In:
Science, American Association for the Advancement of Science (AAAS), Vol. 376, No. 6594 ( 2022-05-13)
Abstract:
Understanding and treating disease requires deep, systematic characterization of different cells and their interactions across human tissues and organs, along with characterization of the genetic variants that causally contribute to disease risk. Recent studies have combined single-cell atlases of specific human tissues and organs with genes associated with human disease to relate risk variants to likely cells of action. However, it has been challenging to extend these studies to profile multiple tissues and organs across the body, conduct studies at population scale, and integrate cell atlases from multiple organs to yield unified insights. RATIONALE Because of the pleiotropy and specificity of disease-associated variants, systematically relating variants to cells and molecular processes requires analysis across multiple tissues and individuals. Prior cell atlases primarily relied on fresh tissue samples from a single organ or tissue. Single-nucleus RNA sequencing (snRNA-seq) can be applied to frozen, archived tissue and captures cell types that do not survive dissociation across many tissues. Deep learning methods can integrate data across individuals and tissues by controlling for batch effects while preserving biological variation. RESULTS We established a framework for multitissue human cell atlases and generated an atlas of 209,126 snRNA-seq profiles from eight tissue types across 16 individuals, archived as frozen tissue as part of the Genotype-Tissue Expression (GTEx) project. We benchmarked four protocols and show how to apply them in a pooled setting to enable larger studies. We integrated the cross-tissue atlas using a conditional variational autoencoder, annotated it with 43 broad and 74 fine categories, and demonstrated its use to decipher tissue residency, such as a macrophage dichotomy and lipid associations that are preserved across tissues, and tissue-specific fibroblast features, including lung alveolar fibroblasts with likely roles in mechanosensation. We relate cells to human disease biology and disease-risk genes for both rare and common diseases, including rare muscle disease gene groups enriched in distinct subsets of myonuclei and nonmyocytes, and cell type–specific enrichment of expression and splicing quantitative trait locus (QTL) target genes mapped to genome-wide association study loci. CONCLUSION Our framework will empower large, cross-tissue population and/or disease studies at single-cell resolution. These frameworks and the cross-tissue perspective provided here will form a basis for larger-scale future studies to improve our understanding of cross-tissue and cross-individual variation of cellular phenotypes in relation to disease-associated genetic variation. Cross-tissue snRNA-seq atlas in eight frozen, archived adult human tissues. Tissue sites and experimental pipeline (top row). The resulting atlas enables a cross-tissue census of tissue-specific and shared cell types (middle left). Differentiation trajectories and compositional analysis of dichotomous macrophage populations improve our understanding of tissue residency (middle center and right). Analyses of fibroblasts across tissues reveal tissue-specific and shared fibroblast features and their functional interpretation (bottom left). Robust and scalable computational methods enable comprehensive associations of monogenic and complex diseases to tissue-specific and shared cell populations (bottom center and right). E. mucosa, esophagus mucosa; E. muscularis, esophagus muscularis; Sk. muscle, skeletal muscle.
Type of Medium:
Online Resource
ISSN:
0036-8075
,
1095-9203
DOI:
10.1126/science.abl4290
Language:
English
Publisher:
American Association for the Advancement of Science (AAAS)
Publication Date:
2022
detail.hit.zdb_id:
128410-1
detail.hit.zdb_id:
2066996-3
detail.hit.zdb_id:
2060783-0
SSG:
11
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