Institute for Computational and Mathematical Engineering, Stanford University, Stanford, United States; Department of Biomedical Data Science, Stanford University, Stanford, United States; Department of Biochemistry, Stanford University, Stanford, United States
Department of Biomedical Data Science, Stanford University, Stanford, United States; Department of Biochemistry, Stanford University, Stanford, United States
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, United States
Serena Y Tan
Department of Pathology, Stanford University Medical Center, Stanford, United States
Jingsi Ming
Academy for Statistics and Interdisciplinary Sciences, Faculty of Economics and Management,East China Normal University, Shanghai, China; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
Angela Ruohao Wu
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Tabula Sapiens Consortium
Stephen R Quake
Chan Zuckerberg Biohub, San Francisco, United States; Department of Bioengineering, Stanford University, Stanford, United States
Mark A Krasnow
Department of Biochemistry, Stanford University, Stanford, United States
Department of Biomedical Data Science, Stanford University, Stanford, United States; Department of Biochemistry, Stanford University, Stanford, United States
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 Chromium 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.
