ArticlePLoS computational biology2022
A deep generative model of 3D single-cell organization.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 43 citations in OpenAlex.
- HiExM Enables Scalable Mapping of Organelle Morphology and Spatial Heterogeneity.bioRxiv : the preprint server for biology · 2026Article
- Generative machine learning unlocks the first proteome-wide image of human cells.bioRxiv : the preprint server for biology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- A scoping study of the whole-cell imaging literature as a foundation for the emerging field of cell anatomy.BMC biology · 2026Article
- Integration and harmonization of cell shape images for generative modeling.bioRxiv : the preprint server for biology · 2026Article
- Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast.Molecular biology of the cell · 2025Article
- Interpretable representation learning for 3D multi-piece intracellular structures using point clouds.Nature methods · 2025Article
- Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.Bioinformatics (Oxford, England) · 2025Article
- A scoping study of the whole-cell imaging literature: a foundational corpus, potential for mesoscale data synthesis, and implications for standardization of an emerging field.bioRxiv : the preprint server for biology · 2025Article
- Automated human induced pluripotent stem cell culture and sample preparation for 3D live-cell microscopy.Nature protocols · 2024Review
- Using Dimensionality Reduction to Visualize Phenotypic Changes in High-Throughput Microscopy.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Virtual staining for pixel-wise and quantitative analysis of single cell images.Scientific reports · 2023Article
- Facilitating cell segmentation with the projection-enhancement network.Physical biology · 2023Article
- Generative models of morphogenesis in developmental biology.Seminars in cell & developmental biology · 2023Review
- Building the next generation of virtual cells to understand cellular biology.Biophysical journal · 2023Review
- Basal body organization and cell geometry during the cell cycle inMolecular biology of the cell · 2023Article
- Article
- Improving and evaluating deep learning models of cellular organization.Bioinformatics (Oxford, England) · 2022Article
- Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.