Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature
Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for i...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037023000855 |
| _version_ | 1827577707021467648 |
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| author | Abbas Habibalahi Jared M. Campbell Stacey N. Walters Saabah B. Mahbub Ayad G. Anwer Shane T. Grey Ewa M. Goldys |
| author_facet | Abbas Habibalahi Jared M. Campbell Stacey N. Walters Saabah B. Mahbub Ayad G. Anwer Shane T. Grey Ewa M. Goldys |
| author_sort | Abbas Habibalahi |
| collection | DOAJ |
| description | Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83–71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments. |
| first_indexed | 2024-03-08T21:31:42Z |
| format | Article |
| id | doaj.art-e3f1f2fe6db1478f866cff0d8c98e40f |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-03-08T21:31:42Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-e3f1f2fe6db1478f866cff0d8c98e40f2023-12-21T07:31:06ZengElsevierComputational and Structural Biotechnology Journal2001-03702023-01-012118511859Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signatureAbbas Habibalahi0Jared M. Campbell1Stacey N. Walters2Saabah B. Mahbub3Ayad G. Anwer4Shane T. Grey5Ewa M. Goldys6Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, Australia; Corresponding author at: Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia.Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, AustraliaGarvan Institute of Medical Research, Darlinghurst, New South Wales, Australia; Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia; St Vincent’s Clinical School, The University of New South Wales, Sydney, NSW, 2010 AustraliaGraduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, AustraliaGraduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, AustraliaGarvan Institute of Medical Research, Darlinghurst, New South Wales, Australia; Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia; St Vincent’s Clinical School, The University of New South Wales, Sydney, NSW, 2010 Australia; Corresponding author at: Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, AustraliaIslets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83–71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments.http://www.sciencedirect.com/science/article/pii/S2001037023000855Pancreatic isletTransplantationDeep morphological signature |
| spellingShingle | Abbas Habibalahi Jared M. Campbell Stacey N. Walters Saabah B. Mahbub Ayad G. Anwer Shane T. Grey Ewa M. Goldys Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature Computational and Structural Biotechnology Journal Pancreatic islet Transplantation Deep morphological signature |
| title | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
| title_full | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
| title_fullStr | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
| title_full_unstemmed | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
| title_short | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
| title_sort | automated pancreatic islet viability assessment for transplantation using bright field deep morphological signature |
| topic | Pancreatic islet Transplantation Deep morphological signature |
| url | http://www.sciencedirect.com/science/article/pii/S2001037023000855 |
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