3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning
Abstract Alzheimer’s Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation of plaques containing extracellular amyloid-β (Aβ) and neurofibrillary tangles composed of hyper-phosphorylated tau proteins (pTau). Aβ gradu...
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BMC
2024-12-01
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| Series: | Acta Neuropathologica Communications |
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| Online Access: | https://doi.org/10.1186/s40478-024-01898-6 |
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| author | Wissam B. Nassrallah Hao Ran Li Lyden Irani Printha Wijesinghe Peter William Hogg Lucy Hui Jean Oh Ian R. Mackenzie Veronica Hirsch-Reinshagen Ging-Yuek Robin Hsiung Wellington Pham Sieun Lee Joanne A. Matsubara |
| author_facet | Wissam B. Nassrallah Hao Ran Li Lyden Irani Printha Wijesinghe Peter William Hogg Lucy Hui Jean Oh Ian R. Mackenzie Veronica Hirsch-Reinshagen Ging-Yuek Robin Hsiung Wellington Pham Sieun Lee Joanne A. Matsubara |
| author_sort | Wissam B. Nassrallah |
| collection | DOAJ |
| description | Abstract Alzheimer’s Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation of plaques containing extracellular amyloid-β (Aβ) and neurofibrillary tangles composed of hyper-phosphorylated tau proteins (pTau). Aβ gradually accumulates in the brain up to 20 years before the clinical onset of dementia, making it a compelling candidate for early detection of AD. It has been shown that there is increased deposition of Aβs in AD patients’ retinas. However, little is known about microglia’s ability to function and clear Aβ within the retina of AD and control eyes. We labelled microglia with ionised calcium-binding adaptor molecule 1 (IBA-1) in AD and age-matched control donor retinas. We then used interactive machine learning to segment individual microglia in 3D. In the temporal mid-peripheral region, we found that the number of microglia was significantly lower in AD retinas compared to controls. Unexpectedly, the size of the microglia was significantly larger in the AD retinas compared to controls. We also labelled retinal microglia for Cluster of Differentiation 68 (CD68), a transmembrane glycoprotein expressed by cells in the monocyte lineage and a marker of phagocytic activity and activated microglia. The size of CD68 + cells was statistically different between AD and control microglial, with CD68 + cells being larger in AD. In contrast, there was no difference in either size or shape for CD68- microglia between the two groups, suggesting an important difference in the active states of CD68 + microglia in AD retina. There was also significantly increased CD68 immunoreactivity in individual microglia within the AD group. Overall, this study reveals unique differences in the size and activity of the retinal microglia, which may relate to their potential chronic activation due to increased levels of Aβs in the AD retina. |
| format | Article |
| id | doaj-art-c6a57b91b95d4fec8e0d192eefaf6c30 |
| institution | Kabale University |
| issn | 2051-5960 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Acta Neuropathologica Communications |
| spelling | doaj-art-c6a57b91b95d4fec8e0d192eefaf6c302024-12-29T12:51:51ZengBMCActa Neuropathologica Communications2051-59602024-12-0112111610.1186/s40478-024-01898-63-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learningWissam B. Nassrallah0Hao Ran Li1Lyden Irani2Printha Wijesinghe3Peter William Hogg4Lucy Hui5Jean Oh6Ian R. Mackenzie7Veronica Hirsch-Reinshagen8Ging-Yuek Robin Hsiung9Wellington Pham10Sieun Lee11Joanne A. Matsubara12Faculty of Medicine, The University of British ColumbiaFaculty of Medicine, The University of British ColumbiaDepartment of Ophthalmology and Visual Sciences, The University of British ColumbiaDepartment of Ophthalmology and Visual Sciences, The University of British ColumbiaDepartment of Cellular and Physiological Sciences, The University of British ColumbiaFaculty of Medicine, The University of British ColumbiaFaculty of Medicine, The University of British ColumbiaDepartment of Pathology and Laboratory Medicine, The University of British ColumbiaDepartment of Pathology and Laboratory Medicine, The University of British ColumbiaDivision of Neurology, Department of Medicine, The University of British ColumbiaVanderbilt University School of Medicine, Vanderbilt University Institute of Imaging ScienceSimon Fraser University School of Engineering ScienceDepartment of Ophthalmology and Visual Sciences, The University of British ColumbiaAbstract Alzheimer’s Disease (AD) is a debilitating neurodegenerative disease that affects 47.5 million people worldwide. AD is characterised by the formation of plaques containing extracellular amyloid-β (Aβ) and neurofibrillary tangles composed of hyper-phosphorylated tau proteins (pTau). Aβ gradually accumulates in the brain up to 20 years before the clinical onset of dementia, making it a compelling candidate for early detection of AD. It has been shown that there is increased deposition of Aβs in AD patients’ retinas. However, little is known about microglia’s ability to function and clear Aβ within the retina of AD and control eyes. We labelled microglia with ionised calcium-binding adaptor molecule 1 (IBA-1) in AD and age-matched control donor retinas. We then used interactive machine learning to segment individual microglia in 3D. In the temporal mid-peripheral region, we found that the number of microglia was significantly lower in AD retinas compared to controls. Unexpectedly, the size of the microglia was significantly larger in the AD retinas compared to controls. We also labelled retinal microglia for Cluster of Differentiation 68 (CD68), a transmembrane glycoprotein expressed by cells in the monocyte lineage and a marker of phagocytic activity and activated microglia. The size of CD68 + cells was statistically different between AD and control microglial, with CD68 + cells being larger in AD. In contrast, there was no difference in either size or shape for CD68- microglia between the two groups, suggesting an important difference in the active states of CD68 + microglia in AD retina. There was also significantly increased CD68 immunoreactivity in individual microglia within the AD group. Overall, this study reveals unique differences in the size and activity of the retinal microglia, which may relate to their potential chronic activation due to increased levels of Aβs in the AD retina.https://doi.org/10.1186/s40478-024-01898-6Alzheimer’s diseaseNeuroretinal microglia3-Dimensional morphologyMicroglia morphologyMicroglia countMicroglia size |
| spellingShingle | Wissam B. Nassrallah Hao Ran Li Lyden Irani Printha Wijesinghe Peter William Hogg Lucy Hui Jean Oh Ian R. Mackenzie Veronica Hirsch-Reinshagen Ging-Yuek Robin Hsiung Wellington Pham Sieun Lee Joanne A. Matsubara 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning Acta Neuropathologica Communications Alzheimer’s disease Neuroretinal microglia 3-Dimensional morphology Microglia morphology Microglia count Microglia size |
| title | 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning |
| title_full | 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning |
| title_fullStr | 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning |
| title_full_unstemmed | 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning |
| title_short | 3-Dimensional morphological characterization of neuroretinal microglia in Alzheimer’s disease via machine learning |
| title_sort | 3 dimensional morphological characterization of neuroretinal microglia in alzheimer s disease via machine learning |
| topic | Alzheimer’s disease Neuroretinal microglia 3-Dimensional morphology Microglia morphology Microglia count Microglia size |
| url | https://doi.org/10.1186/s40478-024-01898-6 |
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