A spatial transcriptomic atlas of acute neonatal lung injury across development and disease severity

A molecular understanding of lung organogenesis requires delineation of the timing and drivers of spatial-temporal cellular movements that ultimately form and support a surface capable of gas exchange. While the advent of single-cell transcriptomics has allowed for the discovery and identification of transcriptionally distinct populations present during lung development, the spatiotemporal dynamics of these transcriptional shifts remain undefined.. With imaging-based spatial transcriptomics, we analyzed the gene expression patterns in 17 human infant lungs at varying stages of development and lung injury, creating a spatial transcriptomics atlas of ~1.2 million cells. We applied computational clustering approaches to identify shared molecular similarities among this cohort, establishing a framework that will generate hypotheses about how tissue architecture and molecular spatial relationships are coordinated during normal development and disrupted in disease. Recognizing that all preterm birth represents an injury to the developing lung, we have moved away from the conventional paradigm of classifying an infants as “disease” or “control” in favor of a linear regression approach that accounted for the routinely collected object measures of gestational age, life span, and disease severity. Within this new framework, we have identified cell type patterns across these variables that would likely be overlooked when using a binary conventional “diseased vs. control” comparison. Together, these data represent a resource for the lung research community, supporting discovery-based inquiry and identification of targetable molecular mechanisms in both normal and arrested human lung development. We profiled 17 infant lung samples that were collected from the Human Infant Lung Repository at Vanderbilt University with a gene probeset of 343 genes using the 10X Genomics Xenium platform. Sample names were generated by appending ‘PDL’ and then ranged from ‘001’ - ‘017’ (ex. PDL001). This dataset is comprised of individuals that range from the canalicular stage of lung development to the alveolar, with variable levels of disease pathologies. We took a 3x3 μ square of each tissue and arranged them in a tissue micro-array. We then took two adjacent serial sections and ran them concurrently, generating two technical replicates per sample. UPDATE: [Jun-26-2025] The transcript.csv.gz files were replaced.