Brain‐wide spatial analysis reveals cell‐type‐specific genetic modifiers of Alzheimer’s disease progression

Abstract

AbstractBackgroundThere is significant variation in the age at onset and severity of cognitive decline among those with Alzheimer’s disease (AD). Despite strong genetic links to AD loci, the specific variants, target genes and cell types that drive AD‐related deterioration remain elusive. Recent advances in brain‐wide cell type and pathology quantification across our AD mouse genetic reference panel provide new opportunities to identify genetic factors that act in a cell‐type‐specific fashion to modify disease progression in response to causal AD mutations; linking, for the first time, genetics to regional cell composition to characterize susceptibility versus resilience to AD.MethodImmunohistochemistry was completed to evaluate neurodegeneration(NeuN), gliosis(Iba1&GFAP), and amyloid pathology(AB1‐42) in 250 mice from the AD‐BXD genetic reference panel. Using the QUINT workflow, hemibrain slices were systematically segmented and registered to the Allen Brain Atlas to gain a global perspective of percent cell/pathology coverage. The heritability of individual cell composition was calculated and quantitative trait loci(QTL) mapping was completed.ResultCompositional differences between the strains, particularly the BXD founder strains, became apparent when evaluating neurodegeneration and microglial proliferation. Despite comparable levels of amyloid, cortical and hippocampal NeuN and Iba1 coverage significantly varied among strains; however, this variation did not explain individual differences in short and long‐term memory capacity. Genetic mapping of individual cell composition traits identified a significant QTL associated with cortical neuron load (h = 0.6) on chromosome 17. Lrfn2 was identified as a gene of interest as it was differentially expressed between mice that had the BB vs BD genotype at the most significant variant location under the peak.ConclusionThis method of combining omics and imaging data to link changes in gene expression, cell composition, and behavior allows for the assessment of disease subtypes with the potential to aid the development of precision medicine solutions to AD. Specifically, changes in cell coverage can be associated with differences in gene expression among the AD‐BXD population to detect genes associated with regional cell composition. These candidates will be validated by creating cell‐type‐specific knock‐in or knock‐out models and measuring cognitive functioning to determine the effect of these genes on memory performance.