Cux1+ proliferative basal cells promote epidermal hyperplasia in chronic dry skin disease identified by single-cell RNA transcriptomics

Liangxue-Qushi-Zhiyang Decoction Ameliorates DNCB-Induced Atopic Dermatitis in Mice through the MAPK Signaling Pathway Based on Network Pharmacology

The traditional prescription of Liangxue-Qushi-Zhiyang decoction (LQZ) has been demonstrated to be efficacious in treating atopic dermatitis (AD), a chronic inflammatory skin disorder marked by intense itching, redness, rashes, and skin thickening. Nevertheless, there has been an inadequate systematic exploration of the potential targets, biological processes, and pathways for AD treatment through LQZ. The study objective was to evaluate the efficacy and possible mechanism of LQZ in AD mice. In our study, we identified the primary compounds of LQZ, analyzed hub targets, and constructed a network. Subsequently, the predicted mechanisms of LQZ in AD were experimentally studied and validated in vivo, as determined by network pharmacological analysis. A total of 80 serum components of LQZ were identified through ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS), among which 49 compounds were absorbed into the bloodstream. Our results indicated that LQZ targets six putative key factors in the MAPK signaling pathway, which play essential roles in AD, namely, EGFR, p-MAPK1/3, p-MAPK14, IL-1β, IL-6, and TNF-α. We observed spleen coefficient, dermatitis scores, and ear thickness were all downregulated in 2,4-dinitrochlorobenzene (DNCB)-induced mice after LQZ treatment. Histological analysis of the dorsal and ear skin further revealed that LQZ significantly decreased skin inflammation, epidermal thickness, and mast cell numbers compared to the DNCB group. Our study demonstrated the effectiveness of LQZ in reducing epidermal and dermal damage in a mouse model of AD. Furthermore, our findings suggest that downregulating the MAPK signaling pathway could be a potential therapeutic strategy for the treatment of AD.

scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders

Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation.

A single-cell transcriptome atlas of Lueyang black-bone chicken skin

As the largest organ of the body, the skin participates in various physiological activities, such as barrier function, sensory function, and temperature regulation, thereby maintaining the balance between the body and the natural environment. To date, compositional and transcriptional profiles in chicken skin cells have not been reported. Here, we report detailed transcriptome analyses of cell populations present in the skin of a black-feather chicken and a white-feather chicken using single-cell RNA sequencing (scRNA-seq). By analyzing cluster-specific gene expression profiles, we identified 12 cell clusters, and their corresponding cell types were also characterized. Subsequently, we characterized the subpopulations of keratinocytes, myocytes, mesenchymal cells, fibroblasts, and melanocytes. It is worth noting that we have identified a subpopulation of keratinocytes involved in pigment granule capture and a subpopulation of melanocytes involved in pigment granule deposition, both of which have a higher cell abundance in black-feather chicken compared to white-feather chicken. Meanwhile, we also compared the cellular heterogeneity features of Lueyang black-bone chicken skin with different feather colors. In addition, we also screened out 12 genes those could be potential markers of melanocytes. Finally, we validated the specific expression of SGK1, WNT5A, CTSC, TYR, and LAPTM5 in black-feather chicken, which may be the key candidate genes determining the feather color differentiation of Lueyang black-bone chicken. In summary, this study first revealed the transcriptome characteristics of chicken skin cells via scRNA-seq technology. These datasets provide valuable information for the study of avian skin characteristics and have important implications for future poultry breeding.