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<p>Wound healing is a dynamic process involving various cell types. Collecting samples from healing wounds and investigating their transcriptomics can provide deeper insights into the underlying processes. In recent years, several experiments have been conducted to gather transcriptomic data from wounds in both humans and animals. However, the temporal resolution of such data often does not adequately match the dynamics of the process, and spatial aspects are frequently overlooked. Here, we present a dataset collected from an experiment on wound healing in pigs, including gene expression profiles at the wound edge and center, and photographs of the wounds. Photographs provide non-invasive data, and advancements in image analysis using artificial intelligence methods are actively being integrated into medical practice. Being collected within the same experiment, these comprehensive data can aid in building intelligent wound diagnostics and treatment algorithms.</p>

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A high-resolution temporal transcriptomic and imaging dataset of porcine wound healing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月10日

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  • Ksenia Zlobina,
  • Hsin-ya Yang,
  • Manasa Kesapragada,
  • Fan Lu,
  • Anthony Gallegos,
  • Guillermo Villa-Martinez,
  • Moyasar A. Alhamo,
  • Kan Zhu,
  • Cynthia Recendez,
  • Craig Collins,
  • Marco Rolandi,
  • Athena Soulika,
  • Elham Aslankoohi,
  • Min Zhao,
  • Marcella Gomez,
  • R. Rivkah Isseroff

摘要

Wound healing is a dynamic process involving various cell types. Collecting samples from healing wounds and investigating their transcriptomics can provide deeper insights into the underlying processes. In recent years, several experiments have been conducted to gather transcriptomic data from wounds in both humans and animals. However, the temporal resolution of such data often does not adequately match the dynamics of the process, and spatial aspects are frequently overlooked. Here, we present a dataset collected from an experiment on wound healing in pigs, including gene expression profiles at the wound edge and center, and photographs of the wounds. Photographs provide non-invasive data, and advancements in image analysis using artificial intelligence methods are actively being integrated into medical practice. Being collected within the same experiment, these comprehensive data can aid in building intelligent wound diagnostics and treatment algorithms.