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Patients with systemic mastocytosis (SM) are at increased risk of hypersensitivity reactions (HRs). Although Hymenoptera venoms are the predominant triggers, cases of contrast media-induced HR (CMIHR) have also been reported and prophylactic premedication is often performed. However, data from larger series are limited and differences between indolent and advanced SM have not yet been investigated.To determine the incidence and severity of CMIHR in all subtypes of SM.We analyzed 162 adult patients with SM (indolent systemic mastocytosis [ISM], n = 65; advanced systemic mastocytosis [advSM], n = 97). First, the cumulative incidence of CMIHR was retrospectively assessed in the patient's history. Second, at our institution, patients underwent 332 contrast media (CM)-enhanced imaging including 80 computed tomography (CT) scans with iodine-based contrast agent and 252 magnetic resonance imaging (MRI) with a gadolinium-based contrast agent, and tolerance was assessed.Previous CMIHRs to CT (vomiting, n = 1, erythema, n = 1, cardiovascular shock, n = 1), and MRI (dyspnea, n = 1, cardiovascular shock, n = 1) had been reported by 4 out of 162 (2.5%) patients (ISM, n = 3; advSM, n = 1). In contrast, during or after 332 CM-enhanced CT or MRI examinations at our institution, no CMIHRs were reported. Premedication was solely given to 3 patients before CT scans, including 1 with previous CMIHR, who tolerated the imaging well.We conclude that: (1) there is a substantial discrepancy between the perception and prevalence of HRs to CM in SM; (2) reactions are scarce in ISM and even rarer in advSM; and (3) in SM patients without previous history of CM hypersensitivity, prophylactic premedication before CM-enhanced CT or MRI is dispensable.

The introduction of a formulaically apportioned common consolidated corporate tax base (CCCTB) could represent a milestone in international taxation. No agreement has yet been reached, however. In contrast, Germany already has a long-standing system that apportions corporate taxes by splitting trade tax and corporate income tax. This conceptual study, presented at the European Accounting Association (EAA) Congress in Bergen in 2022, will examine whether the German method of splitting could lead to some lessons for an appropriate design for an international profit distribution formula. Methodologically, we use a two-step approach: First, we compare the designs, and then we juxtapose both on a factual level. Next, we ask what the objectives these mechanisms have; do they even coincide? If the goals are not comparable, one cannot indisputably serve as a model for the other. We determine that, even though there are partial deviations, a closer look reveals significant overlaps; however, the German implementation is far from consistent and prioritises practicability. This leads us to our main result: The German system makes a clear value decision towards practicability, although there is a different set of aims. For the implementation of formulaic EU profit sharing, the lesson to be learned is that practicability should play a central role in the design of the formula. This lesson is important and helpful to accompany and support the implementation process in the EU.

Acute kidney injury (AKI) manifests as a major health concern, particularly for the elderly. Understanding AKI-related proteome changes is critical for prevention and development of novel therapeutics to recover kidney function and to mitigate the susceptibility for recurrent AKI or development of chronic kidney disease. In this study, mouse kidneys were subjected to ischemia-reperfusion injury, and the contralateral kidneys remained uninjured to enable comparison and assess injury-induced changes in the kidney proteome. A fast-acquisition rate ZenoTOF 7600 mass spectrometer was introduced for data-independent acquisition (DIA) for comprehensive protein identification and quantification. Short microflow gradients and the generation of a deep kidney-specific spectral library allowed for high-throughput, comprehensive protein quantification. Upon AKI, the kidney proteome was completely remodeled, and over half of the 3,945 quantified protein groups changed significantly. Downregulated proteins in the injured kidney were involved in energy production, including numerous peroxisomal matrix proteins that function in fatty acid oxidation, such as ACOX1, CAT, EHHADH, ACOT4, ACOT8, and Scp2. Injured mice exhibited severely declined health. The comprehensive and sensitive kidney-specific DIA assays highlighted here feature high-throughput analytical capabilities to achieve deep coverage of the kidney proteome and will serve as useful tools for developing novel therapeutics to remediate kidney function.

Metabolite-protein interactions regulate diverse cellular processes, prompting the development of methods to investigate the metabolite-protein interactome at a global scale. One such method is our previously developed structural proteomics approach, limited proteolysis-mass spectrometry (LiP-MS), which detects proteome-wide metabolite-protein and drug-protein interactions in native bacterial, yeast, and mammalian systems, and allows identification of binding sites without chemical modification. Here we describe a detailed experimental and analytical workflow for conducting a LiP-MS experiment to detect small molecule-protein interactions, either in a single-dose (LiP-SMap) or a multiple-dose (LiP-Quant) format. LiP-Quant analysis combines the peptide-level resolution of LiP-MS with a machine learning-based framework to prioritize true protein targets of a small molecule of interest. We provide an updated R script for LiP-Quant analysis via a GitHub repository accessible at https://github.com/RolandBruderer/MiMB-LiP-Quant .

Extracellular vesicles (EV) play an important role in melanoma progression but their potential as clinical biomarkers has yet to be realized. EVs can be found in most liquid biopsies (e.g., blood, urine, CSF) and exosomes are the most prominent subcategory of EVs. Exosomes are 50-200 nm lipid-bilayer enclosed particles secreted by all body cells, including tumor cells, and serve as mediators of metastasis formation and typically contain several classes of bioactive molecules such as RNA, proteins, lipids, and metabolites. Blood and its liquid components plasma/serum are the most frequently used matrix for biomarker discovery due to the ease of collection. However, most proteomic platforms for plasma/serum profiling are unable to profile EV proteins due to the high dynamic range of protein concentrations in EV preparations. This is due to 1) EV isolation methods that vary in their potential to separate EVs from free proteins, and 2) the presence of a natural corona of high-abundant blood proteins attached to the EV surface. To tackle this challenge, we developed SEC-DIA-MS an integrated workflow combining size-exclusion chromatography, EV concentration, and optimized mass spectrometry to enable deep profiling of the proteome content of the enriched vesicles. From 200 µl of plasma or serum from a test melanoma patient cohort (6 patients and 3 matched controls), we quantified 2,242 exosome-associated proteins, achieving a 2.5-fold increase in depth compared to previous melanoma studies. To gain a better understanding of the exosome enrichment efficiency, we extensively characterized the plasma/serum proteome by analyzing native, depleted, and EV-enriched blood from the same donors. We successfully validated well-known exosome markers such as CD9, CD63, CD81, PDCD6IP, and TSG101, and found that EV samples are significantly enriched in intact membrane proteins and those related to T cell biology, further underlining the uniqueness of the EV proteome composition. We further assessed the differences between plasma and serum EVs and suggest the use of plasma samples for future studies due to better separation of healthy and melanoma samples. Furthermore, known melanoma markers such as MCAM, TNC, and TGFBI were upregulated in melanoma plasma-derived EV but not depleted plasma samples, highlighting the specific information contained in EVs. Taken together, we demonstrated the applicability and potential of an EV-based proteomic workflow for biomarker discovery in plasma and serum. The ease of automating and scaling up such an approach enables a broader application to other indications and biological matrices. Citation Format: Evelyn Lattmann, Luca Räss, Marco Tognetti, Julia Martinez Gomez, Valérie Lapaire, Roland Bruderer, Lukas Reiter, Yuehan Feng, Lars M. Steinmetz, Mitchell P. Levesque. Leveraging deep proteome profiling of plasma- and serum-derived extracellular vesicles for melanoma biomarker discovery and disease dissection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4352.

Colorectal cancer (CRC) is currently the second most common cause of cancer related mortality in the world. One of the hallmarks of CRC is its complex mutational landscape that contributes to the derivation of neoantigens which aid the immunosurveillance of the cancer. Immunopeptides play an essential role in adaptive immunity by activating and ensuring T-cell specificity. Mass spectrometry (MS) is currently the only technology that can measure and identify the immunopeptide profiles of biological samples at a large scale, however, MS-based studies are frequently limited by sample input and poor scalability. Here, we introduce a semi-automated workflow requiring low sample input to robustly identify immunopeptides and apply it to a cohort of fresh frozen, high quality CRC samples (Indivumed) for immunopeptide profiling and neoantigen identification. We initially optimized a workflow that allowed the native lysis and sequential immunoprecipitation of class-I and class-II immunopeptides while ensuring scalability and reproducibility. The immunopeptides were thereafter subjected to our True Discovery FAIMS-DIA LC-MS/MS platform which we supported through a specific DDA library and searched the resulting data with Spectronaut (Biognosys) by applying a 1% FDR threshold. We utilized data from WGS on both tumor and adjacent normal tissue for the calling of high-confidence somatic variations (Indivumed) and the definition of the resulting neoantigens. We characterized 131,578 unique immunopeptides from 15 mg of fresh-frozen tumor tissue across 20 patients with CRC lesions that mapped to 12,488 genes. On average we identified 9,767 class-I and 16,445 class-II immunopeptides from all patients indicating significant inter-patient heterogeneity. Overall, we found the identification numbers of class-II immunopeptides to be more variable than that of class-I which might be linked to immune infiltration levels. From our pool of identified immunopeptides, we were able to detect 16 class-I and 29 class-II neoantigens, covering 87% of the microsatellite instability-high (MSI-H) samples in the cohort. Coverage of both classes was essential to increase neoantigen discovery as 53% MSI-H samples had neoantigens mapping to a single class. Interestingly we also detected a significant correlation between the mutational burden and the number of detected neoantigens in our patient cohort suggesting that the genetic set up of the cancer might influence the repertoire of presented neoantigens thereby controlling immunosurveillance. In summary, we have established a scalable and efficient pipeline for cell line and tissue immunopeptidomics for both class-I and II immunopeptides. Our pipeline generates high-quality identifications and can be deployed to help shed light on immunopeptidomics heterogeneity through large-scale profiling of patients as exemplified in the case of MSI-H CRC. Citation Format: Ilja E. Shapiro, Luca Räss, Christopher R. Below, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Roland Bruderer, Jonathan Woodsmith, Lukas Reiter. Complementarity of class I and II neoantigen mapping in MSI-high colorectal cancer in needle biopsy size tissue samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2991.

Proteins represent a key class of analytes in pharmacodynamic (PD) analysis, an essential component in preclinical and clinical studies which inform about a biological system’s response to drug treatment. Pharmacodynamic signatures typically comprise target engagement marker and downstream pathway markers. With recent development in targeted therapies and novel therapeutic modalities such as protein degraders, the scope of PD has widened to cover predictive and stratification biomarkers which can inform patient selection and contribute to further understanding of potential drug resistance. Using peptides as surrogate for proteins of interest, mass spectrometry (MS)-based approach alleviates the barriers of reagent specificity of antibody-based methods which can be affected by species and matrices used in preclinical drug development. Moreover, the use of stable-isotope labeled standard (SIS) reference peptides grants targeted MS assays unraveled specificity and the ability to determine absolute quantity of a given analyte. Harnessing the power of targeted MS, we present an oncology-focused assay repository which contains 804 peptides corresponding to 582 proteins. All 804 peptide surrogates were chosen under strict selection criteria such as uniqueness/proteotypicity and stability. Off-the-shelf availability (PQ500 kit) of SIS reference peptides for all 582 proteins facilitates the deployment of any of these assays in a plug-and-play manner. The collection of 582 protein targets cover > 180 cellular pathways (Reactome pathways e.g. cytokine signaling, cell-cell communication, activation of matrix metalloproteinases) together with 49 FDA-approved drug targets. To further evaluate the performance of these assays, we carried out a case control study comprising plasma samples from 5 patient groups (lung, colorectal, pancreatic, breast and prostate cancer) and age and gender-matched healthy subjects. In total, we measured absolute quantities of 582 proteins across 180 plasma samples while validating the expression profiles of well-characterized biomarkers in patient plasma. To conclude, we’ve demonstrated the application of targeted MS approach for the routine analysis of pharmacodynamic biomarkers. Its multiplexing capability, together with the adaptability to various species allow this method to maneuver through preclinical and clinical scenarios. Citation Format: Sebastian Müller, Véronique Laforte, Simonas Savickas, Maik Müller, Yuehan Feng, Lukas Reiter. High-throughput monitoring of proteoforms and pathways through multiplexed and customizable mass spectrometry assay panels. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5059.

DIA profiles of complex biological matrices such as tissues can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. We developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2., Graphical Abstract Highlights Modern DIA methods contain high quality MS1 and MS2. We developed a statistical procedure incorporating MS1 and MS2. Benchmarking, the combined method outperformed the individual use of MS1 or MS2., In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions. We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways. Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

MS-based immunopeptidomics is maturing into an automatized and high-throughput technology, producing small- to large-scale datasets of clinically relevant major histocompatibility complex (MHC) class I-associated and class II-associated peptides. Consequently, the development of quality control (QC) and quality assurance systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semiautomated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition, and MHC specificity to greatly accelerate the “pass–fail” QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan, and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community., Graphical Abstract, Highlights • MhcVizPipe is a software tool for QC in immunopeptidomics. • It provides numerical scores for QC analysis of tumor biopsies. • It performs rapid QC analysis of large clinical immunopeptidomic sample cohorts. • It generates organized and easy-to-understand reports in HTML format., In Brief Automated QC software tools capable of detecting sample and/or measurement issues are important for downstream data interpretation. We created MhcVizPipe, the first semiautomated GUI-based QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS. MVP is intuitive to use, generate QC reports in HTML formats, and will find utility in any immunopeptidomic laboratory.

Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to a perturbation, protein structure may be altered by various molecular events, such as post-translational modifications, protein-protein interactions, aggregation, allostery or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of biological processes and pathways. Here, we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale. In LiP-MS, proteins undergo a brief proteolysis in native conditions followed by complete digestion in denaturing conditions, to generate structurally informative proteolytic fragments that are analyzed by mass spectrometry. We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition-based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. We introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. The experimental procedures take 3 d, mass spectrometric measurement time and data processing depend on sample number and statistical analysis typically requires similar to 1 d. These improvements expand the adaptability of LiP-MS and enable wide use in functional proteomics and translational applications. ISSN:1750-2799 ISSN:1754-2189

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which robust biomarkers are needed. Because protein structure reflects function, we tested whether global, in situ analysis of protein structural changes provides insight into PD pathophysiology and could inform a new concept of structural disease biomarkers. Using limited proteolysis-mass spectrometry (LiP-MS), we identified 76 structurally altered proteins in cerebrospinal fluid (CSF) of individuals with PD relative to healthy donors. These proteins were enriched in processes misregulated in PD, and some proteins also showed structural changes in PD brain samples. CSF protein structural information outperformed abundance information in discriminating between healthy participants and those with PD and improved the discriminatory performance of CSF measures of the hallmark PD protein alpha-synuclein. We also present the first analysis of inter-individual variability of a structural proteome in healthy individuals, identifying biophysical features of variable protein regions. Although independent validation is needed, our data suggest that global analyses of the human structural proteome will guide the development of novel structural biomarkers of disease and enable hypothesis generation about underlying disease processes. ISSN:1545-9993 ISSN:1545-9985

Cancers are immunologically heterogeneous. A range of immunotherapies target abnormal tumor immunity via different mechanisms of actions (MOAs), particularly various tumor-infiltrate leukocytes (TILs). We modeled loss of function (LOF) in four common anti-PD-1 antibody-responsive syngeneic tumors, MC38, Hepa1-6, CT-26 and EMT-6, by systematical depleting a series of TIL lineages to explore the mechanisms of tumor immunity and treatment. CD8+-T-cells, CD4+-T-cells, Treg, NK cells and macrophages were individually depleted through either direct administration of anti-marker antibodies/reagents or using DTR (diphtheria toxin receptor) knock-in mice, for some syngeneic tumors, where specific subsets were depleted following diphtheria toxin (DT) administration. These LOF experiments revealed distinctive intrinsic tumor immunity and thus different MOAs in their responses to anti-PD-1 antibody among different syngeneic tumors. Specifically, the intrinsic tumor immunity and the associated anti-PD-1 MOA were predominately driven by CD8+ cytotoxic TILs (CTL) in all syngeneic tumors, excluding Hepa1-6 where CD4+ Teff TILs played a key role. TIL-Treg also played a critical role in supporting tumor growth in all four syngeneic models as well as M2-macrophages. Pathway analysis using pharmacodynamic readouts of immuno-genomics and proteomics on MC38 and Hepa1-6 also revealed defined, but distinctive, immune pathways of activation and suppression between the two, closely associated with the efficacy and consistent with TIL-pharmacodynamic readouts. Understanding tumor immune-pathogenesis and treatment MOAs in the different syngeneic animal models, not only assists the selection of the right model for evaluating new immunotherapy of a given MOA, but also can potentially help to understand the potential disease mechanisms and strategize optimal immune-therapies in patients.

We established a robust capillary-flow data-independent acquisition MS platform capable of measuring 31 plasma proteomes per day without the need of repeated acquisition of the same sample. We acquired 1508 samples of the DiOGenes study (multicentered, Europa-wide caloric restriction weight loss and maintenance study of overweight and obese, non-diabetic participants). This was achieved using a single analytical column. Comprehensive biological reactions to weight loss and maintenance were observed., Graphical Abstract Highlights Robust capillary-flow DIA was established at 31 clinical samples per day. 1508 samples of dietary intervention study DiOGenes were measured on a single column. Comprehensive biological reactions to weight loss and maintenance were observed. Comparison to independent studies shows high reproducibility of potential biomarkers., Comprehensive, high throughput analysis of the plasma proteome has the potential to enable holistic analysis of the health state of an individual. Based on our own experience and the evaluation of recent large-scale plasma mass spectrometry (MS) based proteomic studies, we identified two outstanding challenges: slow and delicate nano-flow liquid chromatography (LC) and irreproducibility of identification of data-dependent acquisition (DDA). We determined an optimal solution reducing these limitations with robust capillary-flow data-independent acquisition (DIA) MS. This platform can measure 31 plasma proteomes per day. Using this setup, we acquired a large-scale plasma study of the diet, obesity and genes dietary (DiOGenes) comprising 1508 samples. Proving the robustness, the complete acquisition was achieved on a single analytical column. Totally, 565 proteins (459 identified with two or more peptide sequences) were profiled with 74% data set completeness. On average 408 proteins (5246 peptides) were identified per acquisition (319 proteins in 90% of all acquisitions). The workflow reproducibility was assessed using 34 quality control pools acquired at regular intervals, resulting in 92% data set completeness with CVs for protein measurements of 10.9%. The profiles of 20 apolipoproteins could be profiled revealing distinct changes. The weight loss and weight maintenance resulted in sustained effects on low-grade inflammation, as well as steroid hormone and lipid metabolism, indicating beneficial effects. Comparison to other large-scale plasma weight loss studies demonstrated high robustness and quality of biomarker candidates identified. Tracking of nonenzymatic glycation indicated a delayed, slight reduction of glycation in the weight maintenance phase. Using stable-isotope-references, we could directly and absolutely quantify 60 proteins in the DIA. In conclusion, we present herein the first large-scale plasma DIA study and one of the largest clinical research proteomic studies to date. Application of this fast and robust workflow has great potential to advance biomarker discovery in plasma.

In an effort to map the selectivity and understand the mode of action of a CDK9 inhibitor (compound 1), we employed several orthogonal proteomics methods. Our results show a clear selectivity against the CDK family of kinases, with the highest specific affinity towards CDK9. In addition, our multi-method approach allows us to also map the protein binding sites of the inhibitor, identify non-kinase (off-) targets as well as detect the cellular molecular responses to the added inhibitor. Here we describe the methods used, their strengths and weaknesses, how they can be used in the drug discovery pipeline and how they synergize to provide mechanistic insights of compounds of interest. We have used chemoproteomics, kinase affinity tools (kinobeads), Cellular Thermal Shift Assay (CETSA) and Limited Proteolysis (LiP). The results obtained clearly show that CDK9 is the primary target of Compound 1, with affinity curves highly correlated between the different target deconvolution techniques. The chemoproteomic approach rely on a compound derivate, able to bind to a sepharose bead. Subsequently, the binding competition assays are performed on lysed cell material. The choice of a mild lysis buffer allowed us to identify, not only CDK9, but also it’s molecular partners in the p-Tefb complex (Cyclin T1, Cyclin T2 and Aff4) with similar concentration response behavior. The results for the kinases identified in the study were strikingly similar when also profiling the compound without the chemical modification using the kinobeads assay. In the CETSA experiments, where both lysed cells and intact cells were profiled, the lysate experiment most closely resembles that of the previous pull-downs. Here, only direct binders of Compound 1 show a thermal shift, for example several of the pulled down kinases but not the p-Tefb complex partners that were co-competed previously. In the intact cell version of CETSA, not only the direct binders of the compound show stability shifts, but also downstream events and other secondary modulatory effects leave thermal traces in the cell. For example, Compound 1 also binds to GSK3A/B, causing their melting temperature to increase. Inhibition of GSK3 further affects the phosphorylation state and cellular location of FOXK1, which in turn is identified as a destabilized protein. Finally, Limited Proteolysis was used to identify target protein and using the LiP-Quant approach their LiP scores were assigned. Further, out of the identified CDK targets, mapping of peptide cleavage pattern was performed for the members of the CDK family for which structural data is published. The result identified the peptides to be directly adjacent to the ATP binding pocket of CDK9 or regions of high homology. The use of complementary techniques, based on unique biological and biochemical processes, allow robust and confident characterization of inhibitor compounds. Citation Format: Adam Hendricks, Nigel Beaton, Alexey Chernobrovkin, Eric Miele, Ghaith Hamza, Piero Ricchiuto, Ronald Tomlinson, Tomas Friman, Cassandra Borenstein, Bernard Barlaam, Sudhir Hande, Chris De Savi, Rick Davies, Martin Main, Joakim Hellner, Kristina Beeler, Yuehan Feng, Roland Bruderer, Lukas Reiter, Daniel Martinez Molina, Maria Paola Castaldi. Target identification, selectivity profiling and mechanistic insights of a Cdk9 inhibitor using complementary proteomics methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2924.

Blood is the most frequently used sample in diagnostic processes, and its liquid component is known as plasma. Plasma potentially contains molecular clues coming from all the tissues that make up an organism, thus possibly enable the holistic analysis of the health state of an individual. The major challenge in plasma protein analysis is the 1013- fold dynamic range and the strong prevalence of few, very abundant proteins. Depletion of the most abundant proteins is used to increase coverage of the plasma proteome. To be able to profile human plasma at large scale and at maximal depth, we developed and optimized two main aspects. Firstly, we automated the depletion of the top 14 most abundant proteins in human plasma to a throughput of 40 depletions per day per setup. A comparison of native and depletion workflows with a controlled quantitative experiment demonstrated that depletion led to a significant increase in the number of identifications (&gt200%) and number of true hits (&gt300% at controlled actual FDR &lt1%), while conserving quantitative accuracy. Secondly, we further optimized FAIMS-DIA methods for deep plasma proteome profiling and applied the optimized workflow to a cohort coming from the deadliest five solid tumor types. To research large cohorts’ feasibility, we analyzed a cohort comprising 30 each of healthy, lung, colorectal, pancreatic, breast and prostate cancer. Altogether, we processed 180 samples (plus 24 quality controls) and quantified 2,732 proteins, of which 1,804 in at least 50% of the runs. Based on quality control samples, we could characterize variance introduced on each level, all much lower than the inter-individual variability. We reached 8 orders of magnitude of dynamic range, within this range we covered extensively tissue leakage proteome, interleukins and signaling proteins such as Egf, Klk3 (PSA), Akt1, Cd86, Met, Erbb2 and Cd33. Using machine learning we reduced to an average of 129 (5% of the quantified proteins) biomarker candidates per cancer type, making biological interpretation more feasible while enabling classification of health and disease. The model performance was 86-100% on the 20% hold-out validation set when healthy and overall disease status were considered. Importantly, the biomarker candidates were predominantly (70%) coming from low abundance regions clearly demonstrating the need to measure deeply because they would be missed by shallow plasma profiling. Hence, we showed that our automated plasma depletion workflow has the potential to enable the unbiased and reproducible quantification of more than 2,700 proteins across very large cohorts and unlock biomarker candidate panels in different cancer types. Furthermore, with the latest analytical method iteration applied to a subset of the samples, we could quantify more than 3,500 proteins, demonstrating the full potential of the workflow. Citation Format: Marco Tognetti, Kamil Sklodowski, Sebastian Mueller, Dominique Kamber, Jan Muntel, Yuehan Feng, Roland Bruderer, Lukas Reiter. Precise solid tumor classification through unbiased quantification of proteoforms deep into tissue leakage [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3920.

Human leukocyte antigen-associated peptides, known as immunopeptides, play an essential role in adaptive immunity by activating and ensuring the specificity of T-cells. The identification and quantification of the immunopeptidome bear the potential to enable personalized treatments, especially in cancers, vaccines, infectious, and autoimmune diseases. Mass spectrometry is currently the only technology that can reliably measure and identify immunopeptide profiles of biological samples on a large scale. However, the usually high sample input amount and poor scalability are limiting. Here, we introduce a semi-automated workflow to robustly identify immunopeptides from low amounts of cultured cells and tissue samples by systematically optimizing each step of the sample preparation and acquisition. We optimized the native lysis and immunoprecipitation workflow while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. The established sample preparation offers high reproducibility and identifications of good quality. For class-I immunopeptides, &gt60% of the peptides identified are 9-mers, &gt80% predicted strong binders, and the expected amino acids are enriched at the anchor positions. For class-II, &gt50% of the peptides identified are 14-to-16-mers, and &gt50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing as little as 2.5 mg fresh frozen tissue and &gt9,000 class-I and &gt12,000 class-II immunopeptides when preparing 10 million JY cells. Overall, the pipeline is scalable, highly reproducible, and results in high-quality identifications while supporting very limited sample input. Finally, we measured a cohort of 12 cancerous and matched healthy lung tissues from as little as 15 mg tissue, whereby we could identify &gt11,000 class-I immunopeptides and &gt9,000 class-II on average. For class-I, matched samples clustered together, while &gt3,000 immunopeptides were upregulated in the cancer tissues, with a significant enrichment for proteins related to lung cancer. Overall, we established a scalable, efficient pipeline for cell line and tissues immunopeptidomics for class-I and II that generates high-quality identifications and that only requires small amounts of input material and is ready to shed light into immunopeptidomics heterogeneity through large-scale profiling of patients. Citation Format: Ilja E. Shapiro, Luca Raess, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Yuehan Feng, Roland Bruderer, Lukas Reiter. Discovery of MHC class I and class II neoantigens in lung cancer in needle biopsy tissue samples using an optimized high-throughput workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1374.

Background: Beyond phenotypic efficacy and safety categorization, high resolution profiling of drug-protein interactions and binding mechanisms remains a major hurdle during lead selection and optimization. A key milestone in structure-based drug design is compound binding site identification and characterization. Structure-activity relationship (SAR) studies utilize techniques such as nuclear magnetic resonance (NMR), x-ray crystallography (X-ray), cryo-electron microscopy (cryo-EM) and the mass spectrometry-based hydrogen-deuterium exchange (HDX) to address these hurdles but they are labor, time and cost intensive. Further, SAR studies are often complicated by protein size (i.e. large proteins) and location (i.e. membrane proteins), which can lead to protocol adaptations (e.g. recombinant protein usage and/or protein truncation) that can introduce artifacts. Using limited proteolysis (LiP) coupled to next-generation mass spectrometry we have developed a high-throughput, high-resolution approach (HR-LiP) that utilizes peptide-level resolution to characterize drug-protein interactions including for proteins that hindered by the previously mentioned limitations. Methods: To boost protein abundance in their native environment, proteins of interest were overexpressed in HEK293 cells using a simple plasmid construct. Cell lysates were incubated with the compounds of interest at increasing concentrations. Samples were then subjected to a limited digest using proteinase K and further processed for data independent acquisition (DIA)-MS analysis using trypsin. Data was analyzed using a directDIA workflow in Spectronaut. Results: Two well-characterized drug target proteins, bromodomain-containing protein 4 (BRD4) and epidermal growth factor receptor (EGFR), were selected for analysis. Using HR-LiP we identify the binding site of the BRD4 inhibitor JQ1 in the full-length protein, which is typically too large to be used directly in with conventional methods. Further, we map the intracellular binding location of both gefitinib and afatinib, two inhibitors of the membrane protein EGFR. Our data for both proteins are in good accordance with orthogonal data obtained by HDX-MS, NMR and X-ray studies. Conclusions: We demonstrate that HR-LiP can be used to dissect small molecule-protein binding events, including compound binding site prediction for protein targets classically considered to be difficult. Given its biological power, broad applicability and ease of implementation, we envision the use of HR-LiP as a routine approach for target validation and lead optimization in small molecule drug discovery pipelines. Citation Format: Nigel Beaton, Jagat Adhikari, Roland Bruderer, Ron Tomlinson, Yuehan Feng, Ivan Cornella-Taracido, Lukas Reiter. Prediction of small molecule-protein binding events for BRD4 and EGFR inhibitors using HR-LiP, a novel structural proteomics approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2136.

We report on 45 patients with myeloid neoplasms and concurrent Janus kinase 2 (JAK2) V617F and KIT proto-oncogene, receptor tyrosine kinase (KIT) D816V (JAK2 pos ./KIT pos .) mutations, which are individually identified in >60% of patients with classical myeloproliferative neoplasms (MPN) and >90% of patients with systemic mastocytosis (SM) respectively. In SM, the concurrent presence of a clonal non-mast cell neoplasm [SM with associated haematological neoplasm (SM-AHN)] usually constitutes a distinct subtype associated with poor survival. All 45 patients presented with a heterogeneous combination of clinical/morphological features typical of the individual disorders (e.g. leuco-/erythro-/thrombocytosis and elevated lactate dehydrogenase for MPN; elevated serum tryptase and alkaline phosphatase for SM). Overlapping features identified in 70% of patients included splenomegaly, cytopenia(s), bone marrow fibrosis and additional somatic mutations. Molecular dissection revealed discordant development of variant allele frequency for both mutations and absence of concurrently positive single-cell derived colonies, indicating disease evolution in two independent clones rather than monoclonal disease in >60% of patients examined. Overall survival of JAK2 pos ./KIT pos . patients without additional somatic high-risk mutations [HRM, e.g. in serine and arginine-rich splicing factor 2 (SRSF2), additional sex combs like-1 (ASXL1) or Runt-related transcription factor 1 (RUNX1)] at 5 years was 77%, indicating that the mutual impact of JAK2 V617F and KIT D816V on prognosis is fundamentally different from the adverse impact of additional HRM in the individual disorders.

In systemic mastocytosis (SM), qualitative and serial quantitative assessment of the KIT D816V mutation is of diagnostic and prognostic relevance. We investigated peripheral blood and bone marrow samples of 161 patients (indolent SM (ISM), n = 40, advanced SM, AdvSM, n = 121) at referral and during follow-up for the KIT D816V variant allele frequency (VAF) at the DNA-level and the KIT D816V expressed allele burden (EAB) at the RNA-level. A round robin test with four participating laboratories revealed an excellent correlation (r &gt, 0.99, R2 &gt, 0.98) between three different DNA-assays. VAF and EAB strongly correlated in ISM (r = 0.91, coefficient of determination, R2 = 0.84) but only to a lesser extent in AdvSM (r = 0.71, R2 = 0.5). However, as compared to an EAB/VAF ratio ≤2 (cohort A, 77/121 patients, 64%) receiver operating characteristic (ROC) analysis identified an EAB/VAF ratio of &gt, 2 (cohort B, 44/121 patients, 36%) as predictive for an advanced phenotype and a significantly inferior median survival (3.3 vs. 11.7 years, p = 0.005). In terms of overall survival, Cox-regression analysis was only significant for the EAB/VAF ratio &gt, 2 (p = 0.006) but not for VAF or EAB individually. This study demonstrates for the first time that the transcriptional activity of KIT D816V may play an important role in the pathophysiology of SM.

Aging is a complex biological process associated with progressive loss of physiological function and susceptibility to several diseases, such as cancer and neurodegeneration. Exosomes are involved in many cellular signaling pathways, and their cargo may serve as promising disease or aging biomarkers. These membrane-bound extracellular vesicles facilitate the transport of intracellular contents to proximal and distal cells in the body. Here, we investigated two omics approaches for exosome analysis. To overcome the challenges of plasma exosome contamination with abundant soluble plasma proteins, we developed a high-throughput method to isolate highly purified exosomes from human plasma by sequential size-exclusion chromatography and ultrafiltration. First, we used data-dependent acquisitions from offline high-pH reversed-phase fractions of exosome lysate to generate a deep spectral library comprising ∼2,300 exosome proteins. Second, in a pilot aging study, we used comprehensive data-independent acquisitions to compare plasma exosomes from young (20–26 yrs) and old (60–66 yrs) individuals. We quantified 1,318 exosome proteins, and levels of 144 proteins were significantly different in young and old plasma groups (Q1.5-fold change). We also analyzed exosome miRNA cargo and detected 331 miRNAs. Levels of several were significantly different in young and old individuals. In addition, 88 and 17 miRNAs were unique to old and young individuals, respectively. Plasma exosome biomarkers have great potential for translational studies investigating biomarkers of aging and age-related diseases and to monitor therapeutic aging interventions.

Immunopeptidomics refers to the science of investigating the composition and dynamics of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS). Here, we aim to provide a technical report to any non-expert in the field wishing to establish and/or optimize an immunopeptidomic workflow with relatively limited computational knowledge and resources. To this end, we thoroughly describe step-by-step instructions to isolate MHC class I and II-associated peptides from various biological sources, including mouse and human biospecimens. Most notably, we created MhcVizPipe (MVP) (https://github.com/CaronLab/MhcVizPipe), a new and easy-to-use open-source software tool to rapidly assess the quality and the specific enrichment of immunopeptidomic datasets upon the establishment of new workflows. In fact, MVP enables intuitive visualization of multiple immunopeptidomic datasets upon testing sample preparation protocols and new antibodies for the isolation of MHC class I and II peptides. In addition, MVP enables the identification of unexpected binding motifs and facilitates the analysis of non-canonical MHC peptides. We anticipate that the experimental and bioinformatic resources provided herein will represent a great starting point for any non-expert and will therefore foster the accessibility and expansion of the field to ultimately boost its maturity and impact.

MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry-based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset. This work was supported in part by NSF CAREER award no. DBI-1054826, grant no. NSF DBI-1759736 and the Chan-Zuckerberg foundation to O.V., grant no. NIH-NLM 1R01LM013115 to N.B. and O.V., NSF award no. ABI 1759980, NIH award nos. P41GM103484 and R24GM127667 to N.B. and the Personalized Health and Related Technologies (grant no. PHRT 0-21411-18) strategic focus area of ETH to B.W. The CRG/UPF Proteomics Unit is part of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech) and it is a member of the ProteoRed PRB3 consortium that is supported by grant no. PT17/0019 of the PE I+D+i 2013–2016 from the Instituto de Salud Carlos III (ISCIII) and ERDF. We acknowledge support from the Spanish Ministry of Science, Innovation and Universities, ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012–0208 and Secretaria d’Universitats i Recerca del Departament d’Economia i Coneixement de la Generalitat de Catalunya (grant no. 2017SGR595). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 823839 (EPIC-XS). Y.P.-R. acknowledges the Wellcome Trust (grant no. 208391/Z/17/Z).

Neste artigo, prop?e-se um di?logo entre o trabalho de relevantes expoentes da escola de pensamento do institucionalismo americano; Walton Hamilton (1919), Thorstein Veblen (1898, 1899), e os escritos de Geoffrey Hogdson (1998; 2004; 2010), Ulrich Witt (2008; 2010; 2011; 2013), Wolfhard Kaus (2013) e Karin Knottenbauer (2010). Argumenta-se que o que une tais autores ? a busca por tra?os evolutivos do comportamento econ?mico. Analisa-se o caso da categoria consumo consp?cuo e de suas ra?zes evolutivas. Para tal, primeiro apresentamos ao leitor um resumo do que ? a ess?ncia do institucionalismo americano e as proposi??es de Hamilton (1919) para uma teoria econ?mica que adota a din?mica em vez da est?tica. Em seguida, ? apresentada uma exposi??o da teoria do consumo consp?cuo de Veblen (1899), possibilitando o posterior v?nculo entre caracter?sticas evolutivas e o t?pico da conspicuidade. O artigo prossegue com um apelo para que os economistas evolucionistas modernos prestem mais aten??o ?s quest?es ontol?gicas e epistemol?gicas ao abordar o t?pico do consumo na ci?ncia econ?mica. Aqui os trabalhos de Campbell (1993) e Zahavi (1975) s?o ?teis para esclarecer os fundamentos evolutivos do ato de consumo consp?cuo, seu comportamento emulativo e a poss?vel emerg?ncia de um comportamento de consumo inovativo. A partir desta abordagem estilizada, nossas conclus?es mostram que a economia evolutiva ainda n?o ofereceu uma estrutura interpretativa robusta ao lidar com categorias econ?micas importantes, como o consumo consp?cuo. Al?m disso, a falta de um constructo de categorias explicativas interligadas est? no centro dos desafios ontol?gicas e epistemol?gicas que a teoria enfrenta atualmente. In this paper we aim to propose a dialogue between the work of main exponents of what is known as American Old Institutionalism (AOI); Walton Hamilton (1919), Thorstein Veblen (1898, 1899), and the writings of Geoffrey Hogdson (1998; 2004; 2010), Ulrich Witt (2008; 2010; 2011; 2013), Wolfhard Kaus (2013) and Karin Knottenbauer (2010). It is argued what unites all these authors is the search for evolutionary traits of economic behavior. The case is made for the category of conspicuous consumption and its evolutionary roots. To do so, we first present the reader with a brief of what is the essence of the American Old Institutionalism (AOI) and the propositions of Hamilton (1919) for an economic theory that would embrace dynamics instead of statics. Then, an exhibition of Veblen?s (1899) conspicuous consumption theory is offered, making it possible for the future linkage between evolutionary features and the topic of conspicuity. The paper proceeds with an appeal for modern evolutionary economists to pay more attention to the matter of ontology and epistemology when approaching the topic of consumption in Economics. Here the work of Campbell (1993) and Buss (2008) come handy to clarify the underpinnings of the act of conspicuous consumption, its emulative behavior and the eventual consumption novelty. Within that stylized approach, our conclusions show that evolutionary economics still has not offered a robust framework when dealing with key economic categories such as conspicuous consumption. Also, the lack of linked explanatory categories is at the core of the ontological and epistemological issues the theory faces nowadays. Keywords Evolution ? Evolutionary Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - CAPES

Profiling of biological relationships between different molecular layers dissects regulatory mechanisms that ultimately determine cellular function. To thoroughly assess the role of protein post‐translational turnover, we devised a strategy combining pulse stable isotope‐labeled amino acids in cells (pSILAC), data‐independent acquisition mass spectrometry (DIA‐MS), and a novel data analysis framework that resolves protein degradation rate on the level of mRNA alternative splicing isoforms and isoform groups. We demonstrated our approach by the genome‐wide correlation analysis between mRNA amounts and protein degradation across different strains of HeLa cells that harbor a high grade of gene dosage variation. The dataset revealed that specific biological processes, cellular organelles, spatial compartments of organelles, and individual protein isoforms of the same genes could have distinctive degradation rate. The protein degradation diversity thus dissects the corresponding buffering or concerting protein turnover control across cancer cell lines. The data further indicate that specific mRNA splicing events such as intron retention significantly impact the protein abundance levels. Our findings support the tight association between transcriptome variability and proteostasis and provide a methodological foundation for studying functional protein degradation., Molecular Systems Biology, 16 (3), ISSN:1744-4292

Quantitative phosphoproteomics has transformed investigations of cell signaling, but it remains challenging to scale the technology for high-throughput analyses. Here we report a rapid and reproducible approach to analyze hundreds of phosphoproteomes using data-independent acquisition (DIA) with an accurate site localization score incorporated into Spectronaut. DIA-based phosphoproteomics achieves an order of magnitude broader dynamic range, higher reproducibility of identification, and improved sensitivity and accuracy of quantification compared to state-of-the-art data-dependent acquisition (DDA)-based phosphoproteomics. Notably, direct DIA without the need of spectral libraries performs close to analyses using project-specific libraries, quantifying > 20,000 phosphopeptides in 15 min single-shot LC-MS analysis per condition. Adaptation of a 3D multiple regression model-based algorithm enables global determination of phosphorylation site stoichiometry in DIA. Scalability of the DIA approach is demonstrated by systematically analyzing the effects of thirty kinase inhibitors in context of epidermal growth factor (EGF) signaling showing that specific protein kinases mediate EGF-dependent phospho-regulation., Localizing phosphorylation sites by data-independent acquisition (DIA)-based proteomics is still challenging. Here, the authors develop algorithms for phosphosite localization and stoichiometry determination, and incorporate them into single-shot DIA-phosphoproteomics workflows.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound., Proteomics is often used to map protein-drug interactions but identifying a drug’s protein targets along with the binding interfaces has not been achieved yet. Here, the authors integrate limited proteolysis and machine learning for the proteome-wide mapping of drug protein targets and binding sites.

Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in developing optimized small-molecule compounds. Current unbiased approaches cannot directly pinpoint the interaction surfaces between ligands and protein targets. To address his limitation we have developed a new drug target deconvolution approach based on limited proteolysis coupled with mass spectrometry that works across species including human cells (LiP-Quant). LiP-Quant features an automated data analysis pipeline and peptide-level resolution for the identification of any small-molecule binding sites, Here we demonstrate drug target identification by LiP-Quant across compound classes, including compounds targeting kinases and phosphatases. We demonstrate that LiP-Quant estimates the half maximal effective concentration (EC50) of compound binding sites in whole cell lysates. LiP-Quant identifies targets of both selective and promiscuous drugs and correctly discriminates drug binding to homologous proteins. We finally show that the LiP-Quant technology identifies targets of a novel research compound of biotechnological interest.

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5–8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.

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

BackgroundMass spectrometry-based discovery proteomics has recently emerged as a high-throughput method for the proteomic profiling in biofluid samples from large clinical and population screening cohorts. Despite this progress, a significant fraction of the plasma proteome is currently not covered by state-of-the-art discovery approaches and therefore not accessible for biomarker discovery. To close this analytical gap, we present a novel workflow combining automated plasma depletion and FAIMS-DIA-MS to bridge both sensitivity and scalability. We demonstrate the applicability of this workflow to support biomarker discovery and subject stratification in precision oncology in a case-control cohort.MethodsThe plasma samples were depleted in 96-well format using an automated MARS-14 depletion system. The depleted samples were processed to tryptic peptides and analyzed using a Thermo Scientific Orbitrap Exploris 480 equipped with a FAIMS Pro device. Data processing and analysis were performed using Biognosys’ SpectroMine and Spectronaut software.ResultsUsing the unbiased discovery workflow, we investigated a cohort comprising of 180 plasma samples from healthy donors and subjects diagnosed with pancreatic, breast, prostate, colorectal and lung (NSCLC) cancer at either early or late stage of the disease. Overall, the optimized FAIMS-DIA-MS quantified 2,741 proteins across all samples and 1,849 proteins on average per sample measurement. Based on estimated plasma protein concentrations (Human Protein Atlas), quantified proteins span across 8 orders of magnitude, down to single digit pg/mL. Within this dynamic range, we could interrogate the tissue leakage proteome, interleukins and signaling proteins. Using classification algorithms, we were able to select candidates to build protein panels which provide significant positive predictive values associated with different disease stages, especially in the sub-cohorts for pancreatic and colorectal cancer.ConclusionsWe demonstrate the capabilities of a novel discovery workflow for deep, quantitative profiling of plasma samples at large scale, providing a rich proteomic resource for precision oncology.