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Nat Genet. 2017 May;49(5):708-718. doi: 10.1038/ng.3818. Epub 2017 Mar 20.

Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors.

Author information

1
Computational and Systems Biology, Genome Institute of Singapore, Singapore.
2
Developmental Cellomics Laboratory, Genome Institute of Singapore, Singapore.
3
Department of Computer Science and Engineering and Center for Computational Biology, Indraprastha Institute of Information Technology, Delhi, India.
4
Synthetic Biology, Genome Institute of Singapore, Singapore.
5
Cancer Therapeutics and Stratified Oncology, Genome Institute of Singapore, Singapore.
6
Department of Medical Oncology, National Cancer Centre Singapore, Singapore.
7
Department of Pathology, Singapore General Hospital, Singapore.
8
Department of Colorectal Surgery, Singapore General Hospital, Singapore.
9
Data Analytics Department, Institute for Infocomm Research, Singapore.
10
Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.
11
The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA.
12
Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut, Farmington, Connecticut, USA.
13
Department of Biological Sciences, National University of Singapore, Singapore.

Abstract

Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial-mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA-seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.

PMID:
28319088
DOI:
10.1038/ng.3818
[Indexed for MEDLINE]

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