Data Acquisition and Dissemination in the Large Scale

Digital Cell Analysis System

Michael A. MackeyBiomedical Engineering / Pathology

Holden Comprehensive Cancer Center

University of Iowa

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

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

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

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

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

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

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

Challenge: Light can kill cells

HeLa Clone 3 cells – 1 frame / 30 sec. overnight (1995)

Challenge: Can we make movies without altering cell growth kinetics?

HeLa Clone 3 cells – 1 frame / 5 min - five days (1995)

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?

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

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

Normal Colony Formation

HeLa Clone 3 cells – 1 frame / 45 min – 10 days

Radiation (5 Gy) – Induced Mitotic Catastrophe

HeLa Clone 3 cells – 1 frame / 45 min – 10 days

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

GFP-tagged Adenovirus-Mediated Gene Expression Studies

U87-MG cells – 1 frame / 5 min – 1 day

Cell Motility Analysis

U87-MG cells – 1 frame / 5 min – 5 days

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

Cell Segmentation

U87-MG cells 5 Gy – 1 frame / 5 min – 1 day

Cell Segmentation

Centroid (X,Y)

Analysis of Cell Motility

Determination of Cell Motility

Cell Motility Analysis Results

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

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

Measurement of Intracellular Pro-Oxidants: Fluorescent Probe Influx Studies

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

Large-Scale Digital Cell Analysis System

Data Acquisition Subsystem (5223 MERF)

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

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

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)

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)

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.

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

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

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Platform-Independent Image Viewer

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

Acknowledgements

George Weiner (director, Holden Comprehensive Cancer Center)

Iowa Research Imaging Center

National Institutes of HealthCA58648CA74899GM/CA94801

Whitaker Foundation

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)

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)