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Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics
- Source :
- iScience, iScience, 2020, 23, pp.101061-. ⟨10.1016/j.isci.2020.101061⟩, iScience, Vol 23, Iss 5, Pp-(2020)
- Publication Year :
- 2019
-
Abstract
- Summary Despite advances in single-cell and molecular techniques, it is still unclear how to best quantify phenotypic heterogeneity in cancer cells that evolved beyond normal, known classifications. We present an approach to phenotypically characterize cells based on their activities rather than static classifications. We validated the detectability of specific activities (epithelial-mesenchymal transition, glycolysis) in single cells, using targeted RT-qPCR analyses and in vitro inductions. We analyzed 50 established activity signatures as a basis for phenotypic description in public data and computed cell-cell distances in 28,513 cells from 85 patients and 8 public datasets. Despite not relying on any classification, our measure correlated with standard diversity indices in populations of known structure. We identified bottlenecks as phenotypic diversity reduced upon colorectal cancer initiation. This suggests that focusing on what cancer cells do rather than what they are can quantify phenotypic diversity in universal fashion, to better understand and predict intra-tumor heterogeneity dynamics.<br />Graphical Abstract<br />Highlights • Cells categorized as having the same identity can perform different activities • Single-cell expression data can be used to infer the activities cells take part in • Activity profiles provide a basis to measure phenotypic cell-cell divergence • Cell activity can quantify intra-tumor heterogeneity more fully than identity<br />Biological Sciences; Mathematical Biosciences; Cancer Systems Biology; Cancer
- Subjects :
- 0301 basic medicine
[SDV]Life Sciences [q-bio]
media_common.quotation_subject
Cell
02 engineering and technology
Computational biology
Biology
Article
03 medical and health sciences
Mathematical Biosciences
medicine
lcsh:Science
Biological sciences
media_common
Cancer
Multidisciplinary
Genetic heterogeneity
Biological Sciences
021001 nanoscience & nanotechnology
medicine.disease
Phenotype
030104 developmental biology
medicine.anatomical_structure
Cancer systems biology
Cancer cell
lcsh:Q
0210 nano-technology
Cancer Systems Biology
Diversity (politics)
Subjects
Details
- ISSN :
- 25890042
- Volume :
- 23
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- iScience
- Accession number :
- edsair.doi.dedup.....7585c155b248dccd1d06088bc71cb77d