Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. (November 2018)

• Record Type: Journal Article
• Authors: Ounkomol, Chawin; Seshamani, Sharmishtaa; Maleckar, Mary; Collman, Forrest; Johnson, Gregory
• Is Part Of: Nature methods. Volume 15:Number 11(2018)
• Page Start: 917
• Page End: 920
• Publication Date: 2018-11
• Subjects: Life sciences -- Methodology -- Periodicals; Life sciences -- Research -- Periodicals; Biology -- Methodology -- Periodicals; Biology -- Research -- Periodicals; 570.72
• DOI: 10.1038/s41592-018-0111-2
• Languages: English
• ISSNs: 1548-7091

Abstract

Abstract Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron micrographs.