Industries · Dermatology & Cosmetics
Dermatology & Cosmetics
Skin biology and multi-omics profiling; transcriptomics, proteomics, metabolomics, and microbiome, for dermo-cosmetic innovation.
Industries · Dermatology & Cosmetics
Skin biology and multi-omics profiling; transcriptomics, proteomics, metabolomics, and microbiome, for dermo-cosmetic innovation.
Want to support your product claims with molecular evidence? From the model to the interpretation, Skin Biology Xplorer shows how your cells, explants or 3D models respond to a compound, which biological pathways are involved, and what that says about its mechanism of action.
Differential expression between treated and untreated conditions, at gene and pathway level, with effect sizes and significance reported per comparison.
Protein-level quantification and differential abundance, matched against the transcriptomic result. Targeted panels where a candidate needs confirmation.
Vesicle isolation, size distribution and particle count, with molecular cargo profiled alongside the cells that released it. For actives that act on cell-to-cell communication rather than through a single intracellular pathway.
Skin community composition before and after treatment, where the claim involves the microbiome.
Layers measured on the same samples and read together, so a signal seen in several of them carries more weight than one seen in a single measurement.
Donor effect estimated and separated from treatment effect, because an active that works on three donors out of eight is a different finding from one that works on all eight.
The protocol is fixed at the start of a formulation series and held across it, so a difference between iterations is a difference in the formulation rather than in how it was measured.
Tool versions, parameters and thresholds for every step. QC per sample and per cohort, with exclusions stated and justified. Interpretation written against the question the study started from, stating what the data support and what they do not. Processed matrices, result tables, versioned pipelines and figures, delivered for audit or reproduction by a third party.
How many donors and how many replicates are needed before an effect can be told apart from donor variability at all: that question has to be settled before sampling, not after. A study that cannot separate the two will produce data, but not evidence.
Some dermo-cosmetic actives do not act through a single intracellular pathway. They alter what cells communicate to each other, and that signature appears not only inside the cells but in the extracellular vesicles they release.
Measuring the cellular transcriptome alone misses it. The vesicles have to be isolated, counted, sized and their cargo analysed, which adds a layer that can then be read alongside the response of the cells that produced them.
GenXMap combines omics-based cargo analysis with specialised extracellular-vesicle characterisation capabilities developed with the Faculté de Pharmacie de Marseille. This enables vesicle populations to be characterised through their size, concentration and relevant markers, while their molecular cargo can be profiled and interpreted alongside cellular transcriptomic and multiomics data.
The result is a two-sided view of cell communication: what changes inside the cells, and what those cells communicate to their environment.
By integrating extracellular-vesicle data with transcriptomics, proteomics, metabolomics and, where relevant, microbiome profiling, GenXMap can determine whether an active produces a coherent, reproducible biological response across multiple levels of skin biology.
For claims involving cell communication, this provides a stronger evidence chain than measuring a single intracellular endpoint alone.
The laboratory work behind these analyses runs on OMICS4, GenXMap’s platform site, with sample requirements, logistics and turnaround defined per study.
The analysis does not have to start there. Transcriptomics, proteomics, metabolomics, microbiome or extracellular-vesicle data from an earlier study, an internal programme or another provider can be analysed on its own terms. What determines the analysis is the biological question, the study design and the metadata available.
Where several layers are available, we integrate them to find converging signals, separate robust effects from technical or donor variability, and build the evidence around the claim.
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