Bottom-up Proteomics Research Articles

Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring.

Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.

Predominantly Orphan Secretome in the Lung Pathogen Mycobacterium abscessus Revealed by a Multipronged Growth-Phase-Driven Strategy.

The emerging lung pathogen Mycobacterium abscessus is understudied for its virulence determinants and molecular targets for diagnosis and therapeutics. Here, we report a comprehensive secretome (600 proteins) of this species, which was identified using a multipronged strategy based on genetic/genomic, proteomic, and bioinformatic approaches. In-solution digested bottom-up proteomics from various growth phases identified a total of 517 proteins, while 2D-GE proteomics identified 33 proteins. A reporter-gene-fusion-based genomic library that was custom-generated in this study enabled the detection of 23 secretory proteins. A genome-wide survey for N-terminal signal sequences using bioinformatic tools (Psortb 2.0 and SignalP 3.0) combined with a strategy of the subtraction of lipoproteins and proteins containing multiple transmembrane domains yielded 116 secretory proteins. A homology search against the M. tuberculosis database identified nine additional secretory protein homologs that lacked a secretory signal sequence. Considering the little overlap (80 proteins) among the different approaches used, this study emphasized the importance of using a multipronged strategy for a comprehensive understanding of the secretome. Notably, the majority of the secreted proteins identified (over 50%) turned out to be "orphans" (those with no known functional homologs). The revelation of these species-specific orphan proteins offers a hitherto unexplored repertoire of potential targets for diagnostic, therapeutic, and vaccine research in this emerging lung pathogen.

Spatial MS multiomics on clinical prostate cancer tissues.

Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.

Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk–derived bioactive peptides

Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.

Extending the Coverage of Lys-C/Trypsin-Based Bottom-up Proteomics by Cysteine S-Aminoethylation.

To improve the coverage in bottom-up proteomics, S-aminoethylation of cysteine residues (AE-Cys) was carried out with 2-bromoethylamine, followed by cleavage with lysyl endopeptidase (Lys-C) or Lys-C/trypsin. A model study with bovine serum albumin showed that the C-terminal side of AE-Cys was successfully cleaved by Lys-C. The frequency of side reactions at amino acids other than Cys was less than that in the case of carbamidomethylation of Cys with iodoacetamide. Proteomic analysis of A549 cell extracts in the data-dependent acquisition mode after AE-Cys modification afforded a greater number of identified protein groups, especially membrane proteins. In addition, label-free quantification of proteins in mouse nonsmall cell lung cancer (NSCLC) tissue in the data-independent acquisition mode after AE-Cys modification showed improved NSCLC pathway coverage and greater reproducibility. Furthermore, the AE-Cys method could identify an epidermal growth factor receptor peptide containing the T790 M mutation site, a well-established lung-cancer-related mutation site that has evaded conventional bottom-up methods. Finally, AE-Cys was found to fully mimic Lys in terms of collision-induced dissociation fragmentation, ion mobility separation, and cleavage by Lys-C/trypsin, except for sulfoxide formation during sample preparation.

The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome.

Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.

Density-based lipoprotein depletion improves extracellular vesicle isolation and functional analysis

BackgroundBlood plasma is the main source of extracellular vesicles (EVs) in clinical studies aiming to identify biomarkers and to investigate pathophysiological processes, especially regarding EV roles in inflammation and thrombosis. However, EV isolation from plasma has faced the fundamental issue of lipoprotein contamination, representing an important bias since lipoproteins are highly abundant and modulate cell signaling, metabolism, and thromboinflammation. ObjectivesHere, we aimed to isolate plasma EVs after depleting lipoproteins, thereby improving sample purity and EV thromboinflammatory analysis. MethodsDensity-based gradient ultracentrifugation (G-UC) was used for lipoprotein depletion before EV isolation from plasma through size-exclusion chromatography (SEC) or serial centrifugation (SC). Recovered EVs were analyzed by size, concentration, cellular source, ultrastructure, and bottom-up proteomics. ResultsG-UC efficiently separated lipoproteins from the plasma, allowing subsequent EV isolation through SEC or SC. Combined analysis from EV proteomics, cholesterol quantification, and apoB-100 detection confirmed the significant reduction in lipoproteins from isolated EVs. Proteomic analysis identified similar gene ontology and cellular components in EVs, regardless of lipoprotein depletion, which was consistent with similar EV cellular sources, size, and ultrastructure by flow cytometry and transmission electron microscopy. Importantly, lipoprotein depletion increased the detection of less abundant proteins in EV proteome and enhanced thromboinflammatory responses of platelets and monocytes stimulated in vitro with EV isolates. ConclusionCombination of G-UC+SEC significantly reduced EV lipoprotein contamination without interfering in EV cellular source, gene ontology, and ultrastructure, allowing the recovery of highly pure EVs with potential implications for functional assays and proteomic and lipidomic analyses.

The Eastern Bandy Bandy Vermicella annulata, expresses high abundance of SVMP, CRiSP and Kunitz protein families in its venom proteome

The Australian elapid snake radiation (Hydrophiinae) has evolved in the absence of competition from other advanced snakes. This has resulted in ecological specialisation in Australian elapids and the potential for venom proteomes divergent to other elapids. We characterised the venom of the Australian elapid Vermicella annulata (eastern bandy bandy). The venom was analysed using a two-dimensional fractionation process consisting of reverse-phase high-performance liquid chromatography then sodium dodecyl sulphate polyacrylamide gel electrophoresis, followed by bottom-up proteomics. Resulting peptides were matched to a species-specific transcriptome and 87% of the venom was characterised. We identified 11 toxins in the venom from six families: snake venom metalloproteinases (SVMP; 24.2%; two toxins) that are class P-III SVMPs containing a disintegrin-like domain, three-finger toxins (3FTx; 21.6%; five toxins), kunitz peptides (KUN; 19.5%; one toxin), cysteine-rich secretory proteins (CRiSP; 18%; one toxin), and phospholipase A2 (PLA2; 4%; two toxins). The venom had low toxin diversity with five protein families having one or two toxins, except for 3FTx with five different toxins. V. annulata expresses an unusual venom proteome, with high abundances of CRiSP, KUN and SVMP, which are not normally highly expressed in elapid venoms. This unusual venom composition could be an adaptation to its specialised diet. Biological significanceAlthough the Australian elapid radiation represents the most extensive speciation event of elapids on any continent, with 100 terrestrial species, the venom composition of these snakes has rarely been investigated, with only five species currently characterised. Here we provide the venom proteome of a sixth species, Vermicella annulata. The venom of this species could be particularly informative from an evolutionary perspective, as it is an extreme dietary specialist, only preying on blind snakes (Typhlopidae). We show that V. annulata expresses a highly unusual venom for an elapid, due to the high abundance of the protein families SVMP, CRiSP, and KUN, which together make up 61% of the venom. When averaged across all species, a typical elapid venom is 82% PLA2 and 3FTx. This is the second recorded instance of an Australian elapid having evolved highly divergent venom expression.

Proteomic Investigation of Cape Cobra (Naja nivea) Venom Reveals First Evidence of Quaternary Protein Structures.

Naja nivea (N. nivea) is classed as a category one snake by the World Health Organization since its envenomation causes high levels of mortality and disability annually. Despite this, there has been little research into the venom composition of N. nivea, with only one full venom proteome published to date. Our current study separated N. nivea venom using size exclusion chromatography before utilizing a traditional bottom-up proteomics approach to unravel the composition of the venom proteome. As expected by its clinical presentation, N. nivea venom was found to consist mainly of neurotoxins, with three-finger toxins (3FTx), making up 76.01% of the total venom proteome. Additionally, cysteine-rich secretory proteins (CRISPs), vespryns (VESPs), cobra venom factors (CVFs), 5'-nucleotidases (5'NUCs), nerve growth factors (NGFs), phospholipase A2s (PLA2), acetylcholinesterases (AChEs), Kunitz-type serine protease inhibitor (KUN), phosphodiesterases (PDEs), L-amino acid oxidases (LAAOs), hydrolases (HYDs), snake venom metalloproteinases (SVMPs), and snake venom serine protease (SVSP) toxins were also identified in decreasing order of abundance. Interestingly, contrary to previous reports, we find PLA2 toxins in N. nivea venom. This highlights the importance of repeatedly profiling the venom of the same species to account for intra-species variation. Additionally, we report the first evidence of covalent protein complexes in N. nivea venom, which likely contribute to the potency of this venom.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Lymphocyte-Specific Protein 1 Regulates Expression and Stability of Endothelial Nitric Oxide Synthase.

Nitric oxide (NO), synthesized by endothelial nitric oxide synthase (eNOS), plays a critical role in blood pressure regulation. Genome-wide association studies have identified genetic susceptibility loci for hypertension in human lymphocyte-specific protein 1 (LSP1) gene. LSP1 is recognized as modulator of leukocyte extravasation, and endothelial permeability, however, the role of LSP1 in regulation of NO signaling within endothelial cells (ECs) remains unknown. The present study investigated the role of LSP1 in the regulation of eNOS expression and activity utilizing human macrovascular ECs in vitro and LSP1 knockout (KO) mice. In ECs, specific CRISPR-Cas9 genomic editing deleted LSP1 and caused downregulation of eNOS expression. LSP1 gain-of-function through adenovirus-mediated gene transfer was associated with enhanced expression of eNOS. Co-immunoprecipitation and confocal fluorescence microscopy revealed that eNOS and LSP1 formed a protein complex under basal conditions in ECs. Furthermore, LSP1 deficiency in mice promoted significant upregulation and instability of eNOS. Utilizing a mass-spectrometry-based bottom-up proteomics approach, we identified novel truncated forms of eNOS in immunoprecipitates from LSP1 KO aortae. Our experimental data suggest an important role of endothelial LSP1 in regulation of eNOS expression and activity within human ECs and murine vascular tissues.

Present and future perspectives on mass spectrometry for clinical microbiology

In the last decade, MALDI-TOF Mass Spectrometry (MALDI-TOF MS) has been introduced and broadly accepted by clinical laboratory laboratories throughout the world as a powerful and efficient tool for rapid microbial identification. During the MALDI-TOF MS process, microbes are identified using either intact cells or cell extracts. The process is rapid, sensitive, and economical in terms of both labor and costs involved. Whilst MALDI-TOF MS is currently the gold-standard, it suffers from several shortcomings such as lack of direct information on antibiotic resistance, poor depth of analysis and insufficient discriminatory power for the distinction of closely related bacterial species or for reliably sub-differentiating isolates to the level of clones or strains. Thus, new approaches targeting proteins and allowing a better characterization of bacterial strains are strongly needed, if possible, on a very short time scale after sample collection in the hospital. Bottom-up proteomics (BUP) is a nice alternative to MALDI-TOF MS, offering the possibility for in-depth proteome analysis. Top-down proteomics (TDP) provides the highest molecular precision in proteomics, allowing the characterization of proteins at the proteoform level. A number of studies have already demonstrated the potential of these techniques in clinical microbiology. In this review, we will discuss the current state-of-the-art of MALDI-TOF MS for the rapid microbial identification and detection of resistance to antibiotics and describe emerging approaches, including bottom-up and top-down proteomics as well as ambient MS technologies.

Chloroform/Methanol Protein Extraction and In-solution Trypsin Digestion Protocol for Bottom-up Proteomics Analysis.

Bottom-up proteomics utilizes sample preparation techniques to enzymatically digest proteins, thereby generating identifiable and quantifiable peptides. Proteomics integrates with other omics methodologies, such as genomics and transcriptomics, to elucidate biomarkers associated with diseases and responses to drug or biologics treatment. The methodologies employed for preparing proteomic samples for mass spectrometry analysis exhibit variability across several factors, including the composition of lysis buffer detergents, homogenization techniques, protein extraction and precipitation methodologies, alkylation strategies, and the selection of digestion enzymes. The general workflow for bottom-up proteomics consists of sample preparation, mass spectrometric data acquisition (LC-MS/MS analysis), and subsequent downstream data analysis including protein quantification and differential expression analysis. Sample preparation poses a persistent challenge due to issues such as low reproducibility and inherent procedure complexities. Herein, we have developed a validated chloroform/methanol sample preparation protocol to obtain reproducible peptide mixtures from both rodent tissue and human cell line samples for bottom-up proteomics analysis. The protocol we established may facilitate the standardization of bottom-up proteomics workflows, thereby enhancing the acquisition of reliable biologically and/or clinically relevant proteomic data. Key features • Tissue/cell pellet sample preparation for bottom-up proteomics. • Chloroform/methanol protein extraction from murine tissue samples. • In-solution trypsin digestion proteomics workflow.

Immunoaffinity-free chromatographic purification of ovarian cancer biomarker CA125 (MUC16) from blood serum enables mass spectrometry characterization.

The enrichment of trace proteins from human fluid samples is of great importance in diverse clinical and industrial applications. In clinical diagnostics, such enrichment may enable detection of trace proteins that serve as biomarkers of disease. Affinity-based approaches, such as immunoaffinity pulldown, are widely used to enrich trace proteins, but this strategy relies on the availability and performance of antibodies that act on all proteoforms in an unbiased manner. Our prior work to characterize MUC16 (the mucin protein that carries the ovarian cancer biomarker CA125) by mass spectrometry successfully overcame the reliance on affinity-based enrichment and was used to enrich this biomarker from ascites of individual ovarian cancer patients, however, this strategy was not demonstrated on clinically relevant volumes of serum, a biofluid that is more accessible than ascites. The present work developed a non-affinity-based chromatographic method to enrich MUC16 from serum. The enriched MUC16 sample was further processed using a Midi Top 14 abundant protein depletion column. Peptides identified using bottom-up proteomics yielded 1-8% coverage of MUC16. Additionally, MUC16 was detected in samples containing less than the clinical cut-off level of CA125 (35 U mL-1), suggesting that this strategy of enrichment and bottom-up proteomics can enable analysis of CA125 from the serum of individuals with early-stage ovarian cancer and those whose tumors express CA125 (MUC16) at low levels.

Detection and Quantitation of Endogenous Membrane-Bound RAS Proteins and KRAS Mutants in Cancer Cell Lines Using 1D-SDS-PAGE LC-MS2.

In healthy cells, membrane-anchored wild-type RAS proteins (i.e., HRAS, KRAS4A, KRAS4B, and NRAS) regulate critical cellular processes (e.g., proliferation, differentiation, survival). When mutated, RAS proteins are principal oncogenic drivers in approximately 30% of all human cancers. Among them, KRAS mutants are found in nearly 80% of all patients diagnosed with RAS-driven malignancies and are regarded as high-priority anti-cancer drug targets. Due to the lack of highly qualified/specific RAS isoform and mutant RAS monoclonal antibodies, there is a vital need for an effective antibody-free approach capable of identifying and quantifying membrane-bound RAS proteins in isoform- and mutation-specific manner. Here, we describe the development of a simple antibody-free protocol that relies on ultracentrifugation to isolate the membrane fraction coupled with single-dimensional (1D) sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) to fractionate and enrich membrane-bound endogenous RAS isoforms. Next, bottom-up proteomics that utilizes in-gel digestion followed by reversed-phase high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS2) is used for detection and relative quantitation of all wild-type RAS proteins (i.e., HRAS, KRAS4A, KRAS4B, and NRAS) and corresponding RAS mutants (e.g., G12D, G13D, G12S, G12V). Notably, this simple 1D-SDS-PAGE-HPLC-MS2-based protocol can be automated and widely applied to multiple cancer cell lines to investigate concentration changes in membrane-bound endogenous RAS proteins and corresponding mutants in the context of drug discovery.

Origins, Technological Advancement, and Applications of Peptidomics.

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Test-Time Training for Deep MS/MS Spectrum Prediction Improves Peptide Identification.

In bottom-up proteomics, peptide-spectrum matching is critical for peptide and protein identification. Recently, deep learning models have been used to predict tandem mass spectra of peptides, enabling the calculation of similarity scores between the predicted and experimental spectra for peptide-spectrum matching. These models follow the supervised learning paradigm, which trains a general model using paired peptides and spectra from standard data sets and directly employs the model on experimental data. However, this approach can lead to inaccurate predictions due to differences between the training data and the experimental data, such as sample types, enzyme specificity, and instrument calibration. To tackle this problem, we developed a test-time training paradigm that adapts the pretrained model to generate experimental data-specific models, namely, PepT3. PepT3 yields a 10-40% increase in peptide identification depending on the variability in training and experimental data. Intriguingly, when applied to a patient-derived immunopeptidomic sample, PepT3 increases the identification of tumor-specific immunopeptide candidates by 60%. Two-thirds of the newly identified candidates are predicted to bind to the patient's human leukocyte antigen isoforms. To facilitate access of the model and all the results, we have archived all the intermediate files in Zenodo.org with identifier 8231084.

Characterisation of the forest cobra (Naja melanoleuca) venom using a multifaceted mass spectrometric-based approach

Snake venoms consist of highly biologically active proteins and peptides that are responsible for the lethal physiological effects of snakebite envenomation. In order to guide the development of targeted antivenom strategies, comprehensive understanding of venom compositions and in-depth characterisation of various proteoforms, often not captured by traditional bottom-up proteomic workflows, is necessary. Here, we employ an integrated ‘omics’ and intact mass spectrometry (MS)-based approach to profile the heterogeneity within the venom of the forest cobra (Naja melanoleuca), adopting different analytical strategies to accommodate for the dynamic molecular mass range of venom proteins present. The venom proteome of N. melanoleuca was catalogued using a venom gland transcriptome-guided bottom-up proteomics approach, revealing a venom consisting of six toxin superfamilies. The subtle diversity present in the venom components was further explored using reversed phase-ultra performance liquid chromatography (RP-UPLC) coupled to intact MS. This approach showed a significant increase in the number of venom proteoforms within various toxin families that were not captured in previous studies. Furthermore, we probed at the higher-order structures of the larger venom proteins using a combination of native MS and mass photometry and revealed significant structural heterogeneity along with extensive post-translational modifications in the form of glycosylation in these larger toxins. Here, we show the diverse structural heterogeneity of snake venom proteins in the venom of N. melanoleuca using an integrated workflow that incorporates analytical strategies that profile snake venom at the proteoform level, complementing traditional venom characterisation approaches.

Collision-Induced Unfolding, Tandem MS, Bottom-up Proteomics, and Interactomics for Identification of Protein Complexes in Native Surface Mass Spectrometry.

Endogenously occurring salts and nonvolatile matrix components in untreated biological surfaces can suppress protein ionization and promote adduct formation, challenging protein identification. Characterization of labile proteins within biological specimens is particularly demanding because additional purification or sample treatment steps can be time-intensive and can disrupt noncovalent interactions. It is demonstrated that the combined use of collision-induced unfolding, tandem mass spectrometry, and bottom-up proteomics improves protein characterization in native surface mass spectrometry (NSMS). This multiprong analysis is achieved by acquiring NSMS, MS/MS, ion mobility (IM), and bottom-up proteomics data from a single surface extracted sample. The validity of this multiprong approach was confirmed by the successful characterization of nine surface-deposited proteins, with molecular weights ranging from 8 to 147 kDa, in two separate mixtures. Bottom-up proteomics provided a list of proteins to match against observed proteins in NSMS and their detected subunits in tandem MS. The method was applied to characterize endogenous proteins from untreated chicken liver samples. The subcapsular liver sampling for NSMS analysis allowed for the detection of endogenous proteins with molecular weights of up to ∼220 kDa. Moreover, using IM-MS, collision cross sections and collision-induced unfolding pathways of enzymatic proteins and protein complexes of up to 145 kDa were obtained.

A High-Sensitivity Low-Nanoflow LC-MS Configuration for High-Throughput Sample-Limited Proteomics.

This work demonstrates the utility of high-throughput nanoLC-MS and label-free quantification (LFQ) for sample-limited bottom-up proteomics analysis, including single-cell proteomics (SCP). Conditions were optimized on a 50 μm internal diameter (I.D.) column operated at 100 nL/min in the direct injection workflow to balance method sensitivity and sample throughput from 24 to 72 samples/day. Multiple data acquisition strategies were also evaluated for proteome coverage, including data-dependent acquisition (DDA), wide-window acquisition (WWA), and wide-window data-independent acquisition (WW-DIA). Analyzing 250 pg HeLa digest with a 10-min LC gradient (72 samples/day) provided >900, >1,800, and >3,000 protein group identifications for DDA, WWA, and WW-DIA, respectively. Total method cycle time was further reduced from 20 to 14.4 min (100 samples/day) by employing a trap-and-elute workflow, enabling 70% mass spectrometer utilization. The method was applied to library-free DIA analysis of single-cell samples, yielding >1,700 protein groups identified. In conclusion, this study provides a high-sensitivity, high-throughput nanoLC-MS configuration for sample-limited proteomics.