4 years ago

Digging deep into Golgi Phenotypic diversity with Unsupervised Machine Learning.

Yiping, Bard, Chia, Dong, Yi, Khoon, Le Guezennec, Hussain
The synthesis of glycans and 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 RNAi screening offers opportunities to explore this organelle organisation and the gene network underlying it. To date, image-based Golgi screens were based on a single parameter or supervised analysis with pre-defined Golgi structural classes. Here, we report the use of multi-parametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the 3 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 overlaps partially with previously reported protein-protein interactions as well as suggests novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organisational states and provides a proof of concept for the classification of drugs and genes using fine grained phenotypic information.

Publisher URL: http://doi.org/10.1091/mbc.E17-06-0379

DOI: 10.1091/mbc.E17-06-0379

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