In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Cell. 2018 Apr 19;173(3):792-803.e19. doi: 10.1016/j.cell.2018.03.040. Epub 2018 Apr 12.


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.

Keywords: cancer; computer vision; deep learning; machine learning; microscopy; neuroscience; stem cells.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Cell Line, Tumor
  • Cell Survival
  • Cerebral Cortex / cytology
  • Fluorescent Dyes / chemistry*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Induced Pluripotent Stem Cells / cytology
  • Machine Learning
  • Microscopy, Fluorescence / methods*
  • Motor Neurons / cytology*
  • Neural Networks, Computer
  • Neurosciences
  • Rats
  • Software
  • Stem Cells / cytology


  • Fluorescent Dyes