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

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Junyue Cao, Jonathan S. Packer, Vijay Ramani, Darren A. Cusanovich, Chau Huynh, Riza Daza, Xiaojie Qiu, Choli Lee, Scott N. Furlan, Frank J. Steemers, Andrew Adey, Robert H. Waterston, Cole Trapnell, Jay Shendure
doi: https://doi.org/10.1101/104844
Junyue Cao
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
2Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jonathan S. Packer
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Vijay Ramani
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Darren A. Cusanovich
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chau Huynh
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Riza Daza
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xiaojie Qiu
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
2Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Choli Lee
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Scott N. Furlan
3Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA, USA
4Department of Pediatrics, University of Washington, Seattle, WA, USA
5Fred Hutchinson Cancer Research Center, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Frank J. Steemers
6Illumina Inc., Advanced Research Group, San Diego, CA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew Adey
7Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
8Knight Cardiovascular Institute, Portland, OR, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert H. Waterston
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: coletrap@uw.edu watersto@uw.edu shendure@uw.edu
Cole Trapnell
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: coletrap@uw.edu watersto@uw.edu shendure@uw.edu
Jay Shendure
1Department of Genome Sciences, University of Washington, Seattle, WA, USA
9Howard Hughes Medical Institute, Seattle, WA, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: coletrap@uw.edu watersto@uw.edu shendure@uw.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

Copyright 
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.
Back to top
PreviousNext
Posted February 02, 2017.
Download PDF
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.
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
(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
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Junyue Cao, Jonathan S. Packer, Vijay Ramani, Darren A. Cusanovich, Chau Huynh, Riza Daza, Xiaojie Qiu, Choli Lee, Scott N. Furlan, Frank J. Steemers, Andrew Adey, Robert H. Waterston, Cole Trapnell, Jay Shendure
bioRxiv 104844; doi: https://doi.org/10.1101/104844
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing
Junyue Cao, Jonathan S. Packer, Vijay Ramani, Darren A. Cusanovich, Chau Huynh, Riza Daza, Xiaojie Qiu, Choli Lee, Scott N. Furlan, Frank J. Steemers, Andrew Adey, Robert H. Waterston, Cole Trapnell, Jay Shendure
bioRxiv 104844; doi: https://doi.org/10.1101/104844

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 (4232)
  • Biochemistry (9128)
  • Bioengineering (6774)
  • Bioinformatics (23988)
  • Biophysics (12117)
  • Cancer Biology (9522)
  • Cell Biology (13772)
  • Clinical Trials (138)
  • Developmental Biology (7627)
  • Ecology (11686)
  • Epidemiology (2066)
  • Evolutionary Biology (15504)
  • Genetics (10638)
  • Genomics (14322)
  • Immunology (9477)
  • Microbiology (22832)
  • Molecular Biology (9089)
  • Neuroscience (48952)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2568)
  • Physiology (3844)
  • Plant Biology (8327)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6186)
  • Zoology (1300)