Spatial transcriptomics reveals gene expression patterns driven by cell-to-cell interactions in healthy lung tissue
Gene expression is an important aspect of genomics, and can serve as a driver for major biological processes. There are many technologies that can analyze gene expression in individual cells and cell types, but they typically do not include any spatial-level data at high resolution. With the novel spatial transcriptomics technology MERFISH, we can detect gene expression in a spatial context with single-cell resolution. This powerful technique can be used to assess how proximity between cells and spatial features can impact gene expression. To demonstrate this, we utilized MERFISH to detect the gene expression in a sample of healthy human lung tissue, and analyzed the correlation of gene expression between nearby, interacting cells. Specifically, we focused on AT2 and mesenchymal cells because of their importance within the alveolar niche. To do so, we implemented a pipeline to calculate distances between AT2 and mesenchymal cells and subset to close-proximity cell pairs. We then performed correlation analyses between genes expressed in interacting AT2 and mesenchymal cells. We found that approximately one quarter of the original gene pairs had significant correlations with a false discovery rate ≤ 0.05. Additionally, four separate AT2 biomarker genes had significant correlations with four or more mesenchymal genes. We plan to implement this pipeline on lung samples from patients that have idiopathic pulmonary fibrosis (IPF), so that we can compare cell-cell interactions in healthy vs. diseased tissue and provide insight on gene expression patterns within IPF.
