Spatial Transcriptomics (10X Xenium) Data From Early Postnatal Lung Specimens

Clinical interventions and inflammatory signaling shape the transcriptional and cellular architecture of the early postnatal lung

Spatial Transcriptomics was performed using the 10X Xenium Platform with a 480 custom-designed probe set on 1 tissue section from 5 distinct early postnatal lung specimens. CSV files contain cell type identities as determined by label transfer.

.zip files should be unzipped to the same directory and can be viewed with Xenium Explorer.

.csv files contain cell type annotations as determined by label transfer to hand annotated single nuclei RNA-sequencing data from early postnatal lung. They can be added as a custom cell group in Xenium Explorer.

Code used in analysis of this data is available at:  http://github.com/jason-spence-lab/Frum-et-al.-2025a.git

METHODS
Tissue Preparation for Xenium Spatial Transcriptomics Analysis
Xenium slides were removed from -20°C storage and allowed to come to room temperature for 30 minutes and then were placed on a 42ºC slide warmed and coated with DNAse/RNAse free water (Corning, Cat# 46000CM). Small sections from multiple specimens were carefully placed within the sample placement area. Most of the water was removed when sections had completely flattened. Slides dried on the slide warmer for three hours before transport to the Advanced Genomics Core. Xenium slides were processed by the Advanced Genomics Core using the Xenium In SituGene Expression with Cell Segmentation workflow (10X, #CG000749).

Xenium Data Analysis
Preprocessing/QC Filtering
Centroids and Segmentation coordinates and Gene Expression counts were determined by Xenium Onboard Analysis v4.0 and imported into R using Seurat::ReadXenium(). Gene Expression counts were converted to a Seurat object using Seurat::CreateSeuratObject(). Coordinates for centroids and segmentations were first converted into a field of view using Seurat::CreateFOV() and then appended to the Seurat object. Segmentations with less than 25 gene expression counts were excluded from the analysis.

Label Transfer
To align low-complexity 480 probe Xenium data with higher complexity snRNA-seq data the reference data was transformed using Seurat::SCTransform() with 3000 variable features. Each specimen was processed individually, also undergoing SCTransformation using 250 variable features. Any Xenium probes expressed in over 95% of cells were excluded from analysis. Anchors between each specimen and the snRNA-seq reference were calculated using FindTransferAnchors() using the SCT assay of both datasets, 20 dimensions, k.filter = 200, and considering only the variable features from the Xenium specimen. Cell type annotations from the snRNA-seq data were then transferred to the Xenium specimen using TransferData(), with anchors weighted by the PCs of the Xenium specimen.