A Foundation for Reliable Spatial Proteomics Data Analysis

Abstract

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis. Molecular &

A Portrait of the Human Organelle Proteome In Space and Time during Cytomegalovirus Infection

“…All fractions in the gradient were quantified for each time point, and infection-induced variations in the fractionation process could be accounted for (discussed below). However, the stochastic nature of label-free quantification limits the spatial resolution, and, although subcellular protein localization could be obtained, we noticed that missing values impacted the reliable protein assignment to organelles (as also discussed in (Gatto et al, 2014a)).…”

“…For instance, 15% of the proteins in uninfected samples had more than one predicted localization. Although the extent of multi-localizing proteins is not clear, 60% of the human proteins in Uniprot have multiple localization annotations (Gatto et al, 2014a). Therefore, our assignments likely capture the predominant state of the protein in the cell, and many of these proteins could be simultaneously found in multiple organelles.…”

Cardiovascular proteomics in the era of big data: experimental and computational advances

“…The average intracellular position of many proteins can therefore be discovered by their grouping with known organellar markers. These approaches can be readily adopted to understand dynamic protein translocalization from one organelle to another, using new analytical frameworks that can quantify protein translocation in differential centrifugation experiments [60, 72]. Through proteomics studies, it is also demonstrated that many proteins important in the heart can have multiple localizational isoforms that carry out different functions [73].…”

Spatial and temporal dynamics of the cardiac mitochondrial proteome

“…Given that contamination is both an experimental challenge as well as a biological reality, techniques include the Localization of Organelle Proteins by Isotope Tagging (LOPIT) (19, 20), Protein Correlation Profiling (PCP) (18, 21), and similar methods have been instrumental in providing a comprehensive view of protein localization, dual localization, and translocalization (Figure 2). Much like targeted organelle purification, multi-compartment enrichment techniques rely on differential sedimentation in a density gradient centrifugation to spatially separate cellular parts into multiple fractions, which correlates with known organellar compartments and can be individually collected for protein identification and quantification via high-resolution mass spectrometry (19, 20, 22).…”

“…Much like targeted organelle purification, multi-compartment enrichment techniques rely on differential sedimentation in a density gradient centrifugation to spatially separate cellular parts into multiple fractions, which correlates with known organellar compartments and can be individually collected for protein identification and quantification via high-resolution mass spectrometry (19, 20, 22). A major distinction of multi-compartment enrichment techniques such as LOPIT and PCP is the assumption that proteins from subcellular compartments will co-fractionate and subsequently exhibit similar gradient distributions that can be identified as clusters, even if no single layer on the density gradient would contain only a single purified organelle.…”