Abstract
We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.
Highlights
Decades of biological experimentation, coupled with ever-improving advances in microscopy, have led to the identification and description of many subcellular structures that are key to cellular function
If we are interested in integrating those images into a holistic picture of cellular organization directly from data, there are a number of approaches one might take
Given these two reference channels, we learn a model of cell and nuclear morphology, and use this as a reference frame in which to learn a representation of the localization of each subcellular structure as measured by the third
Summary
Decades of biological experimentation, coupled with ever-improving advances in microscopy, have led to the identification and description of many subcellular structures that are key to cellular function. Understanding the unified role of these component structures in the context of the living cell is a goal of modern-day cell biology. A comprehensive understanding of global cellular organization remains challenging, and no unified model currently exists. Advances in microscopy and live cell fluorescence imaging in particular have led to insight and rich data sets with which to explore subcellular organization. Computational approaches offer a powerful opportunity to mitigate these limitations by integrating data from diverse imaging experiments into a single model, a step toward an integrated representation of the living cell and additional insight into its function
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