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PMID: 32929226 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Review

Single-cell transcriptomics in cancer: computational challenges and opportunities.

Experimental & molecular medicine ·Vol. 52 ·No. 9 ·2020-00-00 ·Pages 1452-1465

Fan J, Slowikowski K, Zhang F

Abstract

Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer.

MeSH Terms
Biomarkers, Tumor Cell Communication/genetics Computational Biology/methods Gene Expression Profiling/methods Genomics/methods High-Throughput Nucleotide Sequencing/methods Humans Neoplasm Grading Neoplasms/genetics,pathology Organ Specificity/genetics Sequence Analysis, RNA Single-Cell Analysis/methods Transcriptome Tumor Microenvironment/genetics
Chemicals
Biomarkers, Tumor
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Fan Jean ORCID
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA. [email protected].
Slowikowski Kamil ORCID
Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, USA.
Zhang Fan ORCID
Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA. | Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Article Info
Journal
Experimental & molecular medicine
Abbr.
Exp Mol Med
ISSN
2092-6413
DOI
10.1038/s12276-020-0422-0
Email
[email protected]
Published
2020-00-00
Epub
2020-00-15
Pages
1452-1465
Language
English
Region
United States
NLM ID
9607880
PMCID
PMC8080633
Subset
IM
Grants
NCI NIH HHS · K00 CA222750 · United States
NHGRI NIH HHS · T32 HG002295 · United States
External Links
PubMed source DOI: 10.1038/s12276-020-0422-0 PMC Full Text View journal details
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