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We would like to thank R Marotta/IIT for support with IM-TEM and acknowledge Kim Mace for reviewing the manuscript. This work was supported by grants from Unilever, UK, and the NIHR Manchester BRC (BRC-1215-20007) "Inflammatory Hair Diseases" program.

Background Acute cutaneous inflammation causes microbiome alterations as well as ultrastructural changes in epidermis stratification. However, the interactions between keratinocyte proliferation and differentiation status and the skin microbiome have not been fully explored. Objectives Hypothesizing that the skin microbiome contributes to regulation of keratinocyte differentiation and can modify antimicrobial responses, we examined the effect of exposure to commensal (Staphylococcus epidermidis, SE) or pathogenic (Staphylococcus aureus, SA) challenge on epidermal models. Methods Explant biopsies were taken to investigate species-specific antimicrobial effects of host factors. Further investigations were performed in reconstituted epidermal models by bulk transcriptomic analysis alongside secreted protein profiling. Single-cell RNA sequencing analysis was performed to explore the keratinocyte populations responsible for SA inflammation. A dataset of 6391 keratinocytes from control (2044 cells), SE challenge (2028 cells) and SA challenge (2319 cells) was generated from reconstituted epidermal models. Results Bacterial lawns of SA, not SE, were inhibited by human skin explant samples, and microarray analysis of three-dimensional epidermis models showed that host antimicrobial peptide expression was induced by SE but not SA. Protein analysis of bacterial cocultured models showed that SA exposure induced inflammatory mediator expression, indicating keratinocyte activation of other epidermal immune populations. Single-cell DropSeq analysis of unchallenged naive, SE-challenged and SA-challenged epidermis models was undertaken to distinguish cells from basal, spinous and granular layers, and to interrogate them in relation to model exposure. In contrast to SE, SA specifically induced a subpopulation of spinous cells that highly expressed transcripts related to epidermal inflammation and antimicrobial response. Furthermore, SA, but not SE, specifically induced a basal population that highly expressed interleukin-1 alarmins. Conclusions These findings suggest that SA-associated remodelling of the epidermis is compartmentalized to different keratinocyte populations. Elucidating the mechanisms regulating bacterial sensing-triggered inflammatory responses within tissues will enable further understanding of microbiome dysbiosis and inflammatory skin diseases, such as atopic eczema.

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The ability to reliably predict and infer cellular responses to environmental exposures would offer a major advance in the investigation of immune regulation in health and disease. One possible approach is the use of in silico modelling. Design of such a mathematical kinetic model would be based on existing knowledge of a biological system and utilise a partial data set to parameterise. However, the process of parameter estimation, key for the accuracy of the model, is difficult to conduct by hand, and thus a computational alternative is necessary. We report the utility of Genetic Algorithm with Rank Selection (GARS) as a parameter estimation tool on multiple biological models, including heat shock, signal transduction via ERK, circadian rhythm and NFκB systems, where it showed strong accuracy and superiority to the Extended Kalman Filter method, Algebraic Difference Equations, and MATLAB fminsearch approaches. GARS parameter estimation is a valuable tool for biological data because it reliably infers system behaviour from partial data sets, allowing for the prediction of cellular responses to environmental exposures.