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. 2020 Jun 24;11:3186. doi: 10.1038/s41467-020-17026-6

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — a Applying FI-MS method with 8 optimized ranges in negative ionization mode for metabolomics and lipidomics analysis of serum samples from 20 donors and adding 250 nM of glucose 6-phosphate (G6P); and in positive ionization mode, adding 150 nM of Met-Arg-Phe-Ala tetrapeptide (MRFA); y axis represents the RSD% of the measured intensity of these compounds across the 20 analyzed samples. Applying FI-MS with eight optimized ranges in positive ionization mode for metabolomics analysis of serum samples, adding increasing concentrations of taurocholic acid (b) and lipidomics analysis in positive ionization mode adding increasing concentrations of 18:1 lyso phosphocholine (LPC) (c), demonstrating a linear response in both cases. Data are mean values ± SD, n = 5 (10 for LPC measurement) independent repetitions of the FI-MS analysis. d Correlation between FI-MS and LC-MS-based concentration measurements of nine amino acids and two lipids in serum. Error bars show 95% confidence intervals. Source data are provided as a Source data file.