Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.

ABSTRACT: Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon pe

SUBMITTER: Spitzer H 

PROVIDER: S-EPMC10333128 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature