1. Computational and Systems Biology
  2. Genetics and Genomics

RNA splicing programs define tissue compartments and cell types at single cell resolution

  1. Julia Eve Olivieri
  2. Roozbeh Dehghannasiri
  3. Peter L Wang
  4. SoRi Jang
  5. Antoine de Morree
  6. Serena Y Tan
  7. Jingsi Ming
  8. Angela Ruohao Wu
  9. Tabula Sapiens Consortium
  10. Stephen R Quake
  11. Mark A Krasnow
  12. Julia Salzman  Is a corresponding author
  1. Stanford University, United States
  2. Stanford University School of Medicine, United States
  3. The Hong Kong University of Science and Technology, Hong Kong
  4. The Hong Kong University of Science and Technology, China
  5. Chan Zuckerberg Biohub, United States
https://doi.org/10.7554/eLife.70692
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Cite this article
  1. Julia Eve Olivieri
  2. Roozbeh Dehghannasiri
  3. Peter L Wang
  4. SoRi Jang
  5. Antoine de Morree
  6. Serena Y Tan
  7. Jingsi Ming
  8. Angela Ruohao Wu
  9. Tabula Sapiens Consortium
  10. Stephen R Quake
  11. Mark A Krasnow
  12. Julia Salzman
(2021)
RNA splicing programs define tissue compartments and cell types at single cell resolution
eLife 10:e70692.
https://doi.org/10.7554/eLife.70692
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Abstract

The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.

Data availability

The fastq files for the Tabula Sapiens data (Consortium et al., 2021) (both 10x and Smart-seq2) were downloaded from https://tabula-sapiens-portal.ds.czbiohub.org/. The pilot 2 individual is referred to as individual 1, and the pilot 1 individual is referred to as individual 2 in this manuscript. Pancreas data was removed from individual 2. Cell type annotations were downloaded on March 19th, 2021, and the "ground truth" column was used as the within-tissue-compartment cell type. The Tabula Muris data was downloaded from a public AWS S3 bucket according to https://registry.opendata.aws/tabula-muris-senis/. The P1 (30-M-2) mouse is referred to as individual 1 and P2 (30-M-4) is referred to as individual 2 in this manuscript. Compartment annotations were assigned based on knowledge of cell type. The fastq files for the Tabula Microcebus mouse lemur data were downloaded from https://tabula-microcebus.ds.czbiohub.org. Lemurs 4 and 2 are referred to as individuals 1 and and 2, respectively, in this manuscript. The propagated_cell_ontology_class column was used as the within-tissue-compartment cell type. Because tissue compartments in the mouse lemur were annotated more finely, we collapsed the lymphoid, myeloid, and megakaryocyte-erythroid compartments into the immune compartment.Human and mouse unselected spermatogenesis data was downloaded from the SRA databases with accession IDs SRR6459190 (AdultHuman_17-3), SRR6459191 (AdultHuman_17-4), and SRR6459192 (AdultHuman_17-5) for human, and accession IDs SRR6459155 (AdultMouse-Rep1), SRR6459156 (AdultMouse-Rep2), and SRR6459157 (AdultMouse-Rep3) for mouse. The files containing SpliZ values can be accessed at the following FigShare repository: DOI: 10.6084/m9.figshare.14531721.

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The following previously published data sets were used

Article and author information

Author details

  1. Julia Eve Olivieri

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0850-5498

  2. Roozbeh Dehghannasiri

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7413-3437

  3. Peter L Wang

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9651-3860

  4. SoRi Jang

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Antoine de Morree

    Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8316-4531

  6. Serena Y Tan

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Jingsi Ming

    The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
    Competing interests
    The authors declare that no competing interests exist.

  8. Angela Ruohao Wu

    The Hong Kong University of Science and Technology, Hong Kong, China
    Competing interests
    The authors declare that no competing interests exist.

  9. Tabula Sapiens Consortium

  10. Stephen R Quake

    Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.

  11. Mark A Krasnow

    Chan Zuckerberg Biohub, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.

  12. Julia Salzman

    Department of Biochemistry, Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
    For correspondence
    julia.salzman@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7630-3436

Funding

National Science Foundation (DGE-1656518)

  • Julia Eve Olivieri

National Institute of General Medical Sciences (R01 GM116847)

  • Julia Salzman

National Science Foundation (MCB1552196)

  • Julia Salzman

National Institutes of Health (T15 LM7033-36)

  • Roozbeh Dehghannasiri

National Cancer Institute (R25 CA180993)

  • Roozbeh Dehghannasiri

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2021, Olivieri et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Julia Eve Olivieri
  2. Roozbeh Dehghannasiri
  3. Peter L Wang
  4. SoRi Jang
  5. Antoine de Morree
  6. Serena Y Tan
  7. Jingsi Ming
  8. Angela Ruohao Wu
  9. Tabula Sapiens Consortium
  10. Stephen R Quake
  11. Mark A Krasnow
  12. Julia Salzman
(2021)
RNA splicing programs define tissue compartments and cell types at single cell resolution
eLife 10:e70692.
https://doi.org/10.7554/eLife.70692

Share this article

https://doi.org/10.7554/eLife.70692

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