bioRxivMolecular BiologyPreprint
A Spatial and Temporal Transcriptomic Atlas of Mouse Intestinal Regeneration
Rachel OferiD, Tyler TraniD, Ping HeiD, Xia QiuiD, Xinyu HuiD, Emmanuel Zachariah, Curtis Krier, Ankit SaxenaiD, Jiekun YangiD, Michael Paul VerziiD
Abstract
Background & AimsThe intestinal epithelium exhibits a remarkable capacity for regeneration following injury. However, the spatial and temporal dynamics of the injury-repair cycle remain incompletely understood. MethodsWe employ spatial transcriptomics to create an atlas of the damage and repair response to ionizing radiation in the mouse intestine. We map molecular events driving epithelial recovery over a six-day period and 23 biological samples, spanning the early apoptotic response to tissue remodeling and repair. ResultsThe datasets capture mRNA of 19,042 genes in [~]26 million bins at 2{micro}m resolution. Analysis revealed transcriptional patterns and niche signals that would remain undetected in bulk or single-cell approaches, including a non-random activation of interferon-target genes. Temporal shifts in cytokine and growth factor gene expression, particularly in the crypt and lower villus regions, corroborate published studies and reveal new predictions of the mechanisms governing intestinal healing. Global transcriptional upregulation was observed in the regenerating epithelium, suggesting hypertranscription is a hallmark of intestinal repair. Furthermore, we observe altered cellular differentiation trajectories and villus patterning at the early stages of regeneration. ConclusionsTogether, our work provides a detailed spatiotemporal map of intestinal regeneration at subcellular resolution and nearly whole-genome scale. These data lay the groundwork for future discoveries and therapeutic strategies to enhance epithelial repair in inflammatory bowel diseases and other gastrointestinal pathologies or in response to side-effects of cancer therapies.
- organism: mouse
- assay: spatial transcriptomics
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 2
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