ImShot: An Open-Source Software for Probabilistic Identification of Proteins In Situ and Visualization of Proteomics Data

Imaging mass spectrometry (IMS) has developed into a powerful tool allowing label-free detection of numerous biomolecules in situ. In contrast to shotgun proteomics, proteins/peptides can be detected directly from biological tissues and correlated to its morphology leading to a gain of crucial clinical information. However, direct identification of the detected molecules is currently challenging for MALDI–IMS, thereby compelling researchers to use complementary techniques and resource intensive experimental setups. Despite these strategies, sufficient information could not be extracted because of lack of an optimum data combination strategy/software. Here, we introduce a new open-source software ImShot that aims at identifying peptides obtained in MALDI–IMS. This is achieved by combining information from IMS and shotgun proteomics (LC–MS) measurements of serial sections of the same tissue. The software takes advantage of a two-group comparison to determine the search space of IMS masses after deisotoping the corresponding spectra. Ambiguity in annotations of IMS peptides is eliminated by introduction of a novel scoring system that identifies the most likely parent protein of a detected peptide in the corresponding IMS dataset. Thanks to its modular structure, the software can also handle LC–MS data separately and display interactive enrichment plots and enriched Gene Ontology terms or cellular pathways. The software has been built as a desktop application with a conveniently designed graphic user interface to provide users with a seamless experience in data analysis. ImShot can run on all the three major desktop operating systems and is freely available under Massachusetts Institute of Technology license.     

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State–Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.     

Recent advancements in mass spectrometry–based tools to investigate newly synthesized proteins

Tight regulation of protein translation drives the proteome to undergo changes under influence of extracellular or intracellular signals. Despite mass spectrometry–based proteomics being an excellent method to study differences in protein abundance in complex proteomes, analyzing minute or rapid changes in protein synthesis and abundance remains challenging. Therefore, several dedicated techniques to directly detect and quantify newly synthesized proteins have been developed, notably puromycin-based, bio-orthogonal noncanonical amino acid tagging–based, and stable isotope labeling by amino acids in cell culture–based methods, combined with mass spectrometry. These techniques have enabled the investigation of perturbations, stress, or stimuli on protein synthesis. Improvements of these methods are still necessary to overcome various remaining limitations. Recent improvements include enhanced enrichment approaches and combinations with various stable isotope labeling techniques, which allow for more accurate analysis and comparison between conditions on shorter timeframes and in more challenging systems. Here, we aim to review the current state in this field.     

Differential Proteome and Interactome Analysis Reveal the Basis of Pleiotropy Associated With the Histidine Methyltransferase Hpm1p

The methylation of histidine is a post-translational modification whose function is poorly understood. Methyltransferase histidine protein methyltransferase 1 (Hpm1p) monomethylates H243 in the ribosomal protein Rpl3p and represents the only known histidine methyltransferase in Saccharomyces cerevisiae. Interestingly, the hpm1 deletion strain is highly pleiotropic, with many extraribosomal phenotypes including improved growth rates in alternative carbon sources. Here, we investigate how the loss of histidine methyltransferase Hpm1p results in diverse phenotypes, through use of targeted mass spectrometry (MS), growth assays, quantitative proteomics, and differential crosslinking MS. We confirmed the localization and stoichiometry of the H243 methylation site, found unreported sensitivities of Δhpm1 yeast to nonribosomal stressors, and identified differentially abundant proteins upon hpm1 knockout with clear links to the coordination of sugar metabolism. We adapted the emerging technique of quantitative large-scale stable isotope labeling of amino acids in cell culture crosslinking MS for yeast, which resulted in the identification of 1267 unique in vivo lysine–lysine crosslinks. By reproducibly monitoring over 350 of these in WT and Δhpm1, we detected changes to protein structure or protein–protein interactions in the ribosome, membrane proteins, chromatin, and mitochondria. Importantly, these occurred independently of changes in protein abundance and could explain a number of phenotypes of Δhpm1, not addressed by expression analysis. Further to this, some phenotypes were predicted solely from changes in protein structure or interactions and could be validated by orthogonal techniques. Taken together, these studies reveal a broad role for Hpm1p in yeast and illustrate how crosslinking MS will be an essential tool for understanding complex phenotypes.     

Membrane transporter identification and modulation via adaptive laboratory evolution

Membrane transport proteins are potential targets for medical and biotechnological applications. However, more than 30% of reported membrane transporter families are either poorly characterized or lack adequate functional annotation. Here, adaptive laboratory evolution was leveraged to identify membrane transporters for a set of four amino acids as well as specific mutations that modulate the activities of these transporters. Specifically, Escherichia coli was adaptively evolved under increasing concentrations of L–histidine, L–phenylalanine, L–threonine, and L–methionine separately with multiple replicate evolutions. Evolved populations and isolated clones displayed growth rates comparable to the unstressed ancestral strain at elevated concentrations (four-to six-fold increases) of the targeted amino acids. Whole genome sequencing of the evolved strains revealed a diverse number of key mutations, including SNPs, small deletions, and copy number variants targeting the transporters leuE for histidine, yddG for phenylalanine, yedA for methionine, and brnQ and rhtC for threonine. Reverse engineering of the mutations in the ancestral strain established mutation causality of the specific mutations for the tolerant phenotypes. The functional roles of yedA and brnQ in the transport of methionine and threonine, respectively, are novel assignments and their functional roles were validated using a flow cytometry cellular accumulation assay. To demonstrate how the identified transporters can be leveraged for production, an L–phenylalanine overproduction strain was shown to be a superior producer when the identified yddG exporter was overexpressed. Overall, the results revealed the striking efficiency of laboratory evolution to identify transporters and specific mutational mechanisms to modulate their activities, thereby demonstrating promising applicability in transporter discovery efforts and strain engineering.     

Heptanol-mediated phase separation determines phase preference of molecules in live cell membranes

The localization of many membrane proteins within cholesterol- and sphingolipid-containing microdomains is essential for proper cell signaling and function. These membrane domains, however, are too small and dynamic to be recorded, even with modern super-resolution techniques. Therefore, the association of membrane proteins with these domains can only be detected with biochemical assays that destroy the integrity of cells require pooling of many cells and take a long time to perform. Here, we present a simple membrane fluidizer–induced clustering approach to identify the phase-preference of membrane-associated molecules in individual live cells within 10–15 min. Experiments in phase-separated bilayers and live cells on molecules with known phase preference show that heptanol hyperfluidizes the membrane and stabilizes phase separation. This results in a transition from nanosized to micronsized clusters of associated molecules allowing their identification using routine microscopy techniques. Membrane fluidizer-induced clustering is an inexpensive and easy to implement method that can be conducted at large-scale and allows easy identification of protein partitioning in live cell membranes.     

Reanalysis of ProteomicsDB Using an Accurate,Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.     

Single-Molecule Force Spectroscopy of Membrane Protein Folding

Single-molecule force spectroscopy is a unique method that can probe the structural changes of single proteins at a high spatiotemporal resolution while mechanically manipulating them over a wide force range. Here, we review the current understanding of membrane protein folding learned by using the force spectroscopy approach. Membrane protein folding in lipid bilayers is one of the most complex biological processes in which diverse lipid molecules and chaperone proteins are intricately involved. The approach of single protein forced unfolding in lipid bilayers has produced important findings and insights into membrane protein folding. This review provides an overview of the forced unfolding approach, including recent achievements and technical advances. Progress in the methods can reveal more interesting cases of membrane protein folding and clarify general mechanisms and principles.     

Principles and Methods in Computational Membrane Protein Design

After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.     

Low Cell Number Proteomic Analysis Using In-Cell Protease Digests Reveals a Robust Signature for Cell Cycle State Classification

Comprehensive proteome analysis of rare cell phenotypes remains a significant challenge. We report a method for low cell number MS-based proteomics using protease digestion of mildly formaldehyde-fixed cells in cellulo, which we call the “in-cell digest.” We combined this with averaged MS1 precursor library matching to quantitatively characterize proteomes from low cell numbers of human lymphoblasts. About 4500 proteins were detected from 2000 cells, and 2500 proteins were quantitated from 200 lymphoblasts. The ease of sample processing and high sensitivity makes this method exceptionally suited for the proteomic analysis of rare cell states, including immune cell subsets and cell cycle subphases. To demonstrate the method, we characterized the proteome changes across 16 cell cycle states (CCSs) isolated from an asynchronous TK6 cells, avoiding synchronization. States included late mitotic cells present at extremely low frequency. We identified 119 pseudoperiodic proteins that vary across the cell cycle. Clustering of the pseudoperiodic proteins showed abundance patterns consistent with “waves” of protein degradation in late S, at the G2&M border, midmitosis, and at mitotic exit. These clusters were distinguished by significant differences in predicted nuclear localization and interaction with the anaphase-promoting complex/cyclosome. The dataset also identifies putative anaphase-promoting complex/cyclosome substrates in mitosis and the temporal order in which they are targeted for degradation. We demonstrate that a protein signature made of these 119 high-confidence cell cycle–regulated proteins can be used to perform unbiased classification of proteomes into CCSs. We applied this signature to 296 proteomes that encompass a range of quantitation methods, cell types, and experimental conditions. The analysis confidently assigns a CCS for 49 proteomes, including correct classification for proteomes from synchronized cells. We anticipate that this robust cell cycle protein signature will be crucial for classifying cell states in single-cell proteomes.     

Comparative Proteomic Analysis Identifies Key Metabolic Regulators of Gemcitabine Resistance in Pancreatic Cancer

Pancreatic adenocarcinoma (PDAC) is highly refractory to treatment. Standard-of-care gemcitabine (Gem) provides only modest survival benefits, and development of Gem resistance (GemR) compromises its efficacy. Highly GemR clones of Gem-sensitive MIAPaCa-2 cells were developed to investigate the molecular mechanisms of GemR and implemented global quantitative differential proteomics analysis with a comprehensive, reproducible ion-current–based MS1 workflow to quantify ～6000 proteins in all samples. In GemR clone MIA-GR8, cellular metabolism, proliferation, migration, and ‘drug response’ mechanisms were the predominant biological processes altered, consistent with cell phenotypic alterations in cell cycle and motility. S100 calcium binding protein A4 was the most downregulated protein, as were proteins associated with glycolytic and oxidative energy production. Both responses would reduce tumor proliferation. Upregulation of mesenchymal markers was prominent, and cellular invasiveness increased. Key enzymes in Gem metabolism pathways were altered such that intracellular utilization of Gem would decrease. Ribonucleoside-diphosphate reductase large subunit was the most elevated Gem metabolizing protein, supporting its critical role in GemR. Lower Ribonucleoside-diphosphate reductase large subunit expression is associated with better clinical outcomes in PDAC, and its downregulation paralleled reduced MIAPaCa-2 proliferation and migration and increased Gem sensitivity. Temporal protein-level Gem responses of MIAPaCa-2 versus GemR cell lines (intrinsically GemR PANC-1 and acquired GemR MIA-GR8) implicate adaptive changes in cellular response systems for cell proliferation and drug transport and metabolism, which reduce cytotoxic Gem metabolites, in DNA repair, and additional responses, as key contributors to the complexity of GemR in PDAC. These findings additionally suggest targetable therapeutic vulnerabilities for GemR PDAC patients.     

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

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.     

Towards the Molecular Mechanism of Pulmonary Surfactant Protein SP-B: At the Crossroad of Membrane Permeability and Interfacial Lipid Transfer

Pulmonary surfactant is a lipid-protein complex that coats the alveolar air-liquid interface, enabling the proper functioning of lung mechanics. The hydrophobic surfactant protein SP-B, in particular, plays an indispensable role in promoting the rapid adsorption of phospholipids into the interface. For this, formation of SP-B ring-shaped assemblies seems to be important, as oligomerization could be required for the ability of the protein to generate membrane contacts and to mediate lipid transfer among surfactant structures. SP-B, together with the other hydrophobic surfactant protein SP-C, also promotes permeability of surfactant membranes to polar molecules although the molecular mechanisms underlying this property, as well as its relevance for the surface activity of the protein, remain undefined. In this work, the contribution of SP-B and SP-C to surfactant membrane permeability has been further investigated, by evaluation of the ability of differently-sized fluorescent polar probes to permeate through giant vesicles with different lipid/protein composition. Our results are consistent with the generation by SP-B of pores with defined size in surfactant membranes. Furthermore, incubation of surfactant with an anti-SP-B antibody not only blocked membrane permeability but also affected lipid transfer into the air-water interface, as observed in a captive bubble surfactometer device. Our findings include the identification of SP-C and anionic phospholipids as modulators required for maintaining native-like permeability features in pulmonary surfactant membranes. Proper permeability through membrane assemblies could be crucial to complement the overall role of surfactant in maintaining alveolar equilibrium, beyond its biophysical function in stabilizing the respiratory air-liquid interface.     

Site-Specific Activity-Based Protein Profiling Using Phosphonate Handles

Most drug molecules target proteins. Identification of the exact drug binding sites on these proteins is essential to understand and predict how drugs affect protein structure and function. To address this challenge, we developed a strategy that uses immobilized metal-affinity chromatography–enrichable phosphonate affinity tags, for efficient and selective enrichment of peptides bound to an activity-based probe, enabling the identification of the exact drug binding site. As a proof of concept, using this approach, termed PhosID–ABPP (activity-based protein profiling), over 500 unique binding sites were reproducibly identified of an alkynylated afatinib derivative (PF-06672131). As PhosID–ABPP is compatible with intact cell inhibitor treatment, we investigated the quantitative differences in approachable binding sites in intact cells and in lysates of the same cell line and observed and quantified substantial differences. Moreover, an alternative protease digestion approach was used to capture the previously reported binding site on the epidermal growth factor receptor, which turned out to remain elusive when using solely trypsin as protease. Overall, we find that PhosID–ABPP is highly complementary to biotin-based enrichment strategies in ABPP studies, with PhosID–ABPP providing the advantage of direct activity-based probe interaction site identification.     

Liquid–Liquid Phase Separation at the Plasma Membrane–Cytosol Interface: Common Players in Adhesion,Motility, and Synaptic Function

Networks of scaffold proteins and enzymes assemble at the interface between the cytosol and specific sites of the plasma membrane, where these networks guide distinct cellular functions. Some of these plasma membrane–associated platforms (PMAPs) include shared core components that are able to establish specific protein–protein interactions, to produce distinct supramolecular assemblies regulating dynamic processes as diverse as cell adhesion and motility, or the formation and function of neuronal synapses. How cells organize such dynamic networks is still an open question. In this review we introduce molecular networks assembling at the edge of migrating cells, and at pre– and postsynaptic sites, which share molecular players that can drive the assembly of biomolecular condensates. Very recent experimental evidence has highlighted the emerging role of some of these multidomain/scaffold proteins belonging to the GIT, liprin-α and ELKS/ERC families as drivers of liquid–liquid phase separation (LLPS). The data point to an important role of LLPS: (i) in the formation of PMAPs at the edge of migrating cells, where LLPS appears to be involved in promoting protrusion and the turnover of integrin–mediated adhesions, to allow forward cell translocation; (ii) in the assembly of the presynaptic active zone and of the postsynaptic density deputed to the release and reception of neurotransmitter signals, respectively. The recent results indicate that LLPS at cytosol–membrane interfaces is suitable not only for the regulation of active cellular processes, but also for the continuous spatial rearrangements of the molecular interactions involved in these dynamic processes.