Proteomics profiling of interactome dynamics by colocalisation analysis (COLA)

Localisation and protein function are intimately linked in eukaryotes, as proteins are localised to specific compartments where they come into proximity of other functionally relevant proteins. Significant co-localisation of two proteins can therefore be indicative of their functional association. We here present COLA, a proteomics based strategy coupled with a bioinformatics framework to detect protein–protein co-localisations on a global scale. COLA reveals functional interactions by matching proteins with significant similarity in their subcellular localisation signatures. The rapid nature of COLA allows mapping of interactome dynamics across different conditions or treatments with high precision.Cancer Research UK; BBSRC

KCML: a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

Abstract Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning (KCML), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. KCML is highly generalisable and applicable to various large‐scale genetic perturbation screens

HCC1954

Files are 16-bit tiffs (which means they will appear black if opened in Preview, Photoshop, or similar program). Each file is a Multi-plane tiff, containing three fluorescence channels: Channel 1 = DAPI (Sigma) Channel 2 = NF-kappaB (anti-p65; Abcam ab16502 / Alexa-488 anti-rabbit; Invitrogen) Channel 3 = DHE (dihydroethidium, hydroethidine; Sigma) File names refer to [row][column]-[field

ZR75.1

Files are 16-bit tiffs (which means they will appear black if opened in Preview, Photoshop, or similar program). Each file is a Multi-plane tiff, containing three fluorescence channels: Channel 1 = DAPI (Sigma) Channel 2 = NF-kappaB (anti-p65; Abcam ab16502 / Alexa-488 anti-rabbit; Invitrogen) Channel 3 = DHE (dihydroethidium, hydroethidine; Sigma) File names refer to [row][column]-[field] See ReadMe file for culture and staining information

hs578T

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MCF10A-no EGF

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HeLa

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AU565

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BT474

Files are 16-bit tiffs (which means they will appear black if opened in Preview, Photoshop, or similar program). Each file is a Multi-plane tiff, containing three fluorescence channels: Channel 1 = DAPI (Sigma) Channel 2 = NF-kappaB (anti-p65; Abcam ab16502 / Alexa-488 anti-rabbit; Invitrogen) Channel 3 = DHE (dihydroethidium, hydroethidine; Sigma) File names refer to [row][column]-[field