Journal
CELL
Volume 173, Issue 3, Pages 792-+Publisher
CELL PRESS
DOI: 10.1016/j.cell.2018.03.040
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Funding
- NCRR NIH HHS [C06 RR018928] Funding Source: Medline
- NIA NIH HHS [RF1 AG058476, RF1 AG056151] Funding Source: Medline
- NIGMS NIH HHS [T32 GM008568] Funding Source: Medline
- NINDS NIH HHS [U54 NS091046, R37 NS101996, R01 NS083390] Funding Source: Medline
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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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