ArticlePLoS computational biology2015
Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Generative machine learning unlocks the first proteome-wide image of human cells.bioRxiv : the preprint server for biology · 2026Article
- CytoSpatio: Learning cell type spatial relationships using multirange, multitype point process models.PLoS computational biology · 2025Article
- Flavinated SDHA underlies the change in intrinsic optical properties of oral cancers.Communications biology · 2023Article
- Flavinated SDHA Underlies the Change in Intrinsic Optical Properties of Oral Cancers.bioRxiv : the preprint server for biology · 2023Article
- Basal body organization and cell geometry during the cell cycle inMolecular biology of the cell · 2023Article
- Improving and evaluating deep learning models of cellular organization.Bioinformatics (Oxford, England) · 2022Article
- Self-Supervised Voxel-Level Representation Rediscovers Subcellular Structures in Volume Electron Microscopy.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2022Article
- Article
- Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.Frontiers in physics · 2020Article
- Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.PLoS computational biology · 2019Article
- Evaluation of methods for generative modeling of cell and nuclear shape.Bioinformatics (Oxford, England) · 2019Article
- From observing to predicting single-cell structure and function with high-throughput/high-content microscopy.Essays in biochemistry · 2019Review
- CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures.Bioinformatics (Oxford, England) · 2019Article
- A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images.PloS one · 2019Article
- Learning the sequence of influenza A genome assembly during viral replication using point process models and fluorescence in situ hybridization.PLoS computational biology · 2019Article
- Article
- Whole-Cell Scale Dynamic Organization of Lysosomes Revealed by Spatial Statistical Analysis.Cell reports · 2018Article
- Point process models for localization and interdependence of punctate cellular structures.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- Building cell models and simulations from microscope images.Methods (San Diego, Calif.) · 2016Review
- Visualizing quantitative microscopy data: History and challenges.Critical reviews in biochemistry and molecular biology · 2016Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply "vesicular". We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.
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