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BackgroundIn previous human skin single-cell data, inflammatory cells constituted only a small fraction of the overall cell population, such that functional subsets were difficult to ascertain.ObjectiveOur aims were to overcome the aforesaid limitation by applying single-cell transcriptomics to emigrating cells from skin and elucidate exvivo gene expression profiles of pathogenic versus regulatory immune cell subsets in the skin of individuals with psoriasis.MethodsWe harvested emigrating cells from human psoriasis skin after incubation in culture medium without enzyme digestion or cell sorting and analyzed cells with single-cell RNA sequencing and flow cytometry simultaneously.ResultsUnsupervised clustering of harvested cells from psoriasis skin and control skin identified natural killer cells, T-cell subsets, dendritic cell subsets, melanocytes, and keratinocytes in different layers. Comparison between psoriasis cells and control cells within each cluster revealed that (1) cutaneous type 17 T cells display highly differing transcriptome profiles depending on IL-17A versus IL-17F expression and IFN-γ versus IL-10 expression; (2) semimature dendritic cells are regulatory dendritic cells with high IL-10 expression, but a subset of semimature dendritic cells expresses IL-23A and IL-36G in psoriasis; and (3) CCL27-CCR10 interaction is potentially impaired in psoriasis because of decreased CCL27 expression in basal keratinocytes.ConclusionWe propose that single-cell transcriptomics applied to emigrating cells from human skin provides an innovative study platform to compare gene expression profiles of heterogenous immune cells in various inflammatory skin diseases.

Physician rating of cutaneous erythema is central to clinical dermatological assessment as well as quantification of outcome measures in clinical trials in a number of dermatologic conditions. However, issues with inter-rater reliability and variability in the setting of higher Fitzpatrick skin types make visual erythema assessment unreliable. We developed and validated a computer-assisted image-processing algorithm (EQscore) to reliably quantify erythema (across a range of skin types) in the dermatology clinical setting. Our image processing algorithm evaluated erythema based upon green light suppression differentials between affected and unaffected skin. A group of four dermatologists used a 4-point Likert scale as a human evaluation of similar erythematous patch tests. The algorithm and dermatologist scores were compared across 164 positive patch test reactions. The intra-class correlation coefficient of groups and the correlation coefficient between groups were calculated. The EQscore was validated on and independent image set of psoriasis, minimal erythema dose testing and steroid-induced blanching images. The reliability of the erythema quantification method produced an intra-class correlation coefficient of 0.84 for the algorithm and 0.67 for dermatologists. The correlation coefficient between groups was 0.85. The EQscore demonstrated high agreement with clinical scoring and superior reliability compared with clinical scoring, avoiding the pitfalls of erythema underrating in the setting of pigmentation. The EQscore is easily accessible (http://lab.rockefeller.edu/krueger/EQscore), user-friendly, and may allow dermatologists to more readily and accurately rate the severity of dermatological conditions and the response to therapeutic treatments.

Tissue homeostasis and regeneration rely on resident stem cells (SCs), whose behaviour is regulated through niche-dependent crosstalk. The mechanisms underlying SC identity are still unfolding. Here, using spatiotemporal gene ablation in murine hair follicles, we uncover a critical role for the transcription factors (TFs) nuclear factor IB (NFIB) and IX (NFIX) in maintaining SC identity. Without NFI TFs, SCs lose their hair-regenerating capability, and produce skin bearing striking resemblance to irreversible human alopecia, which also displays reduced NFIs. Through single-cell transcriptomics, ATAC-Seq and ChIP-Seq profiling, we expose a key role for NFIB and NFIX in governing super-enhancer maintenance of the key hair follicle SC-specific TF genes. When NFIB and NFIX are genetically removed, the stemness epigenetic landscape is lost. Super-enhancers driving SC identity are decommissioned, while unwanted lineages are de-repressed ectopically. Together, our findings expose NFIB and NFIX as crucial rheostats of tissue homeostasis, functioning to safeguard the SC epigenome from a breach in lineage confinement that otherwise triggers irreversible tissue degeneration.