Intra-tumor heterogeneity is a major obstacle to cancer treatment. Existing single-cell studies of intra-tumor heterogeneity have largely focused on DNA mutations; functional heterogeneity is thus less well understood. We performed an unbiased analysis of functional diversity in colorectal cancer cells and their microenvironment using RNA-seq profiling of over 1,500 unsorted single cells from 11 primary tumors and matched normal mucosa (NM). To robustly interpret single-cell transcriptomes, we developed novel algorithms for normalizing expression estimates (pQ), clustering cells (RCA) and identifying differentially expressed genes (NODES). RCA identified 6 major cell types and multiple subtypes within colorectal samples. Single-cell differential expression analysis yielded results that were substantially different from bulk-sample analysis. Notably, epithelial-mesenchymal transition (EMT) genes were upregulated in tumor fibroblasts, but not in cancer cells. Though cancer cells generally lay on a continuum of transcriptomic states, a small "tail" of cells showed exceptionally high stemness signatures. Importantly, colorectal tumors previously assigned to a single subtype based on bulk transcriptomics could be divided into subgroups with divergent survival probability based on single-cell signatures, thus underscoring the prognostic value of our approach. Going forward, we see single-cell transcriptomics becoming an essential tool for cancer biology, biomarker discovery and personalized oncology.
Research And Development
Gene Expression
Dna
Big Data
Cancer Research
Tumor
Biomarkers
Cancer
Earth Science
Oncology
University
Gene Sequencing
Drug Discovery
Mass Cytometry
Cell Culture
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