Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells

Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.