Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
Open accessAbstract
The gene-regulatory landscape of the brain is highly dynamic in health and disease, coordinating a menagerie of biological processes across distinct cell types. Here, we present a multi-omic single-nucleus study of 191,890 nuclei in late-stage Alzheimer's disease (AD), accessible through our web portal, profiling chromatin accessibility and gene expression in the same biological samples and uncovering vast cellular heterogeneity. We identified cell-type-specific, disease-associated candidate cis-regulatory elements and their candidate target genes, including an oligodendrocyte-associated regulatory module containing links to APOE and CLU. We describe cis-regulatory relationships in specific cell types at a subset of AD risk loci defined by genome-wide association studies, demonstrating the utility of this multi-omic single-nucleus approach. Trajectory analysis of glial populations identified disease-relevant transcription factors, such as SREBF1, and their regulatory targets. Finally, we introduce single-nucleus consensus weighted gene coexpression analysis, a coexpression network analysis strategy robust to sparse single-cell data, and perform a systems-level analysis of the AD transcriptome.
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AD DEGs were consistent with published results both for region-specific DEGs and for DEGs computed jointly over all regions for multiple AD variables, and were further corroborated by comparisons with various independent studies.
“AD DEGs were consistent with published results both for region-specific DEGs and for DEGs computed jointly over all regions for multiple AD variables, and were further corroborated by comparisons with various independent studies11,12,15,19,47–53”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [50]
The Results section, ‘Multi-omic characterization of AD cellular heterogeneity’ (page unavailable), reports late-stage AD DEGs by cell cluster and notes that NEAT1 upregulation agrees with a previous finding. This makes the paper a relevant independent AD DEG study, but our copy does not establish consistency with the citing authors’ region-specific and jointly computed DEGs across multiple AD variables. (LLM, conf=0.78)
Experimental — accuracy not yet measuredConsensus co-expression network analysis of microglia from three Alzheimer's disease snRNA-seq studies yielded multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.
“We performed consensus co-expression network analysis of microglia from three Alzheimer’s disease (AD) snRNA-seq studies,10,11,12 yielding multiple gene modules corresponding to disease-associated microglia and polygenic risk of AD.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
All ODC-specific modules were significantly preserved in an independent dataset (Z summary preservation ≥5).
“we performed module preservation analysis20 to test the reproducibility of these modules in an independent dataset12 and found that all of the ODC-specific modules were significantly preserved (Z summary preservation ≥5).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
Multiple cell states of microglia represent a spectrum between homeostatic and disease-associated microglia.
“Transcriptomic and epigenomic studies in human tissue and AD mouse models have identified multiple cell states of microglia, representing a spectrum between homeostatic and disease-associated microglia (DAMs).12,50,51”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
A previous study defined a set of transcription factors, genes, and cis-regulatory elements involved in the shift between homeostatic and DAM cell states in human AD, identifying shared and distinct signatures compared with the DAM signature from 5xFAD mice.
“Our previous study defined a set of transcription factors, genes, and cis-regulatory elements involved in the shift between homeostatic and DAM cell states in human AD, identifying shared and distinct signatures compared with the DAM signature from 5xFAD mice.12”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
Consensus co-expression network analysis using microglia integrated from three human AD snRNA-seq datasets identified four consensus modules.
“Next, we performed consensus co-expression network analysis using microglia integrated from three human AD snRNA-seq datasets,10,11,12 identifying four consensus modules (Figure 6C; Data S1).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
scWGCNA was originally used to identify co-expression networks using bulk and single-cell RNA-seq together.
“scWGCNA was originally used to identify co-expression networks using bulk and single-cell RNA-seq together,12”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
hdWGCNA greatly extends upon scWGCNA, the authors' previous method for co-expression network analysis in single-cell transcriptomics data.
“hdWGCNA greatly extends upon scWGCNA,12 our previous method for co-expression network analysis in single-cell transcriptomics data.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [12]
The study harmonized snRNA-seq data and associated donor metadata from the PFC from ten additional AD studies spanning 707 donors.
“To corroborate the results, we harmonized snRNA-seq data and associated donor metadata from the PFC from ten additional AD studies spanning 707 donors5–14.”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [10]
Previous studies described AD-associated molecular and cellular changes; however, cross-study comparisons are challenging without common cell annotations.
“Previous studies described AD-associated molecular and cellular changes5–15; however, cross-study comparisons are challenging without common cell annotations.”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [10]
Our sample sizes are similar to those reported in previous publications.
“but our sample sizes are similar to those reported in previous publications5–14”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [10]
Stratifying human astrocyte transcriptomes from four published single-nuclei datasets by APOE genotype revealed differential expression patterns consistent with our proteomics data.
“We analyzed human astrocyte transcriptomes taken from four published single-nuclei transcriptomic datasets to determine whether genotype-specific lipid droplet proteome changes are reflected in post-mortem brain samples51–54.”
From APOE genotypes differentially remodel the astrocytic lipid droplet proteome to shape lipid droplet dynamics (2026) · cites this paper as [53]
This epigenetic signature is also seen in macrophages in atherosclerosis models and adipose-associated macrophages, but this signature is not seen in DAM microglia.
“This epigenetic signature is also seen in macrophages in atherosclerosis models23 and adipose-associated macrophages24 but this signature is not seen in DAM microglia25.”
From APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia (2024) · cites this paper as [25]
The Results section on the microglia trajectory reports depletion of a stage 2 DAM gene signature, but does not identify the epigenetic signature meant by the citing sentence or say that it is absent in DAM microglia. Searches for that epigenetic signature and for a comparison with macrophages did not find a passage establishing the cited claim; page numbers were unavailable in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe overall decreases in myelin lipids in Plcg2 KO mice may be due to cell autonomous effects of PLCG2 loss.
“suggesting that perhaps the overall decreases in myelin lipids in Plcg2 KO mice may be due to cell autonomous effects of PLCG2 loss.”
From Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain (2023) · cites this paper as [36]
The Results section on oligodendrocyte co-expression reports reduced expression of a myelination-related gene module in Alzheimer’s disease, and the Discussion describes altered lipid-regulatory signaling in human oligodendrocytes. Neither addresses myelin lipid levels in Plcg2 knockout mice or whether PLCG2 loss has cell-autonomous effects (searched: PLCG2, knockout mice, myelin lipids). (LLM, conf=0.76)
Experimental — accuracy not yet measuredA subset of oligodendrocytes, termed immune oligodendrocytes, express PLCG2 mRNA.
“there is some evidence that a subset of oligodendrocytes, termed immune oligodendrocytes, express PLCG2 mRNA (36–38)”
From Multi-omics analyses reveal novel effects of PLCγ2 deficiency in the mouse brain (2023) · cites this paper as [36]
The Results (Multi-omic analysis of the human prefrontal cortex) identify oligodendrocyte clusters, but do not identify an immune oligodendrocyte subset or report PLCG2 mRNA expression in it. Searches for “PLCG2,” “immune oligodendrocytes,” and a paraphrase of the expression claim found no passage addressing that point; page numbers were unavailable in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe study identified five times as many new candidate CREs than previously reported (319,905 links vs 56,552 gene-linked cCREs).
“As such, we identified five times as many new candidate CREs than previously reported (319,905 links vs 56,552 gene-linked cCREs).19”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [19]
No statistical methods were used to pre-determine sample sizes, but our sample sizes are similar to those reported in previous publications.
“No statistical methods were used to pre-determine sample sizes, but our sample sizes are similar to those reported in previous publications.18,19”
From Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements (2023) · cites this paper as [19]
Networks were generated on all genes in the oligodendrocytes from Morabito et al. because this cell type contained the most cells in the dataset, for building networks that are representative of SCING performance.
“Networks were generated under these settings on all genes in the oligodendrocytes from Morabito et al.27, since this cell type contained the most cells in the dataset, for building networks that are representative of SCING performance.”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [27]
For the train-test split and network consistency assessment, human AD snRNAseq data from Morabito et al. was used.
“For the train-test split and network consistency assessment, we used the human AD snRNAseq data from Morabito et al.27”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [27]
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“and Morabito et al.(19), including 57 PTPRG, which is upregulated in AD microglia across all three studies (Figures 2C and 2D).”
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“18,19 Especially, in some previous reports, non-negative matrix factorization (NMF) 20 has been utilized to extract both cell-type identity and cellular activity programs, thereby addressing the profiling of complex cell populations.”
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- Authors
- Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V.
- Affiliations
- University of California, Irvine (US)
- Corresponding author
- Swarup, Vivek
- ORCID
- Venue
- Nature Genetics
- Published
- 2021-07-08
- Volume
- 53
- Issue
- 8
- Pages
- 1143-1155
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC8766217
- OpenAlex
- W3179788429
- Cited by
- 643
- References
- 89
- Citations by year
- 2026: 106 · 2025: 166 · 2024: 171 · 2023: 120 · 2022: 70 · 2021: 8 · 2020: 1
- FWCI
- 32.02
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- American Federation for Aging Research (AFA-5550485)
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (1RF1AG071683)
- Dr. Miriam and Sheldon G. Adelson Medical Research Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- Alzheimer's disease, single-nucleus transcriptomics, cis-regulatory elements, cellular heterogeneity, oligodendrocytes, apolipoprotein E, clusterin, SREBF1, transcription factors
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cell Nucleus, Chromatin, Female, Humans, Male, Neuroglia, Oligodendroglia, Regulatory Sequences, Nucleic Acid, Transcription Factors, Case-Control Studies, Prefrontal Cortex, Gene Expression Profiling, Sterol Regulatory Element Binding Protein 1, Gene Regulatory Networks, Genome-Wide Association Study
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