RNA splicing programs define tissue compartments and cell types at single cell resolution
- Stanford University, United States
- Stanford University School of Medicine, United States
- The Hong Kong University of Science and Technology, Hong Kong
- The Hong Kong University of Science and Technology, China
- Chan Zuckerberg Biohub, United States
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.
Article and author information
Author details
Julia Eve Olivieri
Roozbeh Dehghannasiri
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.
Reviewing Editor
- Gene W Yeo, University of California, San Diego, United States
Publication history
- Preprint posted: May 2, 2021 (view preprint)
- Received: May 26, 2021
- Accepted: September 10, 2021
- Accepted Manuscript published: September 13, 2021 (version 1)
- Version of Record published: November 2, 2021 (version 2)
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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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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Haemoglobinopathies are the commonest monogenic diseases worldwide and are caused by variants in the globin gene clusters. With over 2400 variants detected to date, their interpretation using the ACMG/AMP guidelines is challenging and computational evidence can provide valuable input about their functional annotation. While many in silico predictors have already been developed, their performance varies for different genes and diseases. In this study, we evaluate 31 in silico predictors using a dataset of 1627 variants in HBA1, HBA2, and HBB. By varying the decision threshold for each tool, we analyse their performance (a) as binary classifiers of pathogenicity, and (b) by using different non-overlapping pathogenic and benign thresholds for their optimal use in the ACMG/AMP framework. Our results show that CADD, Eigen-PC, and REVEL are the overall top performers, with the former reaching moderate strength level for pathogenic prediction. Eigen-PC and REVEL achieve the highest accuracies for missense variants, while CADD is also a reliable predictor of non-missense variants. Moreover, SpliceAI is the top performing splicing predictor, reaching strong level of evidence, while GERP++ and phyloP are the most accurate conservation tools. This study provides evidence about the optimal use of computational tools in globin gene clusters under the ACMG/AMP framework.
