Comparison of pre-processing methods for multiplex bead-based immunoassays

Comprehensive normalization and binary classification methods for enhanced sensitivity and reproducibility in Luminex assay quantitation

The Luminex assay is a powerful tool for large-scale quantitation of antibody levels and cytokines, but its utility can be limited by issues of specificity, sensitivity, and reproducibility. The corrections for background fluorescence and machine drift are essential steps in the normalization process. However, traditional methods often oversimplify these steps, failing to account for the complexity of the data, leading to the introduction of error and decreasing the sensitivity and reproducibility of the analysis. Furthermore, conventional methods to determine cut-points in binary measures do not consider the true distribution of the data, leading to arbitrary cut-points that compromise the integrity of the analysis. Here, we present a novel approach to normalize Luminex data and split the normalized bimodal data. Our method uses orthogonal regression of the measured fluorescence of a negative control bead and a blank bead to correct for background fluorescence, enhancing accuracy by preventing overcorrection due to cross-reactivity. To account for machine drift, we use a generalized additive model (GAM) on the standard curves to calculate a plate correction, thus reducing error and improving reproducibility. To distinguish between positive and negative results in bimodal measures, we use a clustering analysis to accurately split the data based on distribution. Finally, we developed a web application to easily carry out the developed method. These methods collectively increase sensitivity, specificity, and reproducibility of Luminex assay data analysis by effectively addressing the limitations of current normalization techniques, correcting for background fluorescence and machine drift, and improving the specificity and accuracy in splitting bimodal data.

Reproductomics: Exploring the Applications and Advancements of Computational Tools

Over recent decades, advancements in omics technologies, such as proteomics, genomics, epigenomics, metabolomics, transcriptomics, and microbiomics, have significantly enhanced our understanding of the molecular mechanisms underlying various physiological and pathological processes. Nonetheless, the analysis and interpretation of vast omics data concerning reproductive diseases are complicated by the cyclic regulation of hormones and multiple other factors, which, in conjunction with a genetic makeup of an individual, lead to diverse biological responses. Reproductomics investigates the interplay between a hormonal regulation of an individual, environmental factors, genetic predisposition (DNA composition and epigenome), health effects, and resulting biological outcomes. It is a rapidly emerging field that utilizes computational tools to analyze and interpret reproductive data, with the aim of improving reproductive health outcomes. It is time to explore the applications of reproductomics in understanding the molecular mechanisms underlying infertility, identification of potential biomarkers for diagnosis and treatment, and in improving assisted reproductive technologies (ARTs). Reproductomics tools include machine learning algorithms for predicting fertility outcomes, gene editing technologies for correcting genetic abnormalities, and single cell sequencing techniques for analyzing gene expression patterns at the individual cell level. However, there are several challenges, limitations and ethical issues involved with the use of reproductomics, such as the applications of gene editing technologies and their potential impact on future generations are discussed. The review comprehensively covers the applications and advancements of reproductomics, highlighting its potential to improve reproductive health outcomes and deepen our understanding of reproductive molecular mechanisms.

Changes and associations between synovial fluid and magnetic resonance imaging markers of osteoarthritis after high tibial osteotomy

BACKGROUND

Mechanobiological mechanisms of osteoarthritis (OA) are unclear. Our objectives were to explore: 1) changes in knee joint physiology using a large panel of synovial fluid biomarkers from before to one year after high tibial osteotomy (HTO) surgery, and 2) the association of changes in the synovial fluid biomarkers with the changes in MRI measures of knee effusion-synovitis and articular cartilage composition.

METHODS

Twenty-six patients with symptomatic knee OA and varus alignment underwent synovial fluid aspirations and 3 T MRI before and one year after medial opening wedge HTO. Cytokine and growth factor levels in synovial fluid were measured with multiplex assays. Ontology and pathway enrichment was assessed using data protein sets with gene set enrichment analysis (GSEA), and analyzed using linear mixed effects models. MRIs were analyzed for effusion-synovitis and T2 cartilage relaxation time using manual segmentations. Changes in biomarker concentrations were correlated to changes in MRI effusion-synovitis volume and articular cartilage T2 relaxation times.

RESULTS

Decreased enrichment in Toll-like receptor and TNF-α signalling was detected one year after HTO. The leading contributors to this reduction included IL-6, TNF-α and IL-1β, whereas the highest contributors to positive enrichment were EGF, PDGF-BB and FGF-2. Effusion-synovitis volume decreased (mean [95%CI]) one year after HTO (-2811.58 [-5094.40, -528.76mm3]). Effusion-synovitis volume was moderately correlated (r [95% CI]) with decreased MMP-1 (0.44 [0.05; 0.71]), IL-7 (0.41 [0.00; 0.69]) and IL-1β (0.59 [0.25; 0.80]) and increased MIP-1β (0.47 [0.10; 0.73]). Medial tibiofemoral articular cartilage T2 relaxation time decreased (mean [95% CI]) one year after HTO (-0.33 [-2.69; 2.05]ms). Decreased T2 relaxation time was moderately correlated to decreased Flt-3L (0.61 [0.28; 0.81]), IL-10 (0.47 [0.09; 0.73]), IP-10 (0.42; 0.03-0.70) and increased MMP-9 (-0.41 [-0.7; -0.03]) and IL-18 (-0.48 [-0.73; -0.10]).

CONCLUSIONS

Decreased aberrant knee mechanical loading in patients with OA is associated with decreased biological and imaging measures of inflammation (measured in synovial fluid and on MRI) and increased anabolic processes. These exploratory findings suggest that improvement in knee loading can produce long-term (one year) improvement in joint physiology.

Evaluation of concordance of new QuantiFERON-TB Gold Plus platforms for Mycobacterium tuberculosis infection diagnosis in a prospective cohort of household contacts

Interferon-gamma (IFN-γ) release assays play a pivotal role in tuberculosis infection (TBI) diagnosis, with QuantiFERON-TB Gold Plus-an enzyme-linked immunosorbent assay (ELISA)-among the most widely utilized. Newer QuantiFERON-TB platforms with shorter turnaround times were recently released. We aimed to evaluate these platforms' agreement in the diagnosis of TBI. Blood samples from a prospective cohort of tuberculosis household contacts were collected at baseline and after 12 weeks of follow-up, and tested with LIAISON, an automated chemiluminescence immunoassay (CLIA) system, QIAreach, a lateral flow (QFT-LF) semi-automated immunoassay, and the ELISA QuantiFERON-TB Gold Plus platform. Test concordances were analyzed. ELISA vs CLIA overall agreement was 83.3% for all tested samples (120/144) [Cohen's kappa coefficient (κ): 0.66 (95% CI: 0.54-0.77)]. Samples positive with CLIA provided consistently higher IFN-γ levels than with ELISA (P < 0.001). Twenty-four (16.7%) discordant pairs were obtained, all CLIA-positive/ELISA-negative: 15 (62.5%) had CLIA IFN-γ levels within borderline values (0.35-0.99 IU/mL) and 9 (37.5%) >0.99 IU/mL. QFT-LF showed only 76.4% (68/89) overall agreement with ELISA [κ: 0.53 (95% CI: 0.37-0.68)] with 21 (23.6%) discordant results obtained, all QFT-LF-positive/ELISA-negative. Overall concordance between ELISA and CLIA platforms was substantial, and only moderate between ELISA and QFT-LF. The CLIA platform yielded higher IFN-γ levels than ELISA, leading to an almost 17% higher positivity rate. The techniques do not seem interchangeable, and validation against other gold standards, such as microbiologically-confirmed tuberculosis disease, is required to determine whether these cases represent true new infections or whether CLIA necessitates a higher cutoff.

IMPORTANCE

Tuberculosis is an airborne infectious disease caused by Mycobacterium tuberculosis that affects over 10 million people annually, with over 2 billion people carrying an asymptomatic tuberculosis infection (TBI) worldwide. Currently, TBI diagnosis includes tuberculin skin test and the blood-based interferon-gamma (IFN-γ) release assays, with Qiagen QuantiFERON-TB Gold Plus (QFT) being among those most widely utilized. We evaluated Qiagen's newer QFT platforms commercially available in a prospective cohort of tuberculosis contacts. A substantial agreement was obtained between the current QFT-enzyme-linked immunosorbent assay (ELISA) and the new QFT-chemiluminescence immunoassay (CLIA) platform, although QFT-CLIA provided higher concentrations of IFN-γ, leading to a 16.6% higher positivity rate. We highlight that both platforms may not be directly interchangeable and that further validation is required.

Differential levels of anti-Mycobacterium tuberculosis-specific IgAs in saliva of household contacts with latent tuberculosis infection

Introduction

Mucosal immunity is strongly elicited in early stages of many respiratory and enteric infections; however, its role in tuberculosis pathogenesis has been scarcely explored. We aimed to investigate Mycobacterium tuberculosis (Mtb) specific IgA levels in saliva in different stages of latent Tuberculosis Infection (TBI).

Methodology

A multiplex bead-based Luminex immunoassay was developed to detect specific IgA against 12 highly immunogenic Mtb antigens. A prospective cohort of household contacts (>14 years) of pulmonary TB cases was established in Santiago, Chile. Contacts were classified as Mtb-infected or not depending on serial interferon-γ release assay results. Saliva samples were collected and tested at baseline and at a 12-week follow-up.

Results

Mtb-specific IgA was detectable at all visits in all participants (n = 168), including the "non-Mtb infected" (n = 64). Significantly higher median levels of IgA were found in the "Mtb infected" compared to the uninfected for anti-lipoarabinomannan (LAM) (110 vs. 84.8 arbitrary units (AU), p < 0.001), anti-PstS1 (117 vs. 83 AU, p < 0.001), anti-Cell Membrane Fraction (CMF) (140 vs. 103 AU, p < 0.001) and anti-Culture Filtrate Proteins (CFP) (median 125 vs. 96 AU, p < 0.001), respectively. Nonetheless, the discriminatory performance of these specific mucosal IgA for TBI diagnosis was low.

Conclusion

Saliva holds Mtb-specific IgA against several antigens with increased levels for anti-LAM, anti-PstS1, anti-CMF and anti-CFP found in household contacts with an established TBI. The role of these mucosal antibodies in TB pathogenesis, and their kinetics in different stages of Mtb infection merits further exploring.

Metabolic Profiling at COVID-19 Onset Shows Disease Severity and Sex-Specific Dysregulation

Background

metabolic changes through SARS-CoV-2 infection has been reported but not fully comprehended. This metabolic dysregulation affects multiple organs during COVID-19 and its early detection can be used as a prognosis marker of severity. Therefore, we aimed to characterize metabolic and cytokine profile at COVID-19 onset and its relationship with disease severity to identify metabolic profiles predicting disease progression.

Material and Methods

we performed a retrospective cross-sectional study in 123 COVID-19 patients which were stratified as asymptomatic/mild, moderate and severe according to the highest COVID-19 severity status, and a group of healthy controls. We performed an untargeted plasma metabolic profiling (gas chromatography and capillary electrophoresis-mass spectrometry (GC and CE-MS)) and cytokine evaluation.

Results

After data filtering and identification we observed 105 metabolites dysregulated (66 GC-MS and 40 CE-MS) which shown different expression patterns for each COVID-19 severity status. These metabolites belonged to different metabolic pathways including amino acid, energy, and nitrogen metabolism among others. Severity-specific metabolic dysregulation was observed, as an increased transformation of L-tryptophan into L-kynurenine. Thus, metabolic profiling at hospital admission differentiate between severe and moderate patients in the later phase of worse evolution. Several plasma pro-inflammatory biomarkers showed significant correlation with deregulated metabolites, specially with L-kynurenine and L-tryptophan. Finally, we describe a strong sex-related dysregulation of metabolites, cytokines and chemokines between severe and moderate patients. In conclusion, metabolic profiling of COVID-19 patients at disease onset is a powerful tool to unravel the SARS-CoV-2 molecular pathogenesis.

Conclusions

This technique makes it possible to identify metabolic phenoconversion that predicts disease progression and explains the pronounced pathogenesis differences between sexes.

Plasma miRNA profile at COVID-19 onset predicts severity status and mortality

BACKGROUND

MicroRNAs (miRNAs) have a crucial role in regulating immune response against infectious diseases, showing changes early in disease onset and before the detection of the pathogen. Thus, we aimed to analyze the plasma miRNA profile at COVID-19 onset to identify miRNAs as early prognostic biomarkers of severity and survival.

METHODS AND RESULTS

Plasma miRNome of 96 COVID-19 patients that developed asymptomatic/mild, moderate and severe disease was sequenced together with a group of healthy controls. Plasma immune-related biomarkers were also assessed. COVID-19 patients showed 200 significant differentially expressed (SDE) miRNAs concerning healthy controls, with upregulated putative targets of SARS-CoV-2, and inflammatory miRNAs. Among COVID-19 patients, 75 SDE miRNAs were observed in asymptomatic/mild compared to symptomatic patients, which were involved in platelet aggregation and cytokine pathways, among others. Moreover, 137 SDE miRNAs were identified between severe and moderate patients, where miRNAs targeting the SARS CoV-2 genome were the most strongly disrupted. Finally, we constructed a mortality predictive risk score (miRNA-MRS) with ten miRNAs. Patients with higher values had a higher risk of 90-days mortality (hazard ratio=4.60; p-value<0.001). Besides, the discriminant power of miRNA-MRS was significantly higher than the observed for age and gender (AUROC=0.970 vs. 0.881; p=0.042).

CONCLUSIONS

SARS-CoV-2 infection deeply disturbs the plasma miRNome from an early stage of COVID-19, making miRNAs highly valuable as early predictors of severity and mortality.

Blood Nanoparticles - Influence on Extracellular Vesicle Isolation and Characterization

Blood is a rich source of disease biomarkers, which include extracellular vesicles (EVs). EVs are nanometer-to micrometer-sized spherical particles that are enclosed by a phospholipid bilayer and are secreted by most cell types. EVs reflect the physiological cell of origin in terms of their molecular composition and biophysical characteristics, and they accumulate in blood even when released from remote organs or tissues, while protecting their cargo from degradation. The molecular components (e.g., proteins, miRNAs) and biophysical characteristics (e.g., size, concentration) of blood EVs have been studied as biomarkers of cancers and neurodegenerative, autoimmune, and cardiovascular diseases. However, most biomarker studies do not address the problem of contaminants in EV isolates from blood plasma, and how these might affect downstream EV analysis. Indeed, nonphysiological EVs, protein aggregates, lipoproteins and viruses share many molecular and/or biophysical characteristics with EVs, and can therefore co-isolate with EVs from blood plasma. Consequently, isolation and downstream analysis of EVs from blood plasma remain a unique challenge, with important impacts on the outcomes of biomarker studies. To help improve rigor, reproducibility, and reliability of EV biomarker studies, we describe here the major contaminants of EV isolates from blood plasma, and we report on how different EV isolation methods affect their levels, and how contaminants that remain can affect the interpretation of downstream EV analysis.

Towards Building a Quantitative Proteomics Toolbox in Precision Medicine: A Mini-Review

Precision medicine as a framework for disease diagnosis, treatment, and prevention at the molecular level has entered clinical practice. From the start, genetics has been an indispensable tool to understand and stratify the biology of chronic and complex diseases in precision medicine. However, with the advances in biomedical and omics technologies, quantitative proteomics is emerging as a powerful technology complementing genetics. Quantitative proteomics provide insight about the dynamic behaviour of proteins as they represent intermediate phenotypes. They provide direct biological insights into physiological patterns, while genetics accounting for baseline characteristics. Additionally, it opens a wide range of applications in clinical diagnostics, treatment stratification, and drug discovery. In this mini-review, we discuss the current status of quantitative proteomics in precision medicine including the available technologies and common methods to analyze quantitative proteomics data. Furthermore, we highlight the current challenges to put quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data with genomics data for future applications in precision medicine.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

High-throughput Luminex xMAP assay for simultaneous detection of antibodies against rabbit hemorrhagic disease virus, Sendai virus and rabbit rotavirus

Rabbits are widely used as models in biological research, and the pathogen status of rabbits used in studies can directly affect the results of experiments. Serological surveillance is the common monitoring method used in laboratory animals. A rapid, sensitive, and cost-effective high-throughput Luminex xMAP assay could be an attractive alternative to labor-intensive enzyme-linked immunosorbent assay (ELISA) methods. In this study, recombinant proteins from rabbit hemorrhagic disease virus and rabbit rotavirus and whole viral lysates of Sendai virus were used as coating antigens in an xMAP assay for the simultaneous detection of antibodies against these pathogens. The xMAP assay showed high specificity, with no cross-reaction with other pathogens. The coefficient of variation for intra-assay and inter-assay comparisons was less than 3% and 4%, respectively, indicating good repeatability and stability of the assay. The xMAP assay exhibited similar limits of detection for rabbit hemorrhagic virus and Sendai virus and was less sensitive for the detection of rabbit rotavirus when compared with commercial ELISA kits. A total of 52 clinical samples were tested simultaneously using both the xMAP assay and ELISA kits. The results obtained using these two methods were 100% coincident. In summary, the novel xMAP assay offers an alternative choice for rapid and sensitive high-throughput detection of antibodies in rabbit serum and can be used as a daily monitoring tool for laboratory animals.

Senolytics in idiopathic pulmonary fibrosis: Results from a first-in-human, open-label, pilot study

Background

Cellular senescence is a key mechanism that drives age-related diseases, but has yet to be targeted therapeutically in humans. Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal cellular senescence-associated disease. Selectively ablating senescent cells using dasatinib plus quercetin (DQ) alleviates IPF-related dysfunction in bleomycin-administered mice.

Methods

A two-center, open-label study of intermittent DQ (D:100 mg/day, Q:1250 mg/day, three-days/week over three-weeks) was conducted in participants with IPF (n = 14) to evaluate feasibility of implementing a senolytic intervention. The primary endpoints were retention rates and completion rates for planned clinical assessments. Secondary endpoints were safety and change in functional and reported health measures. Associations with the senescence-associated secretory phenotype (SASP) were explored.

Findings

Fourteen patients with stable IPF were recruited. The retention rate was 100% with no DQ discontinuation; planned clinical assessments were complete in 13/14 participants. One serious adverse event was reported. Non-serious events were primarily mild-moderate, with respiratory symptoms (n = 16 total events), skin irritation/bruising (n = 14), and gastrointestinal discomfort (n = 12) being most frequent. Physical function evaluated as 6-min walk distance, 4-m gait speed, and chair-stands time was significantly and clinically-meaningfully improved (p < .05). Pulmonary function, clinical chemistries, frailty index (FI-LAB), and reported health were unchanged. DQ effects on circulat.ing SASP factors were inconclusive, but correlations were observed between change in function and change in SASP-related matrix-remodeling proteins, microRNAs, and pro-inflammatory cytokines (23/48 markers r ≥ 0.50).

Interpretation

Our first-in-humans open-label pilot supports study feasibility and provides initial evidence that senolytics may alleviate physical dysfunction in IPF, warranting evaluation of DQ in larger randomized controlled trials for senescence-related diseases.

ClinicalTrials.gov identifier: NCT02874989 (posted 2016–2018).

Proteomic profiles by multiplex microsphere suspension array

Advances in high-throughput proteomic approaches have provided substantial momentum to novel disease-biomarker discovery research and have augmented the quality of clinical studies. Applications based on multiplexed microsphere suspension array technology are making strong in-roads into the clinical diagnostic/prognostic practice. Conventional proteomic approaches are designed to discover a broad set of proteins that are associated with a specific medical condition. In comparison, multiplex microsphere immunoassays use quantitative measurements of selected set(s) of specific/particular molecular markers such as cytokines, chemokines, pathway signaling or disease-specific markers for detection, metabolic disorders, cancer, and infectious agents causing human, plant and animal diseases. This article provides a foundation to the multiplexed microsphere suspension array technology, with an emphasis on the improvements in the technology, data analysis approaches, and applications to translational and clinical research with implications for personalized and precision medicine.