ArticleNature methods2023
Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational Autoencoders.bioRxiv : the preprint server for biology · 2025Article
- Bayesian estimation of muscle mechanisms and therapeutic targets using variational autoencoders.Biophysical journal · 2025Article
- Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening.Cell systems · 2024Article
- Bayesian Estimation of Muscle Mechanisms and Therapeutic Targets Using Variational Autoencoders.bioRxiv : the preprint server for biology · 2024Article
- Graph Fourier transform for spatial omics representation and analyses of complex organs.Nature communications · 2024Article
- Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology.Journal of proteome research · 2024Review
- Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling.Nature communications · 2024Article
- Pooled multicolour tagging for visualizing subcellular protein dynamics.Nature cell biology · 2024Article
- Classification and functional characterization of regulators of intracellular STING trafficking identified by genome-wide optical pooled screening.bioRxiv : the preprint server for biology · 2024Article
- MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.Communications biology · 2024Article
- Bento: a toolkit for subcellular analysis of spatial transcriptomics data.Genome biology · 2024Article
- Multiplex protein imaging in tumour biology.Nature reviews. Cancer · 2024Review
- PXPermute reveals staining importance in multichannel imaging flow cytometry.Cell reports methods · 2024Article
- Graph Fourier transform for spatial omics representation and analyses of complex organs.Research square · 2024Article
- Differential labelling of human sub-cellular compartments with fluorescent dye esters and expansion microscopy.Nanoscale · 2023Article
- CellSighter: a neural network to classify cells in highly multiplexed images.Nature communications · 2023Article
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Authors and funding
5 authors.
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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 perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.
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