Multi-Omics and Personal Care

Introduction

Multiomics (i.e., multiple omics; hereafter referred to as omics) is an emerging interdisciplinary approach to studying complex systems that involves integration of biological data from multiple "omics" fields. Typically including a combination (or all) of the following: genomics, epigenomics, transcriptomics, metabolomics, and proteomics. This provides a comprehensive understanding of how these systems operate at multiple scales from the molecular to whole-organism level, including the study of individual hosts and their associated microbial communities (Ning and Li, 2023). 

While current approaches to microbiome data analysis mostly involve shotgun metagenomic or 16S rRNA amplicon sequencing, which can provide detailed information on the compositional diversity of these host-associated communities, they fail to provide much depth on their functional role or the complex network of interactions by which they communicate with the host (Chetty and Blekhman, 2024). Omics approaches are able to provide a much more holistic model of these traits by capturing and integrating these multiple omics layers to give a better understanding of the mechanisms underlying these host-microbe interactions, as well as their influence on host health and physiology (Chetty and Blekhman, 2024). This makes them an incredibly ideal technology for use in characterising host microbiomes by providing a comprehensive analysis of these complex systems and their dynamic interplay.

Some examples of the types of omics data can be obtained on these host-microbe systems for integrative omics analysis can be viewed in the table below:

Such approaches are increasingly being implemented in the context of product development for skin care, where they can provide a holistic perspective on the overall impact of personal care products on both skin health and the microbiome, as well as assessing the interplay between the two.

Study 1: Multi-omics analysis to evaluate the effects of solar exposure and a broad-spectrum SPF50+ sunscreen on markers of skin barrier function in a skin ecosystem model (Jacques et al., 2025)

Using an original reconstructed human epidermis (RHE) model colonised with human microbiota and supplemented with human sebum, this study aimed to analyse the effects of simulated solar radiation (SSR) on skin metabolites and lipids. It also assessed the effectiveness of an ultraviolet/blue light (UV/BL) broad-spectrum sunscreen with a high sun protection factor (SPF50+) in protecting the skin using skin biomarkers. This was a follow-up to a previous study conducted by the same authors looking at the effects of SSR on the skin microbiome (Jacques et al., 2025).

Results

Comparative metabolomic analyses of the RHE skin model post-irradiation revealed changes in the levels of several natural moisturising factors (NMFs). This included a reduction in the relative amounts of alanine, N-acetyl putrescin, histidine, and glutamine 24 hours after exposure, while lactate, ornithine, trans-UCA and PCA (both on the skin surface and in the epidermis) were found to be higher. The decrease in some of these NMFs could promote skin dehydration following irradiation. Furthermore, the increased abundance of filaggrin byproducts like UCA and PCA may be attributed to the enhanced breakdown of filaggrin (a skin structural protein) upon SSR radiation, with the degradation of this protein contributing to impairment of the physical and biochemical barrier functions of the stratum corneum.

Glycerophospholipid metabolism was also affected following SSR exposure in this RHE model, with an increase in the levels of glycerol and reduction in epidermal

choline, glycerophosphocholine and phosphorylcholine observed in the treatment group. Such an increase in glycerol could be linked to either enhanced glycerophosphocholine degradation or an impairment in the glycerophospholipid biosynthesis pathway, and may be secreted in response to skin irradiation following irradiation as a way to maintain skin health owing to its hygroscopic nature. Although, this benefit may be offset by the simultaneous decrease in epidermal choline, which may disrupt skin barrier function and promote skin dehydration.

Lipidomic analysis found a lower level of free fatty acids (FFAs) in the RHE following SSR exposure, which could be the result of SSR-induced inhibition of enzymes involved in lipid synthesis pathways. These changes may result in an imbalance in skin lipid composition that destabilises skin barrier organisation and integrity. Elevated levels of skin cholesterol were also observed following treatment, where it may work to activate inflammatory pathways associated with sun exposure and cause skin barrier disruption. Furthermore, SSR-exposure was shown to disrupt ceramide biosynthesis and composition in the skin in a manner that might trigger inflammation and disrupt skin barrier function.

Following these omics analyses, the group then assessed the effectiveness of a broad-spectrum SPF50+ sunscreen in preventing SSR-induced changes to the skin. They found application of this broad-spectrum SPF50+ sunscreen was able to prevent many of the previously observed changes to NMF, metabolite and ceramide levels following SSR exposure in the RHE model, allowing it to protect the skin and its components against irradiation-induced disruption and consequences like dehydration, inflammation and damage (Jacques et al., 2025).

Conclusion

Using a reconstructed skin ecosystem model, this study was able to provide information on the impact of single-dose SSR on the skin metabolome and lipidome, with effects observed in the levels of various skin components like natural moisturising factors, metabolites, and lipids that are predicted to cause irradiation-associated effects such as dehydration, reduced barrier function, and inflammation. They also used similar omics approaches to validate the effectiveness of an innovative UV/BL broad-spectrum SPF50+ sunscreen in preventing these SSR-induced changes to skin components levels, as well as predicting how this might mitigate against any associated negative physical effects (Jacques et al., 2025).

Study 2: Multi-omics approach to understand the impact of sun exposure on an in vitro skin ecosystem and evaluate a new broad-spectrum sunscreen (Jacques et al., 2024)

Using an original reconstructed human epidermis (RHE) model colonised with human microbiota and supplemented with human sebum, this precursor study aimed to analyse the effects of simulated solar radiation (SSR) on skin metabolites and microbiome composition as a way to understand the underlying interactions between the skin and its associated microbiota. It also assessed the effect of a broad-spectrum sunscreen on skin ecosystem metabolites and pathways during SSR exposure (Jacques et al., 2024).

Results

Analysis of the skin metabolome following SSR exposure revealed a change in the composition of the whole RHE metabolome, with 51 significantly altered metabolites identified. These included molecules such as uric acid, glutamine/glutamate, and L-pyroglutamic acid/5-oxoproline, which are predicted to play a role in inducing generation of reactive oxygen species (ROS) and oxidative stress pathways in the skin. Lactate production was also elevated following exposure, which can result in skin acidosis to suppress immune function. Furthermore, three distinct metabolic pathways (glycerophospholipid, starch and sucrose, and tetrahydrobiopterin pathways) were also found be significantly affected post-SSR exposure, with the latter possibly having some involvement in mediating host-microbiome interactions following SSR-exposure. 

Compositional microbiome analysis was then used to examine the crosstalk between skin components and cutaneous communities following SSR exposure. Following irradiation treatment, Burkholderia and Cutibacterium became significantly enriched in the skin microbiota, with the latter genus possibly influencing tyrosine metabolism both in and on the surface of the skin via propionate production, a major Cutibacterium metabolites known to inhibit UVB-induced melanogenesis by inhibiting cellular tyrosinase activity. Further analysis of the skin mycobiome found a reduced abundance of the genus Malassezia, with such a depletion likely influencing the composition of skin metabolites like tryptophan and indole. The lipophilic nature of these yeasts mean they are normally able to convert this tryptophan into indole compounds for immune activation, however, under such conditions their metabolisms might be altered in a way that differentially modules the host immune system and prioritises synthesis of melanin and photoprotective indolic compounds.

Like the previous study, after conducting these -omics analyses, the group then assessed the effectiveness of a broad-spectrum SPF50+ sunscreen in preventing SSR-induced changes to the skin and its microbiome. Once again they found application of this broad-spectrum SPF50+ sunscreen to prevent many of the previously observed changes to metabolite and microbiome profiles following SSR exposure in the RHE model, further demonstrating its ability to buffer irradiation-induced disruption and consequences like dysbiosis or inflammation (Jacques et al., 2024).

Conclusion

This study is the first to explore the underlying mechanisms of sun exposure on skin host–microbiota interactions and their biological consequences using an in vitro model to represent the skin ecosystem (skin surface lipids and microbiota) and an integrated omics approach combining metabolomic and microbiomic data. Doing so allowed for accurate characterisation of the skin’s metabolomic signature following irradiation-exposure, with changes in the metabolite profile associated with reactive oxygen species (ROS) generation, inflammation and oxidative stress pathways in the skin being observed. They also explored how interactions between the skin and cutaneous microbiota can be influenced under such conditions, including reduced microbial diversity and possible altered function. The SPF50+ sunscreen was also shown to protect against the negative effects of SSR exposure, including disruption to host–microbial interactions and microbial diversity (Jacques et al., 2024).

Study 3: Multi-omic approach to decipher the impact of skincare products with pre/postbiotics on skin microbiome and metabolome (Li et al., 2023)

This clinical study aimed to decipher the impact of pre- and postbiotic skincare products using an integrated omics approach involving 16S rRNA gene sequencing, shotgun metagenomics and untargeted mass spectrometry-based metabolomics to explore the mechanism-of-action of a triple-biotic complex containing a combination of a prebiotic (inulin), a “smart biotic” (butyloctanol) and postbiotics (lactic acid and pyruvic acid) on skin health through modulation of the skin microbiome and metabolome over a 6-week treatment course (Li et al., 2023).

Results

The researchers found application of the triple-biotic treatment significantly reduced the abundance of opportunistic pathogens, such as Pseudomonas stutzeri and Sphingomonas anadarae, while also increasing the amount of commensals like Halomonas desiderata and Streptococcus mitis that are positively correlated with skin hydration. Further microbiome metagenomic analysis revealed enrichment of bacterial sugar degradation pathways in the prebiotic treatment group compared with baseline controls. This could serve to generate more lactic acid through active degradation of the inulin prebiotic, promoting skin hydration and maintaining its pH.

Metabolomic analysis revealed enrichment of several clinically relevant metabolites in the prebiotic group, such as long-chain/medium-chain fatty acids, Fatty acid esters, Fatty Acyls, Dicarboxylic acids and derivatives that are known to have positive effects on skin health. For example, fatty acids and esters, and fatty acyls contribute to skin barrier functions, while dicarboxylic acids have antimicrobial and anti-inflammatory properties. Correlation analysis between microbiome and discriminant clinically relevant metabolites revealed a negative correlation between the reduction of S. anadarae and P. stutzeri with fatty acids and dicarboxylic acids, while the increase of H. desiderata was positively correlated to certain metabolites associated with the increase of skin hydration (Li et al., 2023).

Conclusion

This study demonstrated a significant positive effect of a triple-biotic complex consisting of a prebiotic, biotic, and postbiotic on the physical and microbial parameters of skin following 6-weeks of topical application, with enhanced skin hydration and a more favourable shift in microbiome composition towards favourable commensals and away for opportunistic pathogens. Some of these commensal species were also found to positively correlate with skin hydration following analysis of the microbiome metagenome and metabolome, presenting potential bacterial targets for the development of future therapeutics (Li et al., 2023).

Strengths and Limitations of Research (integrated omics to skincare)

Strengths:

The implementation of such integrated omics approaches examining data from various omics datasets has allowed for the successful identification of clinically relevant strains and metabolites of interest that could act as targets for the development of future personal care products addressing a diverse set of issues surrounding skin health, for example, by enriching topical formulations with the desired microbial strains and their metabolites (Li et al., 2023).

Omics can also provide a holistic assessment of product efficacy during testing by accurately modelling how different environmental conditions can affect product performance on a range of parameters such as skin physiology and components by examining the molecular cross-talk that occurs between the three, as well as elucidating the specific mechanism of action by which it is able to do so (Jacques et al., 2025).

The combined analysis of multiple streams of omics data also open up avenues to better understand host-microbiome interactions by linking host physiology to microbiome function, allowing for a more comprehensive analysis of how this dynamic relationship can be altered in response to intrinsic and extrinsic factors, as well as how these interactions can change (or be maintained) by personal product use. This information can be used to improve formulations so that they avoid pushing the microbiome towards unfavourable dysbiosis, or promote a more balanced microbiome in the case of treating dysbiotic disorders (Jacques et al., 2024).

Limitations:

Many existing omics studies rely on the use of in vitro models to gather data on skin physiology and product performance, making it difficult to fully reproduce the complex interplay that exists between host metabolism and the cutaneous microbiota in such a static system (Jacques et al., 2024).

While many omics studies have succeeded in promoting a deeper understanding of host-microbiome interactions, there is still a lacking standardised approach for the integration of these multiomics layers, which can make it difficult to both draw accurate comparisons between studies, and determine whether observations are real or pipeline-related artifacts (Chetty and Blekhman, 2024).

Many of the computational tools and bioinformatics infrastructure currently available are significantly limited in their ability to support analysis, integration, and interpretation of these large omics datasets, making it difficult to obtain meaningful insights from the data. Those tools that are able to support these processes are usually inaccessible to smaller research groups and labs owing to cost, further restricting this field of research (Shi et al., 2025).

Future Directions and Research

Integrated multigenomics technologies could allow for the development of personalised care products via identification and tracking of differential skin biomarkers combining multiple omics signatures. Altered biomarker profiles can be used as indicators of particular conditions, and returned to baseline using specific ingredients that modulate the expression of these altered components, allowing for more individualised and targeted treatment for various disorders and skin types. Furthermore, understanding individual skin biomarkers can be used to predict the suitability of products for specific individuals, permitting more precise and refined product formulation (Dessì et al., 2024).

They can also provide added dimensionality to deciphering host-microbiome interactions by elucidating the functional role of these microbes and their mechanism of action in influencing skin condition by analysing the products they produce, how these are synthesised, and their overall effects on skin health, as well as offering deeper insight into skin disorders and how this host-microbe crosstalk influences progression (Fernández-Carro et al., 2025).

The growing availability of these omics datasets may facilitate the development of in silico models that use machine learning to accurately predict the effects of cosmetic ingredients on the skin using existing omics data, acting as a more ethical alternative to existing animal models. Such “infotechnomics” approaches may pave the way for more rapid and accessible ingredient testing for factors like safety (toxicity) and efficacy, streamlining the product formulation process and allowing for greater standardisation (Kalicińska et al., 2023).

Lastly, integrated omics skin biomarkers can be used to monitor the progression of specific skin conditions, as well as predicting susceptibility to disease. This can further promote the development of predictive and preventative therapeutics via biomarker screening approaches for the identification of early diagnostic markers in order to develop effective, personalised treatments to mitigate or reduce the effects of various skin disorders that might emerge later in life (Wei et al., 2024).

Conclusion

Omics provides an interdisciplinary approach to studying skin health and the microbiome, with research increasingly focusing on the application of these technologies for the development of personal care products and their effects on this dynamic host-microbial system. This allows for a holistic assessment of skin biomarkers and how these components can be altered by various products and external conditions, with many positive implications for product formulations and skin monitoring. However, despite providing an increased understanding of these complex biological systems, much work remains to be done to improve the accuracy and standardisation of such omics models. Improving these limitations can pave the way for the development of personalised products and treatments for various skin conditions, product testing, and preventative diagnostic therapeutics.

References

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