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PubMed2026

Exploring the potential mechanism of Huang'e capsule against spontaneous benign prostatic hyperplasia in beagle dogs using high-performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry, gas chromatography-mass spectrometry, and network pharmacology.

OBJECTIVE: To investigate the therapeutic efficacy and potential mechanisms of Huang'e capsule (, HEC) against benign prostatic hyperplasia (BPH). METHODS: The chemical profile of HEC was characterized using high-performance liquid chromatographyquadrupole-time-of-flight tandem mass spectrometry (HPLC-Q-TOF-MS/MS) and gas chromatography-mass spectrometry (GC-MS) techniques. Network pharmacology was employed to analyze potential active compounds, core targets, and key signaling pathways. A spontaneous canine BPH model was used to evaluate the efficacy of HEC and to validate the predictions from network pharmacology. RESULTS: A total of 51 chemical components of HEC were identified, comprising 19 from HPLC-Q-TOF-MS/MS and 32 from GC-MS analyses. The "components-targets-pathways-disease" network analysis predicted active compounds including (s)-coriolic acid, ethyl linoleate, peroxysimulenoline, physcion, and kaempferol. Core targets identified included cytochrome P450 family 19 subfamily A member 1, estrogen receptor 2 (ESR2), ESR1, and androgen receptor (AR). Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that HEC's effects on BPH involve pathways related to cancer, phosphatidylinositol 3-kinase (PI3K) -protein kinase B (Akt)-signaling, proteoglycans in cancer, and prostate cancer signaling. Animal experiments showed that HEC significantly improved maximum urinary flow rates, reduced prostate weight, volume, and prostate index, and ameliorated histopathological changes. HEC regulated the balance between apoptosis and proliferation by downregulating AR and estrogen receptor alpha expression, while upregulating estrogen receptor beta expression. CONCLUSION: These findings indicate that HEC effectively ameliorates spontaneous BPH in beagle dogs, likely by regulating the balance between cell apoptosis and proliferation through multiple signaling pathways.

Animals↗

PubMed2025

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry. AREAS COVERED: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture. EXPERT OPINION: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Proteomics↗

PubMed2026

To cleave or not to cleave: a systemic evaluation of DSS versus DSSO for cross-linking mass spectrometry analysis.

Cross-linking mass spectrometry is a powerful method for structural analysis, but choosing between cleavable and non-cleavable cross-linkers remains challenging. We rigorously compared non-cleavable DSS with cleavable DSSO and found that DSS consistently yields more cross-link identifications from isolated protein complexes to bacterial lysates. The advantage of DSS diminishes as sample complexity increases. At the highest complexity tested-human cell lysate-the trend reverses, with DSSO outperforming DSS. The superior performance of DSS in less complex samples is likely explained by its longer and more flexible spacer arm, which interrogates a spatial volume >40% larger than that of DSSO. For both cross-linkers, the number of identified cross-links decreases as the search space expands, but more steeply for DSS. This sharper decline arises from DSS cross-links producing slightly lower fragment ion coverage, not from the absence of signature ions that could reduce search space. Fragment ion coverage is key to interactome mapping: when coverage reaches 85% or above, identification sensitivity hardly decreases as the search space expands, regardless of the cross-linker used. In summary, we recommend DSS for samples no more complex than bacterial lysates. For interactome mapping of mammalian cells, although DSSO outperforms DSS, neither achieves deep interactome coverage.

Cross-Linking Reagents↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2025

Seq2Saccharide: Discovering Oligosaccharides and Aminoglycosides Natural Products by Integrating Computational Mass Spectrometry and Genome Mining.

Natural oligosaccharides and aminoglycosides are important sources of new drug candidates, especially in the development of antibiotics. In the past, discovering novel saccharides has been time-consuming and costly. However, the rapid expansion of high-throughput data, including genomic and mass spectrometry data sets, has greatly increased opportunities for natural saccharide discovery. Yet, due to the complex biosynthesis pathways of saccharides, no existing method can predict their structures with high precision. To address this, we introduce Seq2Saccharide, a tool designed to automate saccharide natural product discovery by integrating both genomic and mass spectrometry data. To enhance accuracy, Seq2Saccharide predicts hundreds or thousands of putative structures for each gene cluster. The correct structure is then identified from these predictions using a mass spectral search. Benchmarks against saccharides in the MiBIG database show that Seq2Saccharide outperforms existing methods in predicting the structure of saccharides. Furthermore, mass spectrometry analysis indicates that the variable search module can correct mispredictions from genome mining. By searching genomic and mass spectrometry data of microbial strains, Seq2Saccharide correctly identified the biosynthetic gene cluster for the polysaccharide oligosaccharide trestatin B.

Aminoglycosides↗

PubMed2026

Strategy for Simultaneous Multiomic Survey of N-Glycomic and Extracellular Matrix Proteome by Mass Spectrometry Imaging.

Recent advances in spatially resolved molecular profiling have positioned matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) as a powerful platform for multiomic tissue analyses. However, conventional workflows that sequentially target distinct molecular classes are time- and resource-intensive, requiring repeated sequential sample preparation, imaging, and data integration. Here, we evaluate streamlined strategies for simultaneous or combined acquisition of N-glycan and collagen-derived peptide information using PNGase F and collagenase. In-solution studies demonstrate that simultaneous enzymatic digestion yields comparable peptide identifications and glycan profiles relative to traditional sequential workflows, with minimal impact on enzymatic specificity. On the basis of these findings, we developed and optimized MALDI-MSI protocols enabling either simultaneous enzyme application or sequential enzyme treatment with unified matrix deposition and single-pass imaging. While direct coapplication reduced image uniformity, a hybrid approach that used sequential enzyme deposition with combined imaging preserved spatial fidelity and spectral quality while significantly reducing processing and computational demands. Application to human tissues, including vertebral bone and ocular samples, highlights the utility of this workflow for fragile specimens and exploratory multiomic surveys. Collectively, these results establish a framework for integrated glycomic and proteomic imaging targeting the extracellular microenvironment, expanding multiomic MALDI-MSI analyses.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMed2025

NumSimEX: A method using EXX hydrogen exchange mass spectrometry to map the energetics of protein folding landscapes.

Hydrogen exchange mass spectrometry (HXMS) is a powerful tool to understand protein folding pathways and energetics. However, HXMS experiments to date have used exchange conditions termed EX1 or EX2 which limit the information that can be gained compared to the more general EXX exchange regime. If EXX behavior could be understood and analyzed, a single HXMS timecourse on an intact protein could fully map its folding landscape without requiring denaturation. To address this challenge, we developed a numerical simulation method called NumSimEX that models EXX exchange for arbitrarily complex folding pathways. NumSimEx fits protein folding dynamics to experimental HXMS data by iteratively comparing the simulated and experimental timecourses, allowing for determination of both kinetic and thermodynamic protein folding parameters. After analytically verifying NumSimEX's accuracy, we demonstrated its power on HXMS data from beta-2 microglobulin (β2M), a protein involved in dialysis-related amyloidosis. In particular, using NumSimEX, we identified three-state kinetics that near-perfectly matched experimental observation. This proof-of-principle application of NumSimEX sets the stage for harnessing HXMS to expand our understanding of proteins currently excluded from traditional protein folding methods. NumSimEX is freely available at https://github.com/JaswalLab/NumSimEX_Public.

Protein Folding↗

PubMed2026

Chimeric structural isomer fragments as cost-efficient internal standards for amino acid quantification by mass spectrometry.

Amino acid (AA) profiles from body fluids such as blood and urine are clinical indicators for diagnosing metabolic and hepatic diseases. Current quantitative methods, such as liquid chromatography-mass spectrometry (LC-MS) with isotopically labelled internal standards (ISs), are costly and technically demanding. This study proposes a cost-efficient alternative using structural isomers as ISs in a direct liquid infusion (DLI) tandem mass spectrometry (MS/MS) approach. The method leverages chimeric spectra and fragment intensity ratios to quantify AAs, demonstrating high linearity and precision even with a 3D ion trap mass analyser. This approach offers a viable strategy for AA quantification in preventive medicine, particularly for screening metabolic diseases such as phenylketonuria, diabetes, and liver dysfunction.

Amino Acids↗

PubMed2025

Acoustic ejection mass spectrometry: the potential for personalized medicine.

INTRODUCTION: The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional 'one-size-fits-all' approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM. AREAS COVERED: This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices. EXPERT OPINION: Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform's analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

Humans↗

PubMed2025

Characterization of Micrococcus luteus Lipidome Containing Novel Lipid Families by Multiple Stage Linear Ion-Trap with High Resolution Mass Spectrometry.

Micrococcus luteus (M. luteus) is a ubiquitous, long-existing Gram-positive bacterium with a distinctive yellow or golden color. It is a model organism for laboratory studies due to its small genome and ease of cultivation. However, only limited knowledge about its constituent lipid structure is known, and its entire lipid profile remains unclear. Here, we applied linear ion trap (LIT) multiple-stage mass spectrometry (MSn) with high resolution for structural characterization of the native lipid extract, along with GC/MS analysis of the acid hydrolysate to reveal the structural details of the entire lipidome, which includes the major phosphatidylglycerol (PG), cardiolipin (CL), phosphatidylinositol (PI), dimannosyl diacylglycerol (DMDG), and minor diacylglycerol (DAG) lipid families. Importantly, we also found two extra lipid families, the new phosphatidyl 1,3-propanediol and the known polyprenyl 1-phosphosate that was not previously reported for M. luteus. We also revealed the unique lipidome simplified by the dominance of branched 15:0-fatty acid substituents (>90% branched 15:0-FA with anteiso-15:0 to iso-15:0 abundance ratio of 4/1), which is in line with the small genome of M. luteus. In addition, the applied LIT MSn mass spectrometry revealed a fragmentation pathway that undergoes internal loss of a glycerol residue specific to DMDG, leading to its structural characterization.

Micrococcus luteus↗

PubMed2025

Single-cell proteomics using mass spectrometry.

Over the past 2 to 3 years, mass-spectrometry-based single-cell proteomics (SCP) has experienced transformative improvements in microfluidic and robotic sample preparation, innovative MS1- and MS2-based multiplexing strategies, and specialized hardware (e.g., timsTOF Ultra 2, Astral), which have dramatically boosted sensitivity, throughput, and proteome coverage from picogram-level protein inputs. Concurrently, tailored computational workflows that encompass normalization, imputation, and no-code platforms have addressed pervasive missing data challenges and standardized analyses, collectively enabling high-throughput, reproducible profiling of cellular heterogeneity. This minireview summarizes the latest progress in SCP technology and software solutions, highlighting how the closer integration of analytical, computational, and experimental strategies will facilitate a deeper and broader coverage of single-cell proteomes.

Single-Cell Analysis↗

PubMed2024

Universal Identification of Pathogenic Viruses by Liquid Chromatography Coupled with Tandem Mass Spectrometry Proteotyping.

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.

Tandem Mass Spectrometry↗

PubMed2026

Reframing Proteomics Measurement: Super Mass Spectrometry Framework and the Role of Delayed Electrospray Ionization Technique.

Dynamic range, repeatability, and reproducibility remain the central limitations of data-independent acquisition (DIA) proteomics. Current workflows emphasize protein group identification counts and throughput, but these metrics mask the fundamental measurement challenge: generating a repeatable, reproducible, high-fidelity, and relatively complete digital representation of complex proteomes. In particular, plasma proteomics spans more than 10 orders of magnitude in protein abundance, far exceeding the capacity and dynamic range of any single mass spectrometer. Incremental advances have not closed this gap. In this Perspectives article, I introduce the Super Mass Spectrometry framework and then highlight the Delayed Electrospray Ionization (Delayed-ESI) technique, as a practical approach to address these limitations. By producing compositionally identical but temporally staggered ion beams, the Delayed-ESI technique enables deterministic remeasurement of the same analyte profile, supporting various novel strategies to improve analytical figures of merit. While recent implementations of the Delayed-ESI technique have emphasized throughput, I argue that the broader value of the Delayed-ESI technique lies in extending dynamic range and improving repeatability and reproducibility─objectives that should take precedence if proteomics is to evolve into a robust measurement science capable of supporting population-scale proteomics studies.

Proteomics↗

PubMed2025

Uncovering hidden protein modifications with native top-down mass spectrometry.

Protein modifications drive dynamic cellular processes by modulating biomolecular interactions, yet capturing these modifications within their native structural context remains a significant challenge. Native top-down mass spectrometry promises to preserve the critical link between modifications and interactions. However, current methods often fail to detect uncharacterized or low-abundance modifications, limiting insights into proteoform diversity. To address this gap, we introduce precise and accurate Identification Of Native proteoforms (precisION), an interactive end-to-end software package that leverages a robust, data-driven fragment-level open search to detect, localize and quantify 'hidden' modifications within intact protein complexes. Applying precisION to four therapeutically relevant targets-PDE6, ACE2, osteopontin (SPP1) and a GABA transporter (GAT1)-we discover undocumented phosphorylation, glycosylation and lipidation, and resolve previously uninterpretable density in an electron cryo-microscopy map of GAT1. As an open-source software package, precisION offers an intuitive means for interpreting complex protein fragmentation data. This tool will empower the community to unlock the potential of native top-down mass spectrometry, advancing integrative structural biology, molecular pathology and drug development.

Mass Spectrometry↗

PubMed2025

Retention and Rearrangement of Membrane Protein Complexes' Higher Order Structure by Collisionally Activated Dissociation- and Electron Capture Dissociation-Mass Spectrometry.

Membrane proteins (MPs) challenge biophysical and structural biology methods. Native mass spectrometry (MS) has emerged as a powerful tool to study MP structures and their modulation by lipids. We examine the pathways by which three MPs, tetrameric Aquaporin Z (AqpZ), trimeric ammonium transporter (AmtB), and pentameric mechanosensitive channel of large conductance (MscL) decompose following collisionally activated dissociation (CAD) or electron capture dissociation (ECD) in native top-down MS. MPs subjected to CAD typically decompose along well-characterized pathways releasing highly charged monomers and low-charge state complementary subunits, but a lesser pathway accessed by low pressure collisions cleaves the backbone into fragments covering much of the sequence. Collisions also rearrange structures, e.g., AmtB subunits rearrange to form novel interactions and/or salt bridges that surprisingly retain a formerly surface-exposed segment despite ejecting the binding interface. MscL dissociation pathways depend on precursor charge-state, a behavior observed in a few soluble complexes, yet unaddressed mechanistically. Salt bridges in low charge state complexes stabilize subunits from ejection while facilitating only smaller, local rearrangements that release covalently cleaved products from transmembrane regions. With fewer opposite charges, on average, higher charge state molecules can rearrange intersubunit salt bridges on the experimental time scale to partition charge asymmetrically and free a subunit. ECD with supplemental activation can retain higher order structures of proteins and inform about the strongly interacting regions that preclude product ion release. With extensive regions lacking ionizable residues, MPs enable key interactions that guide the structure and dynamics of gas phase protein assemblies to be probed.

Mass Spectrometry↗

PubMed2024

Stool Protein Mass Spectrometry Identifies Biomarkers for Early Detection of Diffuse-type Gastric Cancer.

There is a high unmet need for early detection approaches for diffuse gastric cancer (DGC). We examined whether the stool proteome of mouse models of gastric cancer (GC) and individuals with hereditary diffuse gastric cancer (HDGC) have utility as biomarkers for early detection. Proteomic mass spectrometry of the stool of a genetically engineered mouse model driven by oncogenic KrasG12D and loss of p53 and Cdh1 in gastric parietal cells [known as Triple Conditional (TCON) mice] identified differentially abundant proteins compared with littermate controls. Immunoblot assays validated a panel of proteins, including actinin alpha 4 (ACTN4), N-acylsphingosine amidohydrolase 2 (ASAH2), dipeptidyl peptidase 4 (DPP4), and valosin-containing protein (VCP), as enriched in TCON stool compared with littermate control stool. Immunofluorescence analysis of these proteins in TCON stomach sections revealed increased protein expression compared with littermate controls. Proteomic mass spectrometry of stool obtained from patients with HDGC with CDH1 mutations identified increased expression of ASAH2, DPP4, VCP, lactotransferrin (LTF), and tropomyosin-2 relative to stool from healthy sex- and age-matched donors. Chemical inhibition of ASAH2 using C6 urea ceramide was toxic to GC cell lines and GC patient-derived organoids. This toxicity was reversed by adding downstream products of the S1P synthesis pathway, which suggested a dependency on ASAH2 activity in GC. An exploratory analysis of the HDGC stool microbiome identified features that correlated with patient tumors. Herein, we provide evidence supporting the potential of analyzing stool biomarkers for the early detection of DGC. Prevention Relevance: This study highlights a novel panel of stool protein biomarkers that correlate with the presence of DGC and has potential use as early detection to improve clinical outcomes.

Feces↗

PubMed2026

Development and validation of a liquid chromatography-tandem mass spectrometry method for the quantification of twenty-five steroids in equine serum.

Steroids are potential biomarkers for monitoring equine pregnancy. However, immunoassays currently used for their quantification suffer from cross-reactivity and limited specificity, thus requiring more accurate methods. This study reports the development and validation of a robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of 25 steroids covering the main biosynthetic pathways of progestogens, corticosteroids, androgens, and estrogens. Steroids were extracted by protein precipitation followed by evaporation, derivatization, and reconstitution before LC-MS/MS analysis. A surrogate matrix was used for calibration and validation to avoid endogenous interference. Validation was performed according to and partly adapted from Clinical and Laboratory Standards Institute guidelines (CLSI), including linearity, trueness, precision, limits of detection and quantification, measurement uncertainty, recovery, matrix effects, carryover, selectivity, and stability. Calibration curves were fitted using the best-performing weighted linear or quadratic regression model, yielding excellent linearity (R2 > 0.990), trueness between -9.0% and 2.3%, and intra- and inter-day precision <6.3%. Lower limits of quantification ranged from 2.07 to 2250 pg/mL depending on physiological analytes concentration. Extraction recovery averaged 24.3-114.9%, matrix effects were acceptable, and accuracy ranged from 94.4% to 98.9%. No carryover or interferences were detected. Measurement uncertainty remained <15%. This study presents the first LC-MS/MS method partially validated per CLSI criteria for the quantification of 24 steroids in equine serum. The method offers a sensitive and specific alternative to immunoassays and provides a robust tool for equine steroid profiling with potential applications in pregnancy monitoring, placentitis diagnosis, and fetal sex determination.

Animals↗