Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Modulation in NF-κB-p65/NLRP3, TXNIP-mediated signaling using an ethanolic fruit extract of Withania coagulans mitigates silica-induced pulmonary fibrosis in rats

Withania coagulans encompasses many active phytoconstituents, which have been used to treat many ailments. Prior research has shown that fruit extract of Withania coagulans has anti-inflammatory properties and effectively reduces oxidative stress in various diseases. Nevertheless, its effects are not obscured in the silica (SiO2) induced pulmonary fibrosis (PF). In the current study, an ethanolic fruit extract of Withania coagulans (WCE) was prepared, and its effects and underlying mechanisms on SiO2-induced PF in rats were elucidated. LC-MS/MS analysis identified various bioactive phytoconstituents, secondary plant metabolites, and flavonoids in the WCE. In vitro, results showed that the WCE exhibited no toxicity towards A549 cells, reduced the production of reactive oxygen species, and inhibited cell migration. Further, WCE abrogated alveolar wall thickening, reduced inflammatory cell infiltration, and maintained lung architecture. It also suppresses collagen accumulation and mucus production, abrogating inflammation by downregulating nuclear factor kappa B (NF-κB-p65)/ NOD-like receptor protein 3 (NLRP3) and cytokine levels. It suppresses oxidative and endoplasmic reticulum stress induced by SiO2 by downregulating thioredoxin-interacting protein (TXNIP), activating transcription factor 6 (ATF6), and C/EBP Homologous Protein (CHOP) proteins. Additionally, WCE, by suppressing EMT and transforming growth factor beta 1 (TGF-β1)/Suppressor of Mothers against Decapentaplegic (Smad) pathway, mitigated PF in rats. Taken together, WCE via anti-inflammatory and anti-oxidative properties inhibited SiO2-induced PF, and therefore, it can be envisaged as an effective antifibrotic agent to treat PF.

Metabolomics-enabled biomarker discovery in breast cancer research

Breast cancer (BC) remains the most prevalent malignancy among women worldwide. While genetic predisposition and reproductive history are key contributors to its development, modifiable risk factors are also important, particularly those linked to lifestyle behaviors, often influencing the endogenous metabolome. Over the past decade, mass spectrometry-based metabolomics has enabled agnostic investigations into correlations between the metabolome and BC risk. Here we review recent results from prospective nested case-control studies, which have led to the identification of significantly different metabolites between women who subsequently developed BC and those who did not. As replication of these findings remains limited, we emphasize the need for robust quantitative validation studies, cancer subtype-specific analyses in diverse populations, and expanded chemical space coverage of analytical assays.

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach

Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from health controls. Using plasma peptides, the neural network, termed as CMS-Net, can perform diagnosis and prediction with an accuracy of 0.9441. The sensitivity and specificity reached 0.9352 and 0.9517 respectively, and the area under the curve was enhanced to 0.9634. Using the gradient-based feature importance method to interpret crucial features, we identify 28 differential peptide sequences from 14 precursor proteins (e.g. hemoglobin, immunoglobulin, albumin, etc.). This work highlights the possibility of molecular diagnosis of MDD with the aid of chemical and computer science.

An Optimized Liquid Chromatography-Mass Spectrometry Method for Ganglioside Analysis in Cell Lines

Gangliosides are glycosphingolipids composed of a sialylated glycan head group and a ceramide backbone. These anionic lipids form lipid rafts and play crucial roles in regulating various proteins involved in signal transduction, adhesion, and cell-cell recognition. Neuroblastoma, a pediatric cancer of the sympathetic nervous system, is treated with intensive chemotherapy, radiation, and an antibody targeting the GD2 ganglioside. Gangliosides are critical in neuroblastoma development and serve as therapeutic targets, making it essential to establish a reliable, rapid, and cost-effective method for profiling gangliosides, particularly one capable of isomeric separation of intact species. In this study, liquid chromatography-mass spectrometry (LC-MS) was optimized using standard gangliosides, followed by the optimization of sphingolipid extraction methods from cell lines by comparing Folch and absolute methanol extraction techniques. Percent recovery and the number of identified sphingolipids were used to evaluate the analytical merits of these methods. A standard gangliosides calibration curve demonstrated excellent linearity (R2 = 0.9961-0.9975). The ZIC-HILIC column provided the best separation of ganglioside GD1 isomers with a 25 min runtime. GD1a elutes before GD1b on the ZIC-HILIC column. Absolute methanol yielded better percent recovery (96 ± 7) and identified 121 different sphingolipids, the highest number between the two extraction methods. The optimized method was applied to profile gangliosides in neuroblastoma (COG-N-683), pancreatic cancer (PSN1), breast cancer (MDA-MB-231BR), and brain tumor (CRL-1620) cell lines. The ganglioside profile of the neuroblastoma cell line COG-N-683 showed an inverse relationship between GD1 and GD2. Ceramide, Hex1Cer, GM1, and GM3 were highly abundant in CRL-1620, PSN1, and MDA-MB-231BR, respectively. These results suggest that our method provides a sensitive, reliable, and high-throughput workflow for ganglioside profiling across different cell types.

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.

Tick exposure biomarkers: A One Health approach to new tick surveillance tools

The spread of tick-borne disease (TBD) is escalating globally, driven by climate change and socio-economic shifts, underlining the urgency to improve surveillance, diagnostics, and control strategies. Ticks can transmit a range of pathogens increasing the risk of transmission of human and veterinary diseases such as Lyme disease, tick-borne encephalitis, theileriosis, anaplasmosis, or Crimean-Congo hemorrhagic fever. Surveillance methods play a crucial role in monitoring the spread of tick-borne pathogens (TBP). However, there are shortcomings in the current surveillance methods regarding risks related to ticks. Human-tick encounters offer a novel metric for disease risk assessment, integrating human behavior into traditional surveillance models. However, to more reliably measure tick exposure, a molecular marker is needed. The identification of antibodies against arthropod salivary proteins as biomarkers for vector exposure represents a promising avenue for enhancing existing diagnostic and surveillance metrics. Here we explore how the use of tick saliva biomarkers targeting recombinant proteins and synthetic peptides could significantly improve the assessment of TBD transmission risk and the effectiveness of vector control measures. With focused efforts on creating a biomarker against tick exposure suitable for humans and domestic animals alike, tick surveillance, diagnosis and control would be more achievable and aid in reducing the mounting threat of TBP through a One Health lens.

Metabolomic and lipidomic fingerprints in inflammatory skin diseases - Systemic illumination of atopic dermatitis, hidradenitis suppurativa and plaque psoriasis

Auto-inflammatory skin diseases place considerable symptomatic and emotional burden on the affected and put pressure on healthcare expenditures. Although most apparent symptoms manifest on the skin, the systemic inflammation merits a deeper analysis beyond the surface. We set out to identify systemic commonalities, as well as differences in the metabolome and lipidome when comparing between diseases and healthy controls. Lipidomic and metabolomic LC-MS profiling was applied, using plasma samples collected from patients suffering from atopic dermatitis, plaque-type psoriasis or hidradenitis suppurativa or healthy controls. Plasma profiles revealed a notable shift in the non-enzymatic anti-oxidant defense in all three inflammatory disorders, placing cysteine metabolism at the center of potential dysregulation. Lipid network enrichment additionally indicated the disease-specific provision of lipid mediators associated with key roles in inflammation signaling. These findings will help to disentangle the systemic components of autoimmune dermatological diseases, paving the way to individualized therapy and improved prognosis.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Study on the effect of calibration standards prepared with different matrix on the accuracy of bile acid quantification using LC-MS/MS

The transition from relative to absolute quantification of metabolites is the future development trend of mass spectrometry-based metabolomics research, which could fundamentally solve the problem of comparability of data between different laboratories. However, absolute quantification of endogenous molecules is largely hampered by the lack of analyte-free matrix, leading to uncertainty and inconsistency in the preparation of calibration standards. Bile acids (BAs) are an important class of biomarkers that play a key role in disease progression. In this paper, the quantitative accuracy of calibration curves prepared in neat solvent (NSCCs), charcoal stripped matrix (SMCCs) and authentic matrix (AMCCs) were validated using quality control samples (QCs) prepared in authentic matrix. Results suggested that AMCCs could largely minimize the confidence interval (C.I.) and the deviation in accuracy compared with NSCCs and SMCCs when measured concentration is higher than 20% of the background level. In addition, experimental data demonstrated that two-step calibration strategy proposed here is a promising and reliable alternative strategy to quantify endogenous BAs in biological sample.

Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS

Quantitative information on blood metabolites can be used in developing advanced medical strategies such as early detection and prevention of disease. Monitoring bioactive lipids such as steroids, bile acids, and PUFA metabolites could be a valuable indicator of health status. However, a method for simultaneously measuring these bioactive lipids has not yet been developed. Here, we report a LC/MS/MS method that can simultaneously measure 144 bioactive lipids, including steroids, bile acids, and PUFA metabolites, from human plasma, and a sample preparation method for these targets. Protein removal by methanol precipitation and purification of bioactive lipids by solid-phase extraction improved the recovery of the targeted compounds in human plasma samples, demonstrating the importance of sample preparation methods for a wide range of bioactive lipid analyses. Using the developed method, we studied the plasma from healthy human volunteers and confirmed the presence of bioactive lipid molecules associated with sex differences and circadian rhythms. The developed method of bioactive lipid analysis can be applied to health monitoring and disease biomarker discovery in precision medicine.

Development and validation of derivatization-based LC-MS/MS method for quantification of short-chain fatty acids in human, rat, and mouse plasma

Short-chain fatty acids (SCFAs), the end products of gut microbial fermentation of dietary fibers and non-digestible polysaccharides, act as a link between the microbiome, immune system, and inflammatory processes. The importance of accurately quantifying SCFAs in plasma has recently emerged to understand their biological role. In this work, a sensitive and reproducible LC-MS/MS method is reported for SCFAs quantification in three different matrices such as human, rat and mouse plasma via derivatization, using as derivatizing agent O-benzylhydroxylamine (O-BHA), coupled with liquid-liquid extraction. First, the instrumental parameters of the mass spectrometer and then the chromatographic conditions were optimized using previously SCFAs derivatives synthetized and used as standards. After that, the best conditions for derivatization and extraction from plasma were studied and a series of determinations were performed on human, rat, and mouse plasma aliquots to validate the overall method (derivatization, extraction, and LC-MS/MS determination). The method showed good performance in terms of recovery (> 80%), precision (RSD <14%), accuracy (RE < ± 10%) and sensitivity (LOQ of 0.01 µM for acetic, butyric, propionic and isobutyric acid) in all plasma samples. The method thus developed and validated was applied to the quantification of major SCFAs in adult and aged mice, germ-free mice and in germ-free recipient mice subjected to fecal transplant from adult and aged donors. Results highlighted how plasma concentrations of SCFAs are correlated with age further highlighting the importance of developing a method that is reliable for the quantification of SCFAs to study their biological role.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.