Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells

View ORCID ProfileLaura Mincarelli, Vladimir Uzun, Stuart A. Rushworth, Wilfried Haerty, Iain C. Macaulay
doi: https://doi.org/10.1101/2020.04.06.027474
Laura Mincarelli
1Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Laura Mincarelli
Vladimir Uzun
1Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Stuart A. Rushworth
2Norwich Medical School, The University of East Anglia, Norwich Research Park, Norwich, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wilfried Haerty
1Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: wilfried.haerty@earlham.ac.uk iain.macaulay@earlham.ac.uk
Iain C. Macaulay
1Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: wilfried.haerty@earlham.ac.uk iain.macaulay@earlham.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling and characterization of novel cell types within heterogeneous cell populations. However, most approaches cannot detect alternatively spliced transcripts, which can profoundly shape cell phenotype by generating functionally distinct proteins from the same gene. Here, we integrate short- and long-read scRNA-seq of hematopoietic stem and progenitor cells to characterize changes in cell type abundance, gene and isoform expression during differentiation and ageing.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 06, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells
Laura Mincarelli, Vladimir Uzun, Stuart A. Rushworth, Wilfried Haerty, Iain C. Macaulay
bioRxiv 2020.04.06.027474; doi: https://doi.org/10.1101/2020.04.06.027474
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Combined single-cell gene and isoform expression analysis in haematopoietic stem and progenitor cells
Laura Mincarelli, Vladimir Uzun, Stuart A. Rushworth, Wilfried Haerty, Iain C. Macaulay
bioRxiv 2020.04.06.027474; doi: https://doi.org/10.1101/2020.04.06.027474

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3686)
  • Biochemistry (7774)
  • Bioengineering (5668)
  • Bioinformatics (21245)
  • Biophysics (10563)
  • Cancer Biology (8162)
  • Cell Biology (11915)
  • Clinical Trials (138)
  • Developmental Biology (6738)
  • Ecology (10388)
  • Epidemiology (2065)
  • Evolutionary Biology (13843)
  • Genetics (9694)
  • Genomics (13056)
  • Immunology (8123)
  • Microbiology (19956)
  • Molecular Biology (7833)
  • Neuroscience (42973)
  • Paleontology (318)
  • Pathology (1276)
  • Pharmacology and Toxicology (2256)
  • Physiology (3350)
  • Plant Biology (7208)
  • Scientific Communication and Education (1309)
  • Synthetic Biology (1999)
  • Systems Biology (5528)
  • Zoology (1126)