Background/Purpose: Despite the recent popularity and utility of modern high-resolution sequencing technologies, leveraging publicly available single-cell studies remains hampered by the need for substantial computational power and expertise. In this study, we assembled and integrated publicly available single-cell datasets from diffuse cutaneous systemic sclerosis (dcSSc) skin and control samples. The processed data, accessible through a web application, enables users to access and perform single-cell analyses without prior coding expertise or high-performance computing resources.

Methods: Single-cell counts and metadata were downloaded from the GEO repositories for each dataset (Table 1); each was then processed and normalized independently using Seurat. Cell typing was performed, hierarchically and then subclustered for detailed analysis (differential expression, CAMML, and SingleR). Gene set enrichment was performed on each dataset and cell subpopulation (VAM). Differential expression and gene set enrichment comparisons were performed using the Wilcoxon Rank Sum test. A cohort of these datasets were processed using uniform genome alignment and counts generation (CellRanger 9.0.0, GRCh38) to generate a novel integrated dataset for disease-based comparisons at a single-cell level.

Results: We standardized and reprocessed all publicly available single-cell datasets analyzing dcSSc skin biopsies. These data were optimized for efficient loading into a user-friendly web interface for gene expression and pathway querying (Fig. 1a-c). From individual objects, we successfully recapitulated the findings presented in the original primary research articles, such as the enrichment of SFRP4 in specific fibroblast subpopulations (Wilcoxon Rank Sum Test, p.adjust = 3.9e-151) (Fig 1d). This tool also enables gene set enrichment tests in specific cell subtypes that were not previously presented in the original manuscripts, such as the enrichment of the complement system in SSc macrophages (Wilcoxon Rank Sum Test, p.adjust 1.95e-203) (Fig. 1e-f). Finally, we generated a fully integrated single-cell dataset across multiple studies as a novel source for comparing SSc skin samples.

Conclusion: The SSc Skin Single Cell Atlas is a user-friendly web application for rapid and detailed analysis of publicly available datasets. This tool enables users to easily ask questions regarding gene and gene set differential expression without the need to download, process, and analyze the data locally. Furthermore, given the heterogeneous nature of SSc, accessing a large cohort of patients for single-cell comparison is paramount to better understand cellular dynamics that drive SSc fibrosis. This novel study integrates publicly available datasets from multiple research reports to provide a more comprehensive understanding of SSc skin fibrosis.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/ssc-skin-cell-atlas-a-scalable-web-portal-for-scrna-seq-analysis/