Sophia Longo
Masters Student in Biomedical Data Science, admitted Winter 2023
SU Student - Summer, Dermatology
All Publications
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Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells.
The Journal of investigative dermatology
2023
Abstract
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 (scRNA-seq) and spatial transcriptomics (ST) data were integrated and compared to mouse skin data. Human skin cell type annotation was improved by using matched ST data, highlighting the importance of spatial context in cell type identity, and ST 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 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.
View details for DOI 10.1016/j.jid.2023.02.040
View details for PubMedID 37142187
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Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.
Nature reviews. Genetics
2021
Abstract
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.
View details for DOI 10.1038/s41576-021-00370-8
View details for PubMedID 34145435