Genetic variants associated with Alzheimer's disease confer different cerebral cortex cell-type population structure.
- Authors
- Li, Zeran; Del-Aguila, Jorge L; Dube, Umber; Budde, John; Martinez, Rita; Black, Kathleen; Xiao, Qingli; Cairns, Nigel J; Dominantly Inherited Alzheimer Network (DIAN); Dougherty, Joseph D; Lee, Jin-Moo; Morris, John C; Bateman, Randall J; Karch, Celeste M; Cruchaga, Carlos; Harari, Oscar
- Year
- 2018
- Journal
- Genome medicine
- PMID
- 29880032
- DOI
- 10.1186/s13073-018-0551-4
- PMCID
- PMC5992755
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.
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
LLM interpretation
This figure is a study design diagram divided into two main columns. The left column illustrates the development of a brain cell-type transcriptomic reference panel, showing the curation of RNA-seq data from microglia, astrocytes, oligodendrocytes, and neurons through a pipeline of panel curation, algorithm evaluation, and sample evaluation. The right column outlines the study of cellular population structure in neurodegenerative disorders, detailing the use of RNA-seq data from multiple cohorts (MSBB, Mayo, DIAN, Knight ADRC) across various brain regions to analyze differential distributions between AD, PA, and control groups, including specific genetic variants like APOE and TREM2.
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)
LLM interpretation
This figure consists of four panels analyzing cell-type distributions in AD brains and controls. Panel (a) uses stacked bar charts to show mean neuronal (blue) and astrocytic (red) proportions across six brain regions, with significant decreases in neurons and increases in astrocytes in AD samples (p < 0.01 to p < 0.0001). Panels (b) and (c) are line graphs showing mean proportions of astrocytes and neurons across increasing CDR scores and Braak stages, respectively. Panel (d) contains four scatter plots with LOESS smooth curves showing the relationship between cell-type proportions and the mean number of amyloid plaques across four MSBB brain regions.
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)
LLM interpretation
This figure consists of four panels (a-d) using stacked bar charts to show the proportions of neurons (blue) and astrocytes (red) across different AD conditions. Panel (a) compares disease types (ADAD, LOAD, and Control), showing a decrease in neuronal proportion and an increase in astrocytic proportion in ADAD compared to controls. Panels (b) and (c) plot these proportions against Braak stage and Clinical Dementia Rating (CDR), respectively, with provided beta coefficients and p-values indicating significant trends. Panel (d) compares LOAD genetic variants (Sporadic AD, PLD3, TREM2) against controls, with asterisks denoting statistical significance levels ranging from *p < 0.05 to ****p < 1.0 × 10⁻⁴.
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)
LLM interpretation
This figure consists of line graphs (a, b) and diverging bar charts (c, d, e) analyzing cellular proportions of neurons (blue) and astrocytes (red) across different genetic cohorts. Panels (a) and (b) show that as the APOE ε4 allele count increases from 0 to 2, the mean proportion of astrocytes generally increases while the proportion of neurons decreases across multiple brain regions. Panels (c) and (d) demonstrate a statistically significant increase in astrocyte proportion and decrease in neuron proportion in APOE ε4 carriers compared to non-carriers (*p < 0.05). Panel (e) compares TREM2 carriers, non-carriers, and controls, showing significant differences in cellular proportions between these groups across the APC, IFG, PHG, and STG regions (p < 0.05 to p < 1.0 × 10⁻⁴).
| # | Section | Preview |
|---|---|---|
| 40 | Results — Development of a reference panel to estimate brain cellular population structure | We first tried to create a transcriptome-wide reference panel by selecting the genes that are… |
| 41 | Results — Development of a reference panel to estimate brain cellular population structure | the species of the reference samples (Additional file 1: Figure S1b; Additional file 1: Table S2).… |
| 42 | Results — Optimization, validation, and accuracy estimation of the reference panel and digital deconvolution method | Once we identified the optimal approach to perform digital deconvolution from brain RNA-seq, we… |
| 43 | Results — Optimization, validation, and accuracy estimation of the reference panel and digital deconvolution method | We first validated the accuracy to predict neuronal composition by generating RNA-seq for eight… |
| 44 | Results — Optimization, validation, and accuracy estimation of the reference panel and digital deconvolution method | To evaluate the accuracy of digital deconvolution for measuring cell-type proportion from cell-type… |
| 45 | Results — Optimization, validation, and accuracy estimation of the reference panel and digital deconvolution method | Finally, we evaluated whether any gene included in the reference panel was dominating the inference… |
| 46 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | Pathologically, AD is associated with neuronal death and gliosis specifically in the cerebral… |
| 47 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | We initially analyzed the RNA-seq from the Mayo Clinic Brain Bank that includes bulk RNA-seq from… |
| 48 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | structure (AD vs neuropath-free controls) from the brains in the Mayo Clinic and Mount Sinai Brain… |
| 49 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | 1.46 × 10−033.11 × 10−02Braak stagingAPC173− 0.011.21 × 10−020.011.27 × 10−03− 3.09… |
| 50 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | The distribution of microglia was similar in the TC and CB from AD and control brains (Table 2;… |
| 51 | Results — Deconvolution of bulk RNA-seq of non-demented and AD brains shows a characteristic signature for neurodegeneration | We also analyzed data from the MSBB, which contains bulk RNA-seq for four additional cerebral cortex… |
| 52 | Results — The cellular population structure differs between ADAD vs LOAD | While the loss of neurons is a common feature of AD, it is not clear whether the mechanism holds… |
| 53 | Results — The cellular population structure differs between ADAD vs LOAD | Using digital deconvolution, we determined the cellular composition for these brains. We observed a… |
| 54 | Results — The cellular population structure differs between ADAD vs LOAD | 2.66 × 10−02 for neurons and β = 0.03 and p = 5.48 × 10−03 for astrocytes; Table 3; Fig. 3c).… |
| 55 | Results — The cellular population structure differs between ADAD vs LOAD | × 10−032.41 × 10−01Clinical Dementia Rating ADa and Controls110− 0.022.66 × 10−020.035.48… |
| 56 | Results — The cellular population structure differs between ADAD vs LOAD | Next, we compared the cell proportion of LOAD vs ADAD and found that the cell composition differs… |
| 57 | Results — The cellular population structure differs between ADAD vs LOAD | 3a; Additional file 1: Table S9) in ADAD brains compared to LOAD. We observed the same cellular… |
| 58 | Results — Specific genetic variants confer a distinctive cell composition profile | A variety of genetic variants increase risk of LOAD; however, it is unclear if the cellular… |
| 59 | Results — Specific genetic variants confer a distinctive cell composition profile | We initially ascertained the effect of APOE ε4 on the cell-type composition. We observed a… |
No entities extracted from this document yet.
No uploaded files.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| The association of Alzheimer's disease-related SNPs with mild cognitive impairment susceptibility in the Chinese population. | Xie Z et al. | — | 2026 | → |
| Alzheimer's disease rewires gene coexpression networks coupling different brain regions. | Mitra S et al. | — | 2024 | → |
| Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics. | Yap CX et al. | — | 2024 | → |
| Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease. | Eteleeb AM et al. | — | 2024 | → |
| Circulating blood circular RNA in Parkinson's Disease; from involvement in pathology to diagnostic tools in at-risk individuals. | Beric A et al. | — | 2024 | → |
| Cognitive resilience to Alzheimer's disease characterized by cell-type abundance. | O'Neill N et al. | — | 2024 | → |
| Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center. | Fernandez MV et al. | — | 2024 | → |
| Multilayer Analysis of RNA Sequencing Data in Alzheimer's Disease to Unravel Molecular Mysteries. | Uzuner D et al. | — | 2024 | → |
| Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. | de Klein N et al. | — | 2023 | → |
| Conserved gene signatures shared among <i>MAPT</i> mutations reveal defects in calcium signaling. | Minaya MA et al. | — | 2023 | → |
| Metabolomic and lipidomic signatures in autosomal dominant and late-onset Alzheimer's disease brains. | Novotny BC et al. | — | 2023 | → |
| A diet rich in docosahexaenoic acid enhances reactive astrogliosis and ramified microglia morphology in apolipoprotein E epsilon 4-targeted replacement mice. | Chappus-McCendie H et al. | — | 2022 | → |
| Bulk and Single-Nucleus Transcriptomics Highlight Intra-Telencephalic and Somatostatin Neurons in Alzheimer's Disease. | Consens ME et al. | — | 2022 | → |
| Circular RNA detection identifies circPSEN1 alterations in brain specific to autosomal dominant Alzheimer's disease. | Chen HH et al. | — | 2022 | → |
| Comprehensive evaluation of deconvolution methods for human brain gene expression. | Sutton GJ et al. | — | 2022 | → |
| Multi-ancestry GWAS reveals excitotoxicity associated with outcome after ischaemic stroke. | Ibanez L et al. | — | 2022 | → |
| Murine roseolovirus does not accelerate amyloid-β pathology and human roseoloviruses are not over-represented in Alzheimer disease brains. | Bigley TM et al. | — | 2022 | → |
| The cholesteryl ester transfer protein (CETP) raises cholesterol levels in the brain. | Oestereich F et al. | — | 2022 | → |
| Advances in Genetic and Molecular Understanding of Alzheimer's Disease. | Ibanez L et al. | — | 2021 | → |
| Alzheimer's disease alters oligodendrocytic glycolytic and ketolytic gene expression. | Saito ER et al. | — | 2021 | → |
| Commonalities between Copper Neurotoxicity and Alzheimer's Disease. | Patel R et al. | — | 2021 | → |
| Comparison of machine learning approaches for enhancing Alzheimer's disease classification. | Li Q et al. | — | 2021 | → |
| Emerging genetic complexity and rare genetic variants in neurodegenerative brain diseases. | Perrone F et al. | — | 2021 | → |
| Quantitative endophenotypes as an alternative approach to understanding genetic risk in neurodegenerative diseases. | Farias FHG et al. | — | 2021 | → |
| Deciphering cellular transcriptional alterations in Alzheimer's disease brains. | Wang X et al. | — | 2020 | → |
| Multi-ancestry genetic study in 5,876 patients identifies an association between excitotoxic genes and early outcomes after acute ischemic stroke | Ibanez L et al. | — | 2020 | — |
| Overlapping genetic architecture between Parkinson disease and melanoma. | Dube U et al. | — | 2020 | → |
| The TMEM106B FTLD-protective variant, rs1990621, is also associated with increased neuronal proportion. | Li Z et al. | — | 2020 | → |
| Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism. | Kapoor M et al. | — | 2019 | → |
| An atlas of cortical circular RNA expression in Alzheimer disease brains demonstrates clinical and pathological associations. | Dube U et al. | — | 2019 | → |
| A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain. | Del-Aguila JL et al. | — | 2019 | → |
| Association Between <i>Aldehyde dehydrogenase-2</i> Polymorphisms and Risk of Alzheimer's Disease and Parkinson's Disease: A Meta-Analysis Based on 5,315 Individuals. | Chen J et al. | — | 2019 | → |
| The <i>MS4A</i> gene cluster is a key modulator of soluble TREM2 and Alzheimer's disease risk. | Deming Y et al. | — | 2019 | → |
| TREM2 brain transcript-specific studies in AD and TREM2 mutation carriers. | Del-Aguila JL et al. | — | 2019 | → |
| Effects of <i>APOE</i> Genotype on Brain Proteomic Network and Cell Type Changes in Alzheimer's Disease. | Dai J et al. | — | 2018 | → |
| Integrative system biology analyses of CRISPR-edited iPSC-derived neurons and human brains reveal deficiencies of presynaptic signaling in FTLD and PSP. | Jiang S et al. | — | 2018 | → |