RATIONALE: Silica dust-induced pulmonary fibrosis is the most common occupational lung disease worldwide. This study combines bulk mRNASeq and spatial transcriptomics to identify fibrotic silicosis's molecular and cellular networks.

METHODS: 53FFPE samples were obtained from 27 explant end-stage fibrotic silica patients and 28 controls. RNA was extracted, and bulkRNA sequencing was performed. For Spatial transcriptomics, a tissue microarray with 24 samples was constructed (14Silica, 6IPFs, 4controls). We performed a spatial transcriptomics analysis using CosMx Spatial Molecular Imager. Data analysis was conducted using R with DESeq2 package, and pathway analysis was conducted. We used the Automx platform and R packages for data integration and analysis in the Spatial analysis.

RESULTS: 26,485 genes were detected. Comparing the 53 silica samples to 28 controls using the DESeq2 method, 5735 genes were increased compared to controls, and 630 genes were decreased in silicosis lungs compared to controls (FDR<0.05 fold-change >2 or<0.5). Among the increased genes were CTHRC1, COL1A1, COL3A1, matrix metalloprotease genes  MMP1, MMP3, MMP9, MMP13, as well as markers of profibrotic macrophages such as SPP1, CHIT1, CHI3L1, MERTK among others. We detected up to 1000genes using spatial technology. By integrating all the samples, we identified main compartments of the lung, including airways, capillaries, endothelium, immune, and alveolar, with distinct changes in cellular and gene expression patterns.

CONCLUSIONS: Our BulkRNASeq analysis and spatial transcriptomics findings provide advanced silicosis?s first in-depth molecular and cellular profile.