In silico labeling: Predicting fluorescent labels in unlabeled images

By Eric M. Christiansen, Samuel J. Yang, D. Ando, A. Javaherian, G. Skibinski, S. Lipnick, Elliot Mount, Alison O’Neil, K. Shah, Alicia K. Lee and 8 more. Cell, 2018

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Summary 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, limitation in number of simultaneous labels due to spectral overlap, and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here we show 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, not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a8e4afbba1f79d2291b3a02f61572b49ecbba982