Applications of single-cell genomics and computational strategies to study common disease and population-level variation

Abstract

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

Figure 1.

Sample selection strategy for population-based single-cell studies. (A) Genetic risk variant–enriched design in which individuals with the genetic risk variant are oversampled in order to achieve enough number of individuals that carry the genetic risk variant. (B) Extreme phenotype sampling design in which individuals with extremely low or extremely high phenotypes are selected. These extreme phenotype individuals are expected to carry more rare genetic risk variants than are individuals with intermediate phenotypes.

Figure 2.

Overview of single-cell data analysis workflow. The typical workflow involves data preprocessing, combination of multiple single-cell data sets into a combined data set, clustering and cell type annotation, differential expression analysis, trajectory inference, and pseudotime analysis.