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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individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures.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 When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.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.

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.

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

Introduction: Current techniques to diagnose and/or monitor critically ill neonates with bronchopulmonary dysplasia (BPD) require invasive sampling of body fluids, which is suboptimal in these frail neonates. We tested our hypothesis that it is feasible to use noninvasively collected urine samples for proteomics from extremely low gestational age newborns (ELGANs) at risk for BPD to confirm previously identified proteins and biomarkers associated with BPD. Methods: We developed a robust high-throughput urine proteomics methodology that requires only 50 μL of urine. We utilized the methodology with a proof-of-concept study validating proteins previously identified in invasively collected sample types such as blood and/or tracheal aspirates on urine collected within 72 h of birth from ELGANs (gestational age [26 ± 1.2] weeks) who were admitted to a single Neonatal Intensive Care Unit (NICU), half of whom eventually developed BPD (n = 21), while the other half served as controls (n = 21). Results: Our high-throughput urine proteomics approach clearly identified several BPD-associated changes in the urine proteome recapitulating expected blood proteome changes, and several urinary proteins predicted BPD risk. Interestingly, 16 of the identified urinary proteins are known targets of drugs approved by the Food and Drug Administration. Conclusion: In addition to validating numerous proteins, previously found in invasively collected blood, tracheal aspirate, and bronchoalveolar lavage, that have been implicated in BPD pathophysiology, urine proteomics also suggested novel potential therapeutic targets. Ease of access to urine could allow for sequential proteomic evaluations for longitudinal monitoring of disease progression and impact of therapeutic intervention in future studies.

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.

To elucidate the role of Tau isoforms and post-translational modification (PTM) stoichiometry in Alzheimer's disease (AD), we generated a high-resolution quantitative proteomics map of 95 PTMs on multiple isoforms of Tau isolated from postmortem human tissue from 49 AD and 42 control subjects. Although Tau PTM maps reveal heterogeneity across subjects, a subset of PTMs display high occupancy and frequency for AD, suggesting importance in disease. Unsupervised analyses indicate that PTMs occur in an ordered manner, leading to Tau aggregation. The processive addition and minimal set of PTMs associated with seeding activity was further defined by analysis of size-fractionated Tau. To summarize, features in the Tau protein critical for disease intervention at different stages of disease are identified, including enrichment of 0N and 4R isoforms, underrepresentation of the C terminus, an increase in negative charge in the proline-rich region (PRR), and a decrease in positive charge in the microtubule binding domain (MBD).

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

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).

Background Genetic mutations in α-actinin-4 (ACTN4)-an important actin crosslinking cytoskeletal protein that provides structural support for kidney podocytes-have been linked to proteinuric glomerulosclerosis in humans. However, the effect of post-translational modifications of ACTN4 on podocyte integrity and kidney function is not known. Methods Using mass spectrometry, we found that ACTN4 is phosphorylated at serine (S) 159 in human podocytes. We used phosphomimetic and nonphosphorylatable ACTN4 to comprehensively study the effects of this phosphorylation in vitro and in vivo. We conducted x-ray crystallography, F-actin binding and bundling assays, and immunofluorescence staining to evaluate F-actin alignment. Microfluidic organ-on-a-chip technology was used to assess for detachment of podocytes simultaneously exposed to fluid flow and cyclic strain. We then used CRISPR/Cas9 to generate mouse models and assessed for renal injury by measuring albuminuria and examining kidney histology. We also performed targeted mass spectrometry to determine whether high extracellular glucose or TGF-β levels increase phosphorylation of ACTN4. Results Compared with the wild type ACTN4, phosphomimetic ACTN4 demonstrated increased binding and bundling activity with F-actin in vitro. Phosphomimetic Actn4 mouse podocytes exhibited more spatially correlated F-actin alignment and a higher rate of detachment under mechanical stress. Phosphomimetic Actn4 mice developed proteinuria and glomerulosclerosis after subtotal nephrectomy. Moreover, we found that exposure to high extracellular glucose or TGF-β stimulates phosphorylation of ACTN4 at S159 in podocytes. Conclusions These findings suggest that increased phosphorylation of ACTN4 at S159 leads to biochemical, cellular, and renal pathology that is similar to pathology resulting from human disease-causing mutations in ACTN4. ACTN4 may mediate podocyte injury as a consequence of both genetic mutations and signaling events that modulate phosphorylation.

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without prior knowledge of the type of modification. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

In mammalian cells, the lysosome is the main organelle for the degradation of macromolecules and the recycling of their building blocks. Correct lysosomal function is essential, and mutations in every known lysosomal hydrolase result in so-called lysosomal storage disorders, a group of rare and often fatal inherited diseases. Furthermore, it is becoming more and more apparent that lysosomes play also decisive roles in other diseases, such as cancer and common neurodegenerative disorders. This leads to an increasing interest in the proteomic analysis of lysosomes for which enrichment is a prerequisite. In this study, we compared the four most common strategies for the enrichment of lysosomes using data-independent acquisition. We performed centrifugation at 20,000 × g to generate an organelle-enriched pellet, two-step sucrose density gradient centrifugation, enrichment by superparamagnetic iron oxide nanoparticles (SPIONs), and immunoprecipitation using a 3xHA tagged version of the lysosomal membrane protein TMEM192. Our results show that SPIONs and TMEM192 immunoprecipitation outperform the other approaches with enrichment factors of up to 118-fold for certain proteins relative to whole cell lysates. Furthermore, we achieved an increase in identified lysosomal proteins and a higher reproducibility in protein intensities for label-free quantification in comparison to the other strategies.

Comprehensive proteome quantification is crucial for a better understanding of underlying mechanisms of diseases. Liquid chromatography mass spectrometry (LC-MS) has become the method of choice for comprehensive proteome quantification due to its power and versatility. Even though great advances have been made in recent years, full proteome coverage for complex samples remains challenging due to the high dynamic range of protein expression. Additionally, when studying disease regulatory proteins, biomarkers or potential drug targets are often low abundant, such as for instance kinases and transcription factors. Here, we show that with improvements in chromatography and data analysis the single shot proteome coverage can go beyond 10 000 proteins in human tissue. In a testis cancer study, we quantified 11 200 proteins using data independent acquisition (DIA). This depth was achieved with a false discovery rate of 1% which was experimentally validated using a two species test. We introduce the concept of hybrid libraries which combines the strength of direct searching of DIA data as well as the use of large project-specific or published DDA data sets. Remarkably deep proteome coverage is possible using hybrid libraries without the additional burden of creating a project-specific library. Within the testis cancer set, we found a large proportion of proteins in an altered expression (in total: 3351; 1453 increased in cancer). Many of these proteins could be linked to the hallmarks of cancer. For example, the complement system was downregulated which helps to evade the immune response and chromosomal replication was upregulated indicating a dysregulated cell cycle.

Label-free quantification (LFQ) and isobaric labeling quantification (ILQ) are among the most popular protein quantification workflows in discovery proteomics. Here, we compared the TMT SPS/MS3 10-plex workflow to a label free single shot data-independent acquisition (DIA) workflow on a controlled sample set. The sample set consisted of ten samples derived from 10 biological replicates of mouse cerebelli spiked with the UPS2 protein standard in five different concentrations. For a fair comparison, we matched the instrument time for the two workflows. The LC-MS data were acquired at two facilities to assess interlaboratory reproducibility. Both methods resulted in a high proteome coverage (>5000 proteins) with low missing values on protein level (

Background Immunotherapies targeting the PD-1/PD-L1 axis have been shown to be effective in only around 20% of cancer patients and the mechanism of action (MOA) underlying the differences between responders and non-responders remains poorly understood. It is therefore critical to understand the roles of different lineages of immune cells play mediating PD-1 response, while gaining an overall understanding of the interplay between tumor microenvironment (TME) and immune cell populations. To address this, tumor-bearing syngeneic mice were treated with an anti-PD-1 antibody in combination with targeted CD4+ or CD8+ T cell depletion. Tumor tissues were subsequently investigated with an unbiased proteomics workflow based on data-independent acquisition (DIA) mass spectrometry to provide insights into TME in different treatment contexts. Methods T cell subpopulations were depleted in two subcutaneous murine syngeneic models (MC38, and Hepa 1-6) followed by anti-PD-1 treatment (10mg/kg). The depletion of CD4+ and CD8+ T cells were accomplished with anti-CD4 (GK1.5) and anti-CD8 (2.43). The effectiveness of each depletion was assessed by tumor growth, followed by flow cytometry analysis of tumor infiltrating lymphocytes. FFPE tumor tissue samples, taken at the end of study (17-20 days), were digested using standard procedures and analyzed using 4h gradients on a C18 column coupled to a Thermo Scientific Q Exactive HF-X mass spectrometer. Data were extracted using Spectronaut™ (Biognosys) with a sample specific library. Results Consistent with expectation, depletion of CD8+ T cells significantly attenuated the antitumor effects of anti-PD-1 treatment in both tumor models, confirming the crucial roles of CD8+ T cells in tumor cell killing and inhibition of growth. However, depletion of CD4+ T cells showed opposing effects in the two models tested with a significant enhancement of anti-PD-1 efficacy in MC38 tumors and a reduction of anti-PD-1 efficacy in Hepa1-6 tumors. These effects were further studied by a deep proteome analysis (>9'000 proteins quantified. Statistical analysis showed large differences on proteome level between the two models (>3,000 proteins differentially expressed). We observed a strong upregulation of IFNγ inducible proteins upon anti-PD-1 therapy in the MC38 model, which is boosted upon CD4+ depletion, while many IFNγ pathway related markers were downregulated in the combo group of anti-PD-1 and anti-CD4 in Hepa 1-6. The data of the proteome analysis will be correlated to additional data from RNAseq analysis. Conclusions This study shows an unequivocal role of CD8+ T cells in anti-PD-1 induced tumor growth inhibition whereas the role of CD4+ T cells depends on the specific tumor type and TME. In our study we demonstrate how a deep proteome analysis improves the understanding of the interdependency of immune cells and the TME. Citation Format: Jan Muntel, Ying Jin, Haochen Yu, Nicholas Dupuis, Yongli Shan, Annie X. An, Wubin Qian, Davy Ouyang, Kristina Beeler, Roland Bruderer. Deep proteomic profiling of CD4+ and CD8+ T cell-depleted tumor models in PD-1 treatment [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 927.

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ~8%; DDA = ~16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (~1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

We describe a 96-well plate compatible membrane-based proteomic sample processing method, which enables the complete processing of 96 samples (or multiples thereof) within a single workday. This method uses a large-pore hydrophobic PVDF membrane that efficiently adsorbs proteins, resulting in fast liquid transfer through the membrane and significantly reduced sample processing times. Low liquid transfer speeds have prevented the useful 96-well plate implementation of FASP as a widely used membrane-based proteomic sample processing method. We validated our approach on whole-cell lysate and urine and cerebrospinal fluid as clinically relevant body fluids. Without compromising peptide and protein identification, our method uses a vacuum manifold and circumvents the need for digest desalting, making our processing method compatible with standard liquid handling robots. In summary, our new method maintains the strengths of FASP and simultaneously overcomes one of the major limitations of FASP without compromising protein identification and quantification.

The function of a large percentage of proteins is modulated by post-translational modifications (PTMs). Currently, mass spectrometry (MS) is the only proteome-wide technology that can identify PTMs. Unfortunately, the inability to detect a PTM by MS is not proof that the modification is not present. The detectability of peptides varies significantly making MS potentially blind to a large fraction of peptides. Learning from published algorithms that generally focus on predicting the most detectable peptides we developed a tool that incorporates protein abundance into the peptide prediction algorithm with the aim to determine the detectability of every peptide within a protein. We tested our tool, “Peptide Prediction with Abundance” (PPA), on in-house acquired as well as published data sets from other groups acquired on different instrument platforms. Incorporation of protein abundance into the prediction allows us to assess not only the detectability of all peptides but also whether a peptide of interest is likely to become detectable upon enrichment. We validated the ability of our tool to predict changes in protein detectability with a dilution series of 31 purified proteins at several different concentrations. PPA predicted the concentration dependent peptide detectability in 78% of the cases correctly, demonstrating its utility for predicting the protein enrichment needed to observe a peptide of interest in targeted experiments. This is especially important in the analysis of PTMs. PPA is available as a web-based or executable package that can work with generally applicable defaults or retrained from a pilot MS data set.

Introduction: Recent approvals of microsatellite instability (MSI) or mismatch repair (MMR) testing, in addition to PD-L1 expression, expand the tools available to identify tumor characteristics that may help predict the likelihood of patient response to immunotherapy treatment. However, even in MSI positive subgroups, not all subjects achieve a durable response and research continues to identify tumor characteristics that further predict the likelihood of patient response. To support and advance this area of research, new tools are being developed that provide deeper and unbiased views of the tumor proteome. Here, we characterize the protein expression profiles of 95 colorectal cancer tumors (CRC) using SWATH acquisition mass spectrometry (SWATH MS) to further probe tumor phenotypic characteristics. Experimental Methods: FFPE colon tissue samples (10 healthy, 95 cancer) were obtained from commercial biobanks. Proteins were extracted from the tissue, processed to peptides with trypsin, and prepared for LC-MS analysis. Peptides for each sample were injected on a Triart C18 column (YMC) coupled to a NanoLC 425 system (SCIEX) using a 43min gradient at a flow rate of 5µl/min. The eluted peptides were then analyzed with a TripleTOF® 6600 system (SCIEX) operated in SWATH mode. Total run time per sample was 1 hour. Data were analyzed in Spectronaut Pulsar X (Biognosys) with a project specific library. All data were filtered with a 1% FDR on peptide and protein level. Results: Across all samples, >4,500 protein groups were quantified (approximately 3,600 per sample). Data analysis revealed a large number of proteins (~1,000) were differentially expressed in the cancer cohort. Consistent with increased tumor cell proliferation, proteins involved in protein translation were upregulated in the tumor samples. Unsupervised clustering of the data separated the healthy and the cancer cohort. Clustering also revealed three main proteomic subtypes within in the cancer cohort (A, B and C), which were largely distinguished by expression of cell adhesion proteins, including neuronal growth regulator 1 (NEGR1), a potential tumor suppressor. Interestingly, hepatocyte nuclear factor 4-alpha (HNF4A), a transcription factor which is known to be elevated in CRC, was only overexpressed in subtype B. Further analysis of key protein networks related to CRC treatment and immunotherapy development will be presented. Conclusions: High-throughput proteomic profiling of FFPE tissues using SWATH-MS enables the deepest phenotypic characterization of tumor tissue. Ultimately, analyses of this type will enable a functional understanding of interplay between the tumor microenvironment, expression of protein networks and response to immune-directed therapies. Citation Format: Nicholas Dupuis, Jan Muntel, Roland Bruderer, Lukas Reiter. Expanding insights into the colorectal cancer tumor proteome; unbiased protein profiling reveals multiple proteomic-based tumor subtypes [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr A066.

The past 15 years have seen significant progress in LC-MS/MS peptide sequencing, including the advent of successful de novo and database search methods; however, analysis of glycopeptide and, more generally, glycoconjugate spectra remains a much more open problem, and much annotation is still performed manually. This is partly because glycans, unlike peptides, need not be linear chains and are instead described by trees. In this study, we introduce SweetSEQer, an extremely simple open source tool for identifying potential glycopeptide MS/MS spectra. We evaluate SweetSEQer on manually curated glycoconjugate spectra and on negative controls, and we demonstrate high quality filtering that can be easily improved for specific applications. We also demonstrate a high overlap between peaks annotated by experts and peaks annotated by SweetSEQer, as well as demonstrate inferred glycan graphs consistent with canonical glycan tree motifs. This study presents a novel tool for annotating spectra and producing glycan graphs from LC-MS/MS spectra. The tool is evaluated and shown to perform similarly to an expert on manually curated data.

Absolute protein quantification was applied to follow the dynamics of the cytoplasmic proteome of Staphylococcus aureus in response to long-term oxygen starvation. For 1,168 proteins, the majority of all expressed proteins, molecule numbers per cell have been determined to monitor the cellular investments in single branches of bacterial life for the first time. In the presence of glucose the anaerobic protein pattern is characterized by increased amounts of glycolytic and fermentative enzymes such as Eno, GapA1, Ldh1, and PflB. Interestingly, the ferritin-like protein FtnA belongs to the most abundant proteins during anaerobic growth. Depletion of glucose finally leads to an accumulation of different enzymes such as ArcB1, ArcB2, and ArcC2 involved in arginine deiminase pathway. Concentrations of 29 exo- and 78 endometabolites were comparatively assessed and have been integrated to the metabolic networks. Here we provide an almost complete picture on the response to oxygen starvation, from signal transduction pathways to gene expression pattern, from metabolic reorganization after oxygen depletion to beginning cell death and lysis after glucose exhaustion. This experimental approach can be considered as a proof of principle how to combine cell physiology with quantitative proteomics for a new dimension in understanding simple life processes as an entity.

Tauopathies, including Alzheimer's disease (AD), are associated with the aggregation of modified microtubule associated protein tau. This pathological state of tau is often referred to as "hyperphosphorylated". Due to limitations in technology, an accurate quantitative description of this state is lacking. Here, a mass spectrometry-based assay, FLEXITau, is presented to measure phosphorylation stoichiometry and provide an unbiased quantitative view of the tau post-translational modification (PTM) landscape. The power of this assay is demonstrated by measuring the state of hyperphosphorylation from tau in a cellular model for AD pathology, mapping, and calculating site occupancies for over 20 phosphorylations. We further employ FLEXITau to define the tau PTM landscape present in AD post-mortem brain. As shown in this study, the application of this assay provides mechanistic understanding of tau pathology that could lead to novel therapeutics, and we envision its further use in prognostic and diagnostic approaches for tauopathies.

Outside In Acquisition and analysis of large data sets promises to move us toward a greater understanding of the mechanisms by which biological systems are dynamically regulated to respond to external cues. Now, two papers explore the responses of a bacterium to changing nutritional conditions (see the Perspective by Chalancon et al. ). Nicolas et al. (p. 1103 ) measured transcriptional regulation for more than 100 different conditions. Greater amounts of antisense RNA were generated than expected and appeared to be produced by alternative RNA polymerase targeting subunits called sigma factors. One transition, from malate to glucose as the primary nutrient, was studied in more detail by Buescher et al. (p. 1099 ) who monitored RNA abundance, promoter activity in live cells, protein abundance, and absolute concentrations of intracellular and extracellular metabolites. In this case, the bacteria responded rapidly and largely without transcriptional changes to life on malate, but only slowly adapted to use glucose, a shift that required changes in nearly half the transcription network. These data offer an initial understanding of why certain regulatory strategies may be favored during evolution of dynamic control systems.

RATIONALE Label-based mass spectrometry is a powerful tool for large-scale protein identification and quantification. However, it requires the chemical or metabolic incorporation of the labeled compound(s) which can be difficult to attain, e.g. for non-cultivable organisms or scarce sample, such as biopsies. Therefore, we set out to develop and validate an efficient label-free liquid chromatography/tandem mass spectrometry (LC/MS/MS) workflow based on optimized instrument settings and incremental exclusion lists. METHODS To increase the number of quantified peptides an incremental exclusion list was incorporated along with optimized instrument settings for the used LTQ Orbitrap. As a proof of concept, label-free quantification data from this optimized approach were compared to the results of control measurements without exclusion lists and of an in vivo metabolic labeling GeLC/MS/MS experiment. The data were drawn from Staphylococcus aureus whole cell lysates of non-stressed and nitric oxide (NO)-stressed cells. RESULTS Compared to MS analysis without exclusion lists the new approach resulted in an increased number of identified peptides, enabling label-free quantification of more than 990 S. aureus proteins. With respect to the number of quantified proteins and differences in protein levels between the control and NO-treated samples the results of the new method were consistent with those of the GeLC/MS/MS experiment. CONCLUSIONS The application of exclusion lists and optimized instrument settings in LC/MS/MS analysis significantly enhances the sensitivity and resolution of label-free protein identification and quantification. Therefore, the new workflow is a powerful alternative to label-based quantification methods. Copyright © 2012 John Wiley & Sons, Ltd.

In Methanosarcina mazei several small RNAs have been identified containing a small putative open reading frame (sORF) and thus classified as spRNAs. Here, we report on the first detection of three small proteins in M. mazei encoded by spRNAs using LC–MS/MS analysis of total protein extracts of cells grown under various stress conditions. Each spRNA shows high conservation in Methanosarcina species with regard to the sORF and the flanking non-coding RNA regions, moreover the predicted RNA structures are as well highly conserved. Characterizing the respective transcript levels in response to several stress conditions by northern blots demonstrated an enormous decrease of spRNA36 and spRNA44 during stationary growth (to less than 5%), and a significant increase of spRNA36 (2.5-fold) in response to nitrogen limitation. spRNA41, however, was only detected by RNA-Seq approaches. Quantification of the small proteins by LC–MS/MS using synthetic stable isotope labeled oligopeptides as standards indicated that the concentration of oligopetide36 and 41 in mid exponential phase is induced under nitrogen limitation, which in case of oligopeptide36 is in accordance with its transcript level. The relative amount of the three oligopeptides did not change upon entering stationary growth phase, even though the transcript levels decreased dramatically. Additional production of the oligopeptides in M. mazei did not result in any evident phenotype under standard or nitrogen limiting growth conditions. However, overall the transcript levels of several genes involved in carbon metabolism or in heat shock response were reduced 2–3 fold due to the overproduction, though no sORF specific change was observed. Based on our findings we hypothesize that oligopeptide36 might have a regulatory function in nitrogen metabolism by modulating the activity of a yet unknown target protein involved in the central nitrogen metabolism.

Predicting resource allocation between cell processes is the primary step towards decoding the evolutionary constraints governing bacterial growth under various conditions. Quantitative prediction at genome-scale remains a computational challenge as current methods are limited by the tractability of the problem or by simplifying hypotheses. Here, we show that the constraint-based modeling method Resource Balance Analysis (RBA), calibrated using genome-wide absolute protein quantification data, accurately predicts resource allocation in the model bacterium Bacillus subtilis for a wide range of growth conditions. The regulation of most cellular processes is consistent with the objective of growth rate maximization except for a few suboptimal processes which likely integrate more complex objectives such as coping with stressful conditions and survival. As a proof of principle by using simulations, we illustrated how calibrated RBA could aid rational design of strains for maximizing protein production, offering new opportunities to investigate design principles in prokaryotes and to exploit them for biotechnological applications.

International audience; In the growing field of systems biology, the knowledge of protein concentrations is highly required to truly understand metabolic and adaptational networks within the cells. Therefore we established a workflow relying on long chromatographic separation and mass spectrometric analysis by data independent, parallel fragmentation of all precursor ions at the same time (LC/MSE). By prevention of discrimination of co-eluting low and high abundant peptides a high average sequence coverage of 40% could be achieved, resulting in identification of almost half of the predicted cytosolic proteome of the Gram-positive model organism Bacillus subtilis (>1,050 proteins). Absolute quantification was achieved by correlation of average MS signal intensities of the three most intense peptides of a protein to the signal intensity of a spiked standard protein digest. Comparative analysis with heavily labeled peptides (AQUA approach) showed the use of only one standard digest is sufficient for global quantification. The quantification results covered almost four orders of magnitude, ranging roughly from 10 to 150,000 copies per cell. To prove this method for its biological relevance selected physiological aspects of B. subtilis cells grown under conditions requiring either amino acid synthesis or alternatively amino acid degradation were analyzed. This allowed both in particular the validation of the adjustment of protein levels by known regulatory events and in general a perspective of new insights into bacterial physiology. Within new findings the analysis of “protein costs” of cellular processes is extremely important. Such a comprehensive and detailed characterization of cellular protein concentrations based on data independent, parallel fragmentation in liquid chromatography/mass spectrometry (LC/MSE) data has been performed for the first time and should pave the way for future comprehensive quantitative characterization of microorganisms as physiological entities.

The Gram-positive endospore-forming bacterium Bacillus licheniformis can be found widely in nature and it is exploited in industrial processes for the manufacturing of antibiotics, specialty chemicals, and enzymes. Both in its varied natural habitats and in industrial settings, B. licheniformis cells will be exposed to increases in the external osmolarity, conditions that trigger water efflux, impair turgor, cause the cessation of growth, and negatively affect the productivity of cell factories in biotechnological processes. We have taken here both systems-wide and targeted physiological approaches to unravel the core of the osmostress responses of B. licheniformis. Cells were suddenly subjected to an osmotic upshift of considerable magnitude (with 1 M NaCl), and their transcriptional profile was then recorded in a time-resolved fashion on a genome-wide scale. A bioinformatics cluster analysis was used to group the osmotically up-regulated genes into categories that are functionally associated with the synthesis and import of osmostress-relieving compounds (compatible solutes), the SigB-controlled general stress response, and genes whose functional annotation suggests that salt stress triggers secondary oxidative stress responses in B. licheniformis. The data set focusing on the transcriptional profile of B. licheniformis was enriched by proteomics aimed at identifying those proteins that were accumulated by the cells through increased biosynthesis in response to osmotic stress. Furthermore, these global approaches were augmented by a set of experiments that addressed the synthesis of the compatible solutes proline and glycine betaine and assessed the growth-enhancing effects of various osmoprotectants. Combined, our data provide a blueprint of the cellular adjustment processes of B. licheniformis to both sudden and sustained osmotic stress.

Label-based mass spectrometry is a powerful tool for large-scale protein identification and quantification. However, it requires the chemical or metabolic incorporation of the labeled compound(s) which can be difficult to attain, e.g. for non-cultivable organisms or scarce sample, such as biopsies. Therefore, we set out to develop and validate an efficient label-free liquid chromatography/tandem mass spectrometry (LC/MS/MS) workflow based on optimized instrument settings and incremental exclusion lists.To increase the number of quantified peptides an incremental exclusion list was incorporated along with optimized instrument settings for the used LTQ Orbitrap. As a proof of concept, label-free quantification data from this optimized approach were compared to the results of control measurements without exclusion lists and of an in vivo metabolic labeling GeLC/MS/MS experiment. The data were drawn from Staphylococcus aureus whole cell lysates of non-stressed and nitric oxide (NO)-stressed cells.Compared to MS analysis without exclusion lists the new approach resulted in an increased number of identified peptides, enabling label-free quantification of more than 990 S. aureus proteins. With respect to the number of quantified proteins and differences in protein levels between the control and NO-treated samples the results of the new method were consistent with those of the GeLC/MS/MS experiment.The application of exclusion lists and optimized instrument settings in LC/MS/MS analysis significantly enhances the sensitivity and resolution of label-free protein identification and quantification. Therefore, the new workflow is a powerful alternative to label-based quantification methods.

In prokaryotes, transcription results from the activity of a 400 kDa RNA polymerase (RNAP) protein complex composed of at least five subunits (2 alpha, beta, beta', omega). To ensure adequate responses to changing environmental cues, RNAP activity is tightly controlled by means of interacting regulatory proteins. Here, we report the affinity-purification of the Bacillus subtilis RNAP complexes from cells in different growth states and stress conditions, and the quantitative assessment by mass spectrometry of the dynamic changes in the composition of the RNAP complex. The stoichiometry of RNA polymerase was determined by a comparison of two mass spectrometry-based quantification methods: a label-based and a label-free method. The validated label-free method was then used to quantify the proteins associated with RNAP. The levels of sigma factors bound to RNAP varied during growth and exposure to stress. Elongation factors, helicases such as HelD and PcrA, and novel unknown proteins were also associated with RNAP complexes. The content in 6S RNAs of purified RNAP complexes increased at the onset of the stationary phase. These quantitative variations in the protein and RNA composition of the RNAP complexes well correlate with the known physiology of B. subtilis cells under different conditions.