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CSF and brain proteomics of Trem2-mKate2 KI/wt.APPSAA/SAA.hTfR KI/KI mice

ABSTRACT: Triggering receptor expressed on myeloid cells 2 (TREM2) is a central regulator of microglial activity and loss-of-function coding variants are major risk factors for late onset Alzheimer’s disease (LOAD). To better understand the molecular and functional changes associated with TREM2 signalling in microglia, we generated a TREM2 reporter mouse model. Experimental mice expressed the reporter heterozygous while AppSAA and the hTfR knock-in were homozygous (Trem2-mKate2 KI/wt.APPSAA/SAA.hTfR KI/KI) and were treated with the antibody transport vehicle coupled TREM2 agonist antibody ATV:4D9. After the treatment CSF and brain samples were colleceted for proteomics analyses.

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain, Cerebrospinal Fluid

DISEASE(S): Alzheimer's Disease

SUBMITTER: Stephan Mueller

LAB HEAD: Dr. Stefan F. Lichtenthaler

PROVIDER: PXD065438 | Pride | 2025-12-08

REPOSITORIES: Pride

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			Action	DRS
	AGCH_2501_071_20250220195516.raw	Raw
	AGCH_2501_077_20250221173333.raw	Raw
	AGCH_2501_078_20250222003608.raw	Raw
	AGCH_2501_089_20250222095931.raw	Raw
	AGCH_2501_090_20250221103058.raw	Raw

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Project description:Transcriptional profiling of mouse cerebellar extract comparing SCA7 KI mice with wild type mice. Female mice were killed by decapitation on post natal days 10 and 22 and 11 weeks. Goal was to determine gene expression profiles differing between SCA7 KI mice and wild-type mice during post-natal developement of the cerebellum. Gene expression profiling was performed using RNA extracted from the cerebellum of KI and WT mice at P10 (5 WT and 5 KI), P22 (5 WT and 4 KI) and 11 wks (5WT and 6 KI). After labeling, RNAs were hybridized on dual-label G4122F AgilentM-BM-. chips; a mix of all P10 samples was used as common reference (green channel). Quality control included visual control of the reconstructed image of the chip, M/A plot, corner intensities, outliers, positive and negative intensities, normalization factors. Normalization and statistical analyses were carried out by using BRB-array Tools developed by Dr. Richard Simon and the BRB-ArrayTools development team (Biometrics Research Branch, http://linus.nci.nih.gov/BRB-ArrayTools.html). Genes with less than 50% present calls or with low variability along the arrays (less than 20% of values with at least 2-fold change in either direction from the geneM-bM-^@M-^Ys median value) were excluded from further analysis. For the 1905 remaining probes an interaction between time and genotype was analyzed by regression analysis of the time course of expression. In brief, probes for which variation over time differed for the genotype class were fitted to the following model: log expression ~ time + time**2 + genotype + genotype*time + genotype*time**2. A univiariate p-value < 0.001 (random variance model) was set for significant probes (genotype*time + genotype*time**2) and a False Discovery Rate (FDR) was calculated for each probe (Benjamini & Hochberg, 1995). Differences in profiles were identified with a Self Organisation Tree Algorithm (MultiplExperiment Viewer (MeV), (Saeed et al, 2006). Expression values were averaged by group and then clustered according to their profile as a function of time.

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CSF and brain proteomics of Trem2-mKate2 KI/wt.APPSAA/SAA.hTfR KI/KI mice

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