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Skeletal muscle (SM) acts as a secretory organ, capable of releasing myokines and extracellular vesicles (SM‐EVs) that impact myogenesis and homeostasis. While age‐related changes have been previously reported in murine SM‐EVs, no study has comprehensively profiled SM‐EV in human models. To this end, we provide the first comprehensive comparison of SM‐EVs from young and old human primary skeletal muscle cells (HPMCs) to map changes associated with SM ageing. HPMCs, isolated from young (24 ± 1.7 years old) and older (69 ± 2.6 years old) participants, were immunomagnetically sorted based on the presence of the myogenic marker CD56 (N‐CAM) and cultured as pure (100% CD56+) or mixed populations (MP: 90% CD56+). SM‐EVs were isolated using an optimised protocol combining ultrafiltration and size exclusion chromatography (UF + SEC) and their biological content was extensively characterised using Raman spectroscopy (RS) and liquid chromatography mass spectrometry (LC‐MS). Minimal variations in basic EV parameters (particle number, size, protein markers) were observed between young and old populations. However, biochemical fingerprinting by RS highlighted increased protein (amide I), lipid (phospholipids and phosphatidylcholine) and hypoxanthine signatures for older SM‐EVs. Through LC‐MS, we identified 84 shared proteins with functions principally related to cell homeostasis, muscle maintenance and transcriptional regulation. Significantly, SM‐EVs from older participants were comparatively enriched in proteins involved in oxidative stress and DNA/RNA mutagenesis, such as E3 ubiquitin‐protein ligase TTC3 (TTC3), little elongation complex subunit 1 (ICE1) and Acetyl‐CoA carboxylase 1 (ACACA). These data suggest SM‐EVs could provide an alternative pathway for homeostasis and detoxification during SM ageing. [ABSTRACT FROM AUTHOR]

Identification of features with high levels of confidence in liquid chromatography–mass spectrometry (LC–MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC–MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC–MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors. [ABSTRACT FROM AUTHOR]

Omics-based biomarker technologies, including metabolic profiling (metabolomics/metabonomics) and lipidomics, are making a significant impact on disease understanding, drug development, and translational research. A wide range of patho-physiological processes involve lipids and monitoring changes in lipid abundance can give valuable insights into mechanisms of drug action, off target pharmacology and toxicity. Here we report changes, detected by untargeted LC–MS, in the plasma lipid profiles of male C57Bl/6JRj mice following the PO and IV administration of the epidermal growth factor receptor (EGFR) inhibitor gefitinib. Statistical analysis of the data obtained for both the IV and PO samples showed time-related changes in the amounts of lipids from several different classes. The largest effects were associated with a rapid onset of these changes following gefitinib administration followed by a gradual return by 24 h post dose to the type of lipid profile seen in predose samples. Investigation of the lipids responsible for the variance observed in the data showed that the PI, PC, LPC, PE and TG were subject to the largest disruption with both transient increases and decreases in relative amounts seen in response to administration of the drug. The pattern of the changes in the relative abundances of those lipids subject to variation appeared to be correlated to the pharmacokinetics of gefitinib (and its major metabolites). These observations support the concept of a distinct pharmacolipidodynamic relationship between drug exposure and plasma lipid abundance. [ABSTRACT FROM AUTHOR]

Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance. [ABSTRACT FROM AUTHOR]