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![Page 1: PowerPoint presentation](https://reader035.fdocuments.net/reader035/viewer/2022062821/58a066dd1a28ab06528b51bb/html5/thumbnails/1.jpg)
Data Acquisition and Dissemination in the Large Scale
Digital Cell Analysis System
Michael A. MackeyBiomedical Engineering / Pathology
Holden Comprehensive Cancer Center
University of Iowa
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The Large Scale Digital Cell Analysis System (LSDCAS) Produces Digital Movies of Living Cells
V79 Chinese hamster lung cells (1 frame / 5 min) – 7 days
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What is LSDCAS?
Automated phase / epi-fluorescence microscope systems with optics / camera / illumination / stage under complete computer control
High-capacity fault-tolerant data center for image storage / backup / analysis
LSDCAS has been chosen to be a Holden Comprehensive Cancer Center Core Facility at the University of Iowa
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LSDCAS is Biology-Driven Engineering
Currently, LSDCAS is comprised of over 150,000 lines of programming code
Upon completion of the initial establishment of LSDCAS, all source code will be placed into the public domain as an Open Source project hosted by SourceForge.org
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LSDCAS is Biology-Driven Engineering Implicit within the LSDCAS project is the
development of new mathematical models of biological systems, made possible by the large amount of quantitative data provided by the system
Another goal is to foster the establishment of other LSDCAS installations at other institutions, allowing the biomedical research community access to this technology
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LSDCAS is Biology-Driven Engineering
Since its inception, this system has been continuously modified to accommodate new classes of experiments
With new experimental designs comes new challenges in data acquisition and analysis
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History of LSDCAS Development
LSDCAS was originally developed (starting in 1995) to study radiation-induced mitotic catastrophe using a borrowed microscope, a $400 camera, and a spare PC
This project presented many challenges which were addressed in its early phases, with the goal being to perform non-perturbing measurements of living cell cultures
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LSDCAS Challenges (1995)
Cells don’t like to live on microscope stages Microscopes don’t like computers (sort of) LSDCAS produces large amounts of image
data Good microscopes are expensive Good cameras are expensive Data centers are expensive
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Challenge: Light can kill cells
HeLa Clone 3 cells – 1 frame / 30 sec. overnight (1995)
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Challenge: Can we make movies without altering cell growth kinetics?
HeLa Clone 3 cells – 1 frame / 5 min - five days (1995)
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Glass Plastic
Mean Generation Time19.8 +/- 0.9 h
Mean Generation Time17.2 +/- 0.8 h
N = 120 N=70
Growth Rate Statistics for PC-3 Cells Grown on Either Tissue Culture Plastic or a Glass Coverslip Fragment
Challenge: Can we make movies without altering cell growth kinetics?
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Challenge: LSDCAS produces enormous amounts of data
Data produced at the rate of ~50 gigabytes per week (compressed image data)
LSDCAS runs around the clock, thus storage systems must be fault-tolerant
All data must be backed up routinely Data analysis software must be optimized to
provide a rapid turn-around of experiments
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Current Capabilities of LSDCAS
Cell death analysis Cell motility analysis Gene expression studies using GFP-tagged
adenovirus expression systems Intracellular anti-oxidant / pro-oxidant analysis Cell-cell interaction studies
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Normal Colony Formation
HeLa Clone 3 cells – 1 frame / 45 min – 10 days
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Radiation (5 Gy) – Induced Mitotic Catastrophe
HeLa Clone 3 cells – 1 frame / 45 min – 10 days
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Example of a method used to accurately determine cell borders in LSDCAS image data. (a) Original microscope image (b) The local variance of the original image (c) the binary image obtained after minimum-error thresholding (d) after removing small objects(e) after application of the watershed transform (f) the detected cell borders.
Advanced Image Segmentation for Cell Death Analysis
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GFP-tagged Adenovirus-Mediated Gene Expression Studies
U87-MG cells – 1 frame / 5 min – 1 day
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Cell Motility Analysis
U87-MG cells – 1 frame / 5 min – 5 days
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Cell Motility Studies
Grow cells under conditions thought to alter cell motility (e.g., Wild-Bode et al., Cancer Res 61, 2744-2750, 2001).
Strategy: Segment individual cells in multiple microscope fields
Analyze the segmentation data to provide statistical measures of cell motility
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Cell Segmentation
U87-MG cells 5 Gy – 1 frame / 5 min – 1 day
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Cell Segmentation
Centroid (X,Y)
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Analysis of Cell Motility
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Determination of Cell Motility
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Cell Motility Analysis Results
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Anti-Oxidant / Pro-Oxidant Balance
Oxidative stress occurs when a biological system is perturbed such that an imbalance develops between the production of pro-oxidants and the concentrations of protective anti-oxidants. Damage to cellular macromolecules (e.g., DNA, lipids, proteins) then ensues.
pro-oxidants anti-oxidants
Steady - State
pro-oxidants
Oxidative Stress
anti-oxidants
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Real-Time Measurement of Intracellular Pro-Oxidants
Strategy: Use pro-oxidant-sensitive fluorescent probes to periodically measure intracellular pro-oxidant concentrations under conditions leading to oxidative stress
In parallel, LSDCAS will monitor the growth and clonogenicity of these cell populations
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Measurement of Intracellular Pro-Oxidants: Fluorescent Probe Influx Studies
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LSDCAS Today LSDCAS has evolved into a completely
automated system capable of acquiring any combination of fluorescent / phase contrast images
Analysis of the movie data is accomplished through custom software developed by a team of graduate students / professors in Engineering
New collaborative interactions drive the incorporation of additional capabilities into LSDCAS
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Large-Scale Digital Cell Analysis System
Data Acquisition Subsystem (5223 MERF)
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LSDCAS Today
Two automated microscope systems Perfusion systems to alter cell environment
during an experiment High capacity, highly available data storage
and archiving Custom image / data analysis Biophysical modelling group
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Enterprise-Scale LSDCAS
To handle the data flow and anticipated growth of LSDCAS, an enterprise model has been implemented
Two data centers, one in Medicine and one in Engineering, now process and archive LSDCAS data
Relational Database Technology provides for a robust data management model
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LSDCAS Engineering Data Center Components
(G80 SC)
Storage Area Network 2 Dell PE7155 Quad Itanium
Servers (36 gigabytes total RAM)
Dell PV660F/224F storage arrays – 2.0 terabytes
StorageTek 100 slot / 6 drive DLT library (8 terabyte capacity)
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LSDCAS Medicine Data Center Components
(74 EMRB)
– Storage Area Network– Compaq GS140 (8 cpu's – 16
gigabytes RAM, 300 gigabytes local RAID disk storage
– 2 Compaq ESA12000 - 2.0 terabytes – Dell PV130T 30 slot / 2 tape drive
DLT library (2 terabyte capacity)
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Relational Database Technologies in LSDCAS
To better organize experimental data (not images) and to provide for web-based data analysis and retrieval, LSDCAS now uses PostgreSQL databases.
The databases also contain machine-dependent parameters, thus allowing for a single version of the analysis and acquisition programs to support multiple acquisition systems.
Secure client access to the database is achieved through a series of Java servlet programs running under Apache Tomcat.
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Recent LSDCAS Improvements
Data acquisition now supports both RS-170 and firewire image inputs
Auto-focus subsystem has been designed and implemented
LSDCAS now supports multi-well culture plates We have integrated cell growth and death
analysis into the SQL database system Secure, platform-independent data analysis
and retrieval system
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Software Auto Focus Algorithms
To determine optimal focus, we developed a focus signal function that has a maximum at the point of good focus
The algorithm developed uses band-pass filtering on the Fourier transform of a strip through the image
The integrated spectrum of the filtered frequency-domain data yields the focus signal
Z-Axis (arbitrary units)0 2000 4000 6000 8000 10000 12000 14000
Focu
s Si
gnal
0
500x106
1x109
2x109
2x109
3x109
3x109Frequency
0 50 100 150 200 250 300
Pow
er
100x100
1x103
10x103
100x103
1x106
10x106
100x106
1x109
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Platform-Independent Image Viewer
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LSDCAS: Future Directions
Continue to develop collaboration-driven advances in live-cell imaging
Develop three-dimensional cell imaging technologies
Merge model-driven theories with data obtained using LSDCAS
Establish LSDCAS as a standard for live cell imaging in Systems Biology applications
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Acknowledgements
George Weiner (director, Holden Comprehensive Cancer Center)
Iowa Research Imaging Center
National Institutes of HealthCA58648CA74899GM/CA94801
Whitaker Foundation
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Acknowledgements, College of Engineering
Paul Davis (ECE)Elizabeth Kosmacek (BME)Lacey Bresnahan (BME)
Igor Okulist (BME)
Tamaki Sato (BME)
TJ Moretto (BME)Nikki Baman (BME) Andrew Walters (BME)Teri Duffie (BME)Thuy Nguyen (BME)Lynette Kenjar (BME)
Lin Wang (BME)Yuansheng Sun (BME)Kiersten Anderson (BME)Cheryl Jablonksi (BME)
Michael Squire (BME)
Amy Wheaton (BME)
Tom Chiang (BME)
Marla Johnson (BME)
Ben Keller (BME)
Kyle Rogers (BME)
Mitchell Colemen (BME)
Milan Sonka PhD (ECE)Fuxing Yang (ECE)Greg Gallardo (ECE)
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Acknowledgements – College of Medicine
Fiorenza Ianzini (Radiology)Douglas Spitz (Radiation Oncology)Frederick Domann (Radiation Oncology)Zoya Kurago (Oral Pathology, Radiology and Medicine)Susan Lutgendorf (Psychology)