ArticleMolecular biology of the cell2017
Digging deep into Golgi phenotypic diversity with unsupervised machine learning.
Article in Molecular biology of the cell, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SIGMA: SIgnaling via Golgi Morphology Analysis for pathway-state classification.bioRxiv : the preprint server for biology · 2026Article
- A novel automated image analysis pipeline for quantifying morphological changes to the endoplasmic reticulum in cultured human cells.BMC bioinformatics · 2021Article
- Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations.Cell systems · 2021Review
- Reimagining dots and dashes: Visualizing structure and function of organelles for high-content imaging analysis.Cell chemical biology · 2021Review
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter or supervised analysis with predefined Golgi structural classes. Here, we report the use of multiparametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the three visually definable phenotypes, our framework reproducibly identified 10 Golgi phenotypes. They were used to quantify and stratify phenotypic similarities among genetic perturbations. The derived phenotypic network partially overlaps previously reported protein-protein interactions as well as suggesting novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organizational states and provides a proof of concept for the classification of drugs and genes using fine-grained phenotypic information.
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Registered trials
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