A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages

Robert J. Kimmerling, Gregory Lee Szeto, Jennifer W. Li, Alex S. Genshaft, Samuel W. Kazer, Kristofor R. Payer, Jacob de Riba Borrajo, Paul C. Blainey, Darrell J. Irvine, Alex K. Shalek () and Scott R. Manalis ()
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Robert J. Kimmerling: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Gregory Lee Szeto: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Jennifer W. Li: Massachusetts Institute of Technology
Alex S. Genshaft: Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard
Samuel W. Kazer: Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard
Kristofor R. Payer: Microsystems Technology Laboratory, Massachusetts Institute of Technology
Jacob de Riba Borrajo: Massachusetts Institute of Technology
Paul C. Blainey: Massachusetts Institute of Technology
Darrell J. Irvine: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology
Alex K. Shalek: Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard
Scott R. Manalis: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology

Nature Communications, 2016, vol. 7, issue 1, 1-7

Abstract: Abstract We introduce a microfluidic platform that enables off-chip single-cell RNA-seq after multi-generational lineage tracking under controlled culture conditions. We use this platform to generate whole-transcriptome profiles of primary, activated murine CD8+ T-cell and lymphocytic leukemia cell line lineages. Here we report that both cell types have greater intra- than inter-lineage transcriptional similarity. For CD8+ T-cells, genes with functional annotation relating to lymphocyte differentiation and function—including Granzyme B—are enriched among the genes that demonstrate greater intra-lineage expression level similarity. Analysis of gene expression covariance with matched measurements of time since division reveals cell type-specific transcriptional signatures that correspond with cell cycle progression. We believe that the ability to directly measure the effects of lineage and cell cycle-dependent transcriptional profiles of single cells will be broadly useful to fields where heterogeneous populations of cells display distinct clonal trajectories, including immunology, cancer, and developmental biology.

Date: 2016
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DOI: 10.1038/ncomms10220

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