Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

Kevin Chen; Kivilcim Ozturk; Ryne L Contreras; Jessica Simon; Sean McCann; Wei Ji Chen; Hannah Carter; Stephanie I Fraley

doi:10.1016/j.isci.2020.101991

Phenotypically supervised single-cell sequencing parses within-cell-type heterogeneity

iScience. 2020 Dec 26;24(1):101991. doi: 10.1016/j.isci.2020.101991. eCollection 2021 Jan 22.

Authors

Kevin Chen¹, Kivilcim Ozturk^{2

3}, Ryne L Contreras¹, Jessica Simon¹, Sean McCann¹, Wei Ji Chen¹, Hannah Carter^{2

3

4}, Stephanie I Fraley^{1

4}

Affiliations

¹ Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
² Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA.
³ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA.
⁴ Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA.

Abstract

To better understand cellular communication driving diverse behaviors, we need to uncover the molecular mechanisms of within-cell-type functional heterogeneity. While single-cell RNA sequencing (scRNAseq) has advanced our understanding of cell heterogeneity, linking individual cell phenotypes to transcriptomic data remains challenging. Here, we used a phenotypic cell sorting technique to ask whether phenotypically supervised scRNAseq analysis (pheno-scRNAseq) can provide more insight into heterogeneous cell behaviors than unsupervised scRNAseq. Using a simple 3D in vitro breast cancer (BRCA) model, we conducted pheno-scRNAseq on invasive and non-invasive cells and compared the results to phenotype-agnostic scRNAseq analysis. Pheno-scRNAseq identified unique and more selective differentially expressed genes than unsupervised scRNAseq analysis. Functional studies validated the utility of pheno-scRNAseq in understanding within-cell-type functional heterogeneity and revealed that migration phenotypes were coordinated with specific metabolic, proliferation, stress, and immune phenotypes. This approach lends new insight into the molecular systems underlying BRCA cell phenotypic heterogeneity.

Keywords: Cell Biology; Complex System Biology; Transcriptomics.

Grants and funding

R01 CA238042/CA/NCI NIH HHS/United States