Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

Abstract

The steady-state localisation of proteins provides vital insight into their function. These localisations are context specific with proteins translocating between different sub-cellular niches upon perturbation of the subcellular environment. Differential localisation provides a step towards mechanistic insight of subcellular protein dynamics. Aberrant localisation has been implicated in a number of pathologies, thus differential localisation may help characterise disease states and facilitate rational drug discovery by suggesting novel targets. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we propose a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation, as well quantifying the uncertainty in these estimates. Furthermore, BANDLE allows information to be shared across spatial proteomics datasets to improve statistical power. Extensive simulation studies demonstrate that BANDLE reduces the number of both type I and type II errors compared to existing approaches. Application of BANDLE to datasets studying EGF stimulation and AP-4 dependent localisation recovers well studied translocations, using only two-thirds of the provided data. Moreover, we implicate TMEM199 with AP-4 dependent localisation. In an application to cytomegalovirus infection, we obtain novel insights into the rewiring of the host proteome. Integration of high-throughput transcriptomic and proteomic data, along with degradation assays, acetylation experiments and a cytomegalovirus interactome allows us to provide the functional context of these data.

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

“…Furthermore, FFE can be paired with various tools such as specific antibodies, lectins, chemical ligands, and proteases or other enzymes to optimize organelle separation, by introducing subtle changes in the surface charges of certain compartments with minimal disruption to their functional integrity 221,[271][272][273][274] . FFE has been used in combination with centrifugal separation and MS analysis to resolve subpopulations of organellar networks that are otherwise difficult to capture, including the plasma membrane, components of the ER network, endosomes, lysosomes, phagosomes, peroxisomes, mitochondria, and plant tonoplasts (vacuole membranes) 221,[271][272][273][274][275][276][277][278] . De Michele and colleagues were able to assign peripheral membrane proteins which are normally lost in traditional PM enrichment to the Arabadopsis PM, including the entire exocyst complex 279 .…”

Subcellular proteomics

“…Dynamic classification of proteins and classification of MLPs using these methods is challenging, but has been performed in previous studies 44,47,56,89,[153][154][155] . Bespoke pipelines that use training data have recently facilitated dynamic classifications and MLP classifications, including T-augmented Gaussian mixture model approaches able to quantify the posterior probabilities of proteins residing in multiple organelles and TRANSPIRE (Translocation Analysis of Spatial pRotEomics), which models synthetic translocations from experimental organelle marker profiles to train a classifier to identify true protein dynamic events 52,54,149,156,157 .…”

Comparative Analysis of T Cell Spatial Proteomics and the Influence of HIV Expression

“…Following our analysis of classifiers, we examined various pipelines for identifying protein translocations. We inputted our SVM and TAGM-MAP classified data into BANDLE 10 and a basic labelbased analysis 7 , and inputted unclassified data into TRANSPIRE 9 (Fig. 2A).…”

“…TRANSPIRE is a refined methodology from the Cristea lab that relies on generating synthetic translocations from proteins of known localization and uses Bayesian analysis to determine the likelihood of proteins of unknown localization behaving in a manner consistent with anticipated translocations following a cellular perturbation 9 . Lastly, BANDLE is another method developed by the Lilley group that takes replicated data, both with and without a perturbation, and uses Bayesian analysis to yield a ranked list of possible translocations with their associated likelihood of occurrence 10 . We compared the hits from these various methods by cross-referencing hits with a previous study of the HIV interactome 26 as well as the more broad NIH HIV-1 Human Interaction Database 27 .…”