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Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

Authors
  • Marquina-Sanchez, Brenda1
  • Fortelny, Nikolaus1
  • Farlik, Matthias1, 2
  • Vieira, Andhira3
  • Collombat, Patrick3
  • Bock, Christoph1, 2
  • Kubicek, Stefan1
  • 1 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT 25.3, Vienna, 1090, Austria , Vienna (Austria)
  • 2 Medical University of Vienna, Vienna, 1090, Austria , Vienna (Austria)
  • 3 Université Côte d’Azur, CNRS, Inserm, iBV, Nice, France , Nice (France)
Type
Published Article
Publication Date
May 06, 2020
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s13059-020-02006-2
Source
Springer Nature
License
Green

Abstract

BackgroundSingle-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy—beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses.ResultsWe find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples.ConclusionsThis new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.

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