Single-cell multi-omics: Integrating the transcriptome, epigenome, and proteome from the same cell
Single-cell multi-omics integrates RNA, chromatin, and protein data from individual cells, enabling deep insights into cell function, disease mechanisms, and development using advanced techniques like scRNA-seq and CITE-seq.
Introduction to single-cell multi-omics
In the era of precision biology, single-cell technologies have revolutionized our understanding of cellular diversity and function. Among the most groundbreaking innovations is single-cell multi-omics, a powerful approach that allows scientists to simultaneously profile multiple molecular layers, such as the transcriptome, epigenome, and proteome, from the same individual cell. This integrated strategy is unlocking new dimensions of biological insight, enabling researchers to dissect complex tissues, trace developmental lineages, and understand disease mechanisms with unprecedented resolution.
What is single-cell multi-omics?
Single-cell multi-omics combines several high-throughput techniques into a unified workflow. At its core, it integrates single-cell RNA sequencing (scRNA-seq) to capture gene expression1, single-cell ATAC-seq to assess chromatin accessibility and epigenetic regulation2, and CITE-seq to quantify surface protein expression using oligonucleotide-tagged antibodies3. By capturing these three modalities from the same cell, researchers can construct a comprehensive molecular profile that links gene regulation, transcriptional output, and protein function.
The power of multi-modal integration
The integration of transcriptomic, epigenomic, and proteomic data is particularly transformative in fields like immunology, oncology, and developmental biology. For example, in immune profiling, combining RNA and protein data helps distinguish closely related cell types that may express similar transcripts but differ in surface markers4. In cancer research, linking chromatin accessibility with gene expression can reveal regulatory elements driving tumor progression or therapy resistance5. In developmental biology, multi-omics enables the reconstruction of lineage trajectories and the identification of key transcription factors guiding cell fate decisions6.
Technological advancements in single-cell resolution
Recent advancements in single-cell resolution and analysis are further enhancing the power of multi-omics. One major trend is the refinement of technologies that allow simultaneous profiling of multiple modalities from the same cell. Platforms such as 10x Genomics Multiome7 and emerging methods like TEA-seq8 and SNARE-seq9 are making it possible to capture RNA and ATAC data in parallel, while CITE-seq adds proteomics data into the mix. These innovations reduce technical noise, improve data integration, and enable more accurate cell type classification.
Perturbation screens at the single-cell level
Another exciting development is the rise of perturbation screens at single-cell resolution. Techniques like Perturb-seq10 and CROP-seq combine CRISPR-based gene editing with single-cell RNA-seq to systematically investigate gene function11. Researchers can map gene regulatory networks and identify key drivers of cellular behavior by introducing targeted genetic perturbations and measuring their effects on the transcriptome. This approach is particularly valuable for understanding complex traits, drug responses, and mechanisms of resistance in diseases such as cancer and autoimmune disorders.
Live-cell and real-time monitoring
Live-cell and real-time monitoring represent a third frontier in single-cell analysis. Traditional single-cell sequencing provides static snapshots of cellular states, but new methods are enabling dynamic tracking of molecular changes over time. Live-cell RNA imaging12 and optogenetic tools13 are being integrated with single-cell workflows to capture temporal dynamics of gene expression, signaling pathways, and cellular responses to environmental stimuli. This shift from static to dynamic profiling is crucial for understanding processes like differentiation, immune activation, and cellular adaptation.
Biological and clinical applications
The impact of single-cell multi-omics extends across multiple domains of biology and medicine. In developmental biology, single-cell multi-omics allows researchers to map the progression of stem cells into specialized lineages, identify transient intermediate states, and uncover regulatory hierarchies. In immunology, it provides a detailed view of immune cell diversity, activation states, and responses to infection or vaccination. In oncology, it helps characterize tumor heterogeneity, identify rare subpopulations, and guide personalized treatment strategies. In neuroscience, it enables the classification of neuronal subtypes, mapping brain circuits, and investigating epigenetic regulation in neurological disorders.
Conclusions and future directions
Single-cell multi-omics is revolutionizing molecular biology by enabling the simultaneous analysis of the transcriptome, epigenome, and proteome from the same cell. This integrative approach offers an unprecedented, holistic view of cellular identity, function, and heterogeneity, making it a powerful tool for unraveling complex biological systems. As the technology matures, it is poised to transform our understanding of development, disease mechanisms, and therapeutic responses, paving the way for more precise diagnostics and personalized medicine.
Despite its transformative potential, single-cell multi-omics faces several challenges. The integration of multiple molecular assays within a single workflow demands meticulous experimental design and optimization. Moreover, the computational burden of analyzing and integrating high-dimensional data across modalities requires advanced algorithms and machine learning techniques. Cost and scalability remain significant barriers, particularly for large-scale or clinical applications.
However, the field is advancing rapidly. Innovations in microfluidics, barcoding strategies, and sequencing chemistries are enhancing throughput and reducing costs. Computational frameworks such as Seurat14, Harmony15, and MOFA16 are becoming more robust and accessible, streamlining data integration and interpretation. A fascinating frontier is spatial multi-omics, which combines molecular profiling with spatial context to map cellular interactions and tissue architecture. This is especially valuable for studying tumor microenvironments, developmental biology, and tissue organization.
Looking ahead, the future of single-cell multi-omics lies in expanding accessibility, improving resolution, and integrating spatial and temporal dimensions. As these technologies become more refined and widely adopted, they will not only deepen our understanding of biology but also drive innovation in clinical diagnostics and therapeutic development. For researchers, clinicians, and biotech innovators, single-cell multi-omics is more than a tool, it is a transformative lens through which to explore the complexity of life.
Related resources
References
-
1. Adil A, Kumar V, Jan AT, Asger M. Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis. Front Neurosci. 2021 Apr 22;15:591122. doi: 10.3389/fnins.2021.591122. PMID: 33967674; PMCID: PMC8100238.
2. Shi P, Nie Y, Yang J, Zhang W, Tang Z, Xu J. Fundamental and practical approaches for single-cell ATAC-seq analysis. aBIOTECH. 2022 Sep 27;3(3):212-223. doi: 10.1007/s42994-022-00082-5. Erratum in: aBIOTECH. 2024 Apr 1;5(2):278. doi: 10.1007/s42994-024-00154-8. PMID: 36313930; PMCID: PMC9590475.
3. Song HW, Martin J, Shi X, Tyznik AJ. Key Considerations on CITE-Seq for Single-Cell Multiomics. Proteomics. 2025 Feb 9:e202400011. doi: 10.1002/pmic.202400011. Epub ahead of print. PMID: 39924789.
4. Chen YP, Yin JH, Li WF, Li HJ, Chen DP, Zhang CJ, Lv JW, Wang YQ, Li XM, Li JY, Zhang PP, Li YQ, He QM, Yang XJ, Lei Y, Tang LL, Zhou GQ, Mao YP, Wei C, Xiong KX, Zhang HB, Zhu SD, Hou Y, Sun Y, Dean M, Amit I, Wu K, Kuang DM, Li GB, Liu N, Ma J. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 2020 Nov;30(11):1024-1042. doi: 10.1038/s41422-020-0374-x. Epub 2020 Jul 20. PMID: 32686767; PMCID: PMC7784929.
5. Delacher M, Simon M, Sanderink L, Hotz-Wagenblatt A, Wuttke M, Schambeck K, Schmidleithner L, Bittner S, Pant A, Ritter U, Hehlgans T, Riegel D, Schneider V, Groeber-Becker FK, Eigenberger A, Gebhard C, Strieder N, Fischer A, Rehli M, Hoffmann P, Edinger M, Strowig T, Huehn J, Schmidl C, Werner JM, Prantl L, Brors B, Imbusch CD, Feuerer M. Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells. Immunity. 2021 Apr 13;54(4):702-720.e17. doi: 10.1016/j.immuni.2021.03.007. Epub 2021 Mar 30. PMID: 33789089; PMCID: PMC8050210.
6. Liu C, Li X, Hu Q, Jia Z, Ye Q, Wang X, Zhao K, Liu L, Wang M. Decoding the blueprints of embryo development with single-cell and spatial omics. Semin Cell Dev Biol. 2025 Mar;167:22-39. doi: 10.1016/j.semcdb.2025.01.002. Epub 2025 Jan 31. PMID: 39889540.
7. Huizing GJ, Deutschmann IM, Peyré G, Cantini L. Paired single-cell multi-omics data integration with Mowgli. Nat Commun. 2023 Nov 24;14(1):7711. doi: 10.1038/s41467-023-43019-2. PMID: 38001063; PMCID: PMC10673889.
8. Swanson E, Lord C, Reading J, Heubeck AT, Genge PC, Thomson Z, Weiss MD, Li XJ, Savage AK, Green RR, Torgerson TR, Bumol TF, Graybuck LT, Skene PJ. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife. 2021 Apr 9;10:e63632. doi: 10.7554/eLife.63632. PMID: 33835024; PMCID: PMC8034981.
9. Cheng W, Yin C, Yu S, Chen X, Hong N, Jin W. scMMO-atlas: a single cell multimodal omics atlas and portal for exploring fine cell heterogeneity and cell dynamics. Nucleic Acids Res. 2025 Jan 6;53(D1):D1186-D1194. doi: 10.1093/nar/gkae821. PMID: 39315707; PMCID: PMC11701702.
10. Replogle JM, Saunders RA, Pogson AN, Hussmann JA, Lenail A, Guna A, Mascibroda L, Wagner EJ, Adelman K, Lithwick-Yanai G, Iremadze N, Oberstrass F, Lipson D, Bonnar JL, Jost M, Norman TM, Weissman JS. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell. 2022 Jul 7;185(14):2559-2575.e28. doi: 10.1016/j.cell.2022.05.013. Epub 2022 Jun 9. PMID: 35688146; PMCID: PMC9380471.
11. Pan Y, Tian R, Lee C, Bao G, Gibson G. Fine-mapping within eQTL credible intervals by expression CROP-seq. Biol Methods Protoc. 2020 Mar 28;5(1):bpaa008. doi: 10.1093/biomethods/bpaa008. PMID: 32665975; PMCID: PMC7334875.
12. Xia C, Colognori D, Jiang XS, Xu K, Doudna JA. Single-molecule live-cell RNA imaging with CRISPR-Csm. Nat Biotechnol. 2025 Feb 18. doi: 10.1038/s41587-024-02540-5. Epub ahead of print. PMID: 39966655.
13. Zheng R, Xue Z, You M. Optogenetic Tools for Regulating RNA Metabolism and Functions. Chembiochem. 2024 Dec 16;25(24):e202400615. doi: 10.1002/cbic.202400615. Epub 2024 Nov 4. PMID: 39316432; PMCID: PMC11666399.
14. Pereira WJ, Almeida FM, Conde D, Balmant KM, Triozzi PM, Schmidt HW, Dervinis C, Pappas GJ Jr, Kirst M. Asc-Seurat: analytical single-cell Seurat-based web application. BMC Bioinformatics. 2021 Nov 18;22(1):556. doi: 10.1186/s12859-021-04472-2. PMID: 34794383; PMCID: PMC8600690.
15. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019 Dec;16(12):1289-1296. doi: 10.1038/s41592-019-0619-0. Epub 2019 Nov 18. PMID: 31740819; PMCID: PMC6884693.
16. Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC, Stegle O. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 2020 May 11;21(1):111. doi: 10.1186/s13059-020-02015-1. PMID: 32393329; PMCID: PMC7212577.