Concurrent advances in imaging technologies and deep learning have transformed the nature and scale of data that can now be collected with imaging. Here we discuss the progress that has been made and outline potential research directions at the intersection of deep learning and imaging-based measurements of living systems.
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References
Moffitt, J. R., Lundberg, E. & Heyn, H. Nat. Rev. Genet. 23, 741–759 (2022).
Moses, L. & Pachter, L. Nat. Methods 19, 534–546 (2022).
Schermelleh, L. et al. Nat. Cell Biol. 21, 72–84 (2019).
Moen, E. et al. Nat. Methods 16, 1233–1246 (2019).
Greenwald, N. F. et al. Nat. Biotechnol. 40, 555–565 (2022).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Nat. Methods 18, 100–106 (2021).
Lugagne, J.-B., Lin, H. & Dunlop, M. J. PLoS Comput. Biol. 16, e1007673 (2020).
Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Nat. Biotechnol. 36, 460–468 (2018).
Weigert, M. et al. Nat. Methods 15, 1090–1097 (2018).
Batson, J. & Royer, L. Noise2self: blind denoising by self-supervision. In Int. Conf. Mach. Learn. 97 (eds Chaudhuri, K. & Salakhutdinov, R.) 524–533 (PMLR, 2019).
Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Nat. Methods 15, 917–920, https://doi.org/10.1038/s41592-018-0111-2 (2018).
Christiansen, E. M. et al. Cell 173, 792–803.e19 (2018).
Saka, S. K. et al. Nat. Biotechnol. 37, 1080–1090 (2019).
Shah, S., Lubeck, E., Zhou, W. & Cai, L. Neuron 92, 342–357 (2016).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Science 348, aaa6090 (2015).
Lu, T., Ang, C. E. & Zhuang, X. Cell 185, 4448–4464.e17 (2022).
Feldman, D. et al. Cell 179, 787–799.e17 (2019).
Reicher, A., Koren, A. & Kubicek, S. Genome Res. 30, 1846–1855 (2020).
Vaswani, A. et al. Attention is all you need. In Adv. Neural Inf. Process. Syst. 30 (eds. Guyon, I. et al.) (Curran Associates, 2017).
Kirillov, A. et al. Preprint at https://doi.org/10.48550/arXiv.2304.02643 (2023).
Song, Y. & Ermon, S. Generative modeling by estimating gradients of the data distribution. In Adv. Neural Inf. Process. Syst. 32 (eds Wallach, H. et al.) (Curran Associates, 2019).
Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. In Adv. Neural Inf. Process. Syst. 33 (eds. Larochelle, H. et al.) 6840–6851 (Curran Associates, 2020).
Song, Y. et al. Preprint at https://doi.org/10.48550/arXiv.2011.13456 (2021).
Brown, T. et al. Language models are few-shot learners. In Adv. Neural Inf. Process. Syst. 33 (eds. Larochelle, H. et al.) 1877–1901 (Curran Associates, 2020).
Nitta, N. et al. Cell 175, 266–276.e13 (2018).
Acknowledgements
This Comment is the result of numerous interactions we have had over the years with many bright colleagues, and this space is too short to name them all. We owe tremendous thanks to past and current laboratory members, as many of the ideas described here touch on the idea space that they have explored over the past five years. We thank P. Blainey, I. Cheeseman and M. Leonetti for hosting a recent workshop on ‘Cell Biology at Scale’ that strongly shaped this piece. We also thank several organizations for supporting the work of D.V.V.’s laboratory, including the Shurl and Kay Curci Foundation, the Rita Allen Foundation, the Pew Charitable Trusts, the Alexander and Margaret Stewart Trust, the Gordon and Betty Moore Foundation, the Aligning Science Across Parkinson’s consortium, the Heritage Medical Research Institute, the NIH through the DP2 program and the HuBMAP consortium, and the Howard Hughes Medical Institute through the Freeman Hrabowski Scholar’s program.
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M.S., U.I., X.(J.)W., C.Y., E.L., R.D., Q.L., J.M., J.S., K.Y., E.P., A.A., D.G., R.B., E.P. and D.V.V. conceived the research directions described in the manuscript. D.V.V. wrote the manuscript, with contributions from all authors. All authors read and approved the manuscript.
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D.V.V. is a co-founder and chief scientist of Barrier Biosciences and holds equity in the company. All other authors declare no competing interests.
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Schwartz, M., Israel, U., Wang, X.(. et al. Scaling biological discovery at the interface of deep learning and cellular imaging. Nat Methods 20, 956–957 (2023). https://doi.org/10.1038/s41592-023-01931-x
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DOI: https://doi.org/10.1038/s41592-023-01931-x
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