{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:45:29Z","timestamp":1785545129168,"version":"3.56.0"},"reference-count":15,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["24-75-10091"],"award-info":[{"award-number":["24-75-10091"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]},{"award":["24-75-10091"],"award-info":[{"award-number":["24-75-10091"]}],"id":[{"id":"https:\/\/ror.org\/03y2gwe85","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Data compatibility remains a major challenge in metabolomics, as commonly used measures of biological material\u2014such as sample weight or cell count\u2014are often poorly reproducible. Here, we systematically evaluated practical normalization strategies for GC \u00d7 GC-MS-based metabolomic profiling of two widely used model cell lines: human hepatoblastoma (HepG2) and mesenchymal stromal cells (MSCs). We compared orthogonal biomass estimates, including total protein and double-stranded DNA quantified either directly in aliquots of the cell suspension lysate aliquots or in the post-extraction cell precipitate, alongside normalization based on extracted ion current (XIC). We also assessed three widely used extraction mixtures\u2014methanol\/chloroform\/water (7:2:1); methanol\/water (8:2); acetonitrile\/isopropanol\/water (3:3:2)\u2014for metabolome coverage and normalization robustness. Under realistic biological variability, signal-to-biomass dependencies were moderate. In contrast, under strictly controlled conditions, DNA- and protein-based normalization yielded near-linear relationships with metabolite abundances (R2 &gt; 0.90), demonstrating that biological variability is the dominant source of dispersion rather than technical factors. Methanol\/chloroform\/water system provided the broadest metabolome coverage and strongest correlation with injected biomass. Based on these findings, we recommend normalization to total precipitate protein or DNA using the methanol\/chloroform\/water extraction protocol, with XIC as a complementary quality control metric.<\/jats:p>","DOI":"10.3390\/ijms27073219","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T07:57:55Z","timestamp":1775116675000},"page":"3219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Normalization of GC-MS Metabolomics Data in Adherent Cells: A Practical Comparison of Approaches"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3704-0992","authenticated-orcid":false,"given":"Ilya Yu.","family":"Kurbatov","sequence":"first","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svyatoslav V.","family":"Zakharov","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3032-1983","authenticated-orcid":false,"given":"Olga I.","family":"Kiseleva","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3658-2490","authenticated-orcid":false,"given":"Viktoriia A.","family":"Arzumanian","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9367-6656","authenticated-orcid":false,"given":"Igor V.","family":"Vakhrushev","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2004-1390","authenticated-orcid":false,"given":"Roza Yu.","family":"Saryglar","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4502-6898","authenticated-orcid":false,"given":"Victoria D.","family":"Novikova","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7032-6781","authenticated-orcid":false,"given":"Yan S.","family":"Kim","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1838-3604","authenticated-orcid":false,"given":"Ekaterina V.","family":"Poverennaya","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Chemistry, Pogodinskaya Street, 10, 119121 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1007\/s00216-011-4678-z","article-title":"Sample Amount Alternatives for Data Adjustment in Comparative Cyanobacterial Metabolomics","volume":"399","author":"Huege","year":"2011","journal-title":"Anal. 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