Mass spectrometry (MS) based proteomics provides unbiased quantification of all proteins in plasma, which can dynamically reflect individual health states in real time. However, large-scale proteomics studies are constrained by the excessive dynamic range of plasma proteome and low throughput. Herein, two kinds of magnetic metal-organic frameworks (MOFs) modified with ion exchange functional groups (denoted as MHP-UiO-66-SAX and MHP-HKUST-1-SCX) were designed and fabricated to exhibit large protein adsorption capability, which were combined with an automated Liquid-handling System, thus realizing in-depth, high-throughput and automated proteomics studies. The constructed workflow could automatically complete the sample preparation before MS within only six hours and nearly a thousand protein groups per sample could be quantified. In the cohort study of nearly one hundred breast cancer neoadjuvant chemotherapy (NC) plasma samples, two differentially expressed proteins previously reported as biomarkers were related with the pathological complete response (PCR) of the breast cancer, demonstrating the feasibility of the developed technology for preparing large-scale clinical samples and exhibiting the potential application in monitoring the effect of chemotherapy.

In biological and biomedical research, it's a crucial task to detect or quantify proteins or proteomes accurately across multiple samples. Immunohistochemistry (IHC) and spatial proteomics based on mass spectrometry imaging (MSI) are used to detect proteins in tissue samples. IHC can detect precisely but has a limited throughput, whereas MSI can simultaneously visualize thousands of specific chemical components but hindered by detailed protein annotation. Thereby, the introduction of mass tags may be adopted to expand the potential for integrating MSI and IHC. By enriching optical information for IHC and enhancing MS signals, mass tags can boost the accuracy of qualitative, localization, and quantitative detection of specific proteins in tissue sections, thereby widening the scope of protein detection and annotation results. Consequently, more comprehensive information regarding biological processes and disease states can be obtained, which aids in understanding complex biological processes and disease mechanisms and provides additional perspectives for clinical diagnosis and treatment. In the current review, we aim to discuss the role of different mass tags (e.g., mass tags based on inorganic molecules and organic molecules) in the combined application of MSI and IHC.

Proteomics has emerged as a transformative tool in biomedical research, enabling comprehensive characterization of protein profiles in complex biological systems. In preeclampsia (PE) research, quantitative proteomic analyses of plasma and serum have revealed critical insights into disease mechanisms and potential biomarkers. Through a systematic review of 17 studies (2009-2024), we identified 561 differentially expressed plasma/serum proteins (p < 0.05) in PE patients versus healthy controls, with 122 proteins consistently replicated across ≥2 independent studies. Stratified analysis by clinical subtype (early-vs. late-onset PE) demonstrated both concordant and divergent protein expression patterns, reflecting heterogeneity in PE pathophysiology, methodological variations (e.g., sample processing, proteomic platforms), and differences between discovery-phase and targeted validation studies. The trimester-specific biomarker panels proposed here offer a framework for future large-scale, multicenter validation. By integrating advanced proteomic technologies with standardized preanalytical and analytical protocols, these findings advance opportunities for early prediction (first-trimester biomarker signatures); mechanistic insight (complement system involvement); and personalized management (subtype-specific therapeutic targets). This work underscores the potential of proteomics to reshape PE research, from molecular discovery to clinical translation, ultimately improving outcomes for this leading cause of maternal and perinatal morbidity.

Needle-based blood draws or phlebotomy practice in clinics for centuries, often causing pain, discomfort, and inconvenience. Here, a wearable photonic device is presented by integrating a microlens array (MLA) and an optic microneedle array (OMNA) functionalized with immunobinding for safe and needle-free biomarker sampling and detection. The MLA-integrated OMNA amplifies and transmits LED light at 595 nm into skin through the OMNA, bypassing the light-absorbing melanin in the epidermal layer, and evenly distributing it in the capillary-enriched dermis independent of the skin colors. The 595 nm light is absorbed by hemoglobin (Hb) and oxygen-Hb within the capillaries, triggering thermal dilation of capillaries without damaging them or causing petechiae. The light illumination remarkably increases in the concentrations of various blood biomarkers in the skin through biomarker extravasation. These biomarkers bound specifically to the capture antibodies on OMNA with each microneedle covalently immobilized with one specific antibody. The OMNA is extensively modified to amplify the immunobinding signals and achieve sensitivity superior to that of enzyme-linked immunosorbent assay (ELISA) kits. As proof of concept, the functionality of the prototype for minimally invasive sampling and precise multiplexed blood biomarker detection in two mouse models is validated to quantify acute inflammation and specific antibody production.

Background/Objectives: In Sudan, oral cancer is one of the top ten most common cancers, with OSCC representing the majority of the cases. To date, despite the fact that saliva can be collected simply and non-invasively, there is no approved salivary tumor marker for OSCC. This study aimed to investigate the reliability of salivary CA-125 as a tumor marker for OSCC by measuring and comparing its level among OSCC and healthy individuals as well as its level across different histopathological grades. Methods: A total of 100 subjects were enrolled; 50 were patients with OSCC, while the other 50 were matched healthy individuals. Non-stimulated whole saliva was collected before the administration of definitive treatment, and the concentration of salivary CA-125 was quantified using an automated immunoassay analyzer that employs a one-step sandwich fluorescent enzyme immunoassay (FEIA). Results: The level of salivary CA-125 was 342.65 U/mL in the cases group, which was significantly increased compared with 203.65 U/mL in the healthy controls (p = 0.017). Statistically significant differences in the level of salivary CA-125 among different histopathological grades were observed (p = 0.014). The sensitivity, specificity, accuracy, and positive and negative predictive values were 48%, 78%, 63%, 68.6%, and 60%, respectively. Conclusions: This study suggests that salivary CA-125 could serve as a potential tumor marker for OSCC. However, its clinical application requires further validation.

Reliable and accessible technologies for biological sample analysis are crucial for rapid, accurate illness diagnosis in resource-limited settings. Conventional methods like mass spectrometry and enzyme-linked immunosorbent assay (ELISA) often face challenges of technological complexity and equipment immobility, leading to delayed diagnoses and elevated costs. This study introduces evaporative deposition patterns from sessile droplets as a simple, cost-effective approach for classifying proteins in protein-salt mixtures, leveraging quantitative texture analysis to achieve high diagnostic accuracy. Using mixtures of potassium chloride (KCl) or sodium chloride (NaCl) with Bovine Serum Albumin (BSA) or Ovalbumin (OVA) as model systems, we varied salt (0.25-2 wt%) and protein (0.5-1.5 wt%) concentrations, observing distinct deposition morphologies such as coffee rings, cruciform/dendritic crystals at high salt levels, and volcano-shaped /spoke-like patterns at low or high protein levels. A novel quantitative framework integrating radial density distributions, gray-level co-occurrence matrix (GLCM) texture features, and receiver operating characteristic (ROC) curves achieved a 96.74 % classification accuracy for NaCl-protein mixtures, surpassing traditional texture analysis in simplicity and efficacy. These findings demonstrate the potential of evaporative deposition analysis as a simple tool for distinguishing molecular mixtures, laying the groundwork for future diagnostic applications. Unlike prior qualitative studies, this work provides a robust, quantitative method for protein classification, advancing the understanding of protein-salt interactions.

BackgroundCerebral amyloid angiopathy (CAA) is a form of cerebral small vessel disease (SVD) associated with Alzheimer's disease, intracerebral hemorrhage, and cognitive decline. Despite its clinical significance, no reliable serum biomarker exists for early diagnosis or monitoring of disease progression.ObjectiveThis study hypothesizes that α1-acid glycoprotein (α1-AGP) and other serum biomarkers can aid CAA diagnosis and assessment using gel-based mass spectrometry. A comparative analysis was performed to investigate associations between serum biomarkers and radiological scores.MethodsSerum proteins from individuals with probable or possible CAA (n = 10), classified using the modified Boston criteria, and age-matched controls (n = 10) were analyzed via two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF-MS). Candidate proteins were validated using enzyme-linked immunosorbent assay (ELISA). Outcome measures included biomarker diagnostic accuracy, assessed by receiver operating characteristic (ROC) curve analysis, and correlations between α1-AGP levels and CAA-SVD scores.ResultsFour proteins-hemopexin, complement C3, complement C9, and α1-AGP-were significantly elevated, while apolipoprotein A-1 was reduced in the CAA group. ELISA confirmed higher α1-AGP levels in individuals with CAA (p < 0.0001). ROC analysis demonstrated that α1-AGP could indicate the presence of CAA with a sensitivity and specificity of 1.00 (95%CI: 1.000, 1.000). Additionally, α1-AGP levels correlated with the CAA-SVD score (R² = 0.783).Conclusionsα1-AGP may serve as a novel serum biomarker for CAA. Larger cohorts and external validation are required to substantiate these findings and determine their clinical relevance.

We previously examined plasma metabolic changes before and after botulinum toxin-A injections of cerebral palsy (CP) children and showed that the glycine, serine and threonine metabolism may play a key role in neuritogenesis. This study analysed untargeted metabolomics combined with proteomics of plasma to discussed which substances are meaningfully changed, to what extent they affect the effects of action.

Blood samples were collected from 91 children with spastic CP at 4 time points: pre-injection (T1), 1 month post-injection (T2), 3 months post-injection (T3) and 6 months post-injection (T4). Differentially changed metabolites and proteins were selected, and co-expression pathways were constructed to explore the key molecular processes.

A total of 674 proteins and 354 metabolites were identified. The differential metabolites were mainly involved in the linoleic acid metabolism, beta-Alanine metabolism, citrate cycle, pyruvate metabolism and glycolysis or gluconeogenesis. Differential proteins were primarily associated with glucose metabolism, lipid metabolism, immune and inflammation responses. Co-expression pathways showed that ECM-receptor interaction, complement and coagulation cascades, glycolysis or gluconeogenesis, pyruvate metabolism, and linoleic acid metabolism were the main pathways.

Our results indicated the botulinum toxin-A predominantly activated the glucose metabolism, lipid metabolism, and immune and inflammation responses, and energy metabolism changed significantly in this process.

ChiCTR2000033800, Research on the mechanism of botulinum toxin relieving spasticity in children with cerebral palsy. Approval No. 202023041. Registered 13 June 2020, http://www.chictr.org.cn/showproj.html?proj=52267 .

This is the first study that combined dynamic metabolomics and proteomics analysis to investigate the molecular changes in children with spastic cerebral palsy after botulinum toxin-A injections, which might provide a theoretical reference for the further subsequent study for targets to increase the efficacy and prolong the duration of botulinum toxin-A, and would be a valuable resource for the metabolomics and proteomics field in this group.

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

Analysis of the blood proteome allows identification of proteins related to changes upon certain physiological conditions. The pathophysiology of necrotic enteritis (NE) has been extensively studied. While intestinal changes have been very well documented, data addressing NE-induced alterations in the blood proteome are scant, although these might have merit in diagnostics. In light of recent technological advancements in proteomics and pressing need for tools to access gut health, the current study employs mass-spectrometry (MS)-based proteomics to identify biomarkers for gastrointestinal health of chickens. Here, we report findings of an untargeted proteomics investigation conducted on blood plasma in chickens under NE challenge. Two MS-strategies were used for analysis: conventional data dependent acquisition coupled to standard nanoflow liquid chromatography (LC) (nano-DDA) and recently-developed data independent acquisition coupled to an Evosep One LC system (Evo-DIA). Despite superior completeness and quantification of the Evo-DIA-acquired data, high degree of agreement in identification and quantification was observed between both approaches. Additionally, we identified 15 differentially expressed proteins (shared by nano-DDA and Evo-DIA) that represent responses of animals to infection and may serve as potential biomarkers. Experimental validation through ELISA immunoassays and targeted MS for selected regulated proteins (CFD, HPS5, and MASP2) confirmed medium-to-high levels of inter-protein correlation. A GSEA analysis revealed enrichment in a number of processes related to adaptive and humoral immunity, immune activation and response in infected animals. Data are available via ProteomeXchange with identifiers PXD050461, PXD050473, and PXD061607.

Protein biomarkers in human serum provide critical insights into various physiological conditions and diseases, enabling early diagnosis, prognosis, and personalized treatment. However, detecting low-abundance protein biomarkers is challenging due to the presence of highly abundant proteins that make up ∼99% of the plasma proteome. Here, we report the use of in situ metal-organic framework (MOF) growth in serum to effectively deplete highly abundant serum proteins for integrated proteomic analysis. Through biomolecule-mediated nucleation of a zeolitic imidazolate framework (ZIF-8), abundant plasma proteins are selectively encapsulated within ZIF-8 and removed from serum via centrifugation, leaving a depleted protein fraction in the supernatant. Bottom-up proteomics analysis confirmed significant depletion of the topmost abundant proteins, many at depletion levels exceeding 95%. Such depletion enabled the identification of 277 total proteins in the supernatant (uncaptured) fraction in a single-shot analysis, including 54 proteins that were only identified after depletion, 12 drug targets, and many potential disease biomarkers. Top-down proteomics characterization of the captured and uncaptured protein fractions at the proteoform-level confirmed this method is not biased toward any specific proteoform of individual proteins. These results demonstrate that in situ MOF growth can selectively and effectively deplete high-abundance proteins from serum in a simple, low cost, one-pot synthesis to enable integrated top-down and bottom-up proteomic analysis of serum protein biomarkers.

Difficulties in early-stage diagnosis are among the factors contributing to the high mortality of nonsmall cell lung carcinoma (NSCLC) patients. Unfortunately, diagnostic biomarkers are currently lacking, limiting options in the clinic. To discover proteins that have potential for biomarker applications, we performed an in-depth quantitative proteomic analysis on a cohort of Filipino early-stage NSCLC lung adenocarcinoma (LUAD) patients. Differentially expressed proteins (DEPs) were obtained by using tandem mass tag (TMT) labeling and mass spectrometry (MS)-based quantitative proteomics. A total of 6240 quantified proteins were identified with 3155 significantly upregulated and 1248 significantly downregulated. Integration of the proteomic result with curated transcriptome data allowed the identification of 33 proteins with biomarker potential. This study also provided insights into relevant pathways in NSCLC LUAD, such as protein translation and metabolic pathways. Interestingly, all of the enzymes in the hexosamine biosynthetic pathway (HBP) are found to be upregulated, suggesting its important role in NSCLC LUAD. It is worthwhile to look at the potential of targeting the metabolic vulnerability of NSCLC LUAD as a new strategy in drug development. All MS data were deposited into ProteomeXchange with the identifier PXD050598.

Biomarker analysis enables a deep understanding of physiological and biological processes and offers insights into pathological disease states and conditions. When measured in tissues, the spatial distribution of biomarkers may be evaluated. To meet regulatory and sponsor requirements, guidance on the approach to validation and the parameters to be evaluated is essential. The main goals of this GCC white paper are to disseminate the survey results discussed during the 16th& 17thGCC Closed Forums (2023 & 2024) and to provide recommendations from the GCC members on technical and regulatory considerations for the bioanalysis of biomarkers in tissues.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that poses a significant risk to human health and well-being. The high cost and invasiveness of neuroimaging and cerebrospinal fluid (CSF) analysis underscores the necessity for accessible early screening via blood samples. In this study, we developed an ultrasound-based strategy for emergent macroscopic that enhances the acoustic response enrichment of specific proteins by introducing functionalized microspheres. This customized macroscopic-directed aggregation method combines protein enrichment with specific fluorescent antibody recognition, enabling quantitative detection characterized by high sensitivity and specificity for tau proteins in clinical samples. In comparison with previously reported biosensing platforms, this strategy achieves indirectly driven clustering of tau proteins by integrating an acoustic resonant chamber, realizing facile and low-cost enrichment detection of protein without the need for amplification. Results demonstrate that this method exhibits a linear detection range from 1 pg/mL to 10 ng/mL, with a detection limit of 0.183 pg/mL. AD patients and healthy individuals were successfully distinguished with an accuracy of 90.9%. This ultrasonic biosensing strategy based on protein aggregation exhibits potential as a valuable tool for early screening of AD.

Malaria is a parasitic infectious disease considered a public health problem. Acute respiratory distress syndrome (ARDS) is a complication in malaria-infected individuals with a high mortality rate (80% to 100%) and can occur before, during, or after antimalarial drug treatment. Although inflammation and epithelial/endothelial injury pathways have been determined through these studies, specific circulating malaria-associated ARDS markers have not yet been established. We applied a quantitative mass spectrometry (MS)-based proteomic approach to identify altered molecular pathways in a mouse model of malaria-associated ARDS. Acute-phase response (APR) proteins were regulated in the ARDS group, suggesting their potential involvement in the development of the syndrome. They may serve as biomarkers when analyzed alongside other proteins that require further investigation. Additionally, the regulation of APR proteins in the ARDS group provides valuable insights into the pathophysiology of ARDS, contributing to a better understanding of the syndrome.

Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant). By analyzing gene co-variation and employing machine learning algorithms, the objective was to identify genes that could predict pregnancy outcomes in cattle. Initially, within each tissue type, the top 100 differentially co-expressed genes (DCEGs) were identified based on the analysis of changes in correlation coefficients and network topological structure. Subsequently, these genes were used in models trained by seven different machine learning algorithms. Overall, models trained on DCEGs exhibited superior predictive accuracy compared to those trained on an equivalent number of differential expression genes. Among them, the deep learning models based on differential co-expression genes in blood and endometrial tissue achieved prediction accuracies of 91.7% and 82.6%, respectively. Finally, the importance of DCEGs was ranked using SHapley Additive exPlanations (SHAP) and enrichment analysis, identifying key signaling pathways that influence pregnancy. In summary, this study identified a set of genes potentially affecting pregnancy by analyzing the overall co-variation of gene connections between multiple sources. These key genes facilitated the development of interpretable machine learning models that accurately predict pregnancy outcomes in cattle.

Parkinson's disease (PD) is a progressive neurodegenerative disorder for which reliable blood biomarkers to predict disease progression remain elusive. Plasma extracellular vesicles (EVs) have gained attention as a promising biomarker platform due to their stability and ability to cross the blood-brain barrier. This study explored the potential of EV-cargo proteins, specifically α-synuclein, tau, and β-amyloid, as biomarkers of PD progression. A cohort of 55 people with PD (PwP) and 58 healthy controls (HCs) underwent annual assessments of plasma EV proteins, cognition, and motor symptoms. EVs were isolated and validated using standardized methods, with pathognomonic proteins quantified via immunomagnetic reduction assays. Associations between biomarker changes and clinical symptom progression were analyzed. Over an average of 3.96 visits for PwP and 2.25 visits for HCs, PwP exhibited a distinct pattern of plasma EV protein changes linked to motor symptom progression, particularly in the Unified PD Rating Scale (UPDRS) part II score. Notably, changes in plasma EV α-synuclein levels were significantly correlated with changes in motor and cognitive symptoms, suggesting its central role in disease progression. These findings highlight the potential of plasma EV biomarkers, especially α-synuclein, as indicators of ongoing pathogenesis and as candidates for evaluating α-synuclein-targeted therapies in PD.

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy which mainly consists of serous, mucinous, clear cell, and endometrioid subtypes. Due to the lack of classic symptoms at an early stage, EOC usually presented as advanced tumors with local and/or distant metastasis. Although a large portion of EOC was initially platinum-sensitive, most patients would acquire resistance to common chemotherapeutic agents. These aforementioned issues lead to a challenge for clinical treatments and unsatisfying outcomes. Previous studies have demonstrated the genetic features of EOC are hard to target and the alterations at DNA and RNA levels are not fully represented at the protein expression profiles which made it more complex. In recent years, a panel of studies attempted to explore the key proteins involved in the development and progression of EOC using high-throughput proteomic technologies. We herein summarized them to provide a full view of this topic.

Digital enzyme-linked immunosorbent assays (dELISAs) very sensitively detect biomarkers that cannot be measured using traditional methods. The molecules are confined within a small volume, their counts accurately computed, and the results rapidly delivered. Digital ELISAs find many applications. In recent years, such ELISAs have become increasingly used to aid ophthalmological diagnoses and treatments, and have revolutionized the field. This article reviews the applications of dELISAs in clinical practice, especially in the sphere of ophthalmology.

The serum nanoparticle-protein corona (NPC) provides specific disease information, thus opening a new avenue for high-throughput in-depth proteomics to facilitate biomarker discovery. Yet, little is known about the interactions between NPs and proteins, and its role in enhanced depth of serum proteomics. Herein, a series of protein spike-in experiments are conducted to systematically investigate protein depletion and enrichment during NPC formation. Proteomic depth is serum concentration-dependent, and NPC exhibits powerful tolerance to ultra-high abundant proteins. In addition, protein-protein interactions (PPI), especially those involving albumin, play a pivotal role in promoting proteomic depth. Furthermore, a triple-protein assay is established to interrogate the relationship between protein binding affinity and concentration. NPC formation is a product of balancing binding affinity, concentration, and PPI. Overall, this study elucidates how NPs achieve protein depletion and enrichment for enhanced serum proteomic depth to gain a better understanding of NPC as an essential tool of proteome profiling.

Blood plasma is the most informative body fluid, containing large amounts of substances that are released by active secretion or leakage from tissues and cells. Therefore, plasma changes reflect the body state. To explore changes in plasma during the early life of Wistar-rats, the plasma proteomes of newborn and first-week rats were investigated using liquid chromatography-tandem mass spectrometry. A total of 639 proteins were identified at both developmental stages and 570 proteins were used for quantitative analysis. The plasma of first-week rats, compared to that in newborn rats using label-free quantification, showed that the levels of 42 proteins significantly increased while those of 17 proteins decreased. Plasma proteomic patterns at both developmental stages can be easily separated using differential protein cluster analysis. Using the Ingenuity Pathway analysis tool, some pathways including LXR/RXR Activation, DHCR24 Signaling Pathway, Acute Phase Response Signaling, and Detoxification of Reactive Oxygen Species were significantly enhanced. Over 10 categories related to the development and functions were enriched. Plasma proteomes of first-week rats were distinct from those of newborn rats. These changes would make it easier for newborn rats to survive. This is the first study using liquid chromatography-tandem mass spectrometry to investigate newborn rat plasma proteome changes, providing a basis and clues for studying animal development.

Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through extensive datasets. This study focuses on identifying biomarkers in gene expression data related to chemical injuries by mustard gas, covering a spectrum from healthy individuals to severe injuries.

The study utilized RNA-Seq data comprising 52 expression data samples for 54,583 gene transcripts. These samples were categorized into four classes based on the GOLD classification for chemically injured individuals: Severe (n = 14), Moderate (n = 11), Mild (n = 16), and healthy controls (n = 11). Data preparation involved examining an Excel file created in the R programming environment using MLSeq and devtools packages. Feature selection was performed using Genetic Algorithm and Simulated Annealing, with Random Forest algorithm employed for classification. Ab initio methods ensured computational efficiency and result accuracy, while molecular dynamics simulation acted as a virtual experiment bridging the gap between experimental and theoretical experiences.

A total of 12 models were created, each introducing a list of differentially expressed genes as potential biomarkers. The performance of models varied across group comparisons, with the Genetic Algorithm generally outperforming Simulated Annealing in most cases. For the Severe vs. Moderate group, GA achieved the best performance with an accuracy of 94.38%, recall of 91.64%, and specificity of 97.10%. The results highlight the effectiveness of GA in most group comparisons, while SA performed better in specific cases involving Moderate and Mild groups. These biomarkers were evaluated against the gene expression data to assess their expression changes between different groups of chemically injured individuals. Four genes were selected based on level expression for further investigation: CXCR1, EIF2B2, RAD51, and RXFP2. The expression levels of these genes were analyzed to determine their differential expression between the groups.

This study was designed as a computational effort to identify diagnostic biomarkers in basic biological system research. Our findings proposed a list of discriminative biomarkers capable of distinguishing between different groups of chemically injured individuals. The identification of key genes highlights the potential for biomarkers to serve as indicators of chemical injury severity, warranting further investigation to validate their clinical relevance and utility in diagnosis and treatment.

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Accurate detection and quantification of biomarkers at ultra-low levels is critical for disease diagnosis and effective treatment. Traditional detection technologies often lack the sensitivity, specificity, throughput, or multiplexing capacity required for comprehensive diagnostics, providing only a subset of these requirements. Here, we introduce AVAC, an automated optical technology for rapid and accurate biomarker detection with ultra-high sensitivity that significantly outperforms standard clinical assays. The core of this technology is the digital counting of plasmonic nanoparticles used as optical labels, enabling multiplexed, high-throughput detection of biomarkers. Validation studies demonstrate AVAC's high accuracy, with 98.2% specificity and detection limits as low as 26 fg/mL for HIV p24 protein and a quantification range of 160 fg/mL to 850 pg/mL for interleukin-6 (IL-6). The technology supports multiplexed assays without compromising sensitivity, as demonstrated by the simultaneous detection of three key biomarkers associated with cardiovascular disease. A counting range spanning more than four orders of magnitude ensures robust detection from ultra-low to high biomarker concentrations, and its ability to analyze up to 1,000 samples per hour provides high throughput suitable for large laboratories. With its unique combination of capabilities, this versatile platform has significant potential to advance biomarker-based diagnostics in clinical and research settings.

In this mini review, we provide an overview of the challenging field of spatial glycomics/glycoproteomics. Owing to their complexity, sophisticated analytical methods and innovative technologies are needed to advance this field. An agile development approach enables unraveling aberrant glycosylations, glycobiomarkers, and glycotargets for spatial imaging, diagnosis, and therapeutic purposes. We discuss glycopathology and tissue glycomic profiling using highly sensitive lectin-based analyses and introduce deep visual proteomics for glycomic/glycoproteomic sample preparation. Additionally, we highlight the importance of leveraging laser microdissection and artificial intelligence-driven visual software for cell-type assignment and automation techniques, which are crucial for advancing glycomics/glycoproteomics.

Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination. We further developed a MOF-flower assisted simplified and single-pot Sample Preparation (Mass-SP) workflow that incorporates protein capture, digest, and peptide elute into one single PCR tube to maximally avoid adsorptive sample loss. We adopted Mass-SP to decipher aqueous humor (AH) proteome signatures from cataract and retinal vein occlusion (RVO) patients and quantified ∼3900 proteins in merely 1 μL of AH. Combined with machine learning, we further identified PFKL as a prioritization biomarker for RVO disease with the areas under the curves of 0.95 ± 0.04. Mass-SP presents a strategy to identify de novo biomarkers and explore potential therapeutic targets with extremely limited clinical human body fluid resources.

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

Sensitive and accurate detection of protein biomarkers is crucial for disease diagnosis, especially for early diagnosis. Here, we describe surface-enhanced Raman scattering (SERS)-based CRISPR/Cas12a assays (S-CRISPR) for protein biomarker detection. Firstly, an S-CRISPR-driven enzyme-linked immunosorbent assay (S-CasLISA) was developed utilizing a capture antibody coated on a microplate to recognize the target and a detection antibody labeled with active DNA to trigger the activity of CRISPR/Cas12a. With this assay, we achieved detection of prostate-specific antigen (PSA) as models at the picogram level. The limit of detection (LoD) of S-CasLISA was 0.17 pg mL-1 and in the range of 0.1 pg mL-1 to 10 ng mL-1. Further, we applied aptamer to S-CRISPR (S-Apt-CRISPR), combining the high sensitivity of SERS with the high selectivity of aptamers, while simplifying the operation process of CRISPR detection of protein biomarkers. The proposed S-Apt-CRISPR also could detect picogram-level PSA and without repeated washing steps. The LoD of S-Apt-CRISPR was 0.35 pg mL-1 and in the range of 0.1 pg mL-1 to 10 ng mL-1. Both SERS-based CRISPR/Cas12a assays were validated with clinical samples and demonstrated accuracy consistent with the chemiluminescence immunoassay. The introduction of the CRISPR/Cas12a system with SERS has the effect of improving the analytical capabilities of the system, thereby broadening and facilitating its application in the analysis of sensitive and accurate protein biomarkers.

Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure. Biomarker candidates that are discovered by appropriate technologies such as unbiased mass spectrometry cannot be validated or translated to immunoassays in many cases. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important to allow routine clinical use. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, we present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development, while minimal time and effort are required.

A deeper understanding of disease heterogeneity highlights the urgent need for precision medicine. Microfluidics, with its unique advantages, such as high adjustability, diverse material selection, low cost, high processing efficiency, and minimal sample requirements, presents an ideal platform for precision medicine applications. As nanoparticles, both of biological origin and for therapeutic purposes, become increasingly important in precision medicine, microfluidic nanoparticle separation proves particularly advantageous for handling valuable samples in personalized medicine. This technology not only enhances detection, diagnosis, monitoring, and treatment accuracy, but also reduces invasiveness in medical procedures. This review summarizes the fundamentals of microfluidic nanoparticle separation techniques for precision medicine, starting with an examination of nanoparticle properties essential for separation and the core principles that guide various microfluidic methods. It then explores passive, active, and hybrid separation techniques, detailing their principles, structures, and applications. Furthermore, the review highlights their contributions to advancements in liquid biopsy and nanomedicine. Finally, it addresses existing challenges and envisions future development spurred by emerging technologies such as advanced materials science, 3D printing, and artificial intelligence. These interdisciplinary collaborations are anticipated to propel the platformization of microfluidic separation techniques, significantly expanding their potential in precision medicine.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the synovial joint linings, resulting in progressive disability, increased mortality, and considerable economic costs. Early treatment with disease-modifying antirheumatic medications (DMARDs) can significantly improve the overall outlook for people with RA. Contemporary pharmaceutical interventions, encompassing standard, biological, and emerging small molecule disease- modifying anti-rheumatic medications continue to be the cornerstone of RA management, with substantial advancements made in the pursuit of achieving remission from the disease and preventing joint deformities. Nevertheless, a substantial segment of individuals with RA do not experience a satisfactory response to existing treatments, underscoring the pressing need for novel therapeutic options. Biologic DMARDs are among the therapy choices. Non-tumor necrosis factor inhibitors (Non-TNFi) such as abatacept, rituximab, tocilizumab, and sarilumab are examples, as are anti-tumor necrosis factor (TNF) medications such as infliximab, adalimumab, etanercept, golimumab, and certolizumab pegol. More recent biomarkers have emerged and showed usefulness in the early detection of RA. These biomarkers, often referred to simply as "biomarkers", are quantifiable indicators of normal or pathologic processes, and they can also gauge treatment response. The assessment of RA treatment response typically combines patient-reported outcomes, physical evaluations, and laboratory findings, as there isn't a single biomarker that has proven sufficient for measuring disease activity. This review explores the usage of biologic DMARDs as a therapeutic approach for RA, as well as the biomarkers typically used for RA early diagnosis, prognosis prediction, and disease activity evaluation.

Electrochemical biosensors are gaining attention as powerful tools in cancer diagnosis, particularly in liquid biopsy, due to their high efficiency, rapid response, exceptional sensitivity, and specificity. However, the complexity of intra- and intertumor heterogeneity, with variations in genetic and protein expression profiles and epigenetic modifications, makes electrochemical biosensors susceptible to false-positive or false-negative diagnostic outcomes. To address this challenge, there is growing interest in simultaneously analyzing multiple biomarkers to reveal molecular characteristics of tumor heterogeneity for precise cancer diagnosis. In this Perspective, we highlight recent advancements in utilizing electrochemical biosensors for cancer diagnosis, with a specific emphasis on the multitarget analysis of cancer biomarkers including tumor-associated nucleic acids, tumor protein markers, extracellular vesicles, and tumor cells. These biosensors hold significant promise for improving precision in early cancer diagnosis and monitoring, as well as potentially offering new insights into personalized cancer management.

Schizophrenia is a complex and heterogenous psychiatric disorder characterized by positive, negative, and cognitive symptoms. Our previous study identified three subgroups of schizophrenia patients based on plasma microRNA (miRNA) profiles. The present study aims to (1) verify the reproducibility of the miRNA-based patient stratification and (2) explore the pathophysiological pathways linked to the symptoms using plasma miRNAs. We measured levels of 376 miRNAs in plasma samples of schizophrenia patients and obtained their Positive and Negative Syndrome Scale (PANSS) scores and the Brief Assessment of Cognition in Schizophrenia (BACS) scores. The plasma miRNA profiles identified similar subgroups of patients as in the previous study, suggesting miRNA-based patient stratification is potentially reproducible. Our multivariate analysis identified optimal combinations of miRNAs to estimate the PANSS positive and negative subscales and BACS composite scores. Those miRNAs consistently enriched 'inflammation' and 'NFκB1' according to miRNA set enrichment analysis. Our literature-based text mining and survey confirmed that those miRNAs were associated with IL-1β, IL-6, and TNFα, suggesting that exacerbated positive, negative, and cognitive symptoms are associated with high inflammation. In conclusion, miRNAs are a potential biomarker to identify patient subgroups reflecting pathophysiological conditions and to investigate symptom-related molecular mechanisms in schizophrenia.

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

Targeted mass spectrometry (MS) methods are powerful tools for the selective and sensitive analysis of peptides identified in global discovery experiments. Selected reaction monitoring (SRM) is the most widely accepted clinical MS method due to its reliability and performance. However, SRM and parallel reaction monitoring (PRM) are limited in throughput and are typically used for assays with around 100 targets or fewer. Here we introduce a new MS platform featuring a quadrupole mass filter, collision cell, and linear ion trap architecture, capable of targeting 5000-8000 peptides per hour. This high multiplexing capability is facilitated by acquisition rates of 70-100 Hz and real-time chromatogram alignment. We present a Skyline external software tool for building targeted methods based on data-independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. Our platform demonstrates ∼10× lower limits of quantitation (LOQs) than traditional SRM on a triple quadrupole instrument for highly multiplexed assays, due to parallel product ion accumulation. Finally, we explore how analytical figures of merit vary with method duration and the number of analytes, providing insights into optimizing assay performance.

It is well known that when nanoparticles interact with biological fluids, a layer of proteins and biological components forms on them. This layer may alter the biological fate and efficiency of the nanomaterial. Recent studies have shown that illness states have a major impact on the structure of the biocorona, sometimes referred to as the "personalized protein corona." Physiological factors like illness, which impact the proteome and metabolome pattern and result in conformational changes in proteins, give rise to this structure of discrimination in biocorona decoration. Improving the efficiency of precise platforms for developing new molecular biomarkers for accurate illness diagnosis is vitally necessary. The biocorona pattern's discrimination may be a diagnostic tool for designing biosensors. As a result, in this review, we summarize the most current studies on the relationship between physiological conditions and the variety of biocorona patterns that influence the biological responses of nanosystems. The biocorona pattern's flexibility may provide new research directions and be utilized to create nanoparticle-based therapeutic and diagnostic products suited to certain physiological situations.