Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Chloe X Yap^{1

2

3

4}, Daniel D Vo^{3

4

5

6

7

8}, Matthew G Heffel^{5

9}, Arjun Bhattacharya^{10

11

12

13}, Cindy Wen^{3

4

5}, Yuanhao Yang^{1

2}, Kathryn E Kemper², Jian Zeng², Zhili Zheng², Zhihong Zhu^{2

14}, Eilis Hannon¹⁵, Dorothea Seiler Vellame¹⁵, Alice Franklin¹⁵, Christa Caggiano^{9

16}, Brie Wamsley^{3

4

16

17}, Daniel H Geschwind^{3

4

5

16

17}, Noah Zaitlen^{16

18}, Alexander Gusev^{19

20

21}, Bogdan Pasaniuc^{5

9

10

18

22}, Jonathan Mill¹⁵, Chongyuan Luo⁵, Michael J Gandal^{3

4

5

6

7

8}

Affiliations

¹ Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia.
² Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
³ Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁴ Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ Lifespan Brain Institute at Penn Medicine and The Children's Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
⁷ Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁸ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁹ Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
¹¹ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
¹² Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹³ Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹⁴ The National Centre for Register-based Research, Aarhus University, Denmark.
¹⁵ Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK.
¹⁶ Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
¹⁷ Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁸ Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹⁹ Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
²⁰ Division of Genetics, Brigham & Women's Hospital, Boston, MA, USA.
²¹ Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
²² Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.

PMID: 38781333
PMCID: PMC11114225
DOI: 10.1126/sciadv.adn7655

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Chloe X Yap et al. Sci Adv. 2024.

. 2024 May 24;10(21):eadn7655.

doi: 10.1126/sciadv.adn7655. Epub 2024 May 23.

Authors

Affiliations

¹ Mater Research Institute, University of Queensland, Brisbane, Queensland, Australia.
² Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia.
³ Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁴ Program in Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁵ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ Lifespan Brain Institute at Penn Medicine and The Children's Hospital of Philadelphia, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
⁷ Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁸ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
⁹ Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
¹¹ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
¹² Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹³ Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹⁴ The National Centre for Register-based Research, Aarhus University, Denmark.
¹⁵ Department of Clinical and Biomedical Sciences, University of Exeter Medical School, University of Exeter, Exeter, UK.
¹⁶ Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
¹⁷ Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁸ Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA.
¹⁹ Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
²⁰ Division of Genetics, Brigham & Women's Hospital, Boston, MA, USA.
²¹ Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
²² Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.

PMID: 38781333
PMCID: PMC11114225
DOI: 10.1126/sciadv.adn7655

Abstract

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

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Figures

**Fig. 1.. Overview of study design and analyses.**
(A) An overview of the pipeline developed for reference-based deconvolution of CTPs from bulk brain methylation data. UMAP shows 7 major brain cell types clustered by single cell methylome profiles. (B) Bulk prefrontal cortex DNA methylation data was integrated from three studies: ROSMAP (ascertained for Alzheimer’s disease), LIBD (ascertained for schizophrenia), and UCLA_ASD (ascertained for ASD). Table shows the number of participants: Second column shows individuals with bulk DNA methylation data in each study; blue text for the number of individuals without the diagnosis of interest; red (Alzheimer’s disease), yellow (schizophrenia), and green (ASD) are for those with the diagnosis; the numbers in brackets indicates the number of people excluded for case/control analyses to balance study design by age and sex; third column shows the number of people with matching SNP genotypes, with individuals of European ancestry shown in brackets. Density plot shows age distribution for each constituent study, and bar plots show breakdown by sex. (C) Downstream analyses involving deconvolved brain CTPs, phenotype and genotype data. EUR, European; Exc, excitatory neurons; Inh, inhibitory neurons; Astro, astrocytes; Endo, endothelial cells; Micro, microglia; Oligo, oligodendrocyte, OPC, oligodendrocyte precursor cells.

**Fig. 2.. Overview of deconvolution method.**
(A) Single-cell methylome sequencing was performed on 15,030 single cells from postmortem human frontal cortex. (B) Reference panel of marker sites was created from the methylome data to capture differentially hypermethylated and hypomethylated sites for each of the seven major brain cell types. (C) With this reference panel, bulk methylation data from ROSMAP, LIBD, and UCLA_ASD was deconvolved using non-negative matrix factorization as implemented by the Houseman algorithm. (D) We applied the clr-transformation to the seven major brain CTPs. Bar plot below shows the average clr-transformed CTPs (± SE).

**Fig. 3.. Brain cell type shifts are observed across neuropsychiatric diagnoses, age, and sex.**
(A) Boxplot of deconvolved brain CTPs, stratified by study. (B) Diagnosis coefficients (±95% CI) from linear models of brain CTP (clr-transformed) ~ diagnosis + age + age² + sex + batch. This model was run within each study (UCLA_ASD, n = 58; ROSMAP, n = 715; LIBD, n = 402). Labeled results indicate nominally-significant results (P ≤ 0.05). (C) Age effect coefficients (±95% CI) from linear models of brain CTP (clr-transformed) ~ diagnosis + age + sex + batch; coefficients for sex from linear models of brain CTP (clr-transformed) ~ diagnosis + age + age² + sex + batch. This model was run as a mega-analysis including all participants (with and without neuropsychiatric diagnoses) across all studies (n = 1270). (D) Age trajectories of brain CTPs (clr-transformed) aggregating across all studies, correcting for batch effects. (E) Boxplot of endothelial cell proportion (clr-transformed) by final clinical consensus cognitive diagnosis: no cognitive impairment (NCI) versus mild cognitive impairment (MCI) versus Alzheimer’s disease (AZD) versus other primary cause of dementia (Other). (F) Scatterplot of endothelial cell CTP (clr-transformed) versus Braak score (representing histopathological severity of Alzheimer’s disease), demonstrating replication across the ROSMAP and BDR datasets. (G) Increased microglia are replicated in ASD using snRNA-seq count data (n = 60). ASD diagnosis coefficients (±95% CI) from linear models of brain CTP (clr-transformed) ~ diagnosis + age + age² + sex + brain region.

**Fig. 4.. Polygenic scores for neuropsychiatric traits predict brain cell-type shifts.**
(A) Distributions of PGS within each study, comparing individuals with and without the neuropsychiatric diagnosis of interest. (B) Neuropsychiatric trait PGS coefficients (±95% CI) from linear models for brain CTP (clr-transformed) ~ neuropsychiatric trait PGS + age + age² + diagnosis + sex + batch + genotyping PC1–3, subsetting for individuals with the diagnosis of interest (i.e., one of ASD, schizophrenia, or Alzheimer’s disease) and undiagnosed controls. Analyses included individuals with the diagnosis of interest and all controls: n = 531 for the ASD_PGS analysis, n = 591 for the SCZ_PGS analysis, and n = 763 for the AZD_PGS analysis. The White Matter Hyperintensity on MRI (WMH_PGS) PGS analysis included the same n = 763 individuals as for Alzheimer’s disease. (C) Schematic of causal analyses (mediation analysis and SMR). For the mediation analysis, statistics for effect sizes, 95% CI and P value are provided. ACME, average causal mediation effect; ADE, average direct effect.

**Fig. 5.. Manhattan plots for genome-wide association studies for brain CTPs and composition.**
Manhattan plots correspond to GWAS meta-analyses (n = 873), with signficant (P < 5 × 10⁻⁸) hits: inhibitory neuron CTP, astrocyte CTP, CTP_PC2 (↓Oligo / ↑OPC + Exc), CTP_PC3 (↓Micro / ↑Oligo + OPC), and CTP_PC5 (↓Astro + Endo / ↑Exc + Inh + Micro). Left barplots denote the relative loadings of cell types related to the CTP_PC variables. GWAS was performed in a linear model framework, including as covariates age, age², sex, batch, and diagnosis. Red dotted line denotes the P < 5 × 10⁻⁸ threshold.

**Fig. 6.. Investigation of *TMEM106B* locus, focusing on the ROSMAP dataset.**
(A) LocusZoom plot comparing GWAS results on astrocyte CTP at the *TMEM106B* locus within the ROSMAP, LIBD, UCLA_ASD, and meta-analyzed datasets (METAL). (B) rs1990621 GWAS SNP effect size (±95% CI) in the ROSMAP dataset across the seven CTPs and the five CTP_PCs.

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Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Affiliations

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics

Authors

Affiliations

Abstract

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References

MeSH terms

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Medical

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