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Deciphering subcellular organization with multiplexed imaging and deep learning

Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.

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Fig. 1: Unsupervised learning of CSLs using CAMPA.

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This is a summary of: Spitzer, H., Berry, S., Donoghoe, M., Pelkmans, L. & Theis, F. J. Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps. Nat. Methods https://doi.org/10.1038/s41592-023-01894-z (2023).

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Deciphering subcellular organization with multiplexed imaging and deep learning. Nat Methods 20, 995–996 (2023). https://doi.org/10.1038/s41592-023-01895-y

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