Cross-link assisted spatial proteomics to map sub-organelle proteomes and membrane protein topologies.

The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings. Using human mitochondria as a model system, we show that CLASP can elucidate spatial proteomes of all mitochondrial sub-compartments and provide topological insight into the mitochondrial membrane proteome. Biochemical and imaging-based follow-up studies confirm that CLASP allows discovering mitochondria-associated proteins and revising previous protein sub-compartment localization and membrane topology data. We also validate the CLASP concept in synaptic vesicles, demonstrating its applicability to different sub-cellular compartments. This study extends the scope of cross-linking mass spectrometry beyond protein structure and interaction analysis towards spatial proteomics, and establishes a method for concomitant profiling of sub-organelle and membrane proteomes.

RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.

We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.

Shigella IpaA mediates actin bundling through diffusible vinculin oligomers with activation imprint.

Upon activation, vinculin reinforces cytoskeletal anchorage during cell adhesion. Activating ligands classically disrupt intramolecular interactions between the vinculin head and tail domains that bind to actin filaments. Here, we show that Shigella IpaA triggers major allosteric changes in the head domain, leading to vinculin homo-oligomerization. Through the cooperative binding of its three vinculin-binding sites (VBSs), IpaA induces a striking reorientation of the D1 and D2 head subdomains associated with vinculin oligomerization. IpaA thus acts as a catalyst producing vinculin clusters that bundle actin at a distance from the activation site and trigger the formation of highly stable adhesions resisting the action of actin relaxing drugs. Unlike canonical activation, vinculin homo-oligomers induced by IpaA appear to keep a persistent imprint of the activated state in addition to their bundling activity, accounting for stable cell adhesion independent of force transduction and relevant to bacterial invasion.

PatternLab V Handles Multiplex Spectra in Shotgun Proteomic Searches and Increases Identification.

Complex protein mixtures typically generate many tandem mass spectra produced by different peptides coisolated in the gas phase. Widely adopted proteomic data analysis environments usually fail to identify most of these spectra, succeeding at best in identifying only one of the multiple cofragmenting peptides. We present PatternLab V (PLV), an updated version of PatternLab that integrates the YADA 3 deconvolution algorithm to handle such cases efficiently. In general, we expect an increase of 10% in spectral identifications when dealing with complex proteomic samples. PLV is freely available at http://patternlabforproteomics.org.

XlinkCyNET: A Cytoscape Application for Visualization of Protein Interaction Networks Based on Cross-Linking Mass Spectrometry Identifications.

Software tools that allow the visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a great selection of plug-ins for the interpretation of network data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for protein interaction data; however, to date, no Cytoscape tools are available to analyze XL-MS results. In light of the suitability of the Cytoscape platform and to expand its toolbox, here we introduce XlinkCyNET, an open-source Cytoscape Java plug-in for exploring large-scale XL-MS-based protein interaction networks. XlinkCyNET offers the rapid and easy visualization of intra- and interprotein cross-links in a rectangular-bar style as well as on the 3D structure, allowing the interrogation of protein interaction networks at the residue level. XlinkCyNET is freely available from the Cytoscape App Store (http://apps.cytoscape.org/apps/xlinkcynet) and at the Liu lab webpage (https://www.theliulab.com/software/xlinkcynet).

Comprehensive identification of protein disulfide bonds with pepsin/trypsin digestion, Orbitrap HCD and Spectrum Identification Machine.

Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools. Here we present a new protocol tailored toward mapping SS; it uses readily available reagents, instruments, and software. For sample preparation, a 4-h pepsin digestion at pH 1.3 followed by an overnight trypsin digestion at pH 6.5 can maximize the release of SS-containing peptides from non-reduced proteins, while minimizing SS scrambling. For LC/MS/MS analysis, SS-containing peptides can be efficiently fragmented with HCD in a Q Exactive Orbitrap mass spectrometer, preserving SS for subsequent identification. Our bioinformatics protocol describes how we tailored our freely downloadable and easy-to-use software, Spectrum Identification Machine for Cross-Linked Peptides (SIM-XL), to minimize false identification and facilitate manual validation of SS-peptide mass spectra. To substantiate this optimized method, we've comprehensively identified 14 out of 17 known SS in BSA. SIGNIFICANCE: Comprehensive and accurate identification of SS in proteins is critical for elucidating protein structures and functions. Yet, it is far from routine to accomplish this task in many analytical or core laboratories. Numerous published methods require complex sample preparation methods, specialized mass spectrometers and cumbersome or proprietary software tools, thus cannot be easily implemented in unspecialized laboratories. Here, we describe a robust and rapid SS mapping approach that utilizes readily available reagents, instruments, and software; it can be easily implemented in any analytical core laboratories, and tested for its impact on the research community.

XPlex: An Effective, Multiplex Cross-Linking Chemistry for Acidic Residues.

Cross-linking/Mass spectrometry (XLMS) is a consolidated technique for structural characterization of proteins and protein complexes. Despite its success, the cross-linking chemistry currently used is mostly based on N-hydroxysuccinimide (NHS) esters, which react primarily with lysine residues. One way to expand the current applicability of XLMS into several new areas is to increase the number of cross-links obtainable for a target protein. We introduce a multiplex chemistry (denoted XPlex) that targets Asp, Glu, Lys, and Ser residues. XPlex can generate significantly more cross-links with reactions occurring at lower temperatures and enables targeting proteins that are not possible with NHS ester-based cross-linkers. We demonstrate the effectiveness of our approach in model proteins as well as a target Lys-poor protein, SalBIII. Identification of XPlex spectra requires a search engine capable of simultaneously considering multiple cross-linkers on the same run; to achieve this, we updated the SIM-XL search algorithm with a search mode tailored toward XPlex. In summary, we present a complete chemistry/computational solution for significantly increasing the number of possible distance constraints by mass spectrometry experiments, and thus, we are convinced that XPlex poses as a real complementary approach for structural proteomics studies.

Exploring the proteomic landscape of a gastric cancer biopsy with the shotgun imaging analyzer.

Accessing localized proteomic profiles has emerged as a fundamental strategy to understand the biology of diseases, as recently demonstrated, for example, in the context of determining cancer resection margins with improved precision. Here, we analyze a gastric cancer biopsy sectioned into 10 parts, each one subjected to MudPIT analysis. We introduce a software tool, named Shotgun Imaging Analyzer and inspired in MALDI imaging, to enable the overlaying of a protein's expression heat map on a tissue picture. The software is tightly integrated with the NeXtProt database, so it enables the browsing of identified proteins according to chromosomes, quickly listing human proteins never identified by mass spectrometry (i.e., the so-called missing proteins), and the automatic search for proteins that are more expressed over a specific region of interest on the biopsy, all of which constitute goals that are clearly well-aligned with those of the C-HPP. Our software has been able to highlight an intense expression of proteins previously known to be correlated with cancers (e.g., glutathione S-transferase Mu 3), and in particular, we draw attention to Gastrokine-2, a "missing protein" identified in this work of which we were able to clearly delineate the tumoral region from the "healthy" with our approach. Data are available via ProteomeXchange with identifier PXD000584.