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23rd Human Proteome Organization World Congress

Disrupting healthcare using deep data and remote monitoring

Keynote speaker

Keynote lecture

Disease-state and progression biomarker discovery in multiple myeloma plasma

Keynote Lecture

Multiple Myeloma (MM) develops from well-defined precursors—Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM)—where patients remain stable or may rapidly progress. Bone marrow (BM) biopsies are used for staging and identifying high-risk genomic abnormalities associated with progression. However, BM biopsies are invasive and cannot be repeated often for monitoring tumor burden in the precursor setting, and these patients are limited to monitoring a few markers within peripheral blood (PB) for signs of progressive disease. Deep proteome profiling of PB plasma may advance non-invasive precursor disease staging, monitoring, and characterization. Precursor patients are currently monitored via a few peripheral blood biomarkers for signs of progressive disease. Plasma proteome profiling to identify effective biomarkers has been hindered by the wide dynamic range of plasma and often low abundance of disease-specific markers.Here we performed the first comprehensive analysis of the plasma proteome of MM and its precursor conditions using the Olink proximity extension assay (PEA) that targets ca. 3000 proteins. We profiled 529 PB plasma samples from 485 individuals, including MGUS (n=100), SMM (n=203), MM (n=100), and age-matched healthy donors (HDs) (n=82). Samples from patients with progressive disease (n=32) and stable precursor disease (n=32) with matched clinical follow-up time were also profiled. Analysis of the data enabled us to develop a classifier that utilizes plasma proteins alone to accurately classify disease stages and identified a prognostic protein signature associated with progressive disease. Advancements are underway to validate our high-risk disease candidates, test the performance of an improved risk stratification model in external cohorts, and determine how best to implement proteome profiling into clinical practice.

Professor Dr

Cross-platform clinical proteomics using the Charité Open Standard for Plasma Proteomics (OSPP)

New Technology: MS-based Proteomics

Introduction: Human plasma is a preferred body fluid for clinical investigations due to its minimally invasive collection and rich physiological information. LC-MS-based plasma proteomics holds immense promise in characterising human disease, identifying biomarkers, and advancing diagnostic technologies. However, for widespread clinical adoption, there is an ongoing need to improve proteomic workflows in terms of accuracy, reproducibility, platform transferability, and cost-effectiveness. Objective: To develop a peptide panel based on a previously developed targeted panel for COVID-19. The new panel, Charité Open Peptide Standard for Plasma Proteomics (OSPP), consists of 211 stable-isotope labeled peptides from 131 consistently quantified plasma proteins, it is designed for high-throughput targeted and untargeted plasma and serum proteomics studies in large clinical cohorts. Methods: We utilised targeted plasma proteomics MRM (Agilent 6495C), MRM-HR (ZenoTOF 7600) and discovery proteomics techniques on various LC (Waters M Class, Agilent 1290, Thermo Vanquish Neo) and MS platforms (ZenoTOF, TimsTOF, and Exploris 480) to comprehensively test the peptide panel. Results: The targeted peptide panel that was previously established for COVID-19 demonstrated exceptional abilities in categorising the disease, predicting outcomes, and comparing across different platforms. It also has the potential to be applied to comparable types of diseases (Fig 1). By analysing more than 10,000 plasma samples from various disease categories, we have increased the number of peptides in OSPP to 211, derived from 131 plasma proteins that were consistently quantified. These peptides have shown consistent quantification properties in human studies, across different platforms and matrices, and are suitable for chemical synthesis (Fig 2). The OSPP measures proteins that are crucial for human disease, which are derived from proteins involved in many biological processes, including those commonly employed in clinical tests or targeted by FDA-approved medications . In an acute COVID-19 patient cohort (Fig 3), we used the OSPP to i) achieve patient classification and biomarker identification, ii) generate comparable quantitative proteome data with both targeted and untargeted approaches, and iii) estimate absolute peptide quantities for cross-platform alignment across various MS methods. The OSPP is cost-effective, adding only less than 1€ per sample. OSPP also successful implementation in a real-world cohort comprising over 15,000 samples underscores the scalability and practical utility of our peptide panel in addressing critical translational medicine challenges. Conclusion: The OSPP offers high analytical consistency, cost-effectiveness, and versatility, facilitating adaptable and accessible high-throughput plasma proteomics in large clinical cohorts, addressing critical needs in translational medicine.

Oral presentation

Prof

Proteome profiling with precision on a clinical cohort: Pioneering a large-scale ultra-high throughput validation study employing a novel high-speed hybrid nominal mass platform

Protein and peptide quantification methods, such as Multiple Reaction Monitoring (MRM) and Parallel Reaction Monitoring (PRM), utilize LC-MS for the targeted, bottom-up quantification of protein biomarkers by monitoring signature peptides representative of candidate biomarkers. The challenges are balancing throughput with assay/study variables, peptide parameters that define precision, ease for establishing quantifiability (linearity, sensitivity, reproducibly) and data acquisition across large cohort. The recent development of a novel high-speed hybrid nominal mass platform has enabled us to perform large-scale, high-throughput proteomic profiling of 83 Stable Isotope Labeled (SIL) peptides representing 53 Health Surveillance Panel (HSP) proteins (containing 21 FDA-approved proteins assay) in an inflammatory bowel disease (IBD) cohort (n=498) and age/sex-matched healthy individuals (n=547), totaling 1,005 plasma samples. Using a pooled human plasma sample (100 healthy males and 100 healthy females), we evaluated the targeted peptides' sensitivity, linearity, LLOD, LLOQ, and reproducibility across four different throughput levels: 100 samples per day (SPD), 144 SPD, 180 SPD, and 300 SPD. Excellent reproducibility was observed at all four throughputs, with median CV% ranging from 5% to 10% for all 83 SILs. The median points per peak were 10 (100 SPD), 11 (144 SPD), 8 (180 SPD), and 5 (300 SPD). The observed sensitivity for the 83 SIL peptides across the four throughputs was sub-fmol (LOQ &lt; 0.5 fmol), with 93% of the peptides achieving this for 100 SPD, 87% for 144 SPD, 48% for 180 SPD, and 53% for 300 SPD. For LLOQ (&gt;5 fmol), the percentages were 99% for 100 SPD, 97% for 144 SPD, 49% for 180 SPD, and 72% for 300 SPD. For linearity, more than 95% of all peptides had an R² &gt; 0.9. The total median CV% based on injections each day for 5 days for the 83 SIL peptides was less than 10% at all throughputs. The implementation of the Adaptive Retention Time (ART) algorithm, which allows real-time retention time adjustments, proved advantageous in achieving robustness and enhancing reproducibility when acquiring the IBD cohort of 1,005 samples within 14 days (180 SPD). The study included 2,000 injections when accounting for system suitability tests, low, mid, and high-quality controls, washes, and blanks, performed robustly and reproducibly. In conclusion, the combination of a novel high-speed hybrid nominal mass platform and the ART algorithm provides a powerful solution to the challenges of large-scale, high-throughput, targeted, and scheduled MS-based quantification by increasing the ease to establishing quantifiability (establishing linearity, sensitivity, reproducibly) and ease for consistency data acquisition across large cohort is highlighted. This approach allows us to profile a large IBD cohort (n=1,005) with precision and robustness, offering insights into biologically important plasma proteins, including 21 FDA-approved plasma protein markers.

Breaking barriers to proteomics at scale: the nELISA platform profiles hundreds of circulating proteins in high throughput and at low cost

New Technology: Non MS-based Proteomics

Despite the great promises of proteomics, the low abundance of many signalling proteins in circulation, such as cytokines and metabolic mediators, renders mass spectrometry-based analysis challenging. Targeted approaches using affinity binders provide more sensitive quantification of these proteins, but have been challenging to multiplex due to cross-reactivity between reagents (rCR) when scaling content beyond ~25-plex. Recent advances using the Proximity Extension Assay have helped avoid issues with rCR, but high cost and low throughput have limited their widespread adoption for large-scale applications. The nELISA is a high-throughput, highly multiplexed immunoassay platform capable of profiling 1536 samples per instrument per day at a fraction of the cost of other platforms. This is achieved by miniaturizing the sandwich immunoassay, whereby antibody pairs are pre-packaged at the surface of color-coded microparticles that can be readout by high-throughput flow cytometry. We recently demonstrated the nELISA&quot;s ability to profile 10,000 cell culture supernatants in 1 week to reveal immune phenotypes from PBMCs; here, we describe its use in profiling circulating proteins to identify biomarkers of disease, and compare our findings to xMAP (Luminex) and PEA (Olink) platforms.We profiled circulating protein abundance in 44 plasma samples across 4 diseases (type 2 diabetes, colorectal cancer, COVID19, rheumatoid arthritis) and healthy controls using 3 platforms, nELISA (191-plex), PEA (92-plex), and xMAP (48-plex). For targets shared across platforms, nELISA and PEA measurements were generally well-correlated, with a median Spearman correlation of 0.71. In line with this, both platforms distinguished healthy and disease phenotypes, and identified individual protein markers with differing expression patterns between healthy and disease states. In contrast, correlations were significantly lower when comparing PEA (0.53) or nELISA (0.45) to xMAP, purportedly due to well-known cross-reactivity issues with the latter.We then performed a follow up study on an independent set of 96 samples, which we profiled with an expanded nELISA panel (275-plex), as well as a 384-plex PEA panel (Olink Explore). Unsurprisingly, the expanded content of both platforms resulted in the identification of an additional set of biomarkers not available in the initial screen. Among proteins quantified in both sample sets, approximately 40% of biomarkers were validated using the nELISA, but fewer markers were validated by PEA: this may be due to differences in sensitivity between different PEA panels, and/or the use of different reagents in the different panels. In contrast, nELISA reagents and platform sensitivity were maintained regardless of plex level. Of note, the cost of profiling was 15X more expensive using PEA than nELISA. Thus, the nELISA provides a new, high-throughput and cost-effective means of profiling circulating proteins for large-scale studies.

Conference room 1-2

Session

New technologies for large scale proteomics

Session
New technologies for large scale proteomics

Appointment

Chairs

Program

SessionNew technologies for large scale proteomics

Appointment

Chairs

Program

Session
New technologies for large scale proteomics