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 subcellular niches upon perturbation of the subcellular environment. Differential localisation, that is a change in the steady-state subcellular location of a protein, provides a step towards mechanistic insight of subcellular protein dynamics. High-accuracy high-throughput mass spectrometry-based methods now exist to map the steady-state localisation and re-localisation of proteins. Here, we describe a principled Bayesian approach, BANDLE, that uses these data to compute the probability that a protein differentially localises upon cellular perturbation. 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 several datasets recovers well-studied translocations. In an application to cytomegalovirus infection, we obtain insights into the rewiring of the host proteome. Integration of other high-throughput datasets allows us to provide the functional context of these data.

Surface-Charge-Driven Ferroptosis and Mitochondrial Dysfunction Is Involved in Toxicity Diversity in the Marine Bivalve Exposed to Nanoplastics

“…Notably, biological replications within our study exhibited clustering among themselves with clear distinctions between the different treatment groups (Figure S6b). This underscores that the identified DEPs accurately reflect the impact of NP stress on the clams. , Similar to the patterns observed at the mRNA level in the p-NP and n-NP groups, substantial differences were also evident at the protein level. The subcellular localization of the identified DEPs revealed marked variations between p-NP and n-NP groups, with the nuclear compartment (33% and 40.8%, respectively) and cytoplasmic region (27.1% and 28.9%, respectively) being the most affected locations in both groups (Figure b).…”

“…Importantly, a minor percentage of proteins, specially 9.4% in the p-NP group and 13.9% in the n-NP group, demonstrated significant correlations with their corresponding RNA counterparts ( p < 0.05) (Figure b). This implies that proteomic patterns are only partially predicted by the transcriptomic patterns, which is not unexpected given the influence of post-transcriptional and post-translational regulatory mechanisms on protein levels. , Nonetheless, the integration of omics analyses is pivotal for a greater comprehension of the toxicity mechanisms at play in the organisms studied.…”

“…This implies that proteomic patterns are only partially predicted by the transcriptomic patterns, which is not unexpected given the influence of post-transcriptional and post-translational regulatory mechanisms on protein levels. 44,50 Nonetheless, the integration of omics analyses is pivotal for a greater comprehension of the toxicity mechanisms at play in the organisms studied.…”

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

“…BANDLE (Bayesian analysis of differential localization experiments) is a semisupervised functional mixture model used to obtain the probability of a protein being differentially localized upon cellular perturbation. 82 Unsupervised clustering algorithms like k-means clustering or DBSCAN are helpful when training nonmodel organisms with limited marker proteins. These methods are best suited for static and single-locale localization of proteins.…”

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions

“…Subcellular localization classification and translocation predictions were performed using the pRoloc (Gatto et al, 2014) and the BANDLE (Crook et al, 2022) packages in R/Bioconductor. Briefly, the subcellular localization markers were selected from the intersecting proteins with a prior data set generated from human U-2 OS osteosarcoma cells (Geladaki et al, 2019).…”