Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network
(2023) In Heliyon 9(2).- Abstract
The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After... (More)
The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm3) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.
(Less)
- author
- Guo, Qingqing LU ; AlKendi, Abdulla ; Jiang, Xiaoping LU ; Mittone, Alberto ; Wang, Linbo ; Larsson, Emanuel LU ; Bravin, Alberto ; Renström, Erik LU ; Fang, Xianyong and Zhang, Enming LU
- organization
-
- Surgery (research group)
- Diabetes - Islet Patophysiology (research group)
- Solid Mechanics
- LUNARC, Centre for Scientific and Technical Computing at Lund University
- LTH Profile Area: Nanoscience and Semiconductor Technology
- LU Profile Area: Light and Materials
- NanoLund: Centre for Nanoscience
- EXODIAB: Excellence of Diabetes Research in Sweden
- publishing date
- 2023-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Deep learning, Diabetes, Pancreatic islets, Synchrotron radiation, X-ray microtomography, X-ray phase-contrast
- in
- Heliyon
- volume
- 9
- issue
- 2
- article number
- e13081
- publisher
- Elsevier
- external identifiers
-
- pmid:36718155
- scopus:85147385232
- ISSN
- 2405-8440
- DOI
- 10.1016/j.heliyon.2023.e13081
- language
- English
- LU publication?
- yes
- id
- 5edf415f-7dbc-45f5-be3a-e15c85a9bd55
- date added to LUP
- 2023-02-21 15:37:15
- date last changed
- 2024-04-18 12:15:36
@article{5edf415f-7dbc-45f5-be3a-e15c85a9bd55, abstract = {{<p>The pancreatic islet is a highly structured micro-organ that produces insulin in response to rising blood glucose. Here we develop a label-free and automatic imaging approach to visualize the islets in situ in diabetic rodents by the synchrotron radiation X-ray phase-contrast microtomography (SRμCT) at the ID17 station of the European Synchrotron Radiation Facility. The large-size images (3.2 mm × 15.97 mm) were acquired in the pancreas in STZ-treated mice and diabetic GK rats. Each pancreas was dissected by 3000 reconstructed images. The image datasets were further analysed by a self-developed deep learning method, AA-Net. All islets in the pancreas were segmented and visualized by the three-dimension (3D) reconstruction. After quantifying the volumes of the islets, we found that the number of larger islets (=>1500 μm<sup>3</sup>) was reduced by 2-fold (wt 1004 ± 94 vs GK 419 ± 122, P < 0.001) in chronically developed diabetic GK rat, while in STZ-treated diabetic mouse the large islets were decreased by half (189 ± 33 vs 90 ± 29, P < 0.001) compared to the untreated mice. Our study provides a label-free tool for detecting and quantifying pancreatic islets in situ. It implies the possibility of monitoring the state of pancreatic islets in vivo diabetes without labelling.</p>}}, author = {{Guo, Qingqing and AlKendi, Abdulla and Jiang, Xiaoping and Mittone, Alberto and Wang, Linbo and Larsson, Emanuel and Bravin, Alberto and Renström, Erik and Fang, Xianyong and Zhang, Enming}}, issn = {{2405-8440}}, keywords = {{Deep learning; Diabetes; Pancreatic islets; Synchrotron radiation; X-ray microtomography; X-ray phase-contrast}}, language = {{eng}}, number = {{2}}, publisher = {{Elsevier}}, series = {{Heliyon}}, title = {{Reduced volume of diabetic pancreatic islets in rodents detected by synchrotron X-ray phase-contrast microtomography and deep learning network}}, url = {{http://dx.doi.org/10.1016/j.heliyon.2023.e13081}}, doi = {{10.1016/j.heliyon.2023.e13081}}, volume = {{9}}, year = {{2023}}, }