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Abstract: SA-PO826

Spatial Transcriptomic Profiling to Understand Organ Cross-Talk: Insight into Environment-Linked Pulmonary-Renal Disease

Session Information

Category: Glomerular Diseases

  • 1401 Glomerular Diseases: From Inflammation to Fibrosis

Authors

  • Foster, Mary H., Duke University, Durham, North Carolina, United States
  • Jain, Vaibhav, Duke University, Durham, North Carolina, United States
  • Fee, Lanette, Duke University, Durham, North Carolina, United States
  • Tighe, Robert Matthew, Duke University, Durham, North Carolina, United States
Background

Environmental inhalational exposures are implicated in etiopathogenesis of autoimmune pulmonary-renal diseases and CKDu. Underlying mechanisms of disrupted immune control and cross-organ injury are poorly understood. To gain insight into this under-studied field, we leveraged a tractable preclinical model dependent on mouse lung exposure to crystalline silica (cSi) dust. Inhalation of cSi is linked to SLE and ANCA vasculitis in humans and exposure to cSi generated from burned crops is suspected in pathogenesis of CKDu. In mice cSi exposure induces lung inflammation and lymphoid aggregates and promotes autoimmunity. To gain insight into molecular pathways dysregulated by inhaled cSi, we used spatial gene expression (GE) profiling.

Methods

Transcriptome analysis was performed using a 10X Genomics Visium platform with CytAssist at the Molecular Genomics Core of the Duke Molecular Physiology Institute. An FFPE section of lung harvested 3 months after cSi exposure was H&E stained, imaged, hybridized using a mouse transcriptome barcoded probe set, transferred, probes extended, and libraries constructed and paired-end sequenced on a NextSeq 1000 (Illumina). Raw data were preprocessed using Space Ranger, mapped to the mm10 genome, and aligned to the tissue image. Data filtering, normalization, and analysis were performed with the Seurat R package.

Results

Spatial sequencing yielded mean ~57K reads/spot, with median 2,041 genes/spot and total >19K genes detected. After dimensionality reduction, unsupervised spot clustering based on differential GE yielded 8 clusters. Overlay of molecular and image data showed spatial localization of clusters to lymphoid aggregates, granulomas, peribronchiolar loci, and minimal injury areas, supported by marker gene identification, as well as unexpected regional heterogeneity. Two clusters had strong Ig signals, consistent with distinct local adaptive immune niches. Upregulated genes with spatial restriction include a subset linked to immune control and two genes encoding soluble proteins implicated as circulating mediators of cardiopulmonary crosstalk in acute kidney injury.

Conclusion

Spatial transcriptome analysis in cSi-injured lung provides novel insight into the molecular framework of dysregulated tissue niches as well as potential biological pathways engaged in pulmonary-renal crosstalk.

Funding

  • Other NIH Support