Evaluation of 92 cardiovascular proteins in dried blood spots collected under field-conditions: Off-the-shelf affinity-based multiplexed assays work well, allowing for simplified sample collection

Large-scale proteomics reveals precise biomarkers for detection of ovarian cancer in symptomatic women

Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.

Proteome profiling of home-sampled dried blood spots reveals proteins of SARS-CoV-2 infections

BACKGROUND

Self-sampling of dried blood spots (DBS) offers new routes to gather valuable health-related information from the general population. Yet, the utility of using deep proteome profiling from home-sampled DBS to obtain clinically relevant insights about SARS-CoV-2 infections remains largely unexplored.

METHODS

Our study involved 228 individuals from the general Swedish population who used a volumetric DBS sampling device and completed questionnaires at home during spring 2020 and summer 2021. Using multi-analyte COVID-19 serology, we stratified the donors by their response phenotypes, divided them into three study sets, and analyzed 276 proteins by proximity extension assays (PEA). After normalizing the data to account for variances in layman-collected samples, we investigated the association of DBS proteomes with serology and self-reported information.

RESULTS

Our three studies display highly consistent variance of protein levels and share associations of proteins with sex (e.g., MMP3) and age (e.g., GDF-15). Studying seropositive (IgG+) and seronegative (IgG-) donors from the first pandemic wave reveals a network of proteins reflecting immunity, inflammation, coagulation, and stress response. A comparison of the early-infection phase (IgM+IgG-) with the post-infection phase (IgM-IgG+) indicates several proteins from the respiratory system. In DBS from the later pandemic wave, we find that levels of a virus receptor on B-cells differ between seropositive (IgG+) and seronegative (IgG-) donors.

CONCLUSIONS

Proteome analysis of volumetric self-sampled DBS facilitates precise analysis of clinically relevant proteins, including those secreted into the circulation or found on blood cells, augmenting previous COVID-19 reports with clinical blood collections. Our population surveys support the usefulness of DBS, underscoring the role of timing the sample collection to complement clinical and precision health monitoring initiatives.

Toward ovarian cancer screening with protein biomarkers using dried, self-sampled cervico-vaginal fluid

Early detection is key for increased survival in ovarian cancer, but no general screening program exists today due to lack of biomarkers and overall cost versus benefit over traditional clinical methods. Here, we used dried cervico-vaginal fluid (CVF) as sampling matrix coupled with mass spectrometry for detection of protein biomarkers. We find that self-collected CVF on paper cards yields robust results and is suitable for high-throughput proteomics. Artificial intelligence-based methods were used to identify an 11-protein panel that separates cases from controls. In validation data, the panel achieved a sensitivity of 0.97 (95% CI 0.91-1.00) at a specificity of 0.67 (0.40-0.87). Analyses of samples collected prior to development of symptoms indicate that the panel is informative also of future risk of disease. Dried CVF is used in cervical cancer screening, and our results opens the possibility for a screening program also for ovarian cancer, based on self-collected CVF samples.

Development and validation of a quantitative Proximity Extension Assay instrument with 21 proteins associated with cardiovascular risk (CVD-21)

BACKGROUND

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD.

METHODS AND FINDINGS

Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF.

CONCLUSIONS

The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Dried serum spots on pre-punched filter paper discs are ready-to-use storage and shipping devices for blood-borne antigens and antibodies

Dried serum spots that are well prepared can be attractive alternatives to frozen serum samples for shelving specimens in a medical or research center's biobank and mailing freshly prepared serum to specialized laboratories. During the pre-analytical phase, complications can arise which are often challenging to identify or are entirely overlooked. These complications can lead to reproducibility issues, which can be avoided in serum protein analysis by implementing optimized storage and transfer procedures. With a method that ensures accurate loading of filter paper discs with donor or patient serum, a gap in dried serum spot preparation and subsequent serum analysis shall be filled. Pre-punched filter paper discs with a 3 mm diameter are loaded within seconds in a highly reproducible fashion (approximately 10% standard deviation) when fully submerged in 10 μl of serum, named the "Submerge and Dry" protocol. Such prepared dried serum spots can store several hundred micrograms of proteins and other serum components. Serum-borne antigens and antibodies are reproducibly released in 20 μl elution buffer in high yields (approximately 90%). Dried serum spot-stored and eluted antigens kept their epitopes and antibodies their antigen binding abilities as was assessed by SDS-PAGE, 2D gel electrophoresis-based proteomics, and Western blot analysis, suggesting pre-punched filter paper discs as handy solution for serological tests.

Methodologies in Mitochondrial Testing: Diagnosing a Primary Mitochondrial Respiratory Chain Disorder

BACKGROUND

Mitochondria are cytosolic organelles within most eukaryotic cells. Mitochondria generate the majority of cellular energy in the form of adenosine triphosphate (ATP) through oxidative phosphorylation (OxPhos). Pathogenic variants in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) lead to defects in OxPhos and physiological malfunctions (Nat Rev Dis Primer 2016;2:16080.). Patients with primary mitochondrial disorders (PMD) experience heterogeneous symptoms, typically in multiple organ systems, depending on the tissues affected by mitochondrial dysfunction. Because of this heterogeneity, clinical diagnosis is challenging (Annu Rev Genomics Hum Genet 2017;18:257-75.). Laboratory diagnosis of mitochondrial disease depends on a multipronged analysis that can include biochemical, histopathologic, and genetic testing. Each of these modalities has complementary strengths and limitations in diagnostic utility.

CONTENT

The primary focus of this review is on diagnosis and testing strategies for primary mitochondrial diseases. We review tissue samples utilized for testing, metabolic signatures, histologic findings, and molecular testing approaches. We conclude with future perspectives on mitochondrial testing.

SUMMARY

This review offers an overview of the current biochemical, histologic, and genetic approaches available for mitochondrial testing. For each we review their diagnostic utility including complementary strengths and weaknesses. We identify gaps in current testing and possible future avenues for test development.

Microsampling tools for collecting, processing, and storing blood at the point-of-care

In the wake of the COVID-19 global pandemic, self-administered microsampling tools have reemerged as an effective means to maintain routine healthcare assessments without inundating hospitals or clinics. Finger-stick collection of blood is easily performed at home, in the workplace, or at the point-of-care, obviating the need for a trained phlebotomist. While the initial collection of blood is facile, the diagnostic or clinical utility of the sample is dependent on how the sample is processed and stored prior to transport to an analytical laboratory. The past decade has seen incredible innovation for the development of new materials and technologies to collect low-volume samples of blood with excellent precision that operate independently of the hematocrit effect. The final application of that blood (i.e., the test to be performed) ultimately dictates the collection and storage approach as certain materials or chemical reagents can render a sample diagnostically useless. Consequently, there is not a single microsampling tool that is capable of addressing every clinical need at this time. In this review, we highlight technologies designed for patient-centric microsampling blood at the point-of-care and discuss their utility for quantitative sampling as a function of collection material and technique. In addition to surveying methods for collecting and storing whole blood, we emphasize the need for direct separation of the cellular and liquid components of blood to produce cell-free plasma to expand clinical utility. Integrating advanced functionality while maintaining simple user operation presents a viable means of revolutionizing self-administered microsampling, establishing new avenues for innovation in materials science, and expanding access to healthcare.

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer

Simple Summary

Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4–7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort.

Abstract

Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Toward proteome-wide exploration of proteins in dried blood spots using liquid chromatography-coupled mass spectrometry

Dried blood spot (DBS) sampling is a method with advantages over conventional blood sampling in relation to collection, cost, storage, and transportation. Such advantages have led to its wide use in newborn screening (NBS). Although target analysis of various biomolecules is conducted in NBS, protein quantification-based NBS is still in its infancy. Thus, it is important to clarify how many proteins could be quantitatively detected in DBS samples using advanced liquid chromatography-mass spectrometry (LC-MS/MS) technologies; a catalog of proteins detectable in DBSs by LC-MS/MS will enable us to judge which causative proteins in genetic diseases can be monitored at the protein level in NBS. In this review, we outline conventional proteome analyses of DBSs with a distinction between target and nontarget approaches. Additionally, we discuss the future perspectives for proteome analysis of DBSs in NBS of genetic diseases. This article is protected by copyright. All rights reserved.