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. 2023 Nov 20;9(12):e22461.
doi: 10.1016/j.heliyon.2023.e22461. eCollection 2023 Dec.

Cellular and molecular mechanisms of fibrosis and resolution in bleomycin-induced pulmonary fibrosis mouse model revealed by spatial transcriptome analysis

Qingsong Li  1 , Yue Wang  1 , Liu Ji  2 , Jianhan He  3 , Haixia Liu  1 , Weizhen Xue  1 , Huihui Yue  3 , Ruihan Dong  3 , Xin Liu  1 , Daqing Wang  2 , Huilan Zhang  3
Affiliations

Cellular and molecular mechanisms of fibrosis and resolution in bleomycin-induced pulmonary fibrosis mouse model revealed by spatial transcriptome analysis

Qingsong Li et al. Heliyon. .

Abstract

The bleomycin-induced pulmonary fibrosis mouse model is commonly used in idiopathic pulmonary fibrosis research, but its cellular and molecular changes and efficiency as a model at the molecular level are not fully understood. In this study, we used spatial transcriptome technology to investigate the cellular and molecular changes in the lungs of bleomycin-induced pulmonary fibrosis mouse models. Our analyses revealed cell dynamics during fibrosis in epithelial cells, mesenchymal cells, immunocytes, and erythrocytes with their spatial distribution available. We confirmed the differentiation of the alveolar type II (AT2) cell type expressing Krt8, and we inferred their trajectories from both the AT2 cells and club cells. In addition to the fibrosis process, we also noticed evidence of self-resolving, especially to identify possible self-resolving related genes, including Prkca. Our findings provide insights into the cellular and molecular mechanisms underlying fibrosis resolution and represent the first spatiotemporal transcriptome dataset of the bleomycin-induced fibrosis mouse model.

Keywords: Bleomycin-induced pulmonary fibrosis; Self-resolving; spatial transcriptome.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Establishing a spatiotemporal transcriptome atlas of lungs from bleomycin induced pulmonary fibrosis mouse model. A, Schematic diagram of the experimental procedure and the whole study. B, Quality control of the cell bins to show their boxplots of nFeature (representing genes captured), nCount (RNA molecular captured) and percent.mt (percentage of the mitochondria) in the control lung (Con), the 7d lung (D7, 7 days after bleomycin instillation) and the 21d lung (D21, 21 days after bleomycin instillation). C, Uniform Manifold Approximation and Projection (UMAP) plot to show all the thirteen cells and their cell type annotation. D, Spatiotemporal distribution of the thirteen cell types. We showed one slice of each lung here and here after. E, Spatiotemporal distribution of PVM cells.
Fig. 2
Fig. 2
Investigating known features during fibrosis in the spatiotemporal atlas. A, Mapping a published single-cell dataset of entire mouse lungs onto our spatiotemporal atlas. B, Mapping another published single-cell dataset of mouse lung epithelial cells onto our spatiotemporal atlas. C, Spatiotemporal distribution of AT2 cells expressing Krt8 in our spatiotemporal atlas. D, Enrichment scores of genes known to have been associated with fibrosis in cell subtypes of our spatiotemporal atlas. E, GO enrichment analysis of the highly expressed genes in clubBpifa1+. F, GO enrichment analysis of genes co-expressed with MUC5B in clubBpifa1+.
Fig. 3
Fig. 3
Cell dynamics after bleomycin instillation and during fibrosis. A, Pie chart to show the number and proportion of each cell type. B, Histogram to show the proportions of cell types after bleomycin instillation (control, 7d and 21d). C, Proportional changes, and spatiotemporal distribution of epithelial subtypes during fibrosis. D, Proportional changes and spatiotemporal distribution of fibroblast subtypes during fibrosis. E, Proportional changes and spatiotemporal distribution of Macrophage subtypes during fibrosis. F, Proportional changes, and spatiotemporal distribution of red blood cells during fibrosis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
Cell-cell interaction changes during fibrosis. A, Number, and strength of cell-cell interactions among all cells. B, Information flows fulfilled by different pathways during fibrosis. C, Input, and output signal strengths of cell types during fibrosis. D, Strength of various signaling pathways for cell types during fibrosis.
Fig. 5
Fig. 5
Epithelial cell differentiation during fibrosis. A, Differentiation trajectory of AT2 cells. B, Genes related to AT2 cell differentiation. C, UMAP of epithelial cell subtypes related to differentiation during fibrosis. D, UMAP and pseudotime of epithelial cells related to differentiation.
Fig. 6
Fig. 6
Possible genes related to fibrosis and self-resolving. A, Significantly differentially expressed genes in different cell types comparing the 7d lung days to the control lung. B, Differentially Expressed Gene Protein Interaction Network comparing the 7d lung days to the control lung. C, Significantly differentially expressed genes in different cell types comparing the 21d lung to the 7d lung. D, Network of genes which can interact with Prkca protein.
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