Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function

Abstract

Understanding gene function and regulation in homeostasis and disease requires knowledge of the cellular and tissue contexts in which genes are expressed. Here, we applied four single-nucleus RNA sequencing methods to eight diverse, archived, frozen tissue types from 16 donors and 25 samples, generating a cross-tissue atlas of 209,126 nuclei profiles, which we integrated across tissues, donors, and laboratory methods with a conditional variational autoencoder. Using the resulting cross-tissue atlas, we highlight shared and tissue-specific features of tissue-resident cell populations; identify cell types that might contribute to neuromuscular, metabolic, and immune components of monogenic diseases and the biological processes involved in their pathology; and determine cell types and gene modules that might underlie disease mechanisms for complex traits analyzed by genome-wide association studies.

Cartography of human cells

The function of disease genes active in different cell types is modulated to meet the needs of the different tissues and organs in which the cells reside. Resolving these differences is critical to understanding homeostasis and disease. However, single-cell atlases generated to date have largely focused on individual tissues. Eraslan et al . applied single-nucleus RNA sequencing to frozen, banked samples from eight healthy human organs from 16 donors and characterized cell populations across tissues, including tissue-resident myeloid and fibroblast populations, and their role in tissue support and immunity (see the Perspective by Liu and Zhang). Using this cross-tissue atlas, the authors linked specific cell populations to monogenic and polygenic diseases, suggesting cell- and tissue-specific programs. —LZ and DJ

Abstract

Optimized single-nucleus RNA sequencing of eight frozen, banked human tissues helps reveal cell types and mechanisms underlying monogenic diseases and complex traits.

Abstract

INTRODUCTION

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.