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Updated: Jan 15, 2026
Isolation and Flow Cytometric Assessment of Neuroimmune Interactions in a Mini-Stroke Murine Model
Published on: June 20, 2025
Data-driven modeling and prediction of microglial cell dynamics in the ischemic penumbra
1Bioinformatics & Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
Abstract:
Neuroinflammation immediately follows the onset of ischemic stroke. During this process, microglial cells are activated in and recruited to the tissue surrounding the irreversibly injured infarct core, referred to as the penumbra. Microglial cells can be activated into two distinct phenotypes; however, the dynamics between the detrimental M1 phenotype and beneficial M2 phenotype are not fully understood. Using phenotype-specific cell count data obtained from experimental studies on middle cerebral artery occlusion-induced stroke in mice, we employ sparsity-promoting system identification techniques combined with Bayesian statistical methods for uncertainty quantification to generate continuous and discrete-time predictive models of the M1 and M2 microglial cell dynamics. The resulting sparse, data-driven models explain the data using constant and linear terms. Results emphasize an initial M2 dominance followed by a takeover of M1 cells, capture potential long-term dynamics of microglial cells, and suggest a persistent inflammatory response.
Insights
This study models microglial cell dynamics following ischemic stroke. Results show an initial M2 phenotype dominance, followed by M1 phenotype takeover, suggesting a persistent neuroinflammatory response.
Area of Science:
- Neuroscience
- Immunology
- Computational Biology
Background:
- Ischemic stroke triggers neuroinflammation, involving microglial cells in the brain's penumbra.
- Microglial cells exhibit M1 (detrimental) and M2 (beneficial) phenotypes, but their dynamic interplay post-stroke is unclear.
Purpose of the Study:
- To model the dynamic behavior of M1 and M2 microglial phenotypes after ischemic stroke.
- To understand the temporal shifts between detrimental and beneficial microglial responses.
Main Methods:
- Utilized phenotype-specific cell count data from mouse models of middle cerebral artery occlusion (MCAO).
- Employed sparsity-promoting system identification and Bayesian statistics for model development and uncertainty quantification.
- Generated continuous- and discrete-time predictive models of microglial cell dynamics.
Main Results:
- Developed sparse, data-driven models explaining M1 and M2 dynamics with constant and linear terms.
- Observed an initial dominance of the M2 microglial phenotype.
- Documented a subsequent shift towards M1 phenotype dominance, indicating a potential long-term inflammatory response.
Conclusions:
- The study provides predictive models for microglial cell dynamics post-ischemic stroke.
- Findings suggest a complex, evolving inflammatory response with a transition from beneficial to detrimental microglial states.
- Highlights the potential for persistent neuroinflammation following stroke.
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