Diabetic Retinopathy Gene Expression Analysis via Single-Cell Sequencing

Two candidate genes, AHR and SLC1A5, were identified as potentially associated with diabetic retinopathy through integrative bioinformatics analysis. The dataset includes single-cell RNA sequencing data characterizing cellular landscapes within DR fibrous membranes. Zhenghong Jin authored this exploratory study, last updated in April 2026.

Use Cases

• Identify DNL-associated genes like AHR and SLC1A5 correlated with diabetic retinopathy from transcriptomic data.
• Map gene expression patterns to specific cell populations such as conventional dendritic cell type 2 (cDC2) and classical monocytes using single-cell RNA sequencing data.
• Predict potential gene-compound interactions for AHR and SLC1A5 using the GraphBAN model and molecular docking results.
• Construct a diagnostic nomogram for DR risk prediction based on the expression levels of identified signature genes.
• Perform functional enrichment analysis on identified genes to explore their involvement in DR-related biological pathways.

Strengths

• Integrates multiple data types: public transcriptomics, single-cell RNA sequencing, and GraphBAN model predictions.
• Validation of key gene expression (AHR and SLC1A5) via RT-qPCR on patient samples.

Limitations

• Small dataset scale at 3.7 MB, indicating limited raw data volume.
• Unknown row count for underlying transcriptomic and sequencing data tables.
• Exploratory study with preliminary findings requiring independent validation.

Provenance

Source

Public databases for DR-related transcripts and DNL-associated genes.

Collection Method

Integrated bioinformatics analysis, single-cell RNA sequencing, RT-qPCR validation.

Freshness

Last updated April 2026.