The field of single-cell RNA sequencing (scRNA-seq) has been paired with genomics, epigenomics, spatial omics, proteomics and imaging to achieve multimodal measurements of individual cellular phenotypes and genotypes. In its purest form, single-cell multimodal omics involves the simultaneous detection of multiple traits in the same cell. More broadly, multimodal omics also encompasses comparative pairing and computational integration of measurements made across multiple distinct cells to reconstruct phenotypes. Here I highlight some of the biological insights gained from multimodal studies and discuss the challenges and opportunities in this emerging field.
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References
Plasschaert, L. W. et al. Nature 560, 377–381 (2018).
Montoro, D. T. et al. Nature 560, 319–324 (2018).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Nat. Biotechnol. 33, 495–502 (2015).
McKenna, A. & Gagnon, J. A. Development 146, dev169730 (2019).
Baron, C. S. & van Oudenaarden, A. Nat. Rev. Mol. Cell Biol. 20, 753–765 (2019).
Suvà, M. L. & Tirosh, I. Mol. Cell 75, 7–12 (2019).
Nathan, A., Baglaenko, Y., Fonseka, C. Y., Beynor, J. I. & Raychaudhuri, S. Curr. Opin. Immunol. 61, 17–25 (2019).
Zeng, H. & Sanes, J. R. Nat. Rev. Neurosci. 18, 530–546 (2017).
Huang, Z. J. & Paul, A. Nat. Rev. Neurosci. 20, 563–572 (2019).
Gottgens, B. & Kucinski, I. in Stem Cells: From Biological Principles to Regenerative Medicine (eds. Lo Celso, C., Red-Horse, K. & Watt, F. M.) (Cold Spring Harbor Laboratory Press, 2019); https://doi.org/10.17863/CAM.44977.
Trapnell, C. et al. Nat. Biotechnol. 32, 381–386 (2014).
Bendall, S. C. et al. Cell 157, 714–725 (2014).
Farrell, J. A. et al. Science 360, eaar3131 (2018).
Briggs, J. A. et al. Science 360, eaar5780 (2018).
Wagner, D. E. et al. Science 360, 981–987 (2018).
Cao, J. et al. Nature 566, 496–502 (2019).
Pijuan-Sala, B. et al. Nature 566, 490–495 (2019).
Soldatov, R. et al. Science 364, eaas9536 (2019).
Packer, J. S. et al. Science 365, eaax1971 (2019).
Mizeracka, K., Rogers, J. M., Shaham, S., Bulyk, M. L. & Heiman, M. G. Preprint at https://doi.org/10.1101/758508 (2019).
Stubbington, M. J. T. et al. Nat. Methods 13, 329–332 (2016).
Lee-Six, H. et al. Nature 561, 473–478 (2018).
Ludwig, L. S. et al. Cell 176, 1325–1339.e22 (2019).
Sun, J. et al. Nature 514, 322–327 (2014).
Raj, B. et al. Nat. Biotechnol. 36, 442–450 (2018).
Lacin, H. et al. eLife 8, 1113 (2019).
Doe, C. Q. Annu. Rev. Cell Dev. Biol. 33, 219–240 (2017).
He, J. et al. Neuron 75, 786–798 (2012).
Sánchez-Guardado, L. & Lois, C. eLife 8, 766 (2019).
Biddy, B. A. et al. Nature 564, 219–224 (2018).
Achim, K. et al. Nat. Biotechnol. 33, 503–509 (2015).
Lein, E., Borm, L. E. & Linnarsson, S. Science 358, 64–69 (2017).
Eng, C. L. et al. Nature 568, 235–239 (2019).
Frieda, K. L. et al. Nature 541, 107–111 (2017).
Muñoz-Manchado, A. B. et al. Cell Reports 24, 2179–2190.e7 (2018).
Gouwens, N. W. et al. Nat. Neurosci. 22, 1182–1195 (2019).
Li, H. et al. Cell 171, 1206–1220.e22 (2017).
Moffitt, J. R. et al. Science 362, eaau5324 (2018).
Kim, D.-W. et al. Cell 179, 713–728.e17 (2019).
van Galen, P. et al. Cell 176, 1265–1281.e24 (2019).
Cao, J. et al. Science 361, 1380–1385 (2018).
Argelaguet, R. et al. Nature https://doi.org/10.1038/s41586-019-1825-8 (2019).
Hernando-Herraez, I. et al. Nat. Commun. 10, 4361 (2019).
Saka, S. K. et al. Nat. Biotechnol. 37, 1080–1090 (2019).
Goltsev, Y. et al. Cell 174, 968–981.e15 (2018).
Gut, G., Herrmann, M. D. & Pelkmans, L. Science 361, eaar7042 (2018).
Marioni, J. C. & Arendt, D. Annu. Rev. Cell Dev. Biol. 33, 537–553 (2017).
Shafer, M. E. R. Front. Cell Dev. Biol. 7, 175 (2019).
Camp, J. G., Platt, R. & Treutlein, B. Science 365, 1401–1405 (2019).
Wee, C. L. et al. Nat. Neurosci. 22, 1477–1492 (2019).
Lovett-Barron, M. et al. Preprint at bioRxiv https://doi.org/10.1101/745075 (2019).
Shah, S. et al. Cell 174, 363–376.e16 (2018).
Moor, A. E. et al. Science 357, 1299–1303 (2017).
Wan, Y. et al. Cell 179, 355–372.e23 (2019).
Acknowledgements
I am grateful to J. Farrell, S. Mango and B. Raj for comments on the manuscript and to the US National Institutes of Health, the McKnight Endowment Fund for Neuroscience, the Allen Discovery Center for Cell Lineage Tracing, Harvard University and the University of Basel for support.
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Schier, A.F. Single-cell biology: beyond the sum of its parts. Nat Methods 17, 17–20 (2020). https://doi.org/10.1038/s41592-019-0693-3
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DOI: https://doi.org/10.1038/s41592-019-0693-3
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