Peptide correlation: A means to identify high quality quantitative information in large-scale proteomic studies

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Systematic Review and Meta-Analysis of Mass Spectrometry Proteomics Applied to Human Peripheral Fluids to Assess Potential Biomarkers of Schizophrenia

Mass spectrometry (MS)-based techniques can be a powerful tool to identify neuropsychiatric disorder biomarkers, improving prediction and diagnosis ability. Here, we evaluate the efficacy of MS proteomics applied to human peripheral fluids of schizophrenia (SCZ) patients to identify disease biomarkers and relevant networks of biological pathways. Following PRISMA guidelines, a search was performed for studies that used MS proteomics approaches to identify proteomic differences between SCZ patients and healthy control groups (PROSPERO database: CRD42021274183). Nineteen articles fulfilled the inclusion criteria, allowing the identification of 217 differentially expressed proteins. Gene ontology analysis identified lipid metabolism, complement and coagulation cascades, and immune response as the main enriched biological pathways. Meta-analysis results suggest the upregulation of FCN3 and downregulation of APO1, APOA2, APOC1, and APOC3 in SCZ patients. Despite the proven ability of MS proteomics to characterize SCZ, several confounding factors contribute to the heterogeneity of the findings. In the future, we encourage the scientific community to perform studies with more extensive sampling and validation cohorts, integrating omics with bioinformatics tools to provide additional comprehension of differentially expressed proteins. The produced information could harbor potential proteomic biomarkers of SCZ, contributing to individualized prognosis and stratification strategies, besides aiding in the differential diagnosis.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide's change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ∼15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.

NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

The building blocks of successful translation of proteomics to the clinic

Recently, the first two multiplexed tests using selective reaction monitoring (SRM-MS) mass spectrometry have entered clinical practice. Despite different areas of indication, risk stratification in lung cancer and preterm birth, they share multiple steps in their development strategies. Here we review these strategies and their implications for successful translation of biomarkers to clinical practice. We believe that the identification of blood protein panels for the identification of disease phenotypes is now a reproducible and standard (albeit complex) process.

Quantification of proteins by label-free LC-MS(E.)

Quantitative proteomics by LC-MS/MS is a widely used approach for quantifying a significant portion of any complex proteome. Among the different techniques used for this purpose, one is by use of Data Independent Acquisition (DIA). We present a descriptive protocol for label-free quantitation of proteins by one DIA method termed LC-MS(E), which facilitates large-scale quantification of proteins without the need for isotopic labelling and with no theoretical limit to the number of samples included in an experiment.

Evidence for disease and antipsychotic medication effects in post-mortem brain from schizophrenia patients

Extensive research has been conducted on post-mortem brain tissue in schizophrenia (SCZ), particularly the dorsolateral prefrontal cortex (DLPFC). However, to what extent the reported changes are due to the disorder itself, and which are the cumulative effects of lifetime medication remains to be determined. In this study, we employed label-free liquid chromatography-mass spectrometry-based proteomic and proton nuclear magnetic resonance-based metabonomic profiling approaches to investigate DLPFC tissue from two cohorts of SCZ patients grouped according to their lifetime antipsychotic dose, together with tissue from bipolar disorder (BPD) subjects, and normal controls (n=10 per group). Both techniques showed profound changes in tissue from low-cumulative-medication SCZ subjects, but few changes in tissue from medium-cumulative-medication subjects. Protein expression changes were validated by Western blot and investigated further in a third group of subjects who were subjected to high-cumulative-medication over the course of their lifetime. Furthermore, key protein expression and metabolite level changes correlated significantly with lifetime antipsychotic dose. This suggests that the detected changes are present before antipsychotic therapy and, moreover, may be normalized with treatment. Overall, our analyses revealed novel protein and metabolite changes in low-cumulative-medication subjects associated with synaptogenesis, neuritic dynamics, presynaptic vesicle cycling, amino acid and glutamine metabolism, and energy buffering systems. Most of these markers were altered specifically in SCZ as determined by analysis of the same brain region from BPD patients.

Impaired glycolytic response in peripheral blood mononuclear cells of first-onset antipsychotic-naive schizophrenia patients

Little is known about the biological mechanisms underpinning the pathology of schizophrenia. We have analysed the proteome of stimulated and unstimulated peripheral blood mononuclear cells (PBMCs) from schizophrenia patients and controls as a potential model of altered cellular signaling using liquid-chromatography mass spectrometry proteomic profiling. PBMCs from patients and controls were stimulated for 72 h in vitro using staphylococcal enterotoxin B. In total, 18 differentially expressed proteins between first-onset, antipsychotic-naive patients and controls in the unstimulated and stimulated conditions were identified. Remarkably, eight of these proteins were associated with the glycolytic pathway and patient-control differences were more prominent in stimulated compared with unstimulated PBMCs. None of these proteins were altered in chronically ill antipsychotic-treated patients. Non-linear multivariate statistical analysis showed that small subsets of these proteins could be used as a signal for distinguishing first-onset patients from controls with high precision. Functional analysis of PBMCs did not reveal any difference in the glycolytic rate between patients and controls despite increased levels of lactate and the glucose transporter-1, and decreased levels of the insulin receptor in patients. In addition, subjects showed increased serum levels of insulin, consistent with the idea that some schizophrenia patients are insulin resistant. These results show that schizophrenia patients respond differently to PBMC activation and this is manifested at disease onset and may be modulated by antipsychotic treatment. The glycolytic protein signature associated with this effect could therefore be of diagnostic and prognostic value. Moreover, these results highlight the importance of using cells for functional discovery and show that it may not be sufficient to measure protein expression levels in static states.

Proteomic research in psychiatry

Psychiatric disorders such as Alzheimer's disease, schizophrenia and mood disorders are severe and disabling conditions of largely unknown origin and poorly understood pathophysiology. An accurate diagnosis and treatment of these disorders is often complicated by their aetiological and clinical heterogeneity. In recent years proteomic technologies based on mass spectrometry have been increasingly used, especially in the search for diagnostic and prognostic biomarkers in neuropsychiatric disorders. Proteomics enable an automated high-throughput protein determination revealing expression levels, post-translational modifications and complex protein-interaction networks. In contrast to other methods such as molecular genetics, proteomics provide the opportunity to determine modifications at the protein level thereby possibly being more closely related to pathophysiological processes underlying the clinical phenomenology of specific psychiatric conditions. In this article we review the theoretical background of proteomics and its most commonly utilized techniques. Furthermore the current impact of proteomic research on diverse psychiatric diseases, such as Alzheimer's disease, schizophrenia, mood and anxiety disorders, drug abuse and autism, is discussed. Proteomic methods are expected to gain crucial significance in psychiatric research and neuropharmacology over the coming decade.

Global proteomic profiling reveals altered proteomic signature in schizophrenia serum

Schizophrenia is one of the most severe psychiatric disorders affecting 1% of the world population. There is yet no empirical method to validate the diagnosis of the disease. The identification of an underlying molecular alteration could lead to an improved disease understanding and may yield an objective panel of biomarkers to aid in the diagnosis of this devastating disease. Presented is the largest reported liquid chromatography-mass spectrometry-based proteomic profiling study investigating serum samples taken from first-onset drug-naive patients compared with samples collected from healthy volunteers. The results of this large-scale study are presented along with enzyme-linked immunosorbent assay-based validation data.

Role of spectral counting in quantitative proteomics

Spectral count, defined as the total number of spectra identified for a protein, has gained acceptance as a practical, label-free, semiquantitative measure of protein abundance in proteomic studies. In this review, we discuss issues affecting the performance of spectral counting relative to other label-free methods, as well as its limitations. Possible consequences of modifications, which are commonly applied to raw spectral counts to improve abundance estimations, are considered. The use of spectral counting for different types of quantitation studies is explored and critiqued. Different statistical methods and underlying frameworks that have been applied to spectral count analysis are described and compared, and problem areas that undermine confident statistical analysis are considered. Finally, the issue of accurate estimation of false-discovery rates is addressed and identified as a major current challenge in quantitative proteomics.

Expression profiling of fibroblasts identifies cell cycle abnormalities in schizophrenia

Many previous studies have attempted to gain insight into the underlying pathophysiology of schizophrenia by studying postmortem brain tissues of schizophrenia patients. However, such analyses can be confounded by artifactual features of this approach such as lengthy agonal state and postmortem interval times. As several aspects of schizophrenia are also manifested at the peripheral level in proliferating cell types, we have studied the disorder through systematic transcriptomic and proteomic analyses of skin fibroblasts biopsied from living patients. We performed comparative transcriptomic and proteomic profiling to characterize skin fibroblasts from schizophrenia patients compared to healthy controls. Transcriptomic profiling using cDNA array technology showed that pathways associated with cell cycle regulation and RNA processing were altered in the schizophrenia subjects (n = 12) relative to controls (n = 12). LC-MS(E) proteomic profiling led to identification of 16 proteins that showed significant differences in expression between schizophrenia (n = 11) and control (n = 11) subjects. Analysis in silico revealed that these proteins were also associated with proliferation and cell growth pathways. To validate these findings at the protein level, fibroblast protein extracts were analyzed by Western blotting which confirmed the differential expression of three key proteins associated with these pathways. At the functional level, we confirmed the decreased proliferation phenotype by showing that cultured fibroblasts from schizophrenia subjects (n = 5) incorporated less (3)H-thymidine into their nuclei compared to those from controls (n = 6) by day 4 over an 8 day time course study. Similar abnormalities in cell cycle and growth pathways have been reported to occur in the central nervous system in schizophrenia. These studies demonstrate that fibroblasts obtained from living schizophrenia subjects show alterations in cellular proliferation and growth pathways. Future studies aimed at characterizing such pathways in fibroblasts and other proliferating cell types from schizophrenia patients could elucidate the molecular mechanisms associated with the pathophysiology of schizophrenia and provide a useful model to support drug discovery efforts.

Quantification of proteins by label-free LC-MS/MS

Quantitative proteomic profiling is becoming a widely used approach in systems biology and biomarker discovery. There is a growing realization that quantitative studies require high numbers of non-pooled samples for increased statistical power. We present a descriptive protocol for label-free quantitation of proteins by LC-MS/MS that enables to obtain both quantitative and qualitative information in one study without the need to pool samples or label them.

Improvement of performance in label-free quantitative proteome analysis with monolithic electrospray ionization emitter

The postcolumn void volume, which is introduced by the connecting tubing and void ESI emitter in the nanoflow LC coupled with MS/MS system (microLC-MS/MS), is harmful for the analysis of peptides in shotgun proteome analysis. A new type of porous C12 monolithic ESI emitter was prepared to eliminate the disruption and mixing effects occurring in the connecting tubing and void emitter. It was demonstrated that the porous hydrophobic monolith inside the emitter played a key role in retaining the good peak profile, and the average peak capacity of the whole separation system increased 12.8% in contrast to commercially available void emitter. Then, the porous C12 monolithic emitter was applied in label-free quantitative proteome analysis of two standard protein mixtures that were spiked into the tryptic digest of mouse livers extract. Compared to commercially available void ESI emitter, the number of proteins with reliable results in quantification increased greatly. And the relative quantities of the four standard proteins were all determined with the relative error < or = 6.8%. However, quantitative information of only three standard proteins could be obtained when void emitter was used.