Multi-Omics Network Integration Reveals Cross-Domain Molecular Correlates of Fetal Bovine Serum Grade
TL;DR
A multi-omics meta-analysis integrating original proteomics, peptidomics, metabolomics, and lipidomics datasets from two companion studies across three commercial FBS grades provides a reproducible multi-omics framework and provides a reproducible multi-omics framework for researchers to characterize their own serum lots before biologically sensitive work.
Abstract
Fetal bovine serum (FBS) is the dominant supplement in mammalian cell culture, yet its quality grades are typically treated as fixed, defined inputs despite a poorly characterized molecular basis. We present a multi-omics meta-analysis integrating original proteomics, peptidomics, metabolomics, and lipidomics datasets from two companion studies across three commercial FBS grades (Low, Medium, High), using MOFA2, DIABLO, cross-omics correlation networks, and WGCNA. Integrated multi-omics embeddings achieved markedly superior grade separation (silhouette score 0.98) versus any single omics layer (0.80–0.87). MOFA2 identified a shared proteomics–metabolomics–peptidomics quality axis, while ceramides and sphingolipids emerged as central cross-omics hubs, and HERC2P9-derived peptides and diethylstilbestrol as grade-discriminating features. Because each grade was represented by a single commercial lot, grade and batch are confounded here, and the reported differences should be read as lot-associated, pending multi-lot validation. Rather than certifying FBS grade as a fixed molecular category, this study is intended as an open resource: it demonstrates the depth of coordinated molecular variation across commercial FBS products and provides a reproducible multi-omics framework, with all raw data and analysis code openly deposited, for researchers to characterize their own serum lots before biologically sensitive work, rather than assuming compositional equivalence from a grade label alone.