Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq
Abstract
The rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.
Additional Information
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Received 28 April 2021; Accepted 20 September 2021; Published 07 October 2021. We would like to thank Melanie Oakes at UC Irvine Genomics High-Throughput Facility (GHTF) for her help with PacBio sequencing. This work was supported by the National Institutes of Health (UM1 HG009443) to A.M. and B.J.W. Elisabeth Rebboah and Fairlie Reese contributed equally to this work. Author Contributions: E.R., G.B., I.R., C.M., and H.L. performed the experiments. F.R. and E.R. analyzed the data and wrote the manuscript with significant input from K.W., B.W., and A.M. F.R. supplied custom LR-Split-seq demultiplexing code and D.T. provided TSS-calling code. All authors read and approved the final manuscript. Ethical approval and consent to participate: Ethics approval is not applicable. The authors declare no competing financial interests. Review history: The review history is available as Additional file 3. Peer review information: Barbara Cheifet was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.Attached Files
Published - Rebboah2021_Article_MappingAndModelingTheGenomicBa.pdf
Submitted - 2021.04.26.441522v1.full.pdf
Supplemental Material - 13059_2021_2505_MOESM1_ESM.pdf
Supplemental Material - 13059_2021_2505_MOESM2_ESM.gz
Supplemental Material - 13059_2021_2505_MOESM3_ESM.docx
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Additional details
- PMCID
- PMC8495978
- Eprint ID
- 108866
- Resolver ID
- CaltechAUTHORS:20210429-100236539
- UM1 HG009443
- NIH
- Created
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2021-04-29Created from EPrint's datestamp field
- Updated
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2022-10-24Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering