RNA-Seq data for A Ramanomics-Transcriptomics-Metabolomics Approach for Assessing Metabolic Interactions among Human Epithelial Cells, Microbes, and Actives

Metabolic interactions among host cells, pathogens and active compounds are fundamental to the personal care industry, yet current methods for mechanism-based assessment are usually slow, tedious, and costly. Here, we established such an interaction model which consists of human gingival epithelial cells (HGE), oral pathogens (Porphyromonas gingivalis) or symbionts (Streptococcus sanguinis), and stannous fluoride (SnF2). By profiling the single-cell metabolic phenome of HGE after bacterial exposure via the ramanomic approach, we discovered a novel ramanomic biomarker of bacterial hemin in HGE that is not detected by transcriptomics and metabolomics but provides direct evidence of P. gingivalis invasion to the host cells. Moreover, a Ramanomic Index of Metabolic Stress (RIMS) was proposed to quantify the stressing effect of P. gingivalis and the stress-relieving effect of SnF2. Furthermore, integrated analyses of ramanome, transcriptome and metabolome validated that the differential Raman peaks (DRPs) derived from the HGE ramanomes correspond to the activation of P. gingivalis-induced inflammatory responses, oxidative stress, and metabolic alterations. Notably, the addition of SnF2 not only mitigated these adverse effects but specifically enhanced the nicotinate and nicotinamide metabolism pathway, highlighting its dual role in counteracting inflammation and promoting metabolic homeostasis. These multi-omics findings revealed the molecular events underlying HGE responses to P. gingivalis, and moreover advocated for ramanomics-based metrices such as RIMS as a rapid, cost-effective, and mechanism-based tool for assessing the effects of biotic and abiotic factors in personal care.