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Integrating Cytomics with Proteomics Integrating Cytomics with Proteomics J. Paul Robinson Professor of Immunopharmacology Professor of Biomedical Engineering Harvard University November, 2004 Purdue University Cytometry Laboratories Cytome - Cytomics • Cytomes can be defined as cellular systems and subsystems and functional components of the body. • Cytomics is the study of the heterogeneity of cytomes or more precisely the study of molecular single cell phenotypes resulting from genotype and exposure in combination with exhaustive bioinformatics knowledge extraction. • The word Cytomics was first used in 2001 by: Davies E, Stankovic B, Azama K, Shibata K, Abe S. “Novel components of the plant cytoskeleton: A beginning to plant "cytomics" Plant Science, Invited Review, Plant Science (160)2 (2001) pp. 185-196. Cytomics links technology to biology – it relates measurement and detection to structure and function. It Integrates flow and image cytometry with proteomics. Courtesy of Gunter Valet Environment – Cytome (cell) – Genome A web of interactions Environment Cell From Gene to Protein Cytome Genome - Proteome Instead of concentrating on molecular targets within the relatively infinite network of highly redundant molecular pathways of cells, one could focus on the end result, represented by molecular phenotypes of cells as a consequence of both genotype and environment. Exploring the Human Cytome Technology • Advanced microscopy techniques: – LM, EM, Confocal and laser scanning microscopy, spectral imaging, FRET, SEM, TEM, digital microscopy, … • High Content Screening: – High speed and large volume screening, … • Flow Cytometry – Fast imaging in flow, … • Biomolecular analysis techniques: – Single-cell polymerase chain reaction (PCR), labeling of biomolecules by quantum dots, protein identification, etc • Bioinformatics: – Data exploration, statistics and data management, … Predictive Medicine by Cytomics (= therapy-dependent individualized disease course prediction) • Diseases represent molecular changes in cellular systems of organisms (cytomes) •Cytomics: study of molecular cell phenotypes in combination with exhaustive bioinformatic knowledge extraction • Cell phenotypes result from genotype and exposure • Goal: individualized predictions >95% correct Genome Proteome Genomics Proteomics Cytomics Cytome A Human Cytome Project Small working group having a number of roundtable discussions A Human Cytome Project ? From Valet, Tarnok, Murphy, Kriete, & Robinson Systems Integration • Analytical Cytology – Flow cytometry – Single cell analysis systems – Tissue analysis – High Content Analysis (screening) • Image Analysis – Single cells – Tissues and sections – Cell culture systems • Proteomics Study approaches • Study 1: Cell growth in 3D matrix • Study 2: 3D matrix isolation and characterization • Study 3: Standard cell line characterization (Study 4: toxins of phenotypically separated Listeria monocytogenes) Cellular heterogeneity Cellular Heterogeneity • Identification and characterization of cellular systems is advanced • We can separate and very highly define cellular systems by with multivariate analysis High-resolution cytology segmentation Characteristic Spectra Conventional Spectrally RGB Image segmented Image Wavelength (nm) High spectral resolution increases utility of spectrally responsive indicator dyes Slide from Dr. Richard Levenson, CRi, Inc.,35B Cabot Rd.,Woburn, MA 01801, www.cri-inc.com Analysis of complex Microbial Systems solid hydrogel reduce temperature less than 15°C Confocal Microscopy liquid dead Flow Cytometry Count live log green log red Phenotypically different but mixed populations dead Count live log green log red Extracellular Matrix (ECM) Tissue Engineering Applications Scaffold to support tissue growth Structure and function are closely related Need in vitro models to study such as Matrigel Vitrogen Small Intestinal Submucosa (SIS) Basal Lamina of the Avian Ovarian Follicle Visualization of morphology of cells embedded within a collagen matrix Visualization of cell morphology within native ECM Study 1: Cell growth in 3D matrix With Professor Sophie Lelièvre Professor of Veterinary Medicine Purdue University Three-dimensional models of breast epithelial cell cultures recapitulate tissue differentiation and tumor formation A normal S1 phenotype Cross-section indicates the presence of a central lumen acini B tumor T4-2 phenotype Invasive tumor 110nodule days In the presence of extracellular matrix enriched in laminin, non-neoplastic breast epithelial cells (HMT- 3522 S1) form phenotypically normal tissue-like glandular structures (acini) while malignant breast epithelial cells (HMT-3522 T4-2) develop into tumor-like nodules. Cells are plated as single cells at day 1 and full differentiation and formation of large tumors is seen at day 10 of culture. A) In 3D culture, S1 cells arrest growth and differentiate after a few days of proliferation. B) In 3D culture, S1-derived T4 –2 cells continue to proliferate to form tumor-like nodules. As tumors develop they enter in contact with cells from nonmalignant tissue T4-2 phenotype (tumor) 30 microns 6810S1 phenotype days (normal) Contact co-culture: T4-2 cells grow toward S1 glandular structures and surround these structures. T4-2 cells were added to pre-formed S1 acini and cultured for 10 days. T4-2 cells were stained with DiI (red) prior to being plated, and S1 cells are stably transfected with green fluorescent protein (GFP). Images show co-cultures of S1 and T4-2 cells at days 6, 8, and 10. At day 6: Tumor cells (red) proliferate when in contact with S1 structures (green, arrow). Day 8: T4-2 cells expand over several S1 structures (5 glandular structures can be seen in green). Day 10: A single tumor (red) is in development; at least four S1 glandular structures (green) have been engulfed. Study Flow Sorting followed by protein profiling • Sort dsRed stained tumor cells (T4-2_ from GFP-non-malignant cells (S1) population after 3 weeks of co-culture • Analyze the changes in protein expression profile compared to the control (separately cultured) T4-2 and S1 cell populations HMT-3522 epithelial cells Phenotypically S1 Normal Malignant T4-2 Phenotype Differences Study 2 Professor Eli Asem School of Veterinary Medicine, Purdue University Isolation and Characterization of Extracellular matrix Basement Membrane • The maintenance of normal function of vertebrate cells depends on the integrity of the extracellular microenvironment of the tissue/organ. • Basement membranes (basal laminae) are specialized ECM sheets that participate in numerous physiological processes and play key roles in regulating proliferation and differentiation of cells. • Only some of the proteins of basement membranes have been identified due to the complex protein composition and the unavailability of pure preparations. • Recently a pure preparation was identified and shown to be biologically active Background Study 3 • A “standardized” cell culture environment • Establishing a “normal” profile for HepG2 cell line • Interest from pharmaceutical companies for some relatively fast classification systems A Study of HepG2 cells • HepG2 - cell line that is a human, primary liver cancer • Human hepatocellular liver carcinoma cell line, Epithelial cells. • An established human hepatocarcinoma cell line with epithelial morphology. HepG2 cells are used routinely for a variety of biochemical and cell biological studies. HepG2 is the most commonly used cell line for examining the regulation of hepatic protein synthesis by cytokines . HepG2 Cell Cycle analysis Flow cytometry analysis of cell cycle using propidium iodide (PI) staining Cells were sorted on a Beckman-Coulter Altra cell sorter prior to PF2D # cells sorted # cells sorted 1 x 106 3 x 106 Cell/system complexity can be reduced using tools such as flow cytometry Figures from Roederer, et al Concluding Thoughts • Developing an understanding of complex biological systems should be approached by first considering the heterogeneity of the system • Using a cytomics approach reduces the complexity at an earlier stage • Tools such as the PF2D offer a significant opportunity for cell biologists to approach proteomic solutions Beckman-Coulter automated PF2D protein separation system Cell Sorter Acknowledgements • Contributors – Collaborators • Staff Dan Hirleman (ME) Jennie Sturgis Yinlong Sun (CS) Kathy Ragheb Kinam Park (IP) Cheryl Holdman • Postdocs Gretchen Lawler Gerald J. Gregori (Microbiology) Steve Kelley Valery Patsekin (Photonics) Tytus Bernas (Proteomics) Departments & Centers Sang Youp Lee (Fluidics) Purdue University Cytometry Labs Bartek Rajwa (Biophysics) Basic Medical Science Biomedical Engineering • Graduate Students Electrical & Computing Engineering Wamiq Ahmed (Computational) Mechanical Engineering Murugesan Venkatapathi (M.Eng) Bindley Bioscience Center Bulent Bayraktar (Computational) Discovery Park Silas Leavesley (BioEng) Purdue Cancer Center Jia Liu (Cell Biology) Funding: Connie Paul (Pharmaceutical) NIH, NSF, USDA, Purdue University Corporate: Beckman-Coulter, Point-Source, Parker-Hannifin, Bio-Rad, Polysciences Purdue University Cytometry Laboratories http://www.cyto.purdue.edu Free copy from www.bangslabs.com Human Cytome Project: http://www.cytomic.info.
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