Citation

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. 2018. In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images. Cell. 173(3):792-803.e19. Pubmed: 29656897 DOI:10.1016/j.cell.2018.03.040

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.
Copyright © 2018 Elsevier Inc. All rights reserved.

Related Faculty

Photo of Lee Rubin

Lee Rubin investigates the key molecular mediators of different neurodegenerative diseases, with the ultimate goal of finding effective preclinical therapeutic candidates.

Search Menu