The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and a manually curated gene–cell association dictionary specific to pulmonary fibrosis. It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. The results showed that FPCAM and SCSA both achieved an accuracy of 89.7%, outperforming SingleR and SciBet. Furthermore, FPCAM demonstrated high accuracy in annotating the external validation dataset GSE135893, successfully identifying multiple cell subtypes. In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases.

Pulmonary fibrosis is a chronic progressive lung disease characterized by abnormal proliferation and activation of fibroblasts. However, the regulatory mechanisms of miRNAs and their target genes on fibroblast proliferation during silicosis (silica-induced pulmonary fibrosis) remain incompletely understood. A mouse silicosis model was established by dynamic dust inhalation, and lung tissues were collected at 7, 14, 28, and 56 days after dust exposure, followed by multi-time-point miRNA sequencing and transcriptome sequencing. Using integrated omics, bioinformatics analysis, and molecular interaction validation, the key miRNAs and their target genes regulating fibroblast proliferation were screened and verified. The results showed that time-series clustering and H&E staining confirmed successful modeling of the transition from acute inflammation to chronic fibrosis induced by SiO₂. Differentially expressed miR-146b-5p during SiO₂ stress may affect the cell cycle of fibroblasts by regulating Btg2. Mechanistically, IP-MS combined with Co-IP identified interactions between Btg2 and Prrx2 or Myh10, suggesting their potential involvement in this regulatory process. These findings provide new experimental evidence for the molecular mechanisms of silica-induced pulmonary fibrosis in a mouse model, and may offer insights for understanding the pathogenesis of human silicosis.