Catching Stem and Progenitor Cells with Boolean Logic

March 8, 2013
Interschool Lab (750 CEPSR)
Hosted by: Prof. Ken Shepard
Speaker: Prof. Debashis Sahoo (Stem Cell Institute, Stanford University)


Many, if not all organs and tissues consist of self-renewing stem cells that give rise to distinct, sequential progenitors with increasingly limited development potential, ultimately producing functional mature cells. All normal, cancer and other diseased tissues contain a diversity of different cell types with distinct morphological features. The identification and characterization of these different cell types within normal and diseased tissue are not only critical for the understanding of underlying biology but also in developing more effective therapeutic strategies. Previous attempts to identify markers for cells at hierarchical stages of tissue differentiation involved either 1) large screening studies using antibody libraries or gene expression arrays, or 2) focused trials of established markers identified in other normal and diseased tissues. Unfortunately, these approaches are insufficient to trace complex cellular differentiation stages, and thus most often fails. Therefore a systematic approach to identify cells within tissue differentiation hierarchies is required.

We developed systematic computational approaches to identify markers of stem and progenitor cells by analyzing publicly available, high-throughput gene expression datasets consisting of more than 2 billion measurement points. We developed a set of tools - StepMiner, BooleanNet (a network of Boolean implications), MiDReG (Mining Developmentally Regulated Genes) that uses Boolean implications to predict genes in developmental pathways, and HEGEMON (Hierarchical Exploration of Gene Expression Microarray Online) to identify genes expressed in the stem and progenitor cells in both normal and malignant tissue development. We demonstrated that coordinated use of these tools could predict genes involved in developmental stages in human normal and cancer tissues. We use examples of human B cells, bladder cancer and colon cancer to show the power of this computational approach. For the cancer tissues, the newly identified genes using this approach predicted patient outcomes robustly, using samples ranging from early stage to late advanced disease stages. This approach identifies diagnostic and prognostic value in situ for immediate translation into clinical applications.

Speaker Biography

Debashis Sahoo received his MS and PhD in Electrical Engineering at Stanford University, and BTech in Computer Science and Engineering at IIT-Kharagpur. He has several publications in the area of computational biology and formal verification. He finished his doctoral work under the NCI-funded Integrative Cancer Biology Program at Stanford with his advisor Prof. David Dill and co-advisor Prof. Sylvia Plevritis. His postdoctoral advisee includes Prof. Joe Lipsick, Prof. Matt van de Rijn, Prof. Irv Weissman, and Prof. Michael Clarke. He is currently working as a Siebel fellow at the Stem Cell Institute at Stanford. He has used Boolean logic to understand the process of differentiation in normal and cancer tissues. His research interests include systems biology, genetics, immunology, cancer and stem cell biology

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