TRANSPIRE: A Computational Pipeline to Elucidate Intracellular Protein Movements from Spatial Proteomics Data Sets

Abstract

Protein localization is paramount to protein function, and the intracellular movement of proteins underlies the regulation of numerous cellular processes. Given advances in spatial proteomics, the investigation of protein localization at a global scale has become attainable. Also becoming apparent is the need for dedicated analytical frameworks that allow the discovery of global intracellular protein movement events. Here, we describe TRANSPIRE, a computational pipeline that facilitates TRanslocation ANalysis of SPatIal pRotEomics data sets. TRANSPIRE leverages synthetic translocation profiles generated from organelle marker proteins to train a probabilistic Gaussian process classifier that predicts changes in protein distribution. This output is then integrated with information regarding co-translocating proteins and complexes and enriched gene ontology associations to discern the putative regulation and function of movement. We validate TRANSPIRE performance for predicting nuclear-cytoplasmic shuttling events. Analyzing an existing data set of nuclear and cytoplasmic proteomes during Kaposi Sarcoma-associated herpesvirus (KSHV)-induced cellular mRNA decay, we confirm that TRANSPIRE readily discerns expected translocations of RNA binding proteins. We next investigate protein translocations during infection with human cytomegalovirus (HCMV), a β-herpesvirus known to induce global organelle remodeling. We find that HCMV infection induces broad changes in protein localization, with over 800 proteins predicted to translocate during virus replication. Evident are protein movements related to HCMV modulation of host defense, metabolism, cellular trafficking, and Wnt signaling. For example, the low-density lipoprotein receptor (LDLR) translocates to the lysosome early in infection in conjunction with its degradation, which we validate by targeted mass spectrometry. Using microscopy, we also validate the translocation of the multifunctional kinase DAPK3, a movement that may contribute to HCMV activation of Wnt signaling.

Subcellular proteomics

“…In correlation profiling methods such as protein correlation profiling 30,40 , localization of organelle proteins using isotope tagging (LOPIT) 10,[41][42][43] , dynamic organellar maps 44 , Prolocate 45 , COLA 46 and SubCellBarCode 47 , fractions are collected across a separation gradient using either density or differential centrifugation and analysed using MS and multivariate statistics, and machine learning methods are used to compare the abundance distribution of every protein with known organelle markers in order to determine the probable locations of the proteins 10,42,44,[48][49][50][51][52][53] and make inferences regarding protein trafficking 44,47,54,55 . These techniques can identify organelle protein distribution trends even in the presence of structural alterations, which may not be captured by traditional fractionation methods that focus on enriching a specific organelle 53,56 .…”

“…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 .…”

Subcellular Transcriptomics and Proteomics: A Comparative Methods Review

“…Current methods are typically protein-centric or are based on separating RNA along with their protein interactors -e.g. within stress granules or ribosomes [268][269][270] . New approaches, such as ATLAS-seq, have been able to co-sediment different RNA using density J o u r n a l P r e -p r o o f 11 gradients coupled to RNA-seq and then use hierarchical clustering to infer subcellular localization of transcripts 231 .…”

Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE

“…The task can no longer be phrased as a supervised learning problem, but the question under consideration is clear: which proteins have different sub-cellular niches after cellular perturbation? Procedures to answer this question have been presented by authors (Beltran et al ., 2016; Itzhak et al ., 2016, 2017; Kennedy et al, 2020) and reviewed in Crook et al . (2020b).…”

“…Finally, the approach does not quantify uncertainty which is of clear importance when absolute purification of sub-cellular niches is impossible and multi-localising proteins are present. Recently, Kennedy et al . (2020) introduced a computational pipeline for analysing dynamic spatial proteomics experiments by refraining it as a classification task.…”