Bulk tissue cell type deconvolution with multi-subject single-cell expression reference

• Published in: Nature communications 2019-01, Vol.10 (1), p.380-380, Article 380
• Main Authors: Wang, Xuran, Park, Jihwan, Susztak, Katalin, Zhang, Nancy R., Li, Mingyao
• Format: Article
• Language: English
• Subjects: 631/114/2415; 631/337/2019; Animals; Composition; Disease; Gene Expression; Gene Expression Profiling - methods; Gene sequencing; Heterogeneity; Histocompatibility Testing - methods; Humanities and Social Sciences; Humans; Islets of Langerhans - cytology; Kidney - cytology; Kidneys; Mice; Models, Theoretical; multidisciplinary; Music; Pancreas; Rats; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Target recognition; Tissues
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-08023-x

Knowledge of cell type composition in disease relevant tissues is an important step towards the identification of cellular targets of disease. We present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. By appropriate weighting of genes showing cross-subject and cross-cell consistency, MuSiC enables the transfer of cell type-specific gene expression information from one dataset to another. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables the characterization of cellular heterogeneity of complex tissues for understanding of disease mechanisms. As bulk tissue data are more easily accessible than single-cell RNA-seq, MuSiC allows the utilization of the vast amounts of disease relevant bulk tissue RNA-seq data for elucidating cell type contributions in disease. Bulk tissue RNA-seq data reveals transcriptomic profiles but masks the contributions of different cell types. Here, the authors develop a new method for estimating cell type proportions from bulk tissue RNA-seq data guided by multi-subject single-cell expression reference.