Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry

Proteomic analysis reveals modulation of key proteins in follicular thyroid cancer progression

BACKGROUND

Cytopathology cannot be used to reliably distinguish follicular thyroid adenoma (FTA) from follicular thyroid carcinoma (FTC), the second most common form of thyroid cancer, because they exhibit nearly identical cellular morphology. Given the challenges in diagnosis and treatment, this study aims to identify the mechanisms underlying FTC is essential.

METHODS

Using parallel reaction monitoring-mass spectrometry (PRM-MS) assays, we identified and quantified 94 differentially expressed protein candidates from a retrospective cohort of 1085 FTC and FTA tissue samples from 18 clinical centers. Of these targeted proteins, those with the potential for distinguishing FTC from FTA were prioritized using machine learning. Co-immunoprecipitation (co-IP) and immunofluorescence co-localization assays, as well as gene interference, overexpression, and immunohistochemistry (IHC) experiments, were used to investigate the interactions and cellular functions of selected proteins.

RESULTS

Using machine learning models and feature selection methods, 30 of the 94 candidates were prioritized as key proteins. Co-IP and immunofluorescence co-localization assays using FTC cell lines revealed interactions among insulin-like growth factor 2 receptor (IGF2R), major vault protei (MVP), histone deacetylase 1 (HDAC1), and histone H1.5 (H1-5). Gene interference and overexpression experiments in FTC-133 cells confirmed the promotional role of these proteins in cell proliferation. IHC assays of patient samples further confirmed elevated expression of these four proteins in FTC compared with that in FTA.

CONCLUSIONS

Our findings underscore the utility of advanced proteomic techniques in elucidating the molecular underpinnings of FTC, highlighting the potential significance of IGF2R, MVP, HDAC1, and H1-5 in FTC progression, and providing a foundation for the exploration of targeted therapies.

SLB-msSIM: a spectral library-based multiplex segmented SIM platform for single-cell proteomic analysis

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by msSIM technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity among multiple cell lines and to analyze cellular trajectories during epithelial-mesenchymal transition. Our results demonstrate that SLB-msSIM is a highly sensitive and robust platform applicable to a wide range of single-cell proteomic studies.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

VPBrowse: Genome-based representation of MS/MS spectra to quantify 10,000 bovine proteins

SWATH is a data acquisition strategy acclaimed for generating quantitatively accurate and consistent measurements of proteins across multiple samples. Its utility for proteomics studies in nonlaboratory animals, however, is currently compromised by the lack of sufficiently comprehensive and reliable public libraries, either experimental or predicted, and relevant platforms that support their sharing and utilization in an intuitive manner. Here we describe the development of the Veterinary Proteome Browser, VPBrowse (http://browser.proteo.cloud/), an on-line platform for genome-based representation of the Bos taurus proteome, which is equipped with an interactive database and tools for searching, visualization, and building quantitative mass spectrometry assays. In its current version (VPBrowse 1.0), it contains high-quality fragmentation spectra acquired on QToF instrument for over 36,000 proteotypic peptides, the experimental evidence for over 10,000 proteins. Data can be downloaded in different formats to enable analysis using popular software packages for SWATH data processing whilst normalization to iRT scale ensures compatibility with diverse chromatography systems. When applied to published blood plasma dataset from the biomarker discovery study, the resource supported label-free quantification of additional proteins not reported by the authors previously including PSMA4, a tissue leakage protein and a promising candidate biomarker of animal's response to dehorning-related injury.

NPEPPS Is a Druggable Driver of Platinum Resistance

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations.

SIGNIFICANCE

Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Quantitative proteomics reveals pregnancy prognosis signature of polycystic ovary syndrome women based on machine learning

OBJECTIVE

We aimed to screen and construct a predictive model for pregnancy loss in polycystic ovary syndrome (PCOS) patients through machine learning methods.

METHODS

We obtained the endometrial samples from 33 PCOS patients and 7 healthy controls at the Reproductive Center of the Second Hospital of Lanzhou University from September 2019 to September 2020. Liquid chromatography tandem mass spectrometry (LCMS/MS) was conducted to identify the differentially expressed proteins (DEPs) of the two groups. Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to analyze the related pathways and functions of the DEPs. Then, we used machine learning methods to screen the feature proteins. Multivariate Cox regression analysis was also conducted to establish the prognostic models. The performance of the prognostic model was then evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). In addition, the Bootstrap method was conducted to verify the generalization ability of the model. Finally, linear correlation analysis was performed to figure out the correlation between the feature proteins and clinical data.

RESULTS

Four hundred and fifty DEPs in PCOS and controls were screened out, and we obtained some pathways and functions. A prognostic model for the pregnancy loss of PCOS was established, which has good discrimination and generalization ability based on two feature proteins (TIA1, COL5A1). Strong correlation between clinical data and proteins were identified to predict the reproductive outcome in PCOS.

CONCLUSION

The model based on the TIA1 and COL5A1 protein could effectively predict the occurrence of pregnancy loss in PCOS patients and provide a good theoretical foundation for subsequent research.

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.

Alzheimer's disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling

Amyloid-β, tau pathology, and biomarkers of neurodegeneration make up the core diagnostic biomarkers of Alzheimer disease (AD). However, these proteins represent only a fraction of the complex biological processes underlying AD, and individuals with other brain diseases in which AD pathology is a comorbidity also test positive for these diagnostic biomarkers. More AD-specific early diagnostic and disease staging biomarkers are needed. In this study, we performed tandem mass tag proteomic analysis of paired cerebrospinal fluid (CSF) and serum samples in a discovery cohort comprising 98 participants. Candidate biomarkers were validated by parallel reaction monitoring-based targeted proteomic assays in an independent multicenter cohort comprising 288 participants. We quantified 3,238 CSF and 1,702 serum proteins in the discovery cohort, identifying 171 and 860 CSF proteins and 37 and 323 serum proteins as potential early diagnostic and staging biomarkers, respectively. In the validation cohort, 58 and 21 CSF proteins, as well as 12 and 18 serum proteins, were verified as early diagnostic and staging biomarkers, respectively. Separate 19-protein CSF and an 8-protein serum biomarker panels were built by machine learning to accurately classify mild cognitive impairment (MCI) due to AD from normal cognition with areas under the curve of 0.984 and 0.881, respectively. The 19-protein CSF biomarker panel also effectively discriminated patients with MCI due to AD from patients with other neurodegenerative diseases. Moreover, we identified 21 CSF and 18 serum stage-associated proteins reflecting AD stages. Our findings provide a foundation for developing blood-based tests for AD screening and staging in clinical practice.

Characterization of the physicochemical Properties, bacterial community and non-volatile profiles of fermented Yu jiangsuan by Weissella cibaria and Lactobacillus plantarum

Yu jiangsuan (YJS) is a unique traditional fermented condiment in China. Physicochemical, bacterial communities, and non-volatile properties were examined in inoculation Autochthonous Weissella cibaria and Lactobacillus plantarum. The results indicated that inoculation samples did well in shortening fermentation time; amino acid nitrogen (AN) and TCA-soluble peptide contents of fermented YJS were 10.8% and 17.4% higher than those of naturally fermented YJS, respectively. However, its total volatile base nitrogen (TVB-N), thiobarbituric acid (TBARS), and nitrite were only 74.3%, 87.2% and 83.6% of those of naturally fermented YJS. In addition, the dominant bacterial genera were Lactobacillus, Weissella and Pectobacterium, whose contributions were 41.2%, 20.3% and 5.5%, respectively. Moreover, 26 significantly differential metabolites were identified, and involved in 10 metabolic pathways. The decomposition of substrates and the formation of differential metabolites in YJS were primarily centered on the TCA cycle and the metabolism of carbohydrates. Therefore, this study is conducive to discovering the bacterial community structure and metabolite composition of probiotic inoculated YJS fermentation, as well as the potential value of core functional bacteria genera in controlling YJS production in industry.

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.

Osteopontin depletion in macrophages perturbs proteostasis via regulating UCHL1-UPS axis and mitochondria-mediated apoptosis

Introduction

Osteopontin (OPN; also known as SPP1), an immunomodulatory cytokine highly expressed in bone marrow-derived macrophages (BMMΦ), is known to regulate diverse cellular and molecular immune responses. We previously revealed that glatiramer acetate (GA) stimulation of BMMΦ upregulates OPN expression, promoting an anti-inflammatory, pro-healing phenotype, whereas OPN inhibition triggers a pro-inflammatory phenotype. However, the precise role of OPN in macrophage activation state is unknown.

Methods

Here, we applied global proteome profiling via mass spectrometry (MS) analysis to gain a mechanistic understanding of OPN suppression versus induction in primary macrophage cultures. We analyzed protein networks and immune-related functional pathways in BMMΦ either with OPN knockout (OPNKO) or GA-mediated OPN induction compared with wild type (WT) macrophages. The most significant differentially expressed proteins (DEPs) were validated using immunocytochemistry, western blot, and immunoprecipitation assays.

Results and discussion

We identified 631 DEPs in OPNKO or GA-stimulated macrophages as compared to WT macrophages. The two topmost downregulated DEPs in OPNKO macrophages were ubiquitin C-terminal hydrolase L1 (UCHL1), a crucial component of the ubiquitin-proteasome system (UPS), and the anti-inflammatory Heme oxygenase 1 (HMOX-1), whereas GA stimulation upregulated their expression. We found that UCHL1, previously described as a neuron-specific protein, is expressed by BMMΦ and its regulation in macrophages was OPN-dependent. Moreover, UCHL1 interacted with OPN in a protein complex. The effects of GA activation on inducing UCHL1 and anti-inflammatory macrophage profiles were mediated by OPN. Functional pathway analyses revealed two inversely regulated pathways in OPN-deficient macrophages: activated oxidative stress and lysosome-mitochondria-mediated apoptosis (e.g., ROS, Lamp1-2, ATP-synthase subunits, cathepsins, and cytochrome C and B subunits) and inhibited translation and proteolytic pathways (e.g., 60S and 40S ribosomal subunits and UPS proteins). In agreement with the proteome-bioinformatics data, western blot and immunocytochemical analyses revealed that OPN deficiency perturbs protein homeostasis in macrophages-inhibiting translation and protein turnover and inducing apoptosis-whereas OPN induction by GA restores cellular proteostasis. Taken together, OPN is essential for macrophage homeostatic balance via the regulation of protein synthesis, UCHL1-UPS axis, and mitochondria-mediated apoptotic processes, indicating its potential application in immune-based therapies.

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

Plasma proteomic changes in response to surgical trauma and a novel transdermal analgesic treatment in dogs

Assessment of pain responses and inflammation during animal surgery is difficult because traditional methods, such as visual analogue scores, are not applicable while under anaesthesia. Acute phase proteins (APPs), such as C-reactive protein and haptoglobin, that are typically monitored in veterinary research, do not show a significant change until at least 2 h post-surgery and therefore, immediate pathophysiological changes are uncertain. The current study used sequential window acquisition of all theoretical mass spectra (SWATH-MS) to investigate plasma proteome changes that occur immediately following surgery in dogs and also to assess the efficacy of a novel transdermal ketoprofen (TK) formulation. Castration was chosen as surgical model in this study. The procedure was performed on twelve dogs (n = 6 in two groups) and blood samples were collected at 0 h, 1 and 2 h after surgery for proteomic analysis. Following surgery, there was a general downregulation of proteins, including complement C- 3, complement factor B, complement factor D, transthyretin, and proteins associated with lipid, cholesterol, and glucose metabolisms, reflecting the systemic response to surgical trauma. Many of these changes were diminished in the transdermal group (TD) since ketoprofen, a non-steroidal anti-inflammatory drug (NSAID), inhibits prostanoids and the associated chemotactic neutrophil migration to site of tissue injury. SIGNIFICANCE: SWATH-MS Proteomic analysis revealed significant changes in plasma proteins, predominantly involved in early acute phase and inflammatory response at 1 & 2 h after surgery in castrated dogs. Pre-operative application of transdermal ketoprofen formulation had reduced the systemic immune response, which was confirmed by negligible alteration of proteins in transdermal treated group. A key outcome of this experiment was studying the efficacy of a novel transdermal NSAID formulation in dogs.

Glycoprotein 96 in Peritoneal Dialysis Effluent-Derived Extracellular Vesicles: A Tool for Evaluating Peritoneal Transport Properties and Inflammatory Status

Background

Extracellular vesicles (EVs) from peritoneal dialysis effluent (PDE), containing molecules such as proteins and microRNAs (miRNAs), may be potential biological markers to monitor peritoneal function or injury. Peritoneal inflammation is an important determinant of peritoneal solute transport rate (PSTR). Thus, the aim of this study is to determine whether the specific proteins capable of evaluating the PSTR could be found in PDE-EVs, and explore the underlying mechanism for the association between PSTR and peritoneal inflammation.

Methods

Sixty patients undergoing peritoneal dialysis (PD) were divided into two groups: high/high average transport (H/A) group (PET >0.65) and low/low average transport (L/A) group (PET <0.65). EVs derived from PDE (PDE-EVs) were isolated by ultracentrifugation. Proteomic analysis was performed to explore the differentially expressed proteins and identify the potential biomarkers in PDE-EVs from the two groups, and we focused on glycoprotein 96 (GP96) as it could be involved in the inflammatory process. The expression of GP96 in PDE-EVs and inflammatory cytokines was quantified by real-time PCR and enzyme-linked immunosorbent assay. The infiltration of macrophages and neutrophils into the peritoneum was detected using immunohistochemistry in a PD rat model.

Results

The expression of PDE-EVs-GP96 was significantly higher in the H/A group, and was positively correlated with the PSTR and the level of the inflammatory factor interleukin (IL)-6. GP96-enriched EVs enhanced the secretion of proinflammatory cytokines IL-1β, IL-6, tumor necrosis factor (TNF)-α, and IL-8 in macrophages, which was reversed by a pharmacological GP96-specific inhibitor (PU-WS13). The GP96 inhibitor also reduced local peritoneal inflammation by decreasing the infiltration of inflammatory cells and levels of proinflammatory cytokines (IL-6 and TNF-α) and chemokines (CCL2, CXCL1, and CXCL2) in a PD rat model.

Conclusions

PDE-EVs-GP96 is a new promising tool to evaluate the status of peritoneal inflammation and PSTR, and the mechanism may be related to affecting the inflammatory properties of macrophages.

Democratizing data-independent acquisition proteomics analysis on public cloud infrastructures via the Galaxy framework

BACKGROUND

Data-independent acquisition (DIA) has become an important approach in global, mass spectrometric proteomic studies because it provides in-depth insights into the molecular variety of biological systems. However, DIA data analysis remains challenging owing to the high complexity and large data and sample size, which require specialized software and vast computing infrastructures. Most available open-source DIA software necessitates basic programming skills and covers only a fraction of a complete DIA data analysis. In consequence, DIA data analysis often requires usage of multiple software tools and compatibility thereof, severely limiting the usability and reproducibility.

FINDINGS

To overcome this hurdle, we have integrated a suite of open-source DIA tools in the Galaxy framework for reproducible and version-controlled data processing. The DIA suite includes OpenSwath, PyProphet, diapysef, and swath2stats. We have compiled functional Galaxy pipelines for DIA processing, which provide a web-based graphical user interface to these pre-installed and pre-configured tools for their use on freely accessible, powerful computational resources of the Galaxy framework. This approach also enables seamless sharing workflows with full configuration in addition to sharing raw data and results. We demonstrate the usability of an all-in-one DIA pipeline in Galaxy by the analysis of a spike-in case study dataset. Additionally, extensive training material is provided to further increase access for the proteomics community.

CONCLUSION

The integration of an open-source DIA analysis suite in the web-based and user-friendly Galaxy framework in combination with extensive training material empowers a broad community of researches to perform reproducible and transparent DIA data analysis.

Data-independent acquisition-based proteomics analysis correlating type 2 diabetes mellitus with osteoarthritis in total knee arthroplasty patients

BACKGROUND

To explore the effects of type 2 diabetes mellitus (T2DM) on osteoarthritis (OA), 12 bone tissue samples were obtained surgically from the human total knee arthroplasty patients and analyzed by quantitative proteomics.

METHODS

Based on patient clinical histories, patient samples were assigned to diabetes mellitus osteoarthritis (DMOA) and OA groups. A data-independent acquisition method for data collection was used with proteomic data analysis to assess intergroup proteomic differences. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis were used to further find the correlation between T2DM and OA.

RESULTS

GO functional analysis found 153 differentially expressed proteins between DMOA and OA groups, of which 92 differentially expressed proteins were significantly up-regulated and 61 were significantly down-regulated. Kyoto Encyclopedia of Genes and Genome pathway analysis found 180 pathways, including 9 pathways significantly enriched. Further data analysis revealed that 6 signaling pathways were closely associated with T2DM and OA.

CONCLUSION

OA and DMOA onset and progression were closely related to synthesis and metabolism of extracellular matrix components (e.g., fibronectin, decorin, etc.). The effects of T2DM on OA occur though 2 major ways of oxidative stress and low-grade chronic inflammation, involving in 2 inhibited signaling pathways and 4 activated signaling pathways.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Leveraging homologies for cross-species plasma proteomics in ungulates using data-independent acquisition

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.

Comparative Proteomic Analysis of HPV(+) Oropharyngeal Squamous Cell Carcinoma Recurrence

Deintensification therapy for human papillomavirus-related oropharyngeal squamous cell carcinoma (HPV(+) OPSCC) is under active investigation. An adaptive treatment approach based on molecular stratification could identify high-risk patients predisposed to recurrence and better select for appropriate treatment regimens. Collectively, 40 HPV(+) OPSCC FFPE samples (20 disease-free, 20 recurrent) were surveyed using mass spectrometry-based proteomic analysis via data-independent acquisition to obtain fold change and false discovery differences. Ten-year overall survival was 100.0 and 27.7% for HPV(+) disease-free and recurrent cohorts, respectively. Of 1414 quantified proteins, 77 demonstrated significant differential expression. Top enriched functional pathways included those involved in programmed cell death (73 proteins, p = 7.43 × 10^-30), apoptosis (73 proteins, p = 5.56 × 10^-9), β-catenin independent WNT signaling (47 proteins, p = 1.45 × 10^-15), and Rho GTPase signaling (69 proteins, p = 1.09 × 10^-5). PFN1 (p = 1.0 × 10^-3), RAD23B (p = 2.9 × 10^-4), LDHB (p = 1.0 × 10^-3), and HINT1 (p = 3.8 × 10^-3) pathways were significantly downregulated in the recurrent cohort. On functional validation via immunohistochemistry (IHC) staining, 46.9% (PFN1), 71.9% (RAD23B), 59.4% (LDHB), and 84.4% (HINT1) of cases were corroborated with mass spectrometry findings. Development of a multilateral molecular signature incorporating these targets may characterize high-risk disease, predict treatment response, and augment current management paradigms in head and neck cancer.

Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid

Simple Summary

Endometrial cancer is the most common cancer of the female reproductive tract, and its incidence is rising. Early diagnosis has the potential to improve survival as women can receive care at the earliest possible stage when curative treatment is likely. Current tests for endometrial cancer diagnosis are sequentially invasive with low patient acceptability. A detection tool based on minimally invasive samples such as cervico-vaginal fluid would be a major advance in the field. This study focuses on the potential of detecting endometrial cancer based on the proteins and peptides expressed in cervico-vaginal fluid. Using Sequential window acquisition of all theoretical mass spectra (SWATH-MS), we present a spectral library of thousands of proteins in the cervico-vaginal fluid of women with or at risk of endometrial cancer. This important resource will enable the identification of endometrial cancer biomarkers in cervico-vaginal fluid and advances our knowledge of the role of proteomics in endometrial cancer detection.

Abstract

Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. This unique resource enables the study of protein biomarkers for endometrial cancer detection in cervico-vaginal fluid. Data are available via ProteomeXchange with unique identifier PXD025925.

Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells

BACKGROUND

There is interest in deriving megakaryocytes (MKs) from pluripotent stem cells (iPSC) for biological studies. We previously found that genomic structural integrity and genotype concordance is maintained in iPSC-derived MKs.

OBJECTIVE

To establish a comprehensive dataset of genes and proteins expressed in iPSC-derived MKs.

METHODS

iPSCs were reprogrammed from peripheral blood mononuclear cells (MNCs) and MKs were derived from the iPSCs in 194 healthy European American and African American subjects. mRNA was isolated and gene expression measured by RNA sequencing. Protein expression was measured in 62 of the subjects using mass spectrometry.

RESULTS AND CONCLUSIONS

MKs expressed genes and proteins known to be important in MK and platelet function and demonstrated good agreement with previous studies in human MKs derived from CD34+ progenitor cells. The percent of cells expressing the MK markers CD41 and CD42a was consistent in biological replicates, but variable across subjects, suggesting that unidentified subject-specific factors determine differentiation of MKs from iPSCs. Gene and protein sets important in platelet function were associated with increasing expression of CD41/42a, while those related to more basic cellular functions were associated with lower CD41/42a expression. There was differential gene expression by the sex and race (but not age) of the subject. Numerous genes and proteins were highly expressed in MKs but not known to play a role in MK or platelet function; these represent excellent candidates for future study of hematopoiesis, platelet formation, and/or platelet function.

Surveying the Vampire Bat (Desmodus rotundus) Serum Proteome: A Resource for Identifying Immunological Proteins and Detecting Pathogens

Bats are increasingly studied as model systems for longevity and as natural hosts for some virulent viruses. Yet the ability to characterize immune mechanisms of viral tolerance and to quantify infection dynamics in wild bats is often limited by small sample volumes and few species-specific reagents. Here, we demonstrate how proteomics can overcome these limitations by using data-independent acquisition-based shotgun proteomics to survey the serum proteome of 17 vampire bats (Desmodus rotundus) from Belize. Using just 2 μL of sample and relatively short separations of undepleted serum digests, we identified 361 proteins across 5 orders of magnitude. Levels of immunological proteins in vampire bat serum were then compared to human plasma via published databases. Of particular interest were antiviral and antibacterial components, circulating 20S proteasome complex and proteins involved in redox activity. Lastly, we used known virus proteomes to putatively identify Rh186 from Macacine herpesvirus 3 and ORF1a from Middle East respiratory syndrome-related coronavirus, indicating that mass spectrometry-based techniques show promise for pathogen detection. Overall, these results can be used to design targeted mass-spectrometry assays to quantify immunological markers and detect pathogens. More broadly, our findings also highlight the application of proteomics in advancing wildlife immunology and pathogen surveillance.

A Simple Optimization Workflow to Enable Precise and Accurate Imputation of Missing Values in Proteomic Data Sets

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification level-fragment level-improved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set's most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

AKR1B10 negatively regulates autophagy through reducing GAPDH upon glucose starvation in colon cancer

Autophagy is considered to be an important switch for facilitating normal to malignant cell transformation during colorectal cancer development. Consistent with other reports, we found that the membrane receptor Neuropilin1 (NRP1) is greatly upregulated in colon cancer cells that underwent autophagy upon glucose deprivation. However, the mechanism underlying NRP1 regulation of autophagy is unknown. We found that knockdown of NRP1 inhibits autophagy and largely upregulates the expression of aldo-keto reductase family 1 B10 (AKR1B10). Moreover, we demonstrated that AKR1B10 interacts with and inhibits the nuclear importation of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and then subsequently represses autophagy. Interestingly, we also found that an NADPH-dependent reduction reaction could be induced when AKR1B10 interacts with GAPDH, and the reductase activity of AKR1B10 is important for its repression of autophagy. Together, our findings unravel a novel mechanism of NRP1 in regulating autophagy through AKR1B10.

Mitochondrial translation and dynamics synergistically extend lifespan in C. elegans through HLH-30

Mitochondrial form and function are closely interlinked in homeostasis and aging. Inhibiting mitochondrial translation is known to increase lifespan in C. elegans, and is accompanied by a fragmented mitochondrial network. However, whether this link between mitochondrial translation and morphology is causal in longevity remains uncharacterized. Here, we show in C. elegans that disrupting mitochondrial network homeostasis by blocking fission or fusion synergizes with reduced mitochondrial translation to prolong lifespan and stimulate stress response such as the mitochondrial unfolded protein response, UPRMT. Conversely, immobilizing the mitochondrial network through a simultaneous disruption of fission and fusion abrogates the lifespan increase induced by mitochondrial translation inhibition. Furthermore, we find that the synergistic effect of inhibiting both mitochondrial translation and dynamics on lifespan, despite stimulating UPRMT, does not require it. Instead, this lifespan-extending synergy is exclusively dependent on the lysosome biogenesis and autophagy transcription factor HLH-30/TFEB. Altogether, our study reveals the mechanistic crosstalk between mitochondrial translation, mitochondrial dynamics, and lysosomal signaling in regulating longevity.

Assessing technical and biological variation in SWATH-MS-based proteomic analysis of chronic lymphocytic leukaemia cells

Chronic lymphocytic leukaemia (CLL) exhibits variable clinical course and response to therapy, but the molecular basis of this variability remains incompletely understood. Data independent acquisition (DIA)-MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. Here, a CLL-specific spectral library (7736 proteins) is described alongside an analysis of sample replication and data handling requirements for quantitative SWATH-MS analysis of clinical samples. The analysis was performed on 6 CLL samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins across 54 SWATH-MS acquisitions: the sources of variation and different computational approaches for batch correction were assessed. Functional enrichment analysis of proteins associated with IGHV mutational status showed significant overlap with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was implemented. This study provides a valuable resource for researchers working on the proteomics of CLL. It also establishes a sound framework for the design of sufficiently powered clinical proteomics studies. Indeed, this study shows that it is possible to derive biologically plausible hypotheses from a relatively small dataset.

SWATH-MS Protocols in Human Diseases

Identification of molecular biomarkers for human diseases is one of the most important disciplines in translational science as it helps to elucidate their origin and early progression. Thus, it is a key factor in better diagnosis, prognosis, and treatment. Proteomics can help to solve the problem of sample complexity when the most common primary sample specimens were analyzed: organic fluids of easy access. The latest developments in high-throughput and label-free quantitative proteomics (SWATH-MS), together with more advanced liquid chromatography, have enabled the analysis of large sample sets with the sensitivity and depth needed to succeed in this task. In this chapter, we show different sample processing methods (major protein depletion, digestion, etc.) and a micro LC-SWATH-MS protocol to identify/quantify several proteins in different types of samples (serum/plasma, saliva, urine, tears).

Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies

Cancer has no borders: Generation and analysis of molecular data across multiple centers worldwide is necessary to gain statistically significant clinical insights for the benefit of patients. Here we conceived and standardized a proteotype data generation and analysis workflow enabling distributed data generation and evaluated the quantitative data generated across laboratories of the international Cancer Moonshot consortium. Using harmonized mass spectrometry (MS) instrument platforms and standardized data acquisition procedures, we demonstrate robust, sensitive, and reproducible data generation across eleven international sites on seven consecutive days in a 24/7 operation mode. The data presented from the high-resolution MS1-based quantitative data-independent acquisition (HRMS1-DIA) workflow shows that coordinated proteotype data acquisition is feasible from clinical specimens using such standardized strategies. This work paves the way for the distributed multi-omic digitization of large clinical specimen cohorts across multiple sites as a prerequisite for turning molecular precision medicine into reality.

BKCa channels regulate the immunomodulatory properties of WJ-MSCs by affecting the exosome protein profiles during the inflammatory response

Background

Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) from the human umbilical cord have been studied extensively due to their immunomodulatory functions. Large-conductance Ca²⁺-activated K⁺ (BKCa channels) channels are involved in many inflammatory responses, but their involvement in the anti-inflammatory activity of WJ-MSCs is unknown. The underlying molecular mechanism, through which BKCa channels mediate the immunomodulation of WJ-MSC, which may include changes in exosomes proteomics, has not yet been clarified.

Methods

Alizarin staining, Oil Red O staining, and flow cytometry were used to identify WJ-MSCs, which were isolated from human umbilical cord Wharton’s jelly. BKCa channels were detected in WJ-MSCs using western blotting, real-time polymerase chain reaction (real-time PCR), and electrophysiology, and cytokine expression was examined using real-time PCR and enzyme-linked immunosorbent assays (ELISAs). Exosomes were characterized using transmission electron microscopy and nanoparticle tracking analysis. Proteomics analysis was performed to explore exosomal proteomic profiles.

Results

The cells derived from human umbilical cord Wharton’s jelly were identified as MSCs. BKCa channels were detected in the isolated WJ-MSCs, and the expression of these channels increased after lipopolysaccharide (LPS) stimulation. BKCa channels blockade in LPS-treated WJ-MSCs induced apoptosis and decreased interleukin-6 (IL-6) expression. Furthermore, THP-1 cells (human monocytic cell line) stimulated with LPS/interferon gamma (IFN-γ) produced more anti-inflammatory cytokines after treatment with exosomes derived from BKCa channel-knockdown WJ-MSCs (si-exo). We also observed altered expression of mitochondrial ATP synthase alpha subunit (ATP5A1), filamin B, and other proteins in si-exo, which might increase the anti-inflammatory activity of macrophages.

Conclusions

Our study described the functional expression of BKCa channels in WJ-MSCs, and BKCa channels regulated the immunomodulatory properties of WJ-MSCs by affecting the exosomal protein profiles during the inflammatory response.

Lysine and Arginine Protein Post-translational Modifications by Enhanced DIA Libraries: Quantification in Murine Liver Disease

Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Study on the dynamic changes and formation pathways of metabolites during the fermentation of black waxy rice wine

Black waxy rice wine fermentation metabolites are closely related to the product's final quality. However, little is known about dynamic metabolite changes during fermentation. Here, we used gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) metabolomics and multivariate statistical analysis to explore the relationship between metabolites and fermentation time. A total of 159 metabolites were identified during the entire fermentation process. The PCA analysis revealed a clear separation between the samples after 4 days and 2 days, and the samples after 4-24 days clustered together. This indicated that BGRW fermentation progresses rapidly in the first 48 hr of fermentation. A total of 40 metabolites were identified as differential during fermentation (VIP > 1 and p < .05), including 12 organic acids, four amino acids, one fatty acid, 17 sugars and sugar alcohols, one alcohol, and five other metabolites. Pathway analysis showed that the differential metabolites were involved in 28 metabolic pathways, and the most commonly influenced pathways (impact value > 0.1 and p < .05) were galactose metabolism, pyruvate metabolism; starch and sucrose metabolism; alanine, aspartic acid, and glutamate metabolism; the tricarboxylic acid cycle, glyoxylic acid, and dicarboxylic acid metabolism; and amino sugar and nucleotide sugar metabolism. Moreover, the integrated metabolic pathway was generated to understand the transformation and accumulation of differential metabolites. Overall, these results provide a comprehensive overview of metabolite changes during black waxy rice wine fermentation.

Generation of a murine SWATH-MS spectral library to quantify more than 11,000 proteins

Targeted SWATH-MS data analysis is critically dependent on the spectral library. Comprehensive spectral libraries of human or several other organisms have been published, but the extensive spectral library for mouse, a widely used model organism is not available. Here, we present a large murine spectral library covering more than 11,000 proteins and 240,000 proteotypic peptides, which included proteins derived from 9 murine tissue samples and one murine L929 cell line. This resource supports the quantification of 67% of all murine proteins annotated by UniProtKB/Swiss-Prot. Furthermore, we applied the spectral library to SWATH-MS data from murine tissue samples. Data are available via SWATHAtlas (PASS01441).

Measurement(s)	Mouse Protein • mass spectrum • spectral library
Technology Type(s)	mass spectrometry • combined ms-ms + spectral library search
Sample Characteristic - Organism	Mus musculus

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11968230

A Conserved Mito-Cytosolic Translational Balance Links Two Longevity Pathways

Slowing down translation in either the cytosol or the mitochondria is a conserved longevity mechanism. Here, we found a non-interventional natural correlation of mitochondrial and cytosolic ribosomal proteins (RPs) in mouse population genetics, suggesting a translational balance. Inhibiting mitochondrial translation in C. elegans through mrps-5 RNAi repressed cytosolic translation. Transcriptomics integrated with proteomics revealed that this inhibition specifically reduced translational efficiency of mRNAs required in growth pathways while increasing stress response mRNAs. The repression of cytosolic translation and extension of lifespan from mrps-5 RNAi were dependent on atf-5/ATF4 and independent from metabolic phenotypes. We found the translational balance to be conserved in mammalian cells upon inhibiting mitochondrial translation pharmacologically with doxycycline. Lastly, extending this in vivo, doxycycline repressed cytosolic translation in the livers of germ-free mice. These data demonstrate that inhibiting mitochondrial translation initiates an atf-5/ATF4-dependent cascade leading to coordinated repression of cytosolic translation, which could be targeted to promote longevity.

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method

BACKGROUND

SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification.

METHODS

The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1.

RESULTS

Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally.

CONCLUSION

This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.

Generation of a zebrafish SWATH-MS spectral library to quantify 10,000 proteins

Sequential window acquisition of all theoretical mass spectra (SWATH-MS) requires a spectral library to extract quantitative measurements from the mass spectrometry data acquired in data-independent acquisition mode (DIA). Large combined spectral libraries containing SWATH assays have been generated for humans and several other organisms, but so far no publicly available library exists for measuring the proteome of zebrafish, a rapidly emerging model system in biomedical research. Here, we present a large zebrafish SWATH spectral library to measure the abundance of 104,185 proteotypic peptides from 10,405 proteins. The library includes proteins expressed in 9 different zebrafish tissues (brain, eye, heart, intestine, liver, muscle, ovary, spleen, and testis) and provides an important new resource to quantify 40% of the protein-coding zebrafish genes. We employ this resource to quantify the proteome across brain, muscle, and liver and characterize divergent expression levels of paralogous proteins in different tissues. Data are available via ProteomeXchange (PXD010876, PXD010869) and SWATHAtlas (PASS01237).

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

Mass spectrometry quantitation of proteins from small pools of developing auditory and vestibular cells

Hair cells of the inner ear undergo postnatal development that leads to formation of their sensory organelles, synaptic machinery, and in the case of cochlear outer hair cells, their electromotile mechanism. To examine how the proteome changes over development from postnatal days 0 through 7, we isolated pools of 5000 Pou4f3-Gfp positive or negative cells from the cochlea or utricles; these cell pools were analysed by data-dependent and data-independent acquisition (DDA and DIA) mass spectrometry. DDA data were used to generate spectral libraries, which enabled identification and accurate quantitation of specific proteins using the DIA datasets. DIA measurements were extremely sensitive; we were able to detect proteins present at less than one part in 100,000 from only 312 hair cells. The DDA and DIA datasets will be valuable for accurately quantifying proteins in hair cells and non-hair cells over this developmental window.

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.

Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

Comprehensive, reproducible and precise analysis of large sample cohorts is one of the key objectives of quantitative proteomics. Here, we present an implementation of data-independent acquisition using its parallel acquisition nature that surpasses the limitation of serial MS2 acquisition of data-dependent acquisition on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot data-independent acquisition, we identified and quantified 6,383 proteins in human cell lines using 2-or-more peptides/protein and over 7100 proteins when including the 717 proteins that were identified on the basis of a single peptide sequence. 7739 proteins were identified in mouse tissues using 2-or-more peptides/protein and 8121 when including the 382 proteins that were identified based on a single peptide sequence. Missing values for proteins were within 0.3 to 2.1% and median coefficients of variation of 4.7 to 6.2% among technical triplicates. In very complex mixtures, we could quantify 10,780 proteins and 12,192 proteins when including the 1412 proteins that were identified based on a single peptide sequence. Using this optimized DIA, we investigated large-protein networks before and after the critical period for whisker experience-induced synaptic strength in the murine somatosensory cortex 1-barrel field. This work shows that parallel mass spectrometry enables proteome profiling for discovery with high coverage, reproducibility, precision and scalability.

Multiplex and Label-Free Relative Quantification Approach for Studying Protein Abundance of Drug Metabolizing Enzymes in Human Liver Microsomes Using SWATH-MS

We describe a sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) based method for label-free, simultaneous, relative quantification of drug metabolism enzymes in human liver microsomes (HLM; n = 78). In-solution tryptic digestion was aided by a pressure cycling method, which allowed a 90 min incubation time, a significant reduction over classical protocols (12-18 h). Digested peptides were separated on an Acquity UHPLC Peptide BEH C18 column using a 60 min gradient method at a flow rate of 0.100 mL/min. The quadrupole-time-of-flight mass spectrometer (ESI-QTOFMS) was operated in positive electrospray ionization mode, and data were acquired by data-dependent acquisition (DDA) and SWATH-MS^ALL mode. A pooled HLM sample was used as a quality control to evaluate variability in digestion and quantification among different batches, and inter-batch %CV for various proteins was between 3.1 and 7.8%. Spectral library generated from the DDA data identified 1855 distinct proteins and 25 681 distinct peptides at a 1% global false discovery rate (FDR). SWATH data were queried and analyzed for 10 major cytochrome P450 (CYP) enzymes using Skyline, a targeted data extraction software. Further, correlation analysis was performed between functional activity, protein, and mRNA expression for ten CYP enzymes. Pearson correlation coefficient (r) between protein and activity for CYPs ranged from 0.314 (CYP2C19) to 0.767 (CYP2A6). A strong correlation was found between CYP3A4 and CYP3A5 abundance and activity determined using midazolam and testosterone (r > 0.600, p < 0.001). Protein-to-activity correlation was moderate (r > 0.400-0.600, p < 0.001) for CYP1A2, CYP2A6, CYP2B6, CYP2C9, and CYP2E1 and significant but poor (r < 0.400, p < 0.05) for CYP2C8, CYP2C19, and CYP2D6. The findings suggest the suitability of SWATH-MS based method as a valuable and relatively fast analytical technique for relative quantification of proteins in complex biological samples. We also show that protein abundance is a better surrogate than mRNA to predict the activity of CYP activity.

Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues

Mass spectrometry-based targeted proteomics (e.g., selected reaction monitoring, SRM) is emerging as an attractive alternative to immunoassays for protein quantification. Recently we have made significant progress in SRM sensitivity for enabling quantification of low nanograms per milliliter to sub-naograms per milliliter level proteins in nondepleted human blood plasma/serum without affinity enrichment. However, precise quantification of extremely low abundance proteins (e.g., ≤ 100 pg/mL in blood plasma/serum) using targeted proteomics approaches still remains challenging, especially for these samples without available antibodies for enrichment. To address this need, we have developed an antibody-independent deep-dive SRM (DD-SRM) approach that capitalizes on multidimensional high-resolution reversed-phase liquid chromatography (LC) separation for target peptide separation and enrichment combined with precise selection of target peptide fractions of interest, significantly improving SRM sensitivity by ∼5 orders of magnitude when compared to conventional LC-SRM. Application of DD-SRM to human serum and tissue provides precise quantification of endogenous proteins at the ∼10 pg/mL level in nondepleted serum and at <10 copies per cell level in tissue. Thus, DD-SRM holds great promise for precisely measuring extremely low abundance proteins or protein modifications, especially when high-quality antibodies are not available.

Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques

INTRODUCTION

While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.

Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics

The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography-tandem mass spectrometry (LC-MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Support Vector Regression (SVR) algorithm. Corresponding to amino acid locus, each peptide sequence was converted to a featured locus vector consisting of zeros and ones. With locus vector information from LC-MS/MS data sets, an SVR computational process was trained and evaluated. LsRP finally provided a prediction correlation coefficient of 0.95~0.99. We compared our method with two common predictors. Results showed that LsRP outperforms these methods and tracked up to 30% extra peptides in an extraction RT window of 2 min. A new strategy by combining LsRP and calibration peptide approach was then proposed, which open up new opportunities for precision proteomics.

Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms

Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.