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<p>Understanding the spatial distribution of gene expression in the pancreas is essential for establishing the molecular basis of pancreatic function in healthy and disease contexts. Recent platforms offer a robust method for quantifying gene expression within a spatial context. Here, we report spatial transcriptomic profiling from pancreas samples obtained from three donors with type 2 diabetes (T2D) and three donors with normal glucose tolerance (NGT). Our analysis identified a major technical challenge: substantial transcript bleed of highly abundant genes (e.g., <i>INS</i> and <i>GCG</i>) into adjacent tissue regions. We demonstrate that this bleed can be computationally corrected using probabilistic models. Our analysis highlights the importance of incorporating bleed-correction techniques in the preprocessing of spatial transcriptomic profiling data. In summary, this study provides a dataset, methods, and resources to investigate the spatial regulation of gene expression in normal and T2D-affected human pancreas.</p>

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A spatial transcriptomics dataset of pancreas sections in normal glucose tolerance and type 2 diabetic donors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月1日

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  • Nick Howell,
  • Zoe Weiss,
  • Lori L. Bonnycastle,
  • Caleb M. Grenko,
  • Davide Randazzo,
  • Christopher H. Dampier,
  • Neelam Sinha,
  • Narisu Narisu,
  • Amy J. Swift,
  • Michael R. Erdos,
  • Leslie G. Biesecker,
  • Francis S. Collins,
  • Catherine C. Robertson,
  • D. Leland Taylor

摘要

Understanding the spatial distribution of gene expression in the pancreas is essential for establishing the molecular basis of pancreatic function in healthy and disease contexts. Recent platforms offer a robust method for quantifying gene expression within a spatial context. Here, we report spatial transcriptomic profiling from pancreas samples obtained from three donors with type 2 diabetes (T2D) and three donors with normal glucose tolerance (NGT). Our analysis identified a major technical challenge: substantial transcript bleed of highly abundant genes (e.g., INS and GCG) into adjacent tissue regions. We demonstrate that this bleed can be computationally corrected using probabilistic models. Our analysis highlights the importance of incorporating bleed-correction techniques in the preprocessing of spatial transcriptomic profiling data. In summary, this study provides a dataset, methods, and resources to investigate the spatial regulation of gene expression in normal and T2D-affected human pancreas.