ArticleJournal of neuroinflammation2022
MORPHIOUS: an unsupervised machine learning workflow to detect the activation of microglia and astrocytes.
Article in Journal of neuroinflammation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 17 citations in OpenAlex.
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Characterizing Microglial Morphology: Methodological Advances in Confocal Imaging and Analysis.Cells · 2025Article
- Focused ultrasound-induced blood-brain barrier opening promotes glioprotective phenotypes in ACSA-II+ murine astrocytes.iScience · 2025Article
- StainAI: quantitative mapping of stained microglia and insights into brain-wide neuroinflammation and therapeutic effects in cardiac arrest.Communications biology · 2025Article
- Targeted therapy and deep learning insights into microglia modulation for spinal cord injury.Materials today. Bio · 2024Article
- Using focused ultrasound to modulate microglial structure and function.Frontiers in cellular neuroscience · 2023Review
- Microglial morphometric analysis: so many options, so little consistency.Frontiers in neuroinformatics · 2023Review
- Article
- Classification of Activated Microglia by Convolutional Neural Networks.IEEE Biomedical Circuits and Systems Conference : healthcare technology : [proceedings]. IEEE Biomedical Circuits and Systems Conference · 2022Article
- Transgene distribution and immune response after ultrasound delivery of rAAV9 and PHP.B to the brain in a mouse model of amyloidosis.Molecular therapy. Methods & clinical development · 2021Article
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2 authors at 1 institution in 1 country.
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Abstract
backgroundIn conditions of brain injury and degeneration, defining microglial and astrocytic activation using cellular markers alone remains a challenging task. We developed the MORPHIOUS software package, an unsupervised machine learning workflow which can learn the morphologies of non-activated astrocytes and microglia, and from this information, infer clusters of microglial and astrocytic activation in brain tissue.
methodsMORPHIOUS combines a one-class support vector machine with the density-based spatial clustering of applications with noise (DBSCAN) algorithm to identify clusters of microglial and astrocytic activation. Here, activation was triggered by permeabilizing the blood-brain barrier (BBB) in the mouse hippocampus using focused ultrasound (FUS). At 7 day post-treatment, MORPHIOUS was applied to evaluate microglial and astrocytic activation in histological tissue. MORPHIOUS was further evaluated on hippocampal sections of TgCRND8 mice, a model of amyloidosis that is prone to microglial and astrocytic activation.
resultsMORPHIOUS defined two classes of microglia, termed focal and proximal, that are spatially adjacent to the activating stimulus. Focal and proximal microglia demonstrated activity-associated features, including increased levels of ionized calcium-binding adapter molecule 1 expression, enlarged soma size, and deramification. MORPHIOUS further identified clusters of astrocytes characterized by activity-related changes in glial fibrillary acidic protein expression and branching. To validate these classifications following FUS, co-localization with activation markers were assessed. Focal and proximal microglia co-localized with the transforming growth factor beta 1, while proximal astrocytes co-localized with Nestin. In TgCRND8 mice, microglial and astrocytic activation clusters were found to correlate with amyloid-β plaque load. Thus, by only referencing control microglial and astrocytic morphologies, MORPHIOUS identified regions of interest corresponding to microglial and astrocytic activation.
conclusionsOverall, our algorithm is a reliable and sensitive method for characterizing microglial and astrocytic activation following FUS-induced BBB permeability and in animal models of neurodegeneration.
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