Mapping the distribution of HCN1-subunit containing ...

Mapping the distribution of HCN1-subunit containing channels on the dendritic trees of trapezius motoneurons

By

Ethan Yichao Zhao

A thesis submitted to the Center for Neuroscience

In conformity with the requirements for the Degree of Master of Science

Queen’s University

Kingston, Ontario, Canada

July 2012

Copyright © Ethan Yichao Zhao, 2012

i

Abstract

Voltage-dependent channels on the dendrites of motoneurons provide additional current

that amplifies or dampens synaptic current en route to the soma. The specific consequences will

depend on the density and distribution of the voltage-dependent channels. HCN channels generate

a positive inward current in response to hyperpolarization. HCN channels are responsible for a

resonance phenomenon in motoneurons where inputs of certain frequencies are preferentially

amplified. Modelling studies indicate that this resonance behaviour only occurs if HCN channels are

uniformly distributed on the dendritic tree. However, the distribution of HCN channels on the

dendrites of motoneurons is unknown. Furthermore, current techniques for measuring channel

density on dendrites suffer from methodological limitations that prevent sampling on a scale

necessary to map the distribution of voltage-dependent channels across the dendritic tree. The goal

of the present study is to develop a high throughput method for measuring the membrane-

associated density of voltage-dependent channels using immunohistochemical, confocal and three-

dimensional image analysis techniques. Secondly, the proximal to distal distribution of membrane-

associated channels formed by the HCN1-subunit on the dendritic trees of trapezius motoneurons

in the adult feline will be compared. Antidromically identified motoneurons innervating the

trapezius muscle were intracellularly stained in order to visualize the entire dendritic tree. HCN1-

subunit containing channels were labeled with a specific antibody. Dendritic segments (n=27 to 92)

of ten trapezius motoneurons at different distances from the soma were acquired using confocal

microscopy and rendered into a three-dimensional volume. The perimeter of the intracellular stain

was used to define the membrane-cytoplasmic border. Analysis of the HCN1 immunoreactivitywas

constrained to this perimeter. This technique provides a means of extracting membrane-associated

HCN1 labeling and therefore the functional distribution of HCN1 channels. The density of

membrane-associated HCN1 immunoreactvity across the dendritic tree was either uniform or

ii

increased with distance from the soma among the ten trapezius motoneurons. The increase in

HCN1 density with distance from the soma was inversely related to the density of HCN1 on the

soma and proximal dendrites. Lastly, the change in HCN1 density with distance from the soma was

inversely related to the total input conductance of the motoneuron.

iii

Co-Authorship

Dr. P.K. Rose was my supervisor throughout this thesis and was instrumental to the design

of the study, data collection, analysis and interpretation of the results. Dr. Keith Fenrich was

responsible for the conception of the technique described in the present study for extracting

membrane-associated immunoreactivity. Monica Neuber-Hess and Ashley Martin assisted in the

surgical procedures described in the methods. Monica Neuber-Hess also assisted in the histological

procedures described in the methods. The first draft of this thesis was written by Ethan Zhao. All

subsequent drafts were written in collaboration with Dr. P.K. Rose who provided critical feedback

and comments.

iv

Acknowledgements

It is difficult to express in words the respect and appreciation I have Dr. P. Ken Rose, my

supervisor and mentor. From the first day of taking LISC 323, a third year undergraduate course

taught by Ken, I realized that he was different from many of the other professors in the Life

Sciences program. Gone were the lectures bombarded by a relentless barrage of bullet points and

dull minutiae. Instead, we were presented with pictures of what seemed to be a neuron with

processes extending out in all different directions, each very complex in structure. Then, Ken posed

a question along the lines of "How do we know what's a dendrite and what's an axon?" These words

struck a chord with me simply because the question seemed so simple and yet I was completely

baffled. I was quite confident in my understanding of the nervous system before the course had

commenced. When asked, I could provide a nice elegant textbook summary of the different roles

dendrites and axons served but with a simple picture laid out in front of me, I could not decipher

between the two after reading so much about dendrites and axons from textbooks. There was no

resemblance between the picture and the simple dendrite and axon model of a neuron that I had in

mind. This lesson like many of the others Ken gave taught me a very important lesson. All too often

we cling on to the facts believing them to be absolute and true in all instances. The picture that Ken

had presented was designed to provoke a general feeling of unease and encourage discussion. The

neuron was in reality not so simple. It broke many of the rules that we believed had to be satisfied

in all cases. In certain regions, the dendrites had physical characteristics expected of axons and in

other regions, the reverse was seen. From this I learned to appreciate that although facts are crucial

to science, revisions are just as important. Science revels in revision. There will always be

exceptions to the rule and accordingly, facts are always undergoing revisions. For the first time, I

was thinking critically about the information that was given to me, no longer just learning passively

and absorbing facts and figures. This was the reason that prompted me to pursue an undergraduate

v

thesis under his supervision and to this day I am grateful that he took a chance on a student that

was by far not the top student in the class and who really did not stand out in any way. From there,

the rest is, well, history.

As a mentor, Ken has played a vital role in my personal development and academic career.

Despite my initial inexperience, Ken treated me as an equal and valued my insight. He would often

come into the lab to share his ideas with us and although at times these discussions were beyond

the reaches of my understanding, I realize that it was these subtle gestures that helped to build my

self-confidence. As a supervisor, Ken has always made the time and effort to address any concerns I

was having despite his demanding schedule. He has an incredible passion for his work that

motivates everyone around him.

I also want to thank the people that I have had the opportunity to work closely with in the

Rose Lab. Keith, thank you for all the guidance and support you have provided before your untimely

departure from the lab. You were instrumental in conceiving the technique for extracting the

density of membrane-associated proteins. What I admire most about you is that you are not afraid

to take an idea on paper and put it into practice. You have such a passion for science and the work

you do, which can only be rivaled by Ken. You have set the highest standards when it comes to the

quality of work done in the lab and although I doubt I can surpass those standards I am quite

content if I can just come close. Anny, thank you for providing the code that would become the basic

skeleton of the masking program used to extract the juxta-membrane region. Without your

assistance and the insight you have provided me, I do not think the masking program could have

taken off the ground. I also value all the incredibly advice you have given me outside of the lab, a lot

of which have changed my outlook on life. Rob, thanks for being the other half of the dynamic duo.

You are an incredibly bright and talented individual and I think we made a pretty good team these

last two years. It will be sad to see you go but I am happy that you are moving onto bigger and

vi

better things. I hope you have a blast in law school and keep me updated on your life, marriage, etc.

Farin, although it has been short I am confident that you will do a great job in the lab. You are really

dedicated to your work and you have the displayed the patience and persistence to continue to

strive for excellence even when you have hit a rough patch, which is extremely often in science I

find. I could not have finished this thesis without the help of great support staff. Thank you Monica

for taking me under your wing and teaching me the ins and outs of immunohistochemistry and

animal surgery. Thank you Ashley for doing so much work behind the scenes and along with Monica

have made the day to day operations of the lab run so smoothly and efficiently that we often take it

for granted.

A big thank you goes out to Abdullah Abunafeesa and Chris Groten. I am really thankful for

all the laughter and great memories we have had in last couple of years but most of all your

friendship. Having you two around really kept my sanity in check when things got really

overwhelming. I am really going to miss you guys when this is all over. To Abdullah, I am thrilled

that you have been accepted into Law School as well. You definitely deserve it and all the future

success that awaits. It is so good to see that all that effort has paid off. You are so humble and

gracious and I have learned so much about the world from the discussions we have had. I cannot

wait to have you and Rob fight it out in a courtroom showdown someday. To Chris, you are the

nicest and most-down-to-earth guy I have met. As with Abdullah, I have enjoyed all the discussions

and time we spent together in the last couple of years. You have my trust and respect and I will

always value your opinion on any matter. I wish you much success and I have no doubt that will

happen especially with a PhD fast in sight. I would also like to thank the numerous summer and 499

students, past and present, who I have been unfortunate to meet. To Katie, Yuan, Moose, Carrol and

Heather, your passion for learning, maturity and remarkable intellect motivates me to continue

learning, improving, and becoming a better person.

vii

I also want to give a special thanks to Dr. Neil Magoski who is always willing to share his

expertise in electrophysiology, who has appeared to accept me as a friend at times and who has put

up with me always disturbing his prized student, Chris Groten, when he is in the middle of running

experiments. I want to thank Dr. Martin Pare for your care, advice and being a wonderful

neuroscience instructor. I would also like to thank Dr. Michael Kawaja for being on my committee

throughout my entire thesis and offering your insight.

viii

Table of Contents

Abstract................................................................................................................................................................................. i

Co-Authorship.................................................................................................................................................................... iii

Acknowledgements.......................................................................................................................................................... iv

List of Figures..................................................................................................................................................................... xii

List of Equations................................................................................................................................................................ xv

List of Symbols and Abbreviations............................................................................................................................ xvi

Chapter 1. Introduction.............................................................................................................................................. 1

1.1 Input-output properties of motoneurons in response to time-invariant inputs................ 3

1.2 The morphology of the motoneuron dendritic tree................................................................................... 4

1.3 Passive properties of dendrites........................................................................................................................ 5

1.4 Active properties of dendrites........................................................................................................................... 6

Chapter 2. Input-output properties of motoneurons in response to time-varying inputs.. 8

2.1 Membrane capacitance alters the time course of transient synaptic potentials........................... 8

2.2 The voltage response to a brief current injection in the dendrite....................................................... 15

2.3 Impedance as an electrical property to describe the input-output relationship of

motoneurons in response to time-varying inputs...............................................................................................

18

2.4 Resonance..................................................................................................................................................................... 21

Chapter 3. Hyperpolarization-Activated Cyclic Nucleotide-Gated (HCN) channels................ 23

3.1 Properties of HCN channels.................................................................................................................................. 23

3.2 HCN channels and neuronal excitability......................................................................................................... 24

3.3 Modulation of HCN channels................................................................................................................................ 25

ix

3.4 Functional Diversity of HCN channels.............................................................................................................. 25

3.5 HCN channels and resonance in motoneurons............................................................................................ 26

3.6 Distribution of HCN channels............................................................................................................................... 28

Chapter 4. Mapping the distribution of voltage-dependent channels on dendritic

membranes........................................................................................................................................................................ 30

4.1 Patch clamp recordings from dendrites.......................................................................................................... 30

4.2 Immunocytochemistry and electron/confocal microscopy.................................................................. 31

4.3 Statement of the problem...................................................................................................................................... 33

4.4 Statement of the goal............................................................................................................................................... 35

Chapter 5. Materials and Methods....................................................................................................................... 36

5.1 Animal preparation.................................................................................................................................................. 36

5.2 Motoneuron identification and intracellular injections........................................................................... 37

5.3 Perfusion and fixation............................................................................................................................................. 38

5.4 Histological processing and visualization of HCN1 and Neurobiotin................................................. 38

5.5 Specificity of antibody............................................................................................................................................. 40

5.6 Visualization of Neurobiotin and HCN1.......................................................................................................... 43

5.7 Reconstruction of motoneuron dendritic trees........................................................................................... 44

Chapter 6. Results Part I: Quantitative measurements of the distribution and density of

membrane-associated proteins on dendrites of intracellularly labeled neurons using

confocal microscopy in conjunction with three dimensional image analysis and

reconstructions of neuronal morphology........................................................................................................

45

6.1 Absorption and scattering of excitation and emission light................................................................... 45

6.2 Penetration of antibodies...................................................................................................................................... 47

x

6.3 Compensating for light attenuation and antibody penetration............................................................ 48

6.4 HCN1 immunoreactivity in the ventral horn................................................................................................. 50

6.5 Distribution of HCN1 immunoreactivity within the spinal accessory nucleus and on

trapezius motoneurons..................................................................................................................................................

51

6.6 Registration of selected dendritic segments observed using confocal microscopy with

reconstructions of the dendritic tree based on epi-fluorescence observations....................................

53

6.7 Extracting membrane-associated HCN1 immunoreactivity................................................................... 55

6.8 Determination of the intracellular to extracellular boundary............................................................... 57

6.9 Creation of a three-dimensional layer of voxels to capture the juxta-membrane region.......... 57

6.10 Independent confirmation of the accuracy of the membrane extraction technique................. 58

Chapter 7. Results Part II: Proximal to distal gradients in the distribution of

membrane-associated HCN1 immunoreactivity on the dendritic trees of trapezius

motoneurons..................................................................................................................................................................

61

7.1 Some trapezius motoneurons display a uniform proximal to distal distribution of

HCN1......................................................................................................................................................................................

61

7.2 Some trapezius motoneurons display a proximal to distal graded increase in HCN1................ 64

7.3 The proximal to distal gradients in HCN1 density is inversely related to the somatic HCN1

density...................................................................................................................................................................................

66

7.4 The proximal to distal gradients in HCN1 density is inversely related to the estimated

input conductance of the motoneuron....................................................................................................................

68

Chapter 8. Discussion 71

8.1 The extraction of the membrane-associated density of voltage-dependent channels............... 72

Limitations of the membrane extraction technique.................................................................................. 72

xi

8.2 The resolution in the z dimension is lower than the x and y: consequences of the

difference in resolution..................................................................................................................................................

73

8.3 The juxta-membrane region invariably includes fluorescence from sources in the

neighboring cytoplasm and extracellular space..................................................................................................

73

8.4 The membrane-associated immunoreactivity extends beyond the bounds of the juxta-

membrane layer................................................................................................................................................................

74

8.5 A larger proportion of cytoplasmic immunoreactivity is capture by the juxta-membrane

layer in small dendrites..................................................................................................................................................

75

Regulation of the anatomical and functional distribution of HCN channels............................... 75

8.6 The anatomical distribution of HCN channels may be highly dynamic............................................. 75

8.7 The functional distribution of HCN channels may be altered by cAMP-dependent

modulation...........................................................................................................................................................................

77

Functional consequences.......................................................................................................................................... 78

8.8 The distribution of HCN1 may not reflect the distribution of all HCN channels........................... 78

8.9 Resonance properties as a function of distance from the soma........................................................... 79

8.10 Functional consequences of a linear and uniform proximal to distal distribution of HCN

channels................................................................................................................................................................................

80

8.11 HCN channels and motor unit type................................................................................................................. 81

8.12 Concluding Statements......................................................................................................................................... 82

References.......................................................................................................................................................................... 83

xii

List of Figures

Figure 1.1 The interaction between synapses and voltage-dependent channels............................... 2

Figure 1.2 The relationship between frequency of action potential discharge and constant

long-lasting currents in motoneurons.....................................................................................................................

3

Figure 1.3 The reconstructed dendritic trees of different motoneuron types..................................... 4

Figure 2.1 The lipid bilayer acts as a capacitor.................................................................................................. 9

Figure 2.2 The delivery of a brief current step to a pure lipid bilayer..................................................... 9

Figure 2.3 The change in membrane potential in response to a brief current step of a pure

lipid bilayer.........................................................................................................................................................................

11

Figure 2.4 Leak channels on the neuronal membrane provide a resistive pathway for current

flow..........................................................................................................................................................................................

11

Figure 2.5 The change in membrane potential in response to a brief current step of the

neuronal membrane........................................................................................................................................................

12

Figure 2.6 The delivery of a brief current step to a spherical neuron...................................................... 13

Figure 2.7 The response of the dendrite to a brief current step................................................................. 16

Figure 2.8 The impedance response to a ZAP current stimulus................................................................. 20

Figure 2.9 The production of resonance due to passive and active neuronal properties............... 22

Figure 3.1 The distribution of HCN channels varies across neuronal types.......................................... 29

Figure 5.1 Methods used to fluorescently label HCN1 and Neurobiotin................................................. 40

Figure 5.2 The anti-HCN1 mouse monoclonal IgG1 antibody produces no immunolabling in

HCN1 knockout mice.......................................................................................................................................................

40

Figure 5.3 HCN1 immunoreactivity in the medial and lateral regions of the ventral horn in

transverse spinal cord sections from the rat and feline..................................................................................

41

xiii

Figure 5.4 HCN1 immunoreactivity in the neocortex of transverse brain sections from the

rat.............................................................................................................................................................................................

42

Figure 6.1 Decay in fluorescence intensity of optical sections attributed to the absorption and

scattering of light..............................................................................................................................................................

46

Figure 6.2 Variation in fluorescence intensity of optical sections attributed to the uneven

penetration of antibodies..............................................................................................................................................

47

Figure 6.3 Optical sections of HCN1 immunoreactivity after correcting for the loss of

fluorescence intensity due to light attenuation and uneven antibody penetration............................

49

Figure 6.4 HCN1 and ChATimmunoreactivity in the cervical spinal cord.............................................. 50

Figure 6.5 HCN1 immunoreactivity in the spinal accessory nucleus....................................................... 51

Figure 6.6 HCN1 immunoreactivity at the soma and proximal dendrites of trapezius

motoneurons.......................................................................................................................................................................

52

Figure 6.7 Comparing HCN1 immunoreactivity across dendritic regions at different distances

from the soma of trapeizus motoneurons..............................................................................................................

54

Figure 6.8 Membrane-associated HCN1 immunoreactivity observed along the perimeter of

the intracellular stain......................................................................................................................................................

55

Figure 6.9 Distribution of voxel intensities for a stack of optical sections in the presence and

absence of the intracellular stain...............................................................................................................................

56

Figure 6.10 Creating a three-dimensional layer of voxels to capture the juxta-membrane

region.....................................................................................................................................................................................

58

Figure 6.11 Comparison of the density of KCC2 and gephyrin fluorescence in the juxta-

membrane and adjacent extracellular and cytoplasmic regions.................................................................

60

Figure 7.1 Uniform proximal to distal distribution of HCN1 on dendrites of some trapezius

motoneurons.......................................................................................................................................................................

62

Figure 7.2 The distribution of HCN1 is uniform across the dendritic tree of three trapezius

motoneurons......................................................................................................................................................................

63

xiv

Figure 7.3 Graded proximal to distal increase of HCN1 on dendrites of other trapezius

motoneurons.......................................................................................................................................................................

64

Figure 7.4 Three trapezius motoneurons display a strong linear increase in HCN1 density

with distance from the soma.......................................................................................................................................

65

Figure 7.5 Four trapezius motoneurons display a moderately strong linear increase in HCN1

density with distance from the soma.......................................................................................................................

66

Figure 7.6 The proximal to distal distribution of HCN1 across the ten trapezius motoneurons

and the change in HCN1 density every 1000 µm plotted against the normalized somatic

density of HCN1.................................................................................................................................................................

67

Figure 7.7 Relationship between the computed total input conductance of each motoneuron

and the change in HCN1 density every 1000 µm................................................................................................

69

xv

List of Equations

1.1 The membrane resistance for a dendritic segment of length l............................................................. 5

1.2 The cytoplasmic resistance for a dendritic segment of length l........................................................... 5

1.3 Ratio of membrane resistance and cytoplasmic resistance with respect to the geometry of

dendrites...............................................................................................................................................................................

6

1.4 The membrane capacitance for a dendritic segment of length l.......................................................... 10

1.5 Relationship between the change in potential across a capacitor and the magnitude and

duration of current flowing onto the capacitor...................................................................................................

10

1.6 Relationship between the change in potential with respect to time and the magnitude of

current flowing onto the capacitor...........................................................................................................................

10

1.7 The membrane ionic current............................................................................................................................... 13

1.8 The membrane capacitive current.................................................................................................................... 13

1.9 The total membrane current................................................................................................................................ 14

1.10 The rising phase of the membrane potential change in response to a step current................. 14

1.11 The membrane time constant.......................................................................................................................... 15

1.12 The membrane potential at steady state..................................................................................................... 15

1.13 The decaying phase of the membrane potential change in response to a step current.......... 15

1.14 The decay in fluorescence intensity of optical sections due to the absorption and

scattering of light..............................................................................................................................................................

46

1.15 The loss of fluorescence intensity of optical sections due to light attenuation and uneven

antibody penetration......................................................................................................................................................

48

1.16 Compensation for loss of fluorescence intensity due to light attenuation and uneven

antibody penetration......................................................................................................................................................

49

xvi

List of Symbols and Abbreviations

Ca2+ calcium ion mg milligrams

Cm specific membrane capacitance ml milliliter

cm membrane capacitance Na+ sodium ion

CNS central nervous system NE norepinephrine

EPSP excitatory postsynaptic potential Ω ohm

HCN hyperpolarization-activated π pi

cyclic nucleotide-gated PIC persistent inward current

Ic membrane capacitive current r radius

Im total membrane current Ri specific intracellular resistivity

Ir membrane ionic current ri cytoplasmic resistance

ICaL slow persistent current mediated Rm specific membrane resistivity

by L-type Ca2+ channels rm membrane resistance

Ih hyperpolarization-activated current SK small conductance Ca2+ activated

mediated by HCN channels K+ channel

INaP fast persistent Na+ current TASK tandem of P domain weak inward

IPSP inhibitory postsynaptic potential related acid sensitive K+ channel

K+ potassium ion t time

kg kilogram 𝜏𝜏 membrane time constant

KCl potassium chloride V voltage

l length Vm membrane potential

MΩ mega ohm ZAP impedance amplitude profile

mm millimeter t time

1

Chapter 1. Introduction

Most neurons in the mammalian central nervous system (CNS) communicate via action

potentials. These actions potentials are conveyed to the neuron by multiple axons, which terminate

to form synapses. The vast majority of synapses in the CNS are positioned on the dendritic tree of

neurons. At the synapse, the arrival of an action potential evokes the release of neurotransmitters.

The binding of neurotransmitters to ligand-gated ion channels on the membrane of the dendrites

produces a local positive or negative current in the dendrite referred to as input. The current

generated at each synapse must travel down to the soma where the amount of current is translated

into specific temporal patterns of action potentials, defined as the output. These action potentials, in

turn, are received as the input of the next neuron in the circuit.

The relationship between synaptic input and neuronal output is not fixed, however.

Dendrites do not simply collect and funnel synaptic current to the soma. It is now clear that

dendrites are endowed with a rich assortment of voltage-dependent channels. These voltage-

dependent channels can provide additional current to either amplify or dampen synaptic current en

route to the soma (see Figure 1.1A). In motoneurons, the density of α1D-subunit containing L-type

Ca2+ channels was shown to increase beyond the second or third dendritic branch points (Carlin et

al., 2000; see Figure 1.1 B & C). Thus, the current generated at synapses located distal to these

regions may be selectively amplified by these L-type Ca2+ channels. To complicate matters, in some

cases the additional current is often the result of complex interactions between different classes of

voltage-dependent channels. For instance, in CA1 pyramidal neurons the current mediated by

hyperpolarization-activated cationchannels, Ih, in combination with the voltage-dependent

potassium current identified as IMresults in the amplification of synaptic current at rest but the

dampening of synaptic current at membrane potentials close to the threshold for action potential

firing (George et al., 2009). Thus, the amount of current delivered to the soma will be highly

2

dependent on precisely arranged interactions between (1) synapses and voltage-dependent

channels and less understood, between (2) different classes of voltage-dependent channels.

The long term goal is to define quantitatively how these interactions shape the input-output

relationship of motoneurons. Motoneurons are the “final common pathway” for movement since

they provide the sole link by which the CNS can communicate with muscles – the effectors of

movement (Sherrington, 1906). Therefore, all premotor and sensory feedback signals from

descending and segmental pathways must converge on the motoneuron before influencing the

activity of muscles. The interactions between synapses and voltage-dependent channels on

motoneurons will be determined in part by their relative location on the dendritic tree. However,

few studies have examined the locations of voltage-dependent channels on the dendrites of

motoneurons. Consequently, descriptions of the dendritic distribution of voltage-dependent

channels are largely absent or restricted to the soma and proximal dendrites (e.g. Muennich and

Fyffe, 2003). Thus, an important first step to address the long term goal is to provide a detailed

account of the locations of voltage-dependent channels on the dendritic tree. The goal of this study

is to map the distribution of subunit 1-containing hyperpolarization-activated cation (HCN1)

channels on the dendritic tree of motoneurons.

Figure 1.1 (A) Voltage-dependent channels on the dendritic tree can either provide additional current to amplify (left) or act as a shunt to dampen synaptic current en route to the soma (right). (B). The density of α1D-subunit containing L-type Ca2+ channels increases distal to the second or third dendritic branch points (white arrows) in motoneurons (Carlin et al., 2000). (C) The corresponding soma and dendrites of the motoneuron shown in (B) are outlined (Carline et al., 2000).

3

1.1 Input-output properties of motoneurons in response to time-invariant inputs

Early electrophysiological studies described the response of motoneurons to constant long-

lasting currents injected into the soma via an electrode. If the current was suprathreshold, the

response consisted of a rhythmic series of action potentials (Granit et al., 1963a, 1963b). The

steady-state frequency of action potential firing increased in proportion to the amount of current

delivered to the soma. Therefore, the transformation of synaptic current to increases or decreases

in action potential frequency was proposed to follow a linear relationship (Schwindt and Calvin,

1973) (see Figure 1.2). However, this description does not account for the geometry of the dendritic

tree, the location of active synapses, and the distribution, type and modulation of voltage-

dependent channels. These factors will influence the delivery of current to the soma and thus the

relationship between input and output.

Figure 1.2 The relationship between the steady-state frequency of action potential discharge and current strength from a rat motoneuron (modified to show curve from one motoneuron) (Granit et al., 1963)

4

1.2 The morphology of the motoneuron dendritic tree

Motoneurons have the largest dendritic tree of all neurons in the CNS. A typical dendritic

tree can reach over 2000 µm in length with a surface area on the order of 500,000 µm2 (Rose, 1981;

Rose et al., 1985; Cullheim, 1987a, 1987b). Between 8-15 primary dendrites originate from the

soma, which undergo several orders of branching prior to termination (Rose et al., 1985; Ulfhake

and Kellerth, 1981) (see Figure 1.3). It has been estimated that the dendritic tree of a single

motoneuron receives 20,000 to 50,000 synapses. Over 95% of these synapses are located on the

dendritic tree (Rose and Neuber-Hess, 1991). Thus, the current generated at synapses must travel

great distances along highly branched dendrites before reaching the soma.

Figure 1.3 The reconstructed dendritic trees of motoneurons innervating the trapezius (A) (Meehan, unpublished data), splenius (B) (Montague, 2008), and biventercervicis and complexus (Rose et al., 1995) neck muscles. The motoneurons are shown in the horizontal plane.

5

1.3 Passive properties of dendrites

Dendrites have been viewed traditionally as passive structures that serve as a conduit for

current flow. Under these conditions, the transmission of current from dendritic synapses to the

soma can be described using cable theory (Rall, 1977). This theory assumes that the dendrite can

be conceptualized as a conducting core covered with a thin insulating sheath. However, the

membrane is not a perfect electrical insulator. This leads to a loss of synaptic current through the

membrane during its travel to the soma. This loss is determined by the relative values of the

membrane resistance rmand cytoplasmic resistance ri. For a dendritic segment of length l with

constant radius r, the values for rm and riare described below where Rm is the specific membrane

resistivity (Ω∙cm2) and Ri is the specific intracellular resistivity (Ω∙cm). Rm depends on the lipid and

protein composition of the membrane and includes the density and conductance of so-called ‘leak’

channels that are neither voltage nor time-dependent (e.g. TWIK-related Acid-Sensitive K+

channels) (Lesage and Lazdunski, 2000). These channels introduce an inherent leak in the

membrane. Ri depends on the composition of the cytoplasm.

𝑟𝑟𝑚𝑚 =𝑅𝑅𝑚𝑚

2𝜋𝜋𝑟𝑟𝑙𝑙 (1.1)

𝑟𝑟𝑖𝑖 =𝑅𝑅𝑖𝑖𝑙𝑙𝜋𝜋𝑟𝑟2 (1.2)

Current will always follow the path of least resistance. Therefore, the greater the membrane

resistance (higher rm) and the better the conducting properties of the cytoplasm (lower ri), the

further current spreads along the dendrite before leaking across the membrane. These parameters

are governed by the geometry of the dendritic tree. Assuming a constant Rm and Ri, the ratio of rm to

riis directly proportional to the radius as shown below.

6

𝑟𝑟𝑚𝑚𝑟𝑟𝑖𝑖

= 𝑅𝑅𝑚𝑚𝑅𝑅𝑖𝑖

𝑥𝑥𝑟𝑟2

(1.3)

Therefore, large diameter dendrites are able to transmit current over greater distances than

small diameter dendrites. Synaptic current is also lost as it spreads distally from the synapse and

branch points introduce additional paths for current loss. Compartment models based on the

morphology of intracellularly stained motoneurons have shown that the loss of current can be as

high as 80% (Rose and Cushing, 1999; Korogod et al., 2000; Powers and Binder, 2001; Bui et al.,

2003; Cushing et al., 2005). Furthermore, there is a strong correlation between the magnitude of

current loss and the distance between the synapse and the soma.

1.4 Active properties of dendrites

The predictions based on cable theory noted above ignore the presence of voltage-

dependent channels. There is considerable evidence from optical, electrophysiological and

molecular studies that the dendrites of many CNS neurons including motoneurons possess several

different classes of voltage-dependent channels (Hausser et al., 2000; Vacher et al., 2008). The

opening and closing of these channels impose changes to the effective specific membrane resistivity

Rm. The effectiveRm includes the resistance of the membrane due to passive components (as

described in cable theory) and active components like voltage-dependent channels. For instance,

the increased membrane leak introduced by the opening of some voltage-dependent channels

(lower effective Rm) may provide a powerful means to reduce the transfer of synaptic current from

the dendrites to the soma. Moreover, the additional current provided by voltage-dependent

channels can either amplify or dampen the delivery of synaptic current. The amplification may

counteract the loss of current due to the leakiness of the membrane regardless of whether the

leakiness is due to passive or active properties.

7

There are at least 11 different types of voltage-dependent channels present on

motoneurons. Although most of the known channel locations are somatic there is very persuasive

electrophysiological and anatomical evidence for the presence of L-type Ca2+, persistent Na+, Kv2.1,

and small conductance Ca2+ activated K+ (SK) channels on the dendrites of motoneurons

(Heckmann et al., 2005; Muennich and Fyffe, 2004; Li and Bennett, 2007; Ballou et al., 2006).

Computational studies further suggest that L-type Ca2+ channels are restricted to ‘hotspots’

approximately 100 µm long (Bui et al., 2006). The distance between the hotspots and the soma

varied from 180 to 850 µm and increased with the size of the motoneuron (Elbasiouny et al., 2005;

Grande et al., 2007a). Descriptions of the other channels are currently either cursory or absent.

Furthermore, most of the known distributions are described in very simple terms (e.g. dendritic

versus somatic densities). Although the latter is a useful starting point, it fails to take into account

variations within the dendritic tree that are commonly seen in other types of neurons (Vacher et al.,

2008; Nusser, 2009)

8

Chapter 2. Input-output properties of motoneurons in response to time-varying inputs

Thus far, the input-output properties of motoneurons have been described in the context of

steady long-lasting input. However, synapses do not generate signals that are steady or long-lasting.

Typically, ligand-dependent channels open briefly, usually for periods of less than 1 or 2 ms. Thus,

the current crossing the membrane is equally brief. In addition, in rhythmic motor behaviors such

as walking, scratching and swimming, motoneurons are driven by networks of rhythmically active

neurons in the spinal cord called central pattern generators (Cazalets et al., 1996; Hochman and

Schmidt, 1998; Kiehn et al., 2000). The motoneuron membrane potential oscillates between

alternating periods of hyperpolarization and depolarization during these rhythmic locomotor

movements (Cazalets et al., 1996; Hochman and Schmidt, 1998; Berg et al., 2007). These different

phases result from putative changes in the relative activity of excitatory and inhibitory synapses.

The relationship between these transient inputs and motoneuron output is determined by the same

factors described previously for time-invariant inputs and several additional factors that are

described below.

2.1 Membrane capacitance alters the time course of transient synaptic potentials

An electric capacitor is a device that stores charge. It is comprised of two conducting plates

separated by an insulating barrier. Since the lipid bilayer of the neuronal membrane separates an

internal and external conducting medium, the neuronal membrane acts as a capacitor (see Figure

2.1).

9

Figure 2.1(A) A parallel plate capacitor comprised of two conducting plates separated by an insulating barrier. (B) The common schematic symbol for a capacitor in circuit diagrams. (C) The lipid bilayer acts as a capacitor by separating the electrically conductive media of the cytoplasm and extracellular fluid.

When a brief current step mimicking a transient inputis delivered through an electrode

across a pure lipid bilayer, some ions that carry the current will collect on the inner surface. This

leads to an accumulation of charge that will electrostatically repel similar charges lying on the outer

surface. Consequently, an equal and opposite charge will develop on the two surfaces. This charge

separation produces a change in the potential across the pure lipid bilayer (see Figure 2.2).

Figure 2.2 (A) When a brief step of current is delivered through an electrode, a capacitive current (Ic) is generated across the neuronal membrane. (B) The equivalent electrical circuit representing a pure lipid bilayer. The electrode is represented by an ideal current source that delivers a current step (Istep). The current that flows on to the capacitor (Ic) will deposit charge on one conducting plate. This leads to the displacement of similar charges on the other conducting plate resulting in a separation of charge. A potential difference will now exist between the two plates of the capacitor.

10

The membrane capacitance cmmeasures the ability of the neuronal membrane to store

charge and is defined as the amount of charge required to produce a given membrane potential

difference. The membrane capacitance for a dendrite of length lwith constant radius r is described

below where Cm is the specific membrane capacitance and has an approximate value of 1uF/cm2

(see equation 1.4).

𝑐𝑐𝑚𝑚 = 𝐶𝐶𝑚𝑚2𝜋𝜋𝑟𝑟𝑙𝑙 (1.4)

When the contribution of the membrane capacitance is considered alone, a brief current

step will produce a change in the membrane potential that depends on both the magnitude and

duration of the current. This is described below where ∆V is the change in membrane potential

(mV), Ic is the magnitude of the current flowing across the membrane due to the membrane

capacitance (nA), ∆𝑡𝑡 is the current duration and cm is the membrane capacitance (µF) (see equation

1.5).

∆𝑉𝑉 = 𝐼𝐼𝑐𝑐∆𝑡𝑡𝑐𝑐𝑚𝑚

(1.5)

In response to the current step, the membrane potential will continue to increase linearly with time

for as long as the current step is applied (see equation 1.6 and Figure 2.3).

∆𝑉𝑉∆𝑡𝑡

= 𝐼𝐼𝑐𝑐𝑐𝑐𝑚𝑚

(1.6)

11

Figure 2.3 If the membrane had only capacitive properties, the membrane potential (B) would change linearly with time in response to a current step (A). The membrane potential will continue to increase over the duration of the current step. The slope of the rise in membrane potential is given by equation 1.6.

However, the membrane potential of the neuron does not change linearly with time in

response to the same step of current. In addition to acting as a capacitor, the neuronal membrane

also provides a resistive pathway rm for current flow as mentioned previously. Current crossing the

membrane can either flow onto the membrane capacitor to be stored as charge or through the finite

number of ‘leak’ channels present on the neuronal membrane that provide the resistive pathway.

Thus, a patch of membrane can be represented by a simple electrical circuit with the membrane

resistance and capacitance placed in parallel (see Figure 2.4).

Figure 2.4(A) The neuronal membrane is composed of a lipid bilayer. Inserted into this lipid bilayer, there are many ion-conducting ‘leak’ channels that are neither voltage nor time-dependent. Current crossing the membrane can flow either onto the membrane capacitance or through the resistive pathway provided by ‘leak’ channels. Thus, the membrane acts as both a capacitor and resistor in parallel. (B) The equivalent electrical circuit representing the neuronal membrane. The electrode is represented by a current source that delivers a current step (Istep). Current can flow either onto the capacitor (Ic) to be stored as charge across the membrane or through the resistor (Ir) representing ‘leak’ channels.

12

The time course and magnitude of the membrane potential change will depend on the time

course of the current and the morphology of the neuron. In the simplest case when the neuron is

approximated as a membrane-enclosed sphere, a brief current pulse will produce a characteristic

change in membrane potential (see Figure 2.5). In response to the current step the membrane

potential rises fast initially but slows as it reachesits steady state value. After the current step is

removed, the membrane potential decays with a similar time course.

Figure 2.5When a brief current step is delivered to the neuron, the membrane potential rises and decays more slowly than the step change in current.This is attributed to the time required to store charge across the neuronal membrane. The membrane potential eventually reaches a steady state value after some time during the current step.

To explain the exponential rise and decay of the membrane potential, the spherical neuron

can be represented by the analogous electrical circuit shown below. In the spherical neuron, the

current has equal access to all parts of the neuronal membrane at the same time. Therefore, the

resistance and capacitance for each patch of membrane can be combined in parallel into the total

membrane resistance rm and the total membrane capacitance cm, respectively (see Figure 2.6).

13

Figure 2.6 (A) The equivalent electrical circuit representing a spherical neuron. An intracellular electrode delivers a current step to the neuron. (B) Since the injected current has equal access to all parts of the neuronal membrane at the same time, the resistors and capacitors for each membrane patch can be combined in parallel into the total membrane resistance rm and total membrane capacitance cm. The total membrane resistance is determined by dividing the specific membrane resistance Rm by the total membrane area of the spherical neuron (A = 4πr2). The total membrane capacitance is determined by multiplying the specific membrane capacitance Cm by the total membrane area of the spherical neuron. Thus, the schematic shown in (A) can be reduced to a simple electrical circuit (B) with the total membrane resistance and total membrane capacitance lying in parallel, which are connected in series to a current source representing the electrode.

The current flowing through the membrane resistance is referred to as the membrane ionic

current Ir (see equation 1.7) and the current that flows onto the membrane capacitance is referred

to as the membrane capacitive current Ic(see equation 1.8). Vm is the membrane potential and ∆𝑡𝑡 is

the current step duration.

𝐼𝐼𝑟𝑟 = 𝑉𝑉𝑚𝑚𝑟𝑟𝑚𝑚

(1.7)

𝐼𝐼𝑐𝑐 = 𝑐𝑐𝑚𝑚∆𝑉𝑉𝑚𝑚∆𝑡𝑡

(1.8)

The total current crossing the membrane Im is the sum of the membrane ionic current and the

membrane capacitive current (see equation 1.9).

14

𝐼𝐼𝑚𝑚 = 𝑉𝑉𝑚𝑚𝑟𝑟𝑚𝑚

+ 𝑐𝑐𝑚𝑚∆𝑉𝑉𝑚𝑚∆𝑡𝑡

(1.9)

The capacitance of the membrane has the effect of reducing the rate at which the membrane

potential changes in response to a current step. If the membrane had only resistive properties, a

current step would produce an instantaneous change in the membrane potential. Since the

membrane has both capacitive and resistive properties, the actual change in membrane potential

combines features of both.

At the onset of the current step, all of the current initially flows onto the membrane

capacitance (Im = Ic). As a result of the large initial capacitive current Ic, the potential across the

membrane capacitance will rapidly increase. Since the membrane resistance and capacitance are in

parallel, the potential across each must always be the same and equal to the membrane potential

Vm. The increase in membrane potential will drive progressively more current across the

membrane resistance (this is Ohm’s law). Consequently, increasingly less current flows onto the

membrane capacitance (since Im= Ir+ Ic) and its rate of charging will decrease. This in turn will

decrease the rise of the membrane potential and accounts for the exponential time course.

The rising phase of the membrane potential change is described below by solving equation

1.9 for Vm where 𝜏𝜏 is the membrane time constant and is given by the product of the membrane

resistance rm and membrane capacitance cm (see equations 1.10 and 1.11). The membrane time

constant represents the time taken for the membrane potential to rise to (1 – 1/e) or 63% of its

steady state value.

𝑉𝑉𝑚𝑚 (𝑡𝑡) = 𝐼𝐼𝑚𝑚𝑟𝑟𝑚𝑚 (1 − 𝑒𝑒−𝑡𝑡𝜏𝜏 ) (1.10)

15

𝜏𝜏 = 𝑟𝑟𝑚𝑚𝑐𝑐𝑚𝑚 (1.11)

Eventually, steady state is reached and the membrane potential no longer changes. When

this occurs, the capacitive current is zero and all of the current crossing the membrane will flow

through the membrane resistance (Im= Ir and Ic = 0). The membrane potential at steady state is

described below (see equation 1.12).

𝑉𝑉𝑚𝑚 = 𝐼𝐼𝑟𝑟𝑟𝑟𝑚𝑚 (1.12)

When the current step is removed, the potential difference across the membrane

capacitance Vmwill drive current out of the membrane through the resistive pathway rm. This flow

of current serves to reduce the charge separation across the membrane capacitance. As charge on

the membrane capacitance dissipates the membrane potential Vmwill decrease. The decay in the

membrane potential also follows an exponential time course and is described below (see equation

1.13).

𝑉𝑉𝑚𝑚 (𝑡𝑡) = 𝑉𝑉𝑚𝑚𝑚𝑚𝑥𝑥 (𝑒𝑒−𝑡𝑡𝜏𝜏 ) (1.13)

2.2 The voltage response to a brief current injection in the dendrite

In a spherical neuron, the effect of distance can be ignored since the injected current flows

equally through the resistance and capacitance in all parts of the membrane at the same time.

However, when a brief current step is delivered to the dendrite, the current does not have equal

access to all parts of the membrane. In order to reach distant sites, current must flow along the

length of the cylindrical dendrite. As mentioned previously, the cytoplasmic core of the dendrite

offers afinite resistance to the longitudinal flow of current. Consequently, this resistance to current

flow will lead to some current leaking across the neuronal membrane during travel. However,

16

unlike the previous description, some of the current will cross the membrane via the membrane

capacitance.

The response of the dendrite to a brief current step can be described by dividing an

infinitely long dendrite into smaller unit length cylindrical compartments (see Figure 2.7A).The

membrane of each individual compartment is represented by a simple electrical circuit with the

membrane capacitance and membrane resistance placed in parallel (see Figure 2.7B). The length of

each compartment is very short such that changes in current or voltage are continuous when

examined on a scale of 10’s or 100’s of microns. Neighboring compartments are connected to each

other by an internal resistor, which represents the cytoplasmic resistance of ½ of each

compartment.

Figure 2.7 (A) The response of the dendrite to a brief step of current can be described by dividing an infinitely long dendrite into smaller unit length cylindrical compartments. (B) The equivalent electrical circuit representing an infinitely long dendrite. Each compartment is connected to the next by a resistor that represents the cytoplasmic resistance between.

17

When a brief current step is delivered to a site on the dendrite, some current will flow back

through the membrane in the immediate region (e.g. closest compartment) but most willflow into

the neighboring compartments via the cytoplasmic resistance.This process will continue foreach

successive compartment. Due to the current crossing the membrane, the time course of the

membrane potential change will closely resemble Figure 2.5B. This has important consequences for

the time course of current arriving at adjacent compartments. Since this current must flow through

the cytoplasmic resistance, represented by a simple resistor, the exponential rise and fall in the

membrane potential of the preceding compartment will produce a similar exponential rise and fall

in the current arriving in the subsequent compartment. As a result, the time course of current

reaching distant sites will be slower than at the current injection site.Furthermore, since some

current is lost through the membrane there will be less current arriving at the next

compartment.The cumulative slowing in the time course of the current step combined with the loss

of current results in a progressively slower rate of rise and a smaller peak value for current

travelling along the dendrite.

Lastly, the duration of the initial current step must be considered. If the time required for

the change in membrane potential to reach steady state exceeds the duration of the current step,

the local membrane potential change will be lower and disproportionate to the magnitude of the

current step. This smaller change in membrane potential at the site of injection will drive less

current into neighboring compartments. Since frequency and duration are inversely related, less

current will reach neighboring compartments from transient inputs of short duration (high

frequency) compared to transient inputs of longer duration (low frequency). Modeling studies have

reported a greater than 40 fold difference in the peak of the local membrane potential change in

response to the same brief current step injected at a distal dendritic site compared to the soma.

This arises from differences in the membrane time constant between the two locations. The

18

membrane time constant governs the rate and thus the time taken for the membrane potential to

reach steady state (Rinzel and Rall, 1974).

2.3 Impedance as an electrical property to describe the input-output relationship of

motoneurons in response to time-varying inputs

The previous section described the factors that determine the input-output properties of

motoneurons where the input is defined as a single somatic or dendritic synapse. An important

principle to emerge from this description is the ability of neurons to change the time course and

amplitude of electrical signals (e.g. current and membrane potential changes) due to the electrical

properties (e.g. resistance and capacitance) of neuronal membranes. As a consequence, the change

in membrane potential in response to inputswith high frequency components is selectively reduced.

This behavior is referred to as low-pass filtering since low frequency inputs yield relatively large

voltage responses compared to high frequency inputs.

The brief current step described above was a simple case of a time-varying input. In reality,

the time-varying inputs that a neuron receives are more complex in shape. However, regardless of

their shape, all time-varying inputs are composed of combinations of sine waves with different

frequencies. For example, if the input varies slowly such as oscillations in excitatory or inhibitory

inputs associated with slow walking (approximately 1 Hz), the time course of these oscillations can

be mimicked by the sum of a finite number of sine waves with frequencies between 0.5 and 5 Hz.

On the other hand, the frequencies of sine waves required to mimic fast inputs such as the

asynchronous activation of excitatory or inhibitory synapses lasting 10 to 100 ms will be in the 10

to 1000 Hz range.

19

The impedance of a neuron provides a direct measure of the response of a neuron to time-

varying inputs, expressed in terms of the frequency of the input. Impedance is a concept similar to

resistance. Whereas resistance is generally defined as the opposition to the flow of current,

impedance is a special case of the opposition to current flow when the current varies with time.

Stated formally, impedance is the resistance to the flow of current at a particular frequency

measured in ohms (Ω). The impedance of a neuron at a specific range of frequencies can be

determined by injecting a sinusoidal current of constant amplitude that increases linearly in

frequency with time. This type of time-varying current that sweeps through many frequencies is

referred to as a ‘ZAP’ current (see Figure 2.8 left). The ZAP current is useful because each frequency

in the current is isolated briefly in time so that the voltage response at each frequency can be easily

observed. Since the amplitude of the ZAP current is constant, any changes in the voltage response

will be attributed to changes in the frequency of the current.

Unlike resistance, the impedance cannot be determined simply by the ratio of the voltage

and current at a given point in time. As mentioned previously, the change in membrane potential

‘lags’ behind changes in the inputdue to the finite time required to charge and discharge the

membranecapacitance. As a result, the membrane potential change is ‘out-of-phase’ with the timing

of the input. Thus, it would not be possible to calculate the impedance at a given time point due to

the delay between the current and the corresponding voltage response. Instead, the impedance can

be calculated by the ratio of the voltage and current, each expressed as a function of frequency,

thereby avoiding the problem described above (see Figure 2.8 right).

Consider the case of a simple resistor. When a ZAP current is delivered to a circuit whose

only component is a resistor, the amplitude of the voltage response is the same for all frequencies.

Furthermore, the instantaneous voltage and current at any point in time are ‘in-phase’ since the

20

current and voltage rise and fall simultaneously. Here, the impedance is constant with a value equal

to the resistance of the circuit (see Figure 2.8A).

The outcome is different for a circuit containing a resistor and capacitor in parallel

representing the passive electrical properties of the neuronal membrane (see Figure 2.8B). The

amplitude of the voltage response decreases as the frequency of the current increases. Moreover,

the instantaneous voltage and current at any point in time are ‘out-of-phase’ since the time course

of the current is altered by the capacitor. These effects are seen as a decline in the impedance with

increasing frequency (see Figure 2.8B right) and an increasing phase shift between current and

voltage with increasing frequency. The circuit, therefore, acts as a low-pass filter.

Figure 2.8 (A) ZAP current delivered to a circuit containing a resistor (left), the corresponding voltage response (middle) and the impedance curve (right). (B) ZAP current delivered to a capacitor and resistor in parallel (left), the corresponding voltage response (middle) and impedance curve (right). (C) ZAP current delivered to a circuit containing a capacitor, resistor, and a non-ohmic resistor (Gchannel) mimicking a voltage-dependent channel (left) in parallel. The peak in the voltage response occurs at the resonance frequency (middle) and corresponds to the peak in the impedance curve (right) (Figure adapted from Hutcheon and Yarom, 2000).

21

2.4 Resonance

A distinct property noted in some neurons is a peak in the impedance curve referred to as

resonance (Puil et al., 1994; Gutfreund et al., 1995; Hutcheon et al., 1996; Lampl and Yarom, 1997;

Narayanan and Johnston, 2007; Manuel et al., 2007) (see Figure 2.8C right). Resonance is an

emergent property that results from the interaction between the active and passive properties of a

neuron. When a ZAP current is delivered to a circuit with a capacitor, resistor and non-ohmic

resistor (representing a voltage-dependent channel) placed in parallel, the voltage response is

greatest within a narrow band of current frequencies (see Figure 2.8C left and middle). The

existence of resonance indicates that a neuron is able to discriminate inputs on the basis of their

frequency content and responds best to inputs at preferred frequencies. Specifically, inputs with

frequency components near the resonance frequency will produce the largest changes in

membrane potential.

To create resonance, it is necessary to have two processes that operate selectively at

specific frequency domains – one that reduces the voltage response to high frequency inputs and

another that reduces the voltage response to low frequency inputs. The resulting combination of

low and high-pass filtering creates an intermediate range of preferred input frequencies (see Figure

2.9). As mentioned previously, the voltage response to high frequency inputs is selectively reduced

due to the passive electrical properties of the membrane. The mechanisms that selectively reduce

the voltage response to low frequency inputs are less well understood. However,

electrophysiological studies have shown that changes in the membrane potential at low frequencies

can be reduced by specific classes of voltage-dependent channels. To be effective, these classes of

voltage-dependent channels must display two important features. They must (1) activate slowly

and (2) produce currents that oppose voltage changes (Gutfreund et al., 1995; Hutcheon et al.,

1996; Lampl and Yarom, 1997; Narayanan and Johnston, 2007; Manuel et al., 2007).

22

Figure 2.9 Resonance is produced by the interaction between the passive and active properties of a neuron (black line). At very high frequencies, the voltage response is selectively reduced by passive properties resulting in small impedance values (grey line). At very low frequencies, the voltage response is selectively reduced by specific classes of voltage-dependent channels resulting in small impedance values (dotted black line)(Figure adapted from Hutcheon and Yarom, 2000).

23

Chapter 3. Hyperpolarization-Activated Cyclic Nucleotide-Gated (HCN) channels

3.1 Properties of HCN channels

The hyperpolarization-activated cyclic nucleotide-gated (HCN) channels belong to the

superfamily of voltage-dependent K+ channels (McCormick and Pape, 1990). HCN channels are

unique among voltage-dependent channels in that they activate at hyperpolarized potentials and

deactivate at depolarized potentials. When activated, HCN channels produce a mixed cation current

termed Ihthat is carried by Na+ and K+. This mixed permeability to Na+ and K+ results in a reversal

potential of approximately -40 to -20 mV. Thus, in the range of membrane potentials that are

physiological relevant, Ih is directed inwardwhich will depolarize the membrane potential.

The effect of HCN channels on motoneurons were first described in 1965 by Ito and Oshima.

In response to a hyperpolarizing current step, the membrane potential fell exponentially, but unlike

a strictly passive response, there was a slow depolarizing rebound of the membrane potential

towards the resting membrane potential. When the current step is removed, a small depolarizing

overshoot in the membrane potential occurred. This overshoot recovered slowly back towards the

resting membrane potential with a time course similar to the rebound (Ito and Oshima, 1965) (see

Figure). The depolarizing rebound or “sag” in the membrane potential is a characteristic

consequence of HCN channels and is attributed to the Ih current that activates slowly during a

maintained hyperpolarization. Upon termination of the hyperpolarizing current step, the

depolarizing overshoot occurs from the continued current flow through the slowly deactivating

HCN channels. Furthermore, the onset and amplitude of the sag increases with hyperpolarization,

owing to a faster rate of Ih activation and greater Ihcurrent.

24

3.2 HCN channels and neuronal excitability

The previous section described the unique properties of HCN channels. These properties

contribute to the excitability and responsiveness of the neuron to inputs. Based on measurements of

its voltage dependence, HCN channels begin to activate at membrane potentials between -45 to -60

mV. Half-activation of the channels (V1/2) occurs between -75mV to -85 mV and at membrane

potentials negative to -110 mV, HCN channels are maximally activated (Bayliss et al., 1994). Since

the resting membrane potential of the neuron lies within the voltage range of Ih activation (between

-45 to -110 mV), a subset of HCN channels are active at rest and therefore partially determine the

resting membrane potential. Consequently, the sustained inward current carried through HCN

channels sets the resting membrane potential at more depolarized voltages and thus closer to the

threshold for action potential firing. This increases the excitability of the neuron.

Paradoxically, the opening of HCN channels at rest introduces additional membrane leak,

which lowers the effective membrane resistance Rm. Therefore in the presence of HCN channels,

any given input will produce a smaller local change in the membrane potential (due to Ohm’s law)

and more current will be the lost through the membrane as synaptic current travels from dendritic

synapses towards the soma. This has the effect of reducing the excitability of the neuron and the

responsiveness at the soma to inputs arriving from the dendrites.

Furthermore, the activation and deactivation of HCN channels actively oppose changes in

the membrane potential. This property is a consequence of the unusual relationship between the

activation curve and the reversal potential of HCN channels (approximately -20 mV). In contrast to

voltage-dependent Ca2+ and Na+ channels, the reversal potential of HCN channels falls near the base

of its activation curve. Consequently, when the membrane is hyperpolarized from the resting value,

the fraction of HCN channels that are open increases. This leads to more Ih, which acts to drive the

membrane potential towards the reversal potential of HCN channels thereby restoring the

25

membrane potential back to rest. Conversely, the HCN channels active at rest are deactivated in

response to membrane depolarization. The loss of an inward depolarizing current (Ih ) results in an

effective outward current that hyperpolarizes the membrane potential thus returning it back to

rest.

3.3 Modulation of HCN channels

As the name implies, HCN channels are also regulated by cyclic nucleotides. HCN channels

posses a cyclic nucleotide-binding domain and are thus targets for neurotransmitters and second

messenger systems. Studies have shown that binding of cyclic adenosine monophosphate (cAMP) to

the cyclic nucleotide-binding domain facilitates the activation of HCN channels by shifting the

voltage dependence of activation to more depolarized values, typically by 10 mV or more. This

results in the activation of a greater fraction of HCN channels at a given membrane potential.

Furthermore, the shift in the voltage dependence of activation accelerates the activation kinetics of

HCN channels (DiFrancesco and Tortora, 1991). Later studies presented evidence of an increase in

HCN channel activation upon stimulation of β-adrenergic and serotonergic receptors (Takahashi

and Berger, 1990; Larkman and Kelly, 1992; Hsiao et al., 1997). These receptors are positively

coupled to adenylyl cyclase, which elevates intracellular levels of cAMP. Conversely,

neurotransmitters that downregulatecAMP levels depress the activation of HCN channels by

shifting its activation curve to more hyperpolarized potentials. Thus, the modulation of the voltage

dependence and activation kinetics of HCN channels by external influences confers an additional

level of complexity to the functional properties of HCN channels.

3.4 Functional Diversity of HCN channels

As mentioned previously, HCN channels are members of the voltage-dependent K+ channel

family. Sharing a similar structure, HCN channels are composed of four subunits arranged around a

central pore, each comprised of six membrane-spanning domains (S1 – S6). In mammals, these

26

subunits exist as four different isoforms (HCN1-4) that are widely expressed in the CNS. These HCN

isoforms have been systematically characterized and differ from each other with respect to their

voltage dependence, activation time constants and extent of cAMP-dependent modulation. For

instance, homotetramers of the HCN1 subunit activate at relatively lower hyperpolarized potentials

(V1/2 between -70 to -90 mV) compared to the other isoforms. In addition, HCN1 homotetramers

respond rapidly to hyperpolarization (in tens of milliseconds) and show a minimal response to

cAMP (Santoro et al., 1998). HCN2 and HCN3 homotetramers activate more slowly (hundreds of

milliseconds). However, HCN2 homotetramers are modulated strongly by cAMP whereas HCN3

homotetramers were found to be insensitive to cAMP (Ludwig et al., 1998; Ludwig et al., 1999;

Santoro and Tibbs, 1999; Mistrik et al., 2005; Stieber et al., 2005). HCN4 homotetramers activate

very slowly (seconds) and respond strongly to cAMP (Ishii et al., 1999; Seifert et al., 1999).

Furthermore, different HCN subunit isoforms may coassemble into heterotetramers. For

instance, HCN1 and HCN2 subunits can combine to form stable heterotetramers that carry an Ih

current with kinetics intermediate to that of homotetrameric HCN1 and HCN2 channels (Ulens and

Tytgat, 2001). Thus, the composition of subunits in addition to modulation by neurotransmitters

and second messengers leads to a great diversity in the properties of HCN channels (Chen et al.,

2001; Ulens and Tytgat, 2001).

3.5 HCN channels and resonance in motoneurons

The slow activation and deactivation kinetics of HCN channels (tens to hundreds of

milliseconds) and their opposition to changes in the membrane potential effectively limits the

duration and amplitude of hyperpolarizing and depolarizing responses to low frequency inputs. Due

to these properties, HCN channels act as a high-pass filter. Together with the low-pass filtering

attributed to passive membrane properties, this leads to resonance behavior in some neurons.

27

HCN channels are essential for the emergence of resonance behavior in CA1 hippocampal

and layer 5 pyramidal neurons (Hutcheon et al., 1996; Luthi and McCormick, 1998; Hu et al., 2002;

Narayanan and Johnston, 2007). These pyramidal neurons are tuned to produce the greatest

membrane potential response to inputs with frequencies near 5 Hz and it was demonstrated that

this resonance frequency was dependent on the activation properties and density of HCN channels

(Narayanan and Johnston, 2007). Most importantly, the resonance was abolished after

pharmacological block of HCN channels (Buzsaki, 2002; Ulrich, 2002; Leung and Yu, 1998; Pike et

al., 2000).

Recent work has shown that motoneurons also display resonance behavior that is

attributed HCN channels (Manuel et al., 2007). In resonant motoneurons, EPSPs induced by muscle

stretches with high frequency components were preferentially amplified by the fast persistent

sodium current, INaP. These high frequency components were associated with the velocity of the

muscle stretch. In contrast, the low frequency components of EPSPs induced by muscle stretches

were preferentially amplified in non-resonant motoneurons by a slow persistent calcium current,

ICaL, mediated by L-type Ca2+ channels. These low frequency components were associated with

maintained changes in muscle length.

Furthermore, resonant motoneurons may belong to fast fatigable (FF-type) or fast fatigue-

resistant (FR-type) motor units. The muscle fibers of these motor units are characteristically fast

contracting and develop large forces (Burke, 1981). On the other hand, non-resonant motoneurons

displaying little Ih may belong to slow contracting (S-type) motor units that are resistant to fatigue

(Burke, 1981; Gustafsson and Pinter, 1984). Thus, the presence and absence of this resonance

behavior may offer a potential strategy to facilitate motor unit recruitment. The amplification of

high frequency inputs induced by rapid muscle stretches lowers the recruitment threshold of FF-

type and FR-type motoneurons. These motor units may in turn produce ballistic movements in

28

response to rapid muscle stretches (e.g. to restore balance). In non-resonant motoneurons, the

preferential amplification of low frequency inputs may promote the recruitment of S-type

motoneurons during sustained muscle stretches. Since S-type motor units are resistant to fatigue,

the resulting sustained contraction might contribute to the maintenance of posture.

In order to replicate the experimental findings, simulations based on a simple motoneuron

model suggest that HCN channels must be present on the motoneuron dendritic tree to produce this

effect (Manuel et al., 2007). However, these models assume a uniform density of HCN channels

across the dendritic tree and this assumption has not been verified.

3.6 Distribution of HCN channels

The distribution of HCN channels in the CNS is one of the best studied among voltage-

dependent channels and is remarkably diverse across neurons. In both hippocampal CA1 and layer

5 neocortical pyramidal neurons, dendritic patch-clamp recordings have revealed a gradient of Ih

that increases over six-fold in density with distance from the soma along the apical dendrite

(Magee, 1998; Williams and Stuart, 2000; Berger et al., 2001; Kole et al., 2006). A similar gradient

(but up to 70 fold) has recently been confirmed at the ultrastructural level using immunogold

electron microscopy (Lorincz et al., 2002; Notomi and Shigemoto, 2004) (see Figure 3.1B).

In contrast, a reverse gradient of Ih is displayed by a class of hippocampal neurons with

pyramidal morphology (Bullis et al., 2007). These pyramidal-like principal (PLP) neurons express a

high density of Ih at the soma that declined with distance along the apical dendrite (see Figure

3.1C). Moreover, the biophysical properties of Ih (e.g. voltage-dependence of activation and

activation kinetics) were similar in both PLP neurons and CA1 pyramidal neurons suggesting that a

similar composition of HCN subunits underlie the different gradients of Ih. This finding

demonstrates that an increasing somatodendritic gradient of Ih is not a general feature of pyramidal

neurons.

29

A different Ih distribution is seen in cerebellar Purkinje neurons where the density of Ih is

uniform on the dendritic tree (see Figure 3.1A). Dendritic patch-clamp recordings at different

distances from the soma yielded similar densities of Ih (Hausser and Clark, 1997; Angelo et al.,

2007). The voltage-dependent properties of Ih in Purkinje neurons were similar to those of Ih in

pyramidal neurons; however, the activation and deactivation kinetics were slower. The discrepancy

in the kinetics suggests that other slower HCN subunits may also be expressed in Purkinje neurons

(Notomi and Shigemoto, 2004).

The functional significance of these different distributions of HCN channels observed across

neurons remains poorly understood. Motoneurons express the HCN1 and HCN2 subunits with an

apparent predominance of the HCN1 subunit and dense HCN1 immunoreactivity has been reported

at the soma and proximal dendrites of motoneurons (Moosmang et al., 1999; Monteggia et al., 2000;

Santoro et al., 2000; Chen et al., 2005; Milligan et al., 2006). However, no efforts have been made to

Figure 3.1 Yellow circles represent arbitrary units of density. (A) The density of HCN channels is uniform on the dendritic tree of cerebellar Purkinjes neurons (Hausser and Clark, 1997; Angelo et al.., 2007). (B) The density of HCN channels increases with distance from the soma along the apical dendrites of layer 5 pyramidal and hippocampal CA1 neurons. The highest densities are observed on the distal tuft dendrites (Magee, 1998; Williams and Stuart, 2000; Berger et al., 2001; Lorincz et al., 2002; Kole et al., 2006). (C) The density of HCN channels decreases with distance along the apical dendrite of another class of hippocampal neurons with pyramidal morphology (Bullis et al., 2007).

30

systematically describe the distribution of HCN channels across the entire motoneuron dendritic

tree.

Chapter 4.Mapping the distribution of voltage-dependent channelson dendritic membranes

Few studies have quantitatively examined the distribution of voltage-dependent channels

across the entire dendritic tree and thus descriptions of the distribution pattern of voltage-

dependent channels are sparse. This absence of a detailed account has hindered efforts to

determine the influence of voltage-dependent channels on the input-output properties of neurons.

As will be explained, mapping the distribution of voltage-dependent channels across the dendritic

tree is not a trivial task. Current descriptions rely on two techniques, dendritic patch-clamp

recordings or the high-resolution immunolocalization of channel subunits using electron or

confocal microscopy. Although both techniques have yielded useful insights, they also have

limitations, which are described below.

4.1 Patch clamp recordings from dendrites

Descriptions of the distribution of voltage-dependent channels based on dendritic patch-

clamp recordings rely on the ability to record the current carried by channels located in small

membrane patches on the dendritic tree in response to voltage-clamp commands. Thus, the power

of this technique is based on direct measurements from channels engaged in the very function that

gives them their name. Provided each channel has the same conductance, the magnitude of the

current measured from a patch of dendritic membrane is directly proportional to the number of

open channels open and area of membrane within the patch (Magee and Johnston, 1995; Colbert

and Johnston, 1996; Hoffman et al., 1997; Schwindt and Crill, 1997; Stuart and Spruston, 1998;

Bekkers and Stuart, 1998; Magee, 1998). It is also possible to describe key functional properties of

the channels such as voltage dependence, kinetics, and modulation by second messenger systems.

These advantages are partially offset by other factors. The dendrites of many neurons including

31

motoneurons taper and branch profusely giving rise to smaller diameter daughter branches. Unless

these dendrites occupy discrete regions close to their parent branches (e.g. tuft dendrites of layer 5

pyramidal neurons)(Nevian et al., 2007; Larkum et al., 2009), small diameter dendrites at great

distances from the soma remain inaccessible to patch clamp techniques. In addition, the number of

channels estimated in a given patch may be highly variable due to the limited area of the membrane

patch, especially for channels that are present at low density. Furthermore, this approach does not

necessarily measure the total number of voltage-dependent channels. For instance, as will be

discussed later the activity of many voltage-dependent channels can be regulated by

neuromodulators such as serotonin. In the absence of these neuromodulators, few channels may

open regardless of the membrane potential (Hounsgaard and Kiehn, 1989; Harvey et al., 2006).

Lastly, a limited number of patch clamp recordings can be taken before the viability of the neuron is

compromised. Thus, it is common practice to pool observations from many neurons to form one

data set. This inherently assumes that all neurons of that population display the same distribution

pattern of the voltage-dependent channel under study.

4.2 Immunocytochemistry and electron/confocal microscopy

The application of electron or confocal microscopy in conjunction with

immunohistochemistry is also a powerful tool for describing the distribution of voltage-dependent

channels (Lorincz et al., 2002;Muennich and Fyffe,2003; Lorincz et al., 2010). In principle, if these

techniques are combined with intracellular staining and three-dimensional reconstructions of

dendritic segments found on adjacent serial sections, it should be possible to compare the density

of voltage-dependent channels throughout the dendritic tree of single neurons. However, the

realization of this goal faces several obstacles.

The high spatial resolution provided by electron and confocal microscopy permits the

visualization of voltage-dependent channels and other proteins present on the surface of the

32

membrane. However, the immunohistochemical identification and localization of voltage-

dependent channels have been hampered by the scarcity of rigorously-verified antibodies specific

for channel subunits. In addition, intracellular staining techniques and methods to reconnect

dendritic segments found on multiple serial histological sections are required to sample

immunoreactive labeling from different dendritic regions across a single neuron. Furthermore, the

limited field of view that is inherent to electron microscopy restricts the dendritic surface area that

can be quantitatively examined for channel density. As a result, studies of the distribution of

voltage-dependent channels on motoneuron dendrites havebeen limited carefully selected regions

usually a few hundred microns from the soma (Milligan et al. 2006;Muennich and Fyffe, 2004).

Lastly, some anatomical descriptions of the distribution of voltage-dependent channels

report the total channel density observed within a dendritic volume (Zhang et al., 2006; Ballouet al.,

2006).These measurements include immunoreactivity due to channels in both the membrane and

cytoplasmic compartments of the dendrite. To obtain the density of voltage-dependent channels

that are localized to the membrane and thus responsive to changes in the membrane potential, the

immunoreactivity occurring in the cytoplasm must be excluded from the measurement. In principle,

this approach should yield the distribution of functional voltage-dependent channels and be

comparable to measurements of channel density collected from patch clamp recordings.

33

4.3 Statement of the problem

The dendrites of neurons in the CNS are the principal sites for receiving synaptic input.

Traditionally, dendrites were viewed as passive structures that served to funnel the current

generated at each synapse to the soma. However, the advent of techniques to voltage-clamp small

patches of dendritic membrane and the high resolution imaging provided by electron and confocal

microscopy have revealed that dendrites are endowed with a rich assortment of voltage-dependent

channels (Magee and Johnston, 1995; Colbert and Johnston, 1996; Hoffman et al., 1997; Schwindt

and Crill, 1997; Stuart and Spruston, 1998; Bekkers and Stuart, 1998; Magee, 1998; Lorincz et al.,

2002; Muennich and Fyffe, 2004; Lorincz et al., 2010). Voltage-dependent channels can either

amplify or dampen synaptic current en route to the soma. Thus, the amount of current delivered to

the soma and the resulting train of action potentials, defined as the output, will depend on the

density and distribution of voltage-dependent channels across the dendritic tree.

The relationship between synaptic input and neuronal output is crucial to an understanding

of motor control. To produce purposeful movement, the CNS must activate muscles in a highly

coordinated fashion. The amount of force generated by muscles is determined by either varying the

number of muscle fibers activated or by varying the rate at which these muscle fibers discharge

(Denny-Brown and Sherrington, 1928; Adrian and Bronk, 1929). Both of these mechanisms are

dependent on the activity of motoneurons, which provide the sole link by which the CNS can

communicate with muscles. Thus, all premotor and sensory feedback signals must be processed by

the motoneuron before influencing the activity of muscles. Understanding the role that voltage-

dependent channels play in this step is central to understanding how voltage-dependent channels

govern the input-output relationship of motoneurons. This study examines one the key factors that

will define this role, the distribution of voltage-dependent channels on the dendritic tree. However,

current imaging and recording techniques that provide direct measurements of channel density on

34

dendrites suffer from methodological limitations that preclude sampling on a scale necessary to

comprehensively map the distribution of voltage-dependent channels across the dendritic tree.

The distribution of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels is

one of the best studied among voltage-dependent channels in the CNS (Johnston et al., 1996; Reyes,

2001; Migliore and Shepherd, 2002; Lai and Jan, 2006; Vacher et al., 2008). HCN channels are an

unorthodox class of voltage-dependent channels that activate at hyperpolarized potentials to

produce a positive inward current (Ih) and deactivate at depolarized potentials. The distribution of

HCN channels across neurons is remarkably diverse. There is a pronounced increased in HCN

channel density from soma to distal dendrites in CA1 hippocampal and neocortical pyramidal

neurons (Magee, 1998; Williams and Stuart, 2000; Berger et al., 2001; Kole et al., 2006; Lorincz et

al., 2002; Notomi and Shigemoto, 2004). In contrast, a reverse gradient is present in another class of

hippocampal neurons (Bullis et al., 2007). Lastly, the density of HCN channels is uniform across the

dendritic tree of cerebellar Purkinje neurons (Hausser and Clark, 1997; Angelo et al., 2007). The

functional significance of these fundamental differences remains poorly understood. In

motoneurons, HCN channels underlie resonance behavior whereby EPSPs induced by muscle

stretches at a frequency near the resonance frequency of motoneurons produce a larger change in

the membrane potential. Simulations of these experimental findings based on anatomically reduced

models of the motoneuron suggest that HCN channels must be present on the dendritic tree.

However, these models assume a uniform distribution of HCN channels and this assumption has not

been verified (Manuel et al., 2007). It is known that two of the four HCN subunits, HCN1 and HCN2,

are expressed in motoneurons; however, the distribution of these subunits on the motoneuron

dendritic tree is unknown (Moosmang et al., 1999; Monteggia et al., 2000; Santoro et al., 2000).

35

4.4 Statement of the goal

The goals of this thesis are the following:

1) To develop a high throughput method for measuring the membrane-associated density of

voltage-dependent channels on dendrites of intracellularly stained neurons.

2) To compare the proximal to distal distribution of channels formed by the HCN1-subunit on the

dendritic trees of trapezius motoneurons in the adult feline.

36

Chapter 5.Materials and Methods

5.1 Animal preparation

Experiments were performed on 10 adult female felines weighing 3.1-4.6 kg (Liberty

Research Inc., Waverly New York USA). All experimental protocols were approved by the Queen’s

University Animal Care Committee and conducted in accordance with guidelines established by the

Canadian Council on Animal Care. Animals were pre-medicated with a combination of butorphanol

(0.2 mg/kg; Torbugesic; Wyeth), acepromazine (0.05 mg/kg; Altravet; Ayerst Veterinary

Laboratories), glycopyrrolate (0.01 mg/kg; Sanoz) and hydromorphone (0.075 mg/kg; Purdue

Pharma Inc.) administered subcutaneously. Anesthesia was induced with ketamine (6.25 mg/kg;

Vetalar; Bioniche Animal Health Canada Inc.), medetomidine (0.03 mg/kg; Pfizer) and midazolam

(0.15 mg/kg; Sandoz) administered intravenously. Deep anaesthesia was maintained with

supplementary doses of sodium pentobarbital (2.5-5 mg/kg; Ceva Santé Animale) administered

intravenously in response to sudden increases in heart rate. This protocol maintained the heart rate

between 140-160 beats per minute. Heart rate, O2saturationand end-tidal CO2were continuously

monitored with a digital Cardell Veterinary Monitor 9500 HD monitor (Benson Medical Industries

Inc., Markham, Ontario, Canada). Rectal temperature was monitored using a YSI tele-thermometer

system and maintained at 35-37°C with a heating blanket under negative feedback control (Benson

Medical Industries Inc., Markham, Ontario, Canada). Normosol-R (Hospira Healthcare Corporation,

Montréal, Quebec, Canada) was administered intravenously at a constant rate (10 ml/kg/hour) to

maintain body fluids.

A dorsal laminectomy extending from C1 to C4 was performed to access the underlying

spinal cord. The animals were subsequently secured in a stereotaxic frame (Transvertex, AB). The

vertebra at T2 was clamped to stabilize the upper cervical cord. By adjusting the height of the head

holder, the angle of the upper cervical cord was aligned with the horizontal plane. The spinal

37

accessory nerves innervating the left and right trapezius neck muscle were isolated and positioned

on bipolar stimulating electrodes. The animals were paralyzed with gallaminetriethiodide (2.5-5.0

mg/kg/hour) and artificially respired to maintain end tidal CO2 between 3.7-4.5% as measured

with a medical gas analyzer (LB-2 Beckman). A bilateral pneumothorax was performed to reduce

respiratory-related movement of the spinal cord. The dura and pia mater were removed to facilitate

insertion of the microelectrode into the exposed dorsal surface of the spinal cord.

5.2 Motoneuron identification and intracellular injections

Intracellular recordings were obtained with sharp glass micropipettes broken to yield tip

diameters ranging from 1.5-2.0 µm. The micropipettes were filled with 12% Neurobiotin™ (Vector

Laboratories) in 0.5 M KCL and 0.1 M Trizma buffer (pH 8.2). After advancing the micropipette

several 100 μm, the electrode resistance varied from 9-15 MΩ. To prevent leakage of the positively

charged Neurobiotin, a negative holding current of 5 nA was passed through the microelectrode

while searching for trapezius motoneurons. Trapezius motoneurons in C2 to C4 were

antidromically identified by stimulating the spinal accessory nerve that innervates all three heads

of the trapezius muscle (0.1 ms duration, 2.0-5.0 V). The extracellular field potentials evoked by

antidromic stimulation were used to guide the microelectrode towards the spinal accessory nucleus

where trapezius motoneurons are located (Holomanova et al., 1972; Rapoport, 1978; Keane and

Richmond, 1981; Vanner and Rose, 1984). Advancing the microelectrode in steps of 16-20 µm in

regions with large field potentials often resulted in the impalement of a trapezius motoneuron.

Trapezius motoneurons were injected with Neurobiotin by passing positive current pulses

(450ms at 2 Hz) of 6-10 nA for 3.5-5min. The total charge delivered varied from 10.8-36.9nA∙min.

To minimize overlap of dendritic trees from adjacent trapezius motoneurons, each Neurobiotin-

filled motoneuron was separated by a minimum of 3 to 4 mm. At least one hour was allowed to

38

elapse between intracellular injection and fixation to provide sufficient time for the transport of

Neurobiotin through the motoneuron dendritic tree.

5.3 Perfusion and fixation

Several minutes prior to perfusion, animals were intravenously administered heparin (5

ml) and euthanized with a lethal dose of sodium pentobarbital (3 ml). Animals were subsequently

perfused through the thoracic aorta with 1 liter of saline followed with 1 to 2 liters of fixative (4%

paraformaldehyde in 0.1 sodium phosphate buffer (pH 7.4)). The upper cervical spinal cord was

removed and stored overnight (5-9 hours) at 4°C in 100 ml of the fixative. The tissue was

subsequently stored in 15% sucrose in 0.1 M sodium phosphate buffer for a minimum of 3 days.

This sucrose solution removes water from the tissue through osmosis thus minimizing freezing

artefact when the tissue is cut with the freezing microtome.

5.4 Histological processing and visualization of HCN1 and Neurobiotin

The block of spinal cord containing the Neurobiotin-filled motoneurons was cut into 50 µm

thick serial sections in the horizontal plan using a freezing microtome (LeitzWetzlar Germany;

Model 44011). The tissue sections were rinsed with 0.1 M sodium phosphate buffer and incubated

in 1% sodium borohydride in 0.05M KPBS for 25 minutes to reduce tissue autofluorescence arising

from the fixative (Clancy and Cauller 1998). This was followed with incubation in 0.3% hydrogen

peroxide in 0.05M KPBS for 30 minutes to quench the endogenous peroxidases present in the

tissue. After appropriate rinsing, the sections were incubated in 10% goat serum and 0.4% Triton-

X-100 in 0.05M KPBS pH 7.4 overnight at 4˚C. The goat serum blocks non-specific interactions

between the tissue and the constant (Fc) region of the secondary antibody. Triton-X-100, a

detergent, disrupts the cell membrane thereby improving penetration of the anti-HCN1 primary

antibody, horse radish peroxidase (HRP) conjugated secondary antibody, Cy3 conjugated

39

tyramideandStrept-Avidin DyLight649 conjugate (Jackson ImmunoResearch Laboratories Inc.,

West Grove, PA, USA).

To stain for the HCN1 subunit, tissue sections were incubated for 48 hours in a well

characterized mouse monoclonal anti-HCN1 IgG1 antibody (1:5000, clone #N70/28,LOT#: 444-

1LC-69, NeuroMab Antibodies Inc., Davis, CA, USA) in 0.05M KPBS and 0.5% goat serum at 4˚C.

Sections were then incubated with a HRP conjugated goat anti-mouse IgG1 (1:1000; C#: 115-035-

205, LOT#: 88198, Jackson ImmunoResearch Laboratories Inc., West Grove, PA, USA) for 2 hours at

room temperature in 0.05M KPBS and 0.4% Triton-X-100. This was followed by incubation in the

Cy3 conjugated tyramide reagent (1:50; Perkin Elmer, Waltham, MA, USA) for 9 mins at room

temperature. Compared to using a Cy3 conjugated secondary antibody alone, the intensity of the

Cy3 fluorescence was greatly enhanced by the covalent binding of HRP-activated tyramide

molecules to sites where the primary-secondary antibody complex is bound. This reaction serves to

further amplify the immunofluorescent staining of HCN1 (Bobrow et al., 1992; Adams, 1992;

Shindler and Roth, 1996)(see Figure 5.1 left).

Neurobiotin was visualized by incubating the tissue sections in a Strept-AvidinDylight649

conjugate (1:100; C#: 016-490-084, Jackson ImmunoResearch Laboratories Inc., West Grove, PA,

USA) in 0.05 M KPBS and 0.4% Triton-X-100 for 3 hours at room temperature with constant

agitation (see Figure 5.1 right). After appropriate rinsing, the tissue sections were mounted on

subbed slides with Vectashield® (Vector Laboratories, Burlingame, CA) aqueous mounting

medium.

40

Figure 5.1Methods used to label HCN1 and Neurobiotin. The HCN1 subunit was stained by the covalent binding of tyramide molecules to sites where the primary-secondary antibody complex is bound. Tyramide is converted to a reactive intermediate by the peroxidase and is conjugated to the Cy3 fluorochrome. Neurobiotin was stained using a Strept-Avidin DyLight649 conjugate.

5.5 Specificity of antibody

Themouse monoclonal IgG1 antibody (clone #N70/28) used in the present study was raised

against amino acid residues 778-910 (cytoplasmic C-terminus) of rat HCN1. The antibody satisfies

the gold standard test of specificity by producing noimmunolabeling in HCN1 knockout mice (data

provided by NeuroMab) (see Figure 5.2). The antibody stains a single band of approximately 100

kDa molecular weight on Western blot using membrane fractions prepared from the adult rat and

mouse brain. This band corresponds to the molecular weight of the HCN1 subunit.

Figure 5.2 Western blot using membrane preparations from the adult rat brain (RBM) and adult mouse brain (MBM). The mouse monoclonal IgG1 antibody stains a single band approximately 100 kDa that corresponds to the molecular weight of HCN1 in the rat and wild-type mouse. The band is absent in the HCN1 knockout mouse. (data provided by NeuroMab).

41

To establish a similar level of confidence in the specificity of the antibody for HCN1 in the

adult feline, a battery of tests were conducted. The antibody produced a similar pattern of

immunoreactivity in the cervical spinal cord from the rat and feline cut in the transverse plane (see

Figure 5.3). Notably, immunoreactivity associated with the somatic and dendritic membrane of

motoneurons in the medial and lateral regions of the ventral horn was observed in both the rat and

feline (see Figure 5.3 A3 (rat) and B3 (feline)). These motoneurons were identified by the presence

of choline acetyltransferase (ChAT). Moreover, these observations were consistent with previous

descriptions of the HCN1 immunoreactivity in the spinal cord where an antibody raised against a

different part of the HCN1 subunit was used (Milligan et al., 2006).

Furthermore, the same pattern of immunoreactivity in the rat and feline spinal cord was

produced by an antibody directed to a different epitope of the HCN1 subunit (see Figure 5.3 A1

(rat) and B1 (feline)). This rabbit polyclonal antibody (C#: APC-056, LOT#: AN-09, Alomone Labs,

Ltd., Jerusalem, Israel) was raised against a synthetic peptide corresponding to amino acid residues

6-24 (cytoplasmic N-terminus) of rat HCN1. The immunoreactivity disappears when the rabbit

antibody was preadsorbed with the control antigenic peptide (C#: APC-056, LOT#: AG-04, Alomone

Labs, Ltd., Jerusalem, Israel) (see Figure 5.3 A2 (rat) and B2 feline)).

Figure 5.3. HCN1 immunoreactivity in the (A) medial and (B) lateral regions of the ventral horn in transverse spinal cord sections from the rat (top row) and feline (bottom row) viewed under an epifluorescence light microscope using a 40x objective. (A1 and B1) Immunoreactivity is associated with the somatic and dendritic membrane of motoneurons in both the rat (A1) and feline (B1) using a rabbit polyclonal antibody directed against a different non-overlapping region of the HCN1 subunit. (A2 and B2) The immunoreactivity disappears when the rabbit antibody is pre-incubated with the control antigenic peptide. (A3 and B3) Same pattern of immunoreactivity is observed in the rat (A3) and feline (B3) using the mouse monoclonal IgG1 antibody.

42

Lastly, the mouse monoclonal IgG1 antibody and the rabbit polyclonal antibody both

produced the same regional distribution of HCN1 immunoreactivity across pyramidal neurons in

the rat neocortex (see Figure 5.4 A1 and A2). Strong HCN1 immunoreactivity was present on the

apical dendrites of pyramidal neurons that increased in intensity with distance from the soma

(white arrow). The intensity of HCN1 immunoreactivity was highest on the distal tuft dendrites

(white crossed arrow). In comparison, HCN1 immunoreactivity was not detected on the soma and

proximal dendrites of pyramidal neurons. This pattern of staining matches previous descriptions of

the HCN1 immunoreactivity in rat neocortex using a guinea pig polyclonal antibody recognizing

amino acid residues 850-910 (cytoplasmic C-terminus) of rat HCN1 (Lorincz et al., 2002). Thus,

these sets of observations confirm that the mouse monoclonal IgG1 antibody is (1) specific for the

HCN1 subunit and (2) is cross-reactive in the adult feline.

Figure 5.4HCN1 immunoreactivity in the (A) neocortex of transverse brain sections from the rat viewed under an epifluorescence light microscope using a 20x objective. (A1) HCN1 immunoreactivity using the rabbit polyclonal antibody. (A2) HCN1 immunoreactivity using the mouse monoclonal IgG1 antibody. The intensity of staining was strong on the apical dendrites (white arrow) and highest on the distal tuft dendrites (white crossed arrow) of pyramidal neurons.

43

5.6 Visualization of Neurobiotin and HCN1

Tissue sections containing Neurobiotin-filled motoneurons were initially examined using

standard epifluorescence with appropriate bandpass excitation and emission filters (Chroma

Technology Corp., VT, USA) on an Olympus BX60 microscope. These observations were used to

assess the quality of the staining of HCN1 (e.g. signal to background labelling) and identify

intracellularly stained cells for further examination using confocal microscopy. Only motoneurons

that met the following criteria were selected for further study: 1) the ratio of signal to background

labelling of HCN1in the tissue section was high and 2) small diameter distal dendrites more than

1500 μm rostral or caudal to the soma were easily visualized using a X40 objective.

A Carl Zeiss LSM 710 NLO Laser Scanning Confocal microscope equipped with a 63x oil

immersion DIC objective (Plan-Apochromat, NA 1.4, pixel size = 0.219 µm x 0.219 µm) was used to

collect images of HCN1 immunoreactivity and stained dendrites. Tissue sections were scanned

from top to bottom to yield stacks of 0.4 µm thick optical sections. Ten to sixteen stacks of optical

sections, 224.7 µm x 224.7 µm in the x and y axis, containing dendrites at different rostral and/or

caudal distances from the soma were collected from one to two serial sections for each

motoneuron. One or more of these sections contained the soma.

The Dylight649 and Cy3 were excited with 633 nm and 543 nm helium-neon lasers,

respectively. Light from each fluorochrome was separated using a dichroic mirror. Cross-talk

between the channels was minimized by setting appropriate cut-off wavelengths for the Dylight649

and Cy3 emission filters. Acquisition settings (laser power, detector gain, digital offset, pinhole size)

were identical for all stacks of optical sections.

44

5.7 Reconstruction of motoneuron dendritic trees

All trapezius motoneurons were reconstructed with the aid of an Olympus BX60 microscope

equipped with a digital monochromatic camera (QImagingRetigaEXi Fast 1394) and Neurolucida

(V9.03; Microbrightfield Inc., Williston, Vt, USA) neuron tracing software. Image stacks of regions

containing dendrites from each motoneuron were collected using the Dylight649 filter (Olympus

UPlanFl 40x objective, NA 0.75 dry, pixel size = 0.32 µm x 0.32 µm) and tiled. In order to capture all

of the dendrites on a single tissue section, image stacks from up to fifteen regions were acquired.

After the images were taken, the trajectories of the dendrites were traced by placing a series of

connected points along each dendritic segment. Each point has an associated diameter that

corresponds to the diameter of the dendrite at that location. The dendritic segments that were

connected to the dendritic segments examined under the confocal microscope were followed from

section to section until each dendritic segment examined under the confocal microscope was linked

to the soma. Calculations of the distance to the soma were corrected for tissue shrinkage along the

z-axis.

45

Chapter 6.Results

Part I: Quantitative measurements of the distribution and density of membrane-associated

proteins on dendrites of intracellularly labeled neurons using confocal microscopy in

conjunction with three dimensional image analysis and reconstructions of neuronal

morphology

6.1 Absorption and scattering of excitation and emission light

Light distortion is a problem inherent to confocal microscopy. When a specimen is optically

sectioned to produce a stack of images, the captured fluorescence intensities decline with depth

dueto the absorption and scattering of both the excitation and emission light. The loss of intensity is

readily apparent using confocal microscopy in the epi-fluorescence mode where the objective

serves both to pass excitation light onto the specimen and focus emitted light back to the light

detector. As the excitation light leaves the objective to reach the deeper layers of the specimen,

some of the light is absorbed and scattered in the specimen during travel. This attenuation of the

excitation light will result in less fluorescence emitted from the deeper layers. Furthermore, some

of the emitted light will be lost in the specimen on its return to the objective.

Consequently, optical sections taken from the bottom of the tissue section will appear

darker with some loss of detail compared to optical sections taken from the surface of the tissue

section (See Figure6.1A-D). Thus, accurate quantitative analysis of the fluorescence intensity of

images taken at different tissue depths is not possible without compensating for the absorption and

scattering of light by the tissue. In the presence of light attenuation, the fluorescence intensity with

depth can be described empirically by an exponential decay function (Rigaut and Vassy, 1991;

Rigaut et al., 1996; Conchello, 1995; Kervrann et al., 2004) (see equation 1.14),

46

𝐼𝐼(𝑧𝑧) = 𝐼𝐼𝑜𝑜 𝑒𝑒−𝑏𝑏𝑧𝑧 + 𝑘𝑘 (1.14)

whereI(z) is the mean fluorescence intensity of sequential optical sections as a function of z, z is the

distance from the surface, b is the extinction coefficient, Io is the mean fluorescence intensity of the

optical section at the surface, and k is a constant representing the minimum fluorescence intensity.

Figure 6.1E shows an example of the decay in fluorescence intensity associated with the

HCN1 immunoreactivity in the white matter of the spinal cord. This region was selected because

there is little variation in the distribution of HCN1 in the x, y, and z planes. In addition, the thickness

of the tissue section used in the example was deliberately reduced from the standard thickness of

50 µm to minimize uneven antibody penetration, which is discussed below. Thus, all changes in the

intensity of fluorescence could be attributed to absorption and scattering of light by the tissue. As

indicated by the high correlation coefficient (r2 = 0.99), these changes were precisely predicted by

equation 1.14.

Figure 6.1(A – D) Optical sections of the immunofluorescent HCN1 staining in the white matter of the spinal cord. The fluorescence intensities of optical sections in deeper layers are weaker than optical sections near the surface of the tissue. (E) The loss of intensity with depth can be described empirically by an exponential decay function.

47

6.2 Penetration of antibodies

The penetration of antibodies into fixed tissue is an inherent problem of

immunohistochemistry. The fixation process produces extensive crosslinking between proteins due

to the formation of covalent bonds between adjacent amine-containing groups. This creates a

compact protein network that impedes the penetration of antibodies. As a result, the speed and

depth of antibody penetration are inversely related to the degree of fixationand thickness of the

tissue.

Despite post-fixation treatment with reagents to reduce protein crosslinking and the use of

permeabilizing detergents, the limited ability of antibodies to reach the middle of the tissue section

may reduce the fluorescence intensity of optical sections located midway between the top and

bottom of the tissue section. If not accounted for, the decrease in fluorescence will be attributed

incorrectly to less HCN1 immunoreactivity. In the absence of absorption and scattering of light by

the tissue, the fluorescence intensity as a function of z should follow a parabolic curve where the

intensity is highest at the top and bottom surface and lowest at the center of the tissue (see

Figure6.2).

Figure 6.2 In the absence of light attenuation, the uneven penetration of antibodies results in higher fluorescence intensities in optical sections near the top and bottom surface of the tissue section and lower intensities in optical sections near the tissue center. This variation in fluorescence intensity along the thickness of the tissue section can be described by a parabolic function.

48

6.3 Compensating for light attenuation and antibody penetration

The loss of fluorescence intensity due to absorption and scattering of light and limited

antibody penetration, floss, with depth, z, can be described by the combination of an exponential

decay (representing the loss due to absorption and scattering of light) and a parabolic function

(representing the loss due to limited antibody penetration), such that:

𝑓𝑓𝑙𝑙𝑜𝑜𝑙𝑙𝑙𝑙 (𝑧𝑧) = 𝐴𝐴𝑒𝑒−𝑏𝑏𝑧𝑧 + 𝑐𝑐𝑧𝑧2 + 𝑑𝑑𝑧𝑧 + 𝑘𝑘 (1.15)

Figure 3G1 shows the mean HCN1 fluorescence intensity of each optical section as a

function of z in a region of the spinal accessory nucleus containing an intracellularly stained

trapezius motoneuron. These data were precisely matched by the relationship described by

Equation 1.15 (for this example r2 = 0.99). For all tissue sections examined r2 was always greater

than 0.92. We therefore determined the values of the parameters in equation 1.15 (A, b, c, d, k) for

each tissue section and used equation 1.16 to estimate the mean HCN1 fluorescence intensity for

each optical section in the absence of light absorption and scattering and limited antibody

penetration. All voxels of each optical section were assumed to suffer the same loss of fluorescent

intensity.

The optical section at the top surface of the tissue was selected as the reference section

since the effect of light attenuation and antibody penetration are kept to a minimum (depth ≈ 0).

The mean HCN1 fluorescence intensity for each optical section was calculated with respect to the

reference section. The ratio of the mean HCN1 fluorescence intensity of the reference section, Io, to

the fluorescence intensity predicted at a given optical section due to light absorption and scattering

and antibody penetration, Iofloss(z), was used as a correction factor to compensate for the intensity

lost in that optical section. The observed fluorescence intensity of each voxel belonging to the

optical section was multiplied by the correction factor for that optical section (see equation 1.16and

49

Figure6.3). This is described below where Iobserved[x, y, z] and Icorrected[x, y, z] denote the observed and

corrected fluorescence intensity of the (x, y)th voxel in optical section z.

𝐼𝐼𝑐𝑐𝑜𝑜𝑟𝑟𝑟𝑟𝑒𝑒𝑐𝑐𝑡𝑡𝑒𝑒𝑑𝑑 [𝑥𝑥,𝑦𝑦, 𝑧𝑧] = 𝐼𝐼𝑜𝑜

𝐼𝐼𝑜𝑜𝑓𝑓𝑙𝑙𝑜𝑜𝑙𝑙𝑙𝑙 (𝑧𝑧) . 𝐼𝐼𝑜𝑜𝑏𝑏𝑙𝑙𝑒𝑒𝑟𝑟𝑜𝑜𝑒𝑒𝑑𝑑 [𝑥𝑥,𝑦𝑦, 𝑧𝑧] (1.16)

Figure 6.3 (A1 – F1) Optical sections located at different depths, which capture the HCN1 immunofluorescent staining of the soma and proximal dendrites of a trapezius motoneuron. (A2 – F2) The corresponding optical sections after compensating for the loss of fluorescence intensity due to light attenuation and uneven antibody penetration. (G1) The function f(z) describing the extent of intensity loss (black line) is fitted to the observed mean HCN1 intensity plotted against depth (open red circle). (G2) Corrected mean intensity plotted against depth (solid red circle).

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6.4 HCN1 immunoreactivity in the ventral horn

Previous studies have reported the expression of HCN1 mRNA in the ventral horn of the

spinal cord and HCN1 immunoreactivity has been detected on the somatic and proximal dendritic

membranes of motoneurons (Santoro et al., 2000; Milligan et al., 2006). Consistent with these

observations, the mouse anti-HCN1 monoclonal antibody stained neuronsin the ventral horn (See

Figure 6.4A1 & B1). This staining was largely attributed to labelling of somatic and dendritic

processes belonging to motoneurons, which wereidentified by their immunoreactivity to choline

acetyltransferase (ChAT) (see Figure6.4A2 & B2). However, labelling of HCN1 was absent on the

soma and proximal dendrites of some motoneurons suggesting that HCN1 is either absent or the

amount of HCN1 expressed in these regions is below the detection limit of the assay (see Figure

6.4D). These motoneurons may be γ-motoneurons due to their small somatic diameter. These

observations match previous descriptions of HCN1 immunoreactivity across motoneurons in the

ventral horn (Milligan et al., 2006).

Figure 6.4 (A1-A2) HCN1 and ChATimmunoreactivity in the cervical spinal cord at low magnification. (B1-B2) Higher magnification of HCN1 and ChATimmunoreactivity in the ventral horn. (C) ChAT-positive motoneurons containing HCN1 immunoreactivity. (D) HCN1 immunoreactivity was not detected on some motoneurons immunoreactive for ChAT.

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6.5 Distribution of HCN1 immunoreactivitywithin the spinal accessory nucleus and on

trapezius motoneurons

In the spinal accessory nucleus, the HCN1 immunoreactivitytended to outline the soma and

proximal dendrites of trapezius motoneurons (see Figure6.5A & B). In sections where motoneuron

dendrites could be traced distally within the plane of the section, HCN1 immunoreactivity could be

detected on dendrites several hundred µm from the soma. However, the intensity of labelling on the

soma and proximal dendrites varied among trapezius motoneurons with large somata suggesting

differences in HCN1 between alpha motoneurons. In the surrounding neuropil, the HCN1

immunoreactivity also outlined the circumference of dendritic processes cut perpendicular to the

plane of the section.

Higher magnifications revealed that the HCN1 immunoreactivity was punctate and

organized into clusters ranging in size from 0.5 – 3.0 µm2(see Figure 6.6B). Some of the HCN1

clusters werearranged along the perimeter of the soma and proximal dendrites as defined by the

edge of the intracellular stain (see Figure 6.6C). These clusters are most likely associated with the

Figure 6.5 (A) Single optical section containing the soma of a trapezius motoneuron (white arrow) and dendritic processes oriented perpendicular to the plane of the section (white crossed arrow) that are immunoreactive for HCN1. (B) Single optical section containing proximal dendrites of trapezius motoneurons that immunoreactive for HCN1 (white arrow).

52

neuronal membrane and thus represent membrane-bound HCN1-subunits. These subunits form

functional channels, which are responsive to changes in the membrane potential.

In line with previous observations, HCN1 immunoreactivity was also present in the somatic

and dendritic cytoplasm as indicated by the immunoreactivity occurring in the intracellular stain

(see Figure 6.6B). Ultrastructural studies have shown that these cytoplasmic clusters of HCN1 are

closely associated with the endoplasmic reticulum and Golgi apparatus (Lorincz et al., 2002).

Furthermore, theses cytoplasmic clusters of HCN1 have been shown to co-localize with the early

endosome antigen 1 protein (EEA1), a marker of endosomes (Santoro et al., 2004; Santoro et al.,

2009). Thus, the HCN1 immunoreactivity in the cytoplasm may represent HCN channels being

transported to or from the neuronal membrane. However, it is also likely that these clusters may

represent non-specific immunoreactivity.

Figure 6.6 (A) Single optical section of the intracellular stain at the soma for a trapezius motoneuron. (B) Single optical section of the corresponding HCN1 immunoreactivity at the soma. The HCN1 immunoreactivity is punctate in appearance and organized into clusters that are high in fluorescence intensity. (C) These clusters are mainly observed along the perimeter of the soma suggesting that these clusters are associated with the membrane. HCN1 clusters are also present in the cytoplasm of the soma.

53

6.6 Registration of selected dendritic segments observed using confocal microscopy with

reconstructions of the dendritic tree based on epi-fluorescence observations

The morphology of the dendritic tree of stained trapezius motoneurons was visualized

using a standard epi-fluorescence microscope under the appropriate DyLight649 bandpass

excitation and emission filters (Olympus BX60, 20x/0.50 NA objective).To obtain a quantitative

description of the dendritic distribution of HCN1, segments of stained dendrites (40 - 60 µm in

length) at different distances from the soma and the corresponding HCN1 immunoreactivitywere

collected as stacks of optical sections using confocal microscopy. Dendritic segments were sampled

predominantly from dendrites projecting rostrally and caudally since the dendritic tree of trapezius

motoneurons located between mid C2 and C4 are fusiform in shape with the majority of dendrites

oriented in the rostrocaudal direction (see Figure 6.7) (Vanner and Rose, 1984; Rose et al., 1985).

The distance of each dendritic segment from the soma was determined from detailed

reconstructions of the dendritic tree whereby the dendrites on each serial section were traced and

converted into a series of XYZ coordinates with corresponding diameters (Rose and Cushing, 2004).

Each dendritic segment examined under the confocal microscope was linked to the soma by

connecting these points.

54

Figure 6.7 (Left) A stained trapezius motoneuron from a horizontal tissue section captured using a standard epi-fluorescence microscope under the appropriate DyLight649 bandpass excitation and emission filters. Each boxed area corresponds to a dendritic region where the HCN1 immunoreactivity and stained dendrites were collected into stacks of optical sections using confocal microscopy. (Right) Optical sections of the HCN1 immunoreactivity and intracellular stain at the soma and various dendritic regions.

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6.7 Extracting membrane-associated HCN1 immunoreactivity

As shown in Figure 6.8, the membrane-associated HCN1immunoreactivityoccurs in a small

region less than 1 µm wide at the edge of the intracellular stain where the fluorescence intensity of

the intracellular stain declines abruptly. In order to capture the membrane-associated HCN1

immunoreactivity, a threshold-based approach was used to detect the edge of the intracellular

stain.

As shown in Figure 6.9, the fluorescence intensity of the intracellular stain from all voxels

within each stack of images was bimodal. The peak at 100 arbitrary units invariably corresponded

to voxels that were located within intracellularly stained somata and dendrites. In contrast, the

lower intensity peak at 0 arbitrary units was always associated with voxels located outside

intracellularly stained somata and dendrites. The low fluorescence in these voxels was attributed to

background staining.

Figure 6.8 (A) Membrane-associated HCN1 immunoreactivity observed along the perimeter. (B) The fluorescence intensity of HCN1 and the intracellular stain plotted against distance along the line shown in (A), which crosses over a cluster of HCN1 immunoreactivity.

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To determine the range of voxel intensities that corresponded to regions outside of the

intracellularly stained somata and dendrites, we measured the fluorescence intensity of all voxels

collected from 9-16 stacks of optical sections that did not contain any intracellularly stained

dendrites or somata (see Figure 6.9B). The majority of the voxels hadafluorescence intensity of

zero, however, a number of voxels had non-zerofluorescence intensities. These non-zero

fluorescence intensities were attributed to the Poisson noise inherent to the stochastic detection of

photons during image formation. The maximum voxel intensity of these image stacks was defined

as, Ithreshold, and was used to separate thetwomodes of fluorescence intensity. Due to variability in

histological processing, the range of background voxel intensities varied from cell to cell. Thus, a

separate Ithresholdwas applied to each trapezius motoneuron.

The voxels contained in stacks of optical sections containing intracellularly stained

dendrites and/or somata were separated into two non-overlapping sets. One set contained voxels

with intensities greater than Ithreshold and the other set contained voxels with intensities less than or

equal to Ithreshold. Voxels with intensities higher than Ithreshold were assigned a grey-level intensity of

Figure 6.9 (A) The number of voxels plotted against voxel intensity for a stack of optical sections containing the intracellular stain. (B) The number of voxels plotted against voxel intensity from 9-16 stacks of optical sections that did not contain any intracellularly stained dendrites or somata.

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255 whereas voxels with intensities lower than or equal to Ithreshold were assigned a grey-level

intensity of 0. This resulted in a binary image stack containing two objects of different intensities.

The background was represented by the set of voxels with a grey-level intensity of 0 and the

intracellular stain was represented by the set of voxels with a grey-level intensity of 255. This

process was repeated for each dendritic segment.

6.8 Determination of the intracellular to extracellular boundary

For most dendritic segments, the application of Ithresholdproved to be a robust means of

partitioning the stack of optical sections into voxels belonging to the background and voxels

belonging to the intracellular stain. However, rigid application of a single, fixed Ithresholdled to

obviouserrors if the dendrite was either very strongly stained or weakly stained. The extent of

strongly stained dendrites was overestimated and the extent of weakly stained dendrites was

underestimated. Thus, all dendritic segments were subsequently visually inspected using ImagePro

Plus (MediaCypernetics, Silverspring, MD, USA)and the threshold manually adjusted to ensure that

the intracellular to extracellular boundary was correctly assigned.

6.9 Creation of a three-dimensional layer of voxels to capture the juxta-membrane region

The membrane-cytoplasmic border was defined by the perimeter of the volume containing

voxels that were assigned to the intracellular stain. To constrain the analysis of HCN1

immunoreactivity to within the juxta-membrane zone, the volume was expanded by 1 voxel in the x,

y and z planes and all interior voxels were removed except those adjacent to the perimeter. This

produced a two voxel (0.44 µm) wide layer that captures the region immediately adjacent to the

perimeter. This layer most likely contains the membrane. The HCN1 immunoreactivity located

within this layer was measured and was defined as membrane-associated HCN1 immunoreactivity.

The density of membrane-associated HCN1 immunoreactivity was determined by dividing the sum

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of the fluorescence intensities observed within the membrane layer by the number of voxels

contained in the membrane layer. These steps are shown in Figure 6.10.

6.10 Independent confirmation of the accuracy of the membrane extraction technique

The accuracy of the method described above was validated by staining for gephyrin, an

anchor protein for GABA and glycinergic receptors, and KCC2, a chloride-potassium cotransporter.

Both of these membrane proteins produce similar staining patterns where the majority of the

immunoreactivity is concentrated around the perimeter of the motoneuron with little or no

cytoplasmic staining (see Figure 6.11 B1 & B2). This suggests that the immunoreactivity is localized

specifically to the neuronal membrane. To determine whether the membrane extraction technique

captures the gephyrin and KCC2 immunoreactivity outlining the soma and dendrites, the densities

of gephyrin and KCC2 fluorescence located within the juxta-membrane layer and adjacent

Figure 6.10 (A) Serial optical sections of the intracellularly stained dendritic segment were rendered into a 3-dimensional volume. (B) The stack of optical sections from (A) were partitioned into voxels belonging to the intracellular stain and voxels belonging to the background. (C) The volume containing voxels belonging to the intracellular stain in (B) was expanded by 1 voxel in the x, y and z planes. All interior voxels were removed except those adjacent to the perimeter. This produced a two voxel (0.44 µm) wide layer. (E) The HCN1 immunoreactivity from (D) that was located within the layer in (C) was considered for analysis.

59

extracellular and cytoplasmic layers were compared. The extracellular and cytoplasmic layers were

produced using the same method described above with the exception that the membrane-

cytoplasmic border was deliberately chosen 1 voxel away from the perimeter of the intracellular

stain into the exterior or interior.

As shown in Figure 6.11 E1 & E2 the mean density of KCC2 fluorescence from 8 dendritic

segments was significantly greater in the juxta-membrane layer (43 a.u./voxel ± 6 SEM) compared

to the adjacent extracellular layer (23 a.u./voxel ± 3 SEM) according to a student's test (P<0.05).

However, the density of KCC2 fluorescence in the juxta-membrane layer was not significantly

greater compared to the adjacent cytoplasmic layer (33 a.u./voxel ± 4 SEM). This finding is

consistent with the observation that the KCC2 immunoreactivity located along the perimeter of the

intracellular stain extends approximately 1 µm (4 voxels) into the interior of the intracellular stain

(see Figure 6.11 D1). Since the layer capturing the adjacent cytoplasmic region is restricted to two

voxels in width (0.44 µm), the likelihood of capturing membrane-associated KCC2

immunoreactivity in is high. A similar results were obtained for comparisons of gephyrin densities

between the juxta-membrane layer and adjacent extracellular and cytoplasmic layers. The mean

density of gephyrin fluorescence from 10 dendritic segments was significantly greater in the juxta-

membrane layer (5 a.u./voxel ± 0.5 SEM) compared to the adjacent extracellular layer (3 a.u./voxel

± 0.4 SEM) (P<0.05, student's t-test). The density of gephryin fluorescence in the juxta-membrane

layer was not significantly greater compared to the adjacent cytoplasmic layer (4 a.u./voxel ± 0.4

SEM). Similarly, the gephyrinimmunoreactivity near the perimenter of the intracellular stain

extends approximately 1 µm into the interior of the intracellular stain (see Figure 6.11 D2).

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Figure 6.11 (A1-B1) An optical section of the intracellular stain and KCC2 immunoreacitivty at the soma. (C1) Enlarged view of the region shown in A1. (D1) The fluorescence intensity of KCC2 and the intracellular stain plotted against distance along the line shown in C1. (E1) The mean density of KCC2 fluorescence in the juxta-membrane region was significantly greater than the adjacent extracellular region (P<0.05, t-test) but was not significantly greater than the adjacent cytoplasmic region. Error bars represent SEM. (A2-B2) An optical section of the intracellular stain and gephyrinimmunoreacitivty at a proximal dendrite. (C2) Enlarged view of the region shown in A2. (D2) The fluorescence intensity of gephryin and the intracellular stain plotted against distance along the line shown in C2. (E2) The mean density ofgephyrin fluorescence in the juxta-membrane region was significantly greater than the adjacent extracellular region (P<0.05, t-test) but was not significantly greater than the adjacent cytoplasmic region. Error bars represent SEM.

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Chapter 7 Part II: Proximal to distal gradients in the distribution of membrane-associated

HCN1 immunoreactivity on the dendritic trees of trapezius motoneurons

Ten trapezius motoneurons from six adult felines were stained and the densities of

membrane-associated HCN1immunoreactivity of dendritic segments at different distances from the

soma were compared. Between 25 and 92 dendritic segments were sampled for each trapezius

motoneuron using the membrane extraction technique. These ten trapezius motoneurons were

selected based on the quality of intracellular stain and the HCN1 staining. Dendritic segments were

selected based on the quality of the intracellular stain. Specifically, the entire volume of the

dendrite was uniformly stained by Neurobiotin and the segment of dendrite was not distorted by

histological sectioning and processing.

7.1 Some trapezius motoneuron display a uniform proximal to distal distribution of HCN1

The proximal to distal distribution of HCN1 across the dendritic tree for one of the ten

intracellularly stained trapezius motoneurons is shown in Figure 7.1A. The distance of each

dendritic segment from the soma is plotted against the density of membrane-associated HCN1

immunoreactivity of that segment. The locations of the 39 dendritic segments relative to the

location of all dendrites found within four adjacent tissue sections are shown in Figure 7.1B. The

density of membrane-associated HCN1 immunoreactivity was highly variable across dendritic

segments. The average density of membrane-associated HCN1 immunoreactivity within 0 - 200 µm

from the soma was 21 a.u./voxel ± 6 S.D. (n = 12), from 200 - 800 µm from the soma it was 32

a.u./voxel ± 7 S.D. (n = 9) and greater than 800 µm from the soma it was 25 a.u./voxel ± 7 S.D. (n =

18). A linear regression to the data demonstrated that HCN1 has a uniform distribution (slope = 0.1

a.u./voxel/1000 µm; not significantly different from zero, P>0.5 ANOVA) (see Figure 7.1).

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The proximal to distal distribution of HCN1 was uniform on the dendrites of three of the ten

trapezius motoneurons examined. The slope of the relationship between distance and membrane-

associated HCN1 density for these motoneurons was not significantly different from zero. In order

to minimize the effect of variability in HCN1 labelling due to local differences in fixation and more

accurately compare data collected from different motoneurons, the densities of membrane-

associated HCN1 for each trapezius motoneuron were divided by the density of HCN1 in the tissue

section from which the dendritic segments were selected. The HCN1 density of the tissue section

Figure 7.1 Uniform proximal to distal distributionof HCN1on the dendrites of trapezius motoneurons.(A) The distance of each dendritic segment from the soma is plotted against the density of membrane-associated HCN1 fluorescence for that segment. The density of membrane-associated HCN1 fluorescence is also represented by different colours. Low fluorescence intensities are shown in blue and high fluorescence intensities are shown in red. (B) The locations of the dendritic segments (n=39) shown relative to the positions of other dendrites and the somata. (C1-C3) Membrane-associated HCN1 immunoreactivity from dendritic segments located at 0-200 µm, 200-800 µm, and >800µm from the soma.

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was calculatedbased on the average density of HCN1 observed in all image stacks (224.7 µm x

224.7 µm in the x and y axes) containing stained dendrites. This served as a means of

normalization. Figure 7.2 compares the normalized densities of membrane-associated HCN1 for all

three motoneurons that had a uniform proximal to distal distribution of HCN1. Since the

normalized density of membrane-associated HCN1 is expressed as a ratio, a logarithmic scale is

used for the normalized densities whereby a value of 1 indicates that the density of HCN1 is equal

to the average density of HCN1 for the tissue section.

Figure 7.2 The distribution of HCN1 is uniform across the dendritic tree of three trapezius motoneurons. The distance of each dendritic segment from the soma is plotted against the normalized density of membrane-associated HCN1 fluorescence for that segment. The slopes of the relationship between distance and normalized density of membrane-associated HCN1 for these motoneurons were not significantly different from zero.

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7.2 Some trapezius motoneurons display a proximal to distal graded increase in HCN1

The distribution of HCN1 across the dendritic tree of another trapezius motoneuron is

shown in Figure 7.3. For this cell, the density of membrane-associated HCN1 immunoreactivity

increased from 8 a.u./voxel at a distance of 119 µm from the soma to 16 a.u./voxel at a distance of

1301 µm from the soma. Overall, the density of membrane-associated HCN1

immunoreactivityincreased 7.9a.u./voxel/1000 µm (significantly different from zero; P<0.05

ANOVA).

Figure 7.3 Graded proximal to distal increase of HCN1 on the dendrites of trapezius motoneurons. (A) The distance of each dendritic segment is plotted against the density of membrane-associated HCN1 fluorescence for that segment. The density of membrane-associated HCN1 fluorescence is also represented by different colours. Low fluorescence intensities are shown in blue and high fluorescence intensities are shown in red. (B) The locations of the dendritic segments (n=35) shown relative to the positions of other dendrites and the somata. (C1-C3) Membrane-associated HCN1 immunoreactivity from dendritic segments located at 0-200 µm, 200-800 µm, and >800µm from the soma.

65

For seven of the ten motoneurons examined, the density of membrane-associated HCN1

was significantly greater on distal dendritic segments compared to proximal dendritic segments.

The increase was a linear function of distance such that the density of HCN1 increased 40 to 110%

every 1000 µm. Three of the seven motoneurons displayed a relatively steep linear increase in

HCN1 density with distance from the soma (slope based on normalized data = 1.0 - 1.1

a.u./voxel/1000 µm; P<0.05 ANOVA) (see Figure 7.4). The linear regression lines appear curved

since the normalized densities of membrane-associated HCN1 are plotted on a logarithmic scale.

Figure 7.4 Three trapezius motoneurons display a strong linear increase in HCN1 density with distance from the soma (slope = 1 - 1.1 a.u./voxel·1000 µm). The distance of each dendritic segment from the soma is plotted against the normalized density of membrane-associated HCN1 fluorescence for that segment. The relationship between distance and normalized density of membrane-associated HCN1 is significant for these motoneurons (P<0.05, ANOVA). Linear regression lines appear curved due to the logarithmic scale.

66

The other four motoneurons displayed a more moderate linear increase in HCN1 density with

distance (slopebased on normalized data = 0.4 - 0.8 a.u./voxel/1000 µm; P<0.05 ANOVA) (see

Figure 7.5).

7.3 The proximal to distal gradients in HCN1density is inversely related to the somatic HCN1

density

The different dendritic distributions of HCN1 observed across the ten trapezius

motoneurons occupy a spectrum. The density of HCN1 was uniform on some trapezius

motoneurons (n = 3), increased from 40-80% every 1000 µm on others (n = 4) or increased from

100-110% every 1000 µm (n = 3) in another group of trapezius motoneurons (see Figure 7.6A).

Figure 7.5 Four trapezius motoneurons display a moderately strong linear increase in HCN1 density with distance from the soma (slope = 0.4 - 0.8 a.u./voxel·1000 µm). The distance of each dendritic segment from the soma is plotted against the normalized density of membrane-associated HCN1 fluorescence for that segment. The relationship between distance and normalized density of membrane-associated HCN1 is significant for these motoneurons (P<0.05, ANOVA). Linear regression lines appear curved due to the logarithmic scale

67

This increase in HCN1 density with distance from the soma is inversely related to the HCN1 density

at the soma (represented by the y-intercept of the proximal to distal distance versusdensity

regression analysis) such that the largest proximal to distal gradients of HCN1 were observed on

trapezius motoneurons with low somatic densities of HCN1. In contrast, trapezius motoneurons

with a high somatic density of HCN1 displayed a uniform density of HCN1. This relationship was

significant (P<0.037 ANOVA) (see Figure 7.6B).

Figure 7.6 (A) The proximal to distal distribution of HCN1 across the ten trapezius motoneurons. The density of HCN1 is either uniform (n = 3; black), increases moderately with distance (n = 4; blue) or strongly increases with distance (n = 3; red). (B) The change in HCN1 density every 1000 µm is plotted against the normalized somatic density of HCN1. A significant negative relationship exists (P<0.05 ANOVA).

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7.4 The proximal to distal gradient in HCN1density is inversely related to the estimated

input conductance of the motoneuron

Based on the mechanical response of the innervated muscle fibers, motor units can be

classified as slow twitch fatigue-resistant (S-type), fast twitch fatigue-resistant (FR-type) or fast

twitch fast fatiguing (FF-type) (Burke, 1967; Burke et al., 1973). Past studies have shown that

motor units differ on the basis of the input conductance of the motoneuron (Fleshman et al., 1981;

Burke et al., 1982; Zengel et al., 1985). S-type motoneurons have a low input conductance whereas

FR-type and FF-type motoneurons have higher input conductances. The input conductance of a

motoneuron is largely determined by the specific membrane resistivity of the dendritic tree and the

size and branching pattern of dendrites(Rall, 1977). If the specific membrane resistivity is known,

an accurate estimate of the input conductance of a motoneuron can be obtained by reconstructing

and measuring the geometry of the whole dendritic tree (Fleshman et al., 1988; Clements and

Redman, 1989).

Rall has shown that the electrical behavior of dendritic trees can be roughly approximated

by a simple uniform cylinder of finite length (Rall, 1959; Rall, 1977). For a uniform cylinder, the

input conductance is proportional to the 3/2 power of the dendritic stem diameter, which is

defined as the diameter of proximal dendrites measured at a distance of 40 µm from the soma-

dendrite border (Zwaagstra and Kernell, 1981; Kernell and Zwaastra, 1989). For each of the ten

trapezius motoneurons, the total input conductance was computed from the dendritic stem

diameters. In Figure 7.7A, the computed total input conductance of the motoneuron is plotted

against the change in HCN1 density every 1000 µm. A trend emerges whereby motoneurons

displaying a proximal to distal gradient of HCN1 had low input conductances whereas motoneurons

with a uniform density of HCN1 had high input conductances.To examine whether the proximal to

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distal distribution of HCN1 was related to motor unit type, hierarchical and k-means clustering

analyses were performed to determine if among the 10 trapezius motoneurons two groups

emerged , which correspond to presumably S-type motoneruons or FR- and FF-type motoneurons.

The 10 trapezius motoneurons were organized into the two groupsaccording to the change in HCN1

density per 1000 µm and the computed total input conductance. As shown in Figure 7.7B, the 10

trapezius motoneurons could be segregated into two groups, one group containing the four

trapezius motoneuons shown in the top left of Figure 7.7A displaying a steep increase in HCN1

density with distance from the soma (slopes >= 0.8 a.u./voxel/1000 µm) and the other group

containing the rest. For the first group containing motoneurons that displayed a proximal to distal

gradient of HCN1, the density of HCN1 increased on average 1.0a.u./voxel/1000 µm and the

average total computed input conductance was 0.5µS. For the second group containing

motoneurons that displayed either a unifom or weak increase in HCN1 density with distance from

the soma, the density of HCN1 increased on average 0.3 a.u./voxel/1000 µm and the average total

computed input conductance was 0.7µS. These differences between the two groups were significant

(P<0.05 for both the average change in HCN1 density per 1000 µm and the average total computed

input conductance according to a Mann-Whitney-U test). Thus, motoneurons that displayed a

relatively steep proximal to distal increase in HCN1 density had a low total computed input

conductance and are presumably S-type motoneurons. On the other hand, motoneurons that

displayed a uniform or weak proximal to distal increase In HCN1 density had a higher total

computed input conductance and are presumably FR- or FF-type motoneurons.

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Figure 7.7 (A)The computed total input conductance of each motoneuron is plotted against the change in HCN1 density every 1000 µm. (B) Based on hierarchical clustering analysis, the 10 trapezius motoneurons were organized into two groups according to the change in HCN1 density per 1000 µm and the total computed input conductance.

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Chapter 8. Discussion

The goals of this study were to develop a high throughput method for extracting the

membrane-associated density of voltage dependent channels on dendritesand subsequently to map

the proximal to distal distribution of channels formed by the HCN1-subunit on the dendritic tree of

trapezius motoneuons.The accuracy and robustness of the method were independently confirmed

by the staining of two other membrane proteins, gephyrin and KCC2. The proximal to distal

distribution of membrane-associated HCN1 immunoreactivity across the dendritic tree was either

uniform or increased with distance from the soma among trapezius motoneurons. The increase in

the density of HCN1 with distance from the soma was inversely related to the density of HCN1 on

the soma and proximal dendrites such that the largest proximal to distal gradients of HCN1 were

observed on trapezius motoneurons with a low proximal density of HCN1. In contrast, trapezius

motoneurons with a high proximal density of HCN1 displayed a uniform density of HCN1 across the

dendritic tree. Given this arrangement, it is possible that the average density of HCN1, definedasthe

total number of HCN1-subunit containing channels divided by the surface area of the motoneuron,

maybe the same among the two populations of trapezius motoneurons. Lastly, the distribution of

HCN1 was related to the total input conductance of motoneurons. There was an inverse

relationship between the change in HCN1 density with distance from the soma and the total input

conductance. Motoneurons displaying a prominent proximal to distal gradient of HCN1 had low

input conductances whereas motoneurons with a uniform density of HCN1 had high input

conductances. Past studies have shown that motor units differ based on the input conductance of

the motoneuron such that S-type motoneurons have low input conductances and FR-type and FF-

type motoneurons have higher input conductances. Thus, the differences in HCN1 distribution

across the motoneuron dendritic tree may be related to the type of motor unit.

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8.1 The extraction of the membrane-associated density of voltage-dependent channels

The study of the membrane distribution of voltage-dependent channels currently employ

anatomical techniques such as freeze fractureelectron microscopy, immunogold electron

micoscopy, confocal microscopyor electrophysiological techniques such as dendritic patch clamp

recordings. The high spatial resolution provided by electron microscopy permits the visualization

of voltage-dependent channels directly on the membrane surface but offers a limited field of view

thus restricting the dendritic surface area that can be sampled. Dendritic patch clamp recordings

provide a direct measurement of the functional density of voltage-dependent channels and it is

possible to describe properties such as the voltage dependence and kinetics of the channels.

However, small dendrites at great distances from the soma remain inaccessible. Furthermore, the

number of dendritic recordings that can be performed on a single neuron is limited. In the present

study, we have developed a high throughput method for measuring the membrane-associated

density of voltage-dependent channels from many dendritic locations regardless of diameter on the

same neuron. This technique allows for a large scale sampling of the dendritic tree, which is

necessary to comprehensively map the distribution of voltage-dependent channels. The technique

is also not restricted to only the study of voltage-dependent channels. As evidenced by the

application of the technique on both gephyrin and KCC2 immunoreactivity, the technique is able to

extract the membrane-associated of any membrane bound protein. Thus, the distribution of anchor

proteins, receptors and transporters on the membrane can be determined using the membrane

extraction technique.

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Limitations of the membrane extraction technique

8.2 The resolution in the z dimension is lower than the x and y: consequences of the

difference in resolution

In confocal microscopy, light is focused to illuminate a single point in the specimen. As the

beam of light is scanned point by point across the x, y and z planes of the specimen, a stack of

images is produced. However, the light emitted from an infinitely small point does not appear as an

infinitely small point in the captured image. Rather, the point appears as a larger spot due to the

diffraction of light. In the x- y plane, the spot is comprised of a central bright circular region

surrounded by a series of concentric bright and dark rings. In the x-z and y-z planes, the spot is

elliptical in shape. The dimensions of this spot, which are determined by the numerical aperture of

objective lens and the wavelength of the beam of light, define the lateral (x-y) and axial (z) resolving

limits of the lens system (Nathan et al., 1979). Since the spot is elongated along the z-axis, the axial

resolution is lower compared to the lateral resolution. This difference in resolution leads to an

elongation of features and greater loss of detail along the z-axis. In the present study, confocal

images were acquired using a 63x oil immersion objective with a numerical aperture of 1.4

resulting in lateral and axial resolutions of 0.22 µm and 0.40 µm, respectively. To avoid

overestimations of the membrane surface area when determining the density of membrane-

associated HCN1 immunoreactivity, densities were deliberately expressed as per voxel rather than

per µm3.

8.3 The juxta-membrane region invariably includes fluorescence from sources in the

neighboring cytoplasm and extracellular space

Studies have shown that the thickness of the neuronal membrane falls within the range of 4-

8 nm depending on the method of measurement (Stoeckenius, 1962; Huang and Thompson, 1965).

Given that the thickness of the juxta-membrane layer used to extract-membrane associated HCN1

74

immunoreactivity is approximately 0.44 µm in the x-y plane and 0.80 µm in the x-z and y-z planes, it

must be acknowledged that the juxta-membrane region captured by the membrane layer invariably

includes the fluorescence emitted from sources in the cytoplasm and extracellular space that are

closely apposed to the membrane. Thus, the diffraction-limited resolution of confocal microscopy

cannot resolve channel localization at the ultrastructural level, which can only be achieved with

electron microscopy.

8.4 The membrane-associated immunoreactivity extends beyond the bounds of the juxta-

membrane layer

Although it was demonstrated that the densities of gephyrin and KCC2 fluorescence were

significantly greater in the juxta-membrane layer compared to the adjacent extracellular layer, the

same was not true for the adjacent cytoplasmic layer. This was consistent with the observation that

the membrane-associated immunoreactivity of HCN1, gephyrin and KCC2 all extend approximately

1 µm or more from the edge of the intracellular stain towards the interior. This is not unexpected

given that the antibodies raised against these membrane proteins are directed towards epitopes

located on the intracellular surface of these proteins. Furthermore, given the finite size of the

primary-secondary antibody complex that has been reported to be approximately 14 nm in length,

the fluorochrome, which is the source of the fluorescence, may be located distant to the site of the

antigen (Ban et al., 1994). This may provide another reason as to why the observed membrane-

associated immunoreactivity expands approximately 1 µm into the interior. Studies using electron

microscopy have shown that the HCN1 immunogold particles associated with neuronal membrane

were mainly present on the cytoplasmic side of the membrane (Lorincz et al., 2002). Thus, it must

be acknowledged that although the immunoreactivity captured by the juxta-membrane layer is

membrane-associated, some of the immunoreactivity located along the perimeter of the

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intracellular stain is excluded from the measurement. Since this occurs for all sampled dendritic

segments, the relative comparisons provided in this study remain valid.

8.5 A larger proportion of cytoplasmic immunoreactivity is captured by the juxta-membrane

layer in small diameter dendrites

As mentioned previously, the stacks of optical sections were collected using a lens system

that provided a spatial resolution of 0.22 µm and an axial resolution of 0.40 µm. Thus, the two voxel

(0.44 µm) wide juxta-membrane layer will inevitably capture the fluorescence from sources in the

adjacent extracellular and cytoplasmic compartments. This problem is more severe in small

diameter distal dendrites where the diameters can be less than 1 µm. Compared to large proximal

dendrites that can range from 8 - 12 µm in diameter, the juxta-membrane layer will include a larger

proportion of cytoplasmic HCN1 immunoreactivity in small diameter dendrites.This confound may

potentially skew the extacted membrane-associated density of HCN1.

Regulation of the anatomical and functional distribution of HCN channels

8.6 The anatomicaldistributionof HCN channels may be highly dynamic

Studies have examined the kinetics of HCN channel trafficking and their rapid regulation by

neuronal activity. Live imaging in cultured hippocampal neurons revealed that endogenous HCN1

subunit-containing channels resided in vesicle-like organelles that moved bi-directionally along the

length of the dendrite. This movement was arrested by the activation of ionotropic glutamate

receptors. At the same time, the membrane expression of HCN1subunit-containing channels

increased within minutes (Noam et al., 2010). Thus, the decreased longitudinal movement of HCN1

subunit-containing channels in the cytoplasm may result in their increased insertion into the

neuronal membrane. Similar findings for other proteins have been reported suggesting that the

mobility of vesicles containing membrane proteins is reduced upon vesicular insertion into the

76

membrane (Bezzerides et al., 2004; de Wit et al., 2009). This might be the result of the action of

anchoring complexes. Given these findings, it is possible that trapezius motoneurons displaying a

proximal to distal gradient of HCN1 receive a greater proportion of excitatory activity on distal

dendrites compared to proximal dendrites compared to trapezius motoneurons displaying a

uniform proximal to distal distribution of HCN1.

The increased membrane expression of HCN1 subunit-containing channels by neuronal

activity within a time scale of minutes suggests that the trafficking of these channels to the

membrane can be a rapid and dynamic process. This finding suggests that the distribution of HCN1

subunit-containing channels across the dendritic tree is highly variable and will change with

respect to time due to ongoing neuronal activity. Thus, the dendritic distributions of channels

formed by the HCN1 subunit reported in the present study may only reflect the distribution at a

given point in time. However, the changes in the membane expression of HCN1 subunit-containing

channels described above were based on a very small sample (n = 3) of dendritic segments that

were approximaley 35 µm in length. Furthermore, the observation ofseveralwell characterized

distributions of HCN channels across the dendritic tree suggests that the maintenance of specific

distributions may be critical to the function of the neuron. For instance, the increasing proximal to

distal gradient of HCN channels along the apical dendrite of hippocampal CA1 and layer 5

neocortical pyramidal neurons has been reported and independently confirmed by several studies

(Magee, 1998; Williams and Stuart, 2000; Berger et al., 2001; Lorincz et al., 2002; Kole et al., 2006).

In hippocampal CA1 neurons, perforant path synapses from the cortex selectively terminate on the

distal dendrites whereas schaffer collateral synapses from hippocampal CA3 neurons terminate on

more proximal dendrites. Studies have shown that an intact entorhinal cortex, which projects to

distal dendrites of hippocampal CA1 neurons is critical for the establishment and maintenance of a

distal dendritic enrichment of HCN1 (Shin and Chetkovich, 2007). Removal of the entorhinal cortex

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lead to significant reductions in HCN1 density on distal dendrites resulting in a uniform distribution

of HCN1 across the dendritic tree.

Thus, the local membrane expression of HCN channels may be subject to rapid continuous

changes due to the changes in the activity of nearby synapses. This may provide an explanation for

the punctate organization of the HCN1 immunoreactivity whereby the flourescence intensity of

HCN1 was highly variable on a voxel by voxel basis. On the other hand, the maintenance of specific

distributions of HCN channels across the dendritic tree may be attributed to differences in synaptic

activity between different synaptic pathways. These sets of synapses may bearranged in a specific

pattern across the denditic tree that would give rise to a specific distribution of HCN channels. This

does not, however, exclude a different interpretation. The targeting of HCN channels to dendritic

regions may also be govened by the source of the synaptic input but this would require studying the

influence of different synaptic pathways on the membrane expression of HCN channels

independent of activity.

8.7 The functional distribution of HCN channels may be altered by cAMP-dependent

modulation

Studies have presented evidence of an increase in Ih activation upon stimulation of β-

adrenergic and serotonergic receptors (Bobker and Williams, 1989; Pape and McCormick, 1989).

This facilitation relies on a depolarizing shift of the voltage dependence of activation. The

intracellular messenger system mediating this response involves stimulation of adenylyl cyclase

and a resulting increase in intracellular cAMP levels (Pape and McCormick, 1989; McCormick and

Pape; 1990; Tokimasa and Akasu, 1990). The regulation of HCN channels by cAMP in motoneurons

likely involves the HCN2 subunit, either in a homotetrameric or heterotetrameric configuration,

since the HCN2 subunit is more sensitive to cAMP modulation compared to the HCN1 subunit. In a

recent study, the distributions of serotonin (5-HT) and noradrenaline (NA) synapses on neck

78

extensor motoneurons were investigated. 5-HT and NA synapses were found to be widely

distributed across the motoneuron dendritic tree with a strong bias to distal small diameter

dendrites (Montague et al., 2012). Given this finding, the functional distribution of HCN channels

may be altered in the presence of neuromodulators such a 5-HT and NA.Trapezius motoneurons

that may display a uniform density of Ih across the dendritic tree may have a higher density of Ih on

distal small diameter dendrites, which are more densely innervated by 5-HT and NA. Trapezius

motoneurons that may display a graded proximal to distal increase of Ih may exhibit a steeper

gradient in the presence of neuromodulators. However, the predominate 5-HT receptors on

motoneurons are the 5-HT2A, 5-HT2B, and 5-HT2C receptors, which are present on dendrites. These

receptors are coupled to the Gq/llα subunit, which activates a cAMP-independent signaling pathway

involving the production of inositol triphosphate (IP3) and diacylglycerol (DAG) (Bockaert et al.,

2006). Nonetheless, 5-HT has recently been shown to work through 5-HT7 receptors on

motoneurons, which are positively coupled to Gs α subunit leading to the production of cAMP

(Inoue et al., 2002). Pharmacological studies provide evidence that NA exerts its effects via the α1

NA receptor (Lee and Heckman, 1999; Harvey et al., 2006). Similar to 5-HT2 receptors, the α1 NA

receptor is linked to the Gq/llα subunit, which activates a cAMP-independent signaling pathway.

Functional consequences

8.8 The distribution of HCN1 may not reflect the distribution of all HCN channels

It must be mentioned that the HCN1 subunit is not the only subunit expressed in

motoneurons. Motoneurons also express the HCN2 subunit (Moosmang et al., 1999; Monteggia et

al., 2000; Santoro et al., 2000). It has been shown that HCN1 and HCN2 subunits can coassemble

into heterotetrameric channels that carry an Ih current with activation kinetics, steady-state voltage

dependence, and sensitivity to cAMP intermediate to that of homotetrameric HCN1 and HCN2

channels (Chen et al., 2001; Ulens and Tytgat, 2001). In hippocampal and thalamic neurons,

79

discrepancies between the activation kinetics of the Ih current native to these neurons and the

activation kinetics recorded from homotetrameric HCN1, HCN2, and HCN4 channels was attributed

to differences in the expression of these HCN subunit isoforms. The activation of the native Ih

current in hippocampal CA1 neurons occurred on a time scale that spanned between the activation

of HCN1 and HCN2 homotetrameric channels. In thalamic relay neurons, the native Ih current

activates on a time scale that spanned between the activation of HCN2 and HCN4 homotetrameric

channels (Santoro et al., 2000). Neither the kinetics, voltage-dependence and sensitivity to cAMP in

these cases could be reproduced by the linear sum of independent populations of HCN1, HCN2 and

HCN4 homotetrameric channels. In hypoglossal motoneurons, the properties of the native Ih

current did not match the properties of either homotetrameric HCN1 or HCN2 channels. Given that

the distribution of the HCN2 subunit across the dendritic tree of motoneurons is unknown, the

distribution of HCN1-subunit containing channels observed in the present study may not necessary

reflect the distribution of HCN channels.It is likely that the population of HCN channels on trapezius

motoneurons is largely comprised of heterotetrameric channels containing both the HCN1 and

HCN2 subunit. However, the possibility that some HCN channels may exists as homotetrameric

HCN1 and HCN2 channels cannot be ruled out.

8.9 Resonance properties as a function of distance from the soma

Studies have shown that resonance is not a fixed property but instead is highly variable.

The resonance frequency across the dendritic tree of pyramidal neurons was revealed to be

location-dependent due to regional differences in the density of HCN channels. Anatomical and

electrophysiological studies have demonstrated that the density of HCN channels along the apical

dendrite increases with distance from the soma (Lorincz et al., 2002; Magee, 1998). Similarly, the

injection of a ZAP current stimulus to the soma and sites along the apical dendrite at different

distances from the soma revealed that the resonance frequency increased considerably with

80

increasing distance from the soma (Narayanan and Johnston, 2007). In accordance with the

reported change in HCN channel density, the resonance frequency increased more than 3-fold

between the soma and distal regions of the apical dendrites. Furthermore, the resonance strength

(Q), defined as the ratio of the maximum impedance amplitude to the impedance amplitude at 0 Hz,

increased with distance from the soma. To support a direct graded relationship between the

density of HCN channels and resonance properties, the resonance frequency and resonance

strength were shown based on modeling studies to be dependent on both density of HCN channels

via changes in the conductance of HCN channels and on the voltage-dependence of HCN channels

via changes in V1/2. Lastly, the experimentally measured gradient in resonance properties was

replicated in a morphologically realistic model of a pyramidal neuron with a known gradient in

HCN channel density along the apical dendrite (Narayanan and Johnston, 2007).

These findings suggest that the resonance frequency and resonance strength may be

conserved across the dendritic tree of trapezius motoneurons with a uniform distribution of HCN1.

On the other hand, these properties may be location-dependent across the dendritic tree of

trapezius motoneurons that display a proximal to distal gradient of HCN1. Specifically, the

resonance frequency and resonance strength may be higher on distal dendrites compared to

proximal dendrites. Thus, distal dendrites may be tuned to inputs with high frequency components

whereas proximal dendrites may be tuned to inputs with low frequency components.

8.10 Functional consequences of a linear and uniform proximal to distal distribution of HCN

channels

Motoneurons possess a fast persistent sodium current, INaP, and a slow persistent calcium

current, ICaL. The two persistent inward currents (PICs) have been shown to differentially amplify

synaptic input based on the frequency content of the synaptic input. Synaptic inputs with high

frequency components were preferentially amplifed by INaP whereas synaptic inputs with low

81

frequency components were preferentially amplified by ICaL. This frequency-dependent

amplification arises from a subthreshold resonance attributed to HCN channels. However, these

findings were based on measurements of the voltage response to a ZAP current stimulus injected at

the soma and the two PICs were simulated by a dynamic conductance clamp applied through the

somatic recording electrode. Given strong evidence suggesting that PICs are located on the

dendritic tree (Carlin et al., 2000; Bui et al., 2005) and differences in resonance frequency are

attributed to differences in HCN channel density, the frequency-dependent amplification of synaptic

inputs distributed across the motoneuron dendritic tree in the presence of physiological PICs

remains unclear.

8.11 HCN channels and motor unit type

Burke and colleagues have shown that motor units may be classified based on the

contractile properties of their muscle units (Burke, 1967; Burke et al., 1973). S-type motor units

contain muscle fibers that display slow contraction times, produce small twitch forces and are

highly resistant to fatigue. Among motor units containing muscle fibers that display fast contraction

times and produce large twitch forces, FR-type motor units are resistant to fatigue whereas FF-type

motor units have a low resistance to fatigue. Evidence has also shown that the membrane electrical

properties of motoneurons are tightly coupled with the muscle fiber type. The input conductance of

motoneurons was found to differ significantly among motor units and can reliably predict motor

unit type (Zengel et al., 1985). The measured input conductances from motoneurons belonging to S-

type motor units were considerably lower compared to FR-type and FF-type motor units. Of the ten

trapezius motoneurons, those with a low computed input conductance displayed a graded proximal

to distal increase in the density of HCN1 across the dendritic tree. In contrast, those with a higher

computed input conductance displayed a uniform density of HCN1 across the motoneuron dendritic

tree. This suggests that motor units may also differ based on the distribution of HCN1.

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8.12 Concluding Statements

The present study provides the first description of the distribution of HCN channels on

trapezius motoneurons. Although some trapezius motoneurons display a uniform proximal to distal

distribution of HCN1 comfirming earlier prediction from computational studies, other trapezius

motoneurons display a proximal to distal gradient of HCN1. Based on resonance studies in

pyramidal neurons, it is possible that motoneurons may share in common with pyramidal neurons

the ability to locally tune the response at the dendrite to synaptic input based on differences in

density of HCN channels. Furthermore, the optimal reponse to synaptic input at a specific location

may be changed by local changes in the density of HCN channels or by changing the functional

properties of Ih such as the voltage-dependence and kinetics.

83

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