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. 2022 Sep 10;9:558. doi: 10.1038/s41597-022-01546-5

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — Data processing pipeline. Data record A from dataset 1 (Data record A, Data citation 1) and 2 (Data record A, Data citation 5) were processed using a set of in-house developed post-scanning computational scripts, DFTA (uniform alignment values) and CLAHE (equalizing the contrast of the insulin labelled islets) prior to reconstruction into tomographic images (image processing package “DSPOPT”, including DFTA (“A-value” tuning) and CLAHE can be found: https://github.com/ARDISDataset/DSPOPT). Data record B also include LSFM z-stacks from dataset 3 (Data citation 2, 6 and 9). The tomographic images converted to Imaris native.ims files were analysed (Data record C, 3, 7 and 10). Volumetric and spatial statistics was extracted in Imaris (Data record D, Data citation 4 and 8).