Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis

Changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease

While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.

Acetic acid is a superior ion pairing modifier for sub-nanogram and single cell proteomics

A recent study demonstrated a substantial increase in peptide signal and corresponding proteome coverage when employing 0.5% acetic acid (AA) as the ion pairing modifier in place of the 0.1% formic acid traditionally used in shotgun proteomics. In this study, we investigated the effect of modifier in the context of sub-nanogram and single cell proteomics (SCP). We first evaluated a tryptic digest standard down to 20 picograms total load on column on a TIMSTOF SCP system. In line with the previous results, we observed a signal increase when using AA, leading to increased proteome coverage at every peptide load assessed. Relative improvements were more apparent at lower concentrations, with a 20 picogram peptide digest demonstrating a striking 1.8-fold increase to over 2,000 protein groups identified in a 30 minute analysis. Furthermore, we find that this increase in signal can be leveraged to reduce ramp times, leading to 1.7x more scans across each peak and improvements in quantification as measured by %CVs. When evaluating single cancer cells, approximately 13% more peptide groups were identified on average when employing AA in the place of FA. All vendor raw and processed data are available through ProteomeXchange as PXD046002 and PXD051590.

2023 White Paper on Recent Issues in Bioanalysis: Deuterated Drugs; LNP; Tumor/FFPE Biopsy; Targeted Proteomics; Small Molecule Covalent Inhibitors; Chiral Bioanalysis; Remote Regulatory Assessments; Sample Reconciliation/Chain of Custody (PART 1A - Recommendations on Mass Spectrometry, Chromatography, Sample Preparation Latest Developments, Challenges, and Solutions and BMV/Regulated Bioanalysis PART 1B - Regulatory Agencies' Inputs on Regulated Bioanalysis/BMV, Biomarkers/IVD/CDx/BAV, Immunogenicity, Gene & Cell Therapy and Vaccine)

The 17th Workshop on Recent Issues in Bioanalysis (17th WRIB) took place in Orlando, FL, USA on June 19-23, 2023. Over 1000 professionals representing pharma/biotech companies, CROs, and multiple regulatory agencies convened to actively discuss the most current topics of interest in bioanalysis. The 17th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week to allow an exhaustive and thorough coverage of all major issues in bioanalysis of biomarkers, immunogenicity, gene therapy, cell therapy and vaccines.Moreover, in-depth workshops on "EU IVDR 2017/746 Implementation and impact for the Global Biomarker Community: How to Comply with this NEW Regulation" and on "US FDA/OSIS Remote Regulatory Assessments (RRAs)" were the special features of the 17th edition.As in previous years, WRIB continued to gather a wide diversity of international, industry opinion leaders and regulatory authority experts working on both small and large molecules as well as gene, cell therapies and vaccines to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance, and achieving scientific excellence on bioanalytical issues.This 2023 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2023 edition of this comprehensive White Paper has been divided into three parts for editorial reasons.This publication covers the recommendations on Mass Spectrometry Assays, Regulated Bioanalysis/BMV (Part 1A) and Regulatory Inputs (Part 1B). Part 2 (Biomarkers, IVD/CDx, LBA and Cell-Based Assays) and Part 3 (Gene Therapy, Cell therapy, Vaccines and Biotherapeutics Immunogenicity) are published in volume 16 of Bioanalysis, issues 7 and 8 (2024), respectively.

Peptide set test: a peptide-centric strategy to infer differentially expressed proteins

MOTIVATION

The clinical translation of mass spectrometry-based proteomics has been challenging due to limited statistical power caused by large technical variability and inter-patient heterogeneity. Bottom-up proteomics provides an indirect measurement of proteins through digested peptides. This raises the question whether peptide measurements can be used directly to better distinguish differentially expressed proteins.

RESULTS

We present a novel method called the peptide set test, which detects coordinated changes in the expression of peptides originating from the same protein and compares them to the rest of the peptidome. Applying our method to data from a published spike-in experiment and simulations demonstrates improved sensitivity without compromising precision, compared to aggregation-based approaches. Additionally, applying the peptide set test to compare the tumor proteomes of tamoxifen-sensitive and tamoxifen-resistant breast cancer patients reveals significant alterations in peptide levels of collagen XII, suggesting an association between collagen XII-mediated matrix reassembly and tamoxifen resistance. Our study establishes the peptide set test as a powerful peptide-centric strategy to infer differential expression in proteomics studies.

AVAILABILITY AND IMPLEMENTATION

Peptide set test (PepSetTest) is publicly available at https://github.com/JmWangBio/PepSetTest.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Analysis of gliomas DNA methylation: Assessment of pre-analytical variables

Precision oncology is driven by molecular biomarkers. For glioblastoma multiforme (GBM), the most common malignant adult primary brain tumor, O6-methylguanine-DNA methyltransferase ( MGMT ) gene DNA promoter methylation is an important prognostic and treatment clinical biomarker. Time consuming pre-analytical steps such as biospecimen storage before fixing, sampling, and processing are major sources of errors and batch effects, that are further confounded by intra-tumor heterogeneity of MGMT promoter methylation. To assess the effect of pre-analytical variables on GBM DNA methylation, tissue storage/sampling (CryoGrid), sample preparation multi-sonicator (PIXUL) and 5-methylcytosine (5mC) DNA immunoprecipitation (Matrix MeDIP-qPCR/seq) platforms were used. MGMT promoter CpG methylation was examined in 173 surgical samples from 90 individuals, 50 of these were used for intra-tumor heterogeneity studies. MGMT promoter methylation levels in paired frozen and formalin fixed paraffin embedded (FFPE) samples were very close, confirming suitability of FFPE for MGMT promoter methylation analysis in clinical settings. Matrix MeDIP-qPCR yielded similar results to methylation specific PCR (MS-PCR). Warm ex-vivo ischemia (37°C up to 4hrs) and 3 cycles of repeated sample thawing and freezing did not alter 5mC levels at MGMT promoter, exon and upstream enhancer regions, demonstrating the resistance of DNA methylation to the most common variations in sample processing conditions that might be encountered in research and clinical settings. 20-30% of specimens exhibited intratumor heterogeneity in the MGMT DNA promoter methylation. Collectively these data demonstrate that variations in sample fixation, ischemia duration and temperature, and DNA methylation assay technique do not have significant impact on assessment of MGMT promoter methylation status. However, intratumor methylation heterogeneity underscores the need for histologic verification and value of multiple biopsies at different GBM geographic tumor sites in assessment of MGMT promoter methylation. Matrix-MeDIP-seq analysis revealed that MGMT promoter methylation status clustered with other differentially methylated genomic loci (e.g. HOXA and lncRNAs), that are likewise resilient to variation in above post-resection pre-analytical conditions. These MGMT -associated global DNA methylation patterns offer new opportunities to validate more granular data-based epigenetic GBM clinical biomarkers where the CryoGrid-PIXUL-Matrix toolbox could prove to be useful.

SWATH-MS as a strategy for CHO host cell protein identification and quantification supporting the characterization of mAb purification platforms

Host cell proteins (HCPs) are process-related impurities expressed by the host cells during biotherapeutics' manufacturing, such as monoclonal antibodies (mAbs). Some challenging HCPs evade clearance during the downstream processing and can be co-purified with the molecule of interest, which may impact product stability, efficacy, and safety. Therefore, HCP content is a critical quality attribute to monitor and quantify across the bioprocess. Here we explored a mass spectrometry (MS)-based proteomics tool, the sequential window acquisition of all theoretical fragment-ion spectra (SWATH) strategy, as an orthogonal method to traditional ELISA. The SWATH workflow was applied for high-throughput individual HCP identification and quantification, supporting characterization of a mAb purification platform. The design space of HCP clearance of two polishing resins was evaluated through a design of experiment study. Absolute quantification of high-risk HCPs was achieved (reaching 1.8 and 4.2 ppm limits of quantification, for HCP A and B respectively) using HCP-specific synthetic heavy labeled peptide calibration curves. Profiling of other HCPs was also possible using an average calibration curve (using labeled peptides from different HCPs). The SWATH approach is a powerful tool for HCP assessment during bioprocess development enabling simultaneous monitoring and quantification of different individual HCPs and improving process understanding of their clearance.

Development and application of a multiple reaction monitoring method for the simultaneous quantification of sodium channels Nav 1.1, Nav 1.2, and Nav 1.6 in solubilized membrane proteins from stable HEK293 cell lines, rodents, and human brain tissues

RATIONALE

Nav 1.1, 1.2, and 1.6 are transmembrane proteins acting as voltage-gated sodium channels implicated in various forms of epilepsy. There is a need for knowing their actual concentration in target tissues during drug development.

METHODS

Unique peptides for Nav 1.1, Nav 1.2, and Nav 1.6 were selected as quantotropic peptides for each protein and used for their quantification in membranes from stably transfected HEK293 cells and rodent and human brain samples using ultra-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry.

RESULTS

Nav 1.1, 1.2, and 1.6 protein expressions in three stably individually transfected HEK293 cell lines were found to be 2.1 ± 0.2, 6.4 ± 1.2, and 4.0 ± 0.6 fmol/μg membrane protein, respectively. In brains, Nav 1.2 showed the highest expression, with approximately three times higher (P < 0.003) in rodents than in humans at 3.05 ± 0.57, with 3.35 ± 0.56 in mouse and rat brains and 1.09 ± 0.27 fmol/μg in human brain. Both Nav 1.1 and 1.6 expressions were much lower in the brains, with approximately 40% less expression in human Nav 1.1 than rodent Nav 1.1 at 0.49 ± 0.1 (mouse), 0.43 ± 0.3 (rat), and 0.28 ± 0.04 (humans); whereas Nav 1.6 had approximately 60% less expression in humans than rodents at 0.27 ± 0.09 (mouse), 0.26 ± 0.06 (rat), and 0.11 ± 0.02 (humans) fmol/μg membrane proteins.

CONCLUSIONS

Multiple reaction monitoring was used to quantify sodium channels Nav 1.1, 1.2, and 1.6 expressed in stably transfected HEK293 cells and brain tissues from mice, rats, and humans. We found significant differences in the expression of these channels in mouse, rat, and human brains. Nav expression ranking among the three species was Nav 1.2 ≫ Nav 1.1 > Nav 1.6, with the human brain expressing much lower concentrations overall compared to rodent brain.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

A High-Throughput PIXUL-Matrix-Based Toolbox to Profile Frozen and Formalin-Fixed Paraffin-Embedded Tissues Multiomes

Large-scale high-dimensional multiomics studies are essential to unravel molecular complexity in health and disease. We developed an integrated system for tissue sampling (CryoGrid), analytes preparation (PIXUL), and downstream multiomic analysis in a 96-well plate format (Matrix), MultiomicsTracks96, which we used to interrogate matched frozen and formalin-fixed paraffin-embedded (FFPE) mouse organs. Using this system, we generated 8-dimensional omics data sets encompassing 4 molecular layers of intracellular organization: epigenome (H3K27Ac, H3K4m3, RNA polymerase II, and 5mC levels), transcriptome (messenger RNA levels), epitranscriptome (m6A levels), and proteome (protein levels) in brain, heart, kidney, and liver. There was a high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles confirmed known organ-specific superenhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic profiles, known to be poorly correlated with transcriptomic data, can be more accurately predicted by the full suite of multiomics data, compared with using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Recent Advances in the Detection of Food Toxins Using Mass Spectrometry

Edibles are the only source of nutrients and energy for humans. However, ingredients of edibles have undergone many physicochemical changes during preparation and storage. Aging, hydrolysis, oxidation, and rancidity are some of the major changes that not only change the native flavor, texture, and taste of food but also destroy the nutritive value and jeopardize public health. The major reasons for the production of harmful metabolites, chemicals, and toxins are poor processing, inappropriate storage, and microbial spoilage, which are lethal to consumers. In addition, the emergence of new pollutants has intensified the need for advanced and rapid food analysis techniques to detect such toxins. The issue with the detection of toxins in food samples is the nonvolatile nature and absence of detectable chromophores; hence, normal conventional techniques need additional derivatization. Mass spectrometry (MS) offers high sensitivity, selectivity, and capability to handle complex mixtures, making it an ideal analytical technique for the identification and quantification of food toxins. Recent technological advancements, such as high-resolution MS and tandem mass spectrometry (MS/MS), have significantly improved sensitivity, enabling the detection of food toxins at ultralow levels. Moreover, the emergence of ambient ionization techniques has facilitated rapid in situ analysis of samples with lower time and resources. Despite numerous advantages, the widespread adoption of MS in routine food safety monitoring faces certain challenges such as instrument cost, complexity, data analysis, and standardization of methods. Nevertheless, the continuous advancements in MS-technology and its integration with complementary techniques hold promising prospects for revolutionizing food safety monitoring. This review discusses the application of MS in detecting various food toxins including mycotoxins, marine biotoxins, and plant-derived toxins. It also explores the implementation of untargeted approaches, such as metabolomics and proteomics, for the discovery of novel and emerging food toxins, enhancing our understanding of potential hazards in the food supply chain.

The Human Brainome: changes in expression of VGF, SPECC1L, HLA-DRA and RANBP3L act with APOE E4 to alter risk for late onset Alzheimer's disease

While there are currently over 40 replicated genes with mapped risk alleles for Late Onset Alzheimer's disease (LOAD), the Apolipoprotein E locus E4 haplotype is still the biggest driver of risk, with odds ratios for neuropathologically confirmed E44 carriers exceeding 30 (95% confidence interval 16.59-58.75). We sought to address whether the APOE E4 haplotype modifies expression globally through networks of expression to increase LOAD risk. We have used the Human Brainome data to build expression networks comparing APOE E4 carriers to non-carriers using scalable mixed-datatypes Bayesian network (BN) modeling. We have found that VGF had the greatest explanatory weight. High expression of VGF is a protective signal, even on the background of APOE E4 alleles. LOAD risk signals, considering an APOE background, include high levels of SPECC1L, HLA-DRA and RANBP3L. Our findings nominate several new transcripts, taking a combined approach to network building including known LOAD risk loci.

Top-Down Proteomics of Mouse Islets With Beta Cell CPE Deletion Reveals Molecular Details in Prohormone Processing

Altered prohormone processing, such as with proinsulin and pro-islet amyloid polypeptide (proIAPP), has been reported as an important feature of prediabetes and diabetes. Proinsulin processing includes removal of several C-terminal basic amino acids and is performed principally by the exopeptidase carboxypeptidase E (CPE), and mutations in CPE or other prohormone convertase enzymes (PC1/3 and PC2) result in hyperproinsulinemia. A comprehensive characterization of the forms and quantities of improperly processed insulin and other hormone products following Cpe deletion in pancreatic islets has yet to be attempted. In the present study we applied top-down proteomics to globally evaluate the numerous proteoforms of hormone processing intermediates in a β-cell-specific Cpe knockout mouse model. Increases in dibasic residue-containing proinsulin and other novel proteoforms of improperly processed proinsulin were found, and we could classify several processed proteoforms as novel substrates of CPE. Interestingly, some other known substrates of CPE remained unaffected despite its deletion, implying that paralogous processing enzymes such as carboxypeptidase D (CPD) can compensate for CPE loss and maintain near normal levels of hormone processing. In summary, our quantitative results from top-down proteomics of islets provide unique insights into the complexity of hormone processing products and the regulatory mechanisms.

The Utility of Mixed Effects Models in the Evaluation of Complex Genomic Traits In Vitro

In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this in silico study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.

The amniotic fluid proteome changes across gestation in humans and rhesus macaques

Amniotic fluid is a complex biological medium that offers protection to the fetus and plays a key role in normal fetal nutrition, organogenesis, and potentially fetal programming. Amniotic fluid is also critically involved in longitudinally shaping the in utero milieu during pregnancy. Yet, the molecular mechanism(s) of action by which amniotic fluid regulates fetal development is ill-defined partly due to an incomplete understanding of the evolving composition of the amniotic fluid proteome. Prior research consisting of cross-sectional studies suggests that the amniotic fluid proteome changes as pregnancy advances, yet longitudinal alterations have not been confirmed because repeated sampling is prohibitive in humans. We therefore performed serial amniocenteses at early, mid, and late gestational time-points within the same pregnancies in a rhesus macaque model. Longitudinally-collected rhesus amniotic fluid samples were paired with gestational-age matched cross-sectional human samples. Utilizing LC-MS/MS isobaric labeling quantitative proteomics, we demonstrate considerable cross-species similarity between the amniotic fluid proteomes and large scale gestational-age associated changes in protein content throughout pregnancy. This is the first study to compare human and rhesus amniotic fluid proteomic profiles across gestation and establishes a reference amniotic fluid proteome. The non-human primate model holds promise as a translational platform for amniotic fluid studies.

Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity

Type 1 diabetes (T1D) results from autoimmune destruction of β cells. Insufficient availability of biomarkers represents a significant gap in understanding the disease cause and progression. We conduct blinded, two-phase case-control plasma proteomics on the TEDDY study to identify biomarkers predictive of T1D development. Untargeted proteomics of 2,252 samples from 184 individuals identify 376 regulated proteins, showing alteration of complement, inflammatory signaling, and metabolic proteins even prior to autoimmunity onset. Extracellular matrix and antigen presentation proteins are differentially regulated in individuals who progress to T1D vs. those that remain in autoimmunity. Targeted proteomics measurements of 167 proteins in 6,426 samples from 990 individuals validate 83 biomarkers. A machine learning analysis predicts if individuals would remain in autoimmunity or develop T1D 6 months before autoantibody appearance, with areas under receiver operating characteristic curves of 0.871 and 0.918, respectively. Our study identifies and validates biomarkers, highlighting pathways affected during T1D development.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

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.

The Edge Effect in High-Throughput Proteomics: A Cautionary Tale

In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.

An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples

BACKGROUND

Clinical bronchoalveolar lavage fluid (BALF) samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, mass spectrometry (MS)-based proteomic analysis of BALF is challenged by the dynamic range of protein abundance, and potential for interfering contaminants. A robust, MS-based proteomics compatible sample preparation workflow for BALF samples, including those of small and large volume, would be useful for many researchers.

RESULTS

We have developed a workflow that combines high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, that is compatible with either qualitative or quantitative MS-based proteomic analysis. The workflow includes a value-added collection of endogenous peptides for peptidomic analysis of BALF samples, if desired, as well as amenability to offline semi-preparative or microscale fractionation of complex peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We demonstrate the effectiveness of this workflow on BALF samples collected from COPD patients, including for smaller sample volumes of 1-5 mL that are commonly available from the clinic. We also demonstrate the repeatability of the workflow as an indicator of its utility for quantitative proteomic studies.

CONCLUSIONS

Overall, our described workflow consistently provided high quality proteins and tryptic peptides for MS analysis. It should enable researchers to apply MS-based proteomics to a wide-variety of studies focused on BALF clinical specimens.

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

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

MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background

The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or "omes," measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies.

Methods

Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LC-MS/MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression.

Results

MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LC-MS/MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually.

Conclusions

The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies - for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

Towards absolute quantification of protein genetic variants in Pisum sativum extracts

In recent years, several studies have used proteomics approaches to characterize genetic variant profiles of agricultural raw materials. In such studies, the challenge is the quantification of the individual protein variants. In this study a novel UPLC-PDA-MS method with absolute and label-free UV-based peptide quantification was applied to quantify the genetic variants of legumin, vicilin and albumins in pea extracts. The aim was to investigate the applicability of this method and to identify challenges in determining protein concentration from the measured peptide concentrations. Analysis of the protein mass balance showed significant losses of proteins in extraction (37%) and of peptides in further sample preparation (69%). The challenge in calculating the extractable individual protein concentrations was how to deal with these insoluble peptides. The quantification approach using average amino acid concentrations in each position of the sequence showed most reproducible results and allowed comparison of the genetic protein composition of 8 different cultivars. The extractable protein composition (μM/μM) was remarkably similar for all cultivar extracts and consisted of legumins A1 (12.8 ± 1.2%), A2 (1.1 ± 0.4%), B (9.9 ± 1.6%), J (7.5 ± 1.0%) and K (10.3 ± 2.1%), vicilin (15.2 ± 1.7%), provicilin (15.7 ± 2.5%), convicilin (9.8 ± 0.8%), albumin A1 (7.4 ± 2.0%), albumin 2 (10.0 ± 1.5%) and protease inhibitor (0.4 ± 0.4%).

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.

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

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

pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes

The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry data, tailored to specific characteristics of proteomic (isobaric or labeled), metabolomic, and lipidomic data sets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for transcriptomic data analyses. These improvements have made progress toward a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package's statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.

Mice Placental ECM Components May Provide A Three-Dimensional Placental Microenvironment

Bioethical limitations impair deeper studies in human placental physiology, then most studies use human term placentas or murine models. To overcome these challenges, new models have been proposed to mimetize the placental three-dimensional microenvironment. The placental extracellular matrix plays an essential role in several processes, being a part of the establishment of materno-fetal interaction. Regarding these aspects, this study aimed to investigate term mice placental ECM components, highlighting its collagenous and non-collagenous content, and proposing a potential three-dimensional model to mimetize the placental microenvironment. For that, 18.5-day-old mice placenta, both control and decellularized (n = 3 per group) were analyzed on Orbitrap Fusion Lumos spectrometer (ThermoScientific) and LFQ intensity generated on MaxQuant software. Proteomic analysis identified 2317 proteins. Using ECM and cell junction-related ontologies, 118 (5.1%) proteins were filtered. Control and decellularized conditions had no significant differential expression on 76 (64.4%) ECM and cell junction-related proteins. Enriched ontologies in the cellular component domain were related to cell junction, collagen and lipoprotein particles, biological process domain, cell adhesion, vasculature, proteolysis, ECM organization, and molecular function. Enriched pathways were clustered in cell adhesion and invasion, and labyrinthine vasculature regulation. These preserved ECM proteins are responsible for tissue stiffness and could support cell anchoring, modeling a three-dimensional structure that may allow placental microenvironment reconstruction.

Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum

Multiple reaction monitoring (MRM) mass spectrometry is emerging as a relevant tool for measuring customized molecular markers in freshwater sentinel species. While this technique is typically used for the validation of protein molecular markers preselected from shotgun experiments, recent gains of MRM multiplexing capacity offer new possibilities to conduct large-scale screening of animal proteomes. By combining the strength of active biomonitoring strategies and MRM technologies, this study aims to propose a new strategy for the discovery of candidate proteins that respond to environmental variability. For this purpose, 249 peptides derived from 147 proteins were monitored by MRM in 273 male gammarids caged in 56 environmental sites, representative of the diversity of French water bodies. A methodology is here proposed to identify a set of customized housekeeping peptides (HKPs) used to correct analytical batch effects and allow proper comparison of peptide levels in gammarids. A comparative analysis performed on HKPs-normalized data resulted in the identification of peptides highly modulated in the environment and derived from proteins likely involved in the environmental stress response. Overall, this study proposes a breakthrough approach to screen and identify potential proteins responding to relevant environmental conditions in sentinel species.

Synaptic proteomics reveal distinct molecular signatures of cognitive change and C9ORF72 repeat expansion in the human ALS cortex

Increasing evidence suggests synaptic dysfunction is a central and possibly triggering factor in Amyotrophic Lateral Sclerosis (ALS). Despite this, we still know very little about the molecular profile of an ALS synapse. To address this gap, we designed a synaptic proteomics experiment to perform an unbiased assessment of the synaptic proteome in the ALS brain. We isolated synaptoneurosomes from fresh-frozen post-mortem human cortex (11 controls and 18 ALS) and stratified the ALS group based on cognitive profile (Edinburgh Cognitive and Behavioural ALS Screen (ECAS score)) and presence of a C9ORF72 hexanucleotide repeat expansion (C9ORF72-RE). This allowed us to assess regional differences and the impact of phenotype and genotype on the synaptic proteome, using Tandem Mass Tagging-based proteomics. We identified over 6000 proteins in our synaptoneurosomes and using robust bioinformatics analysis we validated the strong enrichment of synapses. We found more than 30 ALS-associated proteins in synaptoneurosomes, including TDP-43, FUS, SOD1 and C9ORF72. We identified almost 500 proteins with altered expression levels in ALS, with region-specific changes highlighting proteins and pathways with intriguing links to neurophysiology and pathology. Stratifying the ALS cohort by cognitive status revealed almost 150 specific alterations in cognitively impaired ALS synaptic preparations. Stratifying by C9ORF72-RE status revealed 330 protein alterations in the C9ORF72-RE +ve group, with KEGG pathway analysis highlighting strong enrichment for postsynaptic dysfunction, related to glutamatergic receptor signalling. We have validated some of these changes by western blot and at a single synapse level using array tomography imaging. In summary, we have generated the first unbiased map of the human ALS synaptic proteome, revealing novel insight into this key compartment in ALS pathophysiology and highlighting the influence of cognitive decline and C9ORF72-RE on synaptic composition.

Mitochondrial dysfunction is a key pathological driver of early stage Parkinson's

BACKGROUND

The molecular drivers of early sporadic Parkinson's disease (PD) remain unclear, and the presence of widespread end stage pathology in late disease masks the distinction between primary or causal disease-specific events and late secondary consequences in stressed or dying cells. However, early and mid-stage Parkinson's brains (Braak stages 3 and 4) exhibit alpha-synuclein inclusions and neuronal loss along a regional gradient of severity, from unaffected-mild-moderate-severe. Here, we exploited this spatial pathological gradient to investigate the molecular drivers of sporadic PD.

METHODS

We combined high precision tissue sampling with unbiased large-scale profiling of protein expression across 9 brain regions in Braak stage 3 and 4 PD brains, and controls, and verified these results using targeted proteomic and functional analyses.

RESULTS

We demonstrate that the spatio-temporal pathology gradient in early-mid PD brains is mirrored by a biochemical gradient of a changing proteome. Importantly, we identify two key events that occur early in the disease, prior to the occurrence of alpha-synuclein inclusions and neuronal loss: (i) a metabolic switch in the utilisation of energy substrates and energy production in the brain, and (ii) perturbation of the mitochondrial redox state. These changes may contribute to the regional vulnerability of developing alpha-synuclein pathology. Later in the disease, mitochondrial function is affected more severely, whilst mitochondrial metabolism, fatty acid oxidation, and mitochondrial respiration are affected across all brain regions.

CONCLUSIONS

Our study provides an in-depth regional profile of the proteome at different stages of PD, and highlights that mitochondrial dysfunction is detectable prior to neuronal loss, and alpha-synuclein fibril deposition, suggesting that mitochondrial dysfunction is one of the key drivers of early disease.

Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure

Several algorithms for the normalization of proteomic data are currently available, each based on a priori assumptions. Among these is the extent to which differential expression (DE) can be present in the dataset. This factor is usually unknown in explorative biomarker screens. Simultaneously, the increasing depth of proteomic analyses often requires the selection of subsets with a high probability of being DE to obtain meaningful results in downstream bioinformatical analyses. Based on the relationship of technical variation and (true) biological DE of an unknown share of proteins, we propose the “Normics” algorithm: Proteins are ranked based on their expression level–corrected variance and the mean correlation with all other proteins. The latter serves as a novel indicator of the non-DE likelihood of a protein in a given dataset. Subsequent normalization is based on a subset of non-DE proteins only. No a priori information such as batch, clinical, or replicate group is necessary. Simulation data demonstrated robust and superior performance across a wide range of stochastically chosen parameters. Five publicly available spike-in and biologically variant datasets were reliably and quantitively accurately normalized by Normics with improved performance compared to standard variance stabilization as well as median, quantile, and LOESS normalizations. In complex biological datasets Normics correctly determined proteins as being DE that had been cross-validated by an independent transcriptome analysis of the same samples. In both complex datasets Normics identified the most DE proteins. We demonstrate that combining variance analysis and data-inherent correlation structure to identify non-DE proteins improves data normalization. Standard normalization algorithms can be consolidated against high shares of (one-sided) biological regulation. The statistical power of downstream analyses can be increased by focusing on Normics-selected subsets of high DE likelihood.

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Normics is a tool for the normalization of proteomic data based on existing algorithms.

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Specifically addresses data with high shares of differential expression.

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Combines variance and data-inherent correlation structure.

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Provides a ranking of differential expression likelihood.

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Enables normalization based on the most stable proteins.

Equilibration for Electrospray Ionization Mass Spectrometry in Quantitative Analysis

Electrospray ionization mass spectrometry (ESI-MS) is widely used in drug development, therapeutic drug monitoring, and other fields. However, unstable mass spectral signals, especially during the initial stages of instrument operation, plague analysts. Generally, in quantitative experiments, the stability of response can be achieved by running the analytical system for some time. However, the equilibration time required for the responses of different compounds to stabilize has been elusive. To investigate the response stability of the ESI-MS system, 72 compounds with different physicochemical properties were employed on three systems, and flow injection analysis was performed in positive ion mode. With the use of 5.00% (response stable factor, RSF) as the stability limit, about 80% of the compounds were stable within 60 min. Under a 2.00% criterion, the stabilization time was significantly longer. The stabilization time varies with different instruments and physicochemical properties of the compounds. When positive ion detection is performed in an acidic mobile phase, the octanol-water partition coefficient (Log P), molecular weight, and molar volume can all affect the time required to stabilize the response. In general, it is necessary to balance the ESI-MS system for an appropriate time before sample detection, especially for the analysis of compounds with strong hydrophilicity, small molecular weight, or small molar volume under the conditions above.

15N Metabolic Labeling Quantification Workflow in Arabidopsis Using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using ¹⁵N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the ¹⁵N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of ¹⁵N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

Toward optimal clearance - A universal affinity based mass spectrometry approach for comprehensive ELISA reagent coverage evaluation and HCP hitchhiker analysis

In the control strategy for process related impurities in biopharmaceuticals the enzyme linked immunosorbent assay (ELISA) is the method of choice for the quantification of host cell proteins (HCP). Besides two dimensional - western blots (2D-WB), the coverage of ELISA antibodies is increasingly evaluated by affinity purification based liquid chromatography-tandem mass spectrometry (AP-MS) methods. However, all these methods face the problem of unspecific binding issues between antibodies and the matrix, involving the application of arbitrarily defined thresholds during data evaluation. To solve this, a new approach (optimized AP-MS) was developed in this study, for which a cleavable linker was conjugated to the ELISA antibodies enabling the subsequent isolation of specifically interacting HCPs. By comparing both approaches in terms of method variability and the number of false positive or negative hits, we could demonstrate that the optimized AP-MS method is very reproducible and superior in the identification of antibody detection gaps, while previously described strategies suffered from over- or underestimating the coverage. As only antibody associated HCPs were identified, we demonstrated that the method is beneficial for hitchhiker analysis. Overall, the method described herein has proven as a powerful tool for reliable coverage determination of ELISA antibodies, without the need to arbitrarily exclude HCPs during the coverage evaluation. This article is protected by copyright. All rights reserved.

Computational analyses of mechanism of action (MoA): data, methods and integration

The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.

Rapid Targeted Quantitation of Protein Overexpression with Direct Infusion Shotgun Proteome Analysis (DISPA-PRM)

While much effort has been placed on comprehensive quantitative proteome analysis, certain applications demand the measurement of only a few target proteins from complex systems. Traditional approaches to targeted proteomics rely on nanoliquid chromatography (nLC) and targeted mass spectrometry (MS) methods, e.g., parallel reaction monitoring (PRM). However, the time requirement for nLC can limit the throughput of targeted proteomics. To achieve rapid and high-throughput targeted methods, here we show that nLC separations can be eliminated and replaced with direct infusion shotgun proteome analysis (DISPA) using high-field asymmetric waveform ion mobility spectrometry (FAIMS) with PRM. We demonstrate the application of DISPA-PRM for rapid targeted quantification of bacterial enzymes utilized in the production of biofuels by monitoring temporal expression in 72 metabolically engineered bacterial cultures in less than 2.5 h, with a measured dynamic range >1200-fold. We conclude that DISPA-PRM presents a valuable innovative tool with results comparable to nLC-MS/MS, enabling fast and rapid detection of targeted proteins in complex mixtures.

Proteomic analysis of opossum Monodelphis domestica spinal cord reveals the changes of proteins related to neurodegenerative diseases during developmental period when neuroregeneration stops being possible

One of the major challenges of modern neurobiology concerns the inability of the adult mammalian central nervous system (CNS) to regenerate and repair itself after injury. It is still unclear why the ability to regenerate CNS is lost during evolution and development and why it becomes very limited in adult mammals. A convenient model to study cellular and molecular basis of this loss is neonatal opossum (Monodelphis domestica). Opossums are marsupials that are born very immature with the unique possibility to successfully regenerate postnatal spinal cord after injury in the first two weeks of their life, after which this ability abbruptly stops. Using comparative proteomic approach we identified the proteins that are differentially distributed in opossum spinal tissue that can and cannot regenerate after injury, among which stand out the proteins related to neurodegenerative diseases (NDD), such as Huntington, Parkinson and Alzheimer's disease, previously detected by comparative transcriptomics on the analog tissue. The different distribution of the selected proteins detected by comparative proteomics was further confirmed by Western blot (WB), and the changes in the expression of related genes were analysed by quantitative reverse transcription PCR (qRT-PCR). Furthermore, we explored the cellular localization of the selected proteins using immunofluorescent microscopy. To our knowledge, this is the first report on proteins differentially present in developing, non-injured mammalian spinal cord tissue with different regenerative capacities. The results of this study indicate that the proteins known to have an important role in the pathophysiology of neurodegeneration in aged CNS, could also have an important phyisological role during CNS postnatal development and in neuroregeneration process.

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics─currently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.

Metabolic Enzyme Alterations and Astrocyte Dysfunction in a Murine Model of Alexander Disease With Severe Reactive Gliosis

Alexander disease (AxD) is a rare and fatal neurodegenerative disorder caused by mutations in the gene encoding glial fibrillary acidic protein (GFAP). In this report, a mouse model of AxD (GFAPTg;Gfap+/R236H) was analyzed that contains a heterozygous R236H point mutation in murine Gfap as well as a transgene with a GFAP promoter to overexpress human GFAP. Using label-free quantitative proteomic comparisons of brain tissue from GFAPTg;Gfap+/R236H versus wild-type mice confirmed upregulation of the glutathione metabolism pathway and indicated proteins were elevated in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, which had not been reported previously in AxD. Relative protein-level differences were confirmed by a targeted proteomics assay, including proteins related to astrocytes and oligodendrocytes. Of particular interest was the decreased level of the oligodendrocyte protein, 2-hydroxyacylsphingosine 1-beta-galactosyltransferase (Ugt8), since Ugt8-deficient mice exhibit a phenotype similar to GFAPTg;Gfap+/R236H mice (e.g., tremors, ataxia, hind-limb paralysis). In addition, decreased levels of myelin-associated proteins were found in the GFAPTg;Gfap+/R236H mice, consistent with the role of Ugt8 in myelin synthesis. Fabp7 upregulation in GFAPTg;Gfap+/R236H mice was also selected for further investigation due to its uncharacterized association to AxD, critical function in astrocyte proliferation, and functional ability to inhibit the anti-inflammatory PPAR signaling pathway in models of amyotrophic lateral sclerosis (ALS). Within Gfap+ astrocytes, Fabp7 was markedly increased in the hippocampus, a brain region subjected to extensive pathology and chronic reactive gliosis in GFAPTg;Gfap+/R236H mice. Last, to determine whether the findings in GFAPTg;Gfap+/R236H mice are present in the human condition, AxD patient and control samples were analyzed by Western blot, which indicated that Type I AxD patients have a significant fourfold upregulation of FABP7. However, immunohistochemistry analysis showed that UGT8 accumulates in AxD patient subpial brain regions where abundant amounts of Rosenthal fibers are located, which was not observed in the GFAPTg;Gfap+/R236H mice.

Insights into the changes in the proteome of Alzheimer disease elucidated by a meta-analysis

Mass spectrometry (MS)-based proteomics is a powerful tool to explore pathogenic changes of a disease in an unbiased manner and has been used extensively in Alzheimer disease (AD) research. Here, by performing a meta-analysis of high-quality proteomic studies, we address which pathological changes are observed consistently and therefore most likely are of great importance for AD pathogenesis. We retrieved datasets, comprising a total of 21,588 distinct proteins identified across 857 postmortem human samples, from ten studies using labeled or label-free MS approaches. Our meta-analysis findings showed significant alterations of 757 and 1,195 proteins in AD in the labeled and label-free datasets, respectively. Only 33 proteins, some of which were associated with synaptic signaling, had the same directional change across the individual studies. However, despite alterations in individual proteins being different between the labeled and the label-free datasets, several pathways related to synaptic signaling, oxidative phosphorylation, immune response and extracellular matrix were commonly dysregulated in AD. These pathways represent robust changes in the human AD brain and warrant further investigation.

Damage-Net: A program for DNA repair meta-analysis identifies a network of novel repair genes that facilitate cancer evolution

The advent of genome-wide methods for identifying novel components in biological processes including CRISPR screens and proteomic studies, has transformed the research landscape within the biological sciences. However, each study normally investigates a single aspect of a process without integration of other published datasets. Here, we present Damage-Net, a program with a curated database of published results from a broad range of studies investigating DNA repair, that facilitates simple and quick meta-analysis. Users can incorporate their own datasets for analysis, and query genes of interest in the database. Importantly, this program also allows users to examine the correlation of genes of interest with pan-cancer patient survival and mutational burden effects. Interrogating these datasets revealed a network of genes that associated with cancer progression in adrenocortical carcinoma via facilitating mutational burden, ultimately contributing substantially to adrenocortical carcinoma's poor prognosis. Download at www.damage-net.co.uk.

Host cell protein detection gap risk mitigation: quantitative IAC-MS for ELISA antibody reagent coverage determination

Host cell proteins (HCPs) must be sufficiently cleared from recombinant biopharmaceuticals during the downstream process (DSP) to ensure product quality, purity, and patient safety. For monitoring of HCP clearance, the typical method chosen is an enzyme-linked immunosorbent assay (ELISA) using polyclonal anti-HCP antibodies obtained from an immunization campaign. This polyclonal reagent is a critical factor for functionality and confidence of the ELISA. Therefore, it is important to ensure that the pool of ELISA antibodies covers a broad spectrum of the HCPs that potentially could persist in the final drug substance. Typically, coverage is determined by gel-based approaches. Here, we present a quantitative proteomics approach combined with purification of HCPs by immunoaffinity chromatography (qIAC-MS) for assessment of ELISA coverage. The cell culture fluid (CCF) of a mock fermentation and a recombinant monoclonal antibody product were characterized in detail to investigate whether the HCPs used for immunization of animals accurately represent HCPs that are relevant to the process. Using the qIAC-MS approach, the ELISA antibody coverage was determined for mock fermentation and product CCF, as well as several different DSP intermediates. Here, the use of different controls facilitated the identification and quantification of HCPs present in the polyclonal reagent and those that nonspecifically bound to IAC material. This study successfully demonstrates that the described qIAC-MS approach is not only a suitable orthogonal method to commonly used 2D SDS-PAGE-based analysis for evaluating ELISA antibody coverage, but that it further identifies HCPs covered as well as missed by the ELISA, enabling an improved risk assessment of HCP ELISA.

Re-Examining the Impact of Minimal Scans in Liquid Chromatography-Mass Spectrometry Analysis

Liquid chromatography-mass spectrometry (LC-MS) is one of the most widely used analytical tools. High analysis volumes and sample complexity often demand more informative LC-MS acquisition schemes to improve efficiency and throughput without compromising data quality, and such a demand has been always hindered by the prerequisite that a minimum of 13-20 MS scans (data points) across an analyte peak are required for accurate quantitation. The current study systematically re-evaluated and compared the impact of different scan numbers on quantitation analysis using both triple quadrupoles mass spectrometry (TQMS) and high-resolution mass spectrometry (HRMS). Contrary to the 13-20 minimal scan prerequisite, the data obtained from a group of eight commercial drugs in the absence and presence of biological matrices suggest that 6 scans per analyte peak are sufficient to achieve highly comparable quantitation results compared to that obtained using 10 and 20 scans, respectively. The fewer minimal scan prerequisite is presumably attributed to an improved LC system and advanced column technology, better MS detector, and more intelligent peak detection and integration algorithms leading to a more symmetric peak shape and smaller peak standard deviation. As a result, more informative acquisition schemes can be broadly set up for higher throughput and more data-rich LC-MS/MS analysis as demonstrated in a hepatocyte clearance assay in which fewer MS scans executed on HRMS led to broader metabolite coverage without compromising data quality in hepatic clearance assessment. The demonstrated acquisition scheme would substantially increase the throughput, robustness, and richness of the nonregulatory analysis, which can be broadly applied in diverse fields including pharmaceutical, environmental, forensic, toxicological, and biotechnological.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Inter-study and time-dependent variability of metabolite abundance in cultured red blood cells

BACKGROUND

Cultured human red blood cells (RBCs) provide a powerful ex vivo assay platform to study blood-stage malaria infection and propagation. In recent years, high-resolution metabolomic methods have quantified hundreds of metabolites from parasite-infected RBC cultures under a variety of perturbations. In this context, the corresponding control samples of the uninfected culture systems can also be used to examine the effects of these perturbations on RBC metabolism itself and their dependence on blood donors (inter-study variations).

METHODS

Time-course datasets from five independent studies were generated and analysed, maintaining uninfected RBCs (uRBC) at 2% haematocrit for 48 h under conditions originally designed for parasite cultures. Using identical experimental protocols, quadruplicate samples were collected at six time points, and global metabolomics were employed on the pellet fraction of the uRBC cultures. In total, ~ 500 metabolites were examined across each dataset to quantify inter-study variability in RBC metabolism, and metabolic network modelling augmented the analyses to characterize the metabolic state and fluxes of the RBCs.

RESULTS

To minimize inter-study variations unrelated to RBC metabolism, an internal standard metabolite (phosphatidylethanolamine C18:0/20:4) was identified with minimal variation in abundance over time and across all the samples of each dataset to normalize the data. Although the bulk of the normalized data showed a high degree of inter-study consistency, changes and variations in metabolite levels from individual donors were noted. Thus, a total of 24 metabolites were associated with significant variation in the 48-h culture time window, with the largest variations involving metabolites in glycolysis and synthesis of glutathione. Metabolic network analysis was used to identify the production of superoxide radicals in cultured RBCs as countered by the activity of glutathione oxidoreductase and synthesis of reducing equivalents via the pentose phosphate pathway. Peptide degradation occurred at a rate that is comparable with central carbon fluxes, consistent with active degradation of methaemoglobin, processes also commonly associated with storage lesions in RBCs.

CONCLUSIONS

The bulk of the data showed high inter-study consistency. The collected data, quantification of an expected abundance variation of RBC metabolites, and characterization of a subset of highly variable metabolites in the RBCs will help in identifying non-specific changes in metabolic abundances that may obscure accurate metabolomic profiling of Plasmodium falciparum and other blood-borne pathogens.

Integration of peripheral transcriptomics, genomics, and interactomics following trauma identifies causal genes for symptoms of post-traumatic stress and major depression

Posttraumatic stress disorder (PTSD) is a debilitating syndrome with substantial morbidity and mortality that occurs in the aftermath of trauma. Symptoms of major depressive disorder (MDD) are also a frequent consequence of trauma exposure. Identifying novel risk markers in the immediate aftermath of trauma is a critical step for the identification of novel biological targets to understand mechanisms of pathophysiology and prevention, as well as the determination of patients most at risk who may benefit from immediate intervention. Our study utilizes a novel approach to computationally integrate blood-based transcriptomics, genomics, and interactomics to understand the development of risk vs. resilience in the months following trauma exposure. In a two-site longitudinal, observational prospective study, we assessed over 10,000 individuals and enrolled >700 subjects in the immediate aftermath of trauma (average 5.3 h post-trauma (range 0.5-12 h)) in the Grady Memorial Hospital (Atlanta) and Jackson Memorial Hospital (Miami) emergency departments. RNA expression data and 6-month follow-up data were available for 366 individuals, while genotype, transcriptome, and phenotype data were available for 297 patients. To maximize our power and understanding of genes and pathways that predict risk vs. resilience, we utilized a set-cover approach to capture fluctuations of gene expression of PTSD or depression-converting patients and non-converting trauma-exposed controls to find representative sets of disease-relevant dysregulated genes. We annotated such genes with their corresponding expression quantitative trait loci and applied a variant of a current flow algorithm to identify genes that potentially were causal for the observed dysregulation of disease genes involved in the development of depression and PTSD symptoms after trauma exposure. We obtained a final list of 11 driver causal genes related to MDD symptoms, 13 genes for PTSD symptoms, and 22 genes in PTSD and/or MDD. We observed that these individual or combined disorders shared ESR1, RUNX1, PPARA, and WWOX as driver causal genes, while other genes appeared to be causal driver in the PTSD only or MDD only cases. A number of these identified causal pathways have been previously implicated in the biology or genetics of PTSD and MDD, as well as in preclinical models of amygdala function and fear regulation. Our work provides a promising set of initial pathways that may underlie causal mechanisms in the development of PTSD or MDD in the aftermath of trauma.

Proteotyping of knockout mouse strains reveals sex- and strain-specific signatures in blood plasma

We proteotyped blood plasma from 30 mouse knockout strains and corresponding wild-type mice from the International Mouse Phenotyping Consortium. We used targeted proteomics with internal standards to quantify 375 proteins in 218 samples. Our results provide insights into the manifested effects of each gene knockout at the plasma proteome level. We first investigated possible contamination by erythrocytes during sample preparation and labeled, in one case, up to 11 differential proteins as erythrocyte originated. Second, we showed that differences in baseline protein abundance between female and male mice were evident in all mice, emphasizing the necessity to include both sexes in basic research, target discovery, and preclinical effect and safety studies. Next, we identified the protein signature of each gene knockout and performed functional analyses for all knockout strains. Further, to demonstrate how proteome analysis identifies the effect of gene deficiency beyond traditional phenotyping tests, we provide in-depth analysis of two strains, C8a-/- and Npc2+/-. The proteins encoded by these genes are well-characterized providing good validation of our method in homozygous and heterozygous knockout mice. Ig alpha chain C region, a poorly characterized protein, was among the differentiating proteins in C8a-/-. In Npc2+/- mice, where histopathology and traditional tests failed to differentiate heterozygous from wild-type mice, our data showed significant difference in various lysosomal storage disease-related proteins. Our results demonstrate how to combine absolute quantitative proteomics with mouse gene knockout strategies to systematically study the effect of protein absence. The approach used here for blood plasma is applicable to all tissue protein extracts.

Enhancing Top-Down Proteomics of Brain Tissue with FAIMS

Proteomic investigations of Alzheimer's and Parkinson's disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein's "intact" state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer's disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at -50, -40, and -30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ1-42 variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.