Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition

Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

Proteome changes in larval zebrafish (Danio rerio) and fathead minnow (Pimephales promelas) exposed to (±) anatoxin-a

Anatoxin-a and its analogues are potent neurotoxins produced by several genera of cyanobacteria. Due in part to its high toxicity and potential presence in drinking water, these toxins pose threats to public health, companion animals and the environment. It primarily exerts toxicity as a cholinergic agonist, with high affinity at neuromuscular junctions, but molecular mechanisms by which it elicits toxicological responses are not fully understood. To advance understanding of this cyanobacteria, proteomic characterization (DIA shotgun proteomics) of two common fish models (zebrafish and fathead minnow) was performed following  (±) anatoxin-a exposure. Specifically, proteome changes were identified and quantified in larval fish exposed for 96 h (0.01-3 mg/L (±) anatoxin-a and caffeine (a methodological positive control) with environmentally relevant treatment levels examined based on environmental exposure distributions of surface water data. Proteomic concentration - response relationships revealed 48 and 29 proteins with concentration - response relationships curves for zebrafish and fathead minnow, respectively. In contrast, the highest number of differentially expressed proteins (DEPs) varied between zebrafish (n = 145) and fathead minnow (n = 300), with only fatheads displaying DEPs at all treatment levels. For both species, genes associated with reproduction were significantly downregulated, with pathways analysis that broadly clustered genes into groups associated with DNA repair mechanisms. Importantly, significant differences in proteome response between the species was also observed, consistent with prior observations of differences in response using both behavioral assays and gene expression, adding further support to model specific differences in organismal sensitivity and/or response. When DEPs were read across from humans to zebrafish, disease ontology enrichment identified diseases associated with cognition and muscle weakness consistent with the prior literature. Our observations highlight limited knowledge of how (±) anatoxin-a, a commonly used synthetic racemate surrogate, elicits responses at a molecular level and advances its toxicological understanding.

Research progress in the application of proteomics technology in brain injury

The aim of this article is to review the application progress of proteomics technology in brain injury research in recent years, point out the current problems that need to be overcome, and explore the application prospects of proteomics analysis in brain injury. This study also aims to retrieve all literature on brain injury and proteomics and summarize it. Through searching and screening, the widespread application of proteomics technology in the treatment of traumatic brain injury (TBI) and the use of a large number of TBI biomarkers were discovered. The pathways mediated by some biomarkers and the physiological and pathological mechanisms of occurrence were elucidated. The current classification of brain injury is mainly based on subjective evaluation of clinical symptoms, combined with objective imaging. However, its practical value is often limited when applied to prognosis evaluation in brain injury. Proteomics technology can make up for this deficiency and provide a reference for the prevention and treatment of brain injury.

A crustacean neuropeptide spectral library for data-independent acquisition (DIA) mass spectrometry applications

Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method of choice to achieve sensitive and accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and Cancer borealis as a model of choice for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student's t-test, P value < 0.05) in the number of identifications achieved from the TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications was found in the library search compared to the library-free search. This work highlights the necessity for the use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.

Identification and characterization of CHO host-cell proteins in monoclonal antibody bioprocessing

Host-cell proteins (HCPs) are the foremost class of process-related impurities to be controlled and removed in downstream processing steps in monoclonal antibody (mAb) manufacturing. However, some HCPs may evade clearance in multiple purification steps and reach the final drug product, potentially threatening drug stability and patient safety. This study extends prior work on HCP characterization and persistence in mAb process streams by using mass spectrometry (MS)-based methods to track HCPs through downstream processing steps for seven mAbs that were generated by five different cell lines. The results show considerable variability in HCP identities in the processing steps but extensive commonality in the identities and quantities of the most abundant HCPs in the harvests for different processes. Analysis of HCP abundance in the harvests shows a likely relationship between abundance and the reproducibility of quantification measurements and suggests that some groups of HCPs may hinder the characterization. Quantitative monitoring of HCPs persisting through purification steps coupled with the findings from the harvest analysis suggest that multiple factors, including HCP abundance and mAb-HCP interactions, can contribute to the persistence of individual HCPs and the identification of groups of common, persistent HCPs in mAb manufacturing.

Identification and Targeted Quantification of Endogenous Neuropeptides in the Nematode Caenorhabditis elegans Using Mass Spectrometry

The nematode Caenorhabditis elegans lends itself as an excellent model organism for peptidomics studies. Its ease of cultivation and quick generation time make it suitable for high-throughput studies. The nervous system, with its 302 neurons, is probably the best-known and studied endocrine tissue. Moreover, its neuropeptidergic signaling pathways display numerous similarities with those observed in other metazoans. Here, we describe two label-free approaches for neuropeptidomics in C. elegans: one for discovery purposes, and another for targeted quantification and comparisons of neuropeptide levels between different samples. Starting from a detailed peptide extraction procedure, we here outline the liquid chromatography tandem mass spectrometry (LC-MS/MS) setup and describe subsequent data analysis approaches.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Bull Sperm SWATH-MS-Based Proteomics Reveals Link between High Fertility and Energy Production, Motility Structures, and Sperm-Oocyte Interaction

The prediction of male or semen fertility potential remains a persistent challenge that has yet to be fully resolved. This work analyzed several in vitro parameters and proteome of spermatozoa in bulls cataloged as high- (HF; n = 5) and low-field (LF; n = 5) fertility after more than a thousand artificial inseminations. Sperm motility was evaluated by computer-assisted sperm analysis. Sperm viability, mitochondrial membrane potential (MMP) and reactive oxygen species (mROS) of spermatozoa were assessed by flow cytometry. Proteome was evaluated by the SWATH-MS procedure. Spermatozoa of HF bulls showed significantly higher total motility than the LF group (41.4% vs 29.7%). Rates of healthy sperm (live, high MMP, and low mROS) for HF and LF bull groups were 49% and 43%, respectively (p > 0.05). Spermatozoa of HF bulls showed a higher presence of differentially abundant proteins (DAPs) related to both energy production (COX7C), mainly the OXPHOS pathway, and the development of structures linked with the motility process (TPPP2, SSMEM1, and SPAG16). Furthermore, we observed that equatorin (EQTN), together with other DAPs related to the interaction with the oocyte, was overrepresented in HF bull spermatozoa. The biological processes related to protein processing, catabolism, and protein folding were found to be overrepresented in LF bull sperm in which the HSP90AA1 chaperone was identified as the most DAP. Data are available via ProteomeXchange with identifier PXD042286.

CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry

Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

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.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment

DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.

Deep Proteome of the Developing Chick Midbrain

The epithelial-to-mesenchymal transition (EMT) and migration of cranial neural crest cells within the midbrain are critical processes that permit proper craniofacial patterning in the early embryo. Disruptions in these processes not only impair development but also lead to various diseases, underscoring the need for their detailed understanding at the molecular level. The chick embryo has served historically as an excellent model for human embryonic development, including cranial neural crest cell EMT and migration. While these developmental events have been characterized transcriptionally, studies at the protein level have not been undertaken to date. Here, we applied mass spectrometry (MS)-based proteomics to establish a deep proteomics profile of the chick midbrain region during early embryonic development. Our proteomics method combines optimal lysis conditions, offline fractionation, separation on a nanopatterned stationary phase (μPAC) using nanoflow liquid chromatography, and detection using quadrupole-ion trap-Orbitrap tribrid high-resolution tandem MS. Identification of >5900 proteins and >450 phosphoproteins in this study marks the deepest coverage of the chick midbrain proteome to date. These proteins have known roles in pathways related to neural crest cell EMT and migration such as signaling, proteolysis/extracellular matrix remodeling, and transcriptional regulation. This study offers valuable insight into important developmental processes occurring in the midbrain region and demonstrates the utility of proteomics for characterization of tissue microenvironments during chick embryogenesis.

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Integrated Protein Solubility Shift Assays for Comprehensive Drug Target Identification on a Proteome-Wide Scale

Target proteins are often stabilized after binding with a ligand and thereby typically become more resistant to denaturation. Based on this phenomenon, several methods without the need to covalently modify the ligand have been developed to identify target proteins for a specific ligand. These methods usually employ complicated workflows with high cost and limited throughput. Here, we develop an iso-pH shift assay (ipHSA) method, a proteome-wide target identification method that detects ligand-induced protein solubility shifts by precipitating proteins with a single concentration of acidic agent followed by protein quantification via data-independent acquisition (DIA). Using a pan-kinase inhibitor, staurosporine, we demonstrated that ipHSA increased throughput compared to the previously developed pH-dependent protein precipitation (pHDPP) method. ipHSA was found to have high complementarity in staurosporine target identification compared with the improved isothermal shift assay (iTSA) and isosolvent shift assay (iSSA) using DIA instead of tandem mass tags (TMTs) for quantification. To further improve target identification sensitivity, we developed an integrated protein solubility shift assay (IPSSA) by pooling the supernatants yielded from ipHSA, iTSA, and iSSA methods. IPSSA exhibited increased sensitivity in screening staurosporine targets by 38, 29, and 38% compared to individual methods. Increasing the number of replicate experiments further enhanced the sensitivity of target identification. Meanwhile, IPSSA also improved the throughput and reduced the cost compared with previous methods. As a fast and efficient tool for drug target identification, IPSSA is expected to have broad applications in the study of the mechanism of action.

Plasma Proteomics Study Between the Frequent Exacerbation and Infrequent Exacerbation Phenotypes of Chronic Obstructive Pulmonary Disease

Background

Frequent exacerbation (FE) and infrequent exacerbation (IE) are two phenotypes of chronic obstructive pulmonary disease (COPD), of which FE is associated with a higher incidence of exacerbation and a serious threat to human health. Because the pathogenesis mechanisms of FE are unclear, this study aims to identify FE-related proteins in the plasma via proteomics for use as predictive, diagnostic, and therapeutic biomarkers of COPD.

Methods

A cross-sectional study was conducted in which plasma protein profiles were analyzed in COPD patients at stable stage, and differentially expressed proteins (DEPs) were screened out between the FE and IE patients. FE-related DEPs were identified using data-independent acquisition-based proteomics and bioinformatics analyses. In addition, FE-related candidates were verified by enzyme-linked immunosorbent assay.

Results

In this study, 47 DEPs were screened out between the FE and IE groups, including 20 upregulated and 27 downregulated proteins. Key biological functions (eg, neutrophil degranulation, extracellular exosome, protein homodimerization activity) and signaling pathways (eg, arginine and proline metabolism) were enriched in association with the FE phenotype. Receiver operating characteristic (ROC) analysis of the 11 combined DEPs revealed an area under the curve of 0.985 (p <0.05) for discriminating FE from IE. Moreover, correlation and ROC curve analyses indicated that creatine kinase, M-type (CKM) and fat storage-inducing transmembrane protein 1 (FITM1) might be clinically significant in patients with the FE phenotype. In addition, plasma expression levels of CKM and FITM1 were validated to be significantly decreased in the FE group compared with the IE group (CKM: p <0.01; FITM1: p <0.05).

Conclusion

In this study, novel insights into COPD pathogenesis were provided by investigating and comparing plasma protein profiles between the FE and IE patients. CKM, FITM1, and a combinative biomarker panel may serve as useful tools for assisting in the precision diagnosis and effective treatment of the FE phenotype of COPD.

Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling

Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.

Advanced mass spectrometry workflows for accurate quantification of trace-level host cell proteins in drug products: Benefits of FAIMS separation and gas-phase fractionation DIA

Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880 ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

2019 Association of Biomolecular Resource Facilities Multi-Laboratory Data-Independent Acquisition Proteomics Study

Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in proteomics core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in proteomics laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of 4 exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analyzed by focusing on 2 datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings, and skill levels.

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

Integrated proteomic and phosphoproteomic data-independent acquisition data evaluate the personalized drug responses of primary and metastatic tumors in colorectal cancer

Mass spectrometry (MS)-based proteomics and phosphoproteomics are powerful methods to study the biological mechanisms, diagnostic biomarkers, prognostic analysis, and drug therapy of tumors. Data-independent acquisition (DIA) mode is considered to perform better than data-dependent acquisition (DDA) mode in terms of quantitative reproducibility, specificity, accuracy, and identification of low-abundance proteins. Mini patient derived xenograft (MiniPDX) model is an effective model to assess the response to antineoplastic drugs in vivo and is helpful for the precise treatment of cancer patients. Kinases are favorable spots for tumor-targeted drugs, and their functional completion relies on signaling pathways through phosphorylating downstream substrates. Kinase-phosphorylation networks or edge interactions are considered more credible and permanent for characterizing complex diseases. Here, we provide a workflow for personalized drug response assessment in primary and metastatic colorectal cancer (CRC) tumors using DIA proteomic data, DIA phosphoproteomic data, and MiniPDX models. Three kinase inhibitors, afatinib, gefitinib, and regorafenib, are tested pharmacologically. The process mainly includes the following steps: clinical tissue collection, sample preparation, hybrid spectral libraries establishment, MS data acquisition, kinase-substrate network construction, in vivo drug test, and elastic regression modeling. Our protocol gives a more direct data basis for individual drug responses, and will improve the selection of treatment strategies for patients without the druggable mutation.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Resistance prediction in high-grade serous ovarian carcinoma with neoadjuvant chemotherapy using data-independent acquisition proteomics and an ovary-specific spectral library

High-grade serous ovarian carcinoma (HGSOC) is the most common subtype of ovarian cancer with 5-year survival rates below 40%. Neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) is recommended for patients with advanced-stage HGSOC unsuitable for primary debulking surgery (PDS). However, about 40% of patients receiving this treatment exhibited chemoresistance of uncertain molecular mechanisms and predictability. Here, we built a high-quality ovary-specific spectral library containing 130 735 peptides and 10 696 proteins on Orbitrap instruments. Compared to a published DIA pan-human spectral library (DPHL), this spectral library provides 10% more ovary-specific and 3% more ovary-enriched proteins. This library was then applied to analyze data-independent acquisition (DIA) data of tissue samples from an HGSOC cohort treated with NACT, leading to 10 070 quantified proteins, which is 9.73% more than that with DPHL. We further established a six-protein classifier by parallel reaction monitoring (PRM) to effectively predict the resistance to additional chemotherapy after IDS (Log-rank test, P = 0.002). The classifier was validated with 57 patients from an independent clinical center (P = 0.014). Thus, we have developed an ovary-specific spectral library for targeted proteome analysis, and propose a six-protein classifier that could potentially predict chemoresistance in HGSOC patients after NACT-IDS treatment.

Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut microbiota

Metaproteomics can provide valuable insights into the functions of human gut microbiota (GM), but is challenging due to the extreme complexity and heterogeneity of GM. Data-independent acquisition (DIA) mass spectrometry (MS) has been an emerging quantitative technique in conventional proteomics, but is still at the early stage of development in the field of metaproteomics. Herein, we applied library-free DIA (directDIA)-based metaproteomics and compared the directDIA with other MS-based quantification techniques for metaproteomics on simulated microbial communities and feces samples spiked with bacteria with known ratios, demonstrating the superior performance of directDIA by a comprehensive consideration of proteome coverage in identification as well as accuracy and precision in quantification. We characterized human GM in two cohorts of clinical fecal samples of pancreatic cancer (PC) and mild cognitive impairment (MCI). About 70,000 microbial proteins were quantified in each cohort and annotated to profile the taxonomic and functional characteristics of GM in different diseases. Our work demonstrated the utility of directDIA in quantitative metaproteomics for investigating intestinal microbiota and its related disease pathogenesis.

Label-free proteome quantification and evaluation

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

Using SILAC to Develop Quantitative Data-Independent Acquisition (DIA) Proteomic Methods

Proteins are integral to biological systems and functions. Identifying and quantifying proteins can therefore offer systems-wide insights into protein-protein interactions, cellular signaling, and biological pathway activity. The use of quantitative proteomics has become a method of choice for identifying and quantifying proteins in complex matrices. Proteomics allows researchers to survey hundreds to thousands of proteins in a less biased manner than classical antibody-based protein capture strategies. Typically, discovery approaches have used data-dependent acquisition (DDA) methods, but this approach suffers from stochasticity that compromises quantitation. Recent developments in data-independent acquisition (DIA) proteomics workflows enable proteomic profiling of thousands of samples with increased peak picking consistency making it an excellent candidate for discovering and assessing biomarkers in clinical samples. However, quantitation of peptides from DIA datasets is computationally intensive, and guidelines on how to establish DIA methods are lacking. Method development and optimization require novel tools to visualize and filter DIA datasets appropriately. Here, a protocol and novel script workflow for the optimization of quantitative DIA methods using stable isotope labeling of amino acids in culture (SILAC) are presented. This protocol includes steps for cell growth and labeling, peptide digestion and preparation, and optimization of quantitative DIA methods. In addition, important steps for (1) computational analysis to identify and quantify peptides, (2) data visualizations to identify the linear abundance ranges for all peptides in the sample, and (3) descriptions of how to find high confidence quantitation abundance thresholds are described herein.

Data-Independent Acquisition (DIA) Is Superior for High Precision Phospho-Peptide Quantification in Magnaporthe oryzae

The dynamic interplay of signaling networks in most major cellular processes is characterized by the orchestration of reversible protein phosphorylation. Consequently, analytic methods such as quantitative phospho-peptidomics have been pushed forward from a highly specialized edge-technique to a powerful and versatile platform for comprehensively analyzing the phosphorylation profile of living organisms. Despite enormous progress in instrumentation and bioinformatics, a high number of missing values caused by the experimental procedure remains a major problem, due to either a random phospho-peptide enrichment selectivity or borderline signal intensities, which both cause the exclusion for fragmentation using the commonly applied data dependent acquisition (DDA) mode. Consequently, an incomplete dataset reduces confidence in the subsequent statistical bioinformatic processing. Here, we successfully applied data independent acquisition (DIA) by using the filamentous fungus Magnaporthe oryzae as a model organism, and could prove that while maintaining data quality (such as phosphosite and peptide sequence confidence), the data completeness increases dramatically. Since the method presented here reduces the LC-MS/MS analysis from 3 h to 1 h and increases the number of phosphosites identified up to 10-fold in contrast to published studies in Magnaporthe oryzae, we provide a refined methodology and a sophisticated resource for investigation of signaling processes in filamentous fungi.

Proteomic overview of hepatocellular carcinoma cell lines and generation of the spectral library

Cell lines are extensively used tools, therefore a comprehensive proteomic overview of hepatocellular carcinoma (HCC) cell lines and an extensive spectral library for data independent acquisition (DIA) quantification are necessary. Here, we present the proteome of nine commonly used HCC cell lines covering 9,208 protein groups, and the HCC spectral library containing 253,921 precursors, 168,811 peptides and 10,098 protein groups. The proteomic overview reveals the heterogeneity between different cell lines, and the similarity in proliferation and metastasis characteristics and drug targets-expression with tumour tissues. The HCC spectral library generating consumed 108 hours' runtime for data dependent acquisition (DDA) of 48 runs, 24 hours' runtime for database searching by MaxQuant version 2.0.3.0, and 1 hour' runtime for processing by Spectronaut^TM version 15.2. The HCC spectral library supports quantification of 7,637 protein groups of triples 2-hour DIA analysis of HepG2 and discovering biological alteration. This study provides valuable resources for HCC cell lines and efficient DIA quantification on LC-Orbitrap platform, further help to explore the molecular mechanism and candidate therapeutic targets.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

Proteomics Profiling of Stool Samples from Preterm Neonates with SWATH/DIA Mass Spectrometry for Predicting Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is a life-threatening condition for premature infants in neonatal intensive care units. Finding indicators that can predict NEC development before symptoms appear would provide more time to apply targeted interventions. In this study, stools from 132 very-low-birth-weight (VLBW) infants were collected daily in the context of a multi-center prospective study aimed at investigating the potential of fecal biomarkers for NEC prediction using proteomics technology. Eight of the VLBW infants received a stage-3 NEC diagnosis. Stools collected from the NEC infants up to 10 days before their diagnosis were available for seven of them. Their samples were matched with those from seven pairs of non-NEC controls. The samples were processed for liquid chromatography-tandem mass spectrometry analysis using SWATH/DIA acquisition and cross-compatible proteomic software to perform label-free quantification. ROC curve and principal component analyses were used to explore discriminating information and to evaluate candidate protein markers. A series of 36 proteins showed the most efficient capacity with a signature that predicted all seven NEC infants at least a week in advance. Overall, our study demonstrates that multiplexed proteomic signature detection constitutes a promising approach for the early detection of NEC development in premature infants.

Challenges and opportunities for proteomics and the improvement of bread wheat quality

Wheat remains a critical global food source, pressured by climate change and the need to maximize yield, improve processing and nutritional quality and ensure safety. An enormous amount of research has been conducted to understand gluten protein composition and structure in relation to end-use quality, yet progress has become stagnant. This is mainly due to the need and inability to biochemically characterize the intact functional glutenin polymer in order to correlate to quality, necessitating reduction to monomeric subunits and a loss of contextual information. While some individual gluten proteins might have a positive or negative influence on gluten quality, it is the sum total of these proteins, their relative and absolute expression, their sub-cellular trafficking, the amount and size of glutenin polymers, and ratios between gluten protein classes that define viscoelasticity of gluten. The sub-cellular trafficking of gluten proteins during seed maturation is still not completely clear and there is evidence of dual pathways and therefore different destinations for proteins, either constitutively or temporally. The trafficking of proteins is also unclear in endosperm cells as they undergo programmed cell death; Golgi disappear around 12 DPA but protein filling continues at least to 25 DPA. Modulation of the timing of cellular events will invariably affect protein deposition and therefore gluten strength and function. Existing and emerging proteomics technologies such as proteoform profiling and top-down proteomics offer new tools to study gluten protein composition as a whole system and identify compositional patterns that can modify gluten structure with improved functionality.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level

BACKGROUND

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis.

METHODS

We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow.

RESULTS

We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number.

CONCLUSIONS

Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Advances in phosphoproteomics and its application to COPD

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) was the third leading cause of global death in 2019, causing a huge economic burden to society. Therefore, it is urgent to identify specific phenotypes of COPD patients through early detection, and to promptly treat exacerbations. The field of phosphoproteomics has been a massive advancement, compelled by the developments in mass spectrometry, enrichment strategies, algorithms, and tools. Modern mass spectrometry-based phosphoproteomics allows understanding of disease pathobiology, biomarker discovery, and predicting new therapeutic modalities.

AREAS COVERED

In this article, we present an overview of phosphoproteomic research and strategies for enrichment and fractionation of phosphopeptides, identification of phosphorylation sites, chromatographic separation and mass spectrometry detection strategies, and the potential application of phosphorylated proteomic analysis in the diagnosis, treatment, and prognosis of COPD disease.

EXPERT OPINION

The role of phosphoproteomics in COPD is critical for understanding disease pathobiology, identifying potential biomarkers, and predicting new therapeutic approaches. However, the complexity of COPD requires the more comprehensive understanding that can be achieved through integrated multi-omics studies. Phosphoproteomics, as a part of these multi-omics approaches, can provide valuable insights into the underlying mechanisms of COPD.

Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation

Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.

Systematic Qualitative and Quantitative Analyses of Wenxin Granule via Ultra-High Performance Liquid Chromatography Coupled with Ion Mobility Quadrupole Time-of-Flight Mass Spectrometry and Triple Quadrupole–Linear Ion Trap Mass Spectrometry

Wenxin granule (WXG) is a popular traditional Chinese medicine (TCM) preparation for the treatment of arrhythmia disease. Potent analytical technologies are needed to elucidate its chemical composition and assess the quality differences among multibatch samples. In this work, both a multicomponent characterization and quantitative assay of WXG were conducted using two liquid chromatography–mass spectrometry (LC-MS) approaches. An ultra-high performance liquid chromatography–ion mobility quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) approach combined with intelligent peak annotation workflows was developed to characterize the multicomponents of WXG. A hybrid scan approach enabling alternative data-independent and data-dependent acquisitions was established. We characterized 205 components, including 92 ginsenosides, 53 steroidal saponins, 14 alkaloids, and 46 others. Moreover, an optimized scheduled multiple reaction monitoring (sMRM) method was elaborated, targeting 24 compounds of WXG via ultra-high performance liquid chromatography–triple quadrupole linear ion trap mass spectrometry (UHPLC/QTrap-MS), which was validated based on its selectivity, precision, stability, repeatability, linearity, sensitivity, recovery, and matrix effect. By applying this method to 27 batches of WXG samples, the content variations of multiple markers from Notoginseng Radix et Rhizoma (21) and Codonopsis Radix (3) were depicted. Conclusively, we achieved the comprehensive multicomponent characterization and holistic quality assessment of WXG by targeting the non-volatile components.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

Protocol for Increasing the Sensitivity of MS-Based Protein Detection in Human Chorionic Villi

An important step in the proteomic analysis of missing proteins is the use of a wide range of tissues, optimal extraction, and the processing of protein material in order to ensure the highest sensitivity in downstream protein detection. This work describes a purification protocol for identifying low-abundance proteins in human chorionic villi using the proposed “1DE-gel concentration” method. This involves the removal of SDS in a short electrophoresis run in a stacking gel without protein separation. Following the in-gel digestion of the obtained holistic single protein band, we used the peptide mixture for further LC–MS/MS analysis. Statistically significant results were derived from six datasets, containing three treatments, each from two tissue sources (elective or missed abortions). The 1DE-gel concentration increased the coverage of the chorionic villus proteome. Our approach allowed the identification of 15 low-abundance proteins, of which some had not been previously detected via the mass spectrometry of trophoblasts. In the post hoc data analysis, we found a dubious or uncertain protein (PSG7) encoded on human chromosome 19 according to neXtProt. A proteomic sample preparation workflow with the 1DE-gel concentration can be used as a prospective tool for uncovering the low-abundance part of the human proteome.

A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells

Data independent acquisition (DIA) has emerged as a powerful proteomic technique with exceptional reproducibility and throughput, and has been widely applied to clinical sample analysis. DIA approaches normally rely on project-specific spectral libraries generated by data dependent acquisition (DDA), requiring extensive off-line fractionation and large amounts of input material. In this study, we aimed to explore the utility of DIA for the analysis of samples with limited quantities. We employed three software tools (DIA-NN, Spectronaut, and EncyclopeDIA) for data analysis and generated three types of libraries, including an experiment-specific library built by DDA analysis of off-line fractions, a FASTA sequence database, and a library generated by gas-phase fractionation (GPF), resulting in eight analysis pipelines. Then we systematically compared the performance of the eight strategies by analyzing the DIA data from HEK293T cell tryptic peptides with sample loads of 500 ng, 100 ng, 20 ng, and 4 ng. The results showed that DIA-NN with GPF-based libraries outperformed the others in protein identification and retention time calibration. Next, we further evaluated the optimized workflow by analyzing the proteome alteration in 5000 peripheral blood mononuclear cells (PBMCs) induced by lipopolysaccharide (LPS) stimulation. As a result, 3179 protein groups were quantified, and functional analysis revealed activation of multiple signaling pathways, e. g., endocytosis, NF-kappa B signaling, and T cell receptor signaling. The results showed the practicability of using DIA for scarce samples, and the established workflow of PBMC analysis could be easily adapted for biomarker discovery, immune status evaluation, and drug response monitoring, especially for diseases involved with dysfunction of the immune system.

Quantitative Proteomics Reveals Metabolic Reprogramming in Host Cells Induced by Trophozoites and Intermediate Subunit of Gal/GalNAc Lectins from Entamoeba histolytica

Entamoeba histolytica is an intestinal protozoan parasite with remarkable ability to kill and phagocytose host cells, causing amoebic colitis and extraintestinal abscesses. The intermediate subunit (Igl) of galactose (Gal)- and N-acetyl-d-galactosamine (GalNAc)-specific lectins is considered an important surface antigen involved in the pathogenesis of E. histolytica. Here, we applied mass spectrometry-based quantitative proteomics technology to analyze the protein expression profile changes occurring in host Caco2 cells incubated with E. histolytica trophozoites or stimulated by purified native Igl protein. The expression levels of 1,490 and 489 proteins were significantly altered in the E. histolytica-treated and Igl-treated groups, respectively, among 6,875 proteins totally identified. Intriguingly, central carbon metabolism of host cells was suppressed in both E. histolytica-treated and Igl-treated groups, with evidence of decreased expression levels of several key enzymes, including pyruvate kinase muscle type 2, presenting a Warburg-like effect in host cells. Besides, Igl had potential physical interactions with central carbon metabolism enzymes and the proteolytic degradation family members proteasome subunit alpha and beta, which may be responsible for the degradation of key enzymes in carbon metabolism. These results provided a novel perspective on the pathogenic mechanism of E. histolytica and compelling evidence supporting the important role of Igl in the virulence of E. histolytica.

IMPORTANCE Metabolic reprogramming is considered a hallmark of some infectious diseases. However, in amoebiasis, a neglected tropical disease caused by protozoan parasite E. histolytica, metabolic changes in host cells have yet to be proven. In this study, advanced data-independent acquisition mass spectrometry-based quantitative proteomics was applied to investigate the overall host cellular metabolic changes as high-throughput proteomics could measure molecular changes in a cell or tissue with high efficiency. Enrichment analysis of differentially expressed proteins showed biological processes and cellular pathways related to amoeba infection and Igl cytotoxicity. Specifically, central carbon metabolism of host cells was dramatically suppressed in both E. histolytica-treated and Igl-treated groups, indicating the occurrence of a Warburg-like effect induced by trophozoites or Igl from E. histolytica. Distinct differences in ubiquitin-mediated proteolysis, rapamycin (mTOR) signaling pathway, autophagy, endocytosis, and tight junctions provided novel perspectives on the pathogenic mechanism of E. histolytica.

A new mass spectral library for high-coverage and reproducible analysis of the Plasmodium falciparum-infected red blood cell proteome

Background

Plasmodium falciparum causes the majority of malaria mortality worldwide, and the disease occurs during the asexual red blood cell (RBC) stage of infection. In the absence of an effective and available vaccine, and with increasing drug resistance, asexual RBC stage parasites are an important research focus. In recent years, mass spectrometry–based proteomics using data-dependent acquisition has been extensively used to understand the biochemical processes within the parasite. However, data-dependent acquisition is problematic for the detection of low-abundance proteins and proteome coverage and has poor run-to-run reproducibility.

Results

Here, we present a comprehensive P. falciparum–infected RBC (iRBC) spectral library to measure the abundance of 44,449 peptides from 3,113 P. falciparum and 1,617 RBC proteins using a data-independent acquisition mass spectrometric approach. The spectral library includes proteins expressed in the 3 morphologically distinct RBC stages (ring, trophozoite, schizont), the RBC compartment of trophozoite-iRBCs, and the cytosolic fraction from uninfected RBCs. This spectral library contains 87% of all P. falciparum proteins that have previously been reported with protein-level evidence in blood stages, as well as 692 previously unidentified proteins. The P. falciparum spectral library was successfully applied to generate semi-quantitative proteomics datasets that characterize the 3 distinct asexual parasite stages in RBCs, and compared artemisinin-resistant (Cam3.II^R539T) and artemisinin-sensitive (Cam3.II^rev) parasites.

Conclusion

A reproducible, high-coverage proteomics spectral library and analysis method has been generated for investigating sets of proteins expressed in the iRBC stage of P. falciparum malaria. This will provide a foundation for an improved understanding of parasite biology, pathogenesis, drug mechanisms, and vaccine candidate discovery for malaria.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases

Rapid and precise diagnostic methods are required to control emerging infectious diseases effectively. Human body fluids are attractive clinical samples for discovering diagnostic targets because they reflect the clinical statuses of patients and most of them can be obtained with minimally invasive sampling processes. Body fluids are good reservoirs for infectious parasites, bacteria, and viruses. Therefore, recent clinical proteomics methods have focused on body fluids when aiming to discover human- or pathogen-originated diagnostic markers. Cutting-edge liquid chromatography-mass spectrometry (LC-MS)-based proteomics has been applied in this regard; it is considered one of the most sensitive and specific proteomics approaches. Here, the clinical characteristics of each body fluid, recent tandem mass spectroscopy (MS/MS) data-acquisition methods, and applications of body fluids for proteomics regarding infectious diseases (including the coronavirus disease of 2019 [COVID-19]), are summarized and discussed.