High-Resolution Ex Vivo Tissue Clearing, Lightsheet Imaging, and Data Analysis to Support Macromolecular Drug and Biomarker Distribution in Whole Organs and Tumors

Kumar, Niyanta N.; Hrobař, Petr; Vagenknecht, Martin; Soukup, Jindřich; Bloomingdale, Peter; Freshwater, Tomoko; Bardehle, Sophia; Peter, Roman; Patterson, Nadia; Mangadu, Ruban; Pastuskovas, Cinthia V.; Cancilla, Mark T.

doi:10.1017/s1431927622005840

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future model iterations may explore mechanisms such as increased heterogeneity of microglia, subregional variation in the role of Aβ deposition, different sizes of Aβ oligomers and fibrils, and microglial interactions with tau and plaque compaction or using imaging data to initialize simulations. 40,41 This model can also be applied to fractionally up-and downregulate microglial behaviors and can thus be used for exposure-response predictions to yield unique insights into potential pharmacologic interventions that directly modulate microglia activation states and the recently approved plaque clearing immunotherapies such as lecanemab. 42 Furthermore, the addition of other cell types, informed by either AD-specific or generalized data sets, could further expand our understanding of these processes in the larger context of the brain through diverse data integration.…”

Section: Discussionmentioning

confidence: 99%

Microglial roles in Alzheimer's disease: An agent‐based model to elucidate microglial spatiotemporal response to beta‐amyloid

Weathered,

Bardehle,

Yoon

et al. 2024

CPT Pharmacom & Syst Pharma

Self Cite

View full text Add to dashboard Cite

Alzheimer's Disease (AD) is characterized by beta‐amyloid (Aβ) plaques in the brain and widespread neuronal damage. Due to high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aβ plaques. In response to AD pathology, microglia are innate phagocytotic immune cells which transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aβ dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent‐based model (ABM) to characterize the spatiotemporal interactions between microglia and Aβ. Our model qualitatively reproduces mouse data trends reported in Condello et al. 2015, where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p<0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. Additionally, in silico behavioral knock‐out simulations show that phagocytosis knock‐outs have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knock‐outs had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis and replication of activated microglia have complex impacts on plaque volume and coverage, while microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

show abstract

Section: Discussionmentioning

confidence: 99%