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

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.

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Publication Details

Journal
Scientific Data
Published
2025-09-01
DOI
https://doi.org/10.1038/s41597-025-05450-6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

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

D. Leland Taylor, Narisu Narisu, Neelam Sinha, Michael R. Erdos et al.
Scientific Data
Single-cell and spatial transcriptomics
article

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

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

Abstract

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.

Scientific DataVol. 12(1)
National Institutes of Health (US), Mayo Clinic (US), University of Michigan (US), National Institute of Arthritis and Musculoskeletal and Skin Diseases (US), National Human Genome Research Institute (US), Mayo Clinic in Arizona (US), National Cancer Institute (US), Mayo Clinic in Florida (US)
National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Openalex Percentile: Top 80%
Single-cell and spatial transcriptomics
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