Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning
BackgroundAn increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis.New methodHere we used...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.617221/full |
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| author | Michael Nesbit Michael Nesbit John C. Mamo John C. Mamo Maimuna Majimbi Maimuna Majimbi Virginie Lam Virginie Lam Ryusuke Takechi Ryusuke Takechi |
| author_facet | Michael Nesbit Michael Nesbit John C. Mamo John C. Mamo Maimuna Majimbi Maimuna Majimbi Virginie Lam Virginie Lam Ryusuke Takechi Ryusuke Takechi |
| author_sort | Michael Nesbit |
| collection | DOAJ |
| description | BackgroundAn increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis.New methodHere we used snap frozen mouse and rat brain sections that were double-immunofluorescent labeled for immunoglobulin G (IgG; plasma protein) and laminin-α4 (capillary basement membrane). A Machine Learning Image Analysis program (Zeiss ZEN Intellisis) was trained to recognize and segment laminin-α4 to equivocally identify blood vessels in large sets of images. An IgG subclass based on a threshold intensity was segmented and quantitated only in extravascular regions. The residual parenchymal IgG fluorescence is indicative of blood-to-brain extravasation of IgG and was accurately quantitated.ResultsAutomated machine-learning and threshold based segmentation of only parenchymal IgG extravasation accentuates otherwise indistinct capillary permeability, particularly frequent in minor BBB leakage. Comparison with Existing Methods: Large datasets can be processed and analyzed quickly and robustly to provide an overview of vascular permeability throughout the brain. All human bias or ambiguity involved in classifying and measuring leakage is removed.ConclusionHere we describe a fast and precise method of visualizing and quantitating BBB permeability in mouse and rat brain tissue, while avoiding the confounding influence of unphysiological conditions such as perfusion and eliminating any human related bias from analysis. |
| first_indexed | 2024-12-24T05:23:59Z |
| format | Article |
| id | doaj.art-2677a6c93fec4b368a98a30afb0f1bf3 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-24T05:23:59Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-2677a6c93fec4b368a98a30afb0f1bf32022-12-21T17:13:23ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-04-011510.3389/fnins.2021.617221617221Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-LearningMichael Nesbit0Michael Nesbit1John C. Mamo2John C. Mamo3Maimuna Majimbi4Maimuna Majimbi5Virginie Lam6Virginie Lam7Ryusuke Takechi8Ryusuke Takechi9Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, AustraliaFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, AustraliaFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, AustraliaFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, AustraliaFaculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Perth, WA, AustraliaSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, AustraliaBackgroundAn increase in blood brain barrier permeability commonly precedes neuro-inflammation and cognitive impairment in models of dementia. Common methods to estimate capillary permeability have potential confounders, or require laborious and subjective semi-manual analysis.New methodHere we used snap frozen mouse and rat brain sections that were double-immunofluorescent labeled for immunoglobulin G (IgG; plasma protein) and laminin-α4 (capillary basement membrane). A Machine Learning Image Analysis program (Zeiss ZEN Intellisis) was trained to recognize and segment laminin-α4 to equivocally identify blood vessels in large sets of images. An IgG subclass based on a threshold intensity was segmented and quantitated only in extravascular regions. The residual parenchymal IgG fluorescence is indicative of blood-to-brain extravasation of IgG and was accurately quantitated.ResultsAutomated machine-learning and threshold based segmentation of only parenchymal IgG extravasation accentuates otherwise indistinct capillary permeability, particularly frequent in minor BBB leakage. Comparison with Existing Methods: Large datasets can be processed and analyzed quickly and robustly to provide an overview of vascular permeability throughout the brain. All human bias or ambiguity involved in classifying and measuring leakage is removed.ConclusionHere we describe a fast and precise method of visualizing and quantitating BBB permeability in mouse and rat brain tissue, while avoiding the confounding influence of unphysiological conditions such as perfusion and eliminating any human related bias from analysis.https://www.frontiersin.org/articles/10.3389/fnins.2021.617221/fullblood-brain barrierIgG extravasationmachine-learningimmunofluorescencelaminin-α4quantitation |
| spellingShingle | Michael Nesbit Michael Nesbit John C. Mamo John C. Mamo Maimuna Majimbi Maimuna Majimbi Virginie Lam Virginie Lam Ryusuke Takechi Ryusuke Takechi Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning Frontiers in Neuroscience blood-brain barrier IgG extravasation machine-learning immunofluorescence laminin-α4 quantitation |
| title | Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning |
| title_full | Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning |
| title_fullStr | Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning |
| title_full_unstemmed | Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning |
| title_short | Automated Quantitative Analysis of ex vivo Blood-Brain Barrier Permeability Using Intellesis Machine-Learning |
| title_sort | automated quantitative analysis of ex vivo blood brain barrier permeability using intellesis machine learning |
| topic | blood-brain barrier IgG extravasation machine-learning immunofluorescence laminin-α4 quantitation |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2021.617221/full |
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