The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination

Proteomic profiling of human plasma and intervertebral disc tissue reveals matrisomal, but not plasma, biomarkers of disc degeneration

BACKGROUND

Intervertebral disc (IVD) degeneration is a common cause of low back pain, and the most symptomatic patients with neural compression need surgical intervention to relieve symptoms. Current techniques used to diagnose IVD degeneration, such as magnetic resonance imaging (MRI), do not detect changes in the tissue extracellular matrix (ECM) as degeneration progresses. Improved techniques, such as a combination of tissue and blood biomarkers, are needed to monitor the progression of IVD degeneration for more effective treatment plans.

METHODS

To identify tissue and blood biomarkers associated with degeneration progression, we histologically graded 35 adult human degenerate IVD tissues and matched plasma from the individuals into two groups: mild degenerate and severe degenerate. Mass spectrometry was utilised to characterise proteomic differences in tissue and plasma between the two groups. Top differentially distributed proteins were further validated using immunohistochemistry and qRT-PCR. Additionally, correlational analyses were conducted to define similarities and differences between tissue and plasma protein changes in individuals with mild and severe IVD degeneration.

RESULTS

Our data revealed that the abundance of 31 proteins was significantly increased in severe degenerated IVD tissues compared to mild. Functional analyses showed that more than 40% of these proteins were matrisome-related, indicating differences in ECM protein composition between severe and mild degenerate IVD tissues. We confirmed adipocyte enhancer-binding protein 1 (AEBP1) as one of the most significantly enriched core matrisome genes and proteins as degeneration progressed. Compared to others, AEBP1 protein levels best distinguished between mild and severe degenerated IVD tissues with an area under the curve score of 0.768 (95% CI: 0.60-0.93). However, we found that protein changes from associated plasma exhibited a weak relationship with histological grading and AEBP1 tissue levels. Given that systemic plasma changes are complex, a larger sample cohort may be required to identify patterns in blood relating to IVD degeneration progression.

CONCLUSIONS

In this study, we have identified AEBP1 as a tissue marker for monitoring the severity of disc degeneration in humans. Further work to link alterations in tissue AEBP1 levels to changes in blood-related proteins will be beneficial for detailed monitoring of IVD degeneration thereby enabling more personalised treatment approaches.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Proteomic Characterization of Plasma in Lemierre's Syndrome

BACKGROUND

 The underlying mechanisms of thrombosis in Lemierre's syndrome and other septic thrombophlebitis are incompletely understood. Therefore, in this case control study we aimed to generate hypotheses on its pathogenesis by studying the plasma proteome in patients with these conditions.

METHODS

 All patients with Lemierre's syndrome in the Skåne Region, Sweden, were enrolled prospectively during 2017 to 2021 as cases. Age-matched patients with other severe infections were enrolled as controls. Patient plasma samples were analyzed using label-free data-independent acquisition liquid chromatography tandem mass spectrometry. Differentially expressed proteins in Lemierre's syndrome versus other severe infections were highlighted. Functions of differentially expressed proteins were defined based on a literature search focused on previous associations with thrombosis.

RESULTS

 Eight patients with Lemierre's syndrome and 15 with other severe infections were compared. Here, 20/449 identified proteins were differentially expressed between the groups. Of these, 14/20 had functions previously associated with thrombosis. Twelve of 14 had a suggested prothrombotic effect in Lemierre's syndrome, whereas 2/14 had a suggested antithrombotic effect.

CONCLUSION

 Proteins involved in several thrombogenic pathways were differentially expressed in Lemierre's syndrome compared to other severe infections. Among identified proteins, several were associated with endothelial damage, platelet activation, and degranulation, and warrant further targeted studies.

Proteomic changes induced by longevity-promoting interventions in mice

Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.

SWATH-MS Analysis of Blood Plasma and Circulating Small Extracellular Vesicles Enables Detection of Putative Protein Biomarkers of Fertility in Young and Aged Dairy Cows

The development of biomarkers of fertility could provide benefits for the genetic improvement of dairy cows. Circulating small extracellular vesicles (sEVs) show promise as diagnostic or prognostic markers since their cargo reflects the metabolic state of the cell of origin; thus, they mirror the physiological status of the host. Here, we employed data-independent acquisition mass spectrometry to survey the plasma and plasma sEV proteomes of two different cohorts of Young (Peripubertal; n = 30) and Aged (Primiparous; n = 20) dairy cows (Bos taurus) of high- and low-genetic merit of fertility and known pregnancy outcomes (ProteomeXchange data set identifier PXD042891). We established predictive models of fertility status with an area under the curve of 0.97 (sEV; p value = 3.302e-07) and 0.95 (plasma; p value = 6.405e-08). Biomarker candidates unique to high-fertility Young cattle had a sensitivity of 0.77 and specificity of 0.67 (*p = 0.0287). Low-fertility biomarker candidates uniquely identified in sEVs from Young and Aged cattle had a sensitivity and specificity of 0.69 and 1.0, respectively (***p = 0.0005). Our bioinformatics pipeline enabled quantification of plasma and circulating sEV proteins associated with fertility phenotype. Further investigations are warranted to validate this research in a larger population, which may lead to improved classification of fertility status in cattle.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry

BACKGROUND

Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.

METHODS

Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.

RESULTS

The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.

CONCLUSIONS

The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

A Novel Blood Proteomic Signature for Prostate Cancer

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

Rationale, Design, and the Baseline Characteristics of the RHDGen (The Genetics of Rheumatic Heart Disease) Network Study†

BACKGROUND

The genetics of rheumatic heart disease (RHDGen) Network was developed to assist the discovery and validation of genetic variations and biomarkers of risk for rheumatic heart disease (RHD) in continental Africans, as a part of the global fight to control and eradicate rheumatic fever/RHD. Thus, we describe the rationale and design of the RHDGen study, comprising participants from 8 African countries.

METHODS

RHDGen screened potential participants using echocardiography, thereafter enrolling RHD cases and ethnically-matched controls for whom case characteristics were documented. Biological samples were collected for conducting genetic analyses, including a discovery case-control genome-wide association study (GWAS) and a replication trio family study. Additional biological samples were also collected, and processed, for the measurement of biomarker analytes and the biomarker analyses are underway.

RESULTS

Participants were enrolled into RHDGen between December 2012 and March 2018. For GWAS, 2548 RHD cases and 2261 controls (3301 women [69%]; mean age [SD], 37 [16.3] years) were available. RHD cases were predominantly Black (66%), Admixed (24%), and other ethnicities (10%). Among RHD cases, 34% were asymptomatic, 26% had prior valve surgery, and 23% had atrial fibrillation. The trio family replication arm included 116 RHD trio probands and 232 parents.

CONCLUSIONS

RHDGen presents a rare opportunity to identify relevant patterns of genetic factors and biomarkers in Africans that may be associated with differential RHD risk. Furthermore, the RHDGen Network provides a platform for further work on fully elucidating the causes and mechanisms associated with RHD susceptibility and development.

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.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Data independent acquisition (DIA) has been gaining momentum in clinical proteomics. Here, the authors create a benchmark dataset comprising inter-patient heterogeneity to compare popular DIA data analysis workflows for identifying differentially abundant proteins.

Changes in the Proteome Profile of People Achieving Remission of Type 2 Diabetes after Bariatric Surgery

Bariatric surgery (BS) results in metabolic pathway recalibration. We have identified potential biomarkers in plasma of people achieving type 2 diabetes mellitus (T2DM) remission after BS. Longitudinal analysis was performed on plasma from 10 individuals following Roux-en-Y gastric bypass (n = 7) or sleeve gastrectomy (n = 3). Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was done on samples taken at 4 months before (baseline) and 6 and 12 months after BS. Four hundred sixty-seven proteins were quantified by SWATH-MS. Principal component analysis resolved samples from distinct time points after selection of key discriminatory proteins: 25 proteins were differentially expressed between baseline and 6 months post-surgery; 39 proteins between baseline and 12 months. Eight proteins (SHBG, TF, PRG4, APOA4, LRG1, HSPA4, EPHX2 and PGLYRP) were significantly different to baseline at both 6 and 12 months post-surgery. The panel of proteins identified as consistently different included peptides related to insulin sensitivity (SHBG increase), systemic inflammation (TF and HSPA4-both decreased) and lipid metabolism (APOA4 decreased). We found significant changes in the proteome for eight proteins at 6- and 12-months post-BS, and several of these are key components in metabolic and inflammatory pathways. These may represent potential biomarkers of remission of T2DM.

DIMA: Data-Driven Selection of an Imputation Algorithm

Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, it is difficult to assess the performance of different imputation methods and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of an imputation algorithm (DIMA). The performance and broad applicability of DIMA are demonstrated on 142 quantitative proteomics data sets from the PRoteomics IDEntifications (PRIDE) database and on simulated data consisting of 5-50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm, which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 80% of the cases. DIMA implementation is available in MATLAB at github.com/kreutz-lab/OmicsData and in R at github.com/kreutz-lab/DIMAR.

Insights Into the Proteomic Profiling of Extracellular Vesicles for the Identification of Early Biomarkers of Neurodegeneration

Extracellular vesicles (EVs) are involved in the development and progression of neurodegenerative diseases, including Alzheimer's and Parkinson's disease. Moreover, EVs have the capacity to modify the physiology of neuronal circuits by transferring proteins, RNA, lipids, and metabolites. The proteomic characterization of EVs (exosomes and microvesicles) from preclinical models and patient samples has the potential to reveal new proteins and molecular networks that affect the normal physiology prior to the appearance of traditional biomarkers of neurodegeneration. Noteworthy, many of the genetic risks associated to the development of Alzheimer's and Parkinson's disease affect the crosstalk between mitochondria, endosomes, and lysosomes. Recent research has focused on determining the role of endolysosomal trafficking in the onset of neurodegenerative diseases. Proteomic studies indicate an alteration of biogenesis and molecular content of EVs as a result of endolysosomal and autophagic dysfunction. In this review, we discuss the status of EV proteomic characterization and their usefulness in discovering new biomarkers for the differential diagnosis of neurodegenerative diseases. Despite the challenges related to the failure to follow a standard isolation protocol and their implementation for a clinical setting, the analysis of EV proteomes has revealed the presence of key proteins with post-translational modifications that can be measured in peripheral fluids.

Caspase-3 Cleaves Extracellular Vesicle Proteins During Auditory Brainstem Development

Sound localization requires extremely precise development of auditory brainstem circuits, the molecular mechanisms of which are largely unknown. We previously demonstrated a novel requirement for non-apoptotic activity of the protease caspase-3 in chick auditory brainstem development. Here, we used mass spectrometry to identify proteolytic substrates of caspase-3 during chick auditory brainstem development. These auditory brainstem caspase-3 substrates were enriched for proteins previously shown to be cleaved by caspase-3, especially in non-apoptotic contexts. Functional annotation analysis revealed that our caspase-3 substrates were also enriched for proteins associated with several protein categories, including proteins found in extracellular vesicles (EVs), membrane-bound nanoparticles that function in intercellular communication. The proteome of EVs isolated from the auditory brainstem was highly enriched for our caspase-3 substrates. Additionally, we identified two caspase-3 substrates with known functions in axon guidance, namely Neural Cell Adhesion Molecule (NCAM) and Neuronal-glial Cell Adhesion Molecule (Ng-CAM), that were found in auditory brainstem EVs and expressed in the auditory pathway alongside cleaved caspase-3. Taken together, these data suggest a novel developmental mechanism whereby caspase-3 influences auditory brainstem circuit formation through the proteolytic cleavage of extracellular vesicle (EV) proteins.

PulseDIA: Data-Independent Acquisition Mass Spectrometry Using Multi-Injection Pulsed Gas-Phase Fractionation

The performance of data-independent acquisition (DIA) mass spectrometry (MS) depends on the separation efficiency of peptide precursors. In Orbitrap-based mass spectrometers, separation efficiency of peptide precursors is limited by the relatively slow scanning rate compared to time of flight (TOF)-based MS. Here, we present PulseDIA, a multi-injection gas-phase fractionation (GPF) strategy for enhanced DIA-MS. This is achieved by equally dividing the conventional DIA analysis covering the entire mass range into multiple injections for DIA analyses with complementary windows. Using mouse liver digests, the PulseDIA method identified up to 50% more peptides and 29% more protein groups than that by conventional DIA with the same length of effective gradient time. Compared to conventional multi-injection GPF, PusleDIA exhibited higher flexibility and identified up to 18% more peptides and 8% more protein groups using two injections. The gain of peptides per effective time unit was the highest in PulseDIA compared to conventional DIA and GPF. We further applied the PulseDIA method to profile the proteome of 18 human tissue samples (benign and malignant) from nine cholangiocarcinoma (CCA) patients. PulseDIA identified 7796 protein groups in these CCA samples, with a 14% increase of protein group identification compared to the conventional DIA method. The missing value for protein matrix dropped by 7% using PulseDIA compared to DIA. A total of 681 significantly altered proteins were detected in CCA samples using PulseDIA, including several dysregulated proteins, which were absent in the conventional DIA analysis. Taken together, we present PulseDIA as an enhanced DIA-MS method with improved sensitivity and reproducibility.