Single-Cell & Computational Biology Core

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SUMMARY The Single-Cell Sequencing and Computational Biology Core B will be the central hub for devising and implementing all Single-Cell Sequencing experiments, as well as the application of powerful computational algorithms to such data as well as other bulk mRNA sequencing and metabolomic data to generate integrated models of gene networks and regulatory factors underlying metastatic progression. All three Center Projects will approach metastasis systematically, relying on the generation of transcriptomic, ribosomal profiling, single-cell sequencing, proteomic, metabolomic and chromatic accessibility data. As such, this Center will rely heavily on rigorous and statistically sound Computational Biology and Bioinformatics approaches pioneered by Saeed Tavazoie, a leader in Systems Biology, who will be a co-leader of this Core. Similarly, all three Projects will extensively employ Single-Cell Sequencing methods to define and characterize cell-cell interactions and cellular gene expression states within metastatic tumors and to develop novel single-cell methods. Junyue Cao, a leader in Single-Cell Sequencing technology development and application will be a co-leader of this Core. The combined Systems-level focus of these investigators applied to the multi-layered data generated from distinct stages of metastatic progression will enable the establishment of an unprecedented integrated Systems-level model of breast and colorectal cancer metastasis—providing the framework for further mechanistic studies that will refine this model, ultimately revealing critical nodes that when interrupted genetically or pharmacologically will prevent and eradicate metastatic disease. Computational methods that will be foremost applied to the problem of metastatic progression include: 1. iPAGE: an information-theoretic Pathway Analysis of Gene Expression algorithm that allows the systematic discovery of pathways that are differentially modulated across transcriptomes of any cell-types. 2. FIRE: an information-theoretic algorithm that identifies local DNA and RNA elements that underlie gene expression changes, uncovering associated transcription factors and RNA-binding proteins that govern such programs. 3. TEISER: an algorithm that discovers RNA regulatory elements from transcriptomes, enabling identification of their trans-binding factors. 4. An algorithm that integrates transcriptomic and phenotypic features (such as survival) from large-scale cancer compendia to implicate critical clinically-associated genes.