99 - Spatial Transcriptomics of Developing Human Lungs Defines Cellular Phenotypes Associated with Age, Lineage and Location

Poster Number: 99
Publication Number: 99.2655

Background: The mature respiratory system is exceptionally complex at the anatomical and cellular levels. While characterization of lung development has increased dramatically over the past few decades, particularly with the use of animal models, we continue to have a limited understanding of how the processes occur uniquely in humans. A significant knowledge gap exists in that we lack an understanding of regional changes associated with the developmental processes.

Objective: Spatial transcriptomics enables the characterization of gene expression patterns in context to their native tissue, offering a powerful tool to gain insights into complex biological processes.

Design/Methods: Spatial transcriptomics data were generated from normal fetal human lung samples (n=8) ranging in gestational age from 13 – 18 weeks, using 10X Genomics Visium platform. Sequence data demultiplexing and pre-processing was completed using CellRanger and aligned to GRCh38. Data analyses, including quality control, normalization, dimensional reduction, clustering, spatially-variable feature detection, and visualization were performed using Seurat. Cell type annotation and pathway analyses were performed using Toppfun.

Results: The samples yielded greater than 50,000 Mean Reads per Spot, indicating good sample and data quality. An examination of cluster markers and cellular annotations indicated an increase in proportion of epithelial cells as the lung progresses from 13 to 18 weeks of gestation. For lung tissue samples at 13 weeks of gestation, we observed five clusters of spots located in different regions of the lung section, all of which appeared to be of mesenchymal origin. For lung tissue samples at 18 weeks of gestation, we observed six clusters of spots located in different regions of the lung section. Functional assessment of select cluster markers indicated cells clustering based on their lineages. The majority of cells (clusters 1-4) appeared to be of mesenchymal origin indicated by high expression of COL1A1, COL1A2, and COL3A2 (Figure 1A). We observed two clusters that consisted of cells expressing canonical epithelial lineage markers, with cells in cluster 5 appearing to be destined as alveolar epithelial cells (expressing SFTPC), and cluster 6 cells displaying an airway epithelial cell signature (expressing SCGB3A2; Figure 1B), which was not observed at 13 weeks of gestation.

Conclusion(s): These data indicate that the application of spatial transcriptomics methods to pseudoglandular-canalicular stage human lung tissues may provide novel and innovative data ultimately expanding our understanding of key developmental processes.