Quantitative shotgun proteome analysis by direct infusion

Dual-localized PPTC7 limits mitophagy through proximal and dynamic interactions with BNIP3 and NIX

PPTC7 is a mitochondrial-localized phosphatase that suppresses BNIP3- and NIX-mediated mitophagy, but the mechanisms underlying this regulation remain ill-defined. Here, we demonstrate that loss of PPTC7 upregulates BNIP3 and NIX post-transcriptionally and independent of HIF-1α stabilization. Loss of PPTC7 prolongs the half-life of BNIP3 and NIX while blunting their accumulation in response to proteasomal inhibition, suggesting that PPTC7 promotes the ubiquitin-mediated turnover of BNIP3 and NIX. Consistently, overexpression of PPTC7 limits the accumulation of BNIP3 and NIX protein levels, which requires an intact catalytic motif but is surprisingly independent of its targeting to mitochondria. Consistently, we find that PPTC7 is dual-localized to the outer mitochondrial membrane and the matrix. Importantly, anchoring PPTC7 to the outer mitochondrial membrane is sufficient to blunt BNIP3 and NIX accumulation, and proximity labeling and fluorescence co-localization experiments demonstrate that PPTC7 dynamically associates with BNIP3 and NIX within the native cellular environment. Collectively, these data reveal that a fraction of PPTC7 localizes to the outer mitochondrial membrane to promote the proteasomal turnover of BNIP3 and NIX, limiting basal mitophagy.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

PPTC7 antagonizes mitophagy by promoting BNIP3 and NIX degradation via SCFFBXL4

Mitophagy must be carefully regulated to ensure that cells maintain appropriate numbers of functional mitochondria. The SCFFBXL4 ubiquitin ligase complex suppresses mitophagy by controlling the degradation of BNIP3 and NIX mitophagy receptors, and FBXL4 mutations result in mitochondrial disease as a consequence of elevated mitophagy. Here, we reveal that the mitochondrial phosphatase PPTC7 is an essential cofactor for SCFFBXL4-mediated destruction of BNIP3 and NIX, suppressing both steady-state and induced mitophagy. Disruption of the phosphatase activity of PPTC7 does not influence BNIP3 and NIX turnover. Rather, a pool of PPTC7 on the mitochondrial outer membrane acts as an adaptor linking BNIP3 and NIX to FBXL4, facilitating the turnover of these mitophagy receptors. PPTC7 accumulates on the outer mitochondrial membrane in response to mitophagy induction or the absence of FBXL4, suggesting a homoeostatic feedback mechanism that attenuates high levels of mitophagy. We mapped critical residues required for PPTC7-BNIP3/NIX and PPTC7-FBXL4 interactions and their disruption interferes with both BNIP3/NIX degradation and mitophagy suppression. Collectively, these findings delineate a complex regulatory mechanism that restricts BNIP3/NIX-induced mitophagy.

DHA dietary intervention caused different hippocampal lipid and protein profile in ApoE-/- and C57BL/6J mice

BACKGROUND

Changes in protein and lipid levels may occur in the Alzheimer's disease brain, and DHA can have beneficial effects on it. To investigate the impact of DHA dietary intervention on brain protein and lipid profile in ApoE-/- mice and C57 mice.

METHOD

Three-month-old ApoE-/- mice and C57 mice were randomly divided into two groups respectively, and fed with control diet and DHA-fortified diet for five months. Cortical TC, HDL-C and LDL-C levels and cholesterol metabolism-related protein expression were measured by ELISA or immunohistochemistry methods. Hippocampus were collected for proteomic and lipidomics analysis by LC-MS/MS and differential proteins and lipid metabolites were screened and further analyzed by GO functional annotation and KEGG pathway enrichment analysis.

RESULTS

DHA intervention decreased cortical TC level in both C57 and ApoE-/- mice (P < 0.05), but caused different change of cortical HDL-C, LDL-C level and LDL-C/HDL-C ratio in C57 and ApoE-/- mice (P < 0.05). Discrepant cortical and hippocampal LDLR, ABCG1, Lox1 and SORT1 protein expression was found between C57 and ApoE-/- mice (P < 0.05), and DHA treatment caused different changes of these proteins in C57 and ApoE-/- mice (P < 0.05). Differential hippocampal proteins and lipids profile were found in C57 and ApoE-/- mice before and after DHA treatment, which were mainly involved in vesicular transport and phospholipid metabolic pathways.

CONCLUSION

ApoE genetic defect caused abnormal cholesterol metabolism, and affected protein and lipid profile, as well as discrepant response of hippocampal protein and lipids profile in the brain of mice given DHA fortified diet intervention.

Boronic Acid-Rich Lanthanide Metal-Organic Frameworks Enable Deep Proteomics with Ultratrace Biological Samples

Label-free proteomics is widely used to identify disease mechanism and potential therapeutic targets. However, deep proteomics with ultratrace clinical specimen remains a major technical challenge due to extensive contact loss during complex sample pretreatment. Here, a hybrid of four boronic acid-rich lanthanide metal-organic frameworks (MOFs) with high protein affinity is introduced to capture proteins in ultratrace samples jointly by nitrogen-boronate complexation, cation-π and ionic interactions. A MOFs Aided Sample Preparation (MASP) workflow that shrinks sample volume and integrates lysis, protein capture, protein digestion and peptide collection steps into a single PCR tube to minimize sample loss caused by non-specific absorption, is proposed further. MASP is validated to quantify ≈1800 proteins in 10 HEK-293T cells. MASP is applied to profile cerebrospinal fluid (CSF) proteome from cerebral stroke and brain damaged patients, and identified ≈3700 proteins in 1 µL CSF. MASP is further demonstrated to detect ≈9600 proteins in as few as 50 µg mouse brain tissues. MASP thus enables deep, scalable, and reproducible proteome on precious clinical samples with low abundant proteins.

Advances in the Clinical Application of High-throughput Proteomics

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

The Future of Proteomics is Up in the Air: Can Ion Mobility Replace Liquid Chromatography for High Throughput Proteomics?

The coevolution of liquid chromatography (LC) with mass spectrometry (MS) has shaped contemporary proteomics. LC hyphenated to MS now enables quantification of more than 10,000 proteins in a single injection, a number that likely represents most proteins in specific human cells or tissues. Separations by ion mobility spectrometry (IMS) have recently emerged to complement LC and further improve the depth of proteomics. Given the theoretical advantages in speed and robustness of IMS in comparison to LC, we envision that ongoing improvements to IMS paired with MS may eventually make LC obsolete, especially when combined with targeted or simplified analyses, such as rapid clinical proteomics analysis of defined biomarker panels. In this perspective, we describe the need for faster analysis that might drive this transition, the current state of direct infusion proteomics, and discuss some technical challenges that must be overcome to fully complete the transition to entirely gas phase proteomics.

Key Proteomics Tools for Fundamental and Applied Microalgal Research

Microscopic, photosynthetic prokaryotes and eukaryotes, collectively referred to as microalgae, are widely studied to improve our understanding of key metabolic pathways (e.g., photosynthesis) and for the development of biotechnological applications. Omics technologies, which are now common tools in biological research, have been shown to be critical in microalgal research. In the past decade, significant technological advancements have allowed omics technologies to become more affordable and efficient, with huge datasets being generated. In particular, where studies focused on a single or few proteins decades ago, it is now possible to study the whole proteome of a microalgae. The development of mass spectrometry-based methods has provided this leap forward with the high-throughput identification and quantification of proteins. This review specifically provides an overview of the use of proteomics in fundamental (e.g., photosynthesis) and applied (e.g., lipid production for biofuel) microalgal research, and presents future research directions in this field.

Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449-3500, 2278-3257 and 1621-2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

PPTC7 limits mitophagy through proximal and dynamic interactions with BNIP3 and NIX

PPTC7 is a mitochondrial-localized PP2C phosphatase that maintains mitochondrial protein content and metabolic homeostasis. We previously demonstrated that knockout of Pptc7 elevates mitophagy in a BNIP3- and NIX-dependent manner, but the mechanisms by which PPTC7 influences receptor-mediated mitophagy remain ill-defined. Here, we demonstrate that loss of PPTC7 upregulates BNIP3 and NIX post-transcriptionally and independent of HIF-1α stabilization. On a molecular level, loss of PPTC7 prolongs the half-life of BNIP3 and NIX while blunting their accumulation in response to proteasomal inhibition, suggesting that PPTC7 promotes the ubiquitin-mediated turnover of BNIP3 and NIX. Consistently, overexpression of PPTC7 limits the accumulation of BNIP3 and NIX protein levels in response to pseudohypoxia, a well-known inducer of mitophagy. This PPTC7-mediated suppression of BNIP3 and NIX protein expression requires an intact PP2C catalytic motif but is surprisingly independent of its mitochondrial targeting, indicating that PPTC7 influences mitophagy outside of the mitochondrial matrix. We find that PPTC7 exists in at least two distinct states in cells: a longer isoform, which likely represents full length protein, and a shorter isoform, which likely represents an imported, matrix-localized phosphatase pool. Importantly, anchoring PPTC7 to the outer mitochondrial membrane is sufficient to blunt BNIP3 and NIX accumulation, and proximity labeling and fluorescence co-localization experiments suggest that PPTC7 associates with BNIP3 and NIX within the native cellular environment. Importantly, these associations are enhanced in cellular conditions that promote BNIP3 and NIX turnover, demonstrating that PPTC7 is dynamically recruited to BNIP3 and NIX to facilitate their degradation. Collectively, these data reveal that a fraction of PPTC7 dynamically localizes to the outer mitochondrial membrane to promote the proteasomal turnover of BNIP3 and NIX.

Exploiting Charge State Distribution To Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses

Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization is the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public data sets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimensions of the CSD demonstrate high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence and constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments) qualified CSD data suitable to train a deep-learning model capable of accurately predicting CSD values (mean cosine similarity - 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely used retention time dimension, it still holds the potential for application in direct infusion proteomics.

Flash MS/MS proteotyping allows identifying microbial isolates in 36 s of mass spectrometry signal

Rapid identification of microorganisms is essential for medical diagnostics, sanitary controls, and food safety. High-throughput analytical platforms currently rely on whole-cell MALDI-TOF mass spectrometry to process hundreds of samples per day. Although this technology has become a reference method, it is unable to process most environmental isolates and opportunistic pathogens due to an incomplete experimental spectrum database. In most cases, its discriminating power is limited to the species taxonomical rank. By recording much more sequence information at the peptide level, proteotyping by tandem mass spectrometry is able to identify the taxonomic position of any microorganism in the tree of life and can be highly discriminating at the subspecies level. We propose here a methodology for ultra-fast identification of microorganisms by tandem mass spectrometry based on direct sample infusion and a highly sensitive procedure for data processing and taxonomic identification. Results obtained on reference strains and hitherto uncharacterized bacterial isolates show identification to species level in 36 s of tandem mass spectrometry signal, 102 s when including the injection procedure. Flash proteotyping is highly discriminating, as it can provide information down to strain level. The methodology enables high throughput identification of isolates, opening up new prospects, particularly in culturomics, and diagnostics.

Mitochondrial proteome research: the road ahead

Mitochondria are multifaceted organelles with key roles in anabolic and catabolic metabolism, bioenergetics, cellular signalling and nutrient sensing, and programmed cell death processes. Their diverse functions are enabled by a sophisticated set of protein components encoded by the nuclear and mitochondrial genomes. The extent and complexity of the mitochondrial proteome remained unclear for decades. This began to change 20 years ago when, driven by the emergence of mass spectrometry-based proteomics, the first draft mitochondrial proteomes were established. In the ensuing decades, further technological and computational advances helped to refine these 'maps', with current estimates of the core mammalian mitochondrial proteome ranging from 1,000 to 1,500 proteins. The creation of these compendia provided a systemic view of an organelle previously studied primarily in a reductionist fashion and has accelerated both basic scientific discovery and the diagnosis and treatment of human disease. Yet numerous challenges remain in understanding mitochondrial biology and translating this knowledge into the medical context. In this Roadmap, we propose a path forward for refining the mitochondrial protein map to enhance its discovery and therapeutic potential. We discuss how emerging technologies can assist the detection of new mitochondrial proteins, reveal their patterns of expression across diverse tissues and cell types, and provide key information on proteoforms. We highlight the power of an enhanced map for systematically defining the functions of its members. Finally, we examine the utility of an expanded, functionally annotated mitochondrial proteome in a translational setting for aiding both diagnosis of mitochondrial disease and targeting of mitochondria for treatment.

Inclusion of Porous Graphitic Carbon Chromatography Yields Greater Protein Identification and Compartment and Process Coverage and Enables More Reflective Protein-Level Label-Free Quantitation

The ubiquity of mass spectrometry-based bottom-up proteomic analyses as a component of biological investigation mandates the validation of methodologies that increase acquisition efficiency, improve sample coverage, and enhance profiling depth. Chromatographic separation is often ignored as an area of potential improvement, with most analyses relying on traditional reversed-phase liquid chromatography (RPLC); this consistent reliance on a single chromatographic paradigm fundamentally limits our view of the observable proteome. Herein, we build upon early reports and validate porous graphitic carbon chromatography (PGC) as a facile means to substantially enhance proteomic coverage without changes to sample preparation, instrument configuration, or acquisition methods. Analysis of offline fractionated cell line digests using both separations revealed an increase in peptide and protein identifications by 43% and 24%, respectively. Increased identifications provided more comprehensive coverage of cellular components and biological processes independent of protein abundance, highlighting the substantial quantity of proteomic information that may go undetected in standard analyses. We further utilize these data to reveal that label-free quantitative analyses using RPLC separations alone may not be reflective of actual protein constituency. Together, these data highlight the value and comprehension offered through PGC-MS proteomic analyses. RAW proteomic data have been uploaded to the MassIVE repository with the primary accession code MSV000091495.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

A Combined Gas-Phase Separation Strategy for ADP-ribosylated Peptides

ADP-ribosylation (ADPr) is a post-translational modification that is best studied using mass spectrometry. Method developments that are permissive with low inputs or baseline levels of protein ribosylation represent the next frontier in the field. High-field asymmetric waveform ion mobility spectrometry (FAIMS) reduces peptide complexity in the gas phase, providing a means to achieve maximal ADPr peptide sequencing depth. We therefore investigated the extent to which FAIMS with or without traditional gas-phase fractionation-separation (GPS) can increase the number of ADPr peptides. We examined ADPr peptides enriched from mouse spleens. We gleaned additional insight by also reporting findings from the corresponding non-ADPr peptide contaminants and the peptide inputs for ADPr peptide enrichment. At increasingly higher negative compensation voltages, ADPr peptides were more stable, whereas the non-ADPr peptides were filtered out. A combination of 3 GPS survey scans, each with 8 compensation voltages, resulted in 790 high-confidence ADPr peptides, compared to 90 with GPS alone. A simplified acquisition strategy requiring only two injections corresponding to two MS1 scan ranges coupled to optimized compensation voltage settings provided 402 ADPr peptides corresponding to 234 ADPr proteins. We conclude that our combined GPS strategy is a valuable addition to any ADP-ribosylome workflow. The data are available via ProteomeXchange with identifier PXD040898.

Ultra-Fast Mass Spectrometry in Plant Biochemistry: Response of Winter Wheat Proteomics to Pre-Sowing Treatment with Iron Compounds

In recent years, ultrafast liquid chromatography/mass spectrometry methods have been extensively developed for the use in proteome profiling in biochemical studies. These methods are intended for express monitoring of cell response to biotic stimuli and elucidation of correlation of molecular changes with biological processes and phenotypical changes. New technologies, including the use of nanomaterials, are actively introduced to increase agricultural production. However, this requires complex approbation of new fertilizers and investigation of mechanisms underlying the biotic effects on the germination, growth, and development of plants. The aim of this work was to adapt the method of ultrafast chromatography/mass spectrometry for rapid quantitative profiling of molecular changes in 7-day-old wheat seedlings in response to pre-sowing seed treatment with iron compounds. The used method allows to analyze up to 200 samples per day; its practical value lies in the possibility of express proteomic diagnostics of the biotic action of new treatments, including those intended for agricultural needs. Changes in the regulation of photosynthesis, biosynthesis of chlorophyll and porphyrin- and tetrapyrrole-containing compounds, glycolysis (in shoot tissues), and polysaccharide metabolism (in root tissues) were shown after seed treatment with suspensions containing film-forming polymers (PEG 400, Na-CMC, Na2-EDTA), iron (II, III) nanoparticles, or iron (II) sulfate. Observations at the protein levels were consistent with the results of morphometry, superoxide dismutase activity assay, and microelement analysis of 3-day-old germinated seeds and shoots and roots of 7-day-old seedlings. A characteristic molecular signature involving proteins participating in the regulation of photosynthesis and glycolytic process was suggested as a potential marker of the biotic effects of seed treatment with iron compounds, which will be confirmed in further studies.

Sensitive N-Glycopeptide Profiling of Single and Rare Cells Using an Isobaric Labeling Strategy without Enrichment

Single-cell omics is critical in revealing population heterogeneity, discovering unique features of individual cells, and identifying minority subpopulations of interest. As one of the major post-translational modifications, protein N-glycosylation plays crucial roles in various important biological processes. Elucidation of the variation in N-glycosylation patterns at single-cell resolution may largely facilitate the understanding of their key roles in the tumor microenvironment and immune therapy. However, comprehensive N-glycoproteome profiling for single cells has not been achieved due to the extremely limited sample amount and incompatibility with the available enrichment strategies. Here, we have developed an isobaric labeling-based carrier strategy for highly sensitive intact N-glycopeptide profiling for single cells or a small number of rare cells without enrichment. Isobaric labeling has unique multiplexing properties, by which the "total" signal from all channels triggers MS/MS fragmentation for N-glycopeptide identification, while the reporter ions provide quantitative information. In our strategy, a carrier channel using N-glycopeptides obtained from bulk-cell samples significantly improved the "total" signal of N-glycopeptides and, therefore, promoted the first quantitative analysis of averagely 260 N-glycopeptides from single HeLa cells. We further applied this strategy to study the regional heterogeneity of N-glycosylation of microglia in mouse brain and discovered region-specific N-glycoproteome patterns and cell subtypes. In conclusion, the glycocarrier strategy provides an attractive solution for sensitive and quantitative N-glycopeptide profiling of single/rare cells that cannot be enriched by traditional workflows.

Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS)

Combined multi-omics analysis of proteomics, polar metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS) platforms for each omics layer. This requirement for different platforms limits throughput and increases costs, preventing the application of mass spectrometry-based multi-omics to large scale drug discovery or clinical cohorts. Here, we present an innovative strategy for simultaneous multi-omics analysis by direct infusion (SMAD) using one single injection without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of this method and then present two practical applications: mouse macrophage M1/M2 polarization and high throughput drug screening in human 293T cells. Finally, we demonstrate relationships between proteomic and metabolomic data are discovered by machine learning.

Discovery and validation of glucose-sensitive peptide biomarkers from human serum albumin to diagnose type 2 diabetes mellitus

Glycated albumin (GA), which represents the global glycation level of albumin, has emerged as a biomarker for diagnosing prediabetes and diabetes. In our previous study, we developed a peptide-based strategy and found three putative peptide biomarkers from the tryptic peptides of GA to diagnose type 2 diabetes mellitus (T2DM). However, the trypsin cleavage sites at the carboxyl side of lysine (K) and arginine (R) are consistent with the nonenzymatic glycation modification site residues, which considerably increases the number of missed cleavage sites and half-cleaved peptides. To solve this problem, the endoproteinase Glu-C was used to digest GA from human serum to screen putative peptides to diagnose T2DM. In the discovery phase, we found eighteen and fifteen glucose-sensitive peptides from purified albumin and human serum incubated with ¹³C glucose in vitro, respectively. In the validation phase, eight glucose-sensitive peptides were screened and validated in 72 clinical samples (28 healthy controls and 44 patients with diabetes) using label-free LC-ESI-MRM. Three putative sensitive peptides (VAHRFKDLGEE, FKPLVEEPQNLIKQNCE and NQDSISSKLKE) from albumin exhibited good specificity and sensitivity based on receiver operating characteristic analysis. In summary, three peptides were found as promising biomarkers for the diagnosis and assessment of T2DM based on mass spectrometry.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Current Role of Modern Chromatography with Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy in the Investigation of Biomarkers of Endometriosis

Endometriosis has a wide range of clinical manifestations, and the disease course is unpredictable, making the diagnosis a challenging task. Despite significant advances in the pathophysiology of endometriosis and various proposed theories, the exact etiology is not fully understood and is still unknown. The most commonly used biomarker of endometriosis is CA-125, however, it is nonspecific and is applied for cancers diagnosis. Therefore, the development of reliable noninvasive diagnostic tests for the early diagnosis of endometriosis remains one of the top priorities. Omics technologies are very promising approaches for constructing diagnostic models and biomarker discovery. Their use can greatly facilitate the study of such a complex disease as endometriosis. Nowadays, powerful analytical platforms commonly used in omics, such as gas and liquid chromatography with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, have proven to be a promising tools for biomarker discovery. The aim of this review is to summarize the various features of the analytical approaches, practical challenges and features of gas and liquid chromatography with MS and NMR spectroscopy (including sample processing protocols, technological advancements, and methodology) used for profiling of metabolites, lipids, peptides and proteins in physiological fluids and tissues from patients with endometriosis. In addition, this report devotes special attention to the issue of how comprehensive analyses of these profiles can effectively contribute to the study of endometriosis. The search query included reports published between 2012 and 2022 years in PubMed, Web-of-Science, SCOPUS, Science Direct.

Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.

MetaProClust-MS1: an MS1 Profiling Approach for Large-Scale Microbiome Screening

Metaproteomics is used to explore the functional dynamics of microbial communities. However, acquiring metaproteomic data by tandem mass spectrometry (MS/MS) is time-consuming and resource-intensive, and there is a demand for computational methods that can be used to reduce these resource requirements. We present MetaProClust-MS1, a computational framework for microbiome feature screening developed to prioritize samples for follow-up MS/MS. In this proof-of-concept study, we tested and compared MetaProClust-MS1 results on gut microbiome data, from fecal samples, acquired using short 15-min MS1-only chromatographic gradients and MS1 spectra from longer 60-min gradients to MS/MS-acquired data. We found that MetaProClust-MS1 identified robust gut microbiome responses caused by xenobiotics with significantly correlated cluster topologies of comparable data sets. We also used MetaProClust-MS1 to reanalyze data from both a clinical MS/MS diagnostic study of pediatric patients with inflammatory bowel disease and an experiment evaluating the therapeutic effects of a small molecule on the brain tissue of Alzheimer's disease mouse models. MetaProClust-MS1 clusters could distinguish between inflammatory bowel disease diagnoses (ulcerative colitis and Crohn's disease) using samples from mucosal luminal interface samples and identified hippocampal proteome shifts of Alzheimer's disease mouse models after small-molecule treatment. Therefore, we demonstrate that MetaProClust-MS1 can screen both microbiomes and single-species proteomes using only MS1 profiles, and our results suggest that this approach may be generalizable to any proteomics experiment. MetaProClust-MS1 may be especially useful for large-scale metaproteomic screening for the prioritization of samples for further metaproteomic characterization, using MS/MS, for instance, in addition to being a promising novel approach for clinical diagnostic screening. IMPORTANCE Growing evidence suggests that human gut microbiome composition and function are highly associated with health and disease. As such, high-throughput metaproteomic studies are becoming more common in gut microbiome research. However, using a conventional long liquid chromatography (LC)-MS/MS gradient metaproteomics approach as an initial screen in large-scale microbiome experiments can be slow and expensive. To combat this challenge, we introduce MetaProClust-MS1, a computational framework for microbiome screening using MS1-only profiles. In this proof-of-concept study, we show that MetaProClust-MS1 identifies clusters of gut microbiome treatments using MS1-only profiles similar to those identified using MS/MS. Our approach allows researchers to prioritize samples and treatments of interest for further metaproteomic analyses and may be generally applicable to any proteomic analysis. In particular, this approach may be especially useful for large-scale metaproteomic screening or in clinical settings where rapid diagnostic evidence is required.

Paleoproteomics

Paleoproteomics, the study of ancient proteins, is a rapidly growing field at the intersection of molecular biology, paleontology, archaeology, paleoecology, and history. Paleoproteomics research leverages the longevity and diversity of proteins to explore fundamental questions about the past. While its origins predate the characterization of DNA, it was only with the advent of soft ionization mass spectrometry that the study of ancient proteins became truly feasible. Technological gains over the past 20 years have allowed increasing opportunities to better understand preservation, degradation, and recovery of the rich bioarchive of ancient proteins found in the archaeological and paleontological records. Growing from a handful of studies in the 1990s on individual highly abundant ancient proteins, paleoproteomics today is an expanding field with diverse applications ranging from the taxonomic identification of highly fragmented bones and shells and the phylogenetic resolution of extinct species to the exploration of past cuisines from dental calculus and pottery food crusts and the characterization of past diseases. More broadly, these studies have opened new doors in understanding past human-animal interactions, the reconstruction of past environments and environmental changes, the expansion of the hominin fossil record through large scale screening of nondiagnostic bone fragments, and the phylogenetic resolution of the vertebrate fossil record. Even with these advances, much of the ancient proteomic record still remains unexplored. Here we provide an overview of the history of the field, a summary of the major methods and applications currently in use, and a critical evaluation of current challenges. We conclude by looking to the future, for which innovative solutions and emerging technology will play an important role in enabling us to access the still unexplored "dark" proteome, allowing for a fuller understanding of the role ancient proteins can play in the interpretation of the past.

Proteomics of Human Biological Fluids for Biomarker Discoveries: Technical Advances and Recent Applications

INTRODUCTION

Biological fluids are routine samples for diagnostic testing and monitoring. Blood samples are typically measured because of their moderate collection invasiveness and high information content on health and disease. Several body fluids, such as cerebrospinal fluid (CSF), are also studied and suited to specific pathologies. Over the last two decades proteomics has quested to identify protein biomarkers but with limited success. Recent technologies and refined pipelines have accelerated the profiling of human biological fluids.

AREAS COVERED

We review proteomic technologies for the identification of biomarkers. Those are based on antibodies/aptamers arrays or mass spectrometry (MS), but new ones are emerging. Advances in scalability and throughput have allowed to better design studies and cope with the limited sample size that had until now prevailed due to technological constraints. With these enablers, plasma/serum, CSF, saliva, tears, urine, and milk proteomes have been further profiled; we provide a non-exhaustive picture of some recent highlights (mainly covering literature from last five years in the Scopus database) using MS-based proteomics.

EXPERT OPINION

While proteomics has been in the shadow of genomics for years, proteomic tools and methodologies have reached a certain maturity. They are better suited to discover innovative and robust biofluid biomarkers.

An overview of technologies for MS-based proteomics-centric multi-omics

INTRODUCTION

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

AREAS COVERED

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.

EXPERT COMMENTARY

Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand the measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nanoliquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit the throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate the application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in less than 2.5 h, with a measured dynamic range >1200-fold. We conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

CsoDIAq Software for Direct Infusion Shotgun Proteome Analysis

Direct infusion shotgun proteome analysis (DISPA) is a new paradigm for expedited mass spectrometry-based proteomics, but the original data analysis workflow was onerous. Here, we introduce CsoDIAq, a user-friendly software package for the identification and quantification of peptides and proteins from DISPA data. In addition to establishing a complete and automated analysis workflow with a graphical user interface, CsoDIAq introduces algorithmic concepts to spectrum-spectrum matching to improve peptide identification speed and sensitivity. These include spectra pooling to reduce search time complexity and a new spectrum-spectrum match score called match count and cosine, which improves target discrimination in a target-decoy analysis. Fragment mass tolerance correction also increased the number of peptide identifications. Finally, we adapt CsoDIAq to standard LC-MS DIA and show that it outperforms other spectrum-spectrum matching software.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics

Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation-serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond.

Deep learning neural network tools for proteomics

Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategies using artificial deep neural networks (or "deep learning") that have started to break barriers and accelerate progress in the field of shotgun proteomics. Deep learning now accurately predicts physicochemical properties of peptides from their sequence, including tandem mass spectra and retention time. Furthermore, deep learning methods exist for nearly every aspect of the modern proteomics workflow, enabling improved feature selection, peptide identification, and protein inference.

Evaluation and Optimization of High-Field Asymmetric Waveform Ion-Mobility Spectrometry for Multiplexed Quantitative Site-Specific N-Glycoproteomics

The heterogeneity and complexity of glycosylation hinder the depth of site-specific glycoproteomics analysis. High-field asymmetric-waveform ion-mobility spectrometry (FAIMS) has been shown to improve the scope of bottom-up proteomics. The benefits of FAIMS for quantitative N-glycoproteomics have not been investigated yet. In this work, we optimized FAIMS settings for N-glycopeptide identification, with or without the tandem mass tag (TMT) label. The optimized FAIMS approach significantly increased the identification of site-specific N-glycopeptides derived from the purified immunoglobulin M (IgM) protein or human lymphoma cells. We explored in detail the changes in FAIMS mobility caused by N-glycopeptides with different characteristics, including TMT labeling, charge state, glycan type, peptide sequence, glycan size, and precursor m/z. Importantly, FAIMS also improved multiplexed N-glycopeptide quantification, both with the standard MS2 acquisition method and with our recently developed Glyco-SPS-MS3 method. The combination of FAIMS and Glyco-SPS-MS3 methods provided the highest quantitative accuracy and precision. Our results demonstrate the advantages of FAIMS for improved mass spectrometry-based qualitative and quantitative N-glycoproteomics.

Shotgun chemome characterization of Artemisia rupestris L. Using direct infusion-MS/MSALL

In comparison of liquid chromatography, direct infusion is a superior choice to achieve high-throughput measurements. The specificity and selectivity of tandem mass spectrometry (MS/MS) actually result in a so-called MS separation potential when chemical characterization of herbal medicines. Here, a MS/MS^ALL program was introduced to promote DI-MS/MS to be an eligible tool for shotgun chemome characterization of Artemisia rupestris L. that is currently drawing worldwide interests because of the promising antiviral activity. After MS¹ spectral acquisition for the crude extract, the gas phase fractionation concept enabled the precursor ion cohort sequentially entered the collision cell with a stepped unit mass window (step-size as 1 Da) to generate MS² spectra, thus generating a unique property integrating the advantages of both data-dependent and data-independent acquisition manners. Even though being free of chromatographic separation, spectrometric separations were accomplished for by MS/MS^ALL program unless the components shared identical nominal molecular weights. Extensive efforts such as the correlations of MS¹ signals with MS² spectra, structural annotations of fragment ion species, information retrieval in some accessible databases, and referring to the literature data, were devoted for chemical characterization, and as a result, 44 compounds, in total, were structurally identified from 50% aqueous methanol exact of A. rupestris, including 8 caffeoyl quinic acid derivatives, 13 flavonoids, 15 monomeric and dimeric sesquiterpenoids, 4 fatty acids, 2 penylpropanoids, along with 2 other compounds. However, isomers were assigned as an isomeric mixture because their precursor ions always co-existed in a single mass window. Above all, DI-MS/MS^ALL provides an alternative tool for chemome characterization of herbal medicines, in particular when the great measurement workload for a large sample cohort, attributing to the high-throughput advantage.

Deeper Protein Identification Using Field Asymmetric Ion Mobility Spectrometry in Top-Down Proteomics

Field asymmetric ion mobility spectrometry (FAIMS), when used in proteomics studies, provides superior selectivity and enables more proteins to be identified by providing additional gas-phase separation. Here, we tested the performance of cylindrical FAIMS for the identification and characterization of proteoforms by top-down mass spectrometry of heterogeneous protein mixtures. Combining FAIMS with chromatographic separation resulted in a 62% increase in protein identifications, an 8% increase in proteoform identifications, and an improvement in proteoform identification compared to samples analyzed without FAIMS. In addition, utilization of FAIMS resulted in the identification of proteins encoded by lower-abundance mRNA transcripts. These improvements were attributable, in part, to improved signal-to-noise for proteoforms with similar retention times. Additionally, our results show that the optimal compensation voltage of any given proteoform was correlated with the molecular weight of the analyte. Collectively these results suggest that the addition of FAIMS can enhance top-down proteomics in both discovery and targeted applications.

The tryptic peptides of hemoglobin for diagnosis of type 2 diabetes mellitus using label-free and standard-free LC-ESI-DMRM

N-1-(deoxyfructosyl) valine of β-hemoglobin, commonly referred to as HbA1c, is the “gold standard” for clinical detection of diabetes. Instead of quantifying the full-length HbA1c glycated protein, in the present study, we proposed the peptide-based strategy to quantify the depletion of the tryptic peptides of hemoglobin for the diagnosis of type 2 diabetes mellitus (T2DM). The peptides were discovered and validated as T2DM biomarkers by label-free LC-ESI-DMRM method without reference material. The glucose could react with hemoglobin's free amino group of N-terminus and ϵ-amino group of lysine residues and leave the modification on the hemoglobin tryptic peptides. Thus, there are two types of peptides in the hemoglobin: sensitive peptides and insensitive peptides to glucose due to the differential sensitivity of lysine residues to glycation. To discover two types of peptides of hemoglobin, we first developed the assay of liquid chromatography-electrospray ionization mass spectrometry coupled with dynamic multiple reaction monitoring. The protein coverage reaches 94.2%. Moreover, the hemoglobin was incubated with the 500 mmol/L glucose for 20 days, 40 days and 60 days in vitro to screen the sensitive peptides and insensitive peptides to glucose. A total of 14 sensitive peptides and 4 insensitive peptides were discovered. Furthermore, the LC-ESI-DMRM method was also utilized to validated the glucose-sensitive peptides by 40 clinical samples with healthy control individuals (n = 20) and type 2 diabetes mellitus patients (n = 20). Three putative sensitive peptides (LLGNVLVCVLAHHFGK, VVAGVANALAHKYH, LRVDPVNFK) from the hemoglobin showed excellent sensitivity and specificity based on receiver operating characteristic analysis and were verified as the promising biomarkers for the diagnosis of diabetes mellitus. And one peptide (LLVVYPWTQR) was found as glucose-insensitive peptide. Taken together, the findings of this study suggest that quantification of hemoglobin tryptic peptides using label-free and standard-free LC-ESI-DMRM is an alternative method for the diagnosis of T2DM, which could be combined with other MS-based blood biomarkers for diagnosis of multiple diseases in MS single shot.

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The peptide-based strategy is proposed to quantify the depletion of peptides of hemoglobin for the diagnosis of T2DM.

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Three glucose-sensitive peptides of hemoglobin are expected to become the putative biomarkers for T2DM.

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The peptide VVAGVANALAHKYH shows excellent sensitivity and specificity to classify between healthy group and T2DM group.

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The label-free and standard-free assay is very simple and suitable for clinical diagnosis of T2DM.

The clinical potential of prm-PASEF mass spectrometry

INTRODUCTION

The continuous technical improvement in sensitivity and specificity placed mass spectrometry as an alternative method for analyzing clinical samples. In parallel to the rapid development of discovery proteomics, targeted acquisition has been implemented as a complementary option for measuring a small set of proteins with high sensitivity and robustness in a large sample cohort. The combination of trapped ion mobility with a rapid time-of-flight (TOF) mass spectrometer improves the sensitivity even further and triggers the development of prm-PASEF.

AREAS COVERED

This article discusses the development of prm-PASEF and its advantages over the existing targeted and discovery methods for analyzing clinical samples. We are also highlighting the different requirements for the use of prm-PASEF on clinical samples.

EXPERT OPINION

prm-PASEF takes advantage of a dual ion-mobility trap enabling highly multiplexed targeted acquisition. It allows the implementation of a short chromatographic separation setup without sacrificing the number of targeted peptides. Analyzing clinical samples by prm-PASEF holds the promise to significantly improve throughput while maintaining sensitivity to detect the selected target proteins.

Development of a chromatography-free method for high-throughput MS-based bioanalysis of therapeutic monoclonal antibodies

Aim: Our objective was to test the feasibility of developing an LC-free, MS-based approach for high-throughput bioanalysis of humanized therapeutic monoclonal antibodies. Methodology: A universal tryptic peptide from human IgG1, IgG3 and IgG4 was selected as the surrogate peptide for quantitation. After tryptic digestion, the surrogate peptide was fractionated via solid-phase extraction before being subjected to direct infusion-based MS/MS analysis. A high-resolution, multiplexed (MSX = 2) parallel reaction monitoring method was developed for data acquisition. Results & conclusion: This proof-of-concept study demonstrated the feasibility of achieving high-throughput MS-based bioanalysis of monoclonal antibodies using an LC-free workflow with sensitivity comparable to conventional LC-MS/MS-based methods.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.