Quality control in mass spectrometry-based proteomics

A statistical approach to system suitability testing for mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]).

METHODS

A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.

RESULTS

The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.

CONCLUSIONS

This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

Phosphoproteomics analysis of serum from dogs affected with pulmonary hypertension secondary to degenerative mitral valve disease

Pulmonary hypertension (PH), a common complication in dogs affected by degenerative mitral valve disease (DMVD), is a progressive disorder characterized by increased pulmonary arterial pressure (PAP) and pulmonary vascular remodeling. Phosphorylation of proteins, impacting vascular function and cell proliferation, might play a role in the development and progression of PH. Unlike gene or protein studies, phosphoproteomic focuses on active proteins that function as end-target proteins within signaling cascades. Studying phosphorylated proteins can reveal active contributors to PH development. Early diagnosis of PH is crucial for effective management and improved clinical outcomes. This study aimed to identify potential serum biomarkers for diagnosing PH in dogs affected with DMVD using a phosphoproteomic approach. Serum samples were collected from healthy control dogs (n = 28), dogs with DMVD (n = 24), and dogs with DMVD and PH (n = 29). Phosphoproteins were enriched from the serum samples and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Data analysis was performed to identify uniquely expressed phosphoproteins in each group and differentially expressed phosphoproteins among groups. Phosphoproteomic analysis revealed nine uniquely expressed phosphoproteins in the serum of dogs in the DMVD+PH group and 15 differentially upregulated phosphoproteins in the DMVD+PH group compared to the DMVD group. The phosphoproteins previously implicated in PH and associated with pulmonary arterial remodeling, including small nuclear ribonucleoprotein G (SNRPG), alpha-2-macroglobulin (A2M), zinc finger and BTB domain containing 42 (ZBTB42), hemopexin (HPX), serotransferrin (TRF) and complement C3 (C3), were focused on. Their unique expression and differential upregulation in the serum of DMVD dogs with PH suggest their potential as biomarkers for PH diagnosis. In conclusion, this phosphoproteomic study identified uniquely expressed and differentially upregulated phosphoproteins in the serum of DMVD dogs with PH. Further studies are warranted to validate the diagnostic utility of these phosphoproteins.

SpectiCal: m/z Calibration of MS2 Peptide Spectra Using Known Low Mass Ions

Most tandem mass spectrometry fragmentation spectra have small calibration errors that can lead to suboptimal interpretation and annotation. We developed SpectiCal, a software tool that can read mzML files from data-dependent acquisition proteomics experiments in parallel, compute m/z calibrations for each file prior to identification analysis based on known low-mass ions, and produce information about frequently observed peaks and their explanations. Using calibration coefficients, the data can be corrected to generate new calibrated mzML files. SpectiCal was tested using five public data sets, creating a table of commonly observed low-mass ions and their identifications. Information about the calibration and individual peaks is written in PDF and TSV files. This includes information for each peak, such as the number of runs in which it appears, the percentage of spectra in which it appears, and a plot of the aggregated region surrounding each peak. SpectiCal can be used to compute MS run calibrations, examine MS runs for artifacts that might hinder downstream analysis, and generate tables of detected low-mass ions for further analysis. SpectiCal is freely available at https://github.com/PlantProteomes/SpectiCal.

Pursuing Impactful Quantitative Proteomics Using QC-Channels in Every Spectrum and Trend-Design in Experiment

False changes discovered by quantitative proteomics reduce the trust of biologists in proteomics and limit the applications of proteomics to unlock biological mechanisms, which suppresses the application of proteomics techniques in the pharmaceutical industry more than it does in academic research. To remove false changes that arise during LC-MS/MS data acquisition, we evaluated the contributions of peptide abundance and number of unique peptides on reproducibility. Lower abundance and only one unique peptide have a higher risk of generating a higher coefficient of variation (CV), resulting in less accurate quantification. However, the abundance of peptides in samples is not adjustable and discarding proteins quantified by only one unique peptide is not a choice either. Indeed, a large percentage of proteins are accurately quantified by only one unique peptide. Therefore, to improve the calculations of the CV, we leverage a new function in PEAKS called QC-channels which enables technical replicates of each spectrum to be evaluated prior to calculation of the CV. While the QC-channels function in PEAKS significantly reduced the false quantification, random false changes still exist due to known or unknown reasons. To address this challenge, we present the idea of Trend-design to track trend changes rather than changes from two points to remove false quantifications and reveal consequential changes responding to a treatment or condition. The idea was confirmed by molecules with different affinity and dose in the current study. The combination of QC-channels and Trend-design enables a more impactful quantitative proteomics to allow unlocking biological mechanisms using proteomics.

Proteomic analysis of the serum in dogs with pulmonary hypertension secondary to myxomatous mitral valve disease: the preliminary study

Background

Pulmonary hypertension (PH) is a common complication in dogs with myxomatous mitral valve disease (MMVD), characterized by elevated blood pressure in pulmonary artery. Echocardiography is a reliable technique for PH diagnosis in veterinary medicine. However, it is limited to use as an early detection method. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has found extensive application in the discovery of serum protein biomarkers for various diseases. The objective of this study was to identify serum proteins in healthy control dogs and MMVD dogs both with and without PH using LC-MS/MS.

Materials and methods

In this research, a total of 81 small-breed dogs participated, and they were categorized into three groups: the control (n = 28), MMVD (n = 24) and MMVD+PH (n = 29) groups. Serum samples were collected and analyzed by LC-MS/MS.

Results

Differentially expressed proteins were identified, and the upregulated and downregulated proteins in MMVD+PH group including Myomesin 1 (MYOM1) and Histone deacetylase 7 (HDAC7), Pleckstrin homology domain containing M3 (PLEKHM3), Diacylglycerol lipase alpha (DAGLA) and Tubulin tyrosine ligase like 6 (TTLL6) were selected as proteins of interest in MMVD dogs with PH.

Conclusion

Different types of proteins have been identified in healthy dogs and MMVD dogs with and without PH. Additional studies are needed to investigate the potential of these proteins as biomarkers for PH in dogs with MMVD.

Reporting and reproducibility: Proteomics of fish models in environmental toxicology and ecotoxicology

Environmental toxicology and ecotoxicology research efforts are employing proteomics with fish models as New Approach Methodologies, along with in silico, in vitro and other omics techniques to elucidate hazards of toxicants and toxins. We performed a critical review of toxicology studies with fish models using proteomics and reported fundamental parameters across experimental design, sample preparation, mass spectrometry, and bioinformatics of fish, which represent alternative vertebrate models in environmental toxicology, and routinely studied animals in ecotoxicology. We observed inconsistencies in reporting and methodologies among experimental designs, sample preparations, data acquisitions and bioinformatics, which can affect reproducibility of experimental results. We identified a distinct need to develop reporting guidelines for proteomics use in environmental toxicology and ecotoxicology, increased QA/QC throughout studies, and method optimization with an emphasis on reducing inconsistencies among studies. Several recommendations are offered as logical steps to advance development and application of this emerging research area to understand chemical hazards to public health and the environment.

RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control

We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.

Improvement of untargeted proteomics workflow for surfaceome profiling and its evaluation through the implementation of quality controls: Application to multiple myeloma

BACKGROUND

Comprehensive surfaceome profiling of cancer cells using mass spectrometry (MS)-based technologies is a valuable approach to identify new antigens that could be targeted by immunotherapies. Multiple myeloma (MM) is an incurable hematological malignancy in which patients suffer from multiple relapses associated with drug resistance. Nevertheless, only three MM-specific antigens are currently targeted by approved immunotherapies which restrain the availability of efficient treatments for severe refractory patients affected by aggressive forms of the disease. Therefore, the discovery of new antigens in this context could open new perspectives for those patients.

RESULTS

In this study, the first objective was to improve a MS-based untargeted proteomics workflow in order to handle limited patient samples. For this purpose, a highly sensitive and robust miniaturized separation system (LC-Chip) coupled with drift tube ion mobility spectrometry and high-resolution MS was integrated in our workflow to maximize protein identification. As sample preparation can strongly influence the detectability of membrane-associated proteins, the critical steps in sample preparation were carefully optimized. As a result, 4.5 times more membrane-associated proteins were identified and experimental throughput was also drastically improved. In addition to workflow performance, particular attention was paid to assess the quality of the generated data. Indeed, several quality controls (QC) were implemented to assess data quality. Finally, the optimized workflow as well as selected QCs were evaluated in the analysis of samples containing limited number of cells.

SIGNIFICANCE

This work allowed the improvement of an untargeted proteomics workflow for surfaceome profiling in terms of performance. Besides, the reliability of the obtained data was evaluated through the introduction of QCs in the workflow. The applicability of the improved workflow as well as the implemented QCs for the analysis of MM primary cells obtained from patients was confirmed.

Plasma proteomic profiling discovers molecular features associated with upper tract urothelial carcinoma

Upper tract urothelial carcinoma (UTUC) is often diagnosed late and exhibits poor prognosis. Limited data are available on potential non-invasive biomarkers for disease monitoring. Here, we investigate the proteomic profile of plasma in 362 UTUC patients and 239 healthy controls. We present an integrated tissue-plasma proteomic approach to infer the signature proteins for identifying patients with muscle-invasive UTUC. We discover a protein panel that reflects lymph node metastasis, which is of interest in identifying UTUC patients with high risk and poor prognosis. We also identify a ten-protein classifier and establish a progression clock predicting progression-free survival of UTUC patients. Finally, we further validate the signature proteins by parallel reaction monitoring assay in an independent cohort. Collectively, this study portrays the plasma proteomic landscape of a UTUC cohort and provides a valuable resource for further biological and diagnostic research in UTUC.

Tutorial review for peptide assays: An ounce of pre-analytics is worth a pound of cure

The recent increase in peptidomimetic-based medications and the growing interest in peptide hormones has brought new attention to the quantification of peptides for diagnostic purposes. Indeed, the circulating concentrations of peptide hormones in the blood provide a snapshot of the state of the body and could eventually lead to detecting a particular health condition. Although extremely useful, the quantification of such molecules, preferably by liquid chromatography coupled to mass spectrometry, might be quite tricky. First, peptides are subjected to hydrolysis, oxidation, and other post-translational modifications, and, most importantly, they are substrates of specific and nonspecific proteases in biological matrixes. All these events might continue after sampling, changing the peptide hormone concentrations. Second, because they include positively and negatively charged groups and hydrophilic and hydrophobic residues, they interact with their environment; these interactions might lead to a local change in the measured concentrations. A phenomenon such as nonspecific adsorption to lab glassware or materials has often a tremendous effect on the concentration and needs to be controlled with particular care. Finally, the circulating levels of peptides might be low (pico- or femtomolar range), increasing the impact of the aforementioned effects and inducing the need for highly sensitive instruments and well-optimized methods. Thus, despite the extreme diversity of these peptides and their matrixes, there is a common challenge for all the assays: the need to keep concentrations unchanged from sampling to analysis. While significant efforts are often placed on optimizing the analysis, few studies consider in depth the impact of pre-analytical steps on the results. By working through practical examples, this solution-oriented tutorial review addresses typical pre-analytical challenges encountered during the development of a peptide assay from the standpoint of a clinical laboratory. We provide tips and tricks to avoid pitfalls as well as strategies to guide all new developments. Our ultimate goal is to increase pre-analytical awareness to ensure that newly developed peptide assays produce robust and accurate results.

Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome

Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions. IMPORTANCE Metaproteomic analysis can provide valuable insights into the functions of microbial and cellular communities by identifying a broad, untargeted set of proteins. The databases used in the analysis of metaproteomic data influence results by defining what proteins can be identified. Moreover, the size of the database impacts the number of identifications after accounting for false discovery rates (FDRs). Few studies have tested the performance of different strategies for building a protein database to identify proteins from metaproteomic data and those that have largely focused on highly diverse microbial communities. We tested a range of databases on CVL samples and found that a hybrid sample-matched approach, using publicly available proteins from organisms present in the samples, as well as proteins translated from metagenomic sequencing of the samples, had the best performance. However, our results also suggest that public sequence databases will continue to improve as more bacterial genomes are published.

Evaluating cPILOT Data toward Quality Control Implementation

Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMT_pro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMT_zero or TMT_pro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

A Standardized Protocol for Assuring the Validity of Proteomics Results from Liquid Chromatography-High-Resolution Mass Spectrometry

Significant advances in the technological development of mass spectrometry in the field of proteomics and the generation of extremely large amounts of data require a very critical approach to assure the validity of results. Commonly used procedures involved liquid chromatography followed by high-resolution mass spectrometry measurements. Proteomics analysis is used in many fields including the investigation of the metabolism of biologically active substances in organisms. Thus, there is a need to care about the validity of the obtained results. In this work, we proposed a standardized protocol for proteomic analysis using liquid chromatography-high-resolution mass spectrometry, which covers all of these analytical steps to ensure the validity of the results. For this purpose, we explored the requirements of the ISO/IEC 17025:2017 standard as a reference document for quality control in biochemistry research-based mass spectrometry.

Quality Control—A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome

Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow.

Clinical application of serum-based proteomics technology in human tumor research

The rapid development of proteomics technology in the past decades has led to further human understanding of tumor research, and in some ways, the technology plays a very important supporting role in the early detection of tumors. Human serum has been shown to contain a variety of proteins closely related to life activities, and the dynamic change in proteins can often reflect the physiological and pathological conditions of the body. Serum has the advantage of easy extraction, so the application of proteomics technology in serum has become a hot spot and frontier area for the study of malignant tumors. However, there are still many difficulties in the standardized use of proteomic technologies, which inevitably limit the clinical application of proteomic technologies due to the heterogeneity of human proteins leading to incomplete whole proteome populations, in addition to most serum protein markers being now not highly specific in aiding the early detection of tumors. Nevertheless, further development of proteomics technologies will greatly increase our understanding of tumor biology and help discover more new tumor biomarkers with specificity that will enable medical technology.

New thoughts and findings on invasion and metastasis of pancreatic ductal adenocarcinoma (PDAC) from comparative proteomics: multi-target therapy

As one of the most aggressive malignant tumors, pancreatic ductal adenocarcinoma (PDAC) ranks as the fourth cancer-related mortality in the world. The extremely low survival rate is closely related to early invasion and distant metastasis. However, effective target therapy for weakening its malignant behavior remains limited. Over the past decades, many proteins correlating with invasion and metastasis of PDAC have been discovered using proteomics. The discovery of these proteins gives us a deeper understanding of the invasive and migratory processes of PDAC. This review is a systemic integration of these proteomics findings over the past 10 years. The discovered proteins were typically associated with the glycolytic process, hypoxic microenvironment, post-translational modification, extracellular matrix, exosomes, cancer stem cells, and immune escape. Some proteins were found to have multiple functions, and, cooperation between different proteins in the invasive and metastatic processes was found. This cooperation, and not just single protein function, may play a more significant role in the poor prognosis of PDAC. Therefore, multi-target therapy against these cooperative networks should be a primary choice in the future.

Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

A Data-Driven Signaling Network Inference Approach for Phosphoproteomics

Proteins are rapidly and dynamically post-transcriptionally modified as cells respond to changes in their environment. For example, protein phosphorylation is mediated by kinases while dephosphorylation is mediated by phosphatases. Quantifying and predicting interactions between kinases, phosphatases, and target proteins over time will aid the study of signaling cascades under a variety of environmental conditions. Here, we describe methods to statistically analyze label-free phosphoproteomic data and infer posttranscriptional regulatory networks over time. We provide an R-based method that can be used to normalize and analyze label-free phosphoproteomic data using variance stabilizing normalization and a linear mixed model across multiple time points and conditions. We also provide a method to infer regulator-target interactions over time using a discretization scheme followed by dynamic Bayesian modeling computations to validate our conclusions. Overall, this pipeline is designed to perform functional analyses and predictions of phosphoproteomic signaling cascades.

Affinity-Based Interactome Analysis of Endogenous LINE-1 Macromolecules

During their proliferation and the host's concomitant attempts to suppress it, LINE-1 (L1) retrotransposons give rise to a collection of heterogeneous ribonucleoproteins (RNPs); their protein and RNA compositions remain poorly defined. The constituents of L1-associated macromolecules can differ depending on numerous factors, including, for example, position within the L1 life cycle, whether the macromolecule is productive or under suppression, and the cell type within which the proliferation is occurring. This chapter describes techniques that aid the capture and characterization of protein and RNA components of L1 macromolecules from tissues that natively express them. The protocols described have been applied to embryonal carcinoma cell lines that are popular model systems for L1 molecular biology (e.g., N2102Ep, NTERA-2, and PA-1 cells), as well as colorectal cancer tissues. N2102Ep cells are given as the use case for this chapter; the protocols should be applicable to essentially any tissue exhibiting endogenous L1 expression with minor modifications.

Mechanistic insight into female predominance in Alzheimer's disease based on aberrant protein S-nitrosylation of C3

Protein S-nitros(yl)ation (SNO) is a posttranslational modification involved in diverse processes in health and disease and can contribute to synaptic damage in Alzheimer's disease (AD). To identify SNO proteins in AD brains, we used triaryl phosphine (SNOTRAP) combined with mass spectrometry (MS). We detected 1449 SNO proteins with 2809 SNO sites, representing a wide range of S-nitrosylated proteins in 40 postmortem AD and non-AD human brains from patients of both sexes. Integrative protein ranking revealed the top 10 increased SNO proteins, including complement component 3 (C3), p62 (SQSTM1), and phospholipase D3. Increased levels of S-nitrosylated C3 were present in female over male AD brains. Mechanistically, we show that formation of SNO-C3 is dependent on falling β-estradiol levels, leading to increased synaptic phagocytosis and thus synapse loss and consequent cognitive decline. Collectively, we demonstrate robust alterations in the S-nitrosoproteome that contribute to AD pathogenesis in a sex-dependent manner.

The critical role that spectral libraries play in capturing the metabolomics community knowledge

BACKGROUND

Spectral library searching is currently the most common approach for compound annotation in untargeted metabolomics. Spectral libraries applicable to liquid chromatography mass spectrometry have grown in size over the past decade to include hundreds of thousands to millions of mass spectra and tens of thousands of compounds, forming an essential knowledge base for the interpretation of metabolomics experiments.

AIM OF REVIEW

We describe existing spectral library resources, highlight different strategies for compiling spectral libraries, and discuss quality considerations that should be taken into account when interpreting spectral library searching results. Finally, we describe how spectral libraries are empowering the next generation of machine learning tools in computational metabolomics, and discuss several opportunities for using increasingly accessible large spectral libraries.

KEY SCIENTIFIC CONCEPTS OF REVIEW

This review focuses on the current state of spectral libraries for untargeted LC-MS/MS based metabolomics. We show how the number of entries in publicly accessible spectral libraries has increased more than 60-fold in the past eight years to aid molecular interpretation and we discuss how the role of spectral libraries in untargeted metabolomics will evolve in the near future.

Open Modification Searching of SARS-CoV-2-Human Protein Interaction Data Reveals Novel Viral Modification Sites

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

Mesowestern Blot: Simultaneous Analysis of Hundreds of Submicroliter Lysates

Western blotting is a widely used technique for molecular-weight-resolved analysis of proteins and their posttranslational modifications, but high-throughput implementations of the standard slab gel arrangement are scarce. The previously developed Microwestern requires a piezoelectric pipetting instrument, which is not available for many labs. Here, we report the Mesowestern blot, which uses a 3D-printable gel casting mold to enable high-throughput Western blotting without piezoelectric pipetting and is compatible with the standard sample preparation and small (∼1 μL) sample sizes. The main tradeoffs are reduced molecular weight resolution and higher sample-to-sample CV, making it suitable for qualitative screening applications. The casted polyacrylamide gel contains 336, ∼0.5 μL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution profiles. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder and cell lysate (RIPA buffer) demonstrate that the protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semidry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is at least 32-fold, and at least ∼500 attomols of β-actin can be detected (∼29 ng of total protein from mammalian cell lysates: ∼100-300 cells). Because the gel mold is 3D-printable, users with access to additive manufacturing cores have significant design freedom for custom layouts. We expect that the technique could be easily adopted by any typical cell and molecular biology laboratory already performing Western blots.

The application of mass spectrometry method for the study and identification of medically important viruses (review of literature)

It is difficult to overestimate the urgency of the problem of well-timed diagnosis of viral infections. According to the WHO, dozens of outbreaks of viral diseases are recorded annually, both in developing and developed countries. Moreover, the seasonal flu virus alone is capable of infecting up to 20% of the population, even in European countries with a high level of medicine. And the annual number of deaths due to viral infections, according to official statistics, exceeds 600 thousand people around the world. That's why the provision of a reliable and fairly rapid diagnosis of viruses, along with subsequent therapy, makes a significant contribution to reducing the incidence of mortality. Despite the fact that PCR-based methods currently remain the most common method for identifying viruses in clinical practice, as recent experience shows, in addition to the already known disadvantages, in the event of large outbreaks, such test systems may simply not be in the required amount. In this regard, it is necessary to supplement and improve the existing tools for identification and research of clinically significant viruses. The MALDI-TOF mass spectrometry method combines a degree of accuracy and versatility, sufficient both for the identification of clinical strains isolated from patients, and for the study of the phenotypic properties of viruses in research laboratories and centers. This article presents and summarizes the main data on the existing or potential application of the method of time-of-flight mass spectrometry with matrix-associated laser desorption / ionization for the identification or study of viruses.

A Rapid and Universal Workflow for Label-Free-Quantitation-Based Proteomic and Phosphoproteomic Studies in Cereals

Proteomics and phosphoproteomics are robust tools to analyze dynamics of post-transcriptional processes during growth and development. A variety of experimental methods and workflows have been published, but most of them were developed for model plants and have not been adapted to high-throughput platforms. Here, we describe an experimental workflow for proteome and phosphoproteome studies tailored to cereal crop tissues. The workflow consists of two parallel parts that are suitable for analyzing protein/phosphoprotein from total proteins and the microsomal membrane fraction. We present phosphoproteomic data regarding quantification coverage and analytical reproducibility for example preparations from maize root and shoot, wheat leaf, and a microsomal protein preparation from maize leaf. To enable users to adjust for tissue specific requirements, we provide two different methods of protein clean-up: traditional ethanol precipitation (PC) and a recently developed technology termed single-pot, solid-phase-enhanced sample preparation (SP3). Both the PC and SP3 methods are effective in the removal of unwanted substances in total protein crude extracts. In addition, two different methods of phosphopeptide enrichment are presented: a TiO₂ -based method and Fe(III)-NTA cartridges on a robotized platform. Although the overall number of phosphopeptides is stable across protein clean-up and phosphopeptide enrichment methods, there are differences in the preferred phosphopeptides in each enrichment method. The preferred protocol depends on laboratory capabilities and research objective. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Total protein crude extraction Basic Protocol 2: Total protein clean-up with ethanol precipitation Alternate Protocol 1: Total protein clean-up with SP3 method Basic Protocol 3: Microsomal fraction protein extraction Basic Protocol 4: Protein concentration determination by Bradford assay Basic Protocol 5: In-solution digestion with trypsin Basic Protocol 6: Phosphopeptide enrichment with TiO₂ Alternate Protocol 2: Phosphopeptide enrichment with Fe(III)-NTA cartridges Basic Protocol 7: Peptide desalting with C₁₈ material Basic Protocol 8: LC-MS/MS analysis of (phospho)peptides and spectrum matching.

A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies

Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).

Impact of hemolysis on multi-OMIC pancreatic biomarker discovery to derisk biomarker development in precision medicine studies

Cancer biomarker discovery is critically dependent on the integrity of biofluid and tissue samples acquired from study participants. Multi-omic profiling of candidate protein, lipid, and metabolite biomarkers is confounded by timing and fasting status of sample collection, participant demographics and treatment exposures of the study population. Contamination by hemoglobin, whether caused by hemolysis during sample preparation or underlying red cell fragility, contributes 0–10 g/L of extraneous protein to plasma, serum, and Buffy coat samples and may interfere with biomarker detection and validation. We analyzed 617 plasma, 701 serum, and 657 buffy coat samples from a 7-year longitudinal multi-omic biomarker discovery program evaluating 400+ participants with or at risk for pancreatic cancer, known as Project Survival. Hemolysis was undetectable in 93.1% of plasma and 95.0% of serum samples, whereas only 37.1% of buffy coat samples were free of contamination by hemoglobin. Regression analysis of multi-omic data demonstrated a statistically significant correlation between hemoglobin concentration and the resulting pattern of analyte detection and concentration. Although hemolysis had the greatest impact on identification and quantitation of the proteome, distinct differentials in metabolomics and lipidomics were also observed and correlated with severity. We conclude that quality control is vital to accurate detection of informative molecular differentials using OMIC technologies and that caution must be exercised to minimize the impact of hemolysis as a factor driving false discovery in large cancer biomarker studies.

Comprehensive comparison of sample preparation workflows for proteomics

Mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining deep and accurate protein identification. Here, to obtain an optimal sample preparation workflow...

MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets

Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) 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 semi-automated 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.

Quantitative Proteomics Using Isobaric Labeling: A Practical Guide

In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.

Multiplex Mass Spectrometry Analysis of Amyloid Proteins in Human Plasma for Alzheimer's Disease Diagnosis

Direct analysis of amyloid proteins in human plasma will promote rapid screening of brain amyloidosis, the earliest pathological signature of Alzheimer's disease. We developed a microflow liquid chromatography-targeted mass spectrometry assay for quantitation of four intact β-amyloid proteins starting from 1 mL of human plasma samples. This method showed 90% accuracy for predicting brain amyloid using plasma Aβ42/Aβ40 values from 36 cognitively normal individuals in a prospective clinical study (raw data deposited in MassIVE, Data set ID MSV000087451). Our method may contribute to early diagnosis of Alzheimer's disease.

Techniques for Detection of Clinical Used Heparins

Heparins and sulfated polysaccharides have been recognized as effective clinical anticoagulants for several decades. Heparins exhibit heterogeneity depending on the sources. Meanwhile, the adverse effect in the clinical uses and the adulteration of oversulfated chondroitin sulfate (OSCS) in heparins develop additional attention to analyze the purity of heparins. This review starts with the description of the classification, anticoagulant mechanism, clinical application of heparins and focuses on the existing methods of heparin analysis and detection including traditional detection methods, as well as new methods using fluorescence or gold nanomaterials as probes. The in-depth understanding of these techniques for the analysis of heparins will lay a foundation for the further development of novel methods for the detection of heparins.

The Time Has Come for Quantitative Protein Mass Spectrometry Tests That Target Unmet Clinical Needs

Protein mass spectrometry (MS) is an enabling technology that is ideally suited for precision diagnostics. In contrast to immunoassays with indirect readouts, MS quantifications are multiplexed and include identification of proteoforms in a direct manner. Although widely used for routine measurements of drugs and metabolites, the number of clinical MS-based protein applications is limited. In this paper, we share our experience and aim to take away the concerns that have kept laboratory medicine from implementing quantitative protein MS. To ensure added value of new medical tests and guarantee accurate test results, five key elements of test evaluation have been established by a working group within the European Federation for Clinical Chemistry and Laboratory Medicine. Moreover, it is emphasized to identify clinical gaps in the contemporary clinical pathways before test development is started. We demonstrate that quantitative protein MS tests that provide an additional layer of clinical information have robust performance and meet long-term desirable analytical performance specifications as exemplified by our own experience. Yet, the adoption of quantitative protein MS tests into medical laboratories is seriously hampered due to its complexity, lack of robotization and high initial investment costs. Successful and widespread implementation in medical laboratories requires uptake and automation of this next generation protein technology by the In-Vitro Diagnostics industry. Also, training curricula of lab workers and lab specialists should include education on enabling technologies for transitioning to precision medicine by quantitative protein MS tests.

Comprehensive Proteomics Analysis of In Vitro Canine Oviductal Cell-Derived Extracellular Vesicles

Simple Summary

As the dog shows unique and peculiar reproductive characteristics, assisted reproductive techniques such as in vitro maturation and in vitro fertilization have not been well-established compared with those of other mammals. Our recent work demonstrated the interplay between in vitro oviductal cell-derived extracellular vesicles (OC-EVs) and cumulus-oocyte complexes in dogs. Here, we provided for the first time a comprehensive proteomic analysis of OC-EVs. A total of 398 proteins were identified in all OC-EVs samples. A functional enrichment analysis indicated that these core proteins were involved in the key cellular metabolic process related to oocyte maturation and embryonic development. The current comprehensive description of the canine OC-EVs proteome would provide a fundamental resource for further understanding canine reproductive physiology, the interaction of sperms with female counterparts during fertilization, early pregnancy, and establishing an efficient system of in vitro embryo production.

Abstract

Dogs (Canis lupus familiaris) have unique and peculiar reproductive characteristics. While the interplay between in vitro oviductal cell-derived extracellular vesicles (OC-EVs) and cumulus-oocyte complexes in dogs has begun to be elucidated, no study has yet provided extensive information on the biological content and physiological function of OC-EVs and their role in canine oocyte development. Here, we aimed to provide the first comprehensive proteomic analysis of OC-EVs. We identified 398 proteins as present in all OC-EVs samples. The functional enrichment analysis using Gene Ontology terms and an Ingenuity Pathway Analysis revealed that the identified proteins were involved in several cellular metabolic processes, including translation, synthesis, expression, and protein metabolism. Notably, the proteins were also involved in critical canonical pathways with essential functions in oocyte and embryo development, such as ERK/MAPK, EIF2, PI3K/AKT, and mTOR signaling. These data would be an important resource for studying canine reproductive physiology and establishing a successful in vitro embryo production system in dogs.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Combinations of histone post-translational modifications

Histones are essential proteins that package the eukaryotic genome into its physiological state of nucleosomes, chromatin, and chromosomes. Post-translational modifications (PTMs) of histones are crucial to both the dynamic and persistent regulation of the genome. Histone PTMs store and convey complex signals about the state of the genome. This is often achieved by multiple variable PTM sites, occupied or unoccupied, on the same histone molecule or nucleosome functioning in concert. These mechanisms are supported by the structures of ‘readers’ that transduce the signal from the presence or absence of PTMs in specific cellular contexts. We provide background on PTMs and their complexes, review the known combinatorial function of PTMs, and assess the value and limitations of common approaches to measure combinatorial PTMs. This review serves as both a reference and a path forward to investigate combinatorial PTM functions, discover new synergies, and gather additional evidence supporting that combinations of histone PTMs are the central currency of chromatin-mediated regulation of the genome.

The Discovery of Clinically Applicable Biomarkers for Bipolar Disorder: A Review of Candidate and Proteomic Approaches

Bipolar disorder (BD) is a severe psychiatric condition which affects innumerable people across the globe. The etiopathogenesis of BD is multi-faceted with genetic, environmental and psychosocial factors playing a role. Hitherto, the diagnosis and management of BD are purely on empirical grounds as we lack confirmed biomarkers for this condition. In this regard, hypothesis-driven investigations have been unable to identify clinically applicable biomarkers, steering the field towards newer technologies. Innovative, state-of-the-art techniques like multiplex immunoassays and mass spectrometry can potentially investigate the entire proteome. By detecting up or down regulated proteins, novel biomarkers are identified and new postulates about the etiopathogenesis of BD are specified. Hence, biological pathways are uncovered which are involved in the initiation and advancement of the disease and new therapeutic targets are identified. In this manuscript, the extant literature is thoroughly reviewed and the latest findings on candidate BD biomarkers are provided, followed by an overview of the proteomic approaches. It was found that due to the heterogeneous nature of BD no single biomarker is feasible, instead a panel of tests is more likely to be useful. With the application of latest technologies, it is expected that validated biomarkers will be discovered which will be useful as diagnostic tools and help in the delivery of individually tailored therapies to the patients.

RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments

We present RawVegetable, a software for mass spectrometry data assessment and quality control tailored toward shotgun proteomics and cross-linking experiments. RawVegetable provides four main modules with distinct features: (A) The charge state chromatogram that independently displays the ion current for each charge state; useful for optimizing the chromatography for highly charged ions and with lower XIC values such as those typically found in cross-linking experiments. (B) The XL-Artefact determination, which flags possible noncovalently associated peptides. (C) The TopN density estimation, for detecting retention time intervals of under or over-sampling, and (D) The chromatography reproducibility module, which provides pairwise comparisons between multiple experiments. RawVegetable, a tutorial, and the example data are freely available for academic use at: http://patternlabforproteomics.org/rawvegetable. SIGNIFICANCE: Chromatography optimization is a critical step for any shotgun proteomic or cross-linking mass spectrometry experiment. Here, we present a nifty solution with several key features, such as displaying individual charge state chromatograms, highlighting chromatographic regions of under- or over-sampling and checking for reproducibility.

Evaluation of Tumor Interstitial Fluid-Extraction Methods for Proteome Analysis: Comparison of Biopsy Elution versus Centrifugation

The analysis of tumor interstitial fluid (TIF) composition is a valuable procedure to identify antimetastatic targets, and different laboratories have set up techniques for TIF isolation and proteomic analyses. However, those methods had never been compared in samples from the same tumor and patient. In this work, we compared the two most used methods, elution and centrifugation, in pieces of the same biopsy samples of cutaneous squamous cell carcinoma (cSCC). First, we established that high G-force (10 000g) was required to obtain TIF from cSCC by centrifugation. Second, we compared the centrifugation method with the elution method in pieces of three different cSCC tumors. We found that the mean protein intensities based in the number of peptide spectrum matches was significantly higher in the centrifuged samples than in the eluted samples. Regarding the robustness of the methods, we observed higher overlapping between both methods (77-80%) than among samples (50%). These results suggest that there exists an elevated consistence of TIF composition independently of the method used. However, we observed a 3-fold increase of extracellular proteins in nonoverlapped proteome obtained by centrifugation. We therefore conclude that centrifugation is the method of choice to study the proteome of TIF from cutaneous biopsies.

[A genetic analysis of children with Epstein-Barr virus-positive hemophagocytic lymphohistiocytosis and its association with T-helper type 1/T-helper type 2 cytokines]

OBJECTIVE

To study the effect of genetic variation on the prognosis of children with Epstein-Barr virus (EBV)-positive hemophagocytic lymphohistiocytosis (HLH) and its association with cytokines.

METHODS

A total of 81 EBV-positive HLH children who received the sequencing of related genes were enrolled. According to the results of gene detection, they were divided into a non-mutation group and a mutation group. According to the pattern of gene mutation, the mutation group was further divided into three subgroups: single heterozygous mutation (SHM), double heterozygous mutation (DHM), and homozygous or compound heterozygous mutation (H-CHM). The serum levels of cytokines were measured and their association with HLH gene mutations was analyzed.

RESULTS

UNC13D gene mutation had the highest frequency (13/46, 28%). The STXBP2 c.575G>A(p.R192H) and UNC13D c.604C>A(p.L202M) mutations (likely pathogenic) were reported for the first time. The mutation group had a significantly higher level of tumor necrosis factor alpha (TNF-α) than the non-mutation group, while it had a significantly lower level of interferon gamma (IFN-γ) than the non-mutation group (P<0.05). The IL-4 level of the DHM subgroup was higher than that of the non-mutation group, while the IL-4 level of the H-CHM subgroup was lower than that of the DHM group (P<0.0083). The H-CHM subgroup had a significantly lower 1-year overall survival rate than the non-mutation group, the SHM subgroup, and the DHM subgroup (39%±15% vs 85%±6%/86%±7%/91%±9%, P=0.001).

CONCLUSIONS

There is a significant reduction in IFN-γ level in the mutation group. Children with homozygous or compound heterozygous mutation tend to have poorer prognosis, while other mutations do not have a significant impact on prognosis.

Simple Peptide Quantification Approach for MS-Based Proteomics Quality Control

Despite its growing popularity and use, bottom-up proteomics remains a complex analytical methodology. Its general workflow consists of three main steps: sample preparation, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and computational data analysis. Quality assessment of the different steps and components of this workflow is instrumental to identify technical flaws and avoid loss of precious measurement time and sample material. However, assessment of the extent of sample losses along with the sample preparation protocol, in particular, after proteolytic digestion, is not yet routinely implemented because of the lack of an accurate and straightforward method to quantify peptides. Here, we report on the use of a microfluidic UV/visible spectrophotometer to quantify MS-ready peptides directly in the MS-loading solvent, consuming only 2 μL of sample. We compared the performance of the microfluidic spectrophotometer with a standard device and determined the optimal sample amount for LC-MS/MS analysis on a Q Exactive HF mass spectrometer using a dilution series of a commercial K562 cell digest. A careful evaluation of selected LC and MS parameters allowed us to define 3 μg as an optimal peptide amount to be injected into this particular LC-MS/MS system. Finally, using tryptic digests from human HEK293T cells and showing that injecting equal peptide amounts, rather than approximate ones, result in less variable LC-MS/MS and protein quantification data. The obtained quality improvement together with easy implementation of the approach makes it possible to routinely quantify MS-ready peptides as a next step in daily proteomics quality control.

Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies

Adoptive cell therapy is an emerging anti-cancer modality, whereby the patient's own immune cells are engineered to express T-cell receptor (TCR) or chimeric antigen receptor (CAR). CAR-T cell therapies have advanced the furthest, with recent approvals of two treatments by the Food and Drug Administration of Kymriah (trisagenlecleucel) and Yescarta (axicabtagene ciloleucel). Recent developments in proteomic analysis by mass spectrometry (MS) make this technology uniquely suited to enable the comprehensive identification and quantification of the relevant biochemical architecture of CAR-T cell therapies and fulfill current unmet needs for CAR-T product knowledge. These advances include improved sample preparation methods, enhanced separation technologies, and extension of MS-based proteomic to single cells. Innovative technologies such as proteomic analysis of raw material quality attributes (MQA) and final product quality attributes (PQA) may provide insights that could ultimately fuel development strategies and lead to broad implementation.

Voltage Rollercoaster Filtering of Low-Mass Contaminants During Native Protein Analysis

Intact protein mass spectrometry (MS) via electrospray-based methods is often degraded by low-mass contaminants, which can suppress the spectral quality of the analyte of interest via space-charge effects. Consequently, selective removal of contaminants by their mobilities would benefit native MS if achieved without additional hardware and before the mass analyzer regions used for selection, analyte readout, or tandem MS. Here, we use the high-pressure multipole within the source of an Orbitrap Tribrid as the foundation for a coarse ion filter. Using this method, we show complete filtration of 2 mM polyethylene glycol (PEG-1000) during native MS of SILu mAb antibody present at a 200× lower concentration. We also show the generality of the process by rescuing 10 μM tetrameric pyruvate kinase from 2 mM PEG-1000, asserting this voltage rollercoaster filtering (VRF) method for use in native MS as an efficient alternative to conventional purification methods.

The promise and reality of therapeutic discovery from large cohorts

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

Characterization of a human liver reference material fit for proteomics applications

The National Institute of Standards and Technology (NIST) is creating new, economical, qualitative reference materials and data for proteomics comparisons, benchmarking and harmonization. Here we describe a large dataset from shotgun proteomic analysis of RM 8461 Human Liver for Proteomics, a reference material being developed. Consensus identifications using multiple search engines and sample preparations demonstrate a homogeneous and fit-for-purpose material that can be incorporated into automated or manual sample preparation workflows, with the resulting data used to directly assess complete sample-to-data workflows and provide harmonization and benchmarking between laboratories and techniques. Data are available via PRIDE with identifier PXD013608.

Measurement(s)	peptide sequence-level identification attribute • protein expression profiling
Technology Type(s)	liquid chromatography-tandem mass spectrometry
Factor Type(s)	mass of liver sample
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11310485

Quality control of imbalanced mass spectra from isotopic labeling experiments

BACKGROUND

Mass spectra are usually acquired from the Liquid Chromatography-Mass Spectrometry (LC-MS) analysis for isotope labeled proteomics experiments. In such experiments, the mass profiles of labeled (heavy) and unlabeled (light) peptide pairs are represented by isotope clusters (2D or 3D) that provide valuable information about the studied biological samples in different conditions. The core task of quality control in quantitative LC-MS experiment is to filter out low-quality peptides with questionable profiles. The commonly used methods for this problem are the classification approaches. However, the data imbalance problems in previous control methods are often ignored or mishandled. In this study, we introduced a quality control framework based on the extreme gradient boosting machine (XGBoost), and carefully addressed the imbalanced data problem in this framework.

RESULTS

In the XGBoost based framework, we suggest the application of the Synthetic minority over-sampling technique (SMOTE) to re-balance data and use the balanced data to train the boosted trees as the classifier. Then the classifier is applied to other data for the peptide quality assessment. Experimental results show that our proposed framework increases the reliability of peptide heavy-light ratio estimation significantly.

CONCLUSIONS

Our results indicate that this framework is a powerful method for the peptide quality assessment. For the feature extraction part, the extracted ion chromatogram (XIC) based features contribute to the peptide quality assessment. To solve the imbalanced data problem, SMOTE brings a much better classification performance. Finally, the XGBoost is capable for the peptide quality control. Overall, our proposed framework provides reliable results for the further proteomics studies.