Biomarker Discovery Applications Research Articles

Immunoproteomics: A Review of Techniques, Applications, and Advancements.

Immunoproteomics is the branch of proteomics with an emphasis on the study of functional peptides and proteins related to the immune system. Combining proteomics techniques with immunological research aims to uncover the complex interactions of proteins involved in immune responses. This review discusses the methods, applications, and recent advancements in immunoproteomics, highlighting its critical role in understanding immune responses, discovering biomarkers, and developing vaccines and therapeutics. This study offers a comprehensive exploration of the methodologies, applications, and advancements within immunoproteomics. Techniques such as mass spectrometry, antibody-based assays, and computational analysis are pivotal in unraveling the complexities of the immune system at the protein level. Immunoproteomics finds diverse applications in biomarker discovery, vaccine development, autoimmune disease research, infectious disease diagnostics, and cancer immunotherapy. Challenges, including data integration, sample heterogeneity, and biomarker validation, persist, necessitating innovative approaches and interdisciplinary collaborations. In the future, immunoproteomics will likely play a major role in expanding our knowledge of immune-related diseases and accelerating the creation of targeted and precise immunotherapies.

Advancements and Challenges in Biomarkers for Colorectal Cancer Detection: A Comprehensive Review

This study provides an overview of the current landscape of biomarkers for colorectal cancer (CRC) detection, focusing on genetic, proteomic, circulating microRNA (miRNA), and metabolomic biomarkers. CRC remains a significant global health challenge, ranking among the most prevalent cancers worldwide and being a leading cause of cancer-related deaths. Despite advancements in screening methods such as colonoscopy, sigmoidoscopy, and fecal occult blood tests (FOBT), the asymptomatic nature of early-stage CRC often results in late diagnoses, negatively impacting patient outcomes. Genetic biomarkers like APC, KRAS, TP53, and microsatellite instability (MSI) play critical roles in CRC pathogenesis and progression. These biomarkers, detectable through polymerase chain reaction, next-generation sequencing, and other advanced techniques, guide early detection and personalized treatment decisions. Proteomic biomarkers such as CEA, CA 19-9, and novel signatures offer insights into CRC’s physiological changes and disease status, aiding prognosis and treatment response assessments through enzyme-linked immunosorbent assay and mass spectrometry. Circulating miRNAs, including miR-21 and miR-92a, present promising non-invasive biomarkers that can be detected in blood and stool samples, reflecting CRC presence, progression, and therapeutic response. Metabolomic biomarkers, encompassing amino acids, lipids, and TCA cycle intermediates, provide further insights into CRC-associated metabolic alterations, which are crucial for early detection and treatment monitoring using mass spectrometry and nuclear magnetic resonance. Despite these advancements, challenges such as biomarker validation, standardization, and clinical utility remain. Future research directions include integrating multi-omics approaches and leveraging technologies like liquid biopsies and AI for enhanced biomarker discovery and clinical application. By addressing these challenges and advancing research in biomarker development, CRC screening and management could potentially be revolutionized, improving patient outcomes and reducing the global burden of this disease.

ScPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of mass spectrometry (MS), particularly when coupled with liquid chromatography (LC), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized-medicine advancement. MS-based technologies have transformed precision medicine, enabling a comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing the detection of low-abundance molecules and the elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress toward precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. While past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. Here, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semi-parametric generalized additive model (GAM) on fitting tumor volume data from over 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models, the latter is often considered the standard growth model. On the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that eGaIT, a GAM derived efficacy metric, is equivalent to eGR, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on Paclitaxel, anti-PD-1 antibody, Cetuximab, Irinotecan, and Sorafenib, we show that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared eGR-based association analysis and exponential modeling approach in biomarker discovery and found they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

A Carrier-Based Quantitative Proteomics Method Applied to Biomarker Discovery in Pericardial Fluid

Data-dependent liquid chromatography tandem mass spectrometry is challenged by the large concentration range of proteins in plasma and related fluids. We adapted the SCoPE method from single-cell proteomics to pericardial fluid, where a myocardial tissue carrier was used to aid protein quantification. The carrier proteome and patient samples were labeled with distinct isobaric labels, which allowed separate quantification. Undepleted pericardial fluid from patients with type 2 diabetes mellitus and/or heart failure undergoing heart surgery was analyzed with either a traditional liquid chromatography tandem mass spectrometry method or with the carrier proteome. In total, 1398 proteins were quantified with a carrier, compared to 265 without, and a higher proportion of these proteins were of myocardial origin. The number of differentially expressed proteins also increased nearly four-fold. For patients with both heart failure and type 2 diabetes mellitus, pathway analysis of upregulated proteins demonstrated the enrichment of immune activation, blood coagulation, and stress pathways. Overall, our work demonstrates the applicability of a carrier for enhanced protein quantification in challenging biological matrices such as pericardial fluid, with potential applications for biomarker discovery. Mass spectrometry data are available via ProteomeXchange with identifier PXD053450.

IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine.

Artificial intelligence (AI) and machine learning (ML) have advanced in several areas and fields of life; however, its progress in the field of multi-omics is not matching the levels others have attained. Challenges include but are not limited to the handling and analysis of high volumes of complex multi-omics data, and the expertise needed to implement and execute AI/ML approaches. In this article, we present IntelliGenes, an interactive, customizable, cross-platform, and user-friendly AI/ML application for multi-omics data exploration to discover novel biomarkers and predict rare, common, and complex diseases. The implemented methodology is based on a nexus of conventional statistical techniques and cutting-edge ML algorithms, which outperforms single algorithms and result in enhanced accuracy. The interactive and cross-platform graphical user interface of IntelliGenes is divided into three main sections: (i) Data Manager, (ii) AI/ML Analysis, and (iii) Visualization. Data Manager supports the user in loading and customizing the input data and list of existing biomarkers. AI/ML Analysis allows the user to apply default combinations of statistical and ML algorithms, as well as customize and create new AI/ML pipelines. Visualization provides options to interpret a diverse set of produced results, including performance metrics, disease predictions, and various charts. The performance of IntelliGenes has been successfully tested at variable in-house and peer-reviewed studies, and was able to correctly classify individuals as patients and predict disease with high accuracy. It stands apart primarily in its simplicity in use for nontechnical users and its emphasis on generating interpretable visualizations. We have designed and implemented IntelliGenes in a way that a user with or without computational background can apply AI/ML approaches to discover novel biomarkers and predict diseases.

High-throughput surface epitope immunoaffinity isolation of extracellular vesicles and downstream analysis.

Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high-throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilizes affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high-throughput isolation of EVs from biofluids and downstream analysis of RNA, protein, and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new SEI isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. microRNA (miRNA) sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high-throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high-throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high-throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

Optimization of protocols for blood-derived extracellular vesicles for studies in liver diseases

Aim: Extracellular vesicles (EVs) have gained significant attention for their diagnostic and therapeutic potential in various diseases, including liver disorders. This study focuses on optimizing the isolation and characterization of small EVs from plasma and serum samples in patients with liver diseases, aiming to advance our understanding and potential clinical applications of EVs. Methods: Blood samples were collected from patients with end-stage liver disease (ESLD) enlisted in the TransplantLines Cohort and Biobank Study, and healthy donors. We employed differential ultracentrifugation (DUC) to evaluate three distinct protocols: a 3-step DUC, a washing step omitted [samples without washing (WW)], and a contaminant-depleted plasma (CDP) protocol. RNA isolation methodologies were compared, involving the use of TRI-reagent or the commercial AllPrep DNA/RNA kit. Further insights into EV composition were obtained through proteomic analyses, comparing samples subjected to traditional cell lysis (L) with those processed without lysis (NL). Results: We successfully isolated EVs from both plasma and serum samples as confirmed by the presence of specific EV markers, including CD9, CD63, CD81, and tumor susceptibility gene 101 (TSG-101). While some contaminants remained, such as albumin and lipoproteins, the protocol selected to continue EVs analysis was the 3-step protocol. Transmission electron microscopy (TEM) and nanotracking analysis (NTA) further confirmed EVs presence. RNA extraction was achieved using TRI-reagent, but not with the commercial kit highlighting the importance of selecting an appropriate method for RNA isolation. Finally, proteomics analysis showed that lysed samples were significantly more enriched in proteins compared to non-lysed samples, although protein variability was still present in both groups. Conclusions: Optimizing EV isolation techniques is essential for harnessing their potential in liver disease diagnosis and therapy. Further refinement of purification methods, a deep characterization of our cohort and understanding the variability and cargo within EVs will be crucial for future biomarker discovery and therapeutic applications in liver-related diseases.

Abstract 1848: The Cellular Thermal Shift assay and its applications in Target ID, MoA determination and biomarker discovery

Abstract With the Cellular Thermal Shift Assay (CETSA) celebrating its first decade since the PoC publication, the method has gained much interest in the basic science field as well as become an industry standard in applied drug discovery setting. In the last few years it has become apparent that in addition to providing information about drug-protein interaction, CETSA based thermal profiling of cells can help describe additional aspects of cell biology as it gives insight into the activating and rewiring of protein-protein interaction networks inside the cell upon stimuli. In the work presented here, we have used CETSA coupled to high resolution mass spectrometric readout to profile the response patterns of about 8000 proteins to several hundred molecular probes and marketed drugs. The goal has been to provide a map of wanted and unwanted effects following treatment and to correlate it to alternative readouts, by for example cellular imaging and cell death assays. We have applied the CETSA protocol for close to five hundred compounds, many in several cell-lines (HepG2, U87MG and K562). The experimental setup includes the step of treating the cells with a certain compound at a fixed concentration. After incubation for an hour, we heated the cells to a range of temperatures, followed by a lysis protocol and pooling of the samples. After this, the samples have been centrifuged to separate the soluble from insoluble fraction. The soluble fraction has then been prepared for MS acquisition using TMT based multiplexing. Typically, this allowed us to monitor changes in thermal stability among more than 8,000 proteins, as a consequence of compound treatment. This data reveals specific compound “fingerprints” that can be used to decipher mode of action and identify biomarkers. Despite the short incubation time, proteins with compound-induced thermal stability shifts are not only targets/off-targets (direct binders), but also downstream, and sometimes upstream, pathway members and general cellular responses. All data combined makes the basis of the Target Engagement Atlas. Here we can see clusters of compounds, sharing similar protein binding profiles. Among the most prominent ones are mTOR inhibitors, NSAIDs, as well as tubulin binders. Alternatively, one can use this resource to reveal clusters of “pharmacologically” associated protein networks. We show that pharmacological perturbation allows identification of clusters representing tight protein complexes (for example ribosomes), known metabolic pathways (folate biosynthesis), molecular functions (kinases), as well as networks of proteins linked only by ligand binding specificity. CETSA MS allows for unbiased, cellular, and molecular profiling of compound effects. Together with data from clinical settings, as well as in vivo/in vitro end point assays it is possible to correlate the CETSA patterns to wanted but also unwanted responses. Citation Format: Tomas Friman, Alexey Chernobrovkin, Tuomas Tolvanen, Erin Gilson, Stina Lundgren, Victoria Brehmer, Daniel Martinez Molina. The Cellular Thermal Shift assay and its applications in Target ID, MoA determination and biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1848.

Construction of a hydrophilic porphyrin-based MOF@COF hybrid via post-synthetic modification for N-glycopeptides analysis in human serum.

A hybrid material, M@C-Br@GMA@Glu, consisting of a porphyrin-based metal-organic framework-covalent organic framework (MOF@COF), has been meticulously synthesized using a post-synthetic modification approach. This advanced material has demonstrated exceptional effectiveness in glycopeptide enrichment, characterized by an impressively low detection limit (0.2 fmol μL-1), high selectivity (1 : 2000), and loading capacity for glycopeptides (100 mg g-1). In practical applications with complex biological samples, 210 glycopeptides associated with 87 glycoproteins from the serum of the healthy group, and 156 glycopeptides related to 85 glycoproteins from the serum of cervical cancer were identified after enrichment with M@C-Br@GMA@Glu. Subsequent genetic ontology analysis has elucidated the relationship between cervical cancer and glycosylation, focusing on biological processes such as complement activation, innate immune response mechanisms, and the structural dynamics of the extracellular matrix. The collective findings not only validate the material's proficiency in the sensitive and selective enrichment of glycopeptides, but also underscore its potential in biomarker discovery applications.

Mass spectrometry-based proteomic profiling of extracellular vesicle proteins in diabetic and non-diabetic ischemic stroke patients: a case-control study.

Acute ischemic stroke is the most common cause of neurologic dysfunction caused by focal brain ischemia and tissue injury. Diabetes is a major risk factor of stroke, exacerbating disease management and prognosis. Therefore, discovering new diagnostic markers and therapeutic targets is critical for stroke prevention and treatment. Extracellular vesicles (EVs), with their distinctive properties, have emerged as promising candidates for biomarker discovery and therapeutic application. This case-control study utilized mass spectrometry-based proteomics to compare EVs from non-diabetic stroke (nDS = 14), diabetic stroke (DS = 13), and healthy control (HC = 12) subjects. Among 1288 identified proteins, 387 were statistically compared. Statistical comparisons using a general linear model (log2 foldchange ≥0.58 and FDR-p≤0.05) were performed for nDS vs HC, DS vs HC, and DS vs nDS. DS vs HC and DS vs nDS comparisons produced 123 and 149 differentially expressed proteins, respectively. Fibrinogen gamma chain (FIBG), Fibrinogen beta chain (FIBB), Tetratricopeptide repeat protein 16 (TTC16), Proline rich 14-like (PR14L), Inhibitor of nuclear factor kappa-B kinase subunit epsilon (IKKE), Biorientation of chromosomes in cell division protein 1-like 1 (BD1L1), and protein PR14L exhibited significant differences in the DS group. The pathway analysis revealed that the complement system pathways were activated, and blood coagulation and neuroprotection were inhibited in the DS group (z-score ≥2; p ≤ 0.05). These findings underscore the potential of EVs proteomics in identifying biomarkers for stroke management and prevention, warranting further clinical investigation.

REVIEW ON MALDI MASS SPECTROMETRY AND ITS APPLICATION IN CLINICAL RESEARCH

Mass spectrometry has emerged as a powerful analytical tool in various scientific disciplines, and Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) has gained significant prominence, particularly in the field of clinical research. This review provides a comprehensive overview of MALDI-MS, its principles, and its applications in clinical research settings. This study delves into the technical aspects of MALDI-MS, discussing key components such as the matrix, sample preparation methods, and mass analyzers. It highlights recent advancements and innovations in MALDI-MS technology, showcasing its evolution and adaptation to meet the increasing demands of clinical applications. MALDI-MS involves the use of laser energy to desorb and ionize molecules from a solid-phase matrix, enabling the analysis of a wide range of biomolecules, including proteins, peptides, lipids, and metabolites. The technique's ability to analyze complex biological samples with high sensitivity and specificity has made it invaluable in clinical research. In the realm of clinical research, MALDI-MS has found applications in disease diagnosis, biomarker discovery, and therapeutic monitoring. The review explores specific examples of MALDI-MS applications in various clinical areas, including oncology, infectious diseases, and metabolic disorders. Case studies and experimental findings are presented to underscore the practical utility of MALDI-MS in these contexts. Moreover, the review discusses the challenges and limitations associated with MALDI-MS in clinical research, such as standardization of protocols, reproducibility, and sample heterogeneity. Strategies to overcome these challenges are addressed, emphasizing ongoing efforts to enhance the reliability and robustness of MALDI-MS-based analyses in clinical settings. MALDI-MS has proven to be a transformative technology in clinical research, offering unique insights into the molecular landscape of diseases and paving the way for personalized medicine. As MALDI-MS continues to evolve, its integration into routine clinical workflows holds the potential to revolutionize diagnostics and improve patient outcomes. This review serves as a valuable resource for researchers, clinicians, and industry professionals seeking a comprehensive understanding of MALDI-MS and its myriad applications in the field of clinical research.&#x0D; Keywords: MALDI-MS, Clinical Research, Review, Ionization Mass Spectrometry.

Poster Session I: Leveraging AI to accelerate scientific discoveries.

We introduce a structured approach that leverages AI to accelerate scientific discoveries. We showcase the efficacy of this technique via a proof-of-concept study identifying markers of sex in retinal images. Our methodology consists of four stages: In Phase 1, CNN development, we train a VGG model to recognize patient sex from retinal images. Phase 2, Inspiration, involves reviewing post-hoc interpretation tools' visualizations to draw observations and formulate exploratory hypotheses regarding the CNN model's decision process. This yielded 14 testable hypotheses related to potential variances in vasculature and optic disc. In Phase 3, Exploration, we test these hypotheses on an independent dataset, of which nine demonstrated significant differences. In Phase 4, Verification, five out of nine these nine hypotheses are re-tested on a new dataset, verifying five of them: significantly greater length, more nodes and branches of retinal vasculature, a larger area covered by vessels in the superior temporal quadrant, and a darker peri-papillary region in male eyes. Finally, we conducted a psychophysical study and trained a group of ophthalmologists (N=26) to identify these new retinal features for sex classification. Their performance, initially on par with chance and a non-expert group (N=31), significantly improved post-training (p<.001, d=2.63). These outcomes illustrate the potential of our methodology in leveraging AI applications for retinal biomarker discovery.

A Conceptual Framework of Data Readiness for the Health and Aging Brain Study‐Health Disparities

AbstractBackgroundThe Health and Aging Brain Study: Health Disparities (HABS‐HD) seeks to understand the biological, social and environmental factors that impact brain aging among diverse communities. HABS‐HD, like many other NIH funded data‐sharing projects, has important data assets for various uses, including social, environmental and behavioral data, and multiple data flow pathways. Machine learning (ML) develops algorithms and models to continuously improve itself over time, but the determination of data quality and its readiness are needed for these models to operate efficiently. Therefore, developing a data readiness reporting methodology has become a very urgent task for HABS‐HD.MethodIn this study, we developed a conceptual framework of data readiness. First, we analyzed the missing data percentage and used ML‐Based Multiple Imputation (MLMI) for missing data imputation. Then, we performed SVM based on Recursive Feature Elimination and Cross Validation (SVM‐RFE‐CV) for feature elimination and outlier removal. Lastly, we rated the data readiness based on the three metrics: missing data percentage, performance before feature engineering, and performance after feature engineering to rate data readiness. All the three scores were averaged to rate the overall readiness of data.ResultA framework for calculating overall average score for readiness of data was presented (1 stands for completely accessible, 0 for not accessible at all, and 0.5 for neutral). Our results show that the framework of data readiness was straightforward and useful in assessing how ready the HABS‐HD data is for ML.ConclusionThe systematic analysis of readiness of data before building ML models is of utmost importance. And it has a significant impact on biomarker discovery and disease prediction application for Alzheimer’s disease. The conceptual framework of data readiness works well for our Alzheimer’s disease models in HABS‐HD. It can also be applied to other disease data readiness reporting.

Cheaper, faster, simpler trypsin digestion for high-throughput targeted protein quantification

IntroductionLC-MS-based methods for protein quantification have a stigma of being relatively expensive and low-throughput. This is partly due to the cost and speed of trypsin digestion, which has primarily focused on advancements in research-based biomarker discovery applications that rely on protein/peptide identifications rather than clinical biomarker quantification. However, there is a need for simple, fast, and reproducibly efficient surrogate peptide recovery in clinical biomarker quantification. MethodsMultiple methodologies were evaluated to enhance tryptic digestion for the analysis of thyroglobulin, a prototypical serum protein biomarker. The main criteria for assessment were the yield and speed of formation of surrogate peptides. Various factors such as different additives, types of trypsin, microwave- and pressure-assisted systems, and enzyme concentration were considered as key variables, in addition to digestion time. ResultsIt was observed that digestion additives/denaturants had a significant impact on the speed and yield of digestion for each surrogate peptide. Increasing the concentration of trypsin alone was found to accelerate digestions appreciably for most surrogate peptides, without affecting the yield. However, the use of sequencing-grade trypsins and microwave/pressure-assisted systems did not offer significant advantages over the use of 'standard-grade' TPCK-treated trypsin in combination with a conventional incubator, once digestion time and additive had been optimized. ConclusionWe have dispelled the notion that trypsin digestion is inherently slow and expensive for targeted quantification of serum proteins. Additionally, we have established a groundwork for experimentation that can pave the way for the creation of efficient trypsin digestion protocols, aiming to optimize yield, speed, and cost. It is our hope that these advancements will promote the wider incorporation of such assays in clinical laboratories.

One-pot preparation of bi-functional POSS-based hybrid monolith via photo-initiated polymerization for isolation of extracellular vesicles

Extracellular vesicles (EVs) are important participants in numerous pathophysiological processes, and could be used as valuable biomarkers to detect and monitor various diseases. However, facile EV isolation methods are the essential and preliminary issue for their downstream analysis and function investigation. In this work, a polyhedral oligomeric silsesquioxanes (POSS) based hybrid monolith combined metal affinity chromatography (MAC) and distearoyl phospholipid ethanolamine (DSPE) function was developed via photo-initiated thiol-ene polymerization. This synthesis process was facile, simple and convenient, and the obtained hybrid monolith could be applied to efficiently isolate EVs from bio-samples by taking advantages of the specific bond of Ti4+ and phosphate groups on the phospholipid membrane of EVs and the synergistic effect of DSPE insertion. Meanwhile, the eluted EVs could maintain their structural integrity and biological activity, suggesting they could be used for downstream application. Furthermore, 75 up-regulated proteins and 56 down-regulated proteins were identified by comparing the urinary EVs of colorectal cancer (CRC) patients and healthy donors, and these proteins might be used as potential biomarkers for early screening of CRC. These results demonstrated that this hybrid monolith could be used as a simple and convenient tool for isolating EVs from bio-samples and for wider applications in biomarker discovery.

A Thorough Examination of the Recent Advances in Mass Spectrometry

Abstract: Mass spectrometry has become an essential tool in pharmaceutical analysis, revolutionizing drug development, quality assurance, and our understanding of complex biological systems. This review provides a comprehensive overview of recent advances in mass spectrometry for pharmaceutical analysis. We discuss the fundamentals of mass spectrometry, including ionization and mass analysis principles, as well as the various types of mass spectrometers used in pharmaceutical analysis. We explore high-resolution mass spectrometry (HRMS), tandem mass spectrometry (MS/MS), ambient ionization mass spectrometry, and mass spectrometry imaging (MSI), highlighting their applications in drug characterization, quantification, imaging, and biomarker discovery. Furthermore, we examine the challenges faced by mass spectrometry, such as matrix effects and data interpretation, and discuss emerging trends and future perspectives. By understanding the recent advancements and addressing the challenges, mass spectrometry can continue to drive advancements in pharmaceutical analysis and quality assurance

MEvA-X: a hybrid multiobjective evolutionary tool using an XGBoost classifier for biomarkers discovery on biomedical datasets.

Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection (FS) and classification, combining a niche-based multiobjective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multiobjective EA to optimize the hyperparameters of the classifier and perform FS, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important nonredundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. https://github.com/PanKonstantinos/MEvA-X.