Citation: Fecke, A.; Saw, N.M.M.T.; Kale, D.; Kasarla, S.S.; Sickmann, A.; Phapale, P. Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics. Metabolites 2023, 13, 844. https://doi.org/10.3390/ metabo13070844 Academic Editors: Nicole Strittmatter and Regina Verena Taudte Received: 5 May 2023 Revised: 4 July 2023 Accepted: 11 July 2023 Published: 13 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). metabolites H OH OH Article Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics Antonia Fecke 1,2 , Nay Min Min Thaw Saw 1 , Dipali Kale 1 , Siva Swapna Kasarla 1 , Albert Sickmann 1 and Prasad Phapale 1, * 1 Leibniz-Institut für Analytische Wissenschaften—ISAS—e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany; antonia.fecke@isas.de (A.F.); naymin.saw@isas.de (N.M.M.T.S.); dipali.kale@isas.de (D.K.); siva.kasarla@isas.de (S.S.K.); albert.sickmann@isas.de (A.S.) 2 Department Hamm 2, Hochschule Hamm-Lippstadt, Marker-Allee 76-78, 59063 Hamm, Germany * Correspondence: prasad.phapale@isas.de Abstract: Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the com- plexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabo- lite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound’s individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a “quantitative chromatogram library” with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications. Keywords: metabolomics; metabolite quantification; LC-MS; quantitative spectral library; relative response factor 1. Introduction Metabolomics research plays a crucial role in understanding complex biochemical pathways and identifying biomarkers for various clinical and biomedical applications [1]. Untargeted or global metabolomics is a hypothesis-generating approach, which involves the profiling of metabolites without prior knowledge and has provided important insights into several disease and drug response mechanisms [2–4]. However, metabolite quantification using an untargeted metabolomics approach often faces challenges in terms of data quality, reliability, and reproducibility of measured metabolite profiles [5–7]. These quantitative challenges arise from various causes, including variation in electrospray ionization (ESI) response, matrix effect, dynamic concentration range, sensitivity, and even peak curation or integration errors by software [5]. On the other hand, targeted metabolomics methods and data acquisition approaches can provide the absolute or relative quantification and validation of a chosen subset of metabolites, based on a hypothesis-driven approach [8]. Targeted metabolomics allows researchers to circumvent the shortcomings of the global Metabolites 2023, 13, 844. https://doi.org/10.3390/metabo13070844 https://www.mdpi.com/journal/metabolites