SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries

Discrimination of three commercial tuna species through species-specific peptides: From high-resolution mass spectrometry discovery to MRM validation

The risk of tuna adulteration is high driven by economic benefits. The authenticity of tuna is required to protect both consumers and tuna stocks. Given this, the study is designed to identify species-specific peptides for distinguishing three commercial tropical tuna species. The peptides derived from trypsin digestion were separated and detected using ultrahigh-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS) in data-dependent acquisition (DDA) mode. Venn analysis showed that there were differences in peptide composition among the three tested tuna species. The biological specificity screening through the National Center for Biotechnology Information's Basic Local Alignment Search Tool (NCBI BLAST) revealed that 93 peptides could serve as potential species-specific peptides. Finally, the detection specificity of species-specific peptides of raw meats and processed products was carried out by multiple reaction monitoring (MRM) mode based on a Q-Trap mass spectrometer. The results showed that three, one and two peptides of Katsuwonus pelamis, Thunnus obesus and Thunnus albacores, respectively could serve as species-specific peptides.

Comprehensive Proteomic Profiling of Aqueous Humor in Idiopathic Uveitis and Vogt-Koyanagi-Harada Syndrome

Idiopathic uveitis (IU) and Vogt-Koyanagi-Harada (VKH) syndrome are common types of uveitis. However, the exact pathological mechanisms of IU and VKH remain unclear. Proteomic analysis of aqueous humor (AH), the most easily accessible intraocular fluid and a key site of uveitis development, may reveal potential biomarkers and elucidate uveitis pathogenesis. In this study, 44 AH samples, including 12 IU cases, 16 VKH cases, and 16 controls, were subjected to label-free quantitative proteomic analysis. We identified 557 proteins from a comprehensive spectral library of 634 proteins across all samples. The AH proteomic profiles of the IU and VKH groups were different from those of the control group. Differential analysis revealed a shared pattern of extracellular matrix disruption and downregulation of retinal cellular proteins in the IU and VKH groups. Enrichment analysis revealed a protein composition indicative of inflammation in the AH of the IU and VKH groups but not in that of the control group. In the IU and VKH groups, innate immunity played an important role, as indicated by complement cascade activation and overexpression of innate immune cell markers. Extreme gradient boosting (XGBoost), an efficient and robust machine learning algorithm, was subsequently used to screen potential biomarkers for classifying the IU, VKH, and control groups. Transferrin and complement factor B were deemed the most important and represent a promising biomarker panel. These proteins were validated by high-resolution multiple reaction monitoring (HR-MRM) in an independent validation cohort. A classification decision tree was subsequently built for the diagnosis. Our findings further the understanding of the underlying molecular mechanisms in IU and VKH and facilitate the development of potential therapeutic and diagnostic strategies.

Discovery of Alpha-1-Antichymotrypsin as a Marker of Delayed Recovery from Concussion in Children

Of the four million children who experience a concussion each year, 30-50% of children will experience delayed recovery, where they will continue to experience symptoms more than two weeks after their injury. Delayed recovery from concussion encompasses emotional, behavioral, physical, and cognitive symptoms, and as such, there is an increased focus on developing an objective tool to determine risk of delayed recovery. This study aimed to identify a blood protein signature predictive of delayed recovery from concussion in children. Plasma samples were collected from children who presented to the Emergency Department at the Royal Children's Hospital, Melbourne, within 48h post-concussion. This study involved a discovery and validation phase. For the discovery phase, untargeted proteomics analysis was performed using single window acquisition of all theoretical mass spectra to identify blood proteins differentially abundant in samples from children with and without delayed recovery from concussion. A subset of these proteins was then validated in a separate participant cohort using multiple reaction monitoring and enzyme linked immunosorbent assay. A blood protein signature predictive of delayed recovery from concussion was modeled using a Support Vector Machine, a machine learning approach. In the discovery phase, 22 blood proteins were differentially abundant in age- and sex-matched samples from children with (n = 9) and without (n = 9) delayed recovery from concussion, six of whom were chosen for validation. In the validation phase, alpha-1-ACT was shown to be significantly lower in children with delayed recovery (n = 12) compared with those without delayed recovery (n = 28), those with orthopedic injuries (n = 7) and healthy controls (n = 33). A model consisting of alpha-1-ACT concentration stratified children based on recovery from concussion with an 0.88 area under the curve. We have identified that alpha-1-ACT differentiates between children at risk of delayed recovery from those without delayed recovery from concussion. To our knowledge, this is the first study to identify alpha-1-ACT as a potential marker of delayed recovery from concussion in children. Multi-site studies are required to further validate this finding before use in a clinical setting.

Endothelial IL17RD promotes Western diet-induced aortic myeloid cell infiltration

The Interleukin-17 (IL17) family is a group of cytokines implicated in the etiology of several inflammatory diseases. Interleukin-17 receptor D (IL17RD), also known as Sef (similar expression to fibroblast growth factor) belonging to the family of IL17 receptors, has been shown to modulate IL17A-associated inflammatory phenotypes. The objective of this study was to test the hypothesis that IL17RD promotes endothelial cell activation and consequent leukocyte adhesion. We utilized primary human aortic endothelial cells and demonstrated that RNAi targeting of IL17RD suppressed transcript levels by 83 % compared to non-targeted controls. Further, RNAi knockdown of IL17RD decreased the adhesion of THP-1 monocytic cells onto a monolayer of aortic endothelial cells in response to IL17A. Additionally, we determined that IL17A did not significantly enhance the activation of canonical MAPK and NFκB pathways in endothelial cells, and further did not significantly affect the expression of VCAM-1 and ICAM-1 in aortic endothelial cells, which is contrary to previous findings. We also determined the functional relevance of our findings in vivo by comparing the expression of endothelial VCAM-1 and ICAM-1 and leukocyte infiltration in the aorta in Western diet-fed Il17rd null versus wild-type mice. Our results showed that although Il17rd null mice do not have significant alteration in aortic expression of VCAM-1 and ICAM-1 in endothelial cells, they exhibit decreased accumulation of proinflammatory monocytes and neutrophils, suggesting that endothelial IL17RD induced in vivo myeloid cell accumulation is not dependent on upregulation of VCAM-1 and ICAM-1 expression. We further performed proteomics analysis to identify potential molecular mediators of the IL17A/IL17RD signaling axis. Collectively, our results underscore a critical role for Il17rd in the regulation of aortic myeloid cell infiltration in the context of Western diet feeding.

Dysregulation of the Kynurenine Pathway, Cytokine Expression Pattern, and Proteomics Profile Link to Symptomology in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Dysregulation of the kynurenine pathway (KP) is believed to play a significant role in neurodegenerative and cognitive disorders. While some evidence links the KP to myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), further studies are needed to clarify the overall picture of how inflammation-driven KP disturbances may contribute to symptomology in ME/CFS. Here, we report that plasma levels of most bioactive KP metabolites differed significantly between ME/CFS patients and healthy controls in a manner consistent with their known contribution to symptomology in other neurological disorders. Importantly, we found that enhanced production of the first KP metabolite, kynurenine (KYN), correlated with symptom severity, highlighting the relationship between inflammation, KP dysregulation, and ME/CFS symptomology. Other significant changes in the KP included lower levels of the downstream KP metabolites 3-HK, 3-HAA, QUIN, and PIC that could negatively impact cellular energetics. We also rationalized KP dysregulation to changes in the expression of inflammatory cytokines and, for the first time, assessed levels of the iron (Fe)-regulating hormone hepcidin that is also inflammation-responsive. Levels of hepcidin in ME/CFS decreased nearly by half, which might reflect systemic low Fe levels or possibly ongoing hypoxia. We next performed a proteomics screen to survey for other significant differences in protein expression in ME/CFS. Interestingly, out of the seven most significantly modulated proteins in ME/CFS patient plasma, 5 proteins have roles in maintaining gut health, which considering the new appreciation of how gut microbiome and health modulates systemic KP could highlight a new explanation of symptomology in ME/CFS patients and potential new prognostic biomarker/s and/or treatment avenues.

Physiological Temperature and Osmotic Changes Drive Dynamic Proteome Alterations in the Leptospiral Outer Membrane and Enhance Protein Export Systems

Leptospirosis, a remerging zoonosis, has no effective vaccine or an unambiguous early diagnostic reagent. Proteins differentially expressed (DE) under pathogenic conditions will be useful candidates for antileptospiral measures. We employed a multipronged approach comprising high-resolution TMT-labeled LC-MS/MS-based proteome analysis coupled with bioinformatics on leptospiral proteins following Triton X-114 subcellular fractionation of leptospires treated under physiological temperature and osmolarity that mimic infection. Although there were significant changes in the DE proteins at the level of the entire cell, there were notable changes in proteins at the subcellular level, particularly on the outer membrane (OM), that show the significance of subcellular proteome analysis. The detergent-enriched proteins, representing outer membrane proteins (OMPs), exhibited a dynamic nature and upregulation under various physiological conditions. It was found that pathogenic proteins showed a higher proportion of upregulation compared to the nonpathogenic proteins in the OM. Further analysis identified 17 virulent proteins exclusively upregulated in the outer membrane during infection that could be useful for vaccine and diagnostic targets. The DE proteins may aid in metabolic adaptation and are enriched in pathways related to signal transduction and antibiotic biosynthesis. Many upregulated proteins belong to protein export systems such as SEC translocase, T2SSs, and T1SSs, indicating their sequential participation in protein transport to the outer leaflet of the OM. Further studies on OM-localized proteins may shed light on the pathogenesis of leptospirosis and serve as the basis for effective countermeasures.

Profiling the Pyrenophora tritici-repentis secretome: The Pf2 transcription factor regulates the secretion of the effector proteins ToxA and ToxB

The global wheat disease tan spot is caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis (Ptr) which secretes necrotrophic effectors to facilitate host plant colonization. We previously reported a role of the Zn₂ Cys₆ binuclear cluster transcription factor Pf2 in the regulation of the Ptr effector ToxA. Here, we show that Pf2 is also a positive regulator of ToxB, via targeted deletion of PtrPf2 which resulted in reduced ToxB expression and defects in conidiation and pathogenicity. To further investigate the function of Ptr Pf2 in regulating protein secretion, the secretome profiles of two Δptrpf2 mutants of two Ptr races (races 1 and 5) were evaluated using a SWATH-mass spectrometry (MS) quantitative approach. Analysis of the secretomes of the Δptrpf2 mutants from in vitro culture filtrate identified more than 500 secreted proteins, with 25% unique to each race. Of the identified proteins, less than 6% were significantly differentially regulated by Ptr Pf2. Among the downregulated proteins were ToxA and ToxB, specific to race 1 and race 5 respectively, demonstrating the role of Ptr Pf2 as a positive regulator of both effectors. Significant motif sequences identified in both ToxA and ToxB putative promoter regions were further explored via GFP reporter assays.

Comparison of SPEED, S-Trap, and In-Solution-Based Sample Preparation Methods for Mass Spectrometry in Kidney Tissue and Plasma

Mass spectrometry is a powerful technique for investigating renal pathologies and identifying biomarkers, and efficient protein extraction from kidney tissue is essential for bottom-up proteomic analyses. Detergent-based strategies aid cell lysis and protein solubilization but are poorly compatible with downstream protein digestion and liquid chromatography-coupled mass spectrometry, requiring additional purification and buffer-exchange steps. This study compares two well-established detergent-based methods for protein extraction (in-solution sodium deoxycholate (SDC); suspension trapping (S-Trap)) with the recently developed sample preparation by easy extraction and digestion (SPEED) method, which uses strong acid for denaturation. We compared the quantitative performance of each method using label-free mass spectrometry in both sheep kidney cortical tissue and plasma. In kidney tissue, SPEED quantified the most unique proteins (SPEED 1250; S-Trap 1202; SDC 1197). In plasma, S-Trap produced the most unique protein quantifications (S-Trap 150; SDC 148; SPEED 137). Protein quantifications were reproducible across biological replicates in both tissue (R2 = 0.85–0.90) and plasma (SPEED R2 = 0.84; SDC R2 = 0.76, S-Trap R2 = 0.65). Our data suggest SPEED as the optimal method for proteomic preparation in kidney tissue and S-Trap or SPEED as the optimal method for plasma, depending on whether a higher number of protein quantifications or greater reproducibility is desired.

Edaravone and mitochondrial transfer as potential therapeutics for vanishing white matter disease astrocyte dysfunction

INTRODUCTION

Previous research has suggested that vanishing white matter disease (VWMD) astrocytes fail to fully differentiate and respond differently to cellular stresses compared to healthy astrocytes. However, few studies have investigated potential VWMD therapeutics in monoculture patient-derived cell-based models.

METHODS

To investigate the impact of alterations in astrocyte expression and function in VWMD, astrocytes were differentiated from patient and control induced pluripotent stem cells and analyzed by proteomics, pathway analysis, and functional assays, in the absence and presence of stressors or potential therapeutics.

RESULTS

Vanishing white matter disease astrocytes demonstrated significantly reduced expression of astrocyte markers and markers of inflammatory activation or cellular stress relative to control astrocytes. These alterations were identified both in the presence and absence of polyinosinic:polycytidylic acid stimuli, which is used to simulate viral infections. Pathway analysis highlighted differential signaling in multiple pathways in VWMD astrocytes, including eukaryotic initiation factor 2 (EIF2) signaling, oxidative stress, oxidative phosphorylation (OXPHOS), mitochondrial function, the unfolded protein response (UPR), phagosome regulation, autophagy, ER stress, tricarboxylic acid cycle (TCA) cycle, glycolysis, tRNA signaling, and senescence pathways. Since oxidative stress and mitochondrial function were two of the key pathways affected, we investigated whether two independent therapeutic strategies could ameliorate astrocyte dysfunction: edaravone treatment and mitochondrial transfer. Edaravone treatment reduced differential VWMD protein expression of the UPR, phagosome regulation, ubiquitination, autophagy, ER stress, senescence, and TCA cycle pathways. Meanwhile, mitochondrial transfer decreased VWMD differential expression of the UPR, glycolysis, calcium transport, phagosome formation, and ER stress pathways, while further modulating EIF2 signaling, tRNA signaling, TCA cycle, and OXPHOS pathways. Mitochondrial transfer also increased the gene and protein expression of the astrocyte marker, glial fibrillary acidic protein (GFAP) in VWMD astrocytes.

CONCLUSION

This study provides further insight into the etiology of VWMD astrocytic failure and suggests edaravone and mitochondrial transfer as potential candidate VWMD therapeutics that can ameliorate disease pathways in astrocytes related to oxidative stress, mitochondrial dysfunction, and proteostasis.

High-quality and robust protein quantification in large clinical/pharmaceutical cohorts with IonStar proteomics investigation

Robust, reliable quantification of large sample cohorts is often essential for meaningful clinical or pharmaceutical proteomics investigations, but it is technically challenging. When analyzing very large numbers of samples, isotope labeling approaches may suffer from substantial batch effects, and even with label-free methods, it becomes evident that low-abundance proteins are not reliably measured owing to unsufficient reproducibility for quantification. The MS1-based quantitative proteomics pipeline IonStar was designed to address these challenges. IonStar is a label-free approach that takes advantage of the high sensitivity/selectivity attainable by ultrahigh-resolution (UHR)-MS1 acquisition (e.g., 120-240k full width at half maximum at m/z = 200) which is now widely available on ultrahigh-field Orbitrap instruments. By selectively and accurately procuring quantitative features of peptides within precisely defined, very narrow m/z windows corresponding to the UHR-MS1 resolution, the method minimizes co-eluted interferences and substantially enhances signal-to-noise ratio of low-abundance species by decreasing noise level. This feature results in high sensitivity, selectivity, accuracy and precision for quantification of low-abundance proteins, as well as fewer missing data and fewer false positives. This protocol also emphasizes the importance of well-controlled, robust experimental procedures to achieve high-quality quantification across a large cohort. It includes a surfactant cocktail-aided sample preparation procedure that achieves high/reproducible protein/peptide recoveries among many samples, and a trapping nano-liquid chromatography-mass spectrometry strategy for sensitive and reproducible acquisition of UHR-MS1 peptide signal robustly across a large cohort. Data processing and quality evaluation are illustrated using an example dataset ( http://proteomecentral.proteomexchange.org ), and example results from pharmaceutical project and one clinical project (patients with acute respiratory distress syndrome) are shown. The complete IonStar pipeline takes ~1-2 weeks for a sample cohort containing ~50-100 samples.

Improving SWATH-MS analysis by deep-learning

Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectral libraries used for data base searching. Frequently, these libraries need to be generated by labor and time intensive data dependent acquisition (DDA) experiments. Recently, several algorithms have been published that allow the generation of theoretical libraries by an efficient prediction of retention time and intensity of the fragment ions. Sequential windowed acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) is a DIA method that can be applied at an unprecedented speed, but the fragmentation spectra suffer from a lower quality than data acquired on Orbitrap instruments. To reliably generate theoretical libraries that can be used in SWATH experiments, we developed deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins compared to traditional or library-free methods. Based on our analysis we conclude that dpSWATH is a superior prediction framework for SWATH-MS measurements than other algorithms based on Orbitrap data.

Quantification of cytosol and membrane proteins in rumen epithelium of sheep with low or high CH4 emission phenotype

BACKGROUND

Ruminant livestock are a major contributor to Australian agricultural sector carbon emissions. Variation in methane (CH4) produced from enteric microbial fermentation of feed in the reticulo-rumen of sheep differs with different digestive functions.

METHOD

We isolated rumen epithelium enzymatically to extract membrane and cytosol proteins from sheep with high (H) and low (L) CH4 emission. Protein abundance was quantified using SWATH-mass spectrometry.

RESULTS

The research found differences related to the metabolism of glucose, lactate and processes of cell defence against microbes in sheep from each phenotype. Enzymes in the methylglyoxal pathway, a side path of glycolysis, resulting in D-lactate production, differed in abundance. In the H CH4 rumen epithelium the enzyme hydroxyacylglutathione hydrolase (HAGH) was 2.56 fold higher in abundance, whereas in the L CH4 epithelium lactate dehydrogenase D (LDHD) was 1.93 fold higher. Malic enzyme 1 which converts D-lactate to pyruvate via the tricarboxylic cycle was 1.57 fold higher in the L CH4 phenotype. Other proteins that are known to regulate cell defence against microbes had differential abundance in the epithelium of each phenotype.

CONCLUSION

Differences in the abundance of enzymes involved in the metabolism of glucose were associated with H and L CH4 phenotype sheep. Potentially this represents an opportunity to use protein markers in the rumen epithelium to select low CH4 emitting sheep.

The muscle proteome reflects changes in mitochondrial function, cellular stress and proteolysis after 14 days of unilateral lower limb immobilization in active young men

Skeletal muscle unloading due to joint immobilization induces muscle atrophy, which has primarily been attributed to reductions in protein synthesis in humans. However, no study has evaluated the skeletal muscle proteome response to limb immobilization using SWATH proteomic methods. This study characterized the shifts in individual muscle protein abundance and corresponding gene sets after 3 and 14 d of unilateral lower limb immobilization in otherwise healthy young men. Eighteen male participants (25.4 ±5.5 y, 81.2 ±11.6 kg) underwent 14 d of unilateral knee-brace immobilization with dietary provision and following four-weeks of training to standardise acute training history. Participant phenotype was characterized before and after 14 days of immobilization, and muscle biopsies were obtained from the vastus lateralis at baseline (pre-immobilization) and at 3 and 14 d of immobilization for analysis by SWATH-MS and subsequent gene-set enrichment analysis (GSEA). Immobilization reduced vastus group cross sectional area (-9.6 ±4.6%, P <0.0001), immobilized leg lean mass (-3.3 ±3.9%, P = 0.002), unilateral 3-repetition maximum leg press (-15.6 ±9.2%, P <0.0001), and maximal oxygen uptake (-2.9 ±5.2%, P = 0.044). SWATH analyses consistently identified 2281 proteins. Compared to baseline, two and 99 proteins were differentially expressed (FDR <0.05) after 3 and 14 d of immobilization, respectively. After 14 d of immobilization, 322 biological processes were different to baseline (FDR <0.05, P <0.001). Most (77%) biological processes were positively enriched and characterized by cellular stress, targeted proteolysis, and protein-DNA complex modifications. In contrast, mitochondrial organization and energy metabolism were negatively enriched processes. This study is the first to use data independent proteomics and GSEA to show that unilateral lower limb immobilization evokes mitochondrial dysfunction, cellular stress, and proteolysis. Through GSEA and network mapping, we identify 27 hub proteins as potential protein/gene candidates for further exploration.

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.

CFTR Rescue by Lumacaftor (VX-809) Induces an Extensive Reorganization of Mitochondria in the Cystic Fibrosis Bronchial Epithelium

BACKGROUND

Cystic Fibrosis (CF) is a genetic disorder affecting around 1 in every 3000 newborns. In the most common mutation, F508del, the defective anion channel, CFTR, is prevented from reaching the plasma membrane (PM) by the quality check control of the cell. Little is known about how CFTR pharmacological rescue impacts the cell proteome.

METHODS

We used high-resolution mass spectrometry, differential ultracentrifugation, machine learning and bioinformatics to investigate both changes in the expression and localization of the human bronchial epithelium CF model (F508del-CFTR CFBE41o-) proteome following treatment with VX-809 (Lumacaftor), a drug able to improve the trafficking of CFTR.

RESULTS

The data suggested no stark changes in protein expression, yet subtle localization changes of proteins of the mitochondria and peroxisomes were detected. We then used high-content confocal microscopy to further investigate the morphological and compositional changes of peroxisomes and mitochondria under these conditions, as well as in patient-derived primary cells. We profiled several thousand proteins and we determined the subcellular localization data for around 5000 of them using the LOPIT-DC spatial proteomics protocol.

CONCLUSIONS

We observed that treatment with VX-809 induces extensive structural and functional remodelling of mitochondria and peroxisomes that resemble the phenotype of healthy cells. Our data suggest additional rescue mechanisms of VX-809 beyond the correction of aberrant folding of F508del-CFTR and subsequent trafficking to the PM.

Pathophysiological pathway differences in children who present with COVID-19 ARDS compared to COVID -19 induced MIS-C

COVID-19 has infected more than 275 million worldwide (at the beginning of 2022). Children appear less susceptible to COVID-19 and present with milder symptoms. Cases of children with COVID-19 developing clinical features of Kawasaki-disease have been described. Here we utilise Mass Spectrometry proteomics to determine the plasma proteins expressed in healthy children pre-pandemic, children with multisystem inflammatory syndrome (MIS-C) and children with COVID-19 induced ARDS. Pathway analyses were performed to determine the affected pathways. 76 proteins are differentially expressed across the groups, with 85 and 52 proteins specific to MIS-C and COVID-19 ARDS, respectively. Complement and coagulation activation are implicated in these clinical phenotypes, however there was significant contribution of FcGR and BCR activation in MIS-C and scavenging of haem and retinoid metabolism in COVID-19 ARDS. We show global proteomic differences in MIS-C and COVID-ARDS, although both show complement and coagulation dysregulation. The results contribute to our understanding of MIS-C and COVID-19 ARDS in children.

While rare, SARS-CoV-2-infected children can develop severe COVID-19 (ARDS) or inflammatory syndrome (MIS-C). Here, the authors use proteomics to characterize hundreds of blood proteins and identify key biological pathways that differentiate MIS-C and ARDS.

Genome survey sequencing of wild cotton (Gossypium robinsonii) reveals insights into proteomic responses of pollen to extreme heat

Heat stress specifically affects fertility by impairing pollen viability but cotton wild relatives successfully reproduce in hot savannas where they evolved. An Australian arid-zone cotton (Gossypium robinsonii) was exposed to heat events during pollen development then mature pollen was subjected to deep proteomic analysis using 57 023 predicted genes from a genomic database we assembled for the same species. Three stages of pollen development, including tetrads (TEs), uninucleate microspores (UNs) and binucleate microspores (BNs) were exposed to 36°C or 40°C for 5 days and the resulting mature pollen was collected at anthesis (p-TE, p-UN and p-BN, respectively). Using the sequential windowed acquisition of all theoretical mass spectra proteomic analysis, 2704 proteins were identified and quantified across all pollen samples analysed. Proteins predominantly decreased in abundance at all stages in response to heat, particularly after exposure of TEs to 40°C. Functional enrichment analyses demonstrated that extreme heat increased the abundance of proteins that contributed to increased messenger RNA splicing via spliceosome, initiation of cytoplasmic translation and protein refolding in p-TE40. However, other functional categories that contributed to intercellular transport were inhibited in p-TE40, linked potentially to Rab proteins. We ascribe the resilience of reproductive processes in G. robinsonii at temperatures up to 40°C, relative to commercial cotton, to a targeted reduction in protein transport.

Patterns of gene expression in pollen of cotton (Gossypium hirsutum) indicate downregulation as a feature of thermotolerance

Reproductive performance in plants is impaired as maximum temperatures consistently approach 40°C. However, the timing of heatwaves critically affects their impact. We studied the molecular responses during pollen maturation in cotton to investigate the vulnerability to high temperature. Tetrads (TEs), uninucleate and binucleate microspores, and mature pollen were subjected to SWATH-MS and RNA-seq analyses after exposure to 38/28°C (day/night) for 5 days. The results indicated that molecular signatures were downregulated progressively in response to heat during pollen development. This was even more evident in leaves, where three-quarters of differentially changed proteins decreased in abundance during heat. Functional analysis showed that translation of genes increased in TEs after exposure to heat; however, the reverse pattern was observed in mature pollen and leaves. For example, proteins involved in transport were highly abundant in TEs whereas in later stages of pollen formation and leaves, heat suppressed synthesis of proteins involved in cell-to-cell communication. Moreover, a large number of heat shock proteins were identified in heat-affected TEs, but these proteins were less abundant in mature pollen and leaves. We speculate that the sensitivity of TE cells to heat is related to high rates of translation targeted to pathways that might not be essential for thermotolerance. Molecular signatures during stages of pollen development after heatwaves could provide markers for future genetic improvement.

The Extracellular Matrix Environment of Clear Cell Renal Cell Carcinoma Determines Cancer Associated Fibroblast Growth

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and is often caused by mutations in the oxygen-sensing machinery of kidney epithelial cells. Due to its pseudo-hypoxic state, ccRCC recruits extensive vasculature and other stromal components. Conventional cell culture methods provide poor representation of stromal cell types in primary cultures of ccRCC, and we hypothesized that mimicking the extracellular environment of the tumor would promote growth of both tumor and stromal cells. We employed proteomics to identify the components of ccRCC extracellular matrix (ECM) and found that in contrast to healthy kidney cortex, laminin, collagen IV, and entactin/nidogen are minor contributors. Instead, the ccRCC ECM is composed largely of collagen VI, fibronectin, and tenascin C. Analysis of single cell expression data indicates that cancer-associated fibroblasts are a major source of tumor ECM production. Tumor cells as well as stromal cells bind efficiently to a nine-component ECM blend characteristic of ccRCC. Primary patient-derived tumor cells bind the nine-component blend efficiently, allowing to us to establish mixed primary cultures of tumor cells and stromal cells. These miniature patient-specific replicas are conducive to microscopy and can be used to analyze interactions between cells in a model tumor microenvironment.

Computational Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage and Applications to 15 Tumors

Efficient peptide and protein identifications from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on a project-specific spectral library with a suitable size. Here, we describe subLib, a computational strategy for optimizing the spectral library for a specific DIA data set based on a comprehensive spectral library, requiring the preliminary analysis of the DIA data set. Compared with the pan-human library strategy, subLib achieved a 41.2% increase in peptide precursor identifications and a 35.6% increase in protein group identifications in a test data set of six colorectal tumor samples. We also applied this strategy to 389 carcinoma samples from 15 tumor data sets: up to a 39.2% increase in peptide precursor identifications and a 19.0% increase in protein group identifications were observed. Our strategy for spectral library size optimization thus successfully proved to deepen the proteome coverages of DIA-MS data.

Proteome analysis of human adipocytes identifies depot-specific heterogeneity at metabolic control points

Adipose tissue is a primary regulator of energy balance and metabolism. The distribution of adipose tissue depots is of clinical interest because the accumulation of upper-body subcutaneous (ASAT) and visceral adipose tissue (VAT) is associated with cardiometabolic diseases, whereas lower-body glutealfemoral adipose tissue (GFAT) appears to be protective. There is heterogeneity in morphology and metabolism of adipocytes obtained from different regions of the body, but detailed knowledge of the constituent proteins in each depot is lacking. Here, we determined the human adipocyte proteome from ASAT, VAT, and GFAT using high-resolution Sequential Window Acquisition of all Theoretical (SWATH) mass spectrometry proteomics. We quantified 4,220 proteins in adipocytes, and 2,329 proteins were expressed in all three adipose depots. Comparative analysis revealed significant differences between adipocytes from different regions (6% and 8% when comparing VAT vs. ASAT and GFAT, 3% when comparing the subcutaneous adipose tissue depots, ASAT and GFAT), with marked differences in proteins that regulate metabolic functions. The VAT adipocyte proteome was overrepresented with proteins of glycolysis, lipogenesis, oxidative stress, and mitochondrial dysfunction. The GFAT adipocyte proteome predicted the activation of peroxisome proliferator-activated receptor α (PPARα), fatty acid, and branched-chain amino acid (BCAA) oxidation, enhanced tricarboxylic acid (TCA) cycle flux, and oxidative phosphorylation, which was supported by metabolomic data obtained from adipocytes. Together, this proteomic analysis provides an important resource and novel insights that enhance the understanding of metabolic heterogeneity in the regional adipocytes of humans.NEW & NOTEWORTHY Adipocyte metabolism varies depending on anatomical location and the adipocyte protein composition may orchestrate this heterogeneity. We used SWATH proteomics in patient-matched human upper- (visceral and subcutaneous) and lower-body (glutealfemoral) adipocytes and detected 4,220 proteins and distinguishable regional proteomes. Upper-body adipocyte proteins were associated with glycolysis, de novo lipogenesis, mitochondrial dysfunction, and oxidative stress, whereas lower-body adipocyte proteins were associated with enhanced PPARα activation, fatty acid, and BCAA oxidation, TCA cycle flux, and oxidative phosphorylation.

Striated muscle activator of Rho signalling (STARS) overexpression in the mdx mouse enhances muscle functional capacity and regulates the actin cytoskeleton and oxidative phosphorylation pathways

NEW FINDINGS

What is the central question of this study? Striated muscle activator of rho signalling (STARS) is an actin-binding protein that regulates transcriptional pathways controlling muscle function, growth and myogenesis, processes that are impaired in dystrophic muscle: what is the regulation of the STARS pathway in Duchenne muscular dystrophy (DMD)? What is the main finding and its importance? Members of the STARS signalling pathway are reduced in the quadriceps of patients with DMD and in mouse models of muscular dystrophy. Overexpression of STARS in the dystrophic deficient mdx mouse model increased maximal isometric specific force and upregulated members of the actin cytoskeleton and oxidative phosphorylation pathways. Regulating STARS may be a therapeutic approach to enhance muscle health.

ABSTRACT

Duchenne muscular dystrophy (DMD) is characterised by impaired cytoskeleton organisation, cytosolic calcium handling, oxidative stress and mitochondrial dysfunction. This results in progressive muscle damage, wasting and weakness and premature death. The striated muscle activator of rho signalling (STARS) is an actin-binding protein that activates the myocardin-related transcription factor-A (MRTFA)/serum response factor (SRF) transcriptional pathway, a pathway regulating cytoskeletal structure and muscle function, growth and repair. We investigated the regulation of the STARS pathway in the quadriceps muscle from patients with DMD and in the tibialis anterior (TA) muscle from the dystrophin-deficient mdx and dko (utrophin and dystrophin null) mice. Protein levels of STARS, SRF and RHOA were reduced in patients with DMD. STARS, SRF and MRTFA mRNA levels were also decreased in DMD muscle, while Stars mRNA levels were decreased in the mdx mice and Srf and Mrtfa mRNAs decreased in the dko mice. Overexpressing human STARS (hSTARS) in the TA muscles of mdx mice increased maximal isometric specific force by 13% (P < 0.05). This was not associated with changes in muscle mass, fibre cross-sectional area, fibre type, centralised nuclei or collagen deposition. Proteomics screening followed by pathway enrichment analysis identified that hSTARS overexpression resulted in 31 upregulated and 22 downregulated proteins belonging to the actin cytoskeleton and oxidative phosphorylation pathways. These pathways are impaired in dystrophic muscle and regulate processes that are vital for muscle function. Increasing the STARS protein in dystrophic muscle improves muscle force production, potentially via synergistic regulation of cytoskeletal structure and energy production.

Ultra-High-Resolution IonStar Strategy Enhancing Accuracy and Precision of MS1-Based Proteomics and an Extensive Comparison with State-of-the-Art SWATH-MS in Large-Cohort Quantification

Quantitative proteomics in large cohorts is highly valuable for clinical/pharmaceutical investigations but often suffers from severely compromised reliability, accuracy, and reproducibility. Here, we describe an ultra-high-resolution IonStar method achieving reproducible protein measurement in large cohorts while minimizing the ratio compression problem, by taking advantage of the exceptional selectivity of ultra-high-resolution (UHR)-MS1 detection (240k_FWHM@m/z = 200). Using mixed-proteome benchmark sets reflecting large-cohort analysis with technical or biological replicates (N = 56), we comprehensively compared the quantitative performances of UHR-IonStar vs a state-of-the-art SWATH-MS method, each with their own optimal analytical platforms. We confirmed a cutting-edge micro-liquid chromatography (LC)/Triple-TOF with Spectronaut outperforms nano-LC/Orbitrap for SWATH-MS, which was then meticulously developed/optimized to maximize sensitivity, reproducibility, and proteome coverage. While the two methods with distinct principles (i.e., MS1- vs MS2-based) showed similar depth-of-analysis (∼6700-7000 missing-data-free proteins quantified, 1% protein-false discovery rate (FDR) for entire set, 2 unique peptides/protein) and good accuracy/precision in quantifying high-abundance proteins, UHR-IonStar achieved substantially superior quantitative accuracy, precision, and reproducibility for lower-abundance proteins (a category that includes most regulatory proteins), as well as much-improved sensitivity/selectivity for discovering significantly altered proteins. Furthermore, compared to SWATH-MS, UHR-IonStar showed markedly higher accuracy for a single analysis of each sample across a large set, which is an inadequately investigated albeit critical parameter for large-cohort analysis. Finally, we compared UHR-IonStar vs SWATH-MS in measuring the time courses of altered proteins in paclitaxel-treated cells (N = 36), where dysregulated biological pathways have been very well established. UHR-IonStar discovered substantially more well-recognized biological processes/pathways induced by paclitaxel. Additionally, UHR-IonStar showed markedly superior ability than SWATH-MS in accurately depicting the time courses of well known to be paclitaxel-induced biomarkers. In summary, UHR-IonStar represents a reliable, robust, and cost-effective solution for large-cohort proteomic quantification with excellent accuracy and precision.

Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).

DIALib-QC an assessment tool for spectral libraries in data-independent acquisition proteomics

Data-independent acquisition (DIA) mass spectrometry, also known as Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH), is a popular label-free proteomics strategy to comprehensively quantify peptides/proteins utilizing mass spectral libraries to decipher inherently multiplexed spectra collected linearly across a mass range. Although there are many spectral libraries produced worldwide, the quality control of these libraries is lacking. We present the DIALib-QC (DIA library quality control) software tool for the systematic evaluation of a library’s characteristics, completeness and correctness across 62 parameters of compliance, and further provide the option to improve its quality. We demonstrate its utility in assessing and repairing spectral libraries for correctness, accuracy and sensitivity.

Most data-independent acquisition (DIA) methods depend on mass spectral libraries for peptide identification but tools to assess library quality are lacking. Here, the authors develop DIALib- QC for the systematic evaluation and correction of spectral libraries.

Data independent acquisition of plasma biomarkers of response to neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma

The detection of disease-related plasma biomarkers has challenged the proteomic community for years. Attractive features for plasma proteomics includes the ease of collection and small volume needed for analysis, but on the other hand, the presence of highly abundant proteins complicates sample preparation procedures and reduces dynamic range. Data independent acquisition label free quantitation (DIA-LFQ) by mass spectrometry partly overcomes the dynamic range issue; however, generating the peptide spectral reference libraries that allow extensive analysis of the plasma proteome can be a slow and expensive task which is unattainable for many laboratories. We investigated the re-purposing of publically available plasma proteome datasets and the impact on peptide/protein detection for DIA-LFQ. We carried out these studies in the context of identifying putative biomarkers of response to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma, as no useful plasma biomarkers have been clinically adopted. We demonstrated the benefit in searching DIA data against multiple spectral libraries to show that complement proteins were linked to NAC response in PDAC patients, confirming previous observations of the prognostic utility of complement following adjuvant chemotherapy. Our workflow demonstrates that DIA-LFQ can be readily applied in the oncology setting for the putative assignment of clinically relevant plasma biomarkers. STATEMENT OF SIGNIFICANCE: The proteomic mass spectrometry analysis of undepleted, unfractionated human plasma has benefits for sample throughput but remains challenging to obtain deep coverage. This work evaluated the re-purposing of open source peptide mass spectrometry data from human plasma to create spectral reference libraries for use in Data independent acquisition (DIA). We showed how seeding in locally acquired data to integrate iRT peptides into spectral libraries increased identification confidence by facilitating querying of multiple libraries. This workflow was applied to the discovery of putative plasma biomarkers for response to neoadjuvant chemotherapy (NAC) in pancreatic ductal adenocarcinoma patients. There is a paucity of prior information in the literature on this topic and we show that good responder patients have reduced levels of complement proteins.

A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins

Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) is a data-independent acquisition (DIA) strategy that requires a specific spectral library to generate unbiased and consistent quantitative data matrices of all peptides. SWATH-MS is a promising approach for in-depth proteomic profiling of Chinese hamster Ovary (CHO) cell lines, improving mechanistic understanding of process optimization, and real-time monitoring of process parameters in biologics R&D and manufacturing. However, no spectral library for CHO cells is publicly available. Here we present a comprehensive CHO global spectral library to measure the abundance of more than 10,000 proteins consisting of 199,102 identified peptides from a CHO-K1 cell proteome. The robustness, accuracy and consistency of the spectral library were validated for high confidence in protein identification and reproducible quantification in different CHO-derived cell lines, instrumental setups and downstream processing samples. The availability of a comprehensive SWATH CHO global spectral library will facilitate detailed characterization of upstream and downstream processes, as well as quality by design (QbD) in biomanufacturing. The data have been deposited to ProteomeXchange (PXD016047).

PSMD11, PTPRM and PTPRB as novel biomarkers of pancreatic cancer progression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate of all major cancers. Surgery is the only curative intent therapy, but the majority of patients experience disease relapse. Thus, patients who do not benefit from highly morbid surgical resection needs to be identified and offered palliative chemotherapy instead. In this pilot study, we aimed to identify differentially regulated proteins in plasma and plasma derived microparticles from PDAC patients with poor and good prognosis.

METHODS

Plasma and plasma derived microparticle samples were obtained before surgical resection from PDAC patients. Sequential Windowed Acquisition of all Theoretical fragment ion spectra - Mass Spectrometry (SWATH-MS) proteomic analysis was performed to identify and quantify proteins in these samples. Statistical analysis was performed to identify biomarkers for poor prognosis.

RESULTS

A total of 482 and 1024 proteins were identified from plasma and microparticle samples, respectively, by SWATH-MS analysis. Statistical analysis of the data further identified nine and six differentially (log₂ratio > 1, p < .05) expressed proteins in plasma and microparticles, respectively. Protein tyrosine phosphatases, PTPRM and PTPRB, were decreased in plasma of patients with poor PDAC prognosis, while proteasomal subunit PSMD11 was increased in microparticles of patients with poor prognosis.

CONCLUSION AND GENERAL SIGNIFICANCE

A novel blood-based biomarker signature for PDAC prognosis was identified.

Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2

We describe a useful workflow for characterizing proteomics experiments incorporating many conditions and abundance data using the popular weighted gene correlation network analysis (WGCNA) approach and functional annotation with the PloGO2 R package, the latter of which we have extended and made available to Bioconductor. The approach can use quantitative data from labeled or label-free experiments and was developed to handle multiple files stemming from data partition or multiple pairwise comparisons. The WGCNA approach can similarly produce a potentially large number of clusters of interest, which can also be functionally characterized using PloGO2. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate the use of the package on two published data sets using two different biological systems (plant and human plasma) and proteomics platforms (sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and tandem mass tag (TMT)): an analysis of the effect of drought on rice over time generated using TMT and a pediatric plasma sample data set generated using SWATH. In both, the automated workflow recapitulates key insights or observations of the published papers and provides additional suggestions for further investigation. These findings indicate that the data set analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multifaceted proteomics experiments.

iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis

The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.

Analytical techniques for multiplex analysis of protein biomarkers

INTRODUCTION

The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research.

AREAS COVERED

We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses.

EXPERT COMMENTARY

The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.

Two of a kind: transmissible Schwann cell cancers in the endangered Tasmanian devil (Sarcophilus harrisii)

Devil facial tumour disease (DFTD) comprises two genetically distinct transmissible cancers (DFT1 and DFT2) endangering the survival of the Tasmanian devil (Sarcophilus harrisii) in the wild. DFT1 first arose from a cell of the Schwann cell lineage; however, the tissue-of-origin of the recently discovered DFT2 cancer is unknown. In this study, we compared the transcriptome and proteome of DFT2 tumours to DFT1 and normal Tasmanian devil tissues to determine the tissue-of-origin of the DFT2 cancer. Our findings demonstrate that DFT2 expresses a range of Schwann cell markers and exhibits expression patterns consistent with a similar origin to the DFT1 cancer. Furthermore, DFT2 cells express genes associated with the repair response to peripheral nerve damage. These findings suggest that devils may be predisposed to transmissible cancers of Schwann cell origin. The combined effect of factors such as frequent nerve damage from biting, Schwann cell plasticity and low genetic diversity may allow these cancers to develop on rare occasions. The emergence of two independent transmissible cancers from the same tissue in the Tasmanian devil presents an unprecedented opportunity to gain insight into cancer development, evolution and immune evasion in mammalian species.

Proteome of a Moraxella catarrhalis Strain under Iron-Restricted Conditions

Moraxella catarrhalis is a leading cause of otitis media and exacerbations of chronic obstructive pulmonary disease; however, its response to iron starvation during infection is not completely understood. Here, we announce a sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) data set describing the differential expression of the M. catarrhalis CCRI-195ME proteome under iron-restricted versus iron-replete conditions.

Moraxella catarrhalis is a leading cause of otitis media and exacerbations of chronic obstructive pulmonary disease; however, its response to iron starvation during infection is not completely understood. Here, we announce a sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) data set describing the differential expression of the M. catarrhalis CCRI-195ME proteome under iron-restricted versus iron-replete conditions.

Cell-specific non-canonical amino acid labelling identifies changes in the de novo proteome during memory formation

The formation of spatial long-term memory (LTM) requires the de novo synthesis of distinct sets of proteins; however, a non-biased examination of the de novo proteome in this process is lacking. Here, we generated a novel mouse strain, which enables cell-type-specific labelling of newly synthesised proteins with non-canonical amino acids (NCAAs) by genetically restricting the expression of the mutant tRNA synthetase, NLL-MetRS, to hippocampal neurons. By combining this labelling technique with an accelerated version of the active place avoidance task and bio-orthogonal non-canonical amino acid tagging (BONCAT) followed by SWATH quantitative mass spectrometry, we identified 156 proteins that were altered in synthesis in hippocampal neurons during spatial memory formation. In addition to observing increased synthesis of known proteins important in memory-related processes, such as glutamate receptor recycling, we also identified altered synthesis of proteins associated with mRNA splicing as a potential mechanism involved in spatial LTM formation.

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

Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.

Insights into the study and origin of the citrullinome in rheumatoid arthritis

The presence of autoantibodies and autoreactive T cells to citrullinated proteins and citrullinating enzymes in patients with rheumatoid arthritis (RA), together with the accumulation of citrullinated proteins in rheumatoid joints, provides substantial evidence that dysregulated citrullination is a hallmark feature of RA. However, understanding mechanisms that dysregulate citrullination in RA has important challenges. Citrullination is a normal process in immune and non-immune cells, which is likely activated by different conditions (eg, inflammation) with no pathogenic consequences. In a complex inflammatory environment such as the RA joint, unique strategies are therefore required to dissect specific mechanisms involved in the abnormal production of citrullinated proteins. Here, we will review current models of citrullination in RA and discuss critical components that, in our view, are relevant to understanding the accumulation of citrullinated proteins in the RA joint, collectively referred to as the RA citrullinome. In particular, we will focus on potential caveats in the study of citrullination in RA and will highlight methods to precisely detect citrullinated proteins in complex biological samples, which is a confirmatory approach to mechanistically link the RA citrullinome with unique pathogenic pathways in RA.

Bioinformatics approaches for improving seminal plasma proteome analysis

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness.

Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients

Lung cancer is the most common cause of cancer-related mortality worldwide, characterized by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumors in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumors into pulmonary veins. Forty proteins were identified as having an increased abundance in the vein draining from the tumor compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilized the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.

Potential early clinical stage colorectal cancer diagnosis using a proteomics blood test panel

BACKGROUND

One of the most significant challenges in colorectal cancer (CRC) management is the use of compliant early stage population-based diagnostic tests as adjuncts to confirmatory colonoscopy. Despite the near curative nature of early clinical stage surgical resection, mortality remains unacceptably high-as the majority of patients diagnosed by faecal haemoglobin followed by colonoscopy occur at latter stages. Additionally, current population-based screens reliant on fecal occult blood test (FOBT) have low compliance (~ 40%) and tests suffer low sensitivities. Therefore, blood-based diagnostic tests offer survival benefits from their higher compliance (≥ 97%), if they can at least match the sensitivity and specificity of FOBTs. However, discovery of low abundance plasma biomarkers is difficult due to occupancy of a high percentage of proteomic discovery space by many high abundance plasma proteins (e.g., human serum albumin).

METHODS

A combination of high abundance protein ultradepletion (e.g., MARS-14 and an in-house IgY depletion columns) strategies, extensive peptide fractionation methods (SCX, SAX, High pH and SEC) and SWATH-MS were utilized to uncover protein biomarkers from a cohort of 100 plasma samples (i.e., pools of 20 healthy and 20 stages I-IV CRC plasmas). The differentially expressed proteins were analyzed using ANOVA and pairwise t-tests (p < 0.05; fold-change > 1.5), and further examined with a neural network classification method using in silico augmented 5000 patient datasets.

RESULTS

Ultradepletion combined with peptide fractionation allowed for the identification of a total of 513 plasma proteins, 8 of which had not been previously reported in human plasma (based on PeptideAtlas database). SWATH-MS analysis revealed 37 protein biomarker candidates that exhibited differential expression across CRC stages compared to healthy controls. Of those, 7 candidates (CST3, GPX3, CFD, MRC1, COMP, PON1 and ADAMDEC1) were validated using Western blotting and/or ELISA. The neural network classification narrowed down candidate biomarkers to 5 proteins (SAA2, APCS, APOA4, F2 and AMBP) that had maintained accuracy which could discern early (I/II) from late (III/IV) stage CRC.

CONCLUSION

MS-based proteomics in combination with ultradepletion strategies have an immense potential of identifying diagnostic protein biosignature.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

The Generation of a Comprehensive Spectral Library for the Analysis of the Guinea Pig Proteome by SWATH-MS

Advances in liquid chromatography‐mass spectrometry have facilitated the incorporation of proteomic studies to many biology experimental workflows. Data‐independent acquisition platforms, such as sequential window acquisition of all theoretical mass spectra (SWATH‐MS), offer several advantages for label‐free quantitative assessment of complex proteomes over data‐dependent acquisition (DDA) approaches. However, SWATH data interpretation requires spectral libraries as a detailed reference resource. The guinea pig (Cavia porcellus) is an excellent experimental model for translation to many aspects of human physiology and disease, yet there is limited experimental information regarding its proteome. To overcome this knowledge gap, a comprehensive spectral library of the guinea pig proteome is generated. Homogenates and tryptic digests are prepared from 16 tissues and subjected to >200 DDA runs. Analysis of >250 000 peptide‐spectrum matches resulted in a library of 73 594 peptides from 7666 proteins. Library validation is provided by i) analyzing externally derived SWATH files (https://doi.org/10.1016/j.jprot.2018.03.023) and comparing peptide intensity quantifications; ii) merging of externally derived data to the base library. This furnishes the research community with a comprehensive proteomic resource that will facilitate future molecular‐phenotypic studies using (re‐engaging) the guinea pig as an experimental model of relevance to human biology. The spectral library and raw data are freely accessible in the MassIVE repository (MSV000083199).

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

Proteomic Analysis of the Breast Cancer Brain Metastasis Microenvironment

Patients with brain-metastatic breast cancer face a bleak prognosis marked by morbidity and premature death. A deeper understanding of molecular interactions in the metastatic brain tumour microenvironment may inform the development of new therapeutic strategies. In this study, triple-negative MDA-MB-231 breast cancer cells or PBS (modelling traumatic brain injury) were stereotactically injected into the cerebral cortex of NOD/SCID mice to model metastatic colonization. Brain cells were isolated from five tumour-associated samples and five controls (pooled uninvolved and injured tissue) by immunoaffinity chromatography, and proteomic profiles were compared using the Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) discovery platform. Ontology and cell type biomarker enrichment analysis of the 125 differentially abundant proteins (p < 0.05) showed the changes largely represent cellular components involved in metabolic reprogramming and cell migration (min q = 4.59 × 10^-5), with high-throughput PubMed text mining indicating they have been most frequently studied in the contexts of mitochondrial dysfunction, oxidative stress and autophagy. Analysis of mouse brain cell type-specific biomarkers suggested the changes were paralleled by increased proportions of microglia, mural cells and interneurons. Finally, we orthogonally validated three of the proteins in an independent xenograft cohort, and investigated their expression in craniotomy specimens from triple-negative metastatic breast cancer patients, using a combination of standard and fluorescent multiplex immunohistochemistry. This included 3-Hydroxyisobutyryl-CoA Hydrolase (HIBCH), which is integral for gluconeogenic valine catabolism in the brain, and was strongly induced in both graft-associated brain tissue (13.5-fold by SWATH-MS; p = 7.2 × 10^-4), and areas of tumour-associated, reactive gliosis in human clinical samples. HIBCH was also induced in the tumour compartment, with expression frequently localized to margins and haemorrhagic areas. These observations raise the possibility that catabolism of valine is an effective adaptation in metastatic cells able to access it, and that intermediates or products could be transferred from tumour-associated glia. Overall, our findings indicate that metabolic reprogramming dominates the proteomic landscape of graft-associated brain tissue in the intracranial MDA-MB-231 xenograft model. Brain-derived metabolic provisions could represent an exploitable dependency in breast cancer brain metastases.

Influence of surface chemistry on the formation of a protein corona on nanodiamonds

The protein corona of nanodiamonds is dominated by low molecular weight proteins and is largely independent of surface chemistry. The pre-incubation of nanodiamonds in serum and the formation of a protein corona decrease the production of reactive oxygen species, increasing the cell viability of macrophages.

Clinically Relevant Post-Translational Modification Analyses-Maturing Workflows and Bioinformatics Tools

Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation.