2026 Mass Analytica Training
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Whether you want to deepen your knowledge of a specific software, discover new tools, or exchange best data analysis practices, this interactive session combines expert guidance, live demos, and hands-on training tailored to your use cases.
On-site training format
Duration: 2-4 hours (adjustable based on your needs)
Format: In-person, interactive, and hands-on
Content: Training, live demos, and practical examples focused on selected use cases
Note: Contact us to learn more and customize your session: info@mass-analytica.com
Scalable Peptide MRM Transition Prediction for High-Throughput Proteomics via Hashing-Based Sequence Encoding
Scalable Peptide MRM Transition Prediction for High-Throughput Proteomics via Hashing-Based Sequence Encoding
Peptide analysis via Multiple Reaction Monitoring (MRM) is indispensable for quantification and/or biomarker validation and drug development, yet its reliance on experimental transition optimization limits scalability. Current computational models for small molecules fail to address peptide-specific complexities, such as sequence-dependent fragmentation and charge-state variability. We introduce a novel framework that combines hashing-based peptide fragment encoding with gradient-boosted decision trees to predict MRM transitions efficiently. This method eliminates bottlenecks in experimental workflows, enabling rapid, resource-efficient transition identification without compromising accuracy—a critical advancement for high-throughput proteomics pipelines.
Molecular Structure and Mass Spectral Data Quality Driven Processing of High-Resolution Mass Spectrometry Data for Pharmacokinetics Studies
Molecular Structure and Mass Spectral Data Quality Driven Processing of High-Resolution Mass Spectrometry Data for Pharmacokinetics Studies
Our inability to comprehensively process high resolution mass spectrometry data for quantitative analysis has long been an impediment to the broader adoption of this powerful technique. We have developed an approach that agnostically and automatically identifies all ions related to the compound in both the MS and MSMS data. The algorithm uses the structure of the molecule to automatically select the optimal compound related MS and MSMS signals, and parameters (extraction window, S/N) to provide the best overall method to meet the assay acceptance criteria defined by the user. Results using this structure and data driven approach are presented for pharmacokinetic data that were collected using the same set of samples analyzed on both QQQ and HRMS instruments.
Plasma lipidomics analysis reveals altered profile of triglycerides and phospholipids in children with Medium-Chain Acyl-CoA dehydrogenase deficiency
Plasma lipidomics analysis reveals altered profile of triglycerides and phospholipids in children with Medium-Chain Acyl-CoA dehydrogenase deficiency
July 2024
Abstract
Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most prevalent mitochondrial fatty acid β-oxidation disorder. In this study, we assessed the variability of the lipid profile in MCADD by analysing plasma samples obtained from 25 children with metabolically controlled MCADD (following a normal diet with frequent feeding and under l-carnitine supplementation) and 21 paediatric control subjects (CT). Gas chromatography-mass spectrometry was employed for the analysis of esterified fatty acids, while high-resolution C18-liquid chromatography-mass spectrometry was used to analyse lipid species. We identified a total of 251 lipid species belonging to 15 distinct lipid classes. Principal component analysis revealed a clear distinction between the MCADD and CT groups. Univariate analysis demonstrated that 126 lipid species exhibited significant differences between the two groups. The lipid species that displayed the most pronounced variations included triacylglycerols and phosphatidylcholines containing saturated and monounsaturated fatty acids, specifically C14:0 and C16:0, which were found to be more abundant in MCADD. The observed changes in the plasma lipidome of children with non-decompensated MCADD suggest an underlying alteration in lipid metabolism. Therefore, longitudinal monitoring and further in-depth investigations are warranted to better understand whether such alterations are specific to MCADD children and their potential long-term impacts.
Keywords: Lipid profile; Lipidomics; Mass spectrometry; Medium‐chain acyl‐CoA dehydrogenase deficiency (MCADD); Phospholipids (PL); Plasma analysis; Triacylglycerols (TG).