Case Studies

Laboratory pipetting into sample tubes representing microbiome and metabolomics research workflows in a wet lab.

Collaborative Solutions Delivering Scientific and Commercial Impact

From Ingredients to Outcomes

How Multi-Omics Revealed Pre/Postbiotic Skincare’s Impact on the Skin

When biology changes, it rarely changes in just one layer. Multi-omics connects the dots - linking who is present (microbiome), what they can do (functional pathways), and what actually happens (metabolites and clinical readouts).

In this study, Clarity Genomics integrated 16S rRNA sequencing, shotgun metagenomics, and untargeted metabolomics to show how pre/postbiotic skincare shapes the skin ecosystem - and how those shifts relate to improved skin hydration.

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Partner

Colgate-Palmolive

Goal

Understand the mechanism of action of pre-postbiotic body wash + lotion on dry skin

Challenge

Topical pre/pro/postbiotics are promising, but the in-vivo mode of action on skin is not well characterized. It requires linking subtle microbiome shifts to functional pathways, metabolite changes, and clinical endpoints.

Study Overview

Population

female subjects with dry/extremely dry skin

Arms

Prebiotic vs. Control

Timeline

Baseline, 3 weeks, 6 weeks

Clinical measurements
Skin hydration (Corneometer), TEWL

1

Clarity Genomics' Role

Integrated analytics

Brought together 16S, shotgun metagenomics, and LC-MS/MS untargeted metabolomics into a unified workflow and correlation framework

Cross-modal linking

Connected microbial taxa and pathways with discriminant metabolites and clinical hydration outcomes

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Methods

Microbiome

16S rRNA (community profiling), shotgun metagenomics (strain-level & functional pathways)

Metabolome

LC-MS/MS (positive/negative modes), feature detection/annotation of classes, statistics (PCA/PLS-DA, VIP, FDR)

Integration

Clinically relevant metabolite identification and correlation with microbial taxa

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Key Findings

Clinical

Both arms improved hydration; integrity of the skin barrier was maintained.

Microbiome

Taxa: Prebiotic arm reduced opportunists (e.g., P. stutzeri, S. anadarae) and increased commensals (e.g., S. equorum, S. mitis, H. desiderata SP1).

Function

Prebiotic arm showed enrichment of carbohydrate/sugar-acid degradation pathways; control showed reductions in fatty-acid biosynthesis pathways over time.

Metabolome

Product use drove clearer separations in the metabolomics profile than taxonomy alone; the prebiotic arm modulated more clinically relevant metabolites (long/medium-chain fatty acids, esters, dicarboxylic acids, monosaccharides).

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Heatmap showing correlations between microbiome taxa, metabolites, and clinical outcomes.

Figure 6 of manuscript

Heatmap illustrating correlation between microbes, clinical outcomes and discriminant metabolite features identified by LC-MS/MS Neg.

Integrated multi-omics heatmap linking microbiome composition with metabolite profiles.

Supplementary Figure 6 of manuscript

Heatmap illustrating correlation between microbes, clinical outcomes and discriminant metabolite features identified by LC-MS/MS Pos.

Integrated Signals

Beneficial commensals (S. mitis, H. desiderata SP1) positively correlated with hydration.

Opportunists (P. stutzeri, S. anadarae) negatively associated with metabolites linked to improved hydration

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Interpretation

The prebiotic products induced small but targeted microbiome shifts that corresponded with pronounced metabolomic signatures and measurable improvements in skin hydration.

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Business Impact for Partners

Mechanism clarity for R&D and claims: connect ingredients to pathways / metabolites and outcomes.

Biomarker targets for future products (e.g. commensal taxa and metabolite classes associated with hydration)

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Unlocking Human Milk

How B. infantis EVC001 Improved HMO Utilization in Preterm Infants

Early-life biology is a cascade. Diet shapes microbes, microbes shape metabolites, and metabolites shape outcomes. By integrating microbiome profiling with targeted chemistry (HMOs), we can trace that cascade end-to-end and see whether an intervention truly changes function, not just taxonomy.

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Partner

Infinant Health

Goal

Evaluate the tolerability of B. infantis EVC001 and its effects on the fecal microbiota in preterm infants in a Neonatal Intensive Care Unit

Challenge

Show, within NICU constraints, that B. infantis EVC001 is tolerable and delivers a functional benefit, not just taxonomic shifts, i.e., demonstrable HMO utilization (genes ↑, fecal HMOs ↓), requiring integrated multi-omics to link colonization → function → nutrition.

Study Overview

Population

preterm infants in a single NICU. Two sequential cohorts to avoid cross-colonization.

Arms

Control (no product) vs. B. infantis EVC001 daily

Timeline

Baseline, Study Days 14 and 28

Clinical measurements

adverse events, infections, growth, stool frequency; trial registered NCT03939546

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Clarity Genomics' Role

Integrated analytics

Shotgun metagenomics and HPLC-MS HMO profiling link colonization to microbial function and the resulting phenotype.

Cross-modal linking

Mass-spec HMO profiling connected B. infantis colonization to HMO utilization and corresponding shifts in community structure (↑ Bifidobacteriaceae, ↓ Enterobacteriaceae).

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Methods

Microbiome

Shotgun metagenomics processing to identify HMO-utilization gene clusters H1-H5 and functional profiling. qPCR for B. infantis (Blon_2348).

Metabolome

Absolute quantification of key human milk oligosaccharides (e.g., 2′FL, 3′FL, LNT, LNFP-I, 6′SL) by HPLC-MS; statistics with FDR control

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Key Findings

Clinical

No product-related adverse events. Fewer anemia diagnoses post-baseline in EVC001 (P=0.02). Stool frequency decreased in EVC001.

Microbiome

Colonization

B. infantis undetectable at baseline; reached ~9.7–9.9 log₁₀ CFU/ug of fecal DNA by Days 14-28 in EVC001; undetectable in controls.

Function

EVC001 group showed robust HMO-utilization genes and ~250-fold lower fecal HMO abundance vs controls; HMOs negatively correlated with B. infantis levels.

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Interpretation

Targeted colonization with an infant-adapted HMO strain did more than change « who’s there » , it changed what the microbiome does,diverting HMOs into infant-usable metabolism and shifting the community toward beneficial taxa. This is a mechanism-level readoutof nutritional benefit.

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Business Impact for Partners

Mechanism clarify for claims: Direct link from intervention -> HMO genes -> depleted fecal HMOs -> community shift

Biomarkers & endpoints: B. infantis load, fecal HMO depletion, and functional gene panels as sensetive markers of efficacy

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Bar and dot charts showing human milk oligosaccharide abundance and correlation with B. infantis.

Figure 3 c,d) of manuscript C) Absolute abundance and standard error of HMOs in the fecal samples. Statistical differences determined with Wilcoxon Rank Sum test. (D) Spearman's correlation between abundance of HMOs and the absolute abundance (Log10 CFU) of B. infantis in the fecal samples. Data points represent individual infants and are colored by group.

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