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Modes of nano-scale clustering of GPI-anchored

protein at the cell surface.

A Thesis

Submitted to the

Tata Institute of Fundamental Research

Mumbai, India

for the degree of

Doctor of Philosophy

in

Cell Biology

By

Debanjan Goswami

National Centre for Biological Sciences Tata Institute of Fundamental Research

Bangalore, India. 2009

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© 2009 Debanjan Goswami

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Declaration

This thesis is a presentation of my original research work.

Wherever contributions of others are involved, every effort is made to

indicate this clearly, with due reference to the literature, and

acknowledgement of collaborative research and discussions.

The work was done under the guidance of Professor Satyajit

Mayor, at the National Centre for Biological Sciences - Tata Institute of

Fundamental Research, Bangalore, India.

Debanjan Goswami,

Candidate, NCBS-TIFR

Bangalore, India, 560065.

In my capacity as supervisor of the candidate’s thesis, I certify

that the above statements are true to the best of my knowledge.

Prof. Satyajit Mayor

Supervisor, NCBS-TIFR,

Bangalore, India, 560065.

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… dedicated to my parents,

who taught me the best kind of knowledge and upheld my stamina to keep up my best.

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Acknowledgements

I have tried to consolidate in this section the reminiscence from my

journey to this stage of my career. Here, I deeply acknowledge and convey my

sincere thanks to those people who were both for being significant and important

to my life.

During my graduate studies, my thesis supervisor, Jitu, contributed

substantially, by giving me the opportunity to pursue research in his laboratory.

This generosity and opportunity to me was seasoned with thorough and rigorous

guidance. He has always given me the liberty to do experiments of my choice and

at the same time, supported me to streamline those as additions to the story.

Inspiration always came from his one liner – ‘the learning curve should always be

exponential and never saturate’. He is very enthusiastic and energetic and he

always managed to put enormous effort for any scientific discussion. Apart from

our day-to-day project discussions, I have encountered many instances where he

made me to grow up as a person – more specifically as a logical thinker.

My thesis committee members Dr. Madan Rao, Dr. Mathew K. Mathew

and Dr. Sudipta Maiti were always very active, critical and enthusiastic about my

progress in research. Specially, while setting up microscopes, Sudipta-da was

involved right from the beginning and guided me step-by-step. His knowledge in

instrumentation and perception in troubleshooting was always helped me figuring

out problems with the same. Nonetheless, he gave me insights about data analysis

and never stood back from basic criticism of my research. Madan caught hold of

me when I got puzzled with enigmatic biophysical behavior of the cellular plasma

membrane. He always had for biological problems smart applying the explanation

from basic physics. He is fantastic person. He is another reason that I could

perceive draft explanations for unresolved biological phenomena seen in my

experimental results. In many instances he also swerved many of my future

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experiments in the right direction. Mathew (Prof. Mathew K. Mathew) was

always very generous and patient with my projects. He added many important

suggestions regarding fluorescence phenomena in biological samples. I want to

thank Prof. G. Krishnamoorthy from DCS, TIFR for introducing me to time-

resolved fluorescence dynamics in his lab that obviously was a great consequence

for my research later on. Prof. N. Periasamy DCS, TIFR helped me to

understand the fitting routine for time-resolved fluorescence dynamics and has

always been supporting me for any modifications required in analysis program. I

thank Dr. Kulkarni, JNCASR, India for allowing me to work in his laboratory to

make nano-gold particles. I would like to acknowledge Prof. Jim Spudich,

Stanford Univerisity, USA for his criticism, useful suggestions and discussions

during our collaboration. I am grateful to Dr. H. Krishnamurthy (CIFF, NCBS)

for his constant support and useful suggestions while dealing with the problems

in multiple microscopes; actually, I enjoyed whole 5-years working right beside

Krishna. I acknowledge Dr. Gaiti Hasan and Dr. Apurva Sarin as head of

academics. I would like to thank Prof. Vijayraghavan, the director of NCBS, for

helping me to frame and find future scientific research opportunities.

Kripa G., Madan’s student, deployed all necessary tools to couple the

theoretical explanations with our experimental data. She is brilliant. Since I met

her, she has always been more than just a collaborator but a good friend for

hanging out and discussing science in general.

Sameera Bilgrami, the person from whom I learned cell culturing,

microscope handling and wide field anisotropy measurements. I would like to pay

my sincere regards to her for being thorough and rigorous; also for being there

during the initial course of my novice footsteps in to the laboratory. Sriram who

inspired, criticized and nurtured me to grow up in many aspects especially related

to science. I convey my respect to him. My first collaboration with David Altman

at second year was an extraordinary experience for me. I learnt a lot from him

while doing experiments and data analysis. Along with unlimited fun with Gagan,

he encouraged me and helped me to improve scientific writing and computer

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vii

skills. Without Rahul, Manjula and Abhijit (the ‘Kale from Pune’) I can’t even

think of the fulfillment of entertainment in the lab. Me and Neha started working

together for a collaborative project. Precisely, it was an amazing experience for

me. I thank Riya for her effort to make necessary probe that made my

experiments feasible and Sanat from RRI for teaching me lipid related work. I

acknowledge my batch mates for making me participate in the coursework

discussions and all the events and especially Sudha, for all the interesting

interactions in the lab. I enjoyed working with Swetha and Subhasri in the lab. In

the recent past, Suvrajit started working with me and it is a different experience

while passing on some of the expertise. I liked working with him for his aptitude

and analytical understanding. I thank all the present members in the lab for

keeping the lab environment rather cool and cozy. It is difficult to write about

Mr. Gautam Dey – the cool and bright dude in the lab. I lived it up many

evenings with him and certainly, will do in future. I acknowledge Shanta and

Vishalakshi for prompt help with the official and/or academic matter; Mr. Ashok

Rao for all accounts related issues; Ranjith, kitchen stuff and Nagaraj for keeping

up an efficient system to bring us research facilities and materials promptly. I

cannot forget about my seniors, Santanu-da, Samarjit-da and Saikat-da for their

continuous support at multiple levels and especially, because with them NCBS

was a home away from home. I admire them for all the useful and critical

suggestions. I would like to thank Deepak, Bidisha, Feroz and Aprotim from

Shiva’s Lab for teaching me scientific programming in LabView, discussion on

different type of experimental and instrumental problems. I saved the most

important for the last; I am very lucky to have a friend with me and cooled whale

as part of our graduate life.

I found the desired approach towards the interdisciplinary field of science

during my master’s program in Biophysics at Rajabazar Science College, Calcutta

University. I want to express my gratitude to faculties Prof. Chanchal K.

Dasgupta, Prof. Uma Dasgupta, Prof. Utpal Chaudhuri, Prof. Ashok R. Thakur,

Dr. Subhasish Mukherjee for aiding my interest into a more research oriented

arena of scientific studies. And it goes without saying that the world here in

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NCBS been impossible for me without their earnest effort through the rigorous

and rich content based interdisciplinary course that they so willfully provided us.

Definitely, the whole MSc program wouldn’t have been so much fun without all

my classmates; spent good times during scientific discussions, projects and

ofcourse the annual departmental picnic. During my MSc course, I got the

opportunity to do a summer research project at IMTECH, Chandigarh under Dr.

Purnananda Guptasharma. I am very glad to have had Sourav Mukherjee in that

laboratory with whom I experienced the essence of scientific research for the first

time.

Entering undergrad college and choosing Microbiology honours course

was a big decision for me. It was a fairly new interdisciplinary course in Calcutta

University. It did modify the orientation and foundation of my understanding of

biological sciences. I would like to acknowledge faculty members of the college

including Prof. G. Bhattacharya, Dr. Swapna Mukhrejee, Dr. Karuna Baneerjee

Dr. Sathi Das and Dr. Tania Das. Debashis Mukherjee, the guiding star from

Saha Institute of Nuclear Physics, Kolkata who enlighten the path to explore

microbiology as more than just a textbook science.

In my high school, Jadavpur Vidyapith, a first grade Bengali medium

school in south Kolkata, I came across a bunch of marvelous teachers including

my father who helped me build a strong fundamental understanding of different

subjects. I would like to express my tribute to all of them. Remembering school

days, such a fabulous time I spent with all my friends; specially, Subhasish

Chakraborty, with whom life did not end at crazy times and fun stuff, we also

always strangely managed to complement each other academically. With

Devdeep, in high school and in college days, it was different. As he was obsessed

with physics and I being passionate, we enjoyed studying physics together. He

helped me to understand and appreciate the subject, the way it meant to be.

After I got to know Debalina, my wife (not then… duh!! I don’t support

child marriage), I felt myself to be the luckiest. From college through university,

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she was always with me and inspired me in my good and bad times. I was always

very passionate discussing science, history and literature with her and it still

intrigues me that the desire to do so hasn’t come across an inch closer to being

extinguished. She is simply fantastic and the only person who comforts me in all

aspect of life. I am grateful to her for being in my life and she will always be an

inspiration to me.

My parents, the reason for my existence and whose contribution cannot

be put into words, have always given unconditional love and support without

which I would not believe in myself enough to be who I am. Specially, they

taught me the sense of perfection, honesty, hard work, ethics in life and the

aptitude for knowledge. They always allowed me to choose my career. They

trusted my independent decisions and they have confidence in me. My sister, a

lively personality, has always been able to bring me smile anytime. My

grandparents, with whom I partly grew up in my childhood, are esteemed

monuments of inspiration in my life. They are very proud of me not only because

of my achievement but my constant effort to be better myself. I want to convey

my reverence to them. My eldest uncle, who expanded my horizon in terms of

general knowledge and education at my young age, would be the happiest person

seeing me graduate with PhD degree. I would take this opportunity to thank

other family members, all my uncles, aunts and cousins for bringing me love and

fun that could not have been brought to me by any other means. I feel privileged

for having such a nice family.

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x

Table of Contents

DECLARATION ................................................................................................... III

ACKNOWLEDGEMENTS ....................................................................................V

TABLE OF CONTENTS ...................................................................................... X

CHAPTER 1 ............................................................................................................... 1

INTRODUCTION ....................................................................................................... 1

1.A: SUMMARY ....................................................................................................... 11.B: BACKGROUND ................................................................................................... 2

1.B.i: Structure of plasma membrane ............................................................... 21.B.ii: The GPI-anchored proteins and its discovery ........................................... 31.B.iii: The structure and biosynthesis of GPI-APs .............................................. 41.B.iv: The membrane association of GPI-APs ................................................... 5

1.C: STATE OF THE MEMBRANE – ‘RAFT HYPOTHESIS’ ...................................................... 51.C.i: Artificial membrane – description of ‘rafts’ .............................................. 51.C.ii: Rafts in biological membrane redefined .................................................. 7

1.D: BIOPHYSICAL METHODS ....................................................................................... 81.E: OBJECTIVE OF THE THESIS ................................................................................... 121.F: REFERENCES .................................................................................................... 13FIGURE 1.A GPI-ANCHOR AND ANCHORED PROTEINS ..................................................... 16FIGURE 1.B CONCENTRATION INDEPENDENT ANISOTROPY AT CELL SURFACE ........................ 17FIGURE 1.C PICTORIAL REPRESENTATION OF GPI-ANCHORED PROTEIN ORGANIZATION .......... 18

CHAPTER 2 ............................................................................................................. 19

MATERIALS AND METHODS ................................................................................... 19

2.A: Cell lines .................................................................................................. 192.B: Cell culture media and imaging buffer ..................................................... 20

2.C: FLUORESCENCE LABELING ON CELL SURFACE ........................................................... 202.D: Cholesterol depletion procedures ............................................................ 212.E: Actin or myosin perturbing procedures .................................................... 212.F: Wide field microscopy .............................................................................. 222.G: Confocal microscopy ............................................................................... 24

2.H: REFRERENCES ................................................................................................. 25FIGURE 2.A SATURATION BINDING ASSAY FOR PLF PROBE ............................................... 26FIGURE 2.B IMAGES OF GG8 CELLS ............................................................................ 27FIGURE 2.C WIDE-FIELD ILLUMINATION PROFILE ............................................................ 28FIGURE 2.D MEASURING SMALL SYSTEMATIC CHANGES OF ANISOTROPY VALUE .................... 29

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CHAPTER 3 ............................................................................................................. 30

MICROSCOPES, INSTRUMENTATION AND ANALYTICAL METHODS FOR FRET-BASED

MEASUREMENTS ON CELLS .................................................................................... 30

3.A: INTRODUCTION ............................................................................................... 303.A.i: Anisotropy-based FRET measurements .................................................. 303.A.ii: Fluorescence lifetime based FRET measurements .................................. 32

3.B: MULTIPHOTON LASER SCANNING CONFOCAL MICROSCOPE ........................................ 323.B.i: Microscope setup ................................................................................. 323.B.ii: Advantages of multiphoton excitation .................................................. 343.B.iii: Time-resolved fluorescence measurements from living cells using TCSPC 830 card. ........................................................................................................ 353.B.iv: Time-resolved fluorescence studies on live cells .................................... 393.B.v: Time-resolved fluorescence decay analysis ............................................ 413.B.vi: Steady-state anisotropy measurements using TCSPC 830 card. ............ 453.B.vii: Steady-state anisotropy analysis ........................................................ 45

3.C: LINE-SCANNING CONFOCAL MICROSCOPE .............................................................. 463.C.i: Microscope setup .................................................................................. 46

3.D: HETERO-FRET MEASUREMENTS ......................................................................... 483.D.i: Theory ................................................................................................... 483.D.ii: Calculation of R0 .................................................................................. 503.D.iii: Measurement of FRET efficiency .......................................................... 513.D.iv: Hetero FRET measurements by donor lifetime ...................................... 523.D.v: Varying donor to acceptor ratio – Hetero FRET efficiency measurement 54

3.E: REFERENCE ..................................................................................................... 55FIGURE 3.A MICROSCOPE SET UP FOR FIAT AND TIME-RESOLVED FLUORESCENCE

MEASUREMENTS ..................................................................................................... 57FIGURE 3.B ILLUMINATION PROFILE FOR SINGLE PHOTON AND MULTIPHOTON FLUORESCENCE

PROCESS ............................................................................................................... 58FIGURE 3.C CONFIRMATION OF TWO PHOTON EXCITATION .............................................. 59FIGURE 3.D INITIAL ANISOTROPY VALUES FOR DIFFERENT MODE OF EXCITATION ................... 60FIGURE 3.E PRINCIPLE OF TIME CORRELATED SINGLE PHOTON COUNTING (TCSPC) ............... 61FIGURE 3.F INSTRUMENT RESPONSE FUNCTION (IRF) ..................................................... 62FIGURE 3.G MULTIPHOTON ILLUMINATION PROFILE ....................................................... 63FIGURE 3.H EXAMPLES OF TIME- RESOLVED FLUORESCENCE DECAYS .................................. 64FIGURE 3.I LINE-SCANNING CONFOCAL LSM 5 LIVE MICROSCOPE ...................................... 65

CHAPTER 4 ............................................................................................................. 66

AEROLYSIN TOXIN ALTERS GPI-ANCHORED PROTEIN ORGANIZATION AT THE CELL

SURFACE ................................................................................................................ 66

4.A: INTRODUCTION ............................................................................................... 664.B: RESULTS ........................................................................................................ 69

4.B.i: Uniform Surface Distribution of GPI-APs ................................................ 694.B.ii: Aerolysin induces alteration in GPI-AP organization on cell surface ....... 70

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4.B.iii: Aerolysin induced GPI-AP clusters are compact .................................... 714.B.iv: Confirmation of higher order organization with fluorescence lifetime measurements ............................................................................................... 714.B.v: Estimation of cluster size and fraction can be done by theoretical modeling of hetero-FRET ................................................................................ 72

4.C: DISCUSSION .................................................................................................... 764.D: REFERENCES ................................................................................................... 77TABLE 4.A: TRA DATA FOR HOMO-FRET MEASUREMENT ................................................ 80TABLE 4.B: DONOR FLUORESCENCE LIFETIME WITH VARYING A/D ..................................... 81FIGURE 4.A: PROBABLE STRUCTURE OF AEROLYSIN COMPLEX ........................................... 82FIGURE 4.B: DISTRIBUTION OF GPI-ANCHORED PROTEIN ON CELL SURFACE ......................... 83FIGURE 4.D: TIME RESOLVED ANISOTROPY DECAYS FOR GPI-AP ORGANIZATION .................. 85FIGURE 4.D: TIME RESOLVED ANISOTROPY DECAYS FOR GPI-AP ORGANIZATION .................. 85FIGURE 4.E: HETERO FRET OBSERVED BY FLUORESCENCE LIFETIME ................................... 86FIGURE 4.F: ENERGY TRANSFER EFFICIENCY CHANGES WITH D TO A RATIO .......................... 87FIGURE 4.F: ENERGY TRANSFER EFFICIENCY CHANGES WITH D TO A RATIO .......................... 87

CHAPTER 5 ............................................................................................................. 88

GPI-ANCHORED PROTEIN NANO-CLUSTERS ARE IMMOBILE AND

HETEROGENEOUSLY ON LIVING CELL MEMBRANE ................................................. 88

5.A: INTRODUCTION ............................................................................................... 885.B: RESULTS ........................................................................................................ 91

5.B.i: GPI-AP clusters are preferentially distributed in certain regions of the cell surface ........................................................................................................... 915.B.ii: Non-random distribution of sub-resolution clusters of GPI-AP on flat cellscapes ....................................................................................................... 925.B.iii: Nanosclusters are immobile ................................................................. 945.B.iv: Actin perturbation affects nano-cluster reformation – further confirms nanoclusters are immobile ............................................................................. 955.B.v: Formation of nanocluster is sensitive to levels of cholesterol at the plasma membrane ......................................................................................... 96

5.C: DISCUSSION .................................................................................................... 975.D: References .............................................................................................. 99

VARMA, R., AND S. MAYOR. 1998. GPI-ANCHORED PROTEINS ARE ORGANIZED IN SUBMICRON

DOMAINS AT THE CELL SURFACE. NATURE. 394:798-801.FIGURE 5.A SPATIAL DISTRIBUTION OF

NANOCLUSTERS .................................................................................................... 100FIGURE 5.A SPATIAL DISTRIBUTION OF NANOCLUSTERS ................................................. 101FIGURE 5.B STATISTICAL ANALYSIS OF DISTRIBUTION OF NANOCLUSTER ............................ 102FIGURE 5.C ANISOTROPY RECOVERY AFTER PHOTOBLEACHING AT 20°C IMAGES ................ 103FIGURE 5.D GRAPH SHOWS QUANTIFICATION OF ARAP DATA AT 20°C ............................ 104FIGURE 5.E ANISOTROPY RECOVERY AFTER PHOTOBLEACHING AT 37°C IMAGES ................ 105FIGURE 5.F GRAPH SHOWS QUANTIFICATION OF ARAP DATA AT 37°C ............................ 106FIGURE 5.G GRAPH SHOWS QUANTIFICATION OF ARAP DATA AFTER LATRUNCULIN AND

BLEBBISTATIN TREATMENT ...................................................................................... 107

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FIGURE 5.H GRAPH SHOWS QUANTIFICATION OF ARAP DATA AFTER METHYL Β-CYCLODEXTRIN

........................................................................................................................ 108

CHAPTER 6 ........................................................................................................... 109

CORTICAL ACTIN DRIVEN STEADY STATE DYNAMICS OF GPI-AP MONOMERS AND

NANOCLUSTERS – AN ACTIVE PROCESS ............................................................... 109

6.A: INTRODUCTION ............................................................................................. 1096.B: RESULTS ...................................................................................................... 110

6.B.i:Assay to study steady-state dynamics of GPI-AP organization at the cell surface ......................................................................................................... 1106.B.ii: Lipid shows typical concentration dependent FRET signal .................... 1106.B.iii: GPI-AP nanoclusters remain immobile at the scale of confocal area and follows unusual interconversion. .................................................................. 1116.B.iv: Temperature dependence of the dynamics ......................................... 1126.B.v: Spatial heterogeneous nature of association-dissociation kinetics at the cell surface irrespective of temperature. ....................................................... 1136.B.vi: Non-Arrhenius interconversion dynamic ............................................. 1136.B.vii: Cholesterol-sensitive interconversion ................................................ 1146.B.viii: Role of cortical actin in interconversion ............................................ 114

6.C DISCUSSION ................................................................................................... 1176.D: REFERENCES ................................................................................................. 118TABLE I: HETEROFRET+ MEASUREMENT BY DONOR FLUORESCENCE LIFETME ..................... 119TABLE II: TIME RESOLVED HOMOFRET MEASUREMENTS ............................................... 121FIGURE 6.A IMAGING AND INTERCONVERSION DYNAMICS ASSAY ..................................... 123FIGURE 6.B FIAT ASSAY FOR BODIPY-SM AT CELL SURFACE ......................................... 124FIGURE 6.C INTENSITY AND ANISOTROPY TRACES AND IMAGES FROM CELL SURFACE-LABELED

GPI-APS AT 20°C. ............................................................................................... 125FIGURE 6.D INTENSITY AND ANISOTROPY TRACES AND IMAGES FROM CELL SURFACE-LABELED

GPI-APS AT 37°C. ............................................................................................... 126FIGURE 6.E SCHEMATIC REPRESENTATION OF FIAT ASSAY ON CELL SURFACE ...................... 127FIGURE 6.F SCHEMATIC OF DYNAMICS OF GPI-ANCHORED PROTEINS ............................... 128FIGURE 6.G EXAMPLES OF THEORETICAL FITS OBTAINED FROM FIAT ASSAY ....................... 129FIGURE 6.H RATE OF DIFFUSION OF MONOMERS OBTAINED FROM THE FIT ........................ 130FIGURE 6.I INTERCONVERSION RATES OBTAINED FROM THE FIT ....................................... 131FIGURE 6.J TEMPERATURE DEPENDENCE OF INTERCONVERSION RATES ............................. 132FIGURE 6.K PERTURBATION OF CHOLESTEROL LEVELS IN MEMBRANE ALTERS DYNAMICS ....... 133FIGURE 6.L BLEBS ARE DEVOID OF ACTIN .................................................................... 134FIGURE 6.M HETERO-FRET MEASUREMENTS BY DONOR FLUORESCENCE LIFETIME: BLEBS ARE

DEVOID OF NANOCLUSTERS ..................................................................................... 135FIGURE 6.N FLIM DATA SHOWS BLEBS HAS LESS HETERO-FRET COMPARED TO FLAT PART OF CELL

........................................................................................................................ 136FIGURE 6.O TIME-RESOLVED ANISOTROPY DATA FOR HOMO-FRET MEASUREMENT ............ 137FIGURE 6.P NANOCLUSTERS ARE NOT PRESENT IN MEMBRANE STRUCTURE DEVOID OF ACTIN

(BLEBS) AS SEEN IN FIAT EXPERIMENTS ...................................................................... 138FIGURE 6.Q ACTIN PERTURBATION INFLUENCES NANOCLUSTER FORMATION ...................... 139FIGURE 6.R INHIBITION OF MYOSIN ACTIVITY INFLUENCES NANOCLUSTER FORMATION ......... 140

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CHAPTER 7 ........................................................................................................... 141

CONCLUSIONS AND DISCUSSION ......................................................................... 141

Conclusions: ................................................................................................. 141Discussion: ................................................................................................... 142

REFERENCES: ....................................................................................................... 143

PUBLICATIONS ..................................................................................................... 145

SYNOPSIS ............................................................................................................. 146

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1

Chapter 1

Introduction

1.A: Summary

A hallmark in the understanding of cell membrane organization

and structure was encapsulated in the Fluid Mosaic model (Singer and

Nicolson, 1972), where the membrane was visualized as an

equilibrated two-dimensional fluid – a passive mixture of proteins

dissolved in a sea of lipids. According to this model, all lipids and

proteins (ratio varies from 1:4 to 4:1) diffuse freely at all length-scale on

the surface of the cell (Frye and Edidin, 1970). Over the last decade,

the concept of a compartmentalized membrane has emerged where

the cell surface is not a homogeneous mixture, but is segregated into

domains. The mechanism of formation and maintenance of such

domains is hypothesized as arising due to the interaction between

specific lipids such as cholesterol and sphingolipids and associated

proteins. Compartmentalized regions or domains on cell membrane are

referred as ‘lipid-rafts’. ‘Lipid-rafts’ are proposed to be involved in a

variety of important biological roles including endocytosis, trafficking,

signaling complex formation (Simons and Ikonen, 1997). Although

numerous biological functions have been ascribed to ‘lipid-rafts’, the

mechanism behind their formation, structure and dynamics remain

highly debated (Mayor and Rao, 2004).

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1.B: Background

1.B.i: Structure of plasma membrane

The plasma membrane is assembled from a variety of lipids

(which may be broadly classified as phosphoglycerides, sphingolipids,

and sterols) and proteins. All three classes of lipids are amphipathic molecules having a polar (hydrophilic) head group and hydrophobic

tail. The hydrophobic effect and van der Waals interactions cause the

tail groups to self-associate into a micelle or a liposome or a bilayer

with the polar head groups oriented toward water. Although the

common membrane lipids have this amphipathic character in common,

they differ in their chemical structures, abundance, and functions in the

membrane. Phosphoglycerides, the most abundant class of lipids in

most membranes, are derivatives of glycerol-3-phosphate. A typical

phosphoglyceride molecule consists of a hydrophobic tail composed of

two fatty acyl chains esterified to the two hydroxyl groups in glycerol

phosphate and a polar head group attached to the phosphate group.

The two fatty acyl chains may differ in the number of carbons that they

contain (commonly 16 or 18) and their degree of saturation (0, 1, or 2

double bonds). The second class of membrane lipids, sphingolipids, is

derived from sphingosine, an amino alcohol with a long hydrocarbon

chain, and contains a long-chain fatty acid attached to the sphingosine

amino group. Sterols, the third important class of membrane lipids,

consist of cholesterol and its derivatives. The basic structure of

cholesterol is a planar four-ring hydrocarbon with polar hydroxyl group.

The proteins in the plasma membrane are either partially or completely

integrated (known as integral membrane proteins) or loosely attached

with the polar head group of specific lipids (peripheral membrane

proteins).

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3

The lipid and protein profile of membranes varies across cell

type, age, health and also depends on the two leaflets of a particular

bilayer. Differences in lipid composition may also correspond to the

specialization of membrane function. For example, the plasma

membrane of absorptive epithelial cells lining the intestine exhibits two

distinct regions: the apical surface faces the lumen of the gut and is

exposed to widely varying external conditions; the basolateral surface

interacts with other epithelial cells and with underlying extracellular

structures. However, how lipid compositions are modulated and

maintained by the cell and that lead to different physiological processes

is yet to resolve. The role of lipid composition and heterogeneity play in

various endocytic and signaling functions in a cell is unknown.

1.B.ii: The GPI-anchored proteins and its discovery

Amongst several peripheral membrane proteins,

Glycosylphosphatidyl Inositol Anchored Proteins (GPI-APs) is one of

the interesting lipid anchored protein on the cell membrane. Various

enzymes, receptors, signaling molecules, adhesion molecules, cell

surface antigens and prion proteins fall into this category (Paulick and

Bertozzi, 2008). The GPI anchored proteins are attached to only the

outer leaflet of the bilayer unlike other integral membrane proteins via

phospholipids, a part of the GPI moiety. A novel phospholipase that

cleaves phosphatidylinositol was purified in 1976 from Bacillus cereus.

This phospholipase was named phosphatidylinositol phospholipase C

(PIPLC). Subsequently it was shown that purified phopholipase C can

completely remove all the alkaline phophatase activity from membrane

pellets specifically (Low and Finean, 1977) . By 1985, the structural

components of the GPI-APs were identified by multiple

chromatography techqniques from couple of protozoan (Trypanosoma

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4

brucei) surface proteins, variant surface glycoprotein (VSG), and a

glycoprotein expressed on mammalian thymocytes, Thy-1.

1.B.iii: The structure and biosynthesis of GPI-APs

Following their discovery, extensive research commenced to

elucidate the structure of the GPI anchor, biosynthesis of the GPI-APs,

and their localization in cells and has been comprehensively described

in a number of reviews (Chatterjee et al., 2001). Briefly, the signal

peptide is recognized, cleaved and replaced by pre-assembled GPI by

the action of a GPI transamidase residing in the endoplasmic reticulum

(ER). The GPI transamidase is a membrane-bound multi subunit

enzyme containing several PIG gene products which add each

component of the GPI anchor. As we can describe the GPI-anchor, the

membrane anchoring is done by two fatty acyl chains linked to a

glycerol backbone. The third hydroxyl of the glycerol esterified to a

phospho-inositol group that is further linked to an oligosaccharide

consisting of glucosamine and mannose residues and a terminal

phosphoethanolamine that is linked to the carboxy-terminal cysteine of

the protein (Figure 1.A).

The GPI anchor is synthesized with an unsaturated fatty acyl

chain at sn2 position and has a palmitate linked to inositol. Deacylation

of inositol is required for further maturation of the anchor in the Golgi

and is achieved by the deacylase, PGAP1. PGAP3 deacylates the

unsaturated fatty acyl chain at sn2 and PGAP2 reacylates it with

stearic acid (Tanaka et al., 2004). The reason behind the inefficient

process cells adapted for additional deacylation and reacylation

reactions is to ensure that both the fatty acyl chains attached to the

glycerol are saturated.

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The lipid structure is important for its transport and surface

association. In PGAP1-defective CHO cells, when inositolacylated GPI-

APs were expressed (Tanaka et al., 2004), transport of inositol-

acylated GPI-APs from the ER to the Golgi apparatus was at 4-fold

reduced rate compared to that of normal GPI-APs in the wild-type CHO

cells.

1.B.iv: The membrane association of GPI-APs

A well popular notion about GPI-APs is its putative association

with phase segregated membrane domains enriched in selective lipids,

such as sphingolipids and cholesterol, termed ‘rafts’. As shown

previously, GPI-APs are associated with detergent-resistant membrane

(DRM) fraction when cells are extracted with cold non-ionic detergent,

such as Triton X-100 (Brown and Rose, 1992; Mayor and Riezman,

2004). Glycosphingolipids are also efficiently recovered with the DRM,

suggesting that associations among components of these co-extracted

lipid components contribute to resistance against extraction by cold

nonionic detergent. DRMs are classically defined as microdomains or

membrane rafts. The properties and characteristics of DRMs in terms

of its existence on cells and its chemical behavior will be discussed in

the next section.

1.C: State of the membrane – ‘Raft Hypothesis’

1.C.i: Artificial membrane – description of ‘rafts’

Phase segregated domains have been demonstrated in artificial

membranes composed of ternary mixtures of lipids. These mixtures

exhibit three distinct phases depending on temperature and

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composition: gel (so), liquid ordered (lo) and liquid disordered (ld).

Above the chain melting temperature (Tm), the hydrocarbon chains of

lipids are floppy, disordered and loosely packed. This is known as

liquid (disordered) phase (ld). The ld phase has short range positional

correlation. Below Tm, lipids with saturated long acyl chains are tightly

packed and form a phase called the ‘gel phase’. The hydrocarbon

chains are oriented and ordered. The positional correlations in the

plane of the bilayer are long range. However, below Tm, in the

presence of cholesterol, long saturated acyl-chains remain oriented but

the positional correlations are short range, like in a liquid. This is known

as liquid ordered phase (lo). The diffusion coefficient of lipids in lo

phases is higher than in ‘gel-phase’, but lower in the ld phase. Since

rigid cholesterol molecules are inserted inside the lipid molecules (in

gel phase), the surface area per lipid molecule in the lo phase is larger

than in gel phase. However, above Tm in the presence of cholesterol,

no macroscopic phase segregation was observed. But by

spectroscopic studies, such as nuclear magnetic resonance (NMR) and

electron spin resonance (ESR), the two fluid states (lo and ld) were

shown to exist together (Sankaram and Thompson, 1990; Vist and

Davis, 1990). Below Tm, where the gel phase is generally observed, is

replaced by lo phase in presence of high cholesterol (>20 mol%) and

the two fluid (liquid) phases (lo and ld) can coexist (Brown and London,

2000). Experimentally, when the ternary mixture was brought down to

below Tm of specific lipid species, these molecules form liquid ordered

phases (lo phase) in coexistence with disordered phases (ld). These lo

phases coalesce into large scale domains which are resolvable by

optical microscopy. It is this lo phase that is thought to be relevant and

analogous to ‘lipid-rafts’ in biological systems.

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1.C.ii: Rafts in biological membrane redefined

Since, the lo domains exist as large-scale phase-segregated

domains, it was expected that they could be observed in biological

membranes using techniques such as fluorescence microscopy,

electron microscopy, optical tweezers, single molecules studies and

biochemical treatments (chemical cross-linking). However, in contrast

to the situation in artificial membrane, none of the above techniques

had been able to detect the presence of any large scale lo domain on

the native cell membrane (Mayor and Rao, 2004; Munro, 2003) . In this

scenario, several investigators tested the interaction of detergents with

membrane. This technique was used to assess the ‘fluidity’ of

biological membrane (Helenius and Simons, 1975). It was argued that

if biological membrane contains ‘gel-like’ lo patches, similar to artificial

membrane, these would be insoluble in cold-nonionic detergents (for

example Triton X-100). Consequently, it was found that when cell

membranes are extracted with cold (4°C) Triton X-100, a non-ionic

detergent, a small fraction of insoluble membrane residue consisting of

specific subsets of lipids and proteins – called ‘detergent resistant

membrane’ (DRM), is observed (Simons and Ikonen, 1997).

Compositionally, DRM has been correlated to the lo domain on the

model membrane (London and Brown, 2000). This membrane fraction

is enriched in cholesterol, sphingomyelin and many lipid-tethered

proteins such as non-receptor tyrosine kinase,

glycosylphosphatidylinositol anchored proteins (GPI-APs), etc (Simons

and Ikonen, 1997). However, careful biophysical studies in artificial

membrane have showed that Triton X-100 (TX-100) can induce

formation of ‘more ordered phase’ in model membranes from native

‘disordered’ phase (Heerklotz, 2002; Heerklotz et al., 2003).

Furthermore, it has been observed that TX-100 extracted (4°C) DRM

composition matches with the ‘lo’ domain obtained in the ternary phase

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diagram at 37°C, but not with the composition of the lo phase at 4°C

(de Almeida et al., 2003). That means detergent extraction can also

change the composition of preexisting domain on any artificial

membrane. So, a priori existence of lo

1.D: Biophysical methods

domain or ‘lipid-raft’ on native

cell membrane and its composition remains questionable (Mayor and

Rao, 2004).

The scale of lipid domains in cell membrane has remained

controversial; in a few experimental attempts, the size of lipid raft has

been estimated from <10nm to 700nm (Anderson and Jacobson,

2002). Various biophysical tools such as Förster’s Resonance Energy

Transfer (FRET), chemical cross linking, single particle tracking,

fluorescence correlation spectroscopy, laser trap have been used to

investigate the size of large scale organization of different molecules

(lipids, GPI-APs, toxins, trans-membrane proteins) at the cell surface.

However, all these measurements failed to provide consensus scale for

preexisting lipid domains at the cell surface.

GPI-APs have served as marker for rafts since they associate

with DRMs in a cholesterol sensitive fashion (Brown and Rose, 1992).

They also form cholesterol–sensitive nanoclusters (Varma and Mayor,

1998). These were characterized by measuring homo-FRET between

fluorescently labeled GPI-APs on the native cell membrane.

In contrast to hetero-FRET where FRET is monitored between

two different fluorophore, in the homo-FRET process, energy transfer

between two like fluorophores may be measured if they are in close

proximity (<10nm distance) (Varma and Mayor, 1998). This non-

invasive technique showed presence of sub-resolution (<70nm)

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clusters of GPI-APs at the live cell surface (Varma and Mayor, 1998).

These sub-resolution clusters are sensitive to cholesterol and

sphingolipid content in the membrane. However, lack of measurable

hetero-FRET between donor and acceptor fluorescent labeled GPI-APs

on the cell surface contradicted the possibility of nanoclusters in sub-

resolution domain (Kenworthy and Edidin, 1998). A resolution of this

controversy was obtained when Sharma et al measured the scale of

GPI-AP organization. They theoretically modeled the gradual change in

homo-FRET efficiency observed upon photobleaching of fluorophore-

labeled GPI-APs and it to obtain the size of GPI-AP structures giving

rise to the FRET signals (Sharma et al., 2004). Moreover, Sharma et al

provided an explanation for lack of detectable hetero-FRET between

donor-acceptor pair labeled GPI-AP molecules in nanoclusters by

calculating the theoretical values of average hetero-FRET between

them (Sharma et al., 2004).

Since last decade, there are lot of interesting aspects have been

brought up in the field of protein and lipid diffusion at the cell

membrane. Various research groups, using different techniques, have

proposed different models for plasma membrane diffusion. Amongst all

hypotheses, the hop-diffusion model proposed by Akihiro Kusumi

(Fujiwara et al., 2002) has significant evidence for hop diffusion. But in

the contrary, evidences from other laboratories, the hypothesis of hop

diffusion instead of typical Brownian diffusion is still not consensus and

leaves scope for a resolution.

There are groups of researchers who have also shown slowing

down of certain membrane proteins, which are associated to ‘lipid

rafts’. All these findings, however, also depend on the spatio-temporal

scale of measurements. Since it has been established that GPI-

anchored proteins, typical lipid raft associated protein, remain in

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cholesterol dependent sub-resolution clusters at the cell surface,

nobody has attempted to show its mobility and maintenance. Since,

identification of sub-resolution clusters is not straight-forward, it is very

difficult to study its dynamics. It is also important to realize the fact that

cluster mobility, molecular association kinetics and its dynamics is

inter-related. In this context, it is important to use the correct technique

and procedure to measure properties like mobility, kinetics and

maintenance of these clusters at the cell surface.

Previous studies in our laboratory, using fluorescence

anisotropy measurements on images as a measure of homo-FRET,

have shown that GPI-anchored proteins are present in sub-resolution

clusters at the surface of living cells (Varma and Mayor, 1998). Human

Folic acid receptor (FR) expressing CHO cells were labeled with

fluorescent analogues of folic acid Nα-pteroyl-Nε-(4’-

fluoresceinthiocarbamoyl)-L-lysine (PLF). The PLF bound FR were

excited with plane polarized light and wide-field emission fluorescence

anisotropy was measured in steady-state for whole cell. Typically, a

random equilibrated system should give rise to a density dependent

anisotropy profile (emission anisotropy decreases due to depolarization

caused by homo-FRET) as the average inter-fluorophore distance

decreases (one criterion for FRET to occur). The anisotropy profile

obtained from cells, containing different levels of protein on the cell

surface, was independent of protein concentration (direct measure

from total intensity). The partial photo-bleaching of fluorophores

increases the inter-fluorophore distance and hence rise of anisotropy

value (due to loss of homo-FRET originated depolarization). These

confirm existence of sub-resolution clusters. Since, it has also been

shown for a non-specific fluorescence protein (GFP) anchored by GPI

on the cell surface (GFP-GPI), this organization is truly based on the

anchor properties and independent of the protein characteristics

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(Figure 1.B). The anisotropy decay rate obtained from time-resolved

anisotropy measurement on surface of GFP-GPI expressing cell was

another crucial result to determine the properties of these clusters.

Essentially, by modeling the bleaching-profile of fluorophores attached

to the receptors, the characteristic of GPI-AP organization at the cell

surface was elucidated (Figure 1.C). This depicts that nano-clusters

consists 2-4 molecules in each cluster and 20-40% of such clusters are

present on cell surface (Sharma et al., 2004). Furthermore, high

resolution wide-field anisotropy imaging (work done by Sameera

Bilgrami in our laboratory) reveals that nano-clusters are distributed

heterogeneously on the cell surface.

Properties of GPI-AP organization till date can be summarized

into following points: a) GPI-APs remain in a small fraction of

nanoclusters, 20-40% of the total protein on the cell surface, consists

of 2-4 molecules per cluster. b) nanoclusters are cholesterol sensitive.

c) Nanoclusters are always maintained a particular concentration

irrespective of surface protein concentration. This feature does not

obey the ‘law of mass action’ – represents a non-equilibrium state. d)

Multiple GPI-AP can cohabit within a cluster and crosslinking with the

antibodies remodels the cluster composition. This means clusters are

not frozen and they do exchange molecules with monomers present at

the cell surface and hence new clusters are obviously made on the cell

surface.

This description provides an average picture of GPI-AP

organization at the cell surface at 20°C which lacks information about

the dynamics of the aggregation process or its spatial heterogeneity; it

also lacks an understanding of the non-equilibrium state of the whole

process.

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1.E: Objective of the thesis

The present status and understanding of properties of ‘lipid-rafts’

in the view of GPI-AP organization at the cell surface evokes two

obvious questions. Firstly, the nature of the steady state dynamics and

distribution of nanoclusters on the cell surface and secondly, the

underlying mechanism necessary for the maintenance of such unusual

organization. Therefore, I decided to address the following set of

questions and attempt to answer them by developing necessary tools:

1. The diffusivity of nanoclusters vs. monomers at the cell

surface.

2. The distribution of nanoclusters at the cell surface at high

resolution, which requires high resolution anisotropy

imaging.

3. The nature of the steady state dynamic properties of

nanoclusters on the cell surface – the formation and

fragmentation process.

4. Mechanism of maintenance of nanoclusters.

5. Perturbation of the nanocluster organization.

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1.F: References

Anderson, R.G., and K. Jacobson. 2002. A role for lipid shells in

targeting proteins to caveolae, rafts, and other lipid domains.

Science. 296:1821-5.

Brown, D.A., and E. London. 2000. Structure and function of

sphingolipid- and cholesterol-rich membrane rafts. J Biol Chem.

275:17221-4.

Brown, D.A., and J.K. Rose. 1992. Sorting of GPI-anchored proteins to

glycolipid-enriched membrane subdomains during transport to

the apical cell surface. Cell. 68:533-44.

Chatterjee, S., E.R. Smith, K. Hanada, V.L. Stevens, and S. Mayor.

2001. GPI anchoring leads to sphingolipid-dependent retention

of endocytosed proteins in the recycling endosomal

compartment. EMBO J. 20:1583-1592.

de Almeida, R.F., A. Fedorov, and M. Prieto. 2003.

Sphingomyelin/phosphatidylcholine/cholesterol phase diagram:

boundaries and composition of lipid rafts. Biophys J. 85:2406-

16.

Frye, L.D., and M. Edidin. 1970. The rapid intermixing of cell surface

antigens after formation of mouse-human heterokaryons. J Cell

Sci. 7:319-35.

Fujiwara, T., K. Ritchie, H. Murakoshi, K. Jacobson, and A. Kusumi.

2002. Phospholipids undergo hop diffusion in

compartmentalized cell membrane. J Cell Biol. 157:1071-81.

Heerklotz, H. 2002. Triton promotes domain formation in lipid raft

mixtures. Biophys J. 83:2693-701.

Heerklotz, H., H. Szadkowska, T. Anderson, and J. Seelig. 2003. The

sensitivity of lipid domains to small perturbations demonstrated

by the effect of Triton. J Mol Biol. 329:793-9.

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Helenius, A., and K. Simons. 1975. Solubilization of membranes by

detergents. Biochim Biophys Acta. 415:29-79.

Kenworthy, A.K., and M. Edidin. 1998. Distribution of a

glycosylphosphatidylinositol-anchored protein at the apical

surface of MDCK cells examined at a resolution of <100 A using

imaging fluorescence resonance energy transfer. J Cell Biol.

142:69-84.

London, E., and D.A. Brown. 2000. Insolubility of lipids in triton X-100:

physical origin and relationship to sphingolipid/cholesterol

membrane domains (rafts). Biochim Biophys Acta. 1508:182-95.

Low, M.G., and J.B. Finean. 1977. Release of alkaline phosphatase

from membranes by a phosphatidylinositol-specific

phospholipase C. Biochem J. 167:281-4.

Mayor, S., and M. Rao. 2004. Rafts: scale-dependent, active lipid

organization at the cell surface. Traffic. 5:231-40.

Mayor, S., and H. Riezman. 2004. Sorting GPI-anchored proteins. Nat

Rev Mol Cell Biol. 5:110-20.

Munro, S. 2003. Lipid rafts: elusive or illusive? Cell. 115:377-88.

Paulick, M.G., and C.R. Bertozzi. 2008. The

glycosylphosphatidylinositol anchor: a complex membrane-

anchoring structure for proteins. Biochemistry. 47:6991-7000.

Sankaram, M.B., and T.E. Thompson. 1990. Modulation of

phospholipid acyl chain order by cholesterol. A solid-state 2H

nuclear magnetic resonance study. Biochemistry. 29:10676-84.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

Simons, K., and E. Ikonen. 1997. Functional rafts in cell membranes.

Nature. 387:569-72.

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Singer, S.J., and G.L. Nicolson. 1972. The fluid mosaic model of the

structure of cell membranes. Science. 175:720-31.

Tanaka, S., Y. Maeda, Y. Tashima, and T. Kinoshita. 2004. Inositol

deacylation of glycosylphosphatidylinositol-anchored proteins is

mediated by mammalian PGAP1 and yeast Bst1p. J Biol Chem.

279:14256-63.

Varma, R., and S. Mayor. 1998. GPI-anchored proteins are organized

in submicron domains at the cell surface. Nature. 394:798-801.

Vist, M.R., and J.H. Davis. 1990. Phase equilibria of

cholesterol/dipalmitoylphosphatidylcholine mixtures: 2H nuclear

magnetic resonance and differential scanning calorimetry.

Biochemistry. 29:451-64.

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Figure 1.A GPI-anchor and anchored proteins

i. Core structure of GPI-anchor - The membrane anchoring is

done by two fatty acyl chains linked to a glycerol backbone. The third

hydroxyl of the glycerol esterified to a phospho-inositol group that is

further linked to an oligosaccharide consisting of glucosamine and

mannose residues and a terminal phosphoethanolamine that is linked

to the carboxy-terminal cysteine of the protein. Taken from:

Sabharanjak S,et al. Adv Drug Deliv Rev 2004 Apr 29;56(8):1099-109.

ii. Cartoon representation of membrane anchored folate receptor

labeled with fluorescence analogue of folic acid (PLF) and GFP-GPI, a

model protein synthesized inside the cell. Taken from: Sharma P, et

al.Cell. 2004 Feb 20;116(4):577-89.

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Figure 1.B Concentration independent anisotropy at cell surface

Mean fluorescence intensity and anisotropy is plotted in the

graph for GFP-GPI expressing CHO cells grown under normal

condition (black solid circle) and cholesterol depleted condition (red

solid circle). The organization of GPI-anchored protein is independent

of protein concentration at the cell surface. This organization is also

sensitive to level of cholesterol at cell surface. Taken from: Sharma P,

et al.Cell. 2004 Feb 20;116(4):577-89.

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Figure 1.C Pictorial representation of GPI-anchored protein

organization

The picture of GPI-AP organization obtained from the theoretical

estimation of the cluster to monomer ratio at the cell surface. This was

generated by fitting the bleaching profile obtained from the

experimental data of protein organized at the membrane. GPI-APs are

distributed as 20-40% of clusters (with size of 2≥ proteins) and

monomeric proteins. The inter protein distances within the cluster is of

the order of 0R , Forster’s radius (scale bar). Taken from: Sharma P, et

al.Cell. 2004 Feb 20;116(4):577-89.

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Chapter 2

Materials and methods

2.A: Cell lines

Chinese Hamster Ovarian (CHO) cells stably transfected with

two different types of GPI-anchored proteins were used according to

the requirement of experiments. Details of these cell lines are given in

the table below:

Cell

lines

Source Description Reference

1 IA2.2F S. Mayor CHO cells (TRVB), devoid of

transferring receptor (Tfr), were

stably transfected with human

Tfr (geneticin selection) and

GPI-anchored protein – the

human folate receptor (FR-

GPI) (hygromycin selection).

Sabharanjak

et. al. 2002

2 GG8 S. Mayor CHO cells (TRVB), devoid of

transferring receptor (Tfr), were

stably transfected with human

Tfr (geneticin selection) and

GFP-GPI (hygromycin

selection).

Sabharanjak

et. al. 2002

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2.B: Cell culture media and imaging buffer

Ham’s F12 medium (HF12) from HiMedia (Bombay, India)

supplemented with 10% fetal bovine serum (FBS) from GIBCO

(Carshland, CA, USA), appropriate selection antibiotic and combination

of penicillin and streptamycin as antibacterial agent (Chadda et al.,

2007). FR-GPI cells were grown in folic acid free HF12 medium

supplemented with dialysed serum. Appropriate concentration of

fluorescent labels were prepared with either growth medium or medium

1 (150nM NaCl, 5mM KCl, 1mM CaCl2 , 1mM MgCl2

, 20mM HEPES,

pH7.4). Cholesterol depletion reagents were also made in medium 1.

Cells were washed with and imaging was performed in presence of

medium 1 after labeling and / or necessary treatments. For

experiments performed at 37°C 1mg/ml glucose and BSA (bovine

serum albumin) was added to medium 1.

2.C: Fluorescence labeling on cell surface

Folic acid expressing cells were visualized using three different

fluorescence analogues of folic acid PLF (Nα-pteroyl-Nε-(4’-

fluoresceinthiocarbamoyl)-L-lysine, PLBTMR (Nα-pteroyl-Nε-(4’-

BodipyTMR)-L-lysine) or PLR (Nα-pteroyl-Nε-(4’-lissamine rhodamine

thiocarbamoyl)-L-lysine). Fluorescence probe stocks were maintained

at -20°C (described in (Sabharanjak et al., 2002)). PLF, PLBTMR and

PLR saturation binding concentrations for cells were obtained 80nM,

40nM and 400nM respectively, of which PLF saturation profile is shown

in Figure 2.A. Surface labeling was performed by incubating cells with

PLF, PLBTMR or PLR on ice for 1 hour. Excess fluorophore was

removed and cells were washed with ice cold medium 1. GG8 cells

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were treated with 50µM cycloheximide to stop protein expression for 3

hours at 37°C to obtain surface fluorescence only. In Figure 2B, GG8

cell are shown without (i) or with (ii) cycloheximide treatment.

2.D: Cholesterol depletion procedures

Saponin treatment: Labeled cells with appropriate fluorophores

(according to the requirement) were treated with saponin. Pre-chilled

0.3% saponin in medium 1 was used on cells on ice for 30 minutes

(Cerneus et al., 1993).

Cyclodextrin treatment: 10 mM methyl-β-cyclodextrin (mβCD)

solution in medium 1 was prepared at 37°C (Yancey et al., 1996). It

was used on cells according to the protocol applicable to specific

experiment described in chapters later on.

2.E: Actin or myosin perturbing procedures

Two main methods for treatment of cells with actin or myosin

perturbing agents.

To generate blebs:

Cells were incubated at 37°C in growth medium (HAM’s F12) ,

containing 25µM latrunculin or 14µM jasplakinolide for 30 minutes,

either after cycloheximide treatment of GG8 cells or before labeling of

FR-GPI on IA2-2f cells with PLF. Stable membrane deformations and

blebs were formed after 30 minutes of treatment. Time resolved

anisotropy and fluorescence lifetime measurements on blebs were on

these blebs were conducted as described in the next chapter.

During the microphotolysis-type FIAT experiments:

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For microphotolysis type of experiments, 1µl of latrunculin or jas

plakinolide at a concentration of 6mM or 5mM respectively was added

to 1ml of M1 and 1mg/ml glucose and BSA on cells in a 35mm dish

kept on the microscope stage maintained at 37°C. Time of treatment

optimization and photon collection is as described in each experiment.

2.F: Wide field microscopy

TE-2000 or TE-300 from Nikon (Japan) microscope was used

for both wide field fluorescence microscopy and wide field anisotropy

imaging. Briefly, the microscope was equipped with a mercury arc lamp

as an excitation light source and excitation filter wheel for choosing the

excitation wavelength from Shutter instruments (Novato, CA, USA).

Appropriate dichroics and matching excitation and emission filters were

used for imaging. For simultaneous dual color imaging, dual pass

excitation filters were used for simultaneous excitation in two

wavelengths. A secondary beamsplitter was used after the sideport to

separate the emission light intensities for the shorter and longer

wavelengths. In case of anisotropy imaging a polarizing beam splitter

was placed. Images were collected with two 16-bit cooled back

illuminated frame transfer EMCCDs set at 1Mhz transfer rates for

normal gain settings. The instruments were controlled by Metamorph

software.

For 20X steady state anisotropy measurements:

Sub-resolution bead images (Figure 2.C.i-iii) were used to align

two cameras with respect to each other by adjusting the polarizing

beam splitter and position of two cameras. Field illumination was also

optimized to deliver a flat field (Figure 2.C.iv). A set of solution

experiments was performed to test whether the microscope was

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capable of detecting changes in anisotropy. Rhodamine 6 G dye was

dissolved in varying glycerol concentrations to obtain solutions of

different viscosity. Since, fluorescence emission anisotropy reports the

extent of rotational motion, the steady-state anisotropy value will

increase as the molecular rotational motion is restricted (Figure 2.D.i)

when the concentration of glycerol increases. To measure the extent of

depolarization of emission anisotropy due to FRET (Figure 2.D.ii), the

concentration of Rhodamine 6G dye in 70% glycerol solution (70%

glycerol concentration solution was used to hinder the rotation of small

molecule to some extent that has a measurable anisotropy value) was

increased to an extent where average distance between molecules

was less than Forster distance and hence FRET expected in those

concentrations. Furthermore, to verify the accuracy of the microscope a

set standard solution measurements, where anisotropy of fluorescence

emission of Rhodamine dye in 70% glycerol and GFP in PBS

(phosphate buffer saline) were compared before each experiment.

Solution or cell images were acquired simultaneously for parallel

and perpendicular camera by the Metamorph software and split to

obtain separate parallel and perpendicular images using the ‘Split view’

function from Metamorph software. Glycerol or M1 image intensities

were recorded as blanks and were subtracted from the corresponding

solution or cell images. Regions were cut around solution or cells,

intensity logged onto excel worksheet and anisotropy ( r ) calculated as

2I I

rI I

⊥

⊥

−=

+

where, Iand I⊥ are the calculated intensities of fluorescence

emission with polarization parallel and perpendicular to the

excitation polarization, respectively, 2I I I⊥= + is the total

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fluorescence emission intensity, and r is the fluorescence

anisotropy.

The perpendicular image is always corrected for the instrument

bias, called G factor. This was calculated as a ratio Ipa/Ipe obtained

from a concentrated fluorescein solution in water that gave intensity

values, ten times those obtained on cells. It was determined separately

that G factor values remain constant with fluorescein concentration.

Intensity bins of 200-500 units were used to calculate average

anisotropy, and the standard deviation (SD) in that bin. Two dishes per

treatment were imaged, and anisotropy with standard error of the mean

was calculated for each of the intensity bin.

2.G: Confocal microscopy

Zeiss LSM 510 meta and LSM 5 LIVE (Germany) microscope

was used for confocal and multiphoton fluorescence anisotropy

experiments. Details of instrumentation and analytical methods for

fluorescence anisotropy, time resolved fluorescence experiments and

are elaborated in the next chapter.

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2.H: Refrerences

Cerneus, D.P., E. Ueffing, G. Posthuma, G.J. Strous, and A. van der

Ende. 1993. Detergent insolubility of alkaline phosphatase

during biosynthetic transport and endocytosis. Role of

cholesterol. J Biol Chem. 268:3150-5.

Chadda, R., M. Howes, S. Plowman, J. Hancock, R. Parton, and S.

Mayor. 2007. Cholesterol sensitive Cdc42-activation regulates

actin polymerization for endocytosis via the GEEC pathway.

Traffic. in press.

Sabharanjak, S., P. Sharma, R.G. Parton, and S. Mayor. 2002. GPI-

anchored proteins are delivered to recycling endosomes via a

distinct cdc42-regulated, clathrin-independent pinocytic

pathway. Dev Cell. 2:411-23.

Yancey, P.G., W.V. Rodrigueza, E.P. Kilsdonk, G.W. Stoudt, W.J.

Johnson, M.C. Phillips, and G.H. Rothblat. 1996. Cellular

cholesterol efflux mediated by cyclodextrins. Demonstration Of

kinetic pools and mechanism of efflux. J Biol Chem. 271:16026-

34.

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Figure 2.A Saturation binding assay for PLF probe

Example of how saturation binding was obtained for each probe

that binds to FR-GPI expressing CHO cells (IA2.2F). IA2.2F cells

grown in folate free HF12 medium were labeled with varying

concentration of PLF o ice for 1hr. Average fluorescence intensity from

150 cells (integrated intensity was calculated by drawing outline for

each cell in a field) was plotted for each concentration of probe. The

graph showed that the average intensity did not increase as labeling

concentration increased from 80nM to 160nM. Therefore, the

saturation concentration of PLF probe is between 80-160nM.

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Figure 2.B Images of GG8 cells

Images of GG8 cell in a confocal microscope before (i) and after

(ii) cycloheximide treatment for 3 hrs at 37°C, showing that

cycloheximide treatment effectively reduces intracellular GFP-

fluorescence. Scale bar 10µm.

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Figure 2.C Wide-field illumination profile

200nm beads imaged in wide-field two camera setup (i, parallel

camera) and multiphoton (ii, perpendicular camera) was overlapping

with each other (iii, yellow). Uniform fluorescence intensity profile (flat-

field) obtained when fluorescein solution was imaged with both camera

(image in iv, is obtained in parallel camera). Both are important for

anisotropy measurements.

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Figure 2.D Measuring small systematic changes of anisotropy value

i. Anisotropy values obtained from Rhodamine 6G (10µM; at this

concentration there is no FRET) were plotted with varying glycerol

concentration. A systematic increase in anisotropy value was observed

which follows Perin’s equation. ii. Anisotropy values were obtained

from varying concentration of Rhodamine 6G in 70% glycerol (at this

concentration small molecule like Rhodamine 6G has measurable

anisotropy value) concentration. There is systematic decrease of

anisotropy due to homo-FRET.

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Chapter 3

Microscopes, instrumentation and analytical methods for FRET-based measurements on cells

3.A: Introduction

Fluorescence resonance energy transfer (FRET) is a method to

detect proximity between fluorophores at 1-10nm scales. Molecular

interactions, monitored by FRET-based spectroscopic measurement,

can also be implemented in microscope to determine the sizes of

optically unresolved structures at the cell surface. This has been

efficiently employed in our laboratory (Rao and Mayor, 2005; Sharma

et al., 2004; Varma and Mayor, 1998). Previously, in our laboratory,

homo-FRET measurements were performed on a wide-field

fluorescence microscope with a poorly resolved optics where 20X -

0.75 numerical aperture (NA) objective was used to obtain average

information of GPI-anchored protein organization on the cell surface.

To obtain minute details of spatial distribution and formation kinetics of

this organization, high-resolution (both lateral and axial) FRET imaging

was required. In addition, steady-state FRET measurements are often

unable to reveal details of molecular organization. In that case, time

resolved fluorescence measurements are necessary to uncouple the

intertwined contributions of molecular rotation and FRET-contributions

to the steady state measurements.

3.A.i: Anisotropy-based FRET measurements

Fluorescence anisotropy is a property of rotation of fluorophore

or its emission dipole moments. The selection of fluorophore during its

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excitation completely depends on the polarization property of the

exciting light. Homo-FRET is measured using the polarization property

of light. Polarization may be altered due to many physical parameters,

for example, magnetic field, physico-chemical properties and molecular

structure of the conducting medium. After polarized excitation of

fluorophores, its emission may be depolarized due to i) very fast

molecular rotation, ii) a marked angle between emission and excitation

dipoles, or ii) energy transfer to randomly oriented neighbouring

fluorophores (Lakowicz, 1999). In steady-state detection system, a low

anisotropy value can be obtained; the exact source of depolarization

may remain obscure.

Anisotropy of the fluorescence detected also depends on the

microscopic system used. In context of an epi-fluorescence

microscope, specifically, polarization of excitation or emitted light

depends on its interaction with optical components such as dichroic,

optical filter and objective. High NA objectives bend light in steep angle

which causes depolarization of polarized light. In wide-field setup,

usage of high NA (numerical aperture; above 1.2) objectives leads to a

fully depolarized fluorescence emission (of the order of ~0.3) due its

optics. So, any spatial or temporal change in anisotropy, utilized as a

reporter of FRET on the cell surface (Rao and Mayor, 2005), cannot be

distinguished. To retain the anisotropy in the fluorescence emission

with a high NA microscopic system, the dynamic range of anisotropy

detection must be increased. This can efficiently be done in two ways:

i) confocal detection of emitted fluorescence that will not allow

detection of the out-off focus depolarized photons and select relatively

polarized focal plane fluorescence; ii) a further narrow photo-selectivity

by engaging two-photon excitation of fluorophores. A pulsed two-

photon excitation also allows us to monitor time-resolved fluorescence

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measurements with the help of time correlated single photon counting

(TCSPC) method.

3.A.ii: Fluorescence lifetime based FRET measurements

Alteration in the fluorescence lifetime provides direct a

quantitative measure of hetero-FRET, a process when resonance

energy transfer occurs between two different types of fluorophore

having significant spectral overlap. In presence of an acceptor

fluorophore in proximity, donor fluorophore transfer its energy to the

acceptor molecule in non-radiative fashion. In this process the average

lifetime of the donor fluorescence decreases (Lakowicz, 1999). With

the help of TCSPC method, donor fluorescence lifetime can be

measured and the true efficiency of FRET can be calculated.

This is a complementary method also used to determine GPI-AP

organization on the cell surface. According to the theoretical modeling

(Sharma et al., 2004), the size and molecular ratio in a molecular

organization can be predicted from hetero-FRET studies where donor

to acceptor fluorescence ratio is varied. Hetero-FRET measurements

are also quite useful for fluorophores that remain aligned in biological

samples where anisotropy measurement fails.

3.B: Multiphoton laser scanning confocal microscope

3.B.i: Microscope setup

1. Zeiss LSM 510 META confocal microscope. Zeiss

software was used for image acquisition, specifying

region of interest (ROI), laser beam steering and parking.

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Acousto-optic tunable filter (AOTF) is the part of this

microscope for laser power control (Figure 3.A).

2. Single photon lasers: Ar ion laser (LASOS) emitting

475nm, 488nm, 405nm, 514nm; He-Ni lasers (LASOS)

for 532nm and 633nm excitation wavelength respectively.

3. Tsunami Titanium:Sapphire (Ti:Sa), a tunable (700nm-

950nm) multiphoton femto-second pulsed (80 MHz) laser

from Spectra physics was coupled to the microscope.

4. Objectives: 63X (1.45NA), 20X (0.75NA), 10X (0.3NA),

20X (0.5NA), 40X (1.45NA).

5. Two Hamamatsu Photomultiplier tubes (PMTs) at

descanned side and a meta detector with a series of

seven PMTs for spectral imaging.

6. Two Hamamatsu R3809U multi-channel plate

photomultiplier tubes (PMTs; Hamamatsu Photonics) at

the non-descanned detection path.

7. Polarizing beam splitter (PBS) cube (Melles Griot) at the

non-descanned detection path for collection of parallel

and perpendicular emitted photons in case of anisotropy

measurements.

8. Appropriate set of dichroic beam-splitters, emission filters

were chosen and pre-installed before the pinhole, inside

the microscope confocal scan-head.

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9. Time correlated single photon counting card (TCSPC 830

card) was installed from Becker & Hickl, for time resolved

fluorescence data collection that operates in a stop–start

configuration.

3.B.ii: Advantages of multiphoton excitation

1. Fluorescence excitation happens within a confocal

volume (Volkmer et al., 2000; Zipfel et al., 2003) (Figure

3.B). Two-photon excitation was confirmed from log-plot

of power of excitation vs. fluorescence emission from a

fluorescein solution that has a slope of two (Figure 3.C).

2. Narrow photo-selection with respect to single photon

excitation offers higher dynamic range in anisotropy

(Figure 3.D).

3. High NA objectives offer better resolution, higher

convergence of multiphoton laser but result in more

effective depolarization of the emitted fluorescence due to

the high NA.

4. Less bleaching of fluorophores which are out of focus of

the objective.

5. Data can be collected from an isolated point in cell such

as sub-cellular structures, membrane deformities,

endosomes etc.

6. Since multiphoton excitation is pulsed, it can also be used

for time resolved fluorescence studies.

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3.B.iii: Time-resolved fluorescence measurements from living cells using TCSPC 830 card.

Fluorescence lifetime and time-resolved anisotropy

measurement experiments were performed on the TCSPC 830 card

which is connected to the MCP-PMTs attached with above mentioned

microscope (Figure 3.A). Intensity decay profiles (for lifetime or time-

resolved anisotropy) can be obtained by TCSPC method without

accounting all photons arrived at the MCP-PMTs (Becker, 2005;

Lakowicz, 1999). Excitation events occur when laser pulses hit the

sample. Part of the excitation pulse is sent to reference PMT which

generate a start signal. This signal starts time-to-amplitude converter

(TAC), which generates linear ramp voltage unless another pulse from

record PMT (generated by photon from the fluorescence event) stops

it. The TCSPC measurements relies on the concept that the probability

distribution for the emission of a single photon after an excitation yields

the actual intensity versus time distribution of all the photons emitted as

a result of an excitation (Figure 3.E). By multiple sampling of the time

interval between excitation and the arrival of single photon from a very

huge number of excitation events, the card constructs this probability

histogram of counts versus time channel (Figure 3.E). This represents

the actual intensity versus time distribution of the sample. The

description of the setup used for TCSPC measurements is as follows:

1. Ti:Sapphire Tsunami laser was used as the multiphoton

pulsed excitation light source. It is a tunable laser whose

wavelength ranges from 720-1080nm. It was tuned and

mode locked at 790nm and 920nm wavelength for

excitation of GFP and fluorescein respectively. The

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repletion rate of the pulse laser is 80MHz which means

pulse appears in every 12.2ns.

2. A small fraction (1percent) of the laser was deflected to

the reference photodiode which provide the reference

time for incoming laser pulse to the TCSPC 830 card.

3. AOM was used to control the laser power. The laser is

vertically polarized. Reverse dichroic (650nm long pass)

allows IR laser to the sample and fluorescence (less than

650nm wavelength) deflect the photons towards non-

descanned side of the microscope. Emitted photons are

selected with a band-pass filter (IR blocked) according to

their emission wavelength range. Photons are then split,

using a polarizing beam splitter, according to their

polarization and recorded simultaneously in two MCP-

PMTs used for detection. In case of fluorescence lifetime

detection, all fluorescence photons are recorded with a

polarizer in the emission path at 54.7° in a single MCP-

PMT. Zeiss 20X 0.7NA and 63X 1.45NA objectives was

used according the experimental protocol.

4. Due to the random gain mechanisms (secondary

electrons generated by the PMT have wide range of

trajectories and velocities) jitter is inherent in single

photon pulses. The temporal location of incoming single

photon pulses is made more accurate by feeding it to

constant fraction discriminator (CFD). CFDs generate

zero cross-over point by adding input pulse to delayd and

inverted input pulse. The temporal position of zero cross

point is independent of amplitude. Moreover, CFD rejects

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input pulses smaller than detector threshold and larger

than selected amplitude window, leading to significant

improvement of signal to noise. The pulses from start and

record PMTs are individually passed through CFDs

before they reach time-to-amplitude converter (TAC).

5. TAC is the key of single photon counting method. It

measures the time difference between excitation pulse

and the detected photon resulting from fluorescence. A

start pulse received by the TAC generates linear ramp

voltage until signal from stop pulse from the fluorescence

photon detector arrives. The output voltage has linear

dependence on the time difference between arrival of the

start pulse and stop pulse. In ‘start-stop’ mode the laser

pulse can start the TAC and fluorescence photons can

stop it. Alternatively, in a reversed ‘start-stop’ mode,

fluorescence photons can act as a start signal and the

next laser pulse to stop it. Since the ‘start-stop’ mode

results in large dead time for TAC (~99% of the time start

pulse is not followed by any fluorescence emission

photons, resulted aborted events), it can only handle

slower repetition rates. In this set up reverse ‘start-stop’

mode is implemented since the laser repetition rate is 80

MHz.

6. Analogue pulse from TAC is fed into multi channel pulse

height analyzer (MCPHA). Analogue to digital conversion

is done by this MCPHA, where amplitude of TAC pulse

and registers the event into designated time channel.

Multiple such events are counted and histogram of counts

versus time is constructed. The measurements are

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usually continued upto 1,000-10,000 counts (at the peak

time) in each side (parallel and perpendicular channel).

7. After arrival of the first fluorescence photon, TAC can be

started to measure the time until the stop-pulse arrives

from the laser. During this time interval subsequent

photons are lost and that is known as dead-time of the

system (typically in µs order). Hence, if the probability of

photon arrival after each excitation pulse is <1, only early

photons would be detected by TAC leading to

underestimation in the decay time (also known as pile

up). These problems can be avoided by adjusting the

count rate such that the probability of detecting single

photon after excitation of laser pulse is <0.01 (in this

condition probability of detecting 2 photons would be

<(0.01)2, which is less than 1% single photon probability).

Therefore, the fluorescence photon count rate of

detection by MCP-PMT (r CR

r

) must be adjusted by the

following criterion by controlling the laser power or the

density of the fluorophore in the sample:

CR < 0.01 rL

where, rL

8. That means, since laser repetition rate is 80 MHz,

fluorescence photon count rate must be less than 800

KHz.

is the repetition rate of the laser.

9. The 12.5ns time period between two laser pulses is

divided into 1024 time channels which allow us to sample

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intensity decay at the time resolution of 12.2ps per time

channel.

10. Dried colloidal gold particle, which emits instantaneous

photons of half of the wavelength of the excitation (a

phenomenon known as hyper-Rayleigh scattering), was

used collect the instrument response function (IRF)

(Figure 3.F). Scattered photons were collected in two

PMTs simultaneously with the same configuration. The

measured full width at half maximum was 60ps for this

system. This IRF was then used to analyze flurorescence

decays.

3.B.iv: Time-resolved fluorescence studies on live cells

1. Before any measurement on cells or solutions,

multiphoton laser was always aligned with respect to the

pre-aligned single photon laser installed within the

microscope. Both, field uniformity by acquiring image of

uniform density fluorescein solution and sub-resolution

beads distributed on a slide were used for alignment of

the multiphoton laser (Figure 3.G).

2. Before cellular measurements a standard fluorescent

dye, fluorescein in water at pH11, was always taken as

control to verify optimal properties for time-resolved

fluorescence measurements. The lifetime of fluorescein

at pH11 was ~4ns (Figure 3.H.i), which is similar to that of

reported in literature. Correlation time for a 500Da

molecule in water (such as fluorescein) is ~100ps. Hence,

this fluorophore having fluorescence lifetime greater than

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1ns would undergo complete depolarization (Figure

3.H.ii). Anisotropy of fluorescein at pH11 in water rapidly

decayed to zero (Figure 3.H) and was also used to

estimate the G Factor by adjusting the factor multiplied to

the perpendicular photons for correction of the bias

present in the collection optics of the microscope during

fluorescence anisotropy measurements.

3. Time resolved anisotropy (TRA) decay was taken from

GFP in PBS (aqueous solution) at pH7.0 as reference for

anisotropy measurements. The correlation obtained was

similar to that reported the existing literature. Since all

measurements were performed on cells expressing GFP,

r0

was used (with a very narrow window) for fitting data

from cell that was estimated from the multiple fitting trials

of GFP in solution.

4. Cells expressing various GFP-constructs were used for

TRA measurements. For multiphoton excitation of GFP or

fluorescein in cells, we used 920-nm excitation

wavelength. At this wavelength, the two-photon

absorption cross section for GFP is higher, enabling

lower laser excitation power, and auto-fluorescence

signals are minimized. The beam was ‘‘parked’’ at a

single point using routines available in the Zeiss software.

The parked beam was placed at the center of the field to

maintain uniformity of G-Factor, and photons were

collected for 30–50 s. Laser power was kept low such

that photons were collected at a maximum rate of 0.1

MHz and minimum rate of 10 KHz to ensure that TCSPC

conditions were strictly met and to maintain background

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noise <1% of the signal. Since, the low laser power, less

than 10% bleaching was observed during a

measurement.

5. Fluorescence lifetime was obtained from flat regions of

cells, labeled with only donor or donor-acceptor with

varying ratios. Due to low photon counts in individual

pixels, satisfactory fluorescence decay curves were

constructed by scanning the laser over a small ~100x100

pixel area on the cell surface. Measurements on Blebs

were made by collecting photon statistics using a parked

beam located on the bleb. Fluorescence photons were

recorded by selecting photons at emission side

polarization at the magic angle, 54.7° to the excitation

beam. The angle of the polarizer was optimized by

sending low power polarized light in the emission path in

presence of the polarizer at 54.7°and estimating the

anisotropy value to be zero from the parallel and

perpendicular photons obtained at both the detectors.

3.B.v: Time-resolved fluorescence decay analysis

Fluorescence intensity decays:

Fluorescence intensity decays taken at 54.7° angle are analyzed

as sum of discrete exponentials (Grinvald and Steinberg, 1974; Krishna

et al., 2001; Krishnamoorthy et al., 1987; Lakowicz, 1999).

Experimental decay data, F(t) represents a convolution of instrument

response function R(t) with an intensity decay function I(t). It can be

represented as follows

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0

( ) ( ) ( )t

F t R s I t s dsδ= + −∫

where, δ is shift parameter arising due to time shift between

measurement of instrument response and fluorescence photon.

Intensity decay function I(t) is sum of exponential and can be described

as follows:

/

1( ) i

nt

ii

I t e τα −

=

=∑

where, iτ is the i th lifetime;

iα is the pre-exponential factor (amplitude) of i th lifetime

Since the system allows only 12.5ns time of decay, fluorophores

having lifetime more than 2ns would not decay fully. In such cases, the

residual photon out of fluorescence decay would add up at the

beginning of the decay, a process known as ‘roll-over’. Decays were

then fitted by an iterative reconvolution procedure using a Levenberg-

Marquardt minimization algorithm where roll-over effect has been

incorporated. The goodness of the fit was judged when three criteria

were met: a) reduced 2χ was less than 1.2, b) residuals were evenly

distributed across the full extent of the data, and c) visual inspection

ensured that the fit accurately described the decay profile.

Time resolved anisotropy decays:

Polarized emission decays are analyzed to obtain rotational

parameters (Gryczynski et al., 1991; Krishna et al., 2001; Lakowicz,

1999). Parallel and perpendicular decays are described by following

equations:

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1 ( )[1 2 ( )]3

I I t r t= +

1 ( )[1 ( )]3

I I t r t⊥ = −

where, ( )r t is anisotropy as function of time

and ( )I t is total intensity as function of time

( )r t can be represented by the following equation:

( ) ( )( )

( ) 2 ( )I t I t

r tI t I t

⊥

⊥

−=

+

The experimental data is fit to sum of two exponentials to obtain

rotational correlation time and respective amplitudes using following

equation (Krishna et al., 2001; Lakshmikanth and Krishnamoorthy,

1999; Swaminathan et al., 1996):

( / )0

1( ) ( ) rj

mt

jj

r t r r e rτβ −∞ ∞

=

= − +∑

where, rjτ = j th roational correlation time

jβ = pre-exponential (amplitude) for corresponding rjτ

0r = initial anisotropy at 0t =

r∞ =residual anisotropy at infinite time

This equation reduces to the following form for:

a. mono-exponential decay (typical spherical probe that rotates

freely in the solution) ( / )

0( ) rtr t r e τ−=

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b. bi-exponential decay

1 2( / ) ( / )0 1 2( ) r rt tr t r e eτ τβ β− − = +

Rotational diffusion and homoFRET each result in exponential

decays in anisotropy which, for large proteins, occur on very different

time scales: homoFRET results in a rapid anisotropy decay, and

rotational diffusion results in a slower decay. Two decay models (as

explained earlier) for each empirical anisotropy decay: (1) a single

exponential decay and (2) the sum of two exponential decays were

used. Decay profiles, obtained from cells, describe anisotropy decay

through only a single process, presumably rotational diffusion. For a

second class, those were not fit well by a single exponent, but the

addition of a second exponent resulted in a good fit. These profiles

describe decay in anisotropy through two exponential processes, both

homo-FRET and rotational diffusion. Unlike fluorescence lifetime

decay, both parallel and perpendicular decays were analyzed

separately by an iterative reconvolution procedure using a Levenberg-

Marquardt minimization algorithm where roll-over effect was

incorporated (Krishnamoorthy et al., 1987). As previously described,

the goodness of the fit was judged when three criteria were met: a)

reduced 2χ was less than 1.2, b) residuals were evenly distributed

across the full extent of the data, and c) visual inspection ensured that

the fit accurately described the decay profile.

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3.B.vi: Steady-state anisotropy measurements using TCSPC 830 card.

Steady-state imaging was performed on Zeiss LSM 510 META

using multiphoton excitation. Scanner was set for a pixel residence

time of 102 µs/pixel. Imaging was performed using two MCP-PMTs

simultaneously in single photon counting mode on TCSPC 830 card.

The time resolution in the Becker and Hickl card was set to one so that

all the photons were binned into a single time channel. A full image

(512X512 pixel) was collected over 62 seconds.

3.B.vii: Steady-state anisotropy analysis

Steady-state anisotropy was calculated from steady-state

parallel- and perpendicular-polarization images. To account for

differences in the optical paths traversed by the perpendicular and

parallel emissions, a G-Factor correction was applied to the data as

follows. Parallel and perpendicular steady state emission images from

a fluorescein sample were collected. Because fluorescein tumbles

rapidly relative to the time scale of image acquisition, fluorescein emits

identically in both polarizations which is detected pixel-by-pixel readout

in two CCDs. G-Factor image from the parallel and perpendicular

fluorescein emission images were calculated, and perpendicular

images from subsequent experiments were multiplied by this G-Factor

image to apply the appropriate correction. Since pixel values in the

parallel and perpendicular images exhibit Poisson photon noise, the

images were averaged to reduce artifacts arising from dividing signals

containing noise and also to increase the signal-to-noise ratio. Three

pixel nearest neighbor averaging for parallel and perpendicular

fluorescein images were performed and then divided the averaged

images pixel by pixel to create G-Factor image. A new G-Factor image

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was created for each day of experiments. Experimentally obtained

perpendicular image was multiplied with the G-Factor image. Specific

cases, using Metamorph software, regions of interest were manually

selected in the parallel image and were transferred to the perpendicular

image. The mean perpendicular- and parallel-polarization emission

intensities were calculated for each region, and from these, the steady-

state fluorescence anisotropy and fluorescence emission intensity were

calculated using the relations:

2I I

rI I

⊥

⊥

−=

+

where, Iand I⊥ are the calculated intensities of fluorescence

emission with polarization parallel and perpendicular to the

excitation polarization, respectively, 2I I I⊥= + is the total

fluorescence emission intensity, and r is the fluorescence

anisotropy.

3.C: Line-scanning confocal microscope

3.C.i: Microscope setup

1. Customized Zeiss 5 LIVE confocal microscope (Figure

3.I). Here the laser passes through a cylindrical optics by

which the laser beam is spread over a line. This line of

excitation light is then reflected by a one-dimensional

mirror named ‘achrogate mirror’ to the objective placed in

the optical path. Post excitation, the fluorescence

emission from the sample goes to the dichroic mirror with

little regard to the thin achrogate mirror in its path.

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Instead of pinholes for confocality, slits are used to allow

only the fluorescence from focus of the line illumination in

the sample plane.These slits are placed after two

cylindrical lenses in each emission path. High numerical

aperture anisotropy imaging is feasible due to the

confocal arrangement. Acousto-optic tunable filter

(AOTF) is used to control laser power.

2. Primary laser sources are solid state lasers: emitting

488nm, 40nm5, 514nm; He-Ni lasers for 532nm and

633nm excitation wavelength respectively.

3. This set-up is also equipped with a separately steered

laser beam for patterned photo-bleaching.

4. Objectives: 63X (1.45NA), 20X (0.75NA), 10X (0.3NA),

20X (0.5NA), 40X (1.45NA).

5. Two linear array CCDs (dimension is 512 by 1) are used

to detect emitted fluorescence.

6. Appropriate emission filters were mounted in the

emission filter wheels in front of the linear array CCD

detectors.

7. For anisotropy measurements, a nanowire-based

polarization beam splitter (ProFlux™ polarizing

beamsplitter, Moxtek Inc., USA) is used mounted on the

main dichroic filter wheel in the emission path.

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8. Extinction coefficient is 0.96 and G-Factor 0.68 (at

equivalent camera gains).

3.D: Hetero-FRET measurements

3.D.i: Theory

Förster Resonance Energy Transfer (FRET) is description of a

process where energy transfer from an excited donor (D) molecule to

an acceptor (A) happens through long range dipole-dipole coupling and

in a non-radiative fashion (Lakowicz, 1999). FRET can occur if certain

criteria are met:

1. Spectral overlap between donor emission spectrum and

acceptor absorption spectrum.

2. Quantum yield of the donor.

3. Distance between donor and acceptor molecule.

Molecular contact is not required for FRET to occur but

efficiency decreases sharply with respect to the distance,

as described below.

4. Geometric orientation between donor ad acceptor

transition dipoles.

Rate of energy transfer from D to A ( Tk ) as derived from Förster theory

is given by following expression:

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601

TD

Rkrτ

=

or 2

6 5 4

9000(ln10) ( )128

DT

D

k Jr Nnκ λ

τ πΦ =

where, Dτ = lifetime of donor

r = distance between donor and acceptor

0R = Förster’s distance (at which efficiency of energy

transfer is 50%)

DΦ = Qantum yield of donor in absence of acceptor

2κ = orientaton factor depending on relative spatial

orientation of the donor emission dipole and acceptor

absorption dipole

N = Avogadro’s number

n = refraction index of the neighboring medium

( )J λ = Spectral overlap integral that is measure of

overlap between normalized donor emission and

acceptor absorption spectrum

Spectral overlap integral is defined as:

4

0

0

( ) ( )( )

( )

D A

D

F dJ

F d

λ ε λ λ λλ

λ λ

∞

∞=∫

∫

where, DF = Fluorescence intensity of donor

Aε = Molar extinction coefficient of the acceptor

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λ = wavelength

0R can be deduced as where 0R is in Α

and ( )J λ is in M-1 cm3

13 2 4 6

0 9.78 10 ( )DR n Jκ λ− = × Φ

:

The efficiency of energy transfer ( E ) is defined as the ratio of energy

transfer rate to the total decay rate of the donor:

1T

D T

kEkτ −=

+

where, 1Dτ− = donor lifetime without acceptor, Tk = rate of energy

transfer

Substituting Tk in the above equation,

60

6 60

RER r

=+

3.D.ii: Calculation of R0

1. Florescence emission spectrum were measured from

fluorescence spectrophotometer and normalized to its own

maxima ( ( )DF λ ).

2. Absorption spectrum of acceptor was analyzed to get molar

absorption coefficient for each wavelength ( ( )Aε λ ).

3. Spectral overlap was calculated from the equation below:

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4

0

0

( ) ( )( )

( )

D A

D

F dJ

F d

λ ε λ λ λλ

λ λ

∞

∞=∫

∫

4. The 0R was then calculated for pairs of fluorophores (hetero-

FRET) or a single fluorophore (homo-FRET) using the

formula below.

1

3 2 4 60 9.78 10 ( )DR n Jκ λ− = × Φ

3.D.iii: Measurement of FRET efficiency

The efficiency of Energy transfer ( E ) can be calculated by three

different ways:

1. Measuring donor fluorescence lifetime in presence and

absence of acceptor. Due to non-radiative energy transfer

donor fluorescence lifetime or quantum yield reduces

depending on the net amount of energy transfer being

transferred to acceptor. By this experimental technique,

one can obtain true efficiency of FRET.

2. Sensitized fluorescence emission: emission of acceptor

fluorescence in presence and absence of donor.

3. Varying acceptor to donor ratio: Keeping donor

fluorophore constant, donor fluorescence emission

changes in presence of varying acceptor concentration.

FRET efficiency can be calculated directly using following equations:

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1 1 1DA DA DA

D D D

FEF

ττ

Φ= − = − = −

Φ

where, Dτ = lifetime of donor in absence of acceptor

DAτ = lifetime of donor in presence of acceptor

DΦ = donor quantum yield in absence of acceptor

DAΦ = donor quantum yield in presence of acceptor

DF = fluorescence intensity of donor in absence of acceptor

DAF = fluorescence intensity of donor in presence of acceptor

3.D.iv: Hetero FRET measurements by donor lifetime

1. Lifetime of donor only (PLF) labeled FR expressing cells was

measured. PLF labeling was performed such as intensity

matches with respect when cells were labeled with both

donor and acceptor.

2. Photons were selected with band pass filter 500nm to 550nm

placed before MCP detector.

3. Lifetime parameters were obtained by fitting decay data as

described previously.

4. The efficiency of energy transfer is estimated by following

equation

% 1 100DA

D

E ττ

= − ×

where, Dτ = average lifetime of donor in absence of acceptor

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DAτ = average lifetime of donor in presence of acceptor

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3.D.v: Varying donor to acceptor ratio – Hetero FRET efficiency measurement

Since donor lifetime reduces as a result of FRET in presence of

acceptor, varying concentration of acceptor will result varying degree of

energy transfer that will be reflected in donor fluorescence lifetime.

1. Saturating concentration of fluorescent probe was prepared

by mixing two fluorophore (donor and acceptor) conjugated

ligand with varying ratio. Starting from saturated

concentration of donor only (D:A :: 1:0) to a ratio (D:A :: 1:X)

so that the donor signal was three fold higher than the

background count rate in the TCSPC measurement.

2. To obtain donor lifetime at the same donor intensity,

saturated donor labeling was titrated with unlabeled folic

acid. Donor only lifetime was measured at every point

matched with the intensity of donor with varying acceptor

labeling.

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3.E: Reference

Becker, W. 2005. Advanced Time-Correlated Single Photon Counting

Techniques. Springer.

Grinvald, A., and I.Z. Steinberg. 1974. On the analysis of fluorescence

decay kinetics by the method of least-squares. Anal Biochem.

59:583-98.

Gryczynski, I., R.F. Steiner, and J.R. Lakowicz. 1991. Intensity and

anisotropy decays of the tyrosine calmodulin proteolytic

fragments, as studied by GHz frequency-domain fluorescence.

Biophys Chem. 39:69-78.

Krishna, M.M., A. Srivastava, and N. Periasamy. 2001. Rotational

dynamics of surface probes in lipid vesicles. Biophys Chem.

90:123-33.

Krishnamoorthy, G., N. Periasamy, and B. Venkataraman. 1987. On

the origin of heterogeneity of fluorescence decay kinetics of

reduced nicotinamide adenine dinucleotide. Biochem Biophys

Res Commun. 144:387-92.

Lakowicz, J. 1999. Principles of fluorescence spectroscopy. 2nd

Edition.

Lakshmikanth, G.S., and G. Krishnamoorthy. 1999. Solvent-exposed

tryptophans probe the dynamics at protein surfaces. Biophys J.

77:1100-6.

Rao, M., and S. Mayor. 2005. Use of Forster's resonance energy

transfer microscopy to study lipid rafts. Biochim Biophys Acta.

1746:221-33.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

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FRET Microscopy: Theory and Tools

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Swaminathan, R., U. Nath, J.B. Udgaonkar, N. Periasamy, and G.

Krishnamoorthy. 1996. Motional dynamics of a buried

tryptophan reveals the presence of partially structured forms

during denaturation of barstar. Biochemistry. 35:9150-7.

Varma, R., and S. Mayor. 1998. GPI-anchored proteins are organized

in submicron domains at the cell surface. Nature. 394:798-801.

Volkmer, A., V. Subramaniam, D.J. Birch, and T.M. Jovin. 2000. One-

and two-photon excited fluorescence lifetimes and anisotropy

decays of green fluorescent proteins. Biophys J. 78:1589-98.

Zipfel, W.R., R.M. Williams, and W.W. Webb. 2003. Nonlinear magic:

multiphoton microscopy in the biosciences. Nat Biotechnol.

21:1369-77.

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Figure 3.A Microscope set up for FIAT and time-resolved fluorescence

measurements

Zeiss LSM 510 Meta microscope (Zeiss, Germany) equipped

with to steer a femtosecond 80.09 MHz (12 ns) pulsed Tsunami

Titanium:Sapphire (Ti:S) tunable multi-photon excitation laser

(Newport, Mountain View, CA). The Ti:S. The laser can be parked at a

single point for continuous illumination at a single point or scanned

across the field for collecting images. Time correlated single photon

counting (TCSPC) was accomplished using a Becker & Hickl 830 card

(Becker and Hickl, Berlin, Germany) as described (Becker, 2005).

Parallel ( I) and perpendicular ( I⊥ ) emissions were collected

simultaneously into two Hamamatsu R3809U multi-channel plate

photomultiplier tubes (PMTs) using a polarizing beam splitter (Melles

Griot, Carlsbad, CA) to separate the parallel and perpendicular

components of the fluorescence emission, at the non de-scanned side.

This microscope with multiphoton excitation at non de-scanned

emission side has extinction ratio of 96% in the parallel beam path was

characteristic of the system.

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Figure 3.B Illumination profile for single photon and multiphoton

fluorescence process

i. Example of single photon excitation of fluorescein solution by

focusing 488nm light with 0.16 NA objective. ii. Two-photon excitation

using 960nm wavelength pulsed femtosecond laser focused by 0.16

NA objective. (images taken from Nonlinear magic: multiphoton

microscopy in the biosciences. Nat Biotechnol. 21:1369-77.)

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Figure 3.C Confirmation of two photon excitation

Fluorescein solution was taken on cover slip. It was excited at

790nm wavelength with varying laser power (incident peak photon flux

density). Laser power was measured at the back-focal plane of the

microscope. Concentration of the solution remains constant during the

time period of the measurement. Graph shows logarithmic plot of

fluorescence emission intensity versus excitation power, obeyed a

power-squared intensity dependence as indicated by the measured

slope of 1.98, thereby confirming the existence of the two-photon

excitation

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Figure 3.D Initial anisotropy values for different mode of excitation

i. Cone of excitation for one, two, and three photon process. ii.

Excited state distribution for 0r one, two, and three photon excitation.

(taken from: http://www.mi.infm.it/~biolab/tpe/tutor/fpa/anis2.html)

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Figure 3.E Principle of time correlated single photon counting (TCSPC)

The sample is excited with a pulse of light (the red line),

resulting in the fluorescence photon (green graph) waveform shown at

the top of the figure. This is the wave-form that would be observed

when many fluorophores are excited and numerous photons are

observed over multiple such events. TCSPC the conditions are

adjusted so that less than one photon is detected per laser pulse. In

fact, the detection rate is typically 1 photon per 100 excitation pulses.

The time is measured between the excitation pulse and the observed

photon and stored in a histogram. The x-axis is the time difference and

the y-axis the number of photons detected for this time difference.

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Figure 3.F Instrument response function (IRF)

Graph shows instrument response function obtained from the

TCSPC setup. Instantaneous scattered photons from 20-40nm gold

particles on a cover slip were directed to MCP-PMTs to measure the

IRF. The FWHM is 60ps for this system.

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Figure 3.G Multiphoton illumination profile

200nm beads imaged with single photon confocal (i, green) and

multiphoton (ii, red) is overlapping with each other (iii, yellow). Uniform

fluorescence intensity profile obtained when fluorescein solution was

imaged with multiphoton excitation (iv). Both images confirm that the

multiphoton excitation and collection light path are aligned with respect

to the system for imaging purpose. Both criteria are important for

consistent anisotropy measurements. Scale bar is 10µm.

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Figure 3.H Examples of time- resolved fluorescence decays

i. Fuorescence intensity decays for fluorescein dye at pH11

fitted with one exponent decay using iterative re-convolution fitting

routine. This fitting shows 4.01±0.2ns lifetime of fluorescein at ph11. ii.

Fluorescence anisotropy decay of fluorescein dye in water at pH11.

Rotational correlation time was obtained by fitting with one exponential

decay using iterative re-convolution routine and the value is 120±10ps.

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Figure 3.I Line-scanning confocal LSM 5 Live microscope

Custom-designed line-scanning LSM 5 Live microscope (Zeiss,

Jena, Germany) adapted for fluorescence polarization measurements.

This set-up is also equipped with a separately steered laser beam for

patterned photo-bleaching. For the purpose of anisotropy

measurements, main dichroic in the emission path was replaced with a

nanowire-based polarization beam splitter (ProFlux™ polarizing

beamsplitter, Moxtek Inc., USA), and matched emission filters were

mounted in the emission filter wheels in front of the linear array CCD

detectors. The spatial resolution achievable here is 230nm in x-y and

660nm in z (using 1.4NA, 63X objective for 495-530nm fluorescence

emission), and high numerical aperture anisotropy imaging is feasible

due to the confocal arrangement.

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Chapter 4

Aerolysin toxin alters GPI-Anchored Protein organization at the cell surface

4.A: Introduction

Many pathogenic organisms produce toxic substances which

are able to disrupt cellular membranes causing perturbations in the

host system. The plasma membrane of a cell acts as a barrier to

protect from such substances. But plasma membrane is complex

system constituting lipids and proteins. As evident from recent studies

(see preceding Chapters 5 and 6) that the organization of plasma

membrane components is necessary to accomplish multiple functions.

Bacterial pathogens have also used this form of organization at the

plasma membrane as targets and eventually cause its perturbation

(Lafont et al., 2004).

Various bacteria produce different types of toxins. Amongst

them, the largest toxin class (30% of all types) is pore-forming toxins

(PFTs) (Joseph E. Alouf, 2006). PFTs are also produced by higher

organisms. These toxins are secreted as soluble proteins and they

form a transmembrane channel in the target cell membrane. Firstly, the

soluble toxin molecules come toward cells that express a target

receptor on the membrane. Receptors bound PFTs then oligomerize,

followed by membrane insertion leading to channel formation in the

membrane (Parker and Feil, 2005). There are two ways toxin can cross

the membrane as classified alpha helical (α-PFT) or β-barrel (β-PFT).

α-PFTs are predicted to be helical and span lipid bilayers. It contains

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stretches of hydrophobicity. In contrast, β-PFTs are not predicted to be

transmembrane based on the analysis of hydrophobicity. They

construct pairs of amphipathic β-strands. Upon oligomerization (multi-

protein structures), membrane insertion happens by creating a

hydrophobic surface.

The toxin of interest in my studies is produced by Aeromonas

hyhrophila , and is called Aerolysin. This toxin belongs to the second

category that is β -PFT. A. hydrophila secretes a precursor toxin,

proaerolysin, via a type II secretion system in the extra-cellular medium

(Buckley, 1990). Proaerolysin binds to the glycosyl phosphatidyl inositol

(GPI)-anchored proteins on membrane (Cowell et al., 1997; Fivaz et

al., 1999; Fivaz et al., 2002; Hong et al., 2002) . The binding property

depends on the glycan core and the N-linked sugar moiety of the

receptors (Abrami et al., 2002; Hong et al., 2002). Proaerolysin is

activated to aerolysin by removing 40 amino-acids from the C-terminus

(van der Goot et al., 1992). This is done by gut proteases, Aeromonas

proteases or members of the furin family of mammalian endoproteases

(Abrami et al., 1998) in nature. Aerolysin will subsequently oligomerize

into a heptameric ring (Wilmsen et al., 1992); if the concentration is

high, it can oligomerize without binding to its receptor.

The X-ray structure of proaerolysin in the dimeric form revealed

an L-shaped elongated molecule divided into two domains, a small

globular domain, and a large lobe linked together by a long stretch of

residues (Parker 1994). The globular domain, domain 1, is responsible

for binding the toxin to sugar modifications of the receptor (Hong et al.,

2002). The elongated domain of the protein is sub-divided into three

distinct domains. Domain 2 is involved in binding to the GPI-anchor

(MacKenzie et al., 1999) which eventually initiates the oligomerization

process (MacKenzie et al., 1999). Heptameric state is achieved

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through interaction between domain 3 of each monomer too some

extent (Lesieur et al., 1999). There is a loop structure which actually

helps the part of the protein that get inserted into the membrane to

form the transmembrane amphipathic β-barrel (Iacovache et al., 2006;

Melton et al., 2004). The last part, domain 4 contains the pro-peptide,

which is proteolytically removed upon activation (van der Goot et al.,

1994). No high-resolution structure is available for aerolysin in the

transmembrane state. However, cryo-negative staining EM analysis

has performed on the heptameric form of an aerolysin mutant (Tsitrin et

al., 2002); a single point mutation Y221G which converts the wild-type

aerolysin hydrophobic heptamer into a water-soluble complex. Studies

show a conserved mushroom-shaped soluble heptamer (Figure 1.A),

that matches the structure obtained by 2D crystallography determined

from pores of wild type aerolysin (Parker and Feil, 2005; Wilmsen et

al., 1992).

GPI-anchored proteins are present in cholesterol sensitive

nanometer scale clusters of size 2-4 molecules at the plasma

membrane in living cells. After activation by carboxy-terminal cleavage,

soluble aerolysin binds GPI-anchored proteins in the membrane to

form heptameric pore-forming complexes. Thus upon binding, aerolysin

must alters the organization of GPI-anchored proteins at the cell

surface. Here, I attempt to investigate properties of this altered

organization. I use Föster’s resonance energy transfer (FRET) to

extract nanometer length-scale structural information about optically

unresolved aerolysin induced GPI-anchored proteins complexes in

CHO cells expressing a model GPI-anchored protein, GFP-GPI or the

folate receptor (FR). Using steady state and time resolved

measurement to determine the extent of homo- and hetero-FRET, I find

that the relative fraction and size of aerolysin bound GPI-anchored

protein clusters are larger than pre-existing GPI-anchored protein

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clusters. I have also compared the theoretically-modeled hetero-FRET

efficiency with the experimentally observed hetero-FRET efficiencies to

obtain a size of the aerolysin induced structures. Results indicate that

aerolysin reorganizes GPI-anchored proteins to form higher-order

oligomers. These structures could have consequences for the function

of GPI-anchored protein in terms of altered trafficking and signaling

behavior.

4.B: Results

I have used wide-field epifluorescence microscope and a

custom designed multiphoton based laser scanning microscope

(described in Chapter 3.C.i) for anisotropy imaging of emitted

fluorescence from cells. Cells, expressing FR were labeled with PLF

(Nα-pteroyl-Nε-(4’-fluoresceinthiocarbamoyl)-L-lysine and / or PLR (Nε-

pteroyl-Nα

-(4’-lissamine rhodamine thiocarbamoyl)-L-lysine), a

fluorophore tagged ligand to FR and expressing GFP-GPI were used

for experiments.

4.B.i: Uniform Surface Distribution of GPI-APs

Both low and high resolution (where lateral and axial resolution

being 260nm and 890nm respectively) fluorescence intensity image

showed that uniform distribution of GPI-anchored protein on the cell

surface. Cells expressing GFP-GPI or FR-GPI (surface labeled with

various fluorescent analogue of folic acid) were imaged in its

corresponding fluorescent emission wavelength range. It is evident

from the fluorescent images in Figure 4.B.i (widefield) and Figure 4.B.ii

(multiphoton confocal) that upon treatment with activated (nicked)

aerolysin toxin mutant (Y221G; (Fivaz et al., 2002)) between 0.5 to

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1µg/ml, the optically resolvable surface distribution of GPI-anchored

protein at light microscopic resolution remain unaltered.

4.B.ii: Aerolysin induces alteration in GPI-AP organization on cell surface

Using homo-FRET method, previously, it was shown that GPI-

anchored proteins form nanoclusters at the cell surface (Sharma et al.,

2004). I have done homo-FRET measurements on cells treated with

aerolysin. Both wide-field and confocal steady state anisotropy

measurement showed depolarized value compared to control cells

(Figure 4.C.i - wide-field; Figure 4.C.ii- multiphoton confocal). Wide-

field 20X imaging of PLF-labeled IA2.2 cells showed 0.02 unit decrease

in anisotropy value in aerolysin bound cell surface with respect to the

control cells. In case of multiphoton excited GFP-GPI expressing cells

the fluorescence emission was additionally depolarized; aerolysin

bound cell surface exhibited a value of fluorescence anisotropy that

was 0.24±0.005 with respect to the control cells, 0.26±0.004, with a

63X 1.4NA objective. This data implies that aerolysin alters the

organization of GPI-anchored protein on cell surface.

A further depolarized anisotropy value could indicate either

presence of larger and compact clusters or higher fraction of clustered

population of GPI-anchored proteins, as a result of aerolysin induced

clustering process. However, steady state anisotropy measurement

cannot provide a resolution of these possibilities; additional

depolarization caused due to any changes in the rotational properties

after aerolysin binding could also alter any simple extrapolation

between anisotropy values in the bond and unbound states.

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4.B.iii: Aerolysin induced GPI-AP clusters are compact

Time resolved anisotropy measurements on GFP-GPI

expressing cells confirmed that the enhanced depolarization was due

to increaased homo-FRET. The fast rotational decay rate, a signature

of the FRET process was enhanced by 39%. Its amplitude reflects the

fraction of clusters present, and there is a corresponding increase in

this amplitude by 75% compared to the control cells (Figure 4.D.i;

Table 4.A). This data suggests that aerolysin induced GPI-AP clusters

are more compact and involves larger fraction of monomers in a

confocal volume. Furthermore the second rotational correlation time

remain similar suggesting additional depolarization is due to FRET and

not due any alteration in molecular rotation of the GFP monomer.

4.B.iv: Confirmation of higher order organization with fluorescence lifetime measurements

CHO cells, stably expressing folate receptors, were labeled with

PLF –donor and/or PLR –acceptor. Cells were treated with 1.5µg/ml

activated (nicked) aerolysin on ice for 1hr. before fluorescent lifetime

measurements. Fluorescence lifetime decays were obtained from

donor photons from whole cell with high time resolution. This high

precision measurement showed aerolysin treated cells have reduced

donor lifetime compared to control cell in presence of acceptor (Table

4.B; Figure 4.E).

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4.B.v: Estimation of cluster size and fraction can be done by theoretical modeling of hetero-FRET

Efficiency of hetero-FRET can be estimated by donor lifetime in

presence and absence of acceptor; FRET causes a measurable

decrease of the donor lifetime. Theoretical prediction (explained in next

section) of efficiency of energy transfer versus increasing acceptor to

donor ratios can be used to determine cluster characteristics (Sharma

et al., 2004) (Figure 4.F). Cells expressing FR were taken and treated

with aerolysin. Saturating concentration of fluorescent probe was

prepared by mixing two fluorophore (donor and acceptor) conjugated

ligand with varying ratio. Starting from saturated concentration of donor

only (PLF:PLA :: 1:0) to a ratio (PLF:PLR:: 1:0.5). Both aerolysin

treated and untreated cells were labeled with varying ratio (PLF:PLR)

of the probe mixture. To compare actual labeling ratios for donor and

acceptor, images were taken in both channels for PLF and PLR. Since

lifetime measurement may vary when signal to noise ratio decreases,

PLF supplemented with varying concentration of unlabelled folic acid

was also used to measure donor lifetime in each case having

approximately same intensity as compared to PLF-PLR labeled cells.

Lifetime was measured from 1µm2 area on the cell surface. Hetero-

FRET efficiency of was calculated in for each labeling ratios. Acceptor

to donor ratio versus efficiency plot was obtained. The data show that

aerolysin induced clusters display a steeper rise in energy transfer

efficiency consistent with the greater depolarization rate due to homo-

FRET, compared to pre-existing GPI-anchored protein nanoclusters

(Figure 4.F). Thus, aerolysin induced clusters are compact, contain

more number of monomers and larger fraction of molecules are in

clustered form compared to native clusters.

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Explaining the theoretical calculation for determining hetero-FRET efficiency variation with cluster size and fraction:

According to previously published work from our laboratory (Sharma et

al., 2004), done by Dr. Sarasij RC, in an experiment let us consider a

cluster containing n proteins labeled as m acceptors (A) and ( )n m−

donors (D) separated by distances of order 0R (determined for a

specific D-A pair). In this experiment, the n and m vary gradually. For

instance, if the probability of having an A or D in a given cluster is

equal, then the relative abundance amongst all clusters of size n with

at least one donor is

/ (2 1)m n n

n mP C= −

where ( )

!! !

nm

nCn n m

=−

is the combination of m acceptors from a

cluster of size n . For example, when n =3 , the probability of

occurrence of DDD, ADD and AAD are 1/7, 3/7, 3/7 respectively).

The excited donor can transfer its energy to another donor with

probability :p D D∗ → (homo-FRET), to an acceptor with

probability :q D A∗ → or emitted as fluorescence with probability r . We

know that 1p q r+ + = . The possibility that an excited acceptor returns

its energy to a donor ( :s A D∗ → ) is ignored (s = 0). The quantities ,p q

can be obtained from the overlap of the respective emission and

absorption spectra of the fluorophores, D and A, and then r can be

estimated. Obviously, it will be varying with different set of

fluorophores.

The observed emission from A∗ following a transfer from D A∗ → . If

nW is the probability that the excitation of a donor D∗ in a cluster of

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size n is never transferred to A , then the likelihood of observing the

hetero-FRET signal is

2 2 3 3 4 4(1 ) (1 ) (1 ) ...Z x W x W x W= − + − + − +

Now, for very small clusters there is less probability that donor

finds an acceptor to transfer its energy contributing to hetero-FRET

compared to finding another donor and do homo-FRET. In this

scenario, if we change the strength of probability of finding an acceptor

by increasing the acceptor to donor ratio, then, when donor to acceptor

ratio is d , the relative abundance of cluster of size n consisting m

acceptors and n m− donor molecules. The probability becomes

(1 )1 (1 )

n n m mm m

n n

C d dPd

− −=

− −

To calculate nW split it into ( 1)n − parts,

0 1 2 1... n

n n n n nW W W W W −= + + + +

Where the superscript is the number of acceptor in the cluster. If

0m = then donor cannot transfer its energy to the acceptor an hence 0 0

n nW P=

And if 1m n= − , then a) the donor can emit its energy as

fluorescence unless hetero-FRET has occurred; b) if the donor has not

transferred its energy to any of the acceptors, then 1n − independent

events that it does not transfer its energy to each of the 1n − acceptor

in the cluster. So taken together,

1 1 1(1 )n n n

n nW P q− − −= −

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For other values of m ,

1(1 )(1 )

m mn n

m

rW P

r qα

α

= − = − −

This how each solid lines is generated for expected energy

transfer efficiencies for different donor to acceptor ratios where cluster

size and abundance of clusters are varied in Figure 4.F.

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4.C: Discussion

There is significant re-organization of GPI-APs observed after binding

of aerolysin molecules to the GPI-anchor on living cell surface.

Further depolarization in anisotropy implies more molecules in close

proximity. It could either be increase in percentage of closely packed

structures or number of molecules involved per induced cluster or both.

Time resolved anisotropy measurements show that there is more close

packing of molecules, as well as the fraction of molecules undergoing

FRET is increased after treatment.

Hetero-FRET data confirms Homo-FRET-based results about re-

organization of GPI-APs. Predicted theoretical model for hetero-FRET

can be used further for extracting the information about the cluster

characteristics.

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4.D: References Abrami, L., M. Fivaz, E. Decroly, N.G. Seidah, F. Jean, G. Thomas,

S.H. Leppla, J.T. Buckley, and F.G. van der Goot. 1998. The

pore-forming toxin proaerolysin is activated by furin. J Biol

Chem. 273:32656-61.

Abrami, L., M.C. Velluz, Y. Hong, K. Ohishi, A. Mehlert, M. Ferguson,

T. Kinoshita, and F. Gisou van der Goot. 2002. The glycan core

of GPI-anchored proteins modulates aerolysin binding but is not

sufficient: the polypeptide moiety is required for the toxin-

receptor interaction. FEBS Lett. 512:249-54.

Buckley, J.T. 1990. Purification of cloned proaerolysin released by a

low protease mutant of Aeromonas salmonicida. Biochem Cell

Biol. 68:221-4.

Cowell, S., W. Aschauer, H.J. Gruber, K.L. Nelson, and J.T. Buckley.

1997. The erythrocyte receptor for the channel-forming toxin

aerolysin is a novel glycosylphosphatidylinositol-anchored

protein. Mol Microbiol. 25:343-50.

Fivaz, M., M.C. Velluz, and F.G. van der Goot. 1999. Dimer

dissociation of the pore-forming toxin aerolysin precedes

receptor binding. J Biol Chem. 274:37705-8.

Fivaz, M., F. Vilbois, S. Thurnheer, C. Pasquali, L. Abrami, P.E. Bickel,

R.G. Parton, and F.G. van der Goot. 2002. Differential sorting

and fate of endocytosed GPI-anchored proteins. Embo J.

21:3989-4000.

Hong, Y., K. Ohishi, N. Inoue, J.Y. Kang, H. Shime, Y. Horiguchi, F.G.

van der Goot, N. Sugimoto, and T. Kinoshita. 2002.

Requirement of N-glycan on GPI-anchored proteins for efficient

binding of aerolysin but not Clostridium septicum alpha-toxin.

Embo J. 21:5047-56.

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Iacovache, I., P. Paumard, H. Scheib, C. Lesieur, N. Sakai, S. Matile,

M.W. Parker, and F.G. van der Goot. 2006. A rivet model for

channel formation by aerolysin-like pore-forming toxins. Embo J.

25:457-66.

Joseph E. Alouf, M.R.P. 2006. The Comprehensive Sourcebook of

Bacterial Protein Toxins. Academic Press.

Lafont, F., L. Abrami, and F.G. van der Goot. 2004. Bacterial

subversion of lipid rafts. Curr Opin Microbiol. 7:4-10.

Lesieur, C., S. Frutiger, G. Hughes, R. Kellner, F. Pattus, and F.G. van

der Goot. 1999. Increased stability upon heptamerization of the

pore-forming toxin aerolysin. J Biol Chem. 274:36722-8.

MacKenzie, C.R., T. Hirama, and J.T. Buckley. 1999. Analysis of

receptor binding by the channel-forming toxin aerolysin using

surface plasmon resonance. J Biol Chem. 274:22604-9.

Melton, J.A., M.W. Parker, J. Rossjohn, J.T. Buckley, and R.K. Tweten.

2004. The identification and structure of the membrane-

spanning domain of the Clostridium septicum alpha toxin. J Biol

Chem. 279:14315-22.

Parker, M.W., and S.C. Feil. 2005. Pore-forming protein toxins: from

structure to function. Prog Biophys Mol Biol. 88:91-142.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

Tsitrin, Y., C.J. Morton, C. el-Bez, P. Paumard, M.C. Velluz, M. Adrian,

J. Dubochet, M.W. Parker, S. Lanzavecchia, and F.G. van der

Goot. 2002. Conversion of a transmembrane to a water-soluble

protein complex by a single point mutation. Nat Struct Biol.

9:729-33.

van der Goot, F.G., K.R. Hardie, M.W. Parker, and J.T. Buckley. 1994.

The C-terminal peptide produced upon proteolytic activation of

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the cytolytic toxin aerolysin is not involved in channel formation.

J Biol Chem. 269:30496-501.

van der Goot, F.G., J. Lakey, F. Pattus, C.M. Kay, O. Sorokine, A. Van

Dorsselaer, and J.T. Buckley. 1992. Spectroscopic study of the

activation and oligomerization of the channel-forming toxin

aerolysin: identification of the site of proteolytic activation.

Biochemistry. 31:8566-70.

Wilmsen, H.U., K.R. Leonard, W. Tichelaar, J.T. Buckley, and F.

Pattus. 1992. The aerolysin membrane channel is formed by

heptamerization of the monomer. Embo J. 11:2457-63.

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Table 4.A: TRA data for homo-FRET measurement

n

2χ 0r ssr 1rτ

( 1ra )

2rτ

( 2ra )

1Fτ

( 1Fa )

2Fτ

( 2Fa )

GG8

Control

Cell

mean ±

s.d.

6 1.1 ±

0.057

0.44 ±

0.005

0.38

±

0.007

0.22 ±

0.02

(30.08 ±

0.013)

34.5 ± 2

(30.92 ±

0.01)

2.7 ±

0.05

(0.7 ±

0.04)

0.9 ± 0.1

(0.3±0.04)

Aerolysin

treated

GG8 cells

mean ±

s.d.

7 1.2 ±

0.07

0.44 ±

0.006

0.36

±

0.010

0.14 ±

0.032

(0.12 ±

0.02)

29.8 ±

2.6

(0.88 ±

0.02)

2.8 ±

0.11

(0.64 ±

0.041)

0.9 ± 0.09

(0.36 ±

0.041)

n : number of sample; 2χ : chi-square value from the fit; 0r : intial

anisotropy; ssr : steady state anisotropy obtained from the fit; 1rτ : fast

rotational time; 1ra : amplitude of the fast rotational component; 2rτ : slow

rotational time; 2ra : amplitude of the slow rotational component; 1Fτ :

fluorescence lifetime 1; 1Fa : amplitude of the lifetime 1; 2Fτ : fluorescence

lifetime 2; 2Fa : amplitude of the lifetime 2.

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Table 4.B: Donor fluorescence lifetime with varying A/D

D A A / D Treatment n τavg # †† (ns)

PLF 0 10 1.95 (±0.03)

PLF PLA 0.5 31 1.8 (±0.03)

PLF PLA 1 11 1.7 (±0.09)

PLF PLA 2 28 1.59 (±0.09)

PLF 0 Aerolysin 16 2.03 (±0.09)

PLF PLA 0.5 Aerolysin 33 1.814 (±0.064)

PLF PLA 1 Aerolysin 11 1.6 (±0.027)

PLF PLA 2 Aerolysin 21 1.56 (±0.08)

††

1 1 2 2( ) ( )av A Aτ τ τ= × + ×

Average fluorescence lifetime was calculated from the following

equation: . Where, 1τ : fluorescence lifetime 1; 1a :

amplitude of the lifetime 1; 2τ : fluorescence lifetime 2; 2a : amplitude of

the lifetime 2 obtained as fit parameters.

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Figure 4.A: Probable structure of aerolysin complex

i) A simple three-dimensional model for the complex. In this, the

strongly stain-excluding central ring becomes a cylinder projecting from

one side of the 'wheel-like' structure containing seven arms, each

having two globular domains. The cylinder is likely to contain the ion

channel and will presumably integrate into the membrane. The outer

arms, which may be flexible since they exhibit variable curvature when

seen in side view, do not penetrate the membrane but lie close to it.

ii) The fit of the aerolysin monomers into the channel density

obtained from the image of the aerolysin channel derived from the

electron microscopy. The resolution of the image is 25 0Α . The image

consists of a central cylindrical-shaped density of outer diameter

~460Α encircling a water-filled channel 17

0Α in diameter.

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Figure 4.B: Distribution of GPI-anchored protein on cell surface

Multiphoton confocal images of labeled folic acid receptor

expressed on cell surface treated or not treated with aerolysin.

Distribution of GPI-anchored folate receptor is consistent with a

random distribution of monomers or clusters that are smaller than the

diffraction limit of an optical microscope. No further change in

distribution of GPI-AP was observed after aerolysin treatment.

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Figure 4.C: Steady-state anisotropy measurement on cell surface

CHO cells stably expressing GFP-GPI or FR-GPI were treated

with 1.5µg/ml activated (nicked) aerolysin on ice for 1hr. Steady state

anisotropy imaging was performed in wide field microscope (20X

objective) for cell expressing FR-GPI labeled with PLF (i). Same was

done using multiphoton confocal microscope (in TCSPC mode) for

GFP-expressing cells (ii). In both cases, intensity independent

anisotropy distribution was obtained.

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Figure 4.D: Time resolved anisotropy decays for GPI-AP organization

Time resolved anisotropy experiment was performed on GFP-

GPI expressing cells as control and after aerolysin treatment. The

enhanced depolarization observed in steady-state measurements is

validated to be due to enhanced homo-FRET. The rate and the

amplitude of fast depolarization component (due to homo-FRET)

obtained from time resolved anisotropy decay is higher (decay rate is

increased by 39% and corresponding increase in amplitude by 75%)

than that of the control cells suggesting aerolysin induced GPI-AP

clusters are more compact and larger than the pre-existing

organization.

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Figure 4.E: Hetero FRET observed by fluorescence lifetime

Fluorescence lifetime decays are obtained from donor

fluorescence photons from an area of 1µm2 at cell surface where FR-

GPI anchored proteins are labeled with PLF (donor) and PLA546

(acceptor) ratio 1:1. This high precision measurement shows decrease

in donor-lifetime in boh donor-acceptor labeled cells (green graph)

compared to donor alone (violet graph) labeled cells which estimates

detectable hetero-FRET in GPI-AP clusters at the cell surface. Upon

treatment with aerolysin, the donor lifetime decreases further (red

graph). In Aerolysin treated cells a 11% decrease in donor lifetime has

estimated compared to cells without any toxin-treatment.

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Figure 4.F: Energy transfer efficiency changes with D to A ratio

Lines with various colors correspond to the theoretical

predictions with the clusters (20% and 40% molecules are in clusters)

composed entirely of dimmers or trimers or quadramers. Fuorescence

lifetime decay was obtained from a 1µm2

area on the cell surface by

scanning the ROI with multiphoton. Black and red points joined with

dotted lines show experimental results from control and aerolysin

treated cells respectively. Donor lifetime was calculated where

acceptor to donor (PLA and PLF respectively) ratio was varied from 0:1

to 2:1. The efficiency of hetero-FRET was calculated for each case.

This result shows that aerolysin induced clusters display different

nature of graph compared to existing theoretical predictions and control

experimental graph. This data proves that aerolysin induced clusters of

GPI-anchored proteins are bigger than that of the clusters present on

untreated cells.

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Chapter 5

GPI-anchored protein nano-clusters are immobile and heterogeneously distributed on living cell

membranes

5.A: Introduction

Since the last decade, many interesting aspects have been

brought up in the field of protein and lipid diffusion at the cell

membrane. Various research groups, using different techniques, have

proposed different models for plasma membrane diffusion (Bacia and

Schwille, 2007; Chen et al., 2006; Kenworthy, 2007; Lommerse et al.,

2004; Mayor and Rao, 2004; Rao and Mayor, 2005). The hop-diffusion

model proposed by Akihiro Kusumi (Fujiwara et al., 2002) has provided

compelling evidence for hop diffusion where membrane components

experience the confining aspect of an underlying cytoskeleton. But

conflicting evidence from other laboratories regarding the underlying

basis for hop diffusion leaves scope of debate. All these findings,

however, critically depend on the spatio-temporal scale of

measurements. Since it has been established GPI-anchored proteins,

are found in cholesterol dependent sub-resolution clusters at the cell

surface (Sharma et al., 2004; Varma and Mayor, 1998), several studies

have attempted to explore the mobility and organization of these

molecules in an attempt to understand the properties of GPI-anchored

proteins.

Since, the identification of sub-resolution clusters is not direct, it

is very difficult to study its dynamics. It is also important to realize that

cluster mobility, molecular association kinetics and its dynamics are

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Nanocluster dynamics I

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inter-related. In this context, it is important to use the correct technique

and procedure to measure the properties of the clusters at the cell

surface.

In our laboratory, it was shown that GPI-anchored proteins are

distributed as monomers and sub-resolution clusters on the surface of

living cells (Varma and Mayor, 1998). This information was obtained

using wide-field steady-state low-resolution anisotropy imaging on cells

expressing both FR-GPI and GFP-GPI. The fluorescence anisotropy

property of a randomly distributed sample should ideally have density

dependent profile, as shown in case of Rhodamine 6G (Figure 2.D.i),

where the average inter-fluorophore distance decreases (one criterion

for FRET to occur) as concentration of fluorophore increases.

Surprisingly, the anisotropy profile obtained from cells, containing a 40-

fold range of protein levels on the cell surface, was independent of

protein concentration (as measured from the range of fluorescence

intensity) and lower than the value obtained for protein in isolation.

These observations confirm the existence of sub-resolution clusters

that potentially violate the law of mass action. Using both time resolve

anisotropy decay data and a model explaining the bleaching-profile of

fluorophores attached to the receptors, the characteristic of GPI-AP

organization at the cell surface was elucidated (Sharma et al., 2004).

This theoretical model suggests that at the cell surface, 20-40% of the

proteins are present as nanocluster which on an average consists of 2-

4 molecules (Sharma et al., 2004). Furthermore, high resolution wide-

field anisotropy imaging (Goswami et al., 2008) reveals that nano-

clusters are distributed heterogeneously on the cell surface.

These results provided an average picture of the cel surface, but

poorly resolved the spatial distribution of GPI-AP organization, and

clusters to monomer ratio, and gave no information regarding the

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Nanocluster dynamics I

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distribution and exchange between monomer and clusters. To

understand further details about the distribution and maintenance of

clusters at the cell surface, it was necessary to use anisotropy imaging

with high spatial and temporal resolution. A custom designed line-

scanning confocal microscope capable of capturing high-resolution

time-lapse anisotropy images (described in Chapter 3) of emission

fluorescence from cells is used for this purpose. The existence of

heterogeneous, non-random distribution of GPI-AP clusters on baso-

lateral membrane of cell is observed and validated by statistical

distribution analysis. A new assay, Anisotropy Recovery After

Photobleaching (ARAP) is developed using the same microscope to

understand the mobility of GPI-AP clusters and monomers at the cell

surface.

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5.B: Results

I used a custom designed line-scanning microscope (described

in Chapter 3) with high spatio-temporal resolution for time-lapse

anisotropy measurements of emission fluorescence.

Cells, expressing FR were labeled (methods and materials

Chapter 2) with PLBTMR (Nα-pteroyl-Nε-(4’-BodipyTMR)-L-lysine - a more

stable fluorophore with respect to PLF), a fluorophore tagged ligand to

FR for experiment. PLBTMR

5.B.i: GPI-AP clusters are preferentially distributed in certain regions of the cell surface

has already been characterized on cells in

terms of its ability to bind specifically to the membrane bound receptor

and the pathway of internalization (work done by Subhasri Ghosh in

the laboratory).

High resolution (where lateral and axial resolution being 260nm

and 890nm respectively) anisotropy imaging was performed on cells

with PLBTMR-FR-GPI with 60X 1.4NA objective on a line scanning

microscope. Confocal anisotropy images have shown two distinct type

of optically resolvable regions on the cell surface – a. low average

fluorescence anisotropy regions and these are generally associated

with flat regions of the cell membrane, can be defined as flat cellscapes

[Figure 5.A; boxes (i-ii)]; b. regions with high average anisotropy,

associated with membrane overlying protrusive actin architecture such

as dynamic cell edges, such as membrane ruffles or leading edges of

lamellipodia [Figure 5.A; boxes (iii-iv)]. Such a description matches with

previous anisotropy map of cell in wide-field microscope (work done by

Sameera Bilgrami in the laboratory). Clearly, in case of confocal

anisotropy images, single plane of membrane reports higher

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differences in average anisotropy in these two categories of region

compared to wide-field images, where range of difference in anisotropy

is being diluted out because of differential spatial distribution of

receptor organization on both apical and basolateral membrane. Such

distinct distribution is always present on the cells were kept in both

room and physiological temperature (37°C: Figure 5.A.i and 22°C:

5.A.vi).

5.B.ii: Non-random distribution of sub-resolution clusters of GPI-AP on flat cellscapes

Statistical analysis was performed on the sub-resolution cluster

distribution by examining the anisotropy distribution at the level of

individual pixels (since anisotropy is a linear combination of clustered

and monomeric population) in the flat cellscapes from a number of cells

kept at 37 °C. It shows that there is a typical anisotropy distribution

(pixel wise) from flat part of cell membrane since multiple distributions

overlap with the normalised anisotropy distribution (Figure 5.B.i). To

determine whether the nanoclusters are spatially correlated or

randomly distributed, the observed distribution of anisotropy was

compared with the expected distribution by modelling the nanoclusters

and monomers according to a Poisson process.

The experimental values of mean anisotropy A and mean intensity

I yield a mean intensity of clusters cI and mean intensity of

monomers mI . These are given by the following equations:

( )( )

cm

m c

A A II

A A−

=−

( )( )

mc

c m

A A II

A A−

=−

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mI and cI are related to the number of monomers mN and

number of clusters cN via the intensity per fluorophore i

cI = i cN

mI = i mN

If N molecules are scattered randomly on a lattice of L sites, the

probability of a site having n molecules is given by the Poisson

distribution, ( )( ) ( )

,!

n ne nP nn

−

= where Nn L= . It follows that cN

and mN , and therefore cI and mI will independently follow similar

distributions.

We generate strings{ } { },m cI I of intensity values due to

monomers and clusters by drawing random numbers from a Poisson

distribution with means given by mI and cI . These strings are then

used to generate a string of anisotropy values using

{ } { } { }{ } { }

m m c c

m c

A I A IA

I I+

=+

A large enough string provides a well averaged anisotropy

distribution, with which we compare the experimental data. Although,

the distribution analysis of nanoclusters are similar to that of obtained

previously from wide-field images (Goswami et al., 2008), confocal

images provide higher resolution and dynamic range in X-Y-Z planes

and in anisotropy values respectively. The long exponential tail (linear

decay slope in the log plot: shown by the black line in Figure 5.B.ii) is

distinct from both the generated Poissonian distribution (Figure 5.B.ii;

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green line) , as well as solution of GFP (Figure 5.B.ii; blue dots). The

latter two distributions are typical of spatio-temporally uncorrelated

Gaussian random processes. The exponential tails indicates the

existence of correlations in the distributions of nanoclusters from a

single membrane. These results suggest that while nanoclusters are

present at the cell surface, they are not randomly distributed.

5.B.iii: Nanosclusters are immobile

To study the dynamics of the nanoclusters we utilized a

photobleaching-based perturbation strategy. Cell (PLBTMR-FR-GPI)

images were acquired at 20°C on a line scanning microscope with 60X,

1.4 NA objective. Initially, there is a significant depolarization of

fluorescence emission detected in the whole illuminated area (Figure

5.D; Pre-Bleach) which is a characteristic of the steady state

distribution of nanoclusters and monomers. A region 1μm2 was chosen

for bleaching as shown in the first panel of Figure 5.C and average

80% fluorophore was bleached within that area using high-intensity

pulse from a separate bleaching laser. Following the bleaching of

PLBTMR-FR-GPI at the centre of the illuminated area (Figure 5.C;

Bleach, magenta box), it is found that while the fluorescence intensity

recovers (Figure 5.C; Post-Bleach 1 and 4 min), the anisotropy in the

bleached spot does not (Fig.2C; Post-Bleach 1 and 4 min). Images

were taken after one and four minute post bleaching. Notice that in the

regions surrounding the bleached areas, the intensity and anisotropy

remain unchanged during this time (Figure 5.C; brown and blue boxes).

Quantitative analysis from multiple runs of the same experiment

(anisotropy recovery after photo-bleaching or ARAP) confirms this

observation (Figure 5.D). Increase in intensity and high anisotropy

values in the photo-bleached region post recovery may be explained

only if monomeric species diffuse in the bleached area from the

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neighboring regions and that neither nanoclusters are mobile nor do

they reform at this temperature within 4 minute timescale at 20°C.

In contrast at 37°C, in a similar ARAP experiment, while the

fluorescence intensity recovers rapidly after bleaching (Figure 5.E;

magenta boxes), the original depolarized anisotropy value is eventually

recovered after a long delay of 4 minutes (Figure 5.F; magenta boxes).

Quantification of multiple such experiments at 37°C is presented in the

graph (Figure 5.F). These data suggest that at 37°C after a long

interval nanoclusters form from the monomers that have repopulated

the bleached area.

5.B.iv: Actin perturbation affects nano-cluster reformation – further confirms nanoclusters are immobile

To confirm that anisotropy recovers at 37 °C due to formation of

clusters and not diffusion, the anisotropy nanocluster formation is

prevented. Such condition is achievable by treating cell with low level

of actin depolymerizing agent such as latrunculin (6μM). As we show in

the next chapter, there is a major redistribution of nano-cluster at the

cell surface if actin perturbing agents are used. Cells were treated with

6μM latrunculin for two minutes before starting the same ARAP assay,

where no significant morphological change can be visualized over time

period of the experiment. This resulted in an absence of the recovery of

anisotropy in the bleached spot at 37 °C over one or four minute time

points though intensity recovers and neighbouring pixels remain

depolarized as the membrane contains nanoclusters (Figure 5.G.i).

These data are consistent with the idea that while monomers are free

to diffuse, nanoclusters are immobile and formed in situ. The formation

of nanoclusters is sensitive to activity of cortical actin, which I will be

elaborating in the next chapter.

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There is evidence in existing literature that the cortical actin

meshwork activity is related to myosin activity. Therefore, I next

examined whether a block in myosin activity could affect nonocluster

formation. To test this phenomenon in a flat membrane ARAP

experiment was performed after treatment with Blebistatin (50μM).

Similar result in ARAP experiment was obtained when treated when

Myosin activity is blocked (Figure 5.G.ii). These data again suggest

that replenishment of nanoclusters can only happen by formation of

new clusters from existing monomer and activity of acto-myosin

complex is needed for the process.

5.B.v: Formation of nanocluster is sensitive to levels of cholesterol at the plasma membrane

Levels of cholesterol in the membrane play a important role ins

the formation of nanoclusters (Sharma et al., 2004; Varma and Mayor,

1998). Four minutes treatment with 10mM methyl β-cyclodextrin also

causes a reduction in the recovery of homo-FRET in the bleached

region on the cell surface in ARAP experiment at 37 °C (Figure 5.H).

Earlier work showed that low levels of cholesterol depletion leads to a

reduction of CA activity as observed by imaging the dynamics of CA

(Chadda et al., 2007), but not on the net nanocluster concentration

(Sharma et al., 2004). Similar result was found after four minutes of

treatment with methyl β-cyclodextrin where clusters still appear as

depolarized pixels as untreated cells. But these clusters do not diffuse

within the time period of experiment. While pre-formed cluster are

retained on the cell surface after pre-treatment with methyl β-

cyclodextrin, there is no de novo formation of cluster at the treated cell

surface. This data suggests that formation of nanocluster is extremely

sensitive to the levels of cholesterol in the membrane.

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5.C: Discussion

The average picture obtained from previous studies of GPI-AP

organization (Sharma et al., 2004; Varma and Mayor, 1998) describes

presence of cholesterol sensitive sub-resolution (of fluorescence

microscope) clusters of the size of 2-4 molecules and 20-40% cluster

occupancy on the cell surface.

My studies described in this chapter regarding distribution of

clusters and its mobility on the cell surface suggest that there is a

preferential distribution of nanoclusters in specific regions of cell and

the distribution of nanoclusters in those regions is non-random. The

anisotropy recovery assay of nanoclusters shows that these clusters,

when bleached or in other words depleted, are immobile on the cell

surface and can only be reformed at physiological temperature from

mobile monomers. The monomeric population remains mobile at all

temperature. Since the clusters are recovering only at 37°C, the reason

behind this recovery is very much debatable. The explanation could

either be diffusion of clusters or replenishment by formation from

existing monomers. It is difficult to rule out the possibility of cluster

mobility and / or genesis. It is hard to believe that clusters are

transiently becoming mobile at 37°C unless they held by some

unknown entities at the plasma membrane specifically at non-

physiological temperature such as 20°C. Because, theory of simple

membrane diffusion cannot explain the fact that molecules embedded

into membrane and immobile at 20°C become mobile at 37°C only due

to temperature change. On the other hand, the time required to recover

the anisotropy at the bleached area is much highier compared to the

expected time needed to diffuse the clusters of size 2-4 molecules from

the neighboring area. Counter intuitively, it is possible that if I wait for

long time I will see a recovery even at 20°C, but then again I may see

some formation happening at this temperature and rate of which is

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slower than 37°C. My explanation for new synthesis of clusters are

further supported by the fact that at 37°C, I see that there are active

component such actin and myosin are involved in this process. When

the fundamental molecular machines are inactivated by biochemical

agents I do not see recovery of clusters at depleted area (though they

are very much present in the neighboring area, they cannot diffuse).

Note that if the depletion area (bleaching area) at the cell surface is

very large or bleaching time is very long the anisotropy recovery assay

fails because of the overall depletion of the labeled monomers at the

membrane those will take part into the formation new clusters who can

still fluoresce and report to my assay.

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5.D: References

Bacia, K., and P. Schwille. 2007. Fluorescence correlation

spectroscopy. Methods Mol Biol. 398:73-84.

Chadda, R., M. Howes, S. Plowman, J. Hancock, R. Parton, and S.

Mayor. 2007. Cholesterol sensitive Cdc42-activation regulates

actin polymerization for endocytosis via the GEEC pathway.

Traffic. in press.

Chen, Y., B.C. Lagerholm, B. Yang, and K. Jacobson. 2006. Methods

to measure the lateral diffusion of membrane lipids and proteins.

Methods. 39:147-53.

Fujiwara, T., K. Ritchie, H. Murakoshi, K. Jacobson, and A. Kusumi.

2002. Phospholipids undergo hop diffusion in

compartmentalized cell membrane. J Cell Biol. 157:1071-81.

Goswami, D., K. Gowrishankar, S. Bilgrami, S. Ghosh, R. Raghupathy,

R. Chadda, R. Vishwakarma, M. Rao, and S. Mayor. 2008.

Nanoclusters of GPI-anchored proteins are formed by cortical

actin-driven activity. Cell. 135:1085-97.

Kenworthy, A.K. 2007. Fluorescence recovery after photobleaching

studies of lipid rafts. Methods Mol Biol. 398:179-92.

Lommerse, P.H., H.P. Spaink, and T. Schmidt. 2004. In vivo plasma

membrane organization: results of biophysical approaches.

Biochim Biophys Acta. 1664:119-31.

Mayor, S., and M. Rao. 2004. Rafts: scale-dependent, active lipid

organization at the cell surface. Traffic. 5:231-40.

Rao, M., and S. Mayor. 2005. Use of Forster's resonance energy

transfer microscopy to study lipid rafts. Biochim Biophys Acta.

1746:221-33.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

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organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

Varma, R., and S. Mayor. 1998. GPI-anchored proteins are organized in

submicron domains at the cell surface. Nature. 394:798-801.

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Figure 5.A spatial distribution of nanoclusters

CHO cells expressing FR-GPI were labeled with PLBTMR and

fluorescence intensity (grey scale) and anisotropy images (pseudo-

coloured according to the indicated LUT) were imaged at 37°C (i) and

22°C (vi) on a line scanning confocal system. Anisotropy values from

isolated monomeric proteins (A∞) are indicated by a vertical line

(magenta) at the right of the LUT bar. Low anisotropy regions in

relatively constant intensity regions from flat regions of the cell shown

[box(ii,iii)], High anisotropy structures [box (iv,v)] correspond to tips of

lamellipodium, whereas the lamellum exhibits a low anisotropy. Scale

bar 8 µm.

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Figure 5.B Statistical analysis of distribution of nanocluster

Plot of ln (( ))P A versus 2( )A A A− , derived from anisotropy

data from cell images as shown in the previous figure taken in confocal

microscope. This shows a slower, exponentially decaying tail for FR-

GPI-expressing cells, which appears as a linear decay in the log plot

(black line).

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Figure 5.C Anisotropy Recovery After Photobleaching at 20°C images

Fluorescence intensity (grey scale) and anisotropy (pseudo-

coloured) images of PLBTMR–labeled cells were recorded on line

scanning confocal microscope at 20°C , prior to (Pre-Bleach),

immediately-post (Bleach, intensity only) or after 1 or 4 min of (Post-

Bleach, 1 or 4 min, respectively) bleaching the region outlined in the

magenta box. Average anisotropy values from the bleached (magenta)

and unbleached (blue, brown) boxes are shown below pseudo-

coloured anisotropy images from each coloured-box.

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Figure 5.D Graph shows quantification of ARAP data at 20°C

Graphs shows normalized fluorescence intensity (lower panel)

and average (and standard error) anisotropy values (upper panel) from

the respective coloured boxes under the time points indicated on the x-

axis, derived from measurements made on multiple cells (n≥6) at 20°C.

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Figure 5.E Anisotropy Recovery After Photobleaching at 37°C images

Fluorescence intensity (grey scale) and anisotropy (pseudo-

coloured) images of PLBTMR–labeled cells were recorded on line

scanning confocal microscope at 37°C , prior to (Pre-Bleach),

immediately-post (Bleach, intensity only) or after 1 or 4 min of (Post-

Bleach, 1 or 4 min, respectively) bleaching the region outlined in the

magenta box. Average anisotropy values from the bleached (magenta)

and unbleached (blue, brown) boxes are shown below pseudo-

coloured anisotropy images from each coloured-box.

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Figure 5.F Graph shows quantification of ARAP data at 37°C

Graphs shows normalized fluorescence intensity (lower panel)

and average (and standard error) anisotropy values (upper panel) from

the respective coloured boxes under the time points indicated on the x-

axis, derived from measurements made on multiple cells (n≥6) at 37°C.

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Figure 5.G Graph shows quantification of ARAP data after latrunculin

and blebbistatin treatment

Graphs show normalized fluorescence intensity (lower panel)

and average (and standard error) anisotropy values (upper panel) from

an ARAP experiment as described in Figure 5.E, carried out at 37°C on

PLBTMR-labeled FR-GPI expressing cells, pre-treated for 4 min with Lat

( 6 µM) or blebbistatin (F; 50 µM) at 37°C. Magenta symbols represent

data obtained from the area subject to photo-bleaching while blue and

brown symbols are obtained from corresponding neighbouring areas.

Data were derived from measurements made on multiple cells (n≥8) in

a single experiment and averaged over at least two independent

experiments.

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Figure 5.H Graph shows quantification of ARAP data after methyl β-

cyclodextrin

Graphs show normalized fluorescence intensity (lower panel)

and average (and standard error) of anisotropy values (upper panel)

from an ARAP experiment as described in Figure 5.E, carried out at

37°C on PLBTMR-labeled FR-GPI expressing cells, pre-treated with

mβCD (10 mM; 4 min). Magenta symbols represent data obtained from

the area subject to photo-bleaching while blue and brown symbols are

obtained from corresponding neighbouring areas. Data were derived

from measurements made on multiple cells (n≥6) in two independent

experiments.

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Chapter 6

Cortical actin driven steady state dynamics of GPI-AP monomers and nanoclusters – an active

process

6.A: Introduction

Dynamics of membrane anchored proteins is a topic of

research for a long time. Researchers have used different techniques

to study association and dissociation kinetics of membrane proteins at

the cell surface. These include Fluorescence Recovery After

Photobleaching (FRAP), Fluorescence Correlation Spectroscopy

(FCS), Fluorescence Cross-Correlation Spectroscopy (FCCS), dual-

color single molecule dynamics, and a few others. It has been noticed

that across different groups, results from these experiments vary

depending on the modes of observation. Although, detecting diffusion

of membrane anchored molecules is comparatively easy using various

biophysical tools, aggregation kinetics measurement remains a difficult

task. Some of reasons are as follows: – a) little control over the

concentration of reactant and product; b) time lapse observation cannot

be conducted always, since the system is at some steady state; c)

chemical composition of membrane, which is acting as the platform for

aggregation kinetics, may vary locally; d) the read out of association

and dissociation is never unique and quantitative.

In this chapter I focus my attention on the dynamics of GPI-

anchored protein clustering at the cell surface.

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6.B: Results

6.B.i: Assay to study steady-state dynamics of GPI-AP organization at the cell surface

I continue to explore the steady state dynamics of the GPI-AP

monomers and nanoclusters in the nanocluster-rich flat part of cell. In

the previous chapter, I have already described how temperature

influences reorganization of GPI-AP nano-cluster at the cell surface

using a protocol called ARAP. Here, I developed a novel fluorescence

assay, similar to a microphotolysis type assay (Peters et al., 1981),

where a simultaneously intensity and anisotropy trace (FIAT) is

collected from a confocal volume on cell surface (Figure 6.A.i; red

cross) illuminated with a multi-photon laser couple to a single photon

counting device (Figure 4.A). In this assay, I locally perturb the

distribution of PLF-labeled folate receptor (FR-GPI) at the surface of

CHO cells by multi-photon (MP) confocal excitation starting at time t=t0

up to t=t1 which I specify as first illumination period; then the laser is

switched off for a waiting time, tw; after this I switch on again for t2 time,

referred as second illumination period (Figure 6.A.ii). I follow the

dynamical response in fluorescence anisotropy and fluorescence

intensity from the same volume, where intensity represents the local

concentration of the protein and anisotropy represents the

oligomerization status of these receptors.

6.B.ii: Lipid shows typical concentration dependent FRET signal

As a control, the cell membrane was labeled with an

exogenously added fluorescent lipid, BODIPY-SM, at concentrations

high enough to record significant homoFRET. The same recovery

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assay was performed on the cell surface at 20 °C. However, during the

waiting time (tw), the anisotropy of BODIPY-SM recovers to its original

value when the intensity of the probe recovers substantially during this

time interval (Figure 6.B). This result suggests that a cell surface

molecule (largely on the outer leaflet of the plasma membrane) such as

BODIPY-SM, capable of unhindered diffusion (Klein et al., 2003),

recovers its intensity and anisotropy (and hence its original steady

state distribution) following localized photobleaching.

6.B.iii: GPI-AP nanoclusters remain immobile at the scale of confocal area and follows unusual interconversion.

During the first illumination, the time trace of fluorescence

emission intensity shows an initial rapid loss followed by a slower

decay, and significant recovery during tw (Figure 6.C; blue dots). The

corresponding emission anisotropy trace, broadly, shows two kinds of

behaviour that appears correlated with temperature. At 20°C, the initial

anisotropy in the illuminated volume is depolarized (Figure 6.C; red

line), characteristic of a flat membrane where GPI anchored protein

remains as mixture of nanoclusters and monomers. During the first

illumination period (t1) there is a sharp initial rise in fluorescence

anisotropy (corresponding to rapid loss of homoFRET), before

saturating to a high value, due to fluorophores being bleached

continuously. Then the anisotropy value reaches the values

corresponding to the isolated monomers in the membrane (A∞; Figure

6.C, pink band). On the other hand, at 37 °C (Figure 6.D; red line), the

anisotropy rises during t1, as the fluorophores in the confocal volume

get bleached, and saturates to a value significantly lower than A∞.

The recovery of fluorescence intensity in the observation volume

depends on the durations, t1 and tw and this is independent of

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temperature. If t1 is small (<20s) and tw large (>30s), the fluorescence

intensity recovers significantly, implying that fluorophores diffuse in

from the surrounding regions. However, the fluorescence anisotropy at

20 °C, starts out with the same saturation value obtained at the end of

the first illumination, and does not recover to that expected of the

original mixture of nanoclusters and monomers (Figure 6.C, red line).

This implies that nanoclusters neither reform within, nor are

replenished from the reservoir of unbleached fluorophores present

outside the illuminated volume. In contrast, at 37 °C, there is an almost

complete restoration of the original depolarized anisotropy value after

tw, implying that there is substantial reassembly of nanoclusters from

monomers at 37 °C (Figure 6.D, red line).

6.B.iv: Temperature dependence of the dynamics

The dynamical response in FIAT assay (detailed in Figure 6.A)

is systematically recorded from different flat regions of cells, at

temperatures ranging from 15°C–37°C. The fluorescence intensity and

anisotropy profile in each FIAT assay was modeled by reaction-

diffusion type equations (Figure 6.F; this work was done in

collaboration with Kripa G. and Madan Rao; (Goswami et al., 2008)),

incorporating multiple parameters: diffusion of monomers and

nanoclusters (diffusion coefficients, D1 and Dc), bleaching of

fluorophores (bleach rate, b), and the interconversion between

monomers and nanoclusters (aggregation and fragmentation rates, ka

and kf). Using the anisotropy values for monomer and nanocluster, Am

and Ac, we can fit the data to extract these parameters assuming the

fraction of nanoclusters having n proteins, m of which are unbleached,

present within the confocal volume at time, t.

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Using the model, we fit the calculated intensity and anisotropy

profiles to the experimental data at different temperatures and extract

the best fit values for the parameters (Figure 6.G). The diffusion

coefficient of the nanoclusters Dc, obtained from the fit, is found to be

negligibly small (Dc ≈ 0) at all temperatures – this suggests that while

the monomers are mobile (Figure 6.H), the nanoclusters are immobile,

consistent with data shown in previous chapter.

6.B.v: Spatial heterogeneous nature of association-dissociation kinetics at the cell surface irrespective of temperature.

Interconversion parameters at every temperature, extracted by

fitting intensity-anisotropy traces, obtained are spatially variable on the

cell surface. This corroborates the heterogeneity in the steady state

distribution of nanoclusters at the cell surface. Same assay has been

performed on randomly chosen points on multiple cell surface and

analysis showed variation in the rates of association and dissociation

kinetics. Although the interconversion rates are biased for different

temperatures, a distribution of formation kinetics was observed for

every temperature (Figure 6.I).

6.B.vi: Non-Arrhenius interconversion dynamic

At a given temperature, the data for interconversion kinetics

parameters cluster can be divided into four qualitatively distinct

classes: Full Recovery (FR), Partial Recovery (PR), No Recovery (NR)

and No Interconversion (NI) (schematic in Figure 6.E). Interconversion

dynamics is typically absent at lower temperatures and present at

higher temperatures (Figure 6.J). According to Figure 6.G, these

classes reflect the spatial heterogeneity in the organization of

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nanoclusters in the flat regions of the cell surface. Representative class

for each temperature (demarcated in red in Figure 6.I and 6.J.i) was

chosen to construct an Arrhenius-plot from the typical value in each

representative class as a function of the inverse temperature (Figure

6.J.ii); the curve is almost flat at temperatures above 24 °C and

changes sharply below this temperature. This chemical reaction does

not follow a typical Arrhenius behavior.

6.B.vii: Cholesterol-sensitive interconversion

Previousl studies have shown that the steady state distribution

and nanoclustering is sensitive to cholesterol levels in the membrane

(Sharma et al., 2004). Therefore, I tested if the dynamics is affected by

an acute perturbation of cholesterol levels. I conducted similar FIAT

experiments at 37°C with a mild treatment of mβCD (10mM) 30

minutes prior to the first illumination. Whereas on control cells, the

cluster concentration recovers partially or completely after a time t1+tw

(Figure 6.D), I observed that application of mβCD prior to the first

illumination prevents the restoration of the original depolarized

anisotropy value during the waiting period, although the fluorescence

intensity has completely recovered (Figure 6.K). Thus, these results

suggest that the interconversion dynamics is sensitive to cholesterol

levels in the membrane.

6.B.viii: Role of cortical actin in interconversion

Cortical actin activity was perturbed using Jasplakinolide (Jas)

or Latrunculin (Lat) at 37°C to determine if actin polymerization activity

is necessary for maintenance of nanoclusters at the plasma

membrane. Prolonged incubation with Jas / Lat results in the

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generation of micron-sized blebs, devoid of CA (Figure 6.L). Time-

resolved fluorescence measurements show that these fully-formed

blebs lack GPI-AP nanoclusters. Donor fluorescence lifetime obtained

from PLF (donor) and acceptor (PLR) labeled blebs are higher

compared to the flat parts (cluster rich regions) of cells and matches

with the donor alone lifetime data (Figure 6.M; Table I). Fluorescence

lifetime images show (Figure 6.N) blebs with comparatively higher

donor fluorescence lifetime (pseudo-color coded) compared to the flat

region of the cell. Increase in fluorescence lifetime both single decays

and images explain that the hetero-FRET between donor-acceptor

labeled GPI-anchored protein has decreased. The proximity between

molecules in such organization is not present anymore when the

membrane integrity is lost in bleb, which is also devoid of cortical actin.

The steady-state anisotropy values indicate whether the

anisotropy value is integrated with the FRET and rotational component

of the fluorophore. It is always questionable about the origin of the

polarized or depolarized values obtained in the steady-state anisotropy

measurements. But in time resolved anisotropy decay measurements

the fast anisotropy decay component specifically reports the amount of

homo-FRET present in the system. The rate of decay further reports

about the distance between the fluorophore those are in FRET

proximity. Time resolved anisotropy data from blebs, generated from

GFP-GPI expressing cells, showed no or less in amplitude of fast

decay (homo-FRET) component (Figure 6.O and Table II). Since blebs

lack fast decay component or the amplitude is less than obtained from

the flat part of cell, it is confirmed that GFP-GPI organization is lost in

blebs. It is also noticeable that decay data (in case of latrunculin

generated blebs) from blebs having less amount of fast component has

much higher decay rate. This suggests that in latrunculin generated

blebs the GFP-GPI molecules are further separated compared to

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normal organization. It possible that in these blebs few organized

molecules are left with increased intermolecular distance either

because of incomplete block or re-distribute molecules in a cluster too

some extent.

This is again confirmed by the polarized value of anisotropy in

FIAT assay. Polarized value of anisotropy remains unchanged in the

range of A∞ through out the period of illumination (Figure 6.P; red line)

suggesting bleb devoid of nanoclusters.

Next I applied low concentrations of Jas or Lat on cells (5 and 6

µM respectively), which does not cause any morphological change in

cells within the observation time of the experiment, to explore the

status of the dynamics of nanoclusters. Whereas, the nanocluster

concentration recovers completely after time t1+tw on control cells at

37°C (Figure 6.Q, red line), when low concentration of Lat or Jas has

applied either in the beginning or during waiting period prevents the

restoration of the original depolarized anisotropy value (Figure 6.Q.i

and ii; red lines), although the fluorescence intensity has completely

recovered (Figure 6.Q.i and ii; blue lines). I have also shown that pre-

treatment with Lat affect the dynamics of nanoclusters at the plasma

membrane due to dynamic polymerization / depolymerization of actin

near the membrane is restricted (Figure 6.Q.iii). Earlier it had been

shown that treatment with blebbistatin, which restricts the myosin

activity, stops bleb retraction process and nanocluster cannot reform at

the same site after the bleb retracts back to the membrane (Charras et

al., 2007; Charras et al., 2006; Goswami et al., 2008; Keller et al.,

2002). Therefore, I examined the effect of perturbation of myosin

activity using blebbistatin (50µM), on dynamics of interconversion at

the flat membrane (Figure 6.R; red trace). Whereas, at 37°C in

presence of blebbistatin, neither there is de novo synthesis of

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nanoclusters of GPI-APs nor do they diffuse in from the neighboring flat

membrane area. Altogether, these results suggest that the nanocluster

intercoversion dynamics is linked with acto-myosin activity by some

unknown mechanism near the cell surface and nanoclusters are

immobile even at 37°C on the cell surface.

6.C Discussion

The experimental evidence detailed here show that the

dynamics of formation and fragmentation is dependent on the local

concentration of cholesterol and CA remodeling. Salient features of this

unusual kinetics behavior are: (i) nanoclusters are immobile; (ii)

formation of nanoclusters are temperature sensitive (also includes non-

Arrheneius type of chemical reaction with a cross-over at 24°C) and

violates law of mass action; (iii) spatial variation of interconversion

dynamics of nanoclusters due to spatial variation in levels of active

CA; (iv) involvement of actin and myosin activity on interconversion

dynamics (acto-myosin activity has also been reported to show a sharp

crossover at ~24°C); (v) above 28°C, the calculated binding energy of

nanoclusters is ∆E/kBT≈10-2

and this is 2-3 orders of magnitude lower

than the minimal (van der Waals) interactions between molecules on a

membrane at a similar intermolecular distance.

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Nanocluster dynamics II

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6.D: References

Charras, G.T., M. Coughlin, T.J. Mitchison, and L. Mahadevan. 2007.

Life and Times of a Cellular Bleb. Biophys.

J.:biophysj.107.113605.

Charras, G.T., C.K. Hu, M. Coughlin, and T.J. Mitchison. 2006.

Reassembly of contractile actin cortex in cell blebs. J Cell Biol.

175:477-90.

Goswami, D., K. Gowrishankar, S. Bilgrami, S. Ghosh, R. Raghupathy,

R. Chadda, R. Vishwakarma, M. Rao, and S. Mayor. 2008.

Nanoclusters of GPI-anchored proteins are formed by cortical

actin-driven activity. Cell. 135:1085-97.

Keller, H., P. Rentsch, and J. Hagmann. 2002. Differences in cortical

actin structure and dynamics document that different types of

blebs are formed by distinct mechanisms. Exp Cell Res.

277:161-72.

Klein, C., T. Pillot, J. Chambaz, and B. Drouet. 2003. Determination of

plasma membrane fluidity with a fluorescent analogue of

sphingomyelin by FRAP measurement using a standard

confocal microscope. Brain Res Brain Res Protoc. 11:46-51.

Peters, R., A. Brunger, and K. Schulten. 1981. Continuous

fluorescence microphotolysis: A sensitive method for study of

diffusion processes in single cells. Proc Natl Acad Sci U S A.

78:962-966.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

Volkmer, A., V. Subramaniam, D.J. Birch, and T.M. Jovin. 2000. One-

and two-photon excited fluorescence lifetimes and anisotropy

decays of green fluorescent proteins. Biophys J. 78:1589-98.

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Table I: Hetero-FRET+ measurement by Donor Fluorescence Lifetime

Fluorescence Lifetime§§

D

(Donor)

of PLF-labeled FR-GPI at the surface of

FR-GPI-expressing CHO cells

A

(Acceptor) Treatment

τavg #

†† (ns)

PLF Control

(Flat regions)10 @

2.28

(±0.04)

PLF PLR Control

(Flat regions)11 @

2.00

(±0.13)

PLF PLR Saponin 0.2%

5

2.30

(±0.09)

PLF PLR Latrunculin

(Bleb)* 5

2.32

(±0.09)

PLF PLR Jasplakinolide

(Bleb)* 5

2.16

(±0.01)

In all experiments CHO cells-expressing FR-GPI were singly

labeled with donor (D) alone (PLF, 160nM) or with donor (D) and

acceptor (A) fluorophores (PLF, 160 nM; PLR, 200 nM). Time

correlated single photon statistics after excitation with multi-photon

excitation were obtained as described in Chapter 3.

+Efficiency of hetero-FRET in control cells is estimated by

comparing the average lifetimes of PLF in the presence and absence

of the acceptor fluorophore (PLR); shorter lifetime in the presence of

acceptor indicates increased FRET.

Note: Cholesterol removal by saponin-treatment increases the

lifetime of donor PLF to that obtained in the absence of the acceptor.

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PLF/PLR-labeled blebs exhibit a longer donor-lifetime, consistent with

the lack of hetero-FRET on the blebs.

§§ Fluorescence lifetimes were calculated using the fitting routine

outlined in Supplementary Methods and expressed as averages (+

S.D.) from the indicated number of cells (n #).

†† τavg is the amplitude-weighted average of all lifetimes obtained

from the fitting routine; PLF in the absence of acceptor shows multiple

lifetimes as reported earlier(Sharma et al., 2004).

@Measurements on flat regions were made by scanning the

laser over a small ~100x100 pixel area.

* Measurements on Blebs were made by collecting photon

statistics using a parked beam located on the bleb.

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Table II: Time resolved Homo-FRET measurements

Anisotropy decay rates#

Treatment

of GFP-GPI at the surface of GFP-GPI-

expressing CHO cells

# r0 τ1 (ns) * a1 τ2 (ns) a2 rss

Control

(Flat regions) 11

0.40

(± 0.01)

0.17

(± 0.06)

0.10

(± 0.01)

33

(± 11)

0.90

(± 0.01)

0.34

(± 0.02)

Saponin

0.2% 9

0.39

(± 0.02)

23

(± 4)

0.36

(± 0.02)

Latrunculin

(Flat regions) 7

0.40

(± 0.01)

0.2

(±0.06)

0.08

(± 0.04)

27

(± 7)

0.92

(± 0.04)

0.34

(± 0.02)

Jasplakinolide

(Flat regions) 7

0.41

(± 0.01)

0.3

(±0.02)

0.09

(± 0.05)

28

(± 9)

0.92

(± 0.05)

0.35

(± 0.03)

Latrunculin

(Bleb) 6

0.41

(± 0.01)

0.57

(± 0.07)

0.06

(± 0.01)

42

(± 22)

0.94

(± 0.006)

0.38

(± 0.01)

Jasplakinolide

(Bleb) 6

0.41

(± 0.01)

39

(± 13)

0.39

(± 0.01)

In all experiments CHO cells-expressing GFP-GPI were placed

on the microscope stage and the laser beam was parked on indicated

regions. Time correlated single photon statistics were obtained after

excitation with multi-photon excitation using a high NA objective as

described in Supplementary Methods. Measurements on all regions

were made by collecting photon statistics using a parked beam located

on the region.

Note: Cholesterol removal by saponin-treatment eliminates the

fast component due to FRET.

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+Efficiency of homoFRET is obtained by estimating the

amplitude of the fast decay component of the anisotropy decay of GFP

fluorescence emission. While the rate of decay of the short component

is an estimate of the distance between GFP-fluorophores, the longer

decay component relates to the rotational dynamics of the GFP-

fluorophore.

Note: Cholesterol removal by saponin-treatment eliminates the

fast component consistent with earlier studies. Anisotropy decay rates

on blebs also have much smaller amplitude of the shorter decay rate

(Lat) or no short component (Jas), consistent with a reduction/lack of

homoFRET between GFP-GPI-APs in these regions. The presence of

this small component is likely to be due to the large volume of the

confocal excitation (~ 900 nm, Z-resolution), potentially collecting some

emission from the flat-regions surrounding the blebs.

* r0 is the initial anisotropy; its value is depolarized compared to

that obtained using a low NA objective as reported earlier (Volkmer et

al., 2000) (data not shown).

# Anisotropy decay rates were calculated using the fitting routine

eplained in chapter 3 and expressed as averages (+ S.D.) from the

indicated number of cells (n).

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Figure 6.A Imaging and interconversion dynamics assay

PLF-labeled FR-GPI expressing cell imaged on Zeiss 510 meta

microscope (Zeiss, Germany) illuminated by multi-photon excitation at

790 nm. ii) Schematic representation of the strategy of FIAT or

microphotolysis assay developed to study the interconverion of

nanocluster of GPI-APs at the cell surface. Intensity and anisotropy

traces from a fluorescent sample were collected from a multi-photon

excitation volume focused on the sample plane. This was achieved

with a 20x - objective lens (0.7NA). The multiphoton laser was parked

at a single point (as shown by the red crosshair) for continuous

illumination at or near the cell periphery at the center of the field of

observation. Scale bar, 5 µm.

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Figure 6.B FIAT assay for BODIPY-SM at cell surface

BODIPY-SM (N-(4,4-difluoro-5,7-dimethyl-4-bora-3a,4a-diaza-s-

indacene-3-pentanoyl)) complex with BSA (ratio 1:1) in a 5 µM solution

was incorporated onto the surface of CHO cells by incubating for 30

min on ice. Cells were subsequently maintained at 20°C on a Laser

scanning confocal microscope equipped with MP-excitation (790 nm).

Cells were imaged using MP excitation (top) and intensity (blue line)

and anisotropy traces (red line) were obtained simultaneously from a

confocal volume (red crosshair) during an illumination sequence

outlined at the top of the trace (bottom). The concentration of BODIPY-

SM incorporated on the cell surface, is sufficient to record significant

homo-FRET at time, t=0. During the initial illumination period (t1), the

anisotropy of BODIPY-SM increased and approached A∞. A∞ (pink

band) was measured after photobleaching the BODIPY-SM so that

there was no further change in anisotropy. Scale bar 6.6µm.

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Figure 6.C Intensity and anisotropy traces and images from cell

surface-labeled GPI-APs at 20°C.

Intensity (blue line) and anisotropy (red line) traces were

obtained simultaneously from the resultant confocal volume [e.g. red

crosshair, inset in cell image], during the illumination sequence outlined

at the top. The pink band in the graphs are the range of A∞ values

obtained for each experiment representing anisotropy values for PLF

labeled monomeric GPI-anchored protein.

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Figure 6.D Intensity and anisotropy traces and images from cell

surface-labeled GPI-APs at 37°C.

Examples of intensity (blue line) and anisotropy (red line) traces

which were obtained simultaneously from cell surface at 37°C in a

confocal volume during FIAT experiments as the illumination sequence

outlined at the top. The pink band in the graphs are the range of A∞

values obtained for each experiment representing anisotropy values for

PLF labeled monomeric GPI-anchored protein at 37°C.

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Figure 6.E Schematic representation of FIAT assay on cell surface

In this figure, I describe the schematic of various types of

fluorescence intensity and anisotropy traces obtained from FIAT assay

of GPI-APs organization in a confocal volume at the cell surface. A

pictorial description of classification made by visual inspection of

graphs obtained from multiple experiments: Full recovery (FR) and

partial recovery (PR) types would be expected from a region where

there are more nanoclusters and relatively significant monomer-

nanocluster interconversions occur. No recovery type (NR) is expected

from regions where significant fragmentation and no formation of

nanocluster occur. No inerconversion (NI) is type of traces where

monomeric population of GPI-APs is not engaged in formation of

nanocluster.

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Figure 6.F Schematic of dynamics of GPI-anchored proteins

Schematic of the expected dynamics (bleaching, diffusion and

interconversion) of monomers and nanoclusters (depicted as dimers)

within a confocal volume (inner circle). The colour of the circles

represents the bleaching status of the fluorophores, green being active

and black bleached. The arrows represent the transition rates between

the species present. While the fluorophores diffuse both into and out of

the confocal volume, the model assumes that the incoming

fluorophores are predominantly unbleached. The outer circle

represents the pool of unbleached monomers and nanoclusters that

serves to replenish the confocal volume. The dynamics is modeled by

reaction-diffusion equations, involving rates of bleaching (b), diffusion

(monomer d1 and cluster dc), and cluster aggregation (ka) and

fragmentation (kf) as described.

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Figure 6.G Examples of theoretical fits obtained from FIAT assay

Examples of model dependent fit obtained from the schematic

described in previous figure of FIAT experiments of PLF-labeled FR-

GPI, recorded at different temperatures 37°C (i), 24°C (ii) and 15°C (iii,

iv). The fits (dark lines) provide the values of the parameters appearing

on top of each panel. Extracted values of dc were found to lie in the

range (-10-5 s-1 < dc < 10-5 s-1); this being 4-orders of magnitude smaller

than d1. This may safely be taken as immobilized clusters. Pink band

represents the A∞ values.

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Nanocluster dynamics II

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Figure 6.H Rate of diffusion of monomers obtained from the fit

Diffusion of monomers of FR-GPI 1d measured across different

cells as a function of temperature. The range of values for 1d is shown

normalized to a typical value of FR-GPI diffusion (0.1078s-1) extracted

at 37oC. This is consistent with those obtained from measurements on

FR-GPI on similar blebs and BODIPY-SM on the cell surface. In

addition, independently measured variation of diffusion coefficients

from FCS measurements of FR-GPI and GFP-GPI across the

temperature range are 0.516 ± 0.135 µm2/s (20°C) and 1.276 ± 0.469

µm2/s (37°C).

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Nanocluster dynamics II

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Figure 6.I Interconversion rates obtained from the fit

Graph shows the rates of fragmentation ( fk ) versus aggregation

( ak ) measured across different cells at multiple temperatures.

Distribution of the measured parameters was observed upon fitting,

across different cells at temperatures ranging from 15°C - 37°C.

The ranges of data that falls into these categories FR, PR and NR

classification was segmented with dashed lines correspond to the

classes elaborated previously.

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Figure 6.J Temperature dependence of interconversion rates

i) Histogram shows the relative population of each class [FR,

PR, NR, and NI (where the anisotropy stays at a highly polarized value

A∞ at all times)] at different temperatures based on the classification

scheme (Figure 6.E); the representative class for each temperature is

indicated by the red bar. ii) Ratio of typical interconversion rates

obtained from each representative class are represented in an

Arrhenius plot, as ln ka/kf versus roomT T . The line connecting the

typical values of ln ka/kf in each representative class shows strong non-

Arrhenius behaviour.

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Nanocluster dynamics II

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Figure 6.K Perturbation of Cholesterol levels in membrane alters

dynamics

PLF-labeled FR-GPI expressing cells were pre-incubated with

mβCD (10 mM; 30 min) on a microscope stage maintained at 37°C,

were illuminated by multi-photon excitation at 790 nm. Intensity (blue

line) and anisotropy (red line) traces were obtained simultaneously

from the resultant confocal volume, during the illumination sequence

outlined at the top. After waiting time tw, the traces were recorded from

the same area of the cell during t2. The pink band at the top indicates

the range of A∞ values obtained in the experiment.

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Figure 6.L Blebs are devoid of actin

GFP-GPI expressing cells were treated with Jas (14µM) for

30min at 37 °C, fixed and stained with rhodamine phalloidin, before

imaging on confocal. Images from the bottom, medial and top planes of

a confocal stack of images show that membrane blebs (green) are

devoid of polymerized actin (red) as observed by the lack of

Rhodamine-phalloidin staining. Scale bar, 6 µm.

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Nanocluster dynamics II

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Figure 6.M Hetero-FRET measurements by donor fluorescence

lifetime: Blebs are devoid of nanoclusters

FR-GPI expressing CHO cells were labeled with donor and

acceptor (PLF and PLR) cells were imaged using the multiphoton set

up after treatment with Jas for 30 min at 37 °C. The bleb and flat part of

membrane are marked with green box and blue box respectively in the

intensity image. Lower donor lifetimes (corresponding to high levels of

heteroFRET) are observed in the flat regions (blue graph) of cells while

higher donor lifetimes representing less FRET were recorded from the

blebs (green graph). Statistics of lifetime data from large set of data is

represented in Table I. Scale bar, 20 µm.

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Nanocluster dynamics II

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Figure 6.N FLIM data shows blebs has less hetero-FRET compared to

flat part of cell

Either donor (PLF) alone (panel i) or donor and acceptor

(PLF/PLR)-labeled FR-GPI expressing CHO (panel i, iii) cells were

imaged using the FLIM set up either before (panel i, ii) or after

treatment with Jas for 30 min at 37 °C (panel iii). Average donor

fluorescence lifetime image pixel-by-pixel were generated for specific

regions of the cell demarcated by boxes at the cell surface at 1 x 1 µm2

resolution. This was done to achieve the the photon statistic required

for the calculation of donor lifetime. The pseudo-coloured life-time

maps at the right of each intensity image, coloured with respect to the

LUT scale corresponding to the indicated life-time ranges. The spatial

resolution of PLF-fluorescence at signal levels obtained is not sufficient

to resolve the micron-scale features. However, the spatial variation of

heteroFRET signal estimated from the measured distribution of

average fluorophore lifetimes compared to that observed for donor-

alone labeled cells [panel i, boxes (1-6)], reveals regions with high

FRET [panel ii, flat regions in boxes (1-3, 5)] and low FRET [panel ii,

membrane ruffles in boxes (4, 6);] and [panel iii, blebs in boxes (1-6)].

Donor lifetime decay data was also obtained from flat part of cell and

blebs by scanning or parking the laser at a single spot. This set of data

is summarized in Table I.

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Figure 6.O Time-resolved anisotropy data for homo-FRET

measurement

GFP-GPI-expressing CHO cells were treated with Lat A (25 µM,

30 min, 37 °C) to generate blebs, and at the indicated spot on a bleb

(green cross), single point-time resolved anisotropy decay

measurements was carried out using confocal multi-photon excitation

at 37°C. The anisotropy decay profiles obtained from flat region (blue

arrow; blue line) are compared with those blebs (green crosshair;

green line) of cell in the same image. Data from multiple flat

membranes, blebs and from cell membranes treated with saponin are

shown in Table II. Scale bar, 20 µm.

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Nanocluster dynamics II

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Figure 6.P Nanoclusters are not present in membrane structure devoid

of actin (blebs) as seen in FIAT experiments

FIAT experiment was performed on blebs generated by 30 min

incubation at 37 °C with Lat (12 µM; B) or Jas (15 µM; C) show high

anisotropy values close to A∞ (pink band) indicating membranes devoid

of pre-existing GPI-AP nanoclusters. Intensity (blue) and anisotropy

(red) traces are shown.

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Nanocluster dynamics II

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Figure 6.Q Actin perturbation influences nanocluster formation

FIAT experiment was performed on PLF-labeled FR-GPI

expressing cells. With same from the illumination sequence intensity

(blue) and anisotropy (red) traces were obtained at 37°C where

Latrunculin (Lat, 6µM; i) or Jasplakinolide (Jas, 5µM; ii) was added to

cells on the stage after the first illumination period, t1, or pre-incubated

with Latrunculin (Lat, 6µM; iii) for 4 minutes. After waiting time tw, the

traces were recorded from the same area of the cell during t2. During

this treatment there was no detectable change in cell morphology.

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Figure 6.R Inhibition of myosin activity influences nanocluster formation

FR-GPI expressing cells labeled with PLF were pre-incubated

with Blebbistatin (50µM) for 4 minutes on microscope stage at 37°C

and FIAT experiments were performed. Intensity (blue line) and

anisotropy (red line) traces were obtained simultaneously from the

resultant confocal volume, during the illumination sequence outlined at

the top. The pink band at the top of each panel indicates the range of

A∞ values obtained in the experiment. It is evident that when myosin

activity is blocked even at 37°C no further formation of nanoclusters

after depletion by bleaching in a confocal area. During this treatment

there was no detectable change in cell morphology.

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141

Chapter 7

Conclusions and discussion

Conclusions: I summarize the conclusions of my thesis in following points:

1. I have estimated energy transfer efficiency in GPI-AP

organization by hetero-FRET method with varying labeling

ratios of acceptor and donor fluorescent probe. The profile

of the energy transfer efficiency is similar to the theoretical

prediction.

2. I have also shown that both homo- and hetero-FRET

measurement are complementary technique to estimate

altered oligomerization status of GPI-APs obtained upon

generating larger scale oligomer. In general, these

techniques together can be reliably used to determine scale

of molecular organization at the nanometer scale.

3. Both ARAP and FIAT data and analysis show that at the

cell surface nanoclusters are immobile at different length

scale (from confocal area to 1μm2

4. Nanoclusters of GPI-AP are formed at physiological

temperature from existing monomers and maintained at a

specific concentration irrespective of the amount of the

protein present at plasma membrane. Therefore, this

system remains un-equilibrated.

), but monomers remain

mobile.

5. I measure the rate of formation and fragmentation of

nanoclusters in microphotolysis type FIAT assay at multiple

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Conclusions

142

temperatures. In collaboration with Kripa G. and Dr. Madan

Rao, we show the interconversion kinetics between

monomer and clusters do not exhibit Arrhenius type

behavior.

6. Finally, I show that the formation of nanoclusters is

sensitive to the membrane composition (levels of

cholesterol) and cortical actin activity.

Discussion:

The formation and immobilization of native nanoclusters of GPI-

APs are created and maintained by the CA (Goswami et al., 2008). It

represents a new type of molecular complexation in steady-state

kinetics of lipd-anchored protein at the live cell surface. This can be

explained through a framework of active hydrodynamics of the

membrane which is coupled to active cytoskeleton system (Hatwalne et

al., 2004; Kruse et al., 2004; Manville et al., 2001; Ramaswamy and

Rao, 2007). Here, passive cell surface molecules, such as GPI-APs or

transmembrane proteins, irrespective of their direct or indirect

interactions with the CA, can transiently be influenced by the CA.

Theoretical studies speculate such transiently bound molecules can be

actively driven along the polar actin filaments resulting in local

molecular clustering and can further be explained by acto-myosin

contractile forces and tread-milling. These active complexes maybe a

generic mechanism for local nanoclustering of a variety of cell surface

molecules such as GPI-APs (Sharma et al., 2004), Ras-isoforms,

(Plowman et al., 2005), and gangliosides (Fujita et al., 2007). These

nano-complexes can increase the rates of chemical reactions in living

cells (Hancock, 2006). The larger scale (> 100 nm) organization of

these nanoclusters maintained by CA, could possibly be act as

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functional membrane domains responsible for signaling and sorting

functions (Sharma et al., 2004).

The concept of lipid-based sorting and signaling by lateral

segregation of specific molecules as a part of ‘lipid-rafts’ can be

implemented as following (Simons and Ikonen, 1997): I show that lipid

based clustering of molecules may be achieved by active mechanisms

followed by concentration of such nano-domains into specific sites.

These sites can then act as hot-spots for endocytosis and signalling.

This has implications in understanding the role of rafts, especially as

regulatable membrane microdomains as envisaged by recent studies in

cell signalling networks involved in regulating cell shape (Neves et al.,

2008). It is also likely that such structures may exhibit distinct

properties, quite distinct from those dictated by thermodynamic

considerations as explored in artificial membranes.

References:

Fujita, A., J. Cheng, M. Hirakawa, K. Furukawa, S. Kusunoki, and T.

Fujimoto. 2007. Gangliosides GM1 and GM3 in the living cell

membrane form clusters susceptible to cholesterol depletion and

chilling. Mol Biol Cell. 18:2112-22.

Goswami, D., K. Gowrishankar, S. Bilgrami, S. Ghosh, R. Raghupathy,

R. Chadda, R. Vishwakarma, M. Rao, and S. Mayor. 2008.

Nanoclusters of GPI-anchored proteins are formed by cortical

actin-driven activity. Cell. 135:1085-97.

Hancock, J.F. 2006. Lipid rafts: contentious only from simplistic

standpoints. Nat Rev Mol Cell Biol. 7:456-62.

Hatwalne, Y., S. Ramaswamy, M. Rao, and R.A. Simha. 2004.

Rheology of active-particle suspensions. Phys. Rev. Lett. .

92:118101.

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Kruse, K., J.F. Joanny, F. Jülicher, J. Prost, and K. Sekimoto. 2004.

Asters, vortices and rotating spirals in active gels of polar

filaments. Phys. Rev. Lett. . 92:078101.

Manville, J.-B., P. Bassereau, S. Ramaswamy, and J. Prost. 2001.

Active membrane aspirations studied by micropipette

aspirations. Phys. Rev. E. 64:021908.

Neves, S.R., P. Tsokas, A. Sarkar, E.A. Grace, P. Rangamani, S.M.

Taubenfeld, C.M. Alberini, J.C. Schaff, R.D. Blitzer, Moraru, II,

and R. Iyengar. 2008. Cell shape and negative links in

regulatory motifs together control spatial information flow in

signaling networks. Cell. 133:666-80.

Plowman, S.J., C. Muncke, R.G. Parton, and J.F. Hancock. 2005. H-

ras, K-ras, and inner plasma membrane raft proteins operate in

nanoclusters with differential dependence on the actin

cytoskeleton. Proc Natl Acad Sci U S A. 102:15500-5.

Ramaswamy, S., and M. Rao. 2007. Active-filament hydrodynamics:

instabilities, boundary conditions and rheology. New J. Phys. . 9

423.

Sharma, P., R. Varma, R.C. Sarasij, Ira, K. Gousset, G.

Krishnamoorthy, M. Rao, and S. Mayor. 2004. Nanoscale

organization of multiple GPI-anchored proteins in living cell

membranes. Cell. 116:577-89.

Simons, K., and E. Ikonen. 1997. Functional rafts in cell membranes.

Nature. 387:569-72.

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Publications

1. Nanoclusters of GPI-anchored proteins are formed by cortical actin-driven activity.

Goswami D, Gowrishankar K, Bilgrami S, et al

Cell

2. Nanoscale organization of hedgehog is essential for long-range signaling.

. 2008 Dec 12;135(6):1085-97.PMID: 19070578

Vyas N, Goswami D, Manonmani A, Sharma P, Ranganath HA, et

al Cell

3. Precise positioning of myosin VI on endocytic vesicles in vivo.

Altman D*, Goswami D*, Hasson T, Spudich JA, Mayor S.

. 2008 Jun 27;133(7):1214-27. PMID: 18585355

PLoS Biol

4. A DNA nanomachine that maps spatial and temporal pH changes inside living cells.

. 2007 Aug;5(8):e210. PMID: 17683200

Modi S, MGS, Goswami D, Gupta GD, Mayor S, Krishnan Y

Nat. Nanotech. 4

, 325 - 330 (2009)

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Synopsis

Thesis Title: Modes of nano-scale clustering of GPI-anchored protein at the cell surface.

Name: Debanjan Goswami

Degree: Doctor of Philosophy

Subject: Cell Biology

Submitted to: Tata Institute of Fundamental Research (Deemed University)

Thesis Supervisor: Prof. Satyajit Mayor

Institute Address: National Centre for Biological Sciences, Tata Institute of Fundamental Research, UAS- GKVK Campus, Bellary Road, Bangalore. 560065. India.

Date of Submission:

10-June-2008

Signature of Supervisor Signature of Candidate

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Introduction

A hallmark in the understanding of cell membrane organization

and structure was encapsulated in the Fluid Mosaic model (Singer and

Nicolson, 1972), where the membrane was visualized as an

equilibrated two-dimensional fluid – a passive mixture of proteins

dissolved in a sea of lipids. According to this model, all lipids and

proteins (ratio varies from 1:4 to 4:1) diffuse freely at all length-scale on

the surface of the cell (Frye and Edidin, 1970). Over the last decade,

the concept of a compartmentalized membrane has emerged where

the cell surface is not a homogeneous mixture, but is segregated into

domains. The mechanism of formation and maintenance of such

domains is hypothesized as arising due to the interaction between

specific lipids such as cholesterol and sphingolipids and associated

proteins. Compartmentalized regions or domains on cell membrane are

referred as ‘lipid-rafts’. ‘Lipid-rafts’ are proposed to be involved in a

variety of important biological roles including endocytosis, trafficking,

signaling complex formation (Simons and Ikonen, 1997). Although

numerous biological functions have been ascribed to ‘lipid-rafts’, the

mechanism behind their formation, structure and dynamics remain

highly debated (Mayor and Rao, 2004).

Phase segregated domains have been demonstrated in artificial

membranes composed of ternary mixtures of lipids. These mixtures

exhibit three distinct phases depending on temperature and

composition: gel (so), liquid ordered (lo) and liquid disordered (ld).

Above the chain melting temperature (Tm), the hydrocarbon chains of

lipids are floppy, disordered and loosely packed. This is known as

liquid (disordered) phase (ld). The ld phase has short range positional

correlation. Below Tm, lipids with saturated long acyl chains are tightly

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packed and form a phase called the ‘gel phase’. The hydrocarbon

chains are oriented and ordered. The positional correlations in the

plane of the bilayer are long range. However, below Tm, in the

presence of cholesterol, long saturated acyl-chains remain oriented but

the positional correlations are short range, like in a liquid. This is known

as liquid ordered phase (lo). The diffusion coefficient of lipids in lo

phases are higher than in ‘gel-phase’, but lower than in the ld phase.

Since the rigid cholesterol molecule is inserted inside the lipid

molecules (in gel phase), the surface area per lipid molecule in the lo

phase is larger than in gel phase. However, above Tm in the presence

of cholesterol, no macroscopic phase segregation was observed. But

by spectroscopic studies, such as nuclear magnetic resonance (NMR)

and electron spin resonance (ESR), the two fluid state (lo and ld) was

shown to exist together (Sankaram and Thompson, 1990; Vist and

Davis, 1990). Below Tm, whereas the gel phase is generally observed,

in presence of high cholesterol (>20 mol%) gel phase is replaced by lo

phase and the two fluid (liquid) phases (lo and ld) can coexist (Brown

and London, 2000). Experimentally, when the ternary mixture was

brought down to below Tm of specific lipid species, these molecules

form liquid ordered phases (lo phase) in coexistence with disordered

phases (ld). These lo phases coalesce into large scale domains which

are resolvable by optical microscopy. It is this lo phase that is thought to

be relevant and analogous to ‘lipid-rafts’ in biological systems.

Since, the lo domains exist as large-scale phase-segregated

domains, it was expected that they could be observed in biological

membranes using techniques such as fluorescence microscopy,

electron microscopy, optical tweezers, single molecules studies and

biochemical treatments (chemical cross-linking). However, in contrast

to the situation in artificial membrane, none of the above techniques

have been able to detect the presence of any large scale lo domain on

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the native cell membrane (Mayor and Rao, 2004; Munro, 2003). In this

scenario, several investigators tested the interaction of detergents with

membrane. This technique has been used to assess the ‘fluidity’ of

biological membrane (Helenius and Simons, 1975). It was argued that

if biological membrane contains ‘gel-like’ lo patches, similar to artificial

membrane, these would be insoluble in cold-nonionic detergents (for

example Triton X-100). Consequently, it has been shown that when cell

membranes are extracted with cold (4°C) Triton X-100, a non-ionic

detergent, a small fraction of insoluble membrane residue consisting of

specific subsets of lipids and proteins – called ‘detergent resistant

membrane’ (DRM), is observed (Simons and Ikonen, 1997).

Compositionally, DRM has been correlated to the lo domain on the

model membrane (London and Brown, 2000). This membrane fraction

is enriched in cholesterol, sphingomyelin and many lipid-tethered

proteins such as non-receptor tyrosine kinase,

glycosylphosphatidylinositol anchored proteins (GPI-APs), etc (Simons

and Ikonen, 1997). However, careful biophysical studies in artificial

membrane have showed that Triton X-100 (TX-100) can induce

formation of ‘more ordered phase’ in model membranes from native

‘disordered’ phase (Heerklotz, 2002; Heerklotz et al., 2003).

Furthermore, it has been observed that TX-100 extracted (4°C) DRM

composition matches with the ‘lo’ domain obtained in the ternary phase

diagram at 37°C, but not with the composition of the lo phase at 4°C

(de Almeida et al., 2003). That means detergent extraction can also

change the composition of preexisting domain on any artificial

membrane. So, a priori existence of lo domain or ‘lipid-raft’ on native

cell membrane and its composition remains questionable (Mayor and

Rao, 2004).

The scale of lipid domains in cell membrane has remained

controversial; in a few experimental attempts, the size of lipid raft has

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been estimated from <10nm to 700nm (Anderson and Jacobson,

2002). Various biophysical tools such as Förster’s Resonance Energy

Transfer (FRET), chemical cross linking, single particle tracking,

fluorescence correlation spectroscopy, laser trap have been used to

investigate the size of large scale organization of different molecules

(lipids, GPI-APs, toxins, trans-membrane proteins) at the cell surface.

However, all these measurements failed to provide consensus scale of

the preexisting lipid domains at the cell surface.

GPI-APs have served as marker for rafts since they associate

with DRMs and form nanoclusters in a cholesterol sensitive fashion.

The nanoclusters were characterized by measuring homo-FRET

between fluorescently labeled GPI-APs on the native cell membrane. In

contrast to hetero-FRET where FRET is monitored between two

different fluorophore, in the homo-FRET process, energy transfer

between two like fluorophores mey be measured if they are in close

proximity (<10nm distance) (Varma and Mayor, 1998). This non-

invasive technique showed presence of sub-resolution (<70nm)

clusters of GPI-APs at the live cell surface (Varma and Mayor, 1998).

These sub-resolution clusters are sensitive to cholesterol and

sphingolipid content in the membrane. However, lack of measurable

hetero-FRET between donor and acceptor fluorescent labeled GPI-APs

on the cell surface contradicted the possibility of nanoclusters in sub-

resolution domain (Kenworthy and Edidin, 1998). A resolution of this

controversy was obtained when Sharma et al measured the scale of

GPI-AP organization. They theoretically modeled thegradual change in

homo-FRET efficiency observed upon photobleaching of fluorophore-

labeled GPI-APs and it to obtain the size of GPI-AP structures giving

rise to the FRET signals (Sharma et al., 2004). Moreover, Sharma et al

provided an explanation for lack of detectable hetero-FRET between

donor-acceptor pair labeled GPI-AP molecules in nanoclusters by

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calculating the theoretical values of average hetero-FRET between

them (Sharma et al., 2004).

Properties of GPI-AP organization till date can be summarized

into following points: a) GPI-APs remain in a small fraction of

nanoclusters, 20-40% of the total protein on the cell surface, consists

of 2-4 molecules per cluster. b) nanoclusters are cholesterol sensitive.

c) Nanoclusters are always maintained a particular concentration

irrespective of surface protein concentration. This feature does not

obey the ‘law of mass action’ – represents a non-equilibrium state. d)

Multiple GPI-AP can cohabit within a cluster. This means clusters are

not frozen and they do exchange molecules with monomers present at

the cell surface.

This description provides an average picture of GPI-AP

organization at the cell surface at 20°C which lacks information about

the dynamics of the aggregation process or its spatial heterogeneity; it

also lacks explanation for non-equilibrium state of the whole process.

The present status and understanding of properties of ‘lipid-rafts’

in the view of GPI-AP organization at the cell surface evokes two

obvious questions. First, about the steady state dynamics and

distribution of nanocluster on the cell surface and second, about the

mechanism of maintenance of such unusual organization. Therefore, I

decided to address the following set of questions and attempt to

answer them by developing necessary tools:

1. The diffusivity of nanocluster vs. monomers at the cell

surface.

2. How the nanoclusters are distributed on the cell surface,

which requires high resolution anisotropy imaging.

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3. The steady state dynamic properties of nanocluster on the

cell surface – the formation and fragmentation process.

4. Mechanism of maintenance of nanoclusters.

5. Possibility of inducing alteration in the organization.

Results

New experimental tools

Before going into the results, I would like to introduce two new

experimental strategies that I have developed to address these

questions.

1) I have developed a novel fluorescence intensity-anisotropy

trace (FIAT) assay. Here, I measure the intensity and the anisotropy

from a confocal volume on cell surface illuminated with a multi-photon

laser couple to a single photon counting device. In this assay, I locally

perturb the density of fluorophore-labeled folate receptor (FR-GPI) at

the surface of CHO cells by multi-photon (MP) confocal excitation

starting at time t=0 upto t=t1 which I specify as the first illumination

time; then the laser is switched off for a waiting time, tw; after this a

second illumination was perused for a time, t2. I follow the dynamical

response in anisotropy and fluorescence intensity simultaneously from

the same volume, where intensity represents the local concentration of

the protein and the value of the anisotropy reports on the

oligomerization status of these receptors.

2) I used a custom designed line-scanning microscope with high

spatio-temporal resolution for time-lapse anisotropy of emission

fluorescence imaging. A region (1μm2) was chosen on a fluorophore

labeled cell for bleaching and 80% fluorophore was bleached within

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that area using high-intensity pulse from a separate bleaching laser.

Following the bleaching of fluorophore-FR-GPI at the centre of the

illuminated area, I follow the recovery of intensity and anisotropy in this

area at discrete time points. I name this assay as anisotropy recovery

after photobleaching (ARAP).

3) A new application of real time fluorescence dynamics has

been implemented to measure homo- and hetero-FRET on the surface

of a living cell. Using a pulsed laser (80MHz) and a time correlated

single photon counting device, I measure fluorescence lifetime of donor

fluorescence (in case of estimating hetero-FRET) and time resolved

anisotropy of fluorophore tagged GPI molecules (in case estimating

homo-FRET).

Detection of hetero-FRET by fluorescence lifetime measurements

Using labeled acceptor and donor pairs, fluorescence lifetime

measurements report significant changes in donor lifetimes consistent

with the study GPI-AP organization reported in Sharma et al (Sharma

et al., 2004). I have used analogues of folic acid (a ligand for folate

receptor, FR-GPI), conjugated to two different fluorophores having

significant overlap in excitation and emission spectra (an important

criterion for FRET). Cells, expressing FR-GPI, labeled with increasing

acceptor to donor ratio shows gradual increase in energy transfer

efficiency. Energy transfer efficiencies at increasing acceptor to donor

ratio shows similar profile as predicted theoretically in Sharma et. al.

(Sharma et al., 2004). Thus, these fluorescence lifetime measurements

have the sensitivity to detect nanoclusters organization using FRET.

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Aerolysin toxin alters organization of GPI-AP

Aerolysin is a plasma membrane pore-forming toxin, which, after

activation by carboxy-terminal cleavage, binds GPI-anchored proteins

in the membrane (Fivaz et al., 2002) and alters the oligomerization

status of the lipid anchored protein by heptamerizing (Tsitrin et al.,

2002). This heptamer eventually forms an aqueous pore in the

membrane. I have explored the alteration of GPI-AP nanocluster during

the process of toxin-induced GPI-AP clustering at the cell surface. Both

using homo- and hetero-FRET measurements, I show that the tools I

have established are able to describe the altered oligomerization status

of GPI-AP clusters.

GPI-AP nanoclusters are immobile and distributed heterogeneo usly at the surface of living cells.

To study the diffusivity of GPI-AP clusters and monomersat the

cell surface I observed fluorescent tagged GPI-AP under high

resolution (where lateral and axial resolution being 260nm and 890nm

respectively) anisotropy imaging setup via line scanning confocal

microscope. Confocal anisotropy images show two distinct type of

optically resolvable regions on the cell surface – a. low average

fluorescence anisotropy regions, associated with flat regions of the cell

membrane; b. regions with high average anisotropy, associated with

membrane overlying protrusive actin architecture such as dynamic cell

edges, such as membrane ruffles or leading edges of lamellipodia. In

an ARAP experiment, I bleach the fluorophore tagged GPI-AP within a

region of 1μm2 in the flat part of a cell. I find that while the fluorescence

intensity recovers substantially, the anisotropy in the bleached spot

does not recover at 20°C. However, in the regions surrounding the

bleached areas, the anisotropy remains unchanged during the assay.

Interestingly, at 37°C, while the fluorescence intensity recovers rapidly

after bleaching, the original depolarized anisotropy value is eventually

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recover, after a long delay. This suggests that nanoclusters are

immobile and can only be reformed at physiological temperature after a

certain time interval.

Steady state dynamics of GPI-AP nanoclusters

In a FIAT experiment, at 20°C, the anisotropy in the illuminated

volume starts out at a depolarized value which represents a mixture of

nanoclusters and monomers. Then as intensity goes down rapidly (an

effect of photobleaching), and reaches a steady state where

photobleaching and lateral diffusion compensates each other. The

fluorescence anisotropy shows a sharp initial rise (corresponding to

rapid loss of homoFRET). and saturates at a high value, which is a

characteristic of isolated monomers in the membrane (A∞). On the

other hand, at 37°C, the anisotropy rises during t1 and saturates to a

value significantly lower than A∞. During tw, recovery of intensity

indicates fluorophores diffuse in from the surrounding regions. After the

recovery time, the fluorescence anisotropy at 20°C, begins with the

same saturation value obtained at the end of the first illumination. It

never recovers to the depolarized value, obtained in the beginning of t1.

In contrast, at 37°C, there is an almost complete restoration of the

original depolarized anisotropy value after tw, implying that there is

substantial reassembly of nanoclusters from monomers at 37°C.

In collaboration with Kripa Growrishankar, I have tried to fit the

FIAT data precisely using a theoretical model to extract aggregation-

fragmentation kinetics of nanoclusters. The changes in fluorescence

intensity and anisotropy from the fluorophores present in the confocal

volume can be modeled by reaction-diffusion type equations,

incorporating diffusion of monomers and nanoclusters (diffusion

coefficients, D1 and Dc), bleaching of fluorophores (bleach rate, b), and

the interconversion between monomers and nanoclusters (aggregation

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and fragmentation rates, ka and kf). The intensity and anisotropy

profiles can be obtained again knowing the monomer and nanocluster

anisotropy, Am and Ac.

Using the model, we determined the temperature (15°C to 37°C)

dependent aggregation-fragmentation kinetics. A heterogeneous

distribution of formation kinetics was observed for each temperature on

the cell surface. We determined the typical rates at each temperature

and tried to describe the process by a characteristic way of

representing temperature dependent chemical kinetics ─ an Arrhenius

plot. The Arrhenius plot of intercoversion shows two distinctly different

slopes with a crossover at 24°C. Above 24°C the slope is almost zero

and is steep below 24°C, suggesting a non-Arrhenius type behavior.

Membrane composition is important for formation of nanoclusters

Cells, when treated with mβCD (methyl β-cyclodextrin) which

extracts cholesterol from the cell membrane, affects reformation of

clusters as detected in FIAT and ARAP assays at 37°C; whereas

intensity recovered anisotropy did not recovered in both experiments. It

has been shown previously, that GPI-AP nanoclusters are sensitive to

levels of cholesterol in the plasma membrane. It turns out that

cholesterol plays a key role for nanocluster formation and

maintenance.

Role of cortical actin in nanocluster dynamics

I have perturbed actin polymerization using Jasplakinolide (Jas)

or Latrunculin (Lat) at 37°C to explore the involvement of actin for

maintenance of nanoclusters at the membrane. Application of Jas or

Lat during the waiting period of FIAT assay prevents the restoration of

the starting depolarized anisotropy value, although the fluorescence

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intensity recovers completely. Similar results were obtained when actin

polymerization was perturbed before the experiment in both ARAP and

FIAT assay. This clearly shows that cortical actin is actively involved in

interconversion dynamics of GPI-AP monomers and clusters.

Conclusions

In my thesis, I have shown following results:

1. I have estimated energy transfer efficiency in GPI-AP

organization by hetero-FRET method with varying labeling

ratios of acceptor and donor fluorescent probe. The profile

of the energy transfer efficiency is similar to the theoretical

prediction.

2. I have also shown that both homo- and hetero-FRET

measurement are complementary technique to estimate

altered oligomerization status of GPI-APs obtained upon

generating larger scale oligomer. In general, these

techniques together can be reliably used to determine scale

of molecular organization at the nanometer scale.

3. On the cell surface nanoclusters are immobile at different

length scale (from confocal area to 1μm2

4. Nanoclusters of GPI-AP are formed at physiological

temperature from existing monomers and maintained at a

specific concentration irrespective of the amount of the

protein present at plasma membrane. Therefore, this

system remains unequilibrated.

), but monomers

remain mobile.

5. I measure the rate of formation and fragmentation of

nanoclusters with FIAT assay at multiple temperatures. In

collaboration Kripa G. and Dr. Madan Rao, we show the

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inteconversion rate between monomer and clusters do not

exhibit Arrhenius type behavior.

6. Finally, I show that the formation of nanoclusters is

sensitive to the membrane composition (levels of

cholesterol) and cortical actin activity.

Future direction

It is still unclear what other factors could be involved in this

dynamic process. In addition the molecular links between the CA and

the exoplasmic lipid-tethered protein is poorly understood

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