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Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

Chawin Ounkomol, Sharmishtaa Seshamani, Mary M. Maleckar, Forrest Collman, Gregory R. Johnson
doi: https://doi.org/10.1101/289504
Chawin Ounkomol
1Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Sharmishtaa Seshamani
2Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
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Mary M. Maleckar
1Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Forrest Collman
2Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA 98109
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Gregory R. Johnson
1Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Abstract

Understanding cells as integrated systems is a challenge central to modern biology. While different microscopy approaches may be used to probe diverse aspects of biological organization, each method presents limitations which ultimately restrict a view into unified cellular organization. For example, while fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and can damage cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images. We then demonstrate that this same method can be used to predict immunofluorescence from electron micrograph inputs, extending the method to a wider range of bioimaging applications.

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Posted May 23, 2018.
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Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy
Chawin Ounkomol, Sharmishtaa Seshamani, Mary M. Maleckar, Forrest Collman, Gregory R. Johnson
bioRxiv 289504; doi: https://doi.org/10.1101/289504
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Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy
Chawin Ounkomol, Sharmishtaa Seshamani, Mary M. Maleckar, Forrest Collman, Gregory R. Johnson
bioRxiv 289504; doi: https://doi.org/10.1101/289504

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