Computational protocol: Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA

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Protocol publication

[…] Raw MS data was processed using the IDEOM version 19 [] workflow. This utilizes XCMS Centwave [] for peak detection and mzMatch, R [] for peak alignment between triplicates and between samples, for filtering and for the storage of the data in peak ML-formatted files. Feature alignment was performed with a retention time window of 30 s and a mass error window of 5 ppm. Scripts for XCMS [] and mzMatch are coded in the R environment.In the alignment procedure, peaks obtained in three different UHPLC-HRMS experiments (triplicate injections) are determined to be formed from the same compound, based on their appearance at nearly the same retention time and m/z value. Signals of isotopomers were identified and assigned to their respective quasi-molecular ion ([M + H]+ in positive ion mode). The monoisotopic mass of the corresponding neutral was obtained from that of the parent ion by subtracting the proton mass. The alignment procedure results in a list of “features,” each associated with a monoisotopic mass (for the neutral M), a retention time, and a total ion abundance. The calculated mass values for the neutral compounds, M, were used throughout the manuscript, instead of m/z for the MH+ ions. Unless the identification of a parent ion in a group of peaks as MH+ is erroneous, each feature will correspond to an actual compound. Alignment of detected peaks was performed separately for the set of samples extracted into THF and into water, respectively.A major objective of this metabolomic study is to identify (putative) compounds that are over- or under-expressed in renal cancer as opposed to normal renal tissue. For features, the terms “over-abundant” and “under-abundant” were used, while “over-expressed” and “under-expressed” were used for (putatively) identified metabolites. Detailed LC-MS data discussed in this work is available in the Electronic supplementary material (ESM, Table ).Lists of detected features were matched against the IDEOM’s version of the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolite database [] using a mass error tolerance of 4 ppm. Retention times for authentic standards, and a retention time prediction model, were included for ZIC-HILIC chromatography data []. For a putative identification, the maximum difference allowed between calculated and observed RT was 5% for authentic standards and 45% for other metabolites. Putative identifications were also guided by searches on the Madison-Qingdao Metabolomics Consortium Database (MMCD) [] and the Human Metabolome Database (HMDB) [].Multivariate statistical analyses were performed using Metaboanalyst 3.0 []. […]

Pipeline specifications

Software tools Ideom, XCMS, MetaboAnalyst
Databases HMDB MMCD
Application MS-based untargeted metabolomics
Organisms Homo sapiens
Diseases Carcinoma, Renal Cell, Kidney Diseases, Kidney Neoplasms, Neoplasms
Chemicals Riboflavin