Genome Biology (Nov 2021)
Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing
- Luyi Tian,
- Jafar S. Jabbari,
- Rachel Thijssen,
- Quentin Gouil,
- Shanika L. Amarasinghe,
- Oliver Voogd,
- Hasaru Kariyawasam,
- Mei R. M. Du,
- Jakob Schuster,
- Changqing Wang,
- Shian Su,
- Xueyi Dong,
- Charity W. Law,
- Alexis Lucattini,
- Yair David Joseph Prawer,
- Coralina Collar-Fernández,
- Jin D. Chung,
- Timur Naim,
- Audrey Chan,
- Chi Hai Ly,
- Gordon S. Lynch,
- James G. Ryall,
- Casey J. A. Anttila,
- Hongke Peng,
- Mary Ann Anderson,
- Christoffer Flensburg,
- Ian Majewski,
- Andrew W. Roberts,
- David C. S. Huang,
- Michael B. Clark,
- Matthew E. Ritchie
Affiliations
- Luyi Tian
- The Walter and Eliza Hall Institute of Medical Research
- Jafar S. Jabbari
- The Walter and Eliza Hall Institute of Medical Research
- Rachel Thijssen
- The Walter and Eliza Hall Institute of Medical Research
- Quentin Gouil
- The Walter and Eliza Hall Institute of Medical Research
- Shanika L. Amarasinghe
- The Walter and Eliza Hall Institute of Medical Research
- Oliver Voogd
- The Walter and Eliza Hall Institute of Medical Research
- Hasaru Kariyawasam
- The Walter and Eliza Hall Institute of Medical Research
- Mei R. M. Du
- The Walter and Eliza Hall Institute of Medical Research
- Jakob Schuster
- The Walter and Eliza Hall Institute of Medical Research
- Changqing Wang
- The Walter and Eliza Hall Institute of Medical Research
- Shian Su
- The Walter and Eliza Hall Institute of Medical Research
- Xueyi Dong
- The Walter and Eliza Hall Institute of Medical Research
- Charity W. Law
- The Walter and Eliza Hall Institute of Medical Research
- Alexis Lucattini
- Australian Genome Research Facility, Victorian Comprehensive Cancer Centre
- Yair David Joseph Prawer
- Centre for Stem Cell Systems, Department of Anatomy and Neuroscience, The University of Melbourne
- Coralina Collar-Fernández
- The Florey Institute of Neuroscience and Mental Health
- Jin D. Chung
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- Timur Naim
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- Audrey Chan
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- Chi Hai Ly
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- Gordon S. Lynch
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- James G. Ryall
- Centre for Muscle Research, Department of Physiology, The University of Melbourne
- Casey J. A. Anttila
- The Walter and Eliza Hall Institute of Medical Research
- Hongke Peng
- The Walter and Eliza Hall Institute of Medical Research
- Mary Ann Anderson
- The Walter and Eliza Hall Institute of Medical Research
- Christoffer Flensburg
- The Walter and Eliza Hall Institute of Medical Research
- Ian Majewski
- The Walter and Eliza Hall Institute of Medical Research
- Andrew W. Roberts
- The Walter and Eliza Hall Institute of Medical Research
- David C. S. Huang
- The Walter and Eliza Hall Institute of Medical Research
- Michael B. Clark
- Centre for Stem Cell Systems, Department of Anatomy and Neuroscience, The University of Melbourne
- Matthew E. Ritchie
- The Walter and Eliza Hall Institute of Medical Research
- DOI
- https://doi.org/10.1186/s13059-021-02525-6
- Journal volume & issue
-
Vol. 22,
no. 1
pp. 1 – 24
Abstract
Abstract A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.
Keywords
