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Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data

Pool, Allan-Hermann and Poldsam, Helen and Chen, Sisi and Thomson, Matt and Oka, Yuki (2022) Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220505-806027100

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Abstract

Droplet-based 3’ single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3’ scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3’ to known gene boundaries due to poor 3’ UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3’ gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2022.04.26.489449DOIDiscussion Paper
http://www.thepoollab.org/resourcesRelated ItemPool Lab
https://github.com/PoolLab/GenerecoveryRelated ItemCode
ORCID:
AuthorORCID
Pool, Allan-Hermann0000-0002-0811-9861
Chen, Sisi0000-0001-9448-9713
Thomson, Matt0000-0003-1021-1234
Oka, Yuki0000-0003-2686-0677
Additional Information: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 4.0 International license. This version posted April 27, 2022. We thank Lior S. Pachter and members of the M.T. lab for helpful discussion and comments. We thank the Single-Cell Profiling Center (SPEC) in the Beckman Institute at Caltech for technical assistance with scRNA-seq. A.H.P. is supported by Eugene McDermott Scholar funds and by Startup funds from Peter O’Donnell Jr. Brain Institute at UT Southwestern. Y.O. is supported by Startup funds from the President and Provost of the California Institute of Technology and the Biology and Biological Engineering Division of California Institute of Technology, Searle Scholars Program, the Mallinckrodt Foundation, the McKnight Foundation, the Klingenstein-Simons Foundation, the New York Stem Cell Foundation and the NIH (R56MH113030 and R01NS109997). Data Availability: Raw and processed scRNA-seq data are available at the NCBI Gene Expression Omnibus (GEO accession no. GSE198528). Latest versions of the mouse and human reference transcriptomes and genome annotations are available for download at www.thepoollab.org/resources. Code Availability: Code to reproduce data analysis in this manuscript and generate scRNA-seq optimized genomic annotations is available at https://github.com/PoolLab/Generecovery. Author Contributions: A.H.P. conceived and designed the project. A.H.P. and H.P. devised and performed data analysis. A.H.P. and S.C. generated the MnPO scRNA-seq dataset. S.C. and M.T. generated the human PBMC scRNA-seq dataset. S.C., M.T. and Y.O. provided conceptual advice on data analysis. All authors contributed to the manuscript as drafted by A.H.P. and H.P. A.H.P. and Y.O. supervised the overall project. The authors have declared no competing interest.
Funders:
Funding AgencyGrant Number
Eugene McDermott Scholar FundsUNSPECIFIED
University of Texas Southwestern Medical CenterUNSPECIFIED
President and Provost of CaltechUNSPECIFIED
Caltech Division of Biology and Biological EngineeringUNSPECIFIED
Searle Scholars ProgramUNSPECIFIED
Mallinckrodt FoundationUNSPECIFIED
McKnight FoundationUNSPECIFIED
Klingenstein-Simons FoundationUNSPECIFIED
New York Stem Cell FoundationUNSPECIFIED
NIHR56MH113030
NIHR01NS109997
DOI:10.1101/2022.04.26.489449
Record Number:CaltechAUTHORS:20220505-806027100
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220505-806027100
Official Citation:Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data. Allan-Hermann Pool, Helen Poldsam, Sisi Chen, Matt Thomson, Yuki Oka. bioRxiv 2022.04.26.489449; doi: https://doi.org/10.1101/2022.04.26.489449
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114596
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 May 2022 19:09
Last Modified:05 May 2022 19:09

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