Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries. Issue 4 (29th November 2018)

• Record Type: Journal Article
• Authors: Riemondy, Kent A; Ransom, Monica; Alderman, Christopher; Gillen, Austin E; Fu, Rui; Finlay-Schultz, Jessica; Kirkpatrick, Gregory D; Di Paola, Jorge; Kabos, Peter; Sartorius, Carol A; Hesselberth, Jay R
• Is Part Of: Nucleic acids research. Volume 47:Issue 4(2019)
• Page Start: e20
• Page End: e20
• Publication Date: 2018-11-29
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gky1204
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: Single-cell RNA sequencing (scRNA-seq) methods generate sparse gene expression profiles for thousands of single cells in a single experiment. The information in these profiles is sufficient to classify cell types by distinct expression patterns but the high complexity of scRNA-seq libraries often prevents full characterization of transcriptomes from individual cells. To extract more focused gene expression information from scRNA-seq libraries, we developed a strategy to physically recover the DNA molecules comprising transcriptome subsets, enabling deeper interrogation of the isolated molecules by another round of DNA sequencing. We applied the method in cell-centric and gene-centric modes to isolate cDNA fragments from scRNA-seq libraries. First, we resampled the transcriptomes of rare, single megakaryocytes from a complex mixture of lymphocytes and analyzed them in a second round of DNA sequencing, yielding up to 20-fold greater sequencing depth per cell and increasing the number of genes detected per cell from a median of 1313 to 2002. We similarly isolated mRNAs from targeted T cells to improve the reconstruction of their VDJ-rearranged immune receptor mRNAs. Second, we isolated CD3D mRNA fragments expressed across cells in a scRNA-seq library prepared from a clonal T cell line, increasing the number of cells with detected CD3D expression from 59.7% to 100%. Transcriptome resampling is a general approach to recover targeted gene expression information from single-cell RNA sequencing libraries that enhances the utility of these costly experiments, and may be applicable to the targeted recovery of molecules from other single-cell assays.