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Selective targeting and modulation of plaque associated microglia via systemic hydroxyl dendrimer administration in an Alzheimer’s disease mouse model.

ABSTRACT: Selective targeting and modulation of plaque associated microglia via systemic hydroxyl dendrimer administration in an Alzheimer’s disease mouse model.

PROVIDER: PRJNA1107236 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR28878921_1.fastq.gz	Fastqsanger.gz
	SRR28878921_2.fastq.gz	Fastqsanger.gz
	SRR28878922_1.fastq.gz	Fastqsanger.gz
	SRR28878922_2.fastq.gz	Fastqsanger.gz
	SRR28878923_1.fastq.gz	Fastqsanger.gz

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Selective targeting and modulation of plaque associated microglia via systemic hydroxyl dendrimer administration in an Alzheimer’s disease mouse model.

Project description:Background: In Alzheimer’s disease (AD), microglia surround extracellular plaques and mount a sustained inflammatory response, contributing to the pathogenesis of the disease. Identifying approaches to specifically target plaque-associated microglia (PAMs) without interfering in the homeostatic functions of non-plaque associated microglia would afford a powerful tool and potential therapeutic avenue. Methods: Here, we demonstrated that a systemically administered nanomedicine, hydroxyl dendrimers (HDs), can cross the blood brain barrier and are preferentially taken up by PAMs in a mouse model of AD. As proof of principle, to demonstrate biological effects in PAM function, we treated the 5xFAD mouse model of amyloidosis for 4 weeks via systemic administration (ip, 2x weekly) of HDs conjugated to a colony stimulating factor-1 receptor (CSF1R) inhibitor (D-45113). Results: Treatment resulted in significant reductions in amyloid-beta (Aβ) and a stark reduction in the number of microglia and microglia-plaque association in the subiculum and somatosensory cortex, as well as a downregulation in microglial, inflammatory, and synaptic gene expression compared to vehicle treated 5xFAD mice. Conclusions: This study demonstrates that systemic administration of a dendranib may be utilized to target and modulate PAMs.

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Single nucleus RNA-seq of brain regions showing high-low Abeta plaque pathology in a mouse model of Alzheimer’s disease

Project description:Alzheimer’s Disease (AD) pathology and amyloid-beta plaque deposition progress slowly in the cerebellum compared to other regions, while the entorhinal cortex (EC) is one of the most vulnerable regions. Using a knock-in mouse model (App KI) of preclinical AD, we show that within the cerebellum, the deep cerebellar nuclei (DCN) show particularly low Aβ plaque accumulation. To identify factors that might underlie differences in the progression of AD-associated neuropathology across regions, we profiled gene expression in single nuclei (snRNAseq) across all celltypes in the DCN and EC of wild-type and App KI male mice at age 7 months.

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Plaque-induced transcriptional reprogramming of microglia towards lymphoid receptor expression protects against Alzheimer’s disease

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Plaque-induced transcriptional reprogramming of microglia towards lymphoid receptor expression protects against Alzheimer’s disease. [RNA-seq]

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Plaque-induced transcriptional reprogramming of microglia towards lymphoid receptor expression protects against Alzheimer’s disease.

Project description:Plaque-induced transcriptional reprogramming of microglia towards lymphoid receptor expression protects against Alzheimer’s disease.

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Integrative single-plaque analysis reveals signature Aβ and lipid profiles in the Alzheimer’s brain

Project description:Utilizing multimodal mass spectrometry imaging (MSI) combined with machine learning techniques, this study investigates the molecular heterogeneity of amyloid-β (Aβ) plaques and associated lipid profiles in post-mortem brain samples from Alzheimer’s disease (AD) and amyloid-positive cognitively unaffected (AP-CU) individuals. Our analytical approach permitted single-plaque level investigation, revealing distinct populations of amyloid plaques characterized by differential Aβ and lipid compositions. Notably, the integration of MSI data with machine learning based feature extraction enabled the identification of Aβ38 and ganglioside GM1 as significant molecular markers differentiating AD from AP-CU pathology. These findings suggest that the heterogeneity in Aβ metabolism and lipid homeostasis, as revealed through precise analysis, is a key factor in the pathogenesis of AD and implies that total amyloid burden alone is an insufficient marker for the disease. The application of MSI and machine learning based feature extraction in this context exemplifies a progressive analytic strategy to unravel complex biochemical phenomena, offering potential pathways for the refinement of diagnostic tools and deepening the understanding of neurodegenerative diseases from an analytical chemistry perspective.

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