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At least 19 recordsLinked to original sources

PubMed2022

Direct capture and sequencing reveal ultra-short single-stranded DNA in biofluids.

Cell-free DNA (cfDNA) has become the predominant analyte of liquid biopsy; however, recent studies suggest the presence of subnucleosomal-sized DNA fragments in circulation that are likely single-stranded. Here, we report a method called direct capture and sequencing (DCS) tailored to recover such fragments from biofluids by directly capturing them using short degenerate probes followed by single strand-based library preparation and next-generation sequencing. DCS revealed a new DNA population in biofluids, named ultrashort single-stranded DNA (ussDNA). Evaluation of the size distribution and abundance of ussDNA manifested generality of its presence in humans, animal species, and plants. In humans, red blood cells were found to contain abundant ussDNA; plasma-derived ussDNA exhibited modal size at 50 nt. This work reports the presence of an understudied DNA population in circulation, and yet more work is awaiting to study its generation mechanism, tissue of origin, disease implications, etc.

Biological sciences↗

PubMed2026

Integrated landscape of salivary metagenome and multi-biofluid metabolome characterizes a microbial-metabolic axis in upper gastrointestinal cancer progression.

BACKGROUND: Upper gastrointestinal cancer (UGIC) imposes a major global health burden, yet the stage-specific molecular changes along the microbial-metabolic axis remain limited understood. We aimed to delineate this molecular landscape across UGIC progression and evaluate its potential as non-invasive methods for precision screening. RESULTS: Derived from a multi-center population-based UGIC screening program, we enrolled 420 individuals, stratified into normal, low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and UGIC (n = 105 per group). Integrated salivary metagenomics and paired salivary/plasma metabolomics were performed to capture local and systemic dysregulation. We uncovered distinct stage-specific divergence during UGIC progression: profound remodeling of the salivary microbiota (104 differential species) and salivary metabolomics (80 differential metabolites) initiated early at the LGIN stage, whereas plasma metabolic dysregulation (40 differential metabolites) peaked significantly later at the HGIN stage. Integrative analysis revealed salivary microbiota related more closely with salivary metabolome than plasma metabolome. Moreover, statistical evidence suggested that dysbiotic salivary microbiota was associated with altered lysine- and tryptophan-related catabolic pathways converging on Acetyl-CoA-related metabolic nodes, supporting a potential metabolic mechanism in precancerous lesions. Finally, the discriminative model integrating metagenomic and metabolomic markers demonstrated promising diagnostic performance in distinguishing these precancerous lesions (LGIN: area under the curve [AUC] = 0.83; HGIN: AUC = 0.77) and UGIC (AUC = 0.76) from normal. CONCLUSION: This study characterizes a stage-specific microbial-metabolic axis that facilitates the comprehensive understanding of UGIC pathogenesis. These multi-biofluid signatures offer a promising non-invasive triage strategy for detecting precancerous lesions and optimizing endoscopic resource allocation. Video Abstract.

Female↗

PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗

PubMed2026

Proteins as Regulators of Metabolic Changes in Sepsis: Alterations in Body Fluids, Immune Cells, and Organs through the Eyes of Proteomics.

Sepsis is a life-threatening syndrome characterized by a dysregulated host response to infection and profound metabolic alterations that contribute to immune dysfunction and organ failure. This Review synthesizes proteomic evidence on sepsis-associated alterations in proteins involved in metabolic pathways across circulating biofluids, immune cells, and organs. Across plasma and urine, proteomic studies identify disturbances in lipoprotein-associated pathways, redox homeostasis, mitochondrial function, and substrate metabolism, indicating that protein signatures of metabolic dysregulation are systemic and detectable across biofluids. In immune cells, monocytes and neutrophils, proteomic analyses reveal a shift toward glycolysis with concurrent impairment of mitochondrial pathways alongside phenotype-dependent differences in lipid and redox-related programs. Organ-level studies further show that metabolic responses are heterogeneous, with distinct trajectories in the kidney, heart, liver, lung, skeletal muscle, and brain. These observations support the concept that sepsis involves compartment-specific remodeling of metabolism-associated protein networks rather than a single convergent metabolic state. Proteomics also highlights potential translational opportunities by identifying metabolism-associated proteins linked to disease severity, clinical phenotypes, and biologically distinct patient subgroups, although the current evidence remains largely exploratory and context-dependent. Overall, proteomics provides a complementary framework for understanding the molecular regulation of sepsis-associated metabolic dysfunction and may refine biological stratification and therapeutic targeting, particularly when integrated with longitudinal sampling and multiomic data.

Humans↗

PubMed2026

Hypothesis-free evaluation of circulating metabolome provides cell-specific insights regarding the role of energy substrate availability in amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with limited therapeutic options. The circulating metabolome comprises small molecules present in plasma/serum which are the intermediates and end-products of cellular metabolism, and is linked to ALS pathogenesis. METHODS: We conducted hypothesis-free two-sample Mendelian randomisation (MR) analysis of the concentration of 575 plasma/serum metabolites, to determine which are causally linked to risk of ALS. Significant metabolites were validated in an independent GWAS of plasma/serum metabolite concentrations and evaluated for sex-specific effects. Correlations between directly measured patient biofluid metabolite concentrations and ALS risk/severity were examined in 94 ALS patients and 40 controls. We experimentally assessed metabolic function in a murine neurons and human astrocytes carrying an ALS-associated G4C2-repeat expansion within C9orf72. RESULTS: MR causally associated five metabolites with ALS risk after multiple-testing correction. Higher serum concentration of glycoprotein acetyls (P = 9.7e - 9, β = 0.21) and the peptide DSGEGDFXAEGGGVR (P = 8.0e - 6, β = 0.22) was associated with increased ALS risk, whereas higher plasma concentration of phenylalanylserine, isobutyrylcarnitine, and acetylcarnitine was protective (P < 5e - 5, β = - 0.29 to - 0.72). DSGEGDFXAEGGGVR has been linked to glucose metabolism but we have used genetic fine-mapping to link DSGEGDFXAEGGGVR, neuronal glucose uptake through GLUT3, and ALS risk. Direct measurement of metabolite concentrations in patient biofluids revealed elevated acetylcarnitine levels in patients with ALS, which were associated with delayed symptom onset (Cox regression, P = 0.02, HR = 0.4). Similarly, lactate is elevated in ALS patient CSF (ANOVA, P = 1.3e - 3) and in patients with longer survival time (Cox regression, P = 0.03, HR = 0.3). Plasma fructose is elevated in ALS patients with shorter survival time (Cox regression, P = 0.02, HR = 1.1). In vitro, neurons and astrocytes carrying an ALS-associated G4C2-repeat expansion within C9orf72 demonstrated reduced metabolic flexibility. CONCLUSIONS: We provide evidence that impaired energy substrate availability contributes to ALS risk and severity. CNS cell types differ in their use of energy substrates and therefore we postulate the relative importance of different cell types for different stages of disease. Our findings support further investigation of metabolic interventions to treat or prevent ALS.

Amyotrophic Lateral Sclerosis↗

PubMed2026

Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.

Nanoparticles entering biological fluids are rapidly coated by proteins and other biomolecules, converting their synthetic surfaces into biologically active nano-bio interfaces. These coronas regulate colloidal stability, immune recognition, cellular uptake, biodistribution, pharmacokinetics, cargo delivery, and toxicity. Yet a protein list obtained by mass spectrometry captures only part of this interface. Corona identity and function are also shaped by protein organization, binding stability, exchange dynamics, conformational changes, and molecular accessibility. Here, we discuss recent progress in protein corona isolation and analysis from a question-oriented analytical perspective, with emphasis on how centrifugation, magnetic recovery, affinity- or chemistry-enabled capture, chromatography, filtration, and field-flow fractionation (FFF) influence the fidelity, integrity, and comparability of recovered coronas. We then examine how proteomic profiling can be integrated with binding measurements, interfacial structural analysis and functional validation to distinguish descriptive corona signatures from biologically meaningful mechanisms. We further consider how biofluid composition, disease state, tissue interfaces and cellular environments remodel corona identity, presentation, and bioactivity. Finally, we argue that standardized reporting, computational modeling, and AI-enabled approaches are essential for converting protein corona datasets into reproducible and predictive knowledge that can guide the design of drug delivery systems and precision nanomedicines.

Protein Corona↗

PubMed2025

A comprehensive analysis of supermere, exomere, and extracellular vesicle isolation and cargo in colorectal cancer.

Biofluids contain a heterogeneous mixture of extracellular vesicles and non-vesicular nanoparticles (including exomeres and supermeres) that transport a diverse array of proteins, RNA, and lipids. Our previous efforts to characterize the contents of these carriers in colorectal cancer relied on 2D culture systems requiring large-scale setups and time-consuming ultracentrifugation-based isolation. To streamline this process, we have combined 3D hollow-fiber bioreactor production and fast-protein liquid chromatography-based size-exclusion chromatography. Here, we compare the impact of culture methods and purification strategies on small extracellular vesicle, exomere, and supermere cargo. Proteomic analyses show consistently distinct profiles for extracellular vesicles, exomeres, and supermeres regardless of culture conditions or isolation method. In contrast, these two variables influence small RNAs, their base modifications, and lipidomic profiles. We present an online tool to query these and future secretome datasets (https://superomics.shinyapps.io/browse).

Humans↗

PubMed2024

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis.

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

Proteomics↗

PubMed2026

Evaluation of pilocarpine effects on sweat proteome.

BACKGROUND: Sweat is increasingly recognized as a valuable, non-invasive biofluid for biomarker discovery, yet its composition depends on the stimulation method. This study aimed to determine how pharmacological induction with pilocarpine compares to physiologically induced sweat through exercise in shaping the sweat proteome. RESULTS: We analyzed thermoregulatory sweat from exercise, pilocarpine-induced sweat, and combined pilocarpine plus exercise sweat. Total protein concentrations were similar across conditions, but pilocarpine markedly increased proteomic diversity, with combined pilocarpine plus exercise sweat showing the highest number of identifications. The core sweat proteome remained stable, while pilocarpine selectively enriched low-abundance proteins involved in vesicular trafficking, cytoskeletal remodelling, and metabolism. Proteins linked to the canonical M3-Gq-PLC-Ca2+ pathway, including AQP5, CALML5, and CLIC1, were consistently enriched, confirming cholinergic activation. Pilocarpine-induced sweat also contained plasma-derived and immune-related proteins, reflecting enhanced secretion and reduced ductal reabsorption. CONCLUSIONS: Exercise yields a physiologically relevant but less complex proteome, pilocarpine-induced sweat produces a pharmacologically enriched yet biased profile, and combined pilocarpine plus exercise sweat maximizes protein detection at the expense of interpretability. These findings highlight the critical impact of stimulation paradigm on sweat proteomics and provide a reference framework for biomarker research. SIGNIFICANCE: This study employed LC-MS/MS to systematically characterize eccrine sweat and delineate how stimulation paradigms-exercise, pilocarpine, and their combination-shape its proteomic landscape. By demonstrating that pharmacological induction profoundly alters protein diversity and composition compared to physiologically induced sweat, these findings establish a critical benchmark for sweat-based biomarker research and highlight the need for paradigm-aware sampling strategies in clinical and translational contexts. Nonetheless, several methodological constraints warrant consideration: the limited sample size (five individuals per group), the exclusive inclusion of women under combined oral contraceptive treatment (21 active pills followed by 7 pill-free days), which restricts extrapolation to naturally cycling women, and the focus on healthy young adults (18-25 years), limiting generalizability to older or clinically heterogeneous populations. Despite these limitations, this work provides a foundational framework for optimizing sweat collection protocols and advancing precision approaches in non-invasive diagnostics.

Pilocarpine↗

PubMed2026

Urine and Serum Proteome and Lipidome Analysis of Naturally Aging Feline Species.

Aging in companion animals such as cats closely relates to human aging in environmental exposures and disease manifestation, providing a valuable model for identifying biomarkers of age-associated decline. This study provides a combined proteomic and lipidomic analysis of serum and urine from naturally aging domestic cats aged 3.8-16 years, grouped as adult, old, and senior, to identify age-related molecular changes across biofluids. Label-free quantitative proteomics identified 901 urinary and 238 serum proteins, with 75 urinary proteins significantly altered with age that are linked to kidney disease, hypertension, neurodegeneration, and metabolic disorders. In contrast, only six serum proteins differed significantly between adult and old/senior cats, including decreased Apolipoprotein A-I (APOA1) in seniors, a protein linked with cognitive function in aging. Untargeted lipidomics revealed increases in specific serum triacylglycerols, phosphatidylcholines (PCs), and sphingomyelins, while urinary lipid profiles showed limited age-related changes, with some PCs decreasing, and diacylglycerols increasing with age. These results demonstrate distinct systemic and renal molecular remodeling during feline aging and highlight the utility of integrated omics analyses of biological fluids for identifying molecular alterations relevant to both feline and human aging.

Animals↗

PubMed2026

Targeted Modulation of Abundant Proteins Enhances Proteomic Profiling of Ovarian Cancer Ascites: A Pilot Technical Workflow Comparison.

Ascites from ovarian cancer patients are increasingly recognized as a valuable biofluid for cancer research, as its protein composition reflects the disease state and may reveal biomarkers of treatment sensitivity and response. However, the detection of low-abundance proteins is hindered by the presence of highly abundant proteins such as albumin. In this study, we evaluated five protein preparation methods for their effectiveness in depleting high-abundance or enriching low-abundance proteins in ovarian cancer ascites. The Norgen (Nor), Minutes (Min), and Perchloric acid (PerCA) methods were based on abundant protein depletion, while the Urine (Uri) and Nanomics (Nano) kits focused on low-abundance protein enrichment. Processed samples were analyzed using label-free quantitative bottom-up proteomics by LC-MS/MS, followed by a bioinformatics assessment. Compared with undepleted ascites (UnD), Min, Nor, Nano, and PerCA increased protein identifications, whereas Uri produced profiles similar to those of UnD. Notably, PerCA and Nano enabled the identification of distinct protein subsets associated with cancer-related pathways, including immune responses and autophagy. PerCA enriched transmembrane and secreted immunomodulatory glycoproteins, whereas Nano enrichment primarily captured secreted, nuclear, and cytoplasmic soluble proteins. Overall, our results show that both high-abundance protein depletion and low-abundance enrichment improve ascites proteome coverage, each offering distinct advantages in identifying biologically relevant low-abundance proteins.

Female↗

PubMed2026

Proteo-metabolomic integration identifies stage-specific candidate biomarkers for Parkinson's disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder with a prolonged prodromal phase and complex motor symptoms. Despite improved clinical criteria, early diagnosis and longitudinal monitoring remain challenging. While cerebrospinal fluid (CSF) and plasma metabolites and proteins show biomarker potential, their utility in predictive models is insufficiently characterized. We employed a secondary computational approach to integrate proteometabolomic profiles from CSF and plasma samples of >1100 Parkinson's Progression Markers Initiative (PPMI) participants. Using multi-omics machine learning, we identified biofluid-specific signatures and evaluated predictive performance. Twenty-one biomarker candidates were validated across three models (SVM, GLMNET, RF); SVM and GLMNET achieved the highest recall (83-86%) and AUCs of 0.84-0.89. Longitudinal mixed-effects modeling revealed eight candidates associated with progression across diagnostic stages. We identified a three-part molecular framework characterizing neurodegeneration: a diagnostic subpanel reflecting early microbiome dysregulation (secretory granins and metabolites) and synaptic breakdown; a second subpanel monitoring phenoconversion via neurogenesis precursors and extracellular matrix proteins; and a third subpanel tracking progression through chronic neuroinflammation and immune activation. This integrated multi-omics approach provides a robust framework for stage-specific PD monitoring and potential clinical deployment.

Journal Article↗

PubMed2022

Characterizing the metabolic effects of the selective inhibition of gut microbial β-glucuronidases in mice.

The hydrolysis of xenobiotic glucuronides by gut bacterial glucuronidases reactivates previously detoxified compounds resulting in severe gut toxicity for the host. Selective bacterial β-glucuronidase inhibitors can mitigate this toxicity but their impact on wider host metabolic processes has not been studied. To investigate this the inhibitor 4-(8-(piperazin-1-yl)-1,2,3,4-tetrahydro-[1,2,3]triazino[4',5':4,5]thieno[2,3-c]isoquinolin-5-yl)morpholine (UNC10201652, Inh 9) was administered to mice to selectively inhibit a narrow range of bacterial β-glucuronidases in the gut. The metabolomic profiles of the intestinal contents, biofluids, and several tissues involved in the enterohepatic circulation were measured and compared to control animals. No biochemical perturbations were observed in the plasma, liver or gall bladder. In contrast, the metabolite profiles of urine, colon contents, feces and gut wall were altered compared to the controls. Changes were largely restricted to compounds derived from gut microbial metabolism. This work establishes that inhibitors targeted towards bacterial β-glucuronidases modulate the functionality of the intestinal microbiota without adversely impacting the host metabolic system.

Mice↗

PubMed2026

Body mass index-specific nanoparticle protein corona signatures in late pregnancy.

The protein corona (PC) formed on the surface of nanoparticles (NPs) upon exposure to human biofluids is a dynamic interface that reflects the physiological and pathological status of the host. In this study, we investigated how the maternal body mass index (BMI) influences the composition of the NPs' PC during late pregnancy. Polystyrene NPs were incubated with plasma samples collected from third-trimester pregnant individuals across normal weight, overweight, and obese BMI categories. Comprehensive characterization using dynamic light scattering (DLS), zeta potential measurements, and transmission electron microscopy (TEM) confirmed BMI-dependent differences in PC thickness and colloidal stability. SDS-PAGE and label-free quantitative proteomics revealed distinct molecular compositions: PCs from obese individuals were enriched in inflammatory and lipid metabolism-associated proteins (e.g., APOE and CRP), while normal weight-derived PCs showed higher levels of complementary regulators and extracellular matrix proteins. Principal component analysis (PCA) demonstrated clear clustering of proteomic profiles by the BMI group, suggesting BMI-specific PC fingerprints. These findings indicate that the maternal metabolic phenotype shapes nano-bio interactions at the proteomic level and highlight the potential of PC profiling as a non-invasive approach for assessing maternal health and metabolic status. This work lays the foundation for integrating NP-based proteomics into precision nanomedicine for maternal-fetal health monitoring.

Female↗

PubMed2026

The Human Breath Volatilome Responds to Exercise and Recovery: An Untargeted Profiling Study.

Exercise induces metabolic and physiological changes across multiple organs. These changes have been studied via several human biofluids, including urine and blood; however, they remain mainly underexplored in exhaled breath. In this exploratory pilot study, we performed untargeted profiling of breath volatile compounds (VCs) to investigate how exercise and recovery influence breath chemical composition. Breath samples were collected from 71 university athletes from three sporting disciplines. Across 143 breath samples, a total of 1,204 unique breath VCs were detected. There were distinct volatilomic responses to physical activity and recovery, regardless of the athlete's sport. Comparison of paired samples using volcano analysis collected before and during exercise identified 80 breath VCs that significantly increased and 182 that significantly decreased. Similarly, a comparison of samples collected before and after exercise identified 11 compounds that decreased significantly. These findings demonstrate that exercise induces measurable changes in the breath volatilome. However, due to the lack of standardized exercise intensity measures and physiological monitoring, the results should be considered exploratory and interpreted cautiously within the field of exercise science.

Exhaled breath analysis↗

PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗

PubMed2026

MicroRNAs in Oral Bio-Fluids as Predictive Biomarkers of Orthodontic Tooth Movement: A Systematic Review.

This systematic review was designed to assess scientific evidence of the association of microRNA expression during orthodontic tooth movement through various time points. A systematic review was performed in accordance with the PRISMA checklist. A search strategy was developed in electronic databases including Med Line, Scopus, EBSCO Host and ProQuest Dissertations & Theses Global until June 2025. Eligibility criteria included studies that investigated microRNA expression in saliva/GCF during orthodontic treatment. The risk of bias of the included studies was analysed using the QUADAS-2 and RoB-2 tools. The search retrieved 2800 records, of which nine studies were selected. Minor variations in GCF collection were noted, while stimulated saliva was collected in one study. RT-PCR and the Fluro meter accounted for the majority of miRNA estimation. Thirteen miRNAs were identified as target biomarkers for OTM regulation. Despite the high risk of bias, the evidence from the current systematic review indicates that microRNAs can be considered as potential biomarkers of orthodontic tooth movement in oral biofluids. Trial Registration: Prospero ID-CRD420251153064.

Humans↗

PubMed2026

Canine Tear Proteomics: A New Frontier in Veterinary Ophthalmology.

Canine tear proteomics is an emerging field with significant potential for advancing both veterinary ophthalmology and comparative biomedical research. Tears are a readily accessible, non-invasive biofluid that contain a mixture of proteins involved in ocular surface protection, immune defense, and intercellular signaling. In dogs, tear proteomics studies have revealed biomarkers associated with various ocular and non-ocular diseases including keratoconjunctivitis sicca (KCS), glaucoma, neoplasia, and diabetes mellitus. This review compiles all previous studies conducted on the comprehensive canine tear proteome and highlights some of the key tear proteomic studies in human biomedical research. Tear film composition, study design, technological advancements, and select tear proteins are discussed along with key protein alterations and their use as potential biomarkers of disease. Fundamental challenges, clinical implications, and future directions of this rapidly growing field are discussed in detail. As proteomic technology and analytical techniques continue to evolve, canine tear proteomics will become a valuable tool for the veterinary ophthalmologist, enabling the early identification and diagnosis of ocular disease as well as providing a means for monitoring treatment outcomes, disease progression, and overall prognosis for the canine patient.

Animals↗