Clinical Proteomics Studies Research Articles

What Are the Reliable Plasma Biomarkers for Mild Cognitive Impairment? A Clinical 4D Proteomics Study and Validation.

At present, Alzheimer's disease (AD) lacks effective treatment means, and early diagnosis and intervention are the keys to treatment. Therefore, for mild cognitive impairment (MCI) and AD patients, blood sample analysis using the 4D nonstandard (label-free) proteomic in-depth quantitative analysis, looking for specific protein marker expression differences, is important. These marker levels change as AD progresses, and the analysis of these biomarkers changes with this method, which has the potential to show the degree of disease progression and can be used for the diagnosis and preventive treatment of MCI and AD. Patients were recruited according to the inclusion and exclusion criteria and divided into three groups according to scale scores. Elderly patients diagnosed with AD were selected as the AD group (n = 9). Patients diagnosed with MCI were classified into the MCI group (n = 10). Cognitively healthy elderly patients were included in the normal cognition control group (n = 10). Patients' blood samples were used for 4D label-free proteomic in-depth quantitative analysis to identify potential blood biomarkers. The sample size of each group was expanded (n = 30), and the selected biomarkers were verified by enzyme-linked immunosorbent assay (ELISA) to verify the accuracy of the proteomic prediction. Six specific blood markers, namely, APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8, were detected by 4D label-free proteomic quantitative analysis. These markers showed a statistically significant upregulation trend in the MCI and AD groups compared with the normal cognition control group (P < 0.05). ELISA results showed that the levels of these six proteins in the MCI group were significantly higher than those in the normal cognition control group, and the levels of these six proteins in the AD group were significantly higher than those in the MCI group (P < 0.05). The plasma levels of APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8 in cognitively healthy elderly patients and patients with MCI and AD were significantly different and, more importantly, showed a trend of increasing expression. These results indicate that these six human plasma markers have important diagnostic and therapeutic potential in the identification of cognitive impairment and have value for in-depth research and clinical application.

Technical report: The clinically useful selection of proteins protocol: An approach to identify clinically useful proteins for validation

Clinical proteomics studies aiming to develop markers of clinical outcome or disease typically involve distinct discovery and validation stages, neither of which focus on the clinical applicability of the candidate markers studied. Our clinically useful selection of proteins (CUSP) protocol proposes a rational approach, with statistical and non-statistical components, to identify proteins for the validation phase of studies that could be most effective markers of disease or clinical outcome. Additionally, this protocol considers commercially available analysis methods for each selected protein to ensure that use of this prospective marker is easily translated into clinical practice. SignificanceWhen developing proteomic markers of clinical outcomes, there is currently no consideration at the validation stage of how to implement such markers into a clinical setting. This has been identified by several studies as a limitation to the progression of research findings from proteomics studies. When integrated into a proteomic workflow, the CUSP protocol allows for a strategically designed validation study that improves researchers' abilities to translate research findings from discovery-based proteomics into clinical practice.

Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications.

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4-12 with the CHIMERYS search engine identified +51-74% and +59-150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs <7% for low input bulk samples, and accuracy, with deviations <10% from expected fold changes for regular abundance two-proteome mixes. Compared to a conventional workflow, our entire optimized platform discovered 92% more potential interactors in a protein-protein interaction study on the chromatin remodeler Smarca5/Snf2h. These include previously described Smarca5 binding partners and undescribed ones including Arid1a, another chromatin remodeler with key roles in neurodevelopmental and malignant disorders.

A clinical proteomics study of exhaled breath condensate and biomarkers for pulmonary embolism

Pulmonary embolism (PE) can be a diagnostic challenge. Current diagnostic markers for PE are unspecific and new diagnostic tools are needed. The air we exhale is a possible new source for biomarkers which can be tapped into by analysing the exhaled breath condensate (EBC). We analysed the EBC from patients with PE and controls to investigate if the EBC is a useful source for new diagnostic biomarkers of PE. We collected and analysed EBC samples from patients with suspected PE and controls matched on age and sex. Patients in whom PE was ruled out after diagnostic work-up were included in the control group to increase the sensitivity and generalizability of the identified markers. EBC samples were collected using an RTube™. The protein composition of the EBCs were analysed using data dependent label-free quantitative nano liquid chromatography–tandem mass spectrometry. EBC samples from 28 patients with confirmed PE, and 49 controls were analysed. A total of 928 EBC proteins were identified in the 77 EBC samples. As expected, a low protein concentration was determined which resulted in many proteins with unmeasurable levels in several samples. The levels of HSPA5, PEBP1 and SFTPA2 were higher and levels of POF1B, EPPK1, PSMA4, ALDOA, and CFL1 were lower in PE compared with controls. In conclusion, the human EBC contained a variety of endogenous proteins and may be a source for new diagnostic markers of PE and other diseases.

Serum protein biomarkers for HCC risk prediction in HIV/HBV co-infected people: a clinical proteomic study using mass spectrometry.

HBV coinfection is frequent in people living with HIV (PLWH) and is the leading cause of hepatocellular carcinoma (HCC). While risk prediction methods for HCC in patients with HBV monoinfection have been proposed, suitable biomarkers for early diagnosis of HCC in PLWH remain uncommon. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine serum protein alterations in HCC and non-HCC patients with HIV and HBV co-infection. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis were performed on the differentially expressed proteins (DEPs). The risk prediction model was created using five-cross-validation and LASSO regression to filter core DEPs. A total of 124 DEPs were discovered, with 95 proteins up-regulated and 29 proteins down-regulated. Extracellular matrix organization and membrane component were the DEPs that were most abundant in the categories of biological processes (BP) and cellular components (CC). Proteoglycans in cancer were one of the top three DEPs primarily enriched in the KEGG pathway, and 60.0% of DEPs were linked to various neoplasms in terms of DO enrichment. Eleven proteins, including GAPR1, PLTP, CLASP2, IGHV1-69D, IGLV5-45, A2M, VNN1, KLK11, ANPEP, DPP4 and HYI, were chosen as the core DEPs, and a nomogram was created to predict HCC risk. In HIV/HBV patients with HCC, several differential proteins can be detected in plasma by mass spectrometry, which can be used as screening markers for early diagnosis and risk prediction of HCC. Monitoring protease expression differences can help in the diagnosis and prognosis of HCC.

Advances of peptide-centric data-independent acquisition analysis algorithms and software tools

Data-independent acquisition (DIA) is a high-throughput, unbiased mass spectrometry data acquisition method which has good quantitative reproducibility and is friendly to low-abundance proteins. It becomes the preferred choice for clinical proteomic studies especially for large cohort studies in recent years. The mass-spectrometry (MS)/MS spectra generated by DIA is usually heavily mixed with fragment ion information of multiple peptides, which makes the protein identification and quantification more difficult. Currently, DIA data analysis methods fall into two main categories, namely peptide-centric and spectrum-centric. The peptide-centric strategy is more sensitive for identification and more accurate for quantification. Thus, it has become the mainstream strategy for DIA data analysis, which includes four key steps: building a spectral library, extracting ion chromatogram, feature scoring and statistical quality control. This work reviews the peptide-centric DIA data analysis procedure, introduces the corresponding algorithms and software tools, and summarizes the improvements for the existing algorithms. Finally, the future development directions are discussed.

Advanced Multivariable Statistical Analysis Interactive Tool for Handling Missing Data and Confounding Covariates for Label-free LC-MS Proteomics Experiments

Background: Careful consideration is required for detecting significant features (proteins or peptides) in LC-MS proteomics studies using multivariable regression analyses. In proteomics data, missing values can arise due to random errors, bad samples, features below the detection limit in specific samples, etc. Further, expression data are always prone to heterogeneity due to technical/biological reasons. Missing values and heterogeneity in proteomics studies can confound important findings. Moreover, there is additional information in these studies, such as pre-clinical and clinical information (e.g., sex, exposure, etc.), which can be used to supplement the inference. Methods: We introduce a user-friendly web application SATP (Statistical Analysis interactive Tool for label-free LC-MS Proteomics experiments) for differential expression analysis of proteomics data that is scalable to large clinical proteomic studies. Appropriate normalization and imputation methods have been provided. Apart from these, several statistical tests such as t-test, moderated t-test, linear fixed effect model, and linear mixed model with adjustment of effect of extra covariates have also been provided for users' benefit. Results: Our intuitive tool has several advantages over the existing ones, including an extension to multiple factor comparisons after adjusting for covariates. Conclusion: This is a comprehensive tool for analysis of complex experiments with multiple covariates, whereas most of the existing tools were developed for comparing simple experiments mostly with two groups without covariates. Availability: The tool can be accessed freely by the users from https://ulbbf.shinyapps.io/satp/.

Synthesis of magnetic NaY zeolite for plasma proteomics application

Magnetically modified NaY zeolite has shown advantages in high-throughput and large-scale clinical plasma proteomic studies. In this study, magnetic NaY zeolite was prepared via an improved method, "layer by layer adhesion", using epoxy adhesive. The morphology, structure, and magnetic properties of magnetic NaY zeolite were characterized and tested by scanning electron microscope (SEM), fourier transform infrared spectrometer (FT-IR), X-ray diffractometer (XRD), and vibrating sample magnetometer (VSM), and using sodium dodecyl sulfonate-polyacrylamide gelelectrophoresis (SDS-PAGE) and liquid chromatography-tandem mass spectrometry (LC-MS) for plasma proteomics studies. The results showed that NaY zeolite was coated on the surface of Fe3O4 by epoxy adhesive, and such coating did not affect the surface properties of the magnetic zeolite. The saturation magnetization of magnetic NaY zeolite was measured as 9.964 emu g−1. Approximately 2929 proteins could be identified from plasma protein corona formed on magnetic NaY zeolite, reaching 97.6 % of the identification number of pure NaY zeolite. Furthermore, compared with NaY zeolite, the solid-liquid separation processes of magnetic zeolite during plasma proteomics studies were significantly reduced from half an hour to few minutes. Due to excellent plasma protein adsorption performance and rapid magnetic separation property, magnetic NaY zeolite is expected to become a powerful tool for the large-scale adsorption of low-abundant plasma proteins and high-throughput proteomic studies.

The development of the pediatric stroke neuroimaging platform (PEDSNIP)

Childhood stroke occurs from birth to 18 years of age, ranks among the top ten childhood causes of death, and leaves lifelong neurological impairments. Arterial ischemic stroke in infancy and childhood occurs due to arterial occlusion in the brain, resulting in a focal lesion. Our understanding of mechanisms of injury and repair associated with focal injury in the developing brain remains rudimentary. Neuroimaging can reveal important insights into these mechanisms. In adult stroke population, multi-center neuroimaging studies are common and have accelerated the translation process leading to improvements in treatment and outcome. These studies are centered on the growing evidence that neuroimaging measures and other biomarkers (e.g., from blood and cerebrospinal fluid) can enhance our understanding of mechanisms of risk and injury and be used as complementary outcome markers. These factors have yet to be studied in pediatric stroke because most neuroimaging studies in this population have been conducted in single-centred, small cohorts. By pooling neuroimaging data across multiple sites, larger cohorts of patients can significantly boost study feasibility and power in elucidating mechanisms of brain injury, repair and outcomes. These aims are particularly relevant in pediatric stroke because of the decreased incidence rates and the lack of mechanism-targeted trials.Toward these aims, we developed the Pediatric Stroke Neuroimaging Platform (PEDSNIP) in 2015, funded by The Brain Canada Platform Support Grant, to focus on three identified neuroimaging priorities. These were: developing and harmonizing multisite clinical protocols, creating the infrastructure and methods to import, store and organize the large clinical neuroimaging dataset from multiple sites through the International Pediatric Stroke Study (IPSS), and enabling central searchability. To do this, developed a two-pronged approach that included building 1) A Clinical-MRI Data Repository (standard of care imaging) linked to clinical data and longitudinal outcomes and 2) A Research-MRI neuroimaging data set acquired through our extensive collaborative, multi-center, multidisciplinary network. This dataset was collected prospectively in eight North American centers to test the feasibility and implementation of harmonized advanced Research-MRI, with the addition of clinical information, genetic and proteomic studies, in a cohort of children presenting with acute ischemic stroke.Here we describe the process that enabled the development of PEDSNIP built to provide the infrastructure to support neuroimaging research priorities in pediatric stroke. Having built this Platform, we are now able to utilize the largest neuroimaging and clinical data pool on pediatric stroke data worldwide to conduct hypothesis-driven research. We are actively working on a bioinformatics approach to develop predictive models of risk, injury and repair and accelerate breakthrough discoveries leading to mechanism-targeted treatments that improve outcomes and minimize the burden following childhood stroke. This unique transformational resource for scientists and researchers has the potential to result in a paradigm shift in the management, outcomes and quality of life in children with stroke and their families, with far-reaching benefits for other brain conditions of people across the lifespan.

Clinical Proteomics: Liquid Chromatography-Mass Spectrometry (LC-MS) Purification Systems.

Liquid chromatography/mass spectrometry (LC/MS) has become a routine powerful technology in clinical proteomic studies for protein identification, protein characterization, and the discovery of biomarkers. In this chapter, we describe two protocol methods to analyze clinical patient samples using a resin-based depletion column followed by either protein In-Gel enzymatic digestion or protein In-Solution enzymatic digestion using a simple kit-based approach (i.e., using the PreOmics iST sample preparation kit), followed by analysis using one-dimensional reverse-phase chromatography (RPC) or high pH reversed-phase peptide fractionation.

Identifying individuals using proteomics: are we there yet?

Multi-omics approaches including proteomics analyses are becoming an integral component of precision medicine. As clinical proteomics studies gain momentum and their sensitivity increases, research on identifying individuals based on their proteomics data is here examined for risks and ethics-related issues. A great deal of work has already been done on this topic for DNA/RNA sequencing data, but it has yet to be widely studied in other omics fields. The current state-of-the-art for the identification of individuals based solely on proteomics data is explained. Protein sequence variation analysis approaches are covered in more detail, including the available analysis workflows and their limitations. We also outline some previous forensic and omics proteomics studies that are relevant for the identification of individuals. Following that, we discuss the risks of patient reidentification using other proteomics data types such as protein expression abundance and post-translational modification (PTM) profiles. In light of the potential identification of individuals through proteomics data, possible legal and ethical implications are becoming increasingly important in the field.

Application of smart responsive materials in phosphopeptide and glycopeptide enrichment

蛋白质的磷酸化和糖基化作为研究最广泛的两种翻译后修饰(PTMs),在疾病的早期无创诊断、预后和治疗评估中表现出越来越大的潜力。蛋白质的异常磷酸化和糖基化经常被用于临床蛋白质组学研究和疾病相关生物标志物的发现。目前已有多种材料被开发用于磷酸化肽和糖肽的富集研究,其中,智能响应材料由于具有独特的响应特性,已被陆续报道用于磷酸化肽和糖肽的富集。智能响应材料可对外界刺激做出响应,发生结构和性质上的变化,将光、电、热、机械等信号转化为生物化学信号。响应分子是决定智能响应材料响应特性的先决条件,它们在不同刺激条件下(如温度、pH、光、机械应力、电磁场等)的可逆异构化将导致材料的宏观物理和化学性质的动态变化。与传统材料相比,智能响应材料可以可逆地“打开”和“关闭”,具有更好的可调控性。由于引起智能材料响应的刺激信号对其性能具有重要的影响,综述根据施加的刺激种类对智能响应材料进行分类,具体分为外源性响应材料和内源性响应材料,且分别总结了外源性响应材料、内源性响应材料以及内外源共同响应材料在磷酸化肽和糖肽富集方面的工作。此外,综述对智能响应材料在磷酸化肽和糖肽富集方面的发展前景进行了展望,并且提出了智能响应材料在其他蛋白质翻译后修饰方面的应用中存在的挑战。

Altered Bioavailability and Pharmacokinetics in Crohn's Disease: Capturing Systems Parameters for PBPK to Assist with Predicting the Fate of Orally Administered Drugs.

Backgrond and ObjectiveCrohn’s disease (CD) is a chronic inflammatory bowel disease that affects a wide age range. Hence, CD patients receive a variety of drugs over their life beyond those used for CD itself. The changes to the integrity of the intestine and its drug metabolising enzymes and transporters (DMETs) can alter the oral bioavailability of drugs. However, there are other changes in systems parameters determining the fate of drugs in CD, and understanding these is essential for dose adjustment in patients with CD.MethodsThe current analysis gathered all the available clinical data on the kinetics of drugs in CD (by March 2021), focusing on orally administered small molecule drugs. A meta-analysis of the systems parameters affecting oral drug pharmacokinetics was conducted. The systems information gathered on intestine, liver and blood proteins and other physiological parameters was incorporated into a physiologically based pharmacokinetic (PBPK) platform to create a virtual population of CD patients, with a view for guiding dose adjustment in the absence of clinical data in CD.ResultsThere were no uniform trends in the reported changes in reported oral bioavailability. The nature of the drug as well as the formulation affected the direction and magnitude of variation in kinetics in CD patients relative to healthy volunteers. Even for the same drug, the reported changes in exposure varied, possibly due to a lack of distinction between the activity states of CD. The highest alteration was seen with S-verapamil and midazolam, 8.7- and 5.3-fold greater exposure, respectively, in active CD patients relative to healthy volunteers. Only one report was available on liver DMETs in CD, and indicated reduced CYP3A4 activity. In a number of reports, mRNA expression of DMETs in the ileum and colon of CD patients was measured, focussing on P-glycoprotein (p-gp) transporter and CYP3A4 enzyme, and showed contradictory results. No data were available on protein expression in duodenum and jejunum despite their dominant role in oral drug absorption.ConclusionThere are currently inadequate dedicated clinical or quantitative proteomic studies in CD to enable predictive PBPK models with high confidence and adequate verification. The PBPK models for CD with the available systems parameters were able to capture the major physiological influencers and the gaps to be filled by future research. Quantification of DMETs in the intestine and the liver in CD is warranted, alongside well-defined clinical drug disposition studies with a number of index drugs as biomarkers of changes in DMETs in these patients, to avoid large-scale dedicated studies for every drug to determine the effects of disease on the drug’s metabolism and disposition and the consequential safety and therapeutic concerns.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40262-022-01169-4.

IProMix: A Mixture Model for Studying the Function of ACE2 based on Bulk Proteogenomic Data

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and uses a nonparametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in nonasymptotic settings. We apply iProMix to the proteomic data of 110 (tumor-adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies. Supplementary materials for this article are available online.

Differences in clinical characteristics and lesion proteomics between inflammatory linear verrucous epidermal nevus and local verrucous epidermal nevus

BackgroundVerrucous epidermal nevus (VEN) are keratinocytic epidermal nevus that appear at birth or in early childhood. They exhibit a range of manifestations, depending on the patient's age. VEN are rarely encountered in clinical practice, and the systemic and comprehensive clinical characteristics of VEN have not been well investigated. Furthermore, the association between tandem mass tag (TMT)-based quantitative proteomics and the VEN phenotype is still unclear. ObjectivesThis study investigated the differences in the clinical characteristics and lesion proteomics between inflammatory linear VEN (ILVEN) and local VEN. MethodsThis retrospective study enrolled 125 patients with histopathologically diagnosed VEN who presented to our hospital between 2019 and 2021. We collected the clinical data of all patients with VEN using a self-designed questionnaire. The expression of proteins in VEN lesions was analyzed using TMT proteomics technology. ResultsIn total, there were 125 patients with VEN that were evaluated, including 67 (53.60%) patients with local VEN and 58 (46.40%) with ILVEN. No significant differences were found in sex, onset age, and lesion location between patients with local VEN and those with ILVEN (all P > 0.05). Significant differences were found in the onset site and pruritus scores between patients with ILVEN and those with local VEN (all P < 0.05). According to the TMT proteomics results, 89 proteins were up or downregulated with at least 1.3-fold (upregulated: 38, downregulated: 51; P < 0.05) in ILVEN lesions relative to VEN lesions. The top 10 differentially expressed proteins between ILVEN and local VEN lesions were OGN, NT5C3A, ADD1, OLFML1, DHRS1, CALML5, SAMHD1, SFRP2, SPRR1B, and SERPINB13. The upregulated proteins are mainly involved in neutrophil activation, neutrophil-mediated immunity, and p53 signaling pathway (hsa04115). The downregulated proteins are mainly involved in cellular response to cytokine stimulus, cell adhesion, Th1 and Th2 cell differentiation. In total, based on the differentially expressed proteins between ILVEN and local VEN, five pathways that may be associated with the pathogenesis of inflammation, including CAMs (P = 0.006), Th1 and Th2 cell differentiation (P = 0.017), PPAR signaling pathway (P = 0.023), Th17 cell differentiation (P = 0.024), and p53 signaling pathway (P = 0.041). ConclusionsClinical data of the patients revealed that ILVEN lesions presented with intense pruritus and inflammatory change. Differentially expressed proteins between ILVEN and local VEN are mainly involved in multiple inflammation related pathways associated with the pathogenesis mechanisms of pruritus. LimitationsThe small sample size in clinical characteristic and proteomics study is one of the most significant limitations in our study. The inflammation associated proteins and signal pathways in the pathogenesis of pruritus in ILVEN is not explored. SignificanceIn this study, we found the lesions of ILVEN patients presented with intense pruritus and inflammational change. A total of 89 proteins were up or downregulated with at least 1.3-fold (upregulated: 38, downregulated: 51; P < 0.05) in ILVEN lesions relative to VEN lesions. On the other hand, the etiology of itch in ILVEN mainly associated with inflammation, but the exact mechanisms was still unclear. We found the differentially expressed proteins between ILVEN and local VEN enriched five pathways that may be associated with the pathogenesis of inflammation and pruritus.

Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies—A TMT-Based LC-MSMS Proteome Investigation

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.

A Refined Framework for Precision and Translational Proteomics in Clinical Research

Most clinical related proteomic studies were aimed to screen potential biomarkers for clinical usage. However, it is known that only a few candidates can be validated in later stages. Besides clinical biomarkers, it is also necessary to identify key proteins to better understand the molecular mechanism of human diseases. Here, we updated the connotations of precision and translational proteomics and proposed a refined research framework for clinical proteomics. We abstracted the framework as a three-tier system: to determine research objectives and approaches for achieving a certain purpose, to choose the appropriate methods for optimal results, and to apply a standardized experimental procedure for reliable and reproducible results. Started from the initial discovery stage, we divided the following proteomics-driven translational studies into two directions: biomarker screening and mechanism investigation. We also discussed possible causes that may lead to high rates of experimental variation and low rates of follow-up validation. Moreover, we pointed out that the main bottlenecks for the identification of effective targets are data interpretation and statistical inference. Finally, we emphasized that the transparency of experimental design and results can help to ensure data quality. Conclusively, the refined framework provides constructive recommendations and comprehensive guides for designing and performing clinical related proteomic studies with the aim of obtaining reliable and reproducible results. It can also help to further promote the standardization and integration of proteomic studies.

Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass.

seaMass is an R package for protein-level quantification, normalization, and differential expression analysis of proteomics mass spectrometry data after peptide identification, protein grouping, and feature-level quantification. Using the concept of a blocked experimental design, seaMass can analyze all common discovery proteomics paradigms, including label-free (e.g., Waters Progenesis input), SILAC (e.g., MaxQuant input), isotope labelling (e.g., SCIEX ProteinPilot iTraq and Thermo ProteomeDiscoverer TMT input), and data-independent acquisition (e.g., OpenSWATH-PyProphet input), and is able to scale to study with hundreds of assays or more. By utilizing hierarchical Bayesian modelling, seaMass assesses the quantification reliability of each feature and peptide across assays so that only those in consensus influence the resulting protein group quantification strongly. Similarly, unexplained variation in each individual assay is captured, providing both a metric for quality control and automatic down-weighting of suspect assays. To achieve this, each protein group-level quantification outputted by seaMass is accompanied by the standard deviation of its posterior uncertainty. Moreover, seaMass integrates a flexible differential expression analysis subsystem with false discovery rate control based on the popular MCMCglmm package for Bayesian mixed-effects modelling, and also provides uncertainty-aware principal components analysis. We provide a description for using seaMass to perform an end-to-end analysis using a real dataset associated with a published clinical proteomics study.

Assessing technical and biological variation in SWATH-MS-based proteomic analysis of chronic lymphocytic leukaemia cells

Chronic lymphocytic leukaemia (CLL) exhibits variable clinical course and response to therapy, but the molecular basis of this variability remains incompletely understood. Data independent acquisition (DIA)-MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. Here, a CLL-specific spectral library (7736 proteins) is described alongside an analysis of sample replication and data handling requirements for quantitative SWATH-MS analysis of clinical samples. The analysis was performed on 6 CLL samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins across 54 SWATH-MS acquisitions: the sources of variation and different computational approaches for batch correction were assessed. Functional enrichment analysis of proteins associated with IGHV mutational status showed significant overlap with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was implemented. This study provides a valuable resource for researchers working on the proteomics of CLL. It also establishes a sound framework for the design of sufficiently powered clinical proteomics studies. Indeed, this study shows that it is possible to derive biologically plausible hypotheses from a relatively small dataset.

Data Management of Sensitive Human Proteomics Data: Current Practices, Recommendations, and Perspectives for the Future

Today it is the norm that all relevant proteomics data that support the conclusions in scientific publications are made available in public proteomics data repositories. However, given the increase in the number of clinical proteomics studies, an important emerging topic is the management and dissemination of clinical, and thus potentially sensitive, human proteomics data. Both in the United States and in the European Union, there are legal frameworks protecting the privacy of individuals. Implementing privacy standards for publicly released research data in genomics and transcriptomics has led to processes to control who may access the data, so-called “controlled access” data. In parallel with the technological developments in the field, it is clear that the privacy risks of sharing proteomics data need to be properly assessed and managed. In our view, the proteomics community must be proactive in addressing these issues. Yet a careful balance must be kept. On the one hand, neglecting to address the potential of identifiability in human proteomics data could lead to reputational damage of the field, while on the other hand, erecting barriers to open access to clinical proteomics data will inevitably reduce reuse of proteomics data and could substantially delay critical discoveries in biomedical research. In order to balance these apparently conflicting requirements for data privacy and efficient use and reuse of research efforts through the sharing of clinical proteomics data, development efforts will be needed at different levels including bioinformatics infrastructure, policymaking, and mechanisms of oversight.