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Christiansen EM, Yang SJ, Ando DM, Javaherian A, Skibinski G, Lipnick S, Mount E, O'Neil A, Shah K, Lee AK, Goyal P, Fedus W, Poplin R, Esteva A, Berndl M, Rubin LL, Nelson P, Finkbeiner S. In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell 2018; 173:792-803.e19. [PMID: 29656897 PMCID: PMC6309178 DOI: 10.1016/j.cell.2018.03.040] [Citation(s) in RCA: 325] [Impact Index Per Article: 54.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 12/13/2017] [Accepted: 03/15/2018] [Indexed: 01/08/2023]
Abstract
Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.
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Affiliation(s)
| | | | | | - Ashkan Javaherian
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Gaia Skibinski
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Scott Lipnick
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Center for Assessment Technology and Continuous Health, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elliot Mount
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Alison O'Neil
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Kevan Shah
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Alicia K Lee
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Piyush Goyal
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA
| | - William Fedus
- Google, Inc., Mountain View, CA 94043, USA; Montreal Institute of Learning Algorithms, University of Montreal, Montreal, QC, Canada
| | | | - Andre Esteva
- Google, Inc., Mountain View, CA 94043, USA; Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Lee L Rubin
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | | | - Steven Finkbeiner
- Taube/Koret Center for Neurodegenerative Disease Research and DaedalusBio, Gladstone Institutes, San Francisco, CA 94158, USA; Departments of Neurology and Physiology, University of California, San Francisco, 94158, USA.
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