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Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

View ORCID ProfileKent A. Riemondy Jr., Monica Ransom, Christopher Alderman, Austin E. Gillen, Rui Fu, Jessica Finlay-Schultz, Gregory Kirkpatrick, Jorge Di Paola, Peter Kabos, Carol A. Sartorius, View ORCID ProfileJay R. Hesselberth
doi: https://doi.org/10.1101/408740
Kent A. Riemondy
University of Colorado School of Medicine
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Monica Ransom
University of Colorado School of Medicine
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Christopher Alderman
University of Colorado School of Medicine
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Austin E. Gillen
University of Colorado School of Medicine
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Rui Fu
University of Colorado School of Medicine
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Jessica Finlay-Schultz
University of Colorado School of Medicine
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Gregory Kirkpatrick
University of Colorado School of Medicine
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Jorge Di Paola
University of Colorado School of Medicine
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Peter Kabos
University of Colorado School of Medicine
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Carol A. Sartorius
University of Colorado School of Medicine
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Jay R. Hesselberth
University of Colorado School of Medicine
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  • ORCID record for Jay R. Hesselberth
  • For correspondence: jay.hesselberth@gmail.com
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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 1,313 to 2,002. 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 05, 2018.
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Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
Kent A. Riemondy Jr., Monica Ransom, Christopher Alderman, Austin E. Gillen, Rui Fu, Jessica Finlay-Schultz, Gregory Kirkpatrick, Jorge Di Paola, Peter Kabos, Carol A. Sartorius, Jay R. Hesselberth
bioRxiv 408740; doi: https://doi.org/10.1101/408740
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Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries
Kent A. Riemondy Jr., Monica Ransom, Christopher Alderman, Austin E. Gillen, Rui Fu, Jessica Finlay-Schultz, Gregory Kirkpatrick, Jorge Di Paola, Peter Kabos, Carol A. Sartorius, Jay R. Hesselberth
bioRxiv 408740; doi: https://doi.org/10.1101/408740

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