An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients.

OBJECTIVES: Minimal residual disease (MRD) status in multiple myeloma (MM) is an important prognostic biomarker. Personalized blood-based targeted mass spectrometry detecting M-proteins (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to MRD-assessment in bone marrow. However, MS-MRD still comprises of manual steps that hamper upscaling of MS-MRD testing. Here, we introduce a proof-of-concept for a novel workflow using data independent acquisition-parallel accumulation and serial fragmentation (dia-PASEF) and automated data processing. METHODS: Using automated data processing of dia-PASEF measurements, we developed a workflow that identified unique targets from MM patient sera and personalized protein sequence databases. We generated patient-specific libraries linked to dia-PASEF methods and subsequently quantitated and reported M-protein concentrations in MM patient follow-up samples. Assay performance of parallel reaction monitoring (prm)-PASEF and dia-PASEF workflows were compared and we tested mixing patient intake sera for multiplexed target selection. RESULTS: No significant differences were observed in lowest detectable concentration, linearity, and slope coefficient when comparing prm-PASEF and dia-PASEF measurements of serial dilutions of patient sera. To improve assay development times, we tested multiplexing patient intake sera for target selection which resulted in the selection of identical clonotypic peptides for both simplex and multiplex dia-PASEF. Furthermore, assay development times improved up to 25× when measuring multiplexed samples for peptide selection compared to simplex. CONCLUSIONS: Dia-PASEF technology combined with automated data processing and multiplexed target selection facilitated the development of a faster MS-MRD workflow which benefits upscaling and is an important step towards the clinical implementation of MS-MRD.

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow.

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

N-linked glycosylation of the M-protein variable region: glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma.

OBJECTIVES: Multiple myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged suggesting that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity to provide a site-specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. METHODS: Glycoproteogenomics was used for the detailed analysis of de novo N-glycosylation sites of M-proteins. First, Genomic analysis of the M-protein variable region was used to identify de novo N-glycosylation sites. Subsequently glycopeptide analysis with LC-MS/MS was used for detailed analysis of the M-protein glycan sites. RESULTS: Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to Fab Light Chain N-glycosylation of polyclonal antibodies from healthy individuals. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the Fab N-glycosylation sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. CONCLUSIONS: Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias.

A multi-omics data analysis workflow packaged as a FAIR Digital Object.

BACKGROUND: Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS: We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS: Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.

M-protein diagnostics in multiple myeloma patients using ultra-sensitive targeted mass spectrometry and an off-the-shelf calibrator.

OBJECTIVES: Minimal residual disease status in multiple myeloma is an important prognostic biomarker. Recently, personalized blood-based targeted mass spectrometry (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to measure minimal residual disease. However, quantification of MS-MRD requires a unique calibrator for each patient. The use of patient-specific stable isotope labelled (SIL) peptides is relatively costly and time-consuming, thus hindering clinical implementation. Here, we introduce a simplification of MS-MRD by using an off-the-shelf calibrator. METHODS: SILuMAB-based MS-MRD was performed by spiking a monoclonal stable isotope labeled IgG, SILuMAB-K1, in the patient serum. The abundance of both M-protein-specific peptides and SILuMAB-specific peptides were monitored by mass spectrometry. The relative ratio between M-protein peptides and SILuMAB peptides allowed for M-protein quantification. We assessed linearity, sensitivity and reproducibility of SILuMAB-based MS-MRD in longitudinally collected sera from the IFM-2009 clinical trial. RESULTS: A linear dynamic range was achieved of over 5 log scales, allowing for M-protein quantification down to 0.001 g/L. The inter-assay CV of SILuMAB-based MS-MRD was on average 11 %. Excellent concordance between SIL- and SILuMAB-based MS-MRD was shown (R2>0.985). Additionally, signal intensity of spiked SILuMAB can be used for quality control purpose to assess system performance and incomplete SILuMAB digestion can be used as quality control for sample preparation. CONCLUSIONS: Compared to SIL peptides, SILuMAB-based MS-MRD improves the reproducibility, turn-around-times and cost-efficacy of MS-MRD without diminishing its sensitivity and specificity. Furthermore, SILuMAB can be used as a MS-MRD quality control tool to monitor sample preparation efficacy and assay performance.

Ten quick tips for building FAIR workflows.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Plasma glycoproteomics delivers high-specificity disease biomarkers by detecting site-specific glycosylation abnormalities.

INTRODUCTION: The human plasma glycoproteome holds enormous potential to identify personalized biomarkers for diagnostics. Glycoproteomics has matured into a technology for plasma N-glycoproteome analysis but further evolution towards clinical applications depends on the clinical validity and understanding of protein- and site-specific glycosylation changes in disease. OBJECTIVES: Here, we exploited the uniqueness of a patient cohort of genetic defects in well-defined glycosylation pathways to assess the clinical applicability of plasma N-glycoproteomics. METHODS: Comparative glycoproteomics was performed of blood plasma from 40 controls and 74 patients with 13 different genetic diseases that impact the protein N-glycosylation pathway. Baseline glycosylation in healthy individuals was compared to reference glycome and intact transferrin protein mass spectrometry data. Use of glycoproteomics data for biomarker discovery and sample stratification was evaluated by multivariate chemometrics and supervised machine learning. Clinical relevance of site-specific glycosylation changes were evaluated in the context of genetic defects that lead to distinct accumulation or loss of specific glycans. Integrated analysis of site-specific glycoproteome changes in disease was performed using chord diagrams and correlated with intact transferrin protein mass spectrometry data. RESULTS: Glycoproteomics identified 191 unique glycoforms from 58 unique peptide sequences of 34 plasma glycoproteins that span over 3 magnitudes of abundance in plasma. Chemometrics identified high-specificity biomarker signatures for each of the individual genetic defects with better stratification performance than the current diagnostic standard method. Bioinformatic analyses revealed site-specific glycosylation differences that could be explained by underlying glycobiology and protein-intrinsic factors. CONCLUSION: Our work illustrates the strong potential of plasma glycoproteomics to significantly increase specificity of glycoprotein biomarkers with direct insights in site-specific glycosylation changes to better understand the glycobiological mechanisms underlying human disease.

Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens.

Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.

The GlycoPaSER Prototype as a Real-Time N-Glycopeptide Identification Tool Based on the PaSER Parallel Computing Platform.

Real-time database searching allows for simpler and automated proteomics workflows as it eliminates technical bottlenecks in high-throughput experiments. Most importantly, it enables results-dependent acquisition (RDA), where search results can be used to guide data acquisition during acquisition. This is especially beneficial for glycoproteomics since the wide range of physicochemical properties of glycopeptides lead to a wide range of optimal acquisition parameters. We established here the GlycoPaSER prototype by extending the Parallel Search Engine in Real-time (PaSER) functionality for real-time glycopeptide identification from fragmentation spectra. Glycopeptide fragmentation spectra were decomposed into peptide and glycan moiety spectra using common N-glycan fragments. Each moiety was subsequently identified by a specialized algorithm running in real-time. GlycoPaSER can keep up with the rate of data acquisition for real-time analysis with similar performance to other glycoproteomics software and produces results that are in line with the literature reference data. The GlycoPaSER prototype presented here provides the first proof-of-concept for real-time glycopeptide identification that unlocks the future development of RDA technology to transcend data acquisition.

Circulating FGF21 Concentration, Fasting Plasma Glucose, and the Risk of Type 2 Diabetes: Results From the PREVEND Study.

OBJECTIVE: Fibroblast growth factor 21 (FGF21) is a peptide hormone synthesized by several organs and regulates, among others, energy homeostasis. In obesity, insulin resistance and type 2 diabetes (T2D), higher circulating FGF21 concentrations have been found. Temporal analyses in murine studies demonstrate that FGF21 increases before insulin resistance occurs. The current study aims to investigate in time-to-event analyses whether FGF21 may be an early biomarker in the development of T2D. RESEARCH DESIGN AND METHODS: Circulating FGF21 was measured using an immunoassay of the Mesoscale U-PLEX assay platform. The study outcome was incident T2D. Associations of circulating FGF21 concentration with T2D were quantified using Cox proportional hazards models with adjustments for potential confounders. RESULTS: We included 5244 participants aged 52 ± 12 years, of whom 50% were male. Median [interquartile range] circulating FGF21 concentration was 860 [525-1329] pg/mL. During 7.3 [6.1-7.7] years of follow-up, 299 (5.7%) participants developed T2D. In fully adjusted analyses, higher circulating FGF21 concentration was associated with an increased risk of incident T2D (hazard ratio per doubling: 1.26 [95% CI, 1.06-1.51]; P = 0.008), with effect modification by fasting plasma glucose, consistent with strengthening of the association at lower fasting glucose (interaction coefficient: -0.12; P = 0.022). CONCLUSION: Higher circulating FGF21 concentrations are independently associated with an increased risk of incident T2D in participants with a low fasting plasma glucose, making circulating FGF21 concentration a potential early biomarker for type 2 diabetes.