Antibody-based proteomics and biomarker research - current status and limitations

Circulating pancreatic enzyme levels are a causal biomarker of type 1 diabetes

Novel biomarkers of type 1 diabetes (T1D) are needed for earlier detection of disease and identifying therapeutic targets. We identified biomarkers of T1D by combining plasma cis and trans protein QTLs (pQTLs) for 2,922 proteins in the UK Biobank with a T1D genome-wide association study (GWAS) in 157k samples. T1D risk variants at over 20% of known loci colocalized with cis or trans pQTLs, and distinct sets of T1D loci colocalized with immune, pancreatic secretion, or gut-related proteins. We identified 23 proteins with evidence for a causal role in using pQTLs as genetic instruments in Mendelian Randomization which included multiple sensitivity analyses. Proteins increasing T1D risk were involved in immune processes (e.g. HLA-DRA) and, more surprisingly, T1D protective proteins were enriched in pancreatic secretions (e.g. CPA1), cholesterol metabolism (e.g. APOA1), and gut homeostasis. Genetic variants associated with plasma levels of T1D-protective pancreatic enzymes such as CPA1 were enriched in cis-regulatory elements in pancreatic exocrine and gut enteroendocrine cells, and the protective effects of CPA1 and other enzymes on T1D were consistent when using instruments specific to acinar cells. Finally, pancreatic enzymes had decreased acinar expression in T1D, including CPA1 which was altered prior to onset. Together, these results reveal causal biomarkers and highlight processes in the exocrine pancreas, immune system, and gut that modulate T1D risk.

Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds

Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

The enigma of sclera-specific autoimmunity in scleritis

Scleritis is a severe and painful ophthalmic disorder, in which a pathogenic role for collagen-directed autoimmunity was repeatedly suggested. We evaluated the presence of sclera-specific antibodies in a large cohort of patients with non-infectious scleritis. Therefore, we prospectively collected serum samples from 121 patients with non-infectious scleritis in a multicenter cohort study in the Netherlands. In addition, healthy (n = 39) and uveitis controls (n = 48) were included. Serum samples were tested for anti-native human type II collagen antibodies using a validated enzyme-linked immunosorbent assay (ELISA). Further, sclera-specific antibodies were determined using indirect immunofluorescence (IIF) on primate retinal/scleral cryosections. Lastly, human leukocyte antigen (HLA) typing was performed in 111 patients with scleritis. Anti-type II collagen antibodies were found in 13% of scleritis patients, in 10% of healthy controls and in 11% of uveitis controls (p = 0.91). A specific reaction to scleral nerve tissue on IIF was observed in 33% of patients with scleritis, which was higher than in healthy controls (11%; p = 0.01), but similar to uveitis controls (25%; p = 0.36). Reactivity to the scleral nerve tissue was significantly associated with earlier onset of scleritis (48 versus 56 years; p < 0.001), bilateral involvement (65% versus 42%; p = 0.01), and less frequent development of scleral necrosis (5% versus 22%; p = 0.02). HLA-B27 was found to be twice as prevalent in patients with scleritis (15.3%) compared to a healthy population (7.2%). In conclusion, scleral nerve autoantibody reactivity was more common in scleritis and uveitis patients in contrast to healthy controls. Further research is needed to characterize these scleral-nerve directed antibodies and assess their clinical value.

A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Potential Biomarkers for Noninfectious Scleritis Identified by Serum and Tear Fluid Proteomics

Purpose

Scleritis is an extremely painful and potentially blinding inflammation of the sclera with unknown pathogenesis and unpredictable course. To gain insight in its disease process and identify biomarker candidates, we performed extensive proteomics in serum and tear fluid.

Design

Prospective multicenter cohort study.

Participants

A total of 121 patients with noninfectious scleritis (of which 39 active cases), 30 healthy controls, and 23 disease controls (uveitis and rheumatoid arthritis) were enrolled in the Netherlands from 2020 to 2022.

Methods

Serum, tear fluid of both eyes, and clinical data were gathered. The level of 368 inflammatory proteins was measured using proximity extension assays. Results were validated in an independent cohort of 15 patients with scleritis, and using addressable laser bead immunoassay, or enzyme-linked immunoassays. In addition, we studied an extended panel of matrix metalloproteinases in tear fluid of necrotizing scleritis with addressable laser bead immunoassay.

Main Outcome Measures

Statistically significant differences in the level of inflammatory proteins between patients with scleritis and control groups.

Results

Proteomics revealed 18 significantly upregulated or downregulated serum proteins in active scleritis cases compared with all control groups in both the discovery cohort and the validation cohort. The most upregulated protein was nuclear migration protein nudC (NudC; P = 0.0032), a protein involved in neurogenesis. The other significant hits included proteins involved in T-cell activation, apoptosis, epithelial barrier maintenance, and angiogenesis. Our tear fluid analysis showed matrix metalloproteinase 9 (MMP9) to be upregulated in the tear fluid of patients with scleral necrosis.

Conclusions

The results of our proteomics analysis suggest a role for neurogenesis, T-cell activation, disruption of epithelial barrier, and angiogenesis in the pathogenesis of scleritis, and highlight MMP9 and NudC as biomarkers with potential clinical relevance.

Funding Disclosures

The authors have no proprietary or commercial interest in any materials discussed in this article.

Circulating biomarkers of cardiovascular disease are related to aneurysm volume in abdominal aortic aneurysm

BACKGROUND

Surveillance programs in abdominal aortic aneurysms (AAA) are mainly based on imaging and leave room for improvement to timely identify patients at risk for AAA growth. Many biomarkers are dysregulated in patients with AAA, which fuels interest in biomarkers as indicators of disease progression. We examined associations of 92 cardiovascular disease (CVD)-related circulating biomarkers with AAA and sac volume.

METHODS

In a cross-sectional analysis, we separately investigated (1) 110 watchful waiting (WW) patients (undergoing periodic surveillance imaging without planned intervention) and (2) 203 patients after endovascular aneurysm repair (EVAR). The Cardiovascular Panel III (Olink Proteomics AB, Sweden) was used to measure 92 CVD-related circulating biomarkers. We used cluster analyses to investigate protein-based subphenotypes, and linear regression to examine associations of biomarkers with AAA and sac volume on CT scans.

RESULTS

Cluster analyses revealed two biomarker-based subgroups in both WW and EVAR patients, with higher levels of 76 and 74 proteins, respectively, in one subgroup versus the other. In WW patients, uPA showed a borderline significant association with AAA volume. Adjusting for clinical characteristics, there was a difference of -0.092 (-0.148, -0.036) loge mL in AAA volume per SD uPA. In EVAR patients, after multivariable adjustment, four biomarkers remained significantly associated with sac volume. The mean effects on sac volume per SD difference were: LDLR: -0.128 (-0.212, -0.044), TFPI: 0.139 (0.049, 0.229), TIMP4: 0.110 (0.023, 0.197), IGFBP-2: 0.103 (0.012, 0.194).

CONCLUSION

LDLR, TFPI, TIMP4, and IGFBP-2 were independently associated with sac volume after EVAR. Subgroups of patients with high levels of the majority of CVD-related biomarkers emphasize the intertwined relationship between AAA and CVD.ClinicalTrials.gov Identifier: NCT03703947.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Thimet oligopeptidase as a potential CSF biomarker for Alzheimer's disease: A cross-platform validation study

INTRODUCTION

Our previous antibody-based cerebrospinal fluid (CSF) proteomics study showed that Thimet oligopeptidase (THOP1), an amyloid beta (Aβ) neuropeptidase, was increased in mild cognitive impairment with amyloid pathology (MCI-Aβ+) and Alzheimer's disease (AD) dementia compared with controls and dementia with Lewy bodies (DLB), highlighting the potential of CSF THOP1 as an early specific biomarker for AD. We aimed to develop THOP1 immunoassays for large-scale analysis and validate our proteomics findings in two independent cohorts.

METHODS

We developed in-house CSF THOP1 immunoassays on automated Ella and Simoa platforms. The performance of the different assays were compared using Passing-Bablok regression analysis in a subset of CSF samples from the discovery cohort (n = 72). Clinical validation was performed in two independent cohorts (cohort 1: n = 200; cohort 2: n = 165) using the Ella platform.

RESULTS

THOP1 concentrations moderately correlated between proteomics analysis and our novel assays (Rho > 0.580). In both validation cohorts, CSF THOP1 was increased in MCI-Aβ+ (>1.3-fold) and AD (>1.2-fold) compared with controls; and between MCI-Aβ+ and DLB (>1.2-fold). Higher THOP1 concentrations were detected in AD compared with DLB only when both cohorts were analyzed together. In both cohorts, THOP1 correlated with CSF total tau (t-tau), phosphorylated tau (p-tau), and Aβ40 (Rho > 0.540) but not Aβ42.

DISCUSSION

Validation of our proteomics findings underpins the potential of CSF THOP1 as an early specific biomarker associated with AD pathology. The use of antibody-based platforms in both the discovery and validation phases facilitated the translation of proteomics findings, providing an additional workflow that may accelerate the development of biofluid-based biomarkers.

Novel biomarkers associated with thoracic aortic disease

BACKGROUND

Biomarkers might help to improve diagnosis, surveillance and risk stratification of thoracic aortic disease (TAD). We explored the association between a broad spectrum of cardiovascular biomarkers with clinical characteristics and thoracic aortic diameter in TAD patients.

METHODS

Venous blood-samples were obtained in 158 clinically stable TAD patients visiting our outpatient clinic (2017-2020). TAD was defined as a thoracic aortic diameter ≥ 40 mm, or genetic confirmation (hereditary TAD). The cardiovascular panel III of the Olink multiplex platform was used for batch analysis of 92 proteins. A comparison was made between biomarker levels in patients with and without previous aortic dissection and/or surgery, and with and without hereditary TAD. Linear regression analyses were applied to identify (relative, normalized) biomarker concentrations associated with the absolute thoracic aortic diameter (AD_max), and thoracic aortic diameter indexed for body surface area (ID_max).

RESULTS

Median age of study patients was 61.0 (IQR 50.3-68.8) years, 37.3% females. Mean AD_max and ID_max were 43.3 ± 5.4 mm and 21.3 ± 3.3 mm/m². After multivariable adjustment, Matrix Metalloproteinase-3 (MMP-3) and Insulin-like growth factor binding protein 2 (IGFBP-2) showed a significant positive association with AD_max and ID_max, respectively. Patients with previous aortic surgery/dissection had higher N-terminal-pro hormone BNP (NTproBNP) (median 3.67 [IQR 3.01-3.99] vs 2.84 [2.32-3.26], p ≤0.001). Patients with hereditary TAD had higher Trem-like transcript protein 2 (TLT-2) (median 4.64 [IQR 4.45-4.84]) than those with non-heriditary TAD (4.40 [4.17-4.64]; p = 0.00042).

CONCLUSIONS

Among a broad range of biomarkers, MMP-3 and IGFBP-2 were associated with disease severity in TAD patients. The pathophysiological pathways uncovered by these biomarkers, and their potential clinical use warrants further research.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

Plasma membrane proteins are a special class of biomolecules present on the cellular membrane. They provide the transport of ions, small molecules, and water in response to internal and external signals, define a cell's immunological identity, and facilitate intra- and intercellular communication. Since they are vital to almost all cellular functions, their mutants, or aberrant expression is linked to many diseases, including cancer, where they are a part of cancer cell-specific molecular signatures and phenotypes. In addition, their surface-exposed domains make them exciting biomarkers for targeting by imaging agents and drugs. This review looks at the challenges in identifying cancer-related cell membrane proteins and the current methodologies that solve most of the challenges. We classified the methodologies as biased, i.e., search cells for the presence of already known membrane proteins. Second, we discuss the unbiased methods that can identify proteins without prior knowledge of what they are. Finally, we discuss the potential impact of membrane proteins on the early detection and treatment of cancer.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Are the results from a multiplex proteomic assay and a conventional immunoassay for NT-proBNP and GDF-15 comparable?

BACKGROUND

We aimed to compare absolute plasma concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP) and growth differentiation factor 15 (GDF-15) obtained by a conventional immunoassay with the corresponding relative concentrations from a proximity extension assay (PEA) and compare the prognostic impact of the protein levels obtained from these assays.

METHODS

We evaluated 437 patients with peripheral arterial disease (PAD) and a population-based cohort of 643 individuals without PAD. Correlations were calculated using Spearman's rank correlation coefficients (rho). The discriminatory accuracy of the protein levels to predict future cardiovascular events was analyzed with Cox regression and presented as time-dependent areas under the receiver-operator-characteristic curves (tdAUCs).

RESULTS

For NT-proBNP, the two assays correlated with rho 0.93 and 0.93 in the respective cohort. The PEA values leveled off at higher values in both cohorts. The corresponding correlations for GDF-15 were 0.91 and 0.89. At 5 years follow-up, the tdAUCs in the patient cohort were similar for NT-proBNP and GDF-15 regardless of assay used (0.65-0.66). The corresponding tdAUCs in the population-based cohort were between 0.72 and 0.77.

CONCLUSION

Except for the highest levels of NT-proBNP, we suggest that PEA data for NT-proBNP and GDF-15 reliably reflects absolute plasma levels and contains similar prognostic information.

Proteomics of azoospermia: Towards the discovery of reliable markers for non-invasive diagnosis

PURPOSE

Azoospermia, as the most severe form of male infertility, no longer indicates sterility due to modern medical advancements. The current diagnostic procedure based on testicular biopsy has several drawbacks which urges the development of novel, non-invasive diagnostic procedures based on biomarkers. In the last two decades, there have been many proteomics studies investigating potential azoospermia biomarkers. In this review, we aimed to provide a critical evaluation of these studies.

EXPERIMENTAL DESIGN

Published articles were gathered by systematic literature search using Pubmed, Science Direct, and Google Scholar databases until March 2022 and were further preselected to include only studies on human samples.

RESULTS

A detailed review of these studies encompassed the proteomics platforms, sources of material, proposed candidate biomarkers, and their potential diagnostic specificity and sensitivity. In addition, emphasis was put on the top, most identified and validated biomarker candidates and their potential for discriminating azoospermia types and subtypes as well as predicting sperm retrieval success rate.

CONCLUSIONS

Proteomics research of azoospermia has laid the groundwork for the development of a more streamlined biomarker testing. The future research should be focused on well-designed studies including samples from all types/subtypes as well as further testing of the most promising biomarkers identified so far.

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools.

The recent advancements in the early detection of cancer biomarkers by DNAzyme-assisted aptasensors

Clinical diagnostics rely heavily on the detection and quantification of cancer biomarkers. The rapid detection of cancer-specific biomarkers is of great importance in the early diagnosis of cancers and plays a crucial role in the subsequent treatments. There are several different detection techniques available today for detecting cancer biomarkers. Because of target-related conformational alterations, high stability, and target variety, aptamers have received considerable interest as a biosensing system component. To date, several sensitivity-enhancement strategies have been used with a broad spectrum of nanomaterials and nanoparticles (NPs) to improve the limit and sensitivity of analyte detection in the construction of innovative aptasensors. The present article aims to outline the research developments on the potential of DNAzymes-based aptasensors for cancer biomarker detection.

Advances in proteomic profiling of pediatric kidney diseases

Chronic kidney disease (CKD) can progress to kidney failure and require dialysis or transplantation, while early diagnosis can alter the course of disease and lead to better outcomes in both pediatric and adult patients. Significant CKD comorbidities include the manifestation of cardiovascular disease, heart failure, coronary disease, and hypertension. The pathogenesis of chronic kidney diseases can present as subtle and especially difficult to distinguish between different glomerular pathologies. Early detection of adult and pediatric CKD and detailed mechanistic understanding of the kidney damage can be helpful in delaying or curtailing disease progression via precise intervention toward diagnosis and prognosis. Clinically, serum creatinine and albumin levels can be indicative of CKD, but often are a lagging indicator only significantly affected once kidney function has severely diminished. The evolution of proteomics and mass spectrometry technologies has begun to provide a powerful research tool in defining these mechanisms and identifying novel biomarkers of CKD. Many of the same challenges and advances in proteomics apply to adult and pediatric patient populations. Additionally, proteomic analysis of adult CKD patients can be transferred directly toward advancing our knowledge of pediatric CKD as well. In this review, we highlight applications of proteomics that have yielded such biomarkers as PLA2R, SEMA3B, and other markers of membranous nephropathy as well as KIM-1, MCP-1, and NGAL in lupus nephritis among other potential diagnostic and prognostic markers. The potential for improving the clinical toolkit toward better treatment of pediatric kidney diseases is significantly aided by current and future development of proteomic applications.

Damage-free evaluation of cultured cells based on multivariate analysis with a single-polymer probe

We present a pattern-recognition-based sensor that targets cell-derived components in culture media and evaluates cultured cells without damaging them. An array sensor made of a single-polymer probe was employed to obtain fluorescence response patterns of the analyte media, allowing successful identification of the type and state of the cells via multivariate analysis.

A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum

Serum is an important candidate in proteomics analysis as it potentially carries key markers on health status and disease progression. However, several important diagnostic markers found in the circulatory proteome and the low-molecular-weight (LMW) peptidome have become analytically challenging due to the high dynamic concentration range of the constituent protein/peptide species in serum. Herein, we propose a novel approach to improve the limit of detection (LoD) of LMW amino acids by combining mid-IR (MIR) and near-IR spectroscopic data using glycine as a model LMW analyte. This is the first example of near-IR spectroscopy applied to elucidate the detection limit of LMW components in serum; moreover, it is the first study of its kind to combine mid-infrared (25-2.5 μm) and near-infrared (2500-800 nm) to detect an analyte in serum. First, we evaluated the prediction model performance individually with MIR (ATR-FTIR) and NIR spectroscopic methods using partial least squares regression (PLS-R) analysis. The LoD was found to be 0.26 mg/mL with ATR spectroscopy and 0.22 mg/mL with NIR spectroscopy. Secondly, we examined the ability of combined spectral regions to enhance the detection limit of serum-based LMW amino acids. Supervised extended wavelength PLS-R resulted in a root mean square error of prediction (RMSEP) value of 0.303 mg/mL and R² value of 0.999 over a concentration range of 0-50 mg/mL for glycine spiked in whole serum. The LoD improved to 0.17 mg/mL from 0.26 mg/mL. Thus, the combination of NIR and mid-IR spectroscopy can improve the limit of detection for an LMW compound in a complex serum matrix.

Vitreous proteomics, a gateway to improved understanding and stratification of diverse uveitis aetiologies

PURPOSE

The vitreous proteome might provide an attractive gateway to discriminate between various uveitis aetiologies and gain novel insights into the underlying pathophysiological processes. Here, we investigated 180 vitreous proteins to discover novel biomarkers and broaden disease insights by comparing (1). primary vitreoretinal lymphoma ((P)VRL) versus other aetiologies, (2). sarcoid uveitis versus tuberculosis (TB)-associated uveitis and (3). granulomatous (sarcoid and TB) uveitis versus other aetiologies.

METHODS

Vitreous protein levels were determined by proximity extension assay in 47 patients with intraocular inflammation and a prestudy diagnosis (cohort 1; training) and 22 patients with a blinded diagnosis (cohort 2; validation). Differentially expressed proteins identified by t-tests on cohort 1 were used to calculate Youden's indices. Pathway and network analysis was performed by ingenuity pathway analysis. A random forest classifier was trained to predict the diagnosis of blinded patients.

RESULTS

For (P)VRL stratification, the previously reported combined diagnostic value of IL-10 and IL-6 was confirmed. Additionally, CD70 was identified as potential novel marker for (P)VRL. However, the classifier trained on the entire cohort (cohort 1 and 2) relied primarily on the interleukin score for intraocular lymphoma diagnosis (ISOLD) or IL-10/IL-6 ratio and only showed a supportive role for CD70. Furthermore, sarcoid uveitis displayed increased levels of vitreous CCL17 as compared to TB-associated uveitis.

CONCLUSION

We underline the previously reported value of the ISOLD and the IL-10/IL-6 ratio for (P)VRL identification and present CD70 as a potentially valuable target for (P)VRL stratification. Finally, we also show that increased CCL17 levels might help to distinguish sarcoid uveitis from TB-associated uveitis.

Recent progress in the early detection of cancer based on CD44 biomarker; nano-biosensing approaches

CD44 is a cell matrix adhesion molecule overexpressed on the cell surfaces of the major cancers. CD44 as a cancer-related biomarker has an essential role in the invasion and metastasis of cancer. The detection and quantification of CD44 can provide essential information useful for clinical cancer diagnosis. In this regard, biosensors with sensitive and specific properties, give prominence to the development of CD44 detection platforms. To date, various aptamer-based sensitive-enhancers together with nanoparticles (NPs) have been combined into the biosensors systems to provide an innovative biosensing method (aptasensors/nano-aptasensors) with substantially improved detection limit. This review article discusses the recent advances in the field of biosensors, nanobiosensors, and aptasensors for the quantitative determination of CD44 and the detection of CD44-expressing cancer cells.

All on size-coded single bead set: a modular enrich-amplify-amplify strategy for attomolar level multi-immunoassay

Ultrasensitive protein analysis is of great significance for early diagnosis and biological studies. The core challenge is that many critical protein markers at extremely low aM to fM levels are difficult to accurately quantify because the target-induced weak signal may be easily masked by the surrounding background. Hence, we propose herein an ultrasensitive immunoassay based on a modular Single Bead Enrich-Amplify-Amplify (SBEAA) strategy. The highly efficient enrichment of targets on only a single bead (enrich) could confine the target-responsive signal output within a limited tiny space. Furthermore, a cascade tyramide signal amplification design enables remarkable in situ signal enhancement just affixed to the target. As a result, the efficient but space-confined fluorescence deposition on a single bead will significantly exceed the background and provide a wide dynamic range. Importantly, the SBEAA system can be modularly combined to meet different levels of clinical need regarding the detection sensitivity from aM to nM. Finally, a size-coded SBEAA set (SC-SBEAA) is also designed that allows ultrasensitive multi-immunoassay for rare samples in a single tube.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Multi-Omics Characterization of a Human Stem Cell-Based Model of Cardiac Hypertrophy

Cardiac hypertrophy is an important and independent risk factor for the development of cardiac myopathy that may lead to heart failure. The mechanisms underlying the development of cardiac hypertrophy are yet not well understood. To increase the knowledge about mechanisms and regulatory pathways involved in the progression of cardiac hypertrophy, we have developed a human induced pluripotent stem cell (hiPSC)-based in vitro model of cardiac hypertrophy and performed extensive characterization using a multi-omics approach. In a series of experiments, hiPSC-derived cardiomyocytes were stimulated with Endothelin-1 for 8, 24, 48, and 72 h, and their transcriptome and secreted proteome were analyzed. The transcriptomic data show many enriched canonical pathways related to cardiac hypertrophy already at the earliest time point, e.g., cardiac hypertrophy signaling. An integrated transcriptome–secretome analysis enabled the identification of multimodal biomarkers that may prove highly relevant for monitoring early cardiac hypertrophy progression. Taken together, the results from this study demonstrate that our in vitro model displays a hypertrophic response on both transcriptomic- and secreted-proteomic levels. The results also shed novel insights into the underlying mechanisms of cardiac hypertrophy, and novel putative early cardiac hypertrophy biomarkers have been identified that warrant further investigation to assess their potential clinical relevance.

Cytokine Elevation in Severe COVID-19 From Longitudinal Proteomics Analysis: Comparison With Sepsis

Introduction

Coronavirus disease 2019 (COVID-19) is a new viral disease. Uncontrolled inflammation called “cytokine storm” is reported to contribute to disease pathogenesis as well as sepsis. We aimed to identify cytokines related to the pathogenesis of COVID-19 through a proteomics analysis of 1463 plasma proteins, validate these cytokines, and compare them with sepsis.

Materials and Methods

In a derivation cohort of 306 patients with COVID-19, 1463 unique plasma proteins were measured on days 1, 4, and 8. Cytokines associated with disease severity and prognosis were derived. In a validation cohort of 62 COVID-19 patients and 38 sepsis patients treated in the intensive care unit [ICU], these derived cytokines were measured on days 1 (day of ICU admission), 2-3, and 6-8 (maximum: 3 time points/patient). Derived cytokines were compared with healthy controls and between COVID-19 and sepsis patients, and the associations with prognosis were evaluated. The time to wean off mechanical ventilation (MV) was evaluated only for COVID-19.

Results

IL-6, amphiregulin, and growth differentiation factor (GDF)-15 were associated with disease severity and prognosis in the derivation cohort. In the validation cohort, IL-6 and GDF-15 were elevated in COVID-19 and sepsis on day 1, and the levels of these cytokines were higher in sepsis than in COVID-19. IL-6 and GDF-15 were associated with prognosis in sepsis. Cox proportional hazards model with time as a dependent covariate showed a significant relationship between plasma GDF-15 level and time to wean off MV (hazard ratio, 0.549 [95% confidence level, 0.382–0.789]). The GDF-15 level at ICU admission predicted late recovery.

Conclusion

GDF-15 and IL-6 derived from proteomics analysis were related with disease severity of COVID-19. Their values were higher in sepsis than in COVID-19 and were associated with prognosis in sepsis. In COVID-19 patients treated in the ICU, GDF-15 was associated with the time to wean off MV and better predicted late recovery.

Validation of antibodies: Lessons learned from the Common Fund Protein Capture Reagents Program

Large-scale generation of protein capture reagents remains a technical challenge, but their generation is just the beginning. Validation is a critical, iterative process that yields different results for different uses. Independent, community-based validation offers the possibility of transparent data sharing, with use case–specific results made broadly available. This type of resource, which can grow as new validation data are obtained for an expanding group of reagents, provides a community resource that should accompany future reagent-generating efforts. To address a pressing need for antibodies or other reagents that recognize human proteins, the National Institutes of Health Common Fund launched the Protein Capture Reagents Program in 2010 as a pilot to target human transcription factors. Here, we describe lessons learned from this program concerning generation and validation of research reagents, which we believe are generally applicable for future research endeavors working in a similar space.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

Affinity Diversification of a Polymer Probe for Pattern-recognition-based Biosensing Using Chemical Additives

Pattern-recognition-based sensing has attracted attention as a promising alternative to conventional sensing methods that rely on selective recognition. Here, we report on novel strategy using chemical additives with the ability to modulate probe/analyte interactions to more easily construct pattern-recognition-based sensing systems for proteins and cells. The fluorescence of dansyl-modified cationic poly-L-lysine (PLL-Dnc) is enhanced upon binding to proteins in aqueous solution, while the addition of salts, inert polymers, or alcohols modulates the protein/PLL-Dnc interactions via a variety of mechanisms. Subsequent readout of the fluorescence changes produces response patterns that reflect the characteristics of the analytes. Multivariate analysis of the response patterns allowed for accurate identification of not only eight structurally similar albumin homologues, but also four mammalian cells. This strategy, which uses inexpensive and common additives, significantly improves the accessibility of pattern-recognition-based sensing, which will offer new opportunities for the detection of various bioanalytes.

Associations of serially measured PCSK9, LDLR and MPO with clinical outcomes in heart failure

Aim: To investigate the temporal evolution of plasma proprotein convertase subtilisin/kexin type 9 (PCSK9), low-density lipoprotein receptor (LDLR) and myeloperoxidase (MPO) in relation to clinical outcome in chronic heart failure (CHF). Methodology & results: Trimonthly blood sampling was performed during a median follow-up of 2.2 (IQR 1.4-2.5) years in 263 CHF patients. Seventy patients reached the primary end point (PE) (cardiovascular death, heart transplantation, left ventricular assist device implantation or HF-hospitalization). MPO level was independently associated with the PE; the adjusted (for clinical factors) hazard ratio (aHR) per standard deviation difference in MPO was 1.71 (95% CI: 1.23-2.43) at any time during follow-up. PCSK9 level (HR: 1.45 [1.04-2.06]) and LDLR (HR: 0.66 [0.49-0.87]) were statistical significantly associated with the PE but only in unadjusted analyses. Slope of temporal MPO evolution (aHR: 1.34 [1.12-1.76] per 0.1 standard deviation/year difference in slope) and LDLR (aHR: 0.78 [0.61-0.90]) however, were associated with PE. Conclusion: Temporal patterns of MPO and LDLR are independently associated with clinical outcome in CHF, which illustrates the importance of assessing temporal evolutions. Clinical trial registration information: registered in ClinicalTrials.gov, number NCT01851538. https://clinicaltrials.gov/ct2/show/NCT01851538.

Blood-based protein profiling identifies serum protein c-KIT as a novel biomarker for hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is one of the most common hereditary heart diseases and can be classified into an obstructive (HOCM) and non-obstructive (HNCM) form. Major characteristics for HCM are the hypertrophy of cardiomyocytes and development of cardiac fibrosis. Patients with HCM have a higher risk for sudden cardiac death compared to a healthy population. In the present study, we investigated the abundancy of selected proteins as potential biomarkers in patients with HCM. We included 60 patients with HCM and 28 healthy controls and quantitatively measured the rate of a set of 92 proteins already known to be associated with cardiometabolic processes via protein screening using the proximity extension assay technology in a subgroup of these patients (20 HCM and 10 healthy controls). After validation of four hits in the whole cohort of patients consisting of 88 individuals (60 HCM patients, 28 healthy controls) we found only one candidate, c-KIT, which was regulated significantly different between HCM patients and healthy controls and thus was chosen for further analyses. c-KIT is a tyrosine-protein kinase acting as receptor for the stem cell factor and activating several pathways essential for cell proliferation and survival, hematopoiesis, gametogenesis and melanogenesis. Serum protein levels of c-KIT were significantly lower in patients with HCM than in healthy controls, even after adjusting for confounding factors age and sex. In addition, c-KIT levels in human cardiac tissue of patients with HOCM were significant higher compared to controls indicating high levels of c-KIT in fibrotic myocardium. Furthermore, c-KIT concentration in serum significantly correlated with left ventricular end-diastolic diameter in HOCM, but not HCM patients. The present data suggest c-KIT as a novel biomarker differentiating between patients with HCM and healthy population and might provide further functional insights into fibrosis-related processes of HOCM.

Relationship between circulating tumor-associated autoantibodies and clinical outcomes in advanced-stage NSCLC patients receiving PD-1/-L1 directed immune checkpoint inhibition

BACKGROUND

Durable tumor regressions are observed in a subset of advanced-stage non-small cell lung cancer (NSCLC) patients receiving PD-1/-L1 targeted immune checkpoint inhibitors (or 'immunotherapy') alone or in combination with chemotherapy. However, the majority of advanced NSCLC patients receiving these agents do not experience long-term disease control. Existing methods to identify patients most likely to gain clinical benefit from PD-1/-L1 immunotherapy have limitations, creating a need for improved methods to guide treatment selection, particularly for those likely to benefit from single-agent immunotherapy. Here, we describe the development of a series of novel assays for tumor-associated autoantibodies as part of an exploratory study intended to determine if these biomarkers have potential prognostic value in this setting.

METHOD

A selection of recombinant tumor autoantigens previously characterized for their diagnostic utility were developed and preliminarily evaluated by this study. These include: Fumarate Dehydrogenase (FH), Hydroxysteroid 17-Beta Dehydrogenase 10 (HSD17B10), Inosine Monophosphate Dehydrogenase 2 (IMPDH2), New York Esophageal Squamous Cell Carcinoma-1 (NY ESO-1), Phosphoglycerate Mutase 1 (PGAM1), and Vimentin. Custom Luminex immunobead assays were developed for these targets to quantitatively assess autoantibody levels in individual patient sera. Assays were erected as indirect immunoassays on MagPlex® Microspheres using standard carbodiimide/NHS-based chemistries, utilizing a biotin-conjugated secondary (i.e. anti-human IgG) antibody and R-phycoerythrin-conjugated streptavidin reporter system. Standard curves were created for quantitative purposes using commercially-available anti-antigen antibodies and permitted analytical performance characteristics to be calculated. These assays were used to preliminarily evaluate a series of pretreatment serum samples from stage IV NSCLC patients receiving anti PD-1/-L1 therapy after failure of at least one prior line of therapy (n = 40) and their classification efficiency calculated based on 12 months overall survival (OS) threshold.

RESULTS

Six assays were developed that each showed dynamic ranges of four orders of magnitude and provided more than 90% classification accuracy based on the observed clinical outcome data. Inter- and intra-assay precision was assessed within these standards and overall %CVs of ≤7% and ≤ 10%, respectively, were calculated. Generally, the baseline level of autoantibodies were significantly (p < 0.05) lower in the ≥12 months survival group relative to the <12 months survival groups. Serum titers of FH, HSD170B, NY-ESO-1, and vimentin were significantly correlated with ≥12 month survival (p-value 0.0038, 0.0061, 0.0073, and 0.022, respectively). IMPDH2 and PGAM1 were found to have marginal significance (p-value 0.08 and 0.076, respectively).

CONCLUSION

This study demonstrates an efficient and promising means for assessing circulating autoantibody titers that could be useful in selecting advanced NSCLC patients for PD-1/-L1 directed immunotherapy. Further exploration and validation of this paradigm is warranted to further refine current treatment selection methods for this therapeutic strategy.

Multimodal Imaging Mass Spectrometry: Next Generation Molecular Mapping in Biology and Medicine

Imaging mass spectrometry has become a mature molecular mapping technology that is used for molecular discovery in many medical and biological systems. While powerful by itself, imaging mass spectrometry can be complemented by the addition of other orthogonal, chemically informative imaging technologies to maximize the information gained from a single experiment and enable deeper understanding of biological processes. Within this review, we describe MALDI, SIMS, and DESI imaging mass spectrometric technologies and how these have been integrated with other analytical modalities such as microscopy, transcriptomics, spectroscopy, and electrochemistry in a field termed multimodal imaging. We explore the future of this field and discuss forthcoming developments that will bring new insights to help unravel the molecular complexities of biological systems, from single cells to functional tissue structures and organs.

Production of a polyclonal antibody against inosine-uridine preferring nucleoside hydrolase of Acanthamoeba castellanii and its access to diagnosis of Acanthamoeba keratitis

Acanthamoeba keratitis (AK) is a rare disease but its prevalence throughout the globe continues to grow, primarily due to increased contact lens usage. Since early-stage symptoms associated with AK closely resemble those from other corneal infections, accurate diagnosis is difficult and this often results in delayed treatment and exacerbation of the disease, which can lead to permanent visual impairment. Accordingly, developing a rapid Acanthamoeba–specific diagnostic method is highly desired. In the present study, a rapid and differential method for AK diagnosis was developed using the secretory proteins derived from the pathogenic Acanthamoeba. Among the vast quantities of proteins secreted by the pathogenic Acanthamoeba, an open reading frame of the inosine-uridine preferring nucleoside hydrolase (IPNH) gene was obtained. After expressing and purifying the IPNH protein using the pGEX 4T-3 vector system, mice were immunized with the purified proteins for polyclonal antibody generation. Western blot was performed using protein lysates of the human corneal cell, non-pathogenic amoeba, pathogenic amoeba, and clinical amoeba isolate along with lysates from other causes of keratitis such as Staphylococcus aureus, Pseudomonas aeruginosa, and Fusarium solani to confirm Acanthamoeba-specificity. Western blot using the polyclonal IPNH antibody revealed that IPNH was Acanthamoeba-specific since these proteins were only observed in lysates of Acanthamoeba origin or its culture media. Our findings indicate that the IPNH antibody of Acanthamoeba may serve as a potential agent for rapid and differential AK diagnosis.

A Multichannel Pattern-Recognition-Based Protein Sensor with a Fluorophore-Conjugated Single-Stranded DNA Set

Recently, pattern-recognition-based protein sensing has received considerable attention because it offers unique opportunities that complement more conventional antibody-based detection methods. Here, we report a multichannel pattern-recognition-based sensor using a set of fluorophore-conjugated single-stranded DNAs (ssDNAs), which can detect various proteins. Three different fluorophore-conjugated ssDNAs were placed into a single microplate well together with a target protein, and the generated optical response pattern that corresponds to each environment-sensitive fluorophore was read via multiple detection channels. Multivariate analysis of the resulting optical response patterns allowed an accurate detection of eight different proteases, indicating that fluorescence signal acquisition from a single compartment containing a mixture of ssDNAs is an effective strategy for the characterization of the target proteins. Additionally, the sensor could identify proteins, which are potential targets for disease diagnosis, in a protease and inhibitor mixture of different composition ratios. As our sensor benefits from simple construction and measurement procedures, and uses accessible materials, it offers a rapid and simple platform for the detection of proteins.

A rational route to hybrid aptamer-molecularly imprinted magnetic nanoprobe for recognition of protein biomarkers in human serum

Although antibody has played a great role in highly specific recognition of protein biomarkers, it faces poor stability, reproducibility, high-cost and time-consuming preparation, etc. Here, aptamer and molecularly imprinted polymers (MIPs), both as promising substitutes of antibody, were integrated onto magnetic nanoparticles by Au-S bonds and SiO₂ as imprinted layer for preparing a new nanoprobe. Highly specific and sensitive recognition of different protein biomarkers, such as insulin for diabetes and alpha-fetoprotein (AFP) for hepatic carcinoma, were achieved respectively by the system of combining hybrid aptamer-molecularly imprinted magnetic nanoprobe and mass spectrometry. With the double affinities offered by aptamer-MIPs, insulin can be detected at 0.5 ng mL^-1 in human serum dilution, the equlibrium dissociation constant between nanoprobe and insulin is measured as 23.61 ± 2.27 μM. Likewise, AFP can be sufficiently detected in human saliva dilution from 1000 ng mL^-1 to 20 ng mL^-1, and two patients with hepatic carcinoma are discriminated from healthy person due to the abnormally high expression of AFP in serum.

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Pattern-recognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on the pattern recognition of multivariate data, which are generated using an array of multiple probes that cross-reactively interact with analytes. This sensing approach is significantly different from conventional specific cell sensing based on highly specific probes, including antibodies against biomarkers. Encouraged by the advantages of this technique, such as the simplicity, rapidity, and tunability of the systems without requiring a priori knowledge of biomarkers, numerous sensor arrays have been developed over the past decade and used in a variety of cell sensing applications; these include disease diagnosis, drug discovery, and fundamental research. This review summarizes recent progress in pattern-recognition-based cell sensing, with a particular focus on guidelines for designing materials and arrays, techniques for analyzing response patterns, and applications of sensor systems that are focused primarily for the biomedical field.

Aptamer based proteomic pilot study reveals a urine signature indicative of pediatric urinary tract infections

Objective

Current urinary tract infection (UTI) diagnostic strategies that rely on leukocyte esterase have limited accuracy. We performed an aptamer-based proteomics pilot study to identify urine protein levels that could differentiate a culture proven UTI from culture negative samples, regardless of pyuria status.

Methods

We analyzed urine from 16 children with UTIs, 8 children with culture negative pyuria and 8 children with negative urine culture and no pyuria. The urine levels of 1,310 proteins were quantified using the Somascan^™ platform and normalized to urine creatinine. Machine learning with support vector machine (SVM)-based feature selection was performed to determine the combination of urine biomarkers that optimized diagnostic accuracy.

Results

Eight candidate urine protein biomarkers met filtering criteria. B-cell lymphoma protein, C-X-C motif chemokine 6, C-X-C motif chemokine 13, cathepsin S, heat shock 70kDA protein 1A, mitogen activated protein kinase, protein E7 HPV18 and transgelin. AUCs ranged from 0.91 to 0.95. The best prediction was achieved by the SVMs with radial basis function kernel.

Conclusions

Biomarkers panel can be identified by the emerging technologies of aptamer-based proteomics and machine learning that offer the potential to increase UTI diagnostic accuracy, thereby limiting unneeded antibiotics.

Apoptosis regulation in the penumbra after ischemic stroke: expression of pro- and antiapoptotic proteins

Ischemic stroke is the leading cause of human disability and mortality in the world. The main problem in stroke therapy is the search of efficient neuroprotector capable to rescue neurons in the potentially salvageable transition zone (penumbra), which is expanding after brain damage. The data on molecular mechanisms of penumbra formation and expression of diverse signaling proteins in the penumbra during first 24 h after ischemic stroke are discussed. Two basic features of cell death regulation in the ischemic penumbra were observed: (1) both apoptotic and anti-apoptotic proteins are simultaneously over-expressed in the penumbra, so that the fate of individual cells is determined by the balance between these opposite tendencies. (2) Similtaneous and concerted up-regulation in the ischemic penumbra of proteins that execute apoptosis (caspases 3, 6, 7; Bcl-10, SMAC/DIABLO, AIF, PSR), signaling proteins that regulate different apoptosis pathways (p38, JNK, DYRK1A, neurotrophin receptor p75); transcription factors that control expression of various apoptosis regulation proteins (E2F1, p53, c-Myc, GADD153); and proteins, which are normally involved in diverse cellular functions, but stimulate apoptosis in specific situations (NMDAR2a, Par4, GAD65/67, caspase 11). Hence, diverse apoptosis initiation and regulation pathways are induced simultaneously in penumbra from very different initial positions. Similarly, various anti-apoptotic proteins (Bcl-x, p21/WAF-1, MDM2, p63, PKBα, ERK1, RAF1, ERK5, MAKAPK2, protein phosphatases 1α and MKP-1, estrogen and EGF receptors, calmodulin, CaMKII, CaMKIV) are upregulated. These data provide an integral view of neurodegeneration and neuroprotection in penumbra. Some discussed proteins may serve as potential targets for anti-stroke therapy.

Temporal patterns of macrophage- and neutrophil-related markers are associated with clinical outcome in heart failure patients

AIMS

Evidence on the association of macrophage- and neutrophil-related blood biomarkers with clinical outcome in heart failure patients is limited, and, with the exception of C-reactive protein, no data exist on their temporal evolution. We aimed to investigate whether temporal patterns of these biomarkers are related to clinical outcome in patients with stable chronic heart failure (CHF).

METHODS AND RESULTS

In 263 patients with CHF, we performed serial plasma measurements of scavenger receptor cysteine-rich type 1 protein M130 (CD163), tartrate-resistant acid phosphatase type 5 (TRAP), granulins (GRN), spondin-1 (SPON1), peptidoglycan recognition protein 1 (PGLYRP1), and tissue factor pathway inhibitor (TFPI). The Cardiovascular Panel III (Olink Proteomics AB, Uppsala, Sweden) was used. During 2.2 years of follow-up, we collected 1984 samples before the occurrence of the composite primary endpoint (PE) or censoring. For efficiency, we selected 567 samples for the measurements (all baseline samples, the last two samples preceding the PE, and the last sample before censoring in event-free patients). The relationship between repeatedly measured biomarker levels and the PE was evaluated by joint models. Mean (±standard deviation) age was 67 ± 13 years; 189 (72%) were men; left ventricular ejection fraction (%) was 32 ± 11. During follow-up, 70 (27%) patients experienced the PE. Serially measured biomarkers predicted the PE in a multivariable model adjusted for baseline clinical characteristics [hazard ratio (95% confidence interval) per 1-standard deviation change in biomarker]: CD163 [2.07(1.47-2.98), P < 0.001], TRAP [0.62 (0.43-0.90), P = 0.009], GRN [2.46 (1.64-3.84), P < 0.001], SPON1 [3.94 (2.50-6.50), P < 0.001], and PGLYRP1 [1.62 (1.14-2.31), P = 0.006].

CONCLUSIONS

Changes in plasma levels of CD163, TRAP, GRN, SPON1, and PGLYRP1 precede adverse cardiovascular events in patients with CHF.

Temporal Patterns of 14 Blood Biomarker candidates of Cardiac Remodeling in Relation to Prognosis of Patients With Chronic Heart Failure-The Bio- SH i FT Study

Background

Remodeling biomarkers carry high potential for predicting adverse events in chronic heart failure (CHF) patients. However, temporal patterns during the course of CHF, and especially the trajectory before an adverse event, are unknown. We studied the prognostic value of temporal patterns of 14 cardiac remodeling biomarker candidates in stable patients with CHF from the Bio‐SHiFT (Serial Biomarker Measurements and New Echocardiographic Techniques in Chronic Heart Failure Patients Result in Tailored Prediction of Prognosis) study.

Methods and Results

In 263 CHF patients, we performed trimonthly blood sampling during a median follow‐up of 2.2 years. For the analysis, we selected all baseline samples, the 2 samples closest to the primary end point (PE), or the last sample available for end point–free patients. Thus, in 567 samples, we measured suppression of tumorigenicity‐2, galectin‐3, galectin‐4, growth differentiation factor‐15, matrix metalloproteinase‐2, 3, and 9, tissue inhibitor metalloproteinase‐4, perlecan, aminopeptidase‐N, caspase‐3, cathepsin‐D, cathepsin‐Z, and cystatin‐B. The PE was a composite of cardiovascular mortality, heart transplantation, left ventricular assist device implantation, and HF hospitalization. Associations between repeatedly measured biomarker candidates and the PE were investigated by joint modeling. Median age was 68 (interquartile range: 59–76) years with 72% men; 70 patients reached the PE. Repeatedly measured suppression of tumorigenicity‐2, galectin‐3, galectin‐4, growth differentiation factor‐15, matrix metalloproteinase‐2 and 9, tissue inhibitor metalloproteinase‐4, perlecan, cathepsin‐D, and cystatin‐B levels were significantly associated with the PE, and increased as the PE approached. The slopes of biomarker trajectories were also predictors of clinical outcome, independent of their absolute level. Associations persisted after adjustment for clinical characteristics and pharmacological treatment. Suppression of tumorigenicity‐2 was the strongest predictor (hazard ratio: 7.55 per SD difference, 95% CI: 5.53–10.30), followed by growth differentiation factor‐15 (4.06, 2.98–5.54) and matrix metalloproteinase‐2 (3.59, 2.55–5.05).

Conclusions

Temporal patterns of remodeling biomarker candidates predict adverse clinical outcomes in CHF.

Clinical Trial Registration

URL: http://www.clinicaltrials.gov. Unique identifier: NCT01851538.

Circulating Biomarkers of Cell Adhesion Predict Clinical Outcome in Patients with Chronic Heart Failure

Cardiovascular inflammation and vascular endothelial dysfunction are involved in chronic heart failure (CHF), and cellular adhesion molecules are considered to play a key role in these mechanisms. We evaluated temporal patterns of 12 blood biomarkers of cell adhesion in patients with CHF. In 263 ambulant patients, serial, tri-monthly blood samples were collected during a median follow-up of 2.2 (1.4-2.5) years. The primary endpoint (PE) was a composite of cardiovascular mortality, HF hospitalization, heart transplantation and implantation of a left ventricular assist device and was reached in 70 patients. We selected the baseline blood samples in all patients, the two samples closest to a PE, or, for event-free patients, the last sample available. In these 567 samples, associations between biomarkers and PE were investigated by joint modelling. The median age was 68 (59-76) years, with 72% men and 74% New York Heart Association class I-II. Repeatedly measured levels of Complement component C1q receptor (C1qR), Cadherin 5 (CDH5), Chitinase-3-like protein 1 (CHI3L1), Ephrin type-B receptor 4 (EPHB4), Intercellular adhesion molecule-2 (ICAM-2) and Junctional adhesion molecule A (JAM-A) were independently associated with the PE. Their rates of change also predicted clinical outcome. Level of CHI3L1 was numerically the strongest predictor with a hazard ratio (HR) (95% confidence interval) of 2.27 (1.66-3.16) per SD difference in level, followed by JAM-A (2.10, 1.42-3.23) and C1qR (1.90, 1.36-2.72), adjusted for clinical characteristics. In conclusion, temporal patterns of C1qR, CDH5, CHI3L1, EPHB4, ICAM2 and JAM-A are strongly and independently associated with clinical outcome in CHF patients.

Profile of neuronal exosomes in HIV cognitive impairment exposes sex differences

OBJECTIVE

To use plasma neuron-derived exosomes (NDEs) to detect proteins that diagnose HIV-associated neurocognitive disorders (HAND). To compare NDE cargo from HAND with Alzheimer's disease.

DESIGN

Eighty plasma samples were assayed including men (n = 29) and women (n = 51) with and without HAND.

METHODS

Plasma NDEs were isolated by immunoadsorption with neuron specific L1 cell adhesion molecule antibody. NDE proteins were quantified by ELISA and proximity extension assays for 184 targets.

RESULTS

Neuronal enrichment of NDE was confirmed with elevated synaptophysin and normalized to the exosomal marker, apoptosis-linked gene-2-interacting protein X (ALIX). NDE from men and women had significant divergent results. High mobility group box 1 and neurofilament light were significantly increased in NDE from cognitively impaired men and were unchanged in women. NDE from HIV+ men had decreased p-T181-tau, a marker increased in Alzheimer's disease, compared with no difference in women. NDE amyloid beta was not increased in cognitive impairment. Proximity extension assays analysis showed 25 proteins were differentially expressed in HIV infection alone. Seven proteins identified asymptomatic and mild cognitive impairment in HIV+ women. NDE from women had significantly decreased cathepsin S, total tau, neuronal cell adhesion molecule and contactin 5 in mild impairment. Twelve proteins were increased in NDE from cognitively impaired men, including carboxypeptidase M, cadherin 3, colony stimulating factor 2 receptor alpha subunit and mesencephalic astrocyte-derived neurotropic factor.

CONCLUSION

NDE proteins differ in HIV infection alone and cognitive impairment between men and women suggesting mechanistic sex differences associated with HAND. Several NDE targets are different from that reported for Alzheimer's disease.

Time-Resolved Digital Immunoassay for Rapid and Sensitive Quantitation of Procalcitonin with Plasmonic Imaging

Timely diagnosis of acute diseases improves treatment outcomes and saves lives, but it requires fast and precision quantification of biomarkers. Here, we report a time-resolved digital immunoassay based on plasmonic imaging of binding of single nanoparticles to biomarkers captured on a sensor surface. The real-time and high contrast of plasmonic imaging lead to fast and precise counting of the individual biomarkers over a wide dynamic range. We demonstrated the detection principle, evaluated the performance of the method using procalcitonin (PCT) as an example, and achieved a limit of detection of ∼2.8 pg/mL, dynamic range of 4.2-12500 pg/mL, for a total detection time of ∼25 min.