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Single-cell genomics meets human genetics

Nature Reviews Genetics volume 24, pages 535–549 (2023)Cite this article

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Abstract

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.

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Fig. 1: Overview of single-cell expression quantitative trait locus studies.
Fig. 2: Human genetics and single-cell genomics, a 20-year timeline.
Fig. 3: Types of single-cell expression quantitative trait locus.
Fig. 4: Downstream effect of context-dependent single-cell expression quantitative trait locus.

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Authors and Affiliations

  1. Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia

    Anna S. E. Cuomo & Joseph E. Powell

  2. Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia

    Anna S. E. Cuomo & Daniel G. MacArthur

  3. Centre for Population Genomics, Murdoch Children’s Research Institute, Melbourne, Victoria, Australia

    Anna S. E. Cuomo & Daniel G. MacArthur

  4. Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Aparna Nathan & Soumya Raychaudhuri

  5. Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Aparna Nathan & Soumya Raychaudhuri

  6. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Aparna Nathan & Soumya Raychaudhuri

  7. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Aparna Nathan & Soumya Raychaudhuri

  8. UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia

    Joseph E. Powell

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The authors contributed equally to all aspects of the article.

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Correspondence to Anna S. E. Cuomo or Joseph E. Powell.

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Competing interests

D.G.M. is a founder with equity in Goldfinch Bio, a paid adviser to GSK, Insitro, Third Rock Ventures and Foresite Labs and has received research support from AbbVie, Astellas, Biogen, BioMarin, Eisai, Merck, Pfizer and Sanofi-Genzyme; none of these activities is related to the work presented here. S.R. is a founder for Mestag, Inc. and a scientific adviser for Sonoma Biotherapeutics, Pfizer, Jannsen and Sanofi. The other authors declare no competing interests.

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Glossary

Allele

One of two or more alternative DNA sequences occurring at a particular genomic locus.

Ambient RNA

Free-floating RNA captured in a single-cell RNA sequencing droplet or other reaction compartment.

Cell-type annotation

Manual or algorithmic approach to assign labels (corresponding to cell type) to unbiasedly identified cell clusters.

Cell villages

Cell lines derived from multiple donors cultured and differentiated together in a single dish. These are distinct from ‘uni-cultures’, in which each cell line is cultured independently. This makes the strategy particularly valuable for population-scale studies.

Clustering

Algorithmic approach to group cells into clusters, which are groups of similar cells based on their transcriptomes.

Colocalization

Statistical methods that aim to estimate the probability that the same genetic variant is causal for two different traits, for example, an organismal trait (for example, a disease in a genome-wide association study) and a molecular trait (for example, the expression level of a given gene in an expression quantitative trait locus study).

Doublets

Two or more cells (also called multiplet) captured and processed in the same droplet.

Fine-mapping

The process of localizing association signals to causal variants using statistical, bioinformatic or functional methods.

Fluorescence-activated cell sorting (FACS)

Experimental technique to select cells based on physical and chemical characteristics of individual cells. Single cells from a sample are suspended in a fluid and then injected into an instrument that uses lasers to detect cell morphology and fluorescently labelled features and sort cells based on these qualities.

Gene regulatory network (analysis)

A gene regulatory network is a set of interacting regulatory elements and genes that jointly control expression patterns that dictate a specific cell function.

Genome-wide association studies (GWASs)

Statistical procedure to identify associations between individual genetic variants and variation in continuous traits (for example, height) or risk of disease (for example, type 2 diabetes).

Interaction

Interplay between different sources of variation (for example, genetic variation and environmental exposure — GxE) that results in a joint effect on the trait of interest beyond the individual additive effects.

Mendelian randomization

Statistical method using measured variation in an instrumental variable (for example, a genetic variant) to test the causal effect of an exposure (for example, the expression of a gene) on an outcome (for example, a common trait or disease).

Minor allele frequency

Population frequency for the least common (that is, minor) alleles within the population of interest.

Non-negative matrix factorization

Dimensionality reduction method to decompose a matrix of non-negative values into two matrices of vectors capturing the essential features of a data set. Unlike principal component analysis, non-negative matrix factorization components are not orthogonal.

Polygenic risk scores (PRSs)

Quantification of total risk of an individual for a given disease based on genetic contributors alone. PRSs are calculated by summing the dosage of an individual of thousands of variants weighted by the strength of their association with the trait (as estimated from a genome-wide association study for that trait).

Principal component analysis (PCA)

Dimensionality reduction method to identify main orthogonal axes of variation in a dataset, called ‘principal components’.

Pseudotime

Approximate ordering of cells along a latent dimension based on single-cell RNA sequencing data. The ordering represents sequential changes along a transition (for example, during cell differentiation).

Response eQTL

An association between a genetic variant and RNA level (that is, an expression quantitative trait locus) that only becomes apparent when the cells the RNA is measured in are stimulated in some way (for example, immune activation).

Single-cell phenotypes

Cell characteristics (for example, function, gene expression and position along a transition) that can be estimated using single-cell-resolved molecular profiling (for example, single-cell RNA sequencing).

Sparse

Containing a large number of 0s. In single-cell data, sparsity is due to the combination of inefficient sampling and true absence of expression.

Trajectory inference

Also known as trajectory mapping. A computational technique used in single-cell data to determine the form of a dynamic process experienced by cells (for example, lineage specification and differentiation) and then arrange cells based on their progression through the process, usually using a pseudotime approach.

Transcriptome-wide association studies (TWASs)

Statistical method that uses estimated associations between variants and gene expression (for example, from expression quantitative trait locus studies) to infer expression for all individuals in a genome-wide association study and to identify associations between genes and traits/diseases.

Unique molecular identifiers (UMI)

Complex indices added to sequencing libraries before any PCR amplification steps, enabling the accurate bioinformatic identification of PCR duplicates. They are common in many single-cell RNA sequencing protocols.

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Cuomo, A.S.E., Nathan, A., Raychaudhuri, S. et al. Single-cell genomics meets human genetics. Nat Rev Genet 24, 535–549 (2023). https://doi.org/10.1038/s41576-023-00599-5

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