Into the multiverse: advances in single-cell multiomic profiling
Open accessAbstract
Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.
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In single-cell multi-omics applications, multiple profiles have been used to characterize cellular and spatiotemporal genomic regulations.
“In single-cell multi-omics applications, these multiple profiles have been used to characterize the cellular and spatiotemporal genomic regulations [21].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [21]
With advancement of high-throughput single-cell capture and sequencing techniques, multiple types of single-cell omics data have been generated, including transcriptome, chromatin, DNA methylation, histone modification, and others.
“With the advancement of high-throughput single-cell capture and sequencing techniques, multiple types of single-cell omics data have been generated, including transcriptome, chromatin, DNA methylation, histone modification, and others [21].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [21]
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- Authors
- Ogbeide, S., Giannese, F., Mincarelli, L., & Macaulay, I. C.
- Affiliations
- Earlham Institute (GB)
- IRCCS Ospedale San Raffaele (IT)
- Norwich Research Park (GB)
- Corresponding author
- Macaulay, Iain C.
- Venue
- Trends in Genetics
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 38
- Issue
- 8
- Pages
- 831-843
- ISSN
- 0168-9525 (ISSN-L)
- OpenAlex
- W4229335045
- Cited by
- 82
- References
- 101
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 26 · 2023: 30 · 2022: 6 · 2021: 1
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Trends in Genetics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion, other-oa)
- Funders
- UK Research and Innovation (BB/CSP1720/1, BB/CCG1720/1)
- Cancer Research UK (26815, A26815)
- Associazione Italiana per la Ricerca sul Cancro (A26815)
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BBS/E/T/000PR9816, BB/CCG1720/1, BB/P022073/1, BBS/E/T/000PR9817, BB/CSP1720/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell multi-omics, single-cell transcriptomics, cell atlases, intercellular heterogeneity, epigenomics, proteomics, regulatory mechanisms, cell function
- MeSH terms
- Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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