Targeted Proteomics Approach Research Articles

558 Poster - Development of a multiplexed protein panel using a targeted proteomics approach for the study of CDK4/6 inhibitors resistance in hormone receptor positive breast cancer

558 Poster - Development of a multiplexed protein panel using a targeted proteomics approach for the study of CDK4/6 inhibitors resistance in hormone receptor positive breast cancer

Chemerin isoform analysis in human biofluids using an LC/MRM-MS-based targeted proteomics approach with stable isotope-labeled standard

Targeted proteomics has advantages over earlier conventional technologies for protein detection. We developed and validated an LC/MRM-MS-based targeted proteomic method combined with immunoaffinity precipitation for the enrichment and detection of low abundance chemerin isoforms in human biofluids. After tryptic digestion, each chemerin isoform was characterized by isoform-specific peptides, and the absolute quantification was achieved by using stable isotope-labeled peptides as internal standards. In serum, follicular fluid and synovial fluid, a total of 6 chemerin isoforms were identified and quantified, among which a novel natural isoform 153Q was discovered for the first time. The relative content of the six chemerin isoforms in human serum was 157S ≫ 156F ≫ 158K > 154F ≥ 155A > 153Q in the ratio of 25:17:5:2.5:2.2:1, respectively. The absolute contents were in the range of 88–3.5 ng/mL. This distribution remained consistent among the 3 biofluids analyzed. Total chemerin were found to be increased in both polycystic ovary syndrome (serum and follicular fluid) and rheumatoid arthritis (serum) patients. However, chemerin isoform analysis revealed that only 156F & 157S were increased in the former, while 155A, 156F & 157S were increased in the latter. This demonstrates the potential of this method in detailed characterization of changes in chemerin isoforms that may be of clinical relevance.

Abstract 2353: A two-tiered targeted proteomics approach to identify biomarkers of colorectal cancer risk

Abstract Colorectal cancer (CRC) mortality is highly dependent on stage at diagnosis, and measures to increase the likelihood of early detection are urgently needed. Many countries have ongoing screening programs, but the addition of blood-based pre-screening biomarkers for risk stratification could reduce costs and increase compliance. In this study, we used prospectively collected plasma samples from the population-based Northern Sweden Health and Disease Study (NSHDS) to evaluate panels of protein biomarkers in relation to CRC risk. Using a two-tiered approach, we first included a unique set of 58 CRC cases and 58 matched controls with time-matched, repeated, prediagnostic samples (10 years apart, with the second sample collected three months to five years before diagnosis). Relative plasma concentrations of 161 proteins were analyzed by multiplex immunoassay (Olink proteomics, inflammation panel and oncology II panel). In the second tier, 15 of these proteins were selected for further analysis, together with six promising early detection protein markers from the literature. The second sample set included prediagnostic samples from 450 CRC cases and 450 matched controls, of which 33 case/control pairs had repeated samples. Conditional logistic regression and linear mixed models were used to estimate odds ratios for CRC risk. In the second-tier analyses, Lasso regression was used to develop multi-marker models, and the resulting protein combinations were tested using ROC analysis. Of the 15 proteins showing promise as predictive biomarkers in the first phase, none retained statistical significance in the second phase. The most promising marker candidate was FGF-21 (multivariable OR: 1.14 95% CI: 0.99-1.30), a metabolic hormone involved in stimulation of glucose uptake that has been associated with body mass index. FGF-21 was also the only protein consistently selected by the Lasso algorithm. Sensitivity analysis for FGF-21 in subgroups based on tumor site, stage and molecular subtypes including BRAF and KRAS mutations did not reach statistical significance. Our findings highlight the challenge of identifying cancer biomarkers that can be used in the prediagnostic window of opportunity for early detection. For early risk stratification, with the vision of achieving effective precision screening, single biomarkers or small marker panels may not be sufficient. Large studies using prospectively collected samples and machine learning to identify biomarker risk patterns may be a more promising approach. Citation Format: Sophia Harlid, Justin Harbs, Robin Myte, Carl Brunius, Marc Gunter, Xijia Liu, Richard Palmqvist, Bethany Van Guelpen. A two-tiered targeted proteomics approach to identify biomarkers of colorectal cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2353.

High-multiplexed monitoring of protein biomarkers in the sentinel Gammarus fossarum by targeted scout-MRM assay, a new vision for ecotoxicoproteomics

Ecotoxicoproteomics employs mass spectrometry-based approaches centered on proteins of sentinel organisms to assess for instance, chemical toxicity in fresh water. In this study, we combined proteogenomics experiments and a novel targeted proteomics approach free from retention time scheduling called Scout-MRM. This methodology will enable the measurement of simultaneously changes in the relative abundance of multiple proteins involved in key physiological processes and potentially impacted by contaminants in the freshwater sentinel Gammarus fossarum. The development and validation of the assay were performed to target 157 protein biomarkers of this non-model organism. We carefully chose and validated the transitions to monitor using conventional parameters (linearity, repeatability, LOD, LOQ). Finally, the potential of the methodology is illustrated by measuring 277-peptide-plex assay (831 transitions) in sentinel animals exposed in natura to different agricultural sites potentially exposed to pesticide contamination. Multivariate data analyses highlighted the modulation of several key proteins involved in feeding and molting. This multiplex-targeted proteomics assay paves the way for the discovery and the use of a large panel of novel protein biomarkers in emergent ecotoxicological models for environmental monitoring in the future. Biological significanceThe study contributed to the development of Scout-MRM for the high-throughput quantitation of a large panel of proteins in the Gammarus fossarum freshwater sentinel. Increasing the number of markers in ecotoxicoproteomics is of most interest to assess the impact of pollutants in freshwater organisms. The development and validation of the assay enabled the monitoring of a large panel of reporter peptides of exposed gammarids. To illustrate the applicability of the methodology, animals from different agricultural sites were analysed. The application of the assay highlighted the modulation of some biomarker proteins involved in key physiological pathways, such as molting, feeding and general stress response. Increasing multiplexing capabilities and field test will provide the development of diagnostic protein biomarkers for emergent ecotoxicological models in future environmental biomonitoring programs.

Identification of hub genes associated with RNAi-induced silencing of XIAP through targeted proteomics approach in MCF7 cells

BackgroundThe X-linked inhibitor of apoptosis protein (XIAP) is the most potent caspase inhibitor of the IAP family in apoptosis pathway. This study aims to identify the molecular targets of XIAP in human breast cancer cells exposed to XIAP siRNA by proteomics screening. The expression of XIAP was reduced in MCF-7 breast cancer cells by siRNA. Cell viability and the mRNA expression level of this gene were evaluated by MTS and quantitative real-time PCR procedures, respectively. Subsequently, the XIAP protein level was visualized by Western blotting and analyzed by two-dimensional (2D) electrophoresis and LC–ESI–MS/MS.ResultsFollowing XIAP silencing, cell proliferation was reduced in XIAP siRNA transfected cells. The mRNA transcription and protein expression of XIAP were decreased in cells exposed to XIAP siRNA than si-NEG. We identified 30 proteins that were regulated by XIAP, of which 27 down-regulated and 3 up-regulated. The most down-regulated proteins belonged to the Heat Shock Proteins family. They participate in cancer related processes including apoptosis and MAPK signaling pathway. Reduced expression of HSP90B1 was associated with apoptosis induction by androgen receptor and prostate specific antigen. Suppression of XIAP resulted in the enhancement of GDIB, ENO1, and CH60 proteins expression. The network analysis of XIAP-regulated proteins identified HSPA8, HSP90AA1, ENO1, and HSPA9 as key nodes in terms of degree and betweenness centrality methods.ConclusionsThese results suggested that XIAP may have a number of biological functions in a diverse set of non-apoptotic signaling pathways and may provide an insight into the biomedical significance of XIAP over-expression in MCF-7 cells.

Identification of peptide biomarkers for authentication of Atlantic salmon and rainbow trout with untargeted and targeted proteomics approaches and quantitative detection of adulteration

Atlantic salmon is often adulterated or substituted by rainbow trout with much lower price and quality. However, it is extremely difficult to distinguish Atlantic salmon and rainbow trout due to their similar appearance and close relationship in species. In the present work, untargeted and targeted proteomics approaches were both implemented to identify species-specific peptide biomarkers of Atlantic salmon and rainbow trout. Potential peptide biomarkers were obtained through matching HRMS data with UniProt database, screened by BLAST and then verified with real samples. Five peptide biomarkers were identified each for Atlantic salmon and rainbow trout. MRM method was established for quantitative measurement of rainbow trout Adulteration in Atlantic salmon, showing high sensitivity and repeatability. The biomarker peptide GDPGPGGPQGEQGVVGPAGISGDK was used for quantification. The limit of the detection (LOD) of adulteration of rainbow trout is 0.19%, and the limit of quantitation (LOQ) is 0.62%. Furthermore, this method was successfully applied to analyze a number of Atlantic salmon and Rainbow trout samples from different regions and different batches, as well as commercially available processed products.

Quantitative Longitudinal Inventory of the N-Glycoproteome of Human Milk from a Single Donor Reveals the Highly Variable Repertoire and Dynamic Site-Specific Changes

Protein N-glycosylation on human milk proteins assists in protecting an infant’s health and functions among others as competitive inhibitors of pathogen binding and immunomodulators. Due to the individual uniqueness of each mother’s milk and the overall complexity and temporal changes of protein N-glycosylation, analysis of the human milk N-glycoproteome requires longitudinal personalized approaches, providing protein- and N-site-specific quantitative information. Here, we describe an automated platform using hydrophilic-interaction chromatography (HILIC)-based cartridges enabling the proteome-wide monitoring of intact N-glycopeptides using just a digest of 150 μg of breast milk protein. We were able to map around 1700 glycopeptides from 110 glycoproteins covering 191 glycosites, of which 43 sites have not been previously reported with experimental evidence. We next quantified 287 of these glycopeptides originating from 50 glycoproteins using a targeted proteomics approach. Although each glycoprotein, N-glycosylation site, and attached glycan revealed distinct dynamic changes, we did observe a few general trends. For instance, fucosylation, especially terminal fucosylation, increased across the lactation period. Building on the improved glycoproteomics approach outlined above, future studies are warranted to reveal the potential impact of the observed glycosylation microheterogeneity on the healthy development of infants.

Quantitative Analysis of Target Peptides Related to Resistance Against Ascochyta Blight (Peyronellaea pinodes) in Pea.

Peyronellaea pinodes causes Ascochyta blight, one of the major diseases in pea worldwide. Cultivated pea plants have a low resistance to this disease. Although quantitative trait loci (QTLs) involved in the resistance to Ascochyta blight have been identified, the specific genes associated with these QTLs remain unknown, which makes marker-assisted selection difficult. Complex traits alter proteins and their abundance. Quantitative estimation of proteins in pea might therefore be useful in selecting potential markers for breeding. In this work, we developed a strategy using a combination of shotgun proteomics (viz., high performance liquid chromatography-mass spectrometry data-dependent acquisition) and data-independent acquisition (DIA) analysis, to identify putative protein markers associated with resistance to Ascochyta blight and explored its use for breeding selection. For this purpose, an initial list of target peptides based on proteins closely related to resistance to P. pinodes was compiled by using two genotypes with contrasting responses to the disease. Then, targeted data analysis (viz., shotgun proteomics-DIA) was used for constitutive quantification of the target peptides in a representative number of the recombinant inbred line population segregated for resistance as derived from a cross between the two genotypes. Finally, a peptide panel of potential markers for resistance to P. pinodes was built. The results thus obtained are discussed and compared with those of previous gene expression studies using the same parental pea genotypes responding to the pathogen. Also, a molecular defense mechanism against Ascochyta blight in pea is proposed. To the authors' knowledge, this is the first time a targeted proteomics approach based on data analysis has been used to identify peptides associated with resistance to this disease.

The associations of self-rated health with cardiovascular risk proteins: a proteomics approach

BackgroundThough subjective, poor self-rated health (SRH) has consistently been shown to predict cardiovascular disease (CVD). The underlying mechanism is unclear. This study evaluates the associations of SRH with biomarkers for CVD, aiming to explore potential pathways between poor SRH and CVD.MethodsBased on the Malmö Diet and Cancer Cardiovascular Cohort study, a targeted proteomics approach was used to assess the associations of SRH with 88 cardiovascular risk proteins, measured in plasma from 4521 participants without CVD. The false discovery rate (FDR) was controlled using the Benjamini and Hochberg method. Covariates taken into consideration were age, sex, traditional CVD risk factors (low-density lipoprotein cholesterol, systolic blood pressure, anti-hypertensive medication, diabetes, body mass index, smoking), comorbidity, life-style and psycho-social factors (education level, living alone, alcohol consumption, low physical activity, psychiatric medication, sleep duration, and unemployment).ResultsAge and sex-adjusted associations with SRH was found for 34 plasma proteins. Nine of them remained significant after adjustments for traditional CVD risk factors. After further adjustment for comorbidity, life-style and psycho-social factors, only leptin (β = − 0.035, corrected p = 0.016) and C–C motif chemokine 20 (CCL20; β = − 0.054, corrected p = 0.016) were significantly associated with SRH.ConclusionsPoor SRH was associated with raised concentrations of many plasma proteins. However, the relationships were largely attenuated by adjustments for CVD risk factors, comorbidity and psycho-social factors. Leptin and CCL20 were associated with poor SRH in the present study and could potentially be involved in the SRH–CVD link.

Analysis of 5000 year-old human teeth using optimized large-scale and targeted proteomics approaches for detection of sex-specific peptides

The study demonstrates the high potential of MS-based proteomics coupled to an iterative database search strategy for the in-depth investigation of ancient proteomes. An efficient targeted PRM MS-based approach, although limited to the detection of a single pair of sex-specific amelogenin peptides, allowed confirming the sex of individuals in ancient dental remains, an essential information for paleoanthropologists facing the issue of sex determination and dimorphism.

Associations between PM2.5 and mid-pregnancy inflammation measured using a novel proteomics chip

OPS 20: Chemicals, biomarkers, omics, Room 110, Floor 1, August 27, 2019, 4:30 PM - 5:30 PM Background/Aim: Prenatal fine particulate matter (PM2.5) exposure is linked with adverse pregnancy outcomes and developmental problems in children; PM2.5-induced changes to maternal immune activity may underlie these relationships. We examined maternal prenatal PM2.5 exposure in relation to inflammatory signals using a targeted proteomics approach. Methods: Analyses include 373 pregnant women enrolled in a prospective study in the northeastern United States between 2011 and 2015. We estimated daily residential PM2.5 exposure using high-resolution satellite data combined with ground monitoring measures and spatiotemporal predictors. We analyzed 92 inflammatory proteins in mid-pregnancy serum using a novel multiplex proximity extension assay. To explore the influence of exposure timing, we examined PM2.5 averaged across the previous 3 months, 1 month, or week of blood draw. We regressed PM2.5 on each inflammatory marker in separate models adjusting for week of gestation, maternal age, and season. For associations with a Bonferroni-adjusted p-value <0.05, we next used multivariable logistic regression to examine continuous PM2.5 exposure in relation to high (highest 20%) versus low (lowest 80%) inflammatory marker levels. Results: Mothers were primarily minorities (60% black or Hispanic). After Bonferroni correction, monocyte-chemotactic protein-3 (MCP-3), interleukin-8 (IL-8), and oncostatin M (OSM) were (p<0.001) positively associated with PM2.5 for one or more exposure interval. The odds of having high MCP-3 levels increased per unit (µg/m3) increase in PM2.5 when averaged across the previous 3 months (OR=1.48, 95% CI=1.22, 1.80), 1 month (OR=1.30, 95%CI=1.12, 1.51), or week (OR=1.18, 95% CI=1.06, 1.31). Parallel findings were true for OSM (1 month: OR=1.24, 95% CI=1.08, 1.44; 1 week: OR=1.22, 95% CI=1.10, 1.35) and IL-8 (1 month: OR=1.27, 95% CI=1.10, 1.46; 1 week: OR=1.23, 95% CI=1.11, 1.37) for more recent exposure only. Conclusions: These data link increased prenatal PM2.5 with markers of enhanced inflammation. Future analysis in this longitudinal cohort will examine whether identified markers (MCP3, OSM and IL8) mediate associations between PM2.5 and maternal-child health outcomes.

Targeted detection of Dehalococcoides mccartyi microbial protein biomarkers as indicators of reductive dechlorination activity in contaminated groundwater

Dehalococcoides mccartyi (Dhc) bacterial strains expressing active reductive dehalogenase (RDase) enzymes play key roles in the transformation and detoxification of chlorinated pollutants, including chlorinated ethenes. Site monitoring regimes traditionally rely on qPCR to assess the presence of Dhc biomarker genes; however, this technique alone cannot directly inform about dechlorination activity. To supplement gene-centric approaches and provide a more reliable proxy for dechlorination activity, we sought to demonstrate a targeted proteomics approach that can characterize Dhc mediated dechlorination in groundwater contaminated with chlorinated ethenes. Targeted peptide selection was conducted in axenic cultures of Dhc strains 195, FL2, and BAV1. These experiments yielded 37 peptides from housekeeping and structural proteins (i.e., GroEL, EF-TU, rpL7/L2 and the S-layer), as well as proteins involved in the reductive dechlorination activity (i.e., FdhA, TceA, and BvcA). The application of targeted proteomics to a defined bacterial consortium and contaminated groundwater samples resulted in the detection of FdhA peptides, which revealed active dechlorination with Dhc strain-level resolution, and the detection of RDases peptides indicating specific reductive dechlorination steps. The results presented here show that targeted proteomics can be applied to groundwater samples and provide protein level information about Dhc dechlorination activity.

A sensitive and simple targeted proteomics approach to quantify transcription factor and membrane proteins of the unfolded protein response pathway in glioblastoma cells

Many cellular events are driven by changes in protein expression, measurable by mass spectrometry or antibody-based assays. However, using conventional technology, the analysis of transcription factor or membrane receptor expression is often limited by an insufficient sensitivity and specificity. To overcome this limitation, we have developed a high-resolution targeted proteomics strategy, which allows quantification down to the lower attomol range in a straightforward way without any prior enrichment or fractionation approaches. The method applies isotope-labeled peptide standards for quantification of the protein of interest. As proof of principle, we applied the improved workflow to proteins of the unfolded protein response (UPR), a signaling pathway of great clinical importance, and could for the first time detect and quantify all major UPR receptors, transducers and effectors that are not readily detectable via antibody-based-, SRM- or conventional PRM assays. As transcription and translation is central to the regulation of UPR, quantification and determination of protein copy numbers in the cell is important for our understanding of the signaling process as well as how pharmacologic modulation of these pathways impacts on the signaling. These questions can be answered using our newly established workflow as exemplified in an experiment using UPR perturbation in a glioblastoma cell lines.

A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer.

BackgroundGastric cancer (GC) is the third leading cause of cancer death. Early detection is a key factor to reduce its mortality.MethodsWe retrospectively collected pre- and postoperative serum samples as well as tumour tissues and adjacent normal tissues from 100 GC patients. Serum samples from non-cancerous patients were served as controls (n = 50). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure levels of over 300 proteins. Alteration of each protein was analysed by univariate analysis. Elastic-net logistic regression was performed to select serum proteins into the diagnostic model.FindingsWe identified 19 serum proteins (CEACAM5, CA9, MSLN, CCL20, SCF, TGF-alpha, MMP-1, MMP-10, IGF-1, CDCP1, PPIA, DDAH-1, HMOX-1, FLI1, IL-7, ZBTB-17, APBB1IP, KAZALD-1, and ADAMTS-15) that together distinguish GC cases from controls with a diagnostic sensitivity of 93%, specificity of 100%, and area under receiver operating characteristic curve (AUC) of 0·99 (95% CI: 0·98–1). Moreover, the 19-serum protein signature provided an increased diagnostic capacity in patients at TNM I-II stage (sensitivity 89%, specificity 100%, AUC 0·99) and in patients with high microsatellite instability (MSI) (91%, 98%, and 0·99) compared to individual proteins. These promising results will inspire a large-scale independent cohort study to be pursued for validating the proposed protein signature.InterpretationBased on targeted proteomics and elastic-net logistic regression, we identified a 19-serum protein signature which could contribute to clinical GC diagnosis, especially for patients at early stage and those with high MSI.FundThis study was supported by a European H2020-Marie Skłodowska-Curie Innovative Training Networks grant (316,929, GastricGlycoExplorer). Funder had no influence on trial design, data evaluation, and interpretation.

Quantitative assay of targeted proteome in tomato trichome glandular cells using a large-scale selected reaction monitoring strategy

BackgroundGlandular trichomes found in vascular plants are called natural cell factories because they synthesize and store secondary metabolites in glandular cells. To systematically understand the metabolic processes in glandular cells, it is indispensable to analyze cellular proteome dynamics. The conventional proteomics methods based on mass spectrometry have enabled large-scale protein analysis, but require a large number of trichome samples for in-depth analysis and are not suitable for rapid and sensitive quantification of targeted proteins.ResultsHere, we present a high-throughput strategy for quantifying targeted proteins in specific trichome glandular cells, using selected reaction monitoring (SRM) assays. The SRM assay platform, targeting proteins in type VI trichome gland cells of tomato as a model system, demonstrated its effectiveness in quantifying multiple proteins from a limited amount of sample. The large-scale SRM assay uses a triple quadrupole mass spectrometer connected online to a nanoflow liquid chromatograph, which accurately measured the expression levels of 221 targeted proteins contained in the glandular cell sample recovered from 100 glandular trichomes within 120 min. Comparative quantitative proteomics using SRM assays of type VI trichome gland cells between different organs (leaves, green fruits, and calyx) revealed specific organ-enriched proteins.ConclusionsWe present a targeted proteomics approach using the established SRM assays which enables quantification of proteins of interest with minimum sampling effort. The remarkable success of the SRM assay and its simple experimental workflow will increase proteomics research in glandular trichomes.

Proteomics characterisation of central nervous system metastasis biomarkers in triple negative breast cancer.

BackgroundBreast cancer (BC) is the most frequent tumour in women. Triple negative tumours (TNBC)–which are associated with minor survival rates—lack markers predictive of response to anticancer drugs. Triple negative tumours frequently metastasise to the central nervous system (CNS).ObjectiveThe main objective of this study was to study differences in tumour protein expression between patients with CNS metastases and those without this kind of spread, and propose new biomarkers.MethodsA retrospective study was performed. Targeted proteomics and statistical analyses were used to identify possible biomarkers.ResultsProteins were quantified by a targeted proteomics approach and protein expression data were successfully obtained from 51 triple negative formalin-fixed paraffin-embedded samples. ISG15, THBS1 and AP1M1 were identified as possible biomarkers related with CNS metastasis development.ConclusionsThree possible biomarkers associated with CNS metastases in TNBC tumours were identified: ISG15, THBS1 and AP1M1. They may become markers predicting the appearance of CNS infiltration in triple negative BC.

A Targeted Proteomics Approach Reveals a Serum Protein Signature as Diagnostic Biomarker for Resectable Gastric Cancer

Background: Gastric cancer (GC) is the third leading cause of cancer death. Early detection is a key factor to reduce its mortality. Methods: We retrospectively collected pre- and postoperative serum samples as well as tumor tissues and adjacent normal tissues from 100 GC patients. Serum samples from non-cancerous patients were served as controls (n=50). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure levels of over 300 proteins. Alteration of each protein was analyzed by univariate analysis. Elastic-net logistic regression was performed to select serum proteins into the diagnostic model. Findings: We identified 19 serum proteins (CEA, CA9, MSLN, CCL20, SCF, TGF-alpha, MMP-1, MMP-10, IGF-1, CDCP1, PPIA, DDAH-1, HMOX-1, FLI1, IL-7, ZBTB-17, APBB1IP, KAZALD-1, and ADAMTS-15) that together distinguish the GC cases from controls with a diagnostic sensitivity of 93%, specificity of 100%, and area under receiver operating characteristic curve (AUC) of 0·99 (95% CI: 0·98-1). Moreover, the 19-serum protein signature provided a satisfactory diagnostic capacity in patients at TNM I-II stage (sensitivity 89%, specificity 100%, AUC 0·988) and in patients with high microsatellite instability (91%, 98%, and 0·990). A large-scale independent cohort of cases and controls will be necessary for validating the proposed protein signature. Interpretation: Based on targeted proteomics and elastic-net logistic regression, we identified a 19-serum protein signature which could contribute to clinical GC diagnosis. Funding Statement: This study was supported by a Marie Curie ITN grant (316929, GastricGlycoExplorer). Funder had no influence on trial design, data evaluation, and interpretation. Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: This study was conducted following the Declaration of Helsinki. All patients were provided with the informed consent and ethical approval was granted by the Institutional Ethics Committee of University of Siena.

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study

The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42–81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age, and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.

Carrier-Assisted Single-Tube Processing Approach for Targeted Proteomics Analysis of Low Numbers of Mammalian Cells.

Heterogeneity in composition is inherent in all cell populations, even those containing a single cell type. Single-cell proteomics characterization of cell heterogeneity is currently achieved by antibody-based technologies, which are limited by the availability of high-quality antibodies. Herein we report a simple, easily implemented, mass spectrometry (MS)-based targeted proteomics approach, termed cLC-SRM (carrier-assisted liquid chromatography coupled to selected reaction monitoring), for reliable multiplexed quantification of proteins in low numbers of mammalian cells. We combine a new single-tube digestion protocol to process low numbers of cells with minimal loss together with sensitive LC-SRM for protein quantification. This single-tube protocol builds upon trifluoroethanol digestion and further minimizes sample losses by tube pretreatment and the addition of carrier proteins. We also optimized the denaturing temperature and trypsin concentration to significantly improve digestion efficiency. cLC-SRM was demonstrated to have sufficient sensitivity for reproducible detection of most epidermal growth factor receptor (EGFR) pathway proteins expressed at levels ≥30 000 and ≥3000 copies per cell for 10 and 100 mammalian cells, respectively. Thus, cLC-SRM enables reliable quantification of low to moderately abundant proteins in less than 100 cells and could be broadly useful for multiplexed quantification of important proteins in small subpopulations of cells or in size-limited clinical samples. Further improvements of this method could eventually enable targeted single-cell proteomics when combined with either SRM or other emerging ultrasensitive MS detection.

Fluid‐based proteomics targeted on pathophysiological processes and pathologies in neurodegenerative diseases

Neurodegenerative dementias constitute a broad group of diseases in which abnormally folded proteins accumulate in specific brain regions and result in tissue reactions that eventually cause neuronal dysfunction and degeneration. Depending on where in the brain this happens, symptoms appear which may be used to classify the disorders on clinical grounds. However, brain changes in neurodegenerative dementias start to accumulate many years prior to symptom onset and there is a poor correlation between the clinical picture and what pathology that is the most likely to cause it. Thus, novel drug candidates having disease-modifying effects that is targeting the underlying pathology and changes the course of the disease needs to be defined using objective biomarker-based measures since the clinical symptoms are often non-specific and overlap between different disorders. Furthermore, the treatment should ideally be initiated as soon as symptoms are evident or when biomarkers confirm an underlying pathology (pre-clinical phase of the disease) to reduce irreversible damage to, for example, neurons, synapses and axons. Clinical trials in the pre-clinical phase bring a greater importance to biomarkers since by definition the clinical effects are difficult or slow to discern in a population that is not yet clinically affected. Here, we discuss neuropathological changes that may underlie neurodegenerative dementias, including how they can be detected and quantified using currently available biofluid-based biomarkers and how more of them could be identified using targeted proteomics approaches. This article is part of the special issue "Proteomics".