Introduction: Age-related macular degeneration (AMD), an ever-increasing ocular disease, has become one of the leading causes of irreversible blindness. Recent advances in single-cell genomics are improving our understanding of the molecular mechanisms of AMD. However, the pathophysiology of this multifactorial disease is complicated and still an ongoing challenge. To better understand disease pathogenesis and identify effective targets, we conducted an in-depth analysis of the single-cell transcriptome of AMD. Methods: The cell expression specificity of the gene (CESG) was selected as an index to identify the novel cell markers. A computational framework was designed to explore the cell-specific TF regulatory loops, containing the interaction of gene pattern signatures, transcription factors regulons, and differentially expressed genes. Results: Three potential novel cell markers were DNASE1L3 for endothelial cells, ABCB5 for melanocytes, and SLC39A12 for RPE cells detected. We observed a notable change in the cell abundance and crosstalk of fibroblasts cells, melanocytes, schwann cells, and T/NK cells between AMD and controls, representing a complex cellular ecosystem in disease status. Finally, we identified six cell type related and three disease-associated ternary loops and elaborated on the robust association between key immune-pathway and AMD. Discussion: In conclusion, this study facilitates the optimization of screening for AMD-related receptor ligand pathways and proposes to further improve the interpretability of disease associations from single-cell data. It illuminated that immune-related regulation paths could be used as potential diagnostic markers for AMD, and in the future, also as therapeutic targets, providing insights into AMD diagnosis and potential interventions.
CITATION STYLE
Wang, W., Lin, P., Wang, S., Zhang, G., Chen, C., Lu, X., … Xu, L. (2023). In-depth mining of single-cell transcriptome reveals the key immune-regulated loops in age-related macular degeneration. Frontiers in Molecular Neuroscience, 16. https://doi.org/10.3389/fnmol.2023.1173123
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