Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure

Abstract

Background: Alzheimer's disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored.

Methods: We developed and optimized a cell-type-specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies.

Results: We found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies.

Conclusions: These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.

Keywords: Alzheimer’s disease; Autosomal dominant AD; Brain cellular composition; Bulk RNA-sequencing; Digital deconvolution; TREM2.

Fig. 1

Study design development of the brain cell-type transcriptomic reference panel (left column): the expression signatures of key cell types of the brain were curated by compiling publicly available RNA-seq data from neurons, astrocytes, oligodendrocytes, and microglia. The panel was curated iteratively to retain only those samples that showed the most faithful expression signature, while evaluating alternative digital deconvolution methods. The accuracy of digital deconvolution to estimate brain cellular proportion was validated using additional cell-type-specific samples and also by generating chimeric libraries. To study cellular population structure in AD (right column), we accessed publicly available data from the AMP-AD, including Mayo Clinic and MSBB datasets. In addition, we generated RNA-seq from participants of the Knight-ADRC and DIAN studies. These three studies generated RNA-seq data from PA brains, AD cases, and neuropath-free controls in a total of six cerebral cortex regions and cerebellum. We quantified the gene expression for all of the samples included in these studies using the same RNA-seq processing pipeline. Using digital deconvolution methods, we estimated the brain cellular proportions of the samples and compared the proportion between AD cases and controls. We studied the cell structure of brain carriers of Mendelian pathological mutations and variants that confer high-risk to AD. APC anterior prefrontal cortex, STG superior temporal gyrus, PHG parahippocampal gyrus, IFG inferior frontal gyrus, MSBB Mount Sinai Brain Bank, AD Alzheimer’s disease, PA pathological aging

Fig. 2

Cell-type distributions of the samples included in the Mayo Clinic and MSBB. Mean neuronal (blue) and astrocytic proportion (red) for (a) AD affected brains and controls (bars indicate standard deviations). The numbers of participants for each group are shown below the x-axis. Distribution for additional clinical and pathological phenotypes reported for the MSBB: (b) CDR scores and (c) Braak staging. d Brain cell-type proportions (x-axis) plotted against the mean number of amyloid plaque (values > 0; y-axis). Standard errors were depicted in shaded area with LOESS smooth curve fitted to cell-type proportions derived from deconvolution. (**p < 0.01; ***p < 1.0 × 10⁻³; and ****p < 1.0 × 10⁻⁴)

Fig. 3

Neuron and astrocyte distributions from the DIAN and Knight-ADRC brains. a Mean neuronal (blue) and astrocytic (red) proportions for carriers of pathogenic mutations in APP, PSEN1, or PSEN2 (ADAD), late-onset AD (LOAD), and neuropath-free controls (bars indicate standard deviations). Neuronal and astrocytic proportions plotted against (b) Braak staging and (c) by CDR. d Cell-type distributions for carriers of AD genetic risk factors. Lines indicate significance levels (*p < 0.05; **p < 0.01; ***p < 1.0 × 10⁻³; ****p < 1.0 × 10⁻⁴)

Fig. 4

Effect of the APOE ε4 allele and TREM2 coding variants on the cellular population structure. Mean neuronal (blue) and astrocytic (red) proportions for (a) AD cases and controls in the Knight-ADRC brains categorized by APOE ε4 carriers vs non-carriers and (b) AD cases of Knight-ADRC brain bank (bars indicate standard deviations). c AD cases and controls in the Mayo Clinic and MSBB. d AD cases in the Mayo Clinic and MSBB. e Neuronal (blue) and astrocyte (red) distributions for samples included in the MSBB stratified by TREM2 genetic status. APC anterior prefrontal cortex, STG superior temporal gyrus, PHG parahippocampal gyrus, IFG inferior frontal gyrus (n.s. p > 0.05; *p < 0.05; ****p < 1.0 × 10⁻⁴)