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Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets.
- Source :
-
Genome biology [Genome Biol] 2020 May 06; Vol. 21 (1), pp. 106. Date of Electronic Publication: 2020 May 06. - Publication Year :
- 2020
-
Abstract
- Background: Single-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.<br />Results: We 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.<br />Conclusions: This 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.
- Subjects :
- Animals
Artemether pharmacology
Cell Dedifferentiation drug effects
Forkhead Transcription Factors antagonists & inhibitors
Glucagon-Secreting Cells drug effects
Glucagon-Secreting Cells metabolism
Humans
Insulin-Secreting Cells drug effects
Insulin-Secreting Cells metabolism
Islets of Langerhans metabolism
Machine Learning
Mice
Reference Standards
Species Specificity
Transcriptome drug effects
Islets of Langerhans drug effects
RNA-Seq standards
Single-Cell Analysis standards
Subjects
Details
- Language :
- English
- ISSN :
- 1474-760X
- Volume :
- 21
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Genome biology
- Publication Type :
- Academic Journal
- Accession number :
- 32375897
- Full Text :
- https://doi.org/10.1186/s13059-020-02006-2