Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
| Autor: | Iain Beehuat Tan, Axel M. Hillmer, Lim Kiat Hon, Debarka Sengupta, Shyam Prabhakar, Wah Siew Tan, Yuliana Tan, Kok Hao Chen, Clarinda Chua, Jolene Jie Lin Goh, Paul Jongjoon Choi, Lawrence J K Wee, Paul Robson, Mark T. C. Wong, Say Li Kong, Elise T. Courtois, Huipeng Li |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Epithelial-Mesenchymal Transition Colorectal cancer Cell Biology Bioinformatics Cell Line Transcriptome 03 medical and health sciences Genetic Heterogeneity Downregulation and upregulation Cell Line Tumor Genetics medicine Cluster Analysis Humans Survival analysis In Situ Hybridization Fluorescence Regulation of gene expression Principal Component Analysis Genetic heterogeneity Sequence Analysis RNA Gene Expression Profiling Fibroblasts medicine.disease Prognosis Immunohistochemistry Survival Analysis Gene Expression Regulation Neoplastic 030104 developmental biology medicine.anatomical_structure A549 Cells Cancer research Single-Cell Analysis Colorectal Neoplasms K562 Cells Algorithms |
| Zdroj: | Nature genetics. 49(5) |
| ISSN: | 1546-1718 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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