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Gene regulatory network inference in the era of single-cell multi-omics

Nature Reviews Genetics volume 24, pages 739–754 (2023)Cite this article

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Abstract

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data — historically, bulk omics data — and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor–gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

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Fig. 1: Principles of gene regulatory networks.
Fig. 2: Flow chart of methods for gene regulatory network inference.
Fig. 3: Applications of gene regulatory networks.
Fig. 4: Experimental assessment of gene regulatory networks.

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Acknowledgements

The authors thank the developers of the methods discussed for the insightful feedback they provided. S.M.-D. was funded by the LiSyM-cancer network supported by the German Federal Ministry of Education and Research (BMBF; Funding number 031L0257B).

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Authors and Affiliations

  1. Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany

    Pau Badia-i-Mompel, Lorna Wessels, Sophia Müller-Dott, Rémi Trimbour, Ricardo O. Ramirez Flores & Julio Saez-Rodriguez

  2. Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany

    Lorna Wessels

  3. Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France

    Rémi Trimbour

  4. Altos Labs, Granta Park, Cambridge, UK

    Ricard Argelaguet

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All authors researched data for the article. P.B.-i-M., L.W., S.M.-D., R.O.R.F., R.A. and J.S.-R. contributed substantially to discussion of the content. P.B.-i-M., L.W., R.A. and J.S.-R. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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J.S.-R. reports funding from GSK, Pfizer and Sanofi, and consultant fees from Travere Therapeutics, Stadapharm and Astex Pharmaceutical. R.A. is an employee of Altos Labs. The other authors declare no competing interests.

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Supplementary information

Glossary

Assay for transpose-accessible chromatin with sequencing

(ATAC-seq). A technique to identify accessible DNA regions using hyperactive Tn5 transposase.

Betweenness centrality

A network centrality measure representing the number of appearances of a node in the shortest path of any other two nodes in the network.

Chromatin

A higher-order filamentous structure of DNA–protein complex that can exist in a condensed or uncondensed state.

Chromatin immunoprecipitation followed by sequencing

(ChIP-seq). A technique to analyse protein interactions with accessible DNA regions using chromatin immunoprecipitation followed by DNA sequencing.

cis-Regulatory elements

(CREs). Non-coding DNA regions that regulate the transcription of nearby genes upon binding of transcription factors (TFs). These include promoters, enhancers and silencers.

Cleavage under targets and tagmentation

(CUT&Tag). An antibody-based technique to analyse protein interactions with accessible DNA regions using transposase Tn5-mediated tagmentation followed by DNA sequencing.

Closeness centrality

A network centrality measure describing the average distance (length of the shortest path) of a node to all other nodes.

Degree centrality

A network centrality measure describing the number of edges (degree) of a node.

DNA binding sites

DNA sequences where transcription factors can bind to drive gene regulation, usually represented as nucleotide patterns known as motifs.

Eigenvector centrality

A network centrality measure describing the importance of a node in the network based on the centrality of its neighbours.

Enhancers

Distal regulatory DNA regions where transcription regulatory proteins can bind and activate transcription.

Expression quantitative trait loci

Genomic locations whose sequence variation is associated with changes in gene expression.

Gene regulatory networks

(GRNs). Network representations of molecular interactions between transcriptional regulators and target genes.

Genome-wide association studies

Analysis approach to identify frequently appearing single-nucleotide polymorphisms in the genome across a large cohort of individuals.

Hi-C

A technique to study chromatin conformation in three dimensions to identify genomic sequences that might be distal to each other in linear distance but closer in the 3D space.

Metacells

Groups of cells with a similar molecular profile that can be aggregated into a single omics profile to reduce sparsity of the data.

Motif matcher algorithms

String matching algorithms to detect transcription factor binding sites in DNA sequences.

Network centrality

A group of graph theory metrics that defines the relative importance of a node in a network.

Peaks

Regions of accessible chromatin that form the read-out of epigenetic sequencing techniques.

Promoter

A regulatory region in the genome located before the transcriptional start site of a gene.

Silencers

Distal regulatory DNA regions where transcription regulatory proteins can bind and repress transcription.

Single-nucleotide polymorphisms

(SNPs). DNA sequence variations caused by substitution of a single nucleotide in a specific position.

Topologically associating domains

Self-interacting genomic regions with high interaction frequency of sequences within the domain and relative isolation from neighbouring regions, forming a 3D chromosome structure.

Transcription factors

(TFs). Proteins that modify the rate of transcription by binding to specific DNA sequences.

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Badia-i-Mompel, P., Wessels, L., Müller-Dott, S. et al. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 24, 739–754 (2023). https://doi.org/10.1038/s41576-023-00618-5

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