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Building a 3D Integrated Cell

Gregory R. Johnson, Rory M. Donovan-Maiye, Mary M. Maleckar
doi: https://doi.org/10.1101/238378
Gregory R. Johnson
Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Rory M. Donovan-Maiye
Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Mary M. Maleckar
Allen Institute for Cell Science, 615 Westlake Ave N, Seattle, WA 98109
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Abstract

We present a conditional generative model for learning variation in cell and nuclear morphology and predicting the location of subcellular structures from 3D microscopy images. The model generalizes well to a wide array of structures and allows for a probabilistic interpretation of cell and nuclear morphology and structure localization from fluorescence images. We demonstrate the effectiveness of the approach by producing and evaluating photo-realistic 3D cell images using the generative model, and show that the conditional nature of the model provides the ability to predict the localization of unobserved structures, given cell and nuclear morphology. We additionally explore the model’s utility in a number of applications, including cellular integration from multiple experiments and exploration of variation in structure localization. Finally, we discuss the model in the context of foundational and contemporary work and suggest forthcoming extensions.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 21, 2017.
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Building a 3D Integrated Cell
Gregory R. Johnson, Rory M. Donovan-Maiye, Mary M. Maleckar
bioRxiv 238378; doi: https://doi.org/10.1101/238378
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Building a 3D Integrated Cell
Gregory R. Johnson, Rory M. Donovan-Maiye, Mary M. Maleckar
bioRxiv 238378; doi: https://doi.org/10.1101/238378

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