Project description:To investigate dynamic molecular and cellular changes associated with skin tissue remodeling during the human hair cycle, we aimed to obtain single-cell RNA sequencing (scRNA-seq) data from different hair cycle phases. We obtained scRNA-seq from randomly dissected human skin samples containing a single hair. Nineteen small skin pieces with a single hair were dissected from three female donors (single-hair samples). For comparison, we also collected three small skin pieces with no hairs from the same donors (non-hair samples). We enzymatically removed the interfollicular epidermis to focus our analysis on hair follicles and their surrounding dermis, which are the main areas of hair cycle-dependent tissue remodeling.
Project description:Healthy human skin tissue is often used as a control for comparison to diseased skin in patients with skin pathologies, including skin cancers or other inflammatory conditions such as atopic dermatitis or psoriasis. Although non-affected skin from these patients is a more appropriate choice for comparison, there is a paucity of studies examining such tissue. This lack is exacerbated by the difficulty of processing skin tissue for experimental analysis. In addition, choosing a processing protocol for skin tissue which preserves cell viability and identity while sufficiently dissociating cells for single-cell analysis is not a trivial task. Here, we compare three digestion methods for human skin tissue, evaluating the cell yield and viability for each protocol. We find that the use of a sequential dissociation method with multiple enzymatic digestion steps produces the highest cell viability. Using single-cell sequencing, we show this method results in a relative increase in the proportion of non-antigen-presenting mast cells and CD8 T cells as well as a relative decrease in the proportion of antigen-presenting mast cells and KYNU+ CD4 T cells. Overall, our findings support the use of this sequential digestion method on freshly processed human skin samples for optimal cell yield and viability.
