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Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease

Nature Genetics volume 53, pages 1143–1155 (2021)Cite this article

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Abstract

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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Fig. 1: Single-nucleus ATAC-seq and single-nucleus RNA-seq for the study of cellular diversity in the diseased brain.
Fig. 2: Epigenetically and transcriptionally distinct cell subpopulations in human AD prefrontal cortex.
Fig. 3: Linking cis-regulatory elements to downstream target genes in specific cell types.
Fig. 4: Cell-subpopulation-specific transcription factor regulation in late-stage AD.
Fig. 5: Multi-omic oligodendrocyte trajectory analysis.
Fig. 6: Multi-omic microglia and astrocyte trajectory analyses.
Fig. 7: Cell-type-specific regulatory landscapes of GWAS loci in the AD brain.
Fig. 8: Robust gene expression modules revealed using integrated bulk and single-cell coexpression network analysis.

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Data availability

All the multi-omics raw and processed data are available at https://www.synapse.org/#!Synapse:syn22079621/. Raw sequencing data have been deposited into the National Center for Biotechnology Information Gene Expression Omnibus under accession number GSE174367. Additionally, these data can be accessed through our web app at http://swaruplab.bio.uci.edu/singlenucleiAD. Source data are provided with this paper.

Code availability

The custom code used for this paper is available on GitHub74 (https://doi.org/10.5281/zenodo.4681643) at https://github.com/swaruplab/Single-nuclei-epigenomic-and-transcriptomic-landscape-in-Alzheimer-disease/.

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Acknowledgements

Funding for this work was provided by UCI and UCI MIND start-up funds, a National Institute on Aging grant 1RF1AG071683, Adelson Medical Research Foundation funds and an American Federation of Aging Research young investigator award to V.S. The funders had no role in the study design, data collection and analysis, decision to publish or in the preparation of the manuscript. We thank the New York Genome Center for sequencing our bulk and single-nucleus RNA-seq libraries and UCI’s Genomic High Throughput Facility for providing their facilities and sequencing our single-nucleus ATAC-seq libraries. In addition, we thank J. Atwood and UCI’s Institute for Immunology Flow Cytometry Core for assisting us in FACS sorting. We also thank K. Green and F. LaFerla for generously providing their imaging facilities and M. Arreola, Y.-Q. Xue and I. Cobos for their technical advice. This work utilized the infrastructure for high-performance and high-throughput computing, research data storage and analysis, and scientific software tool integration built, operated and updated by the Research Cyberinfrastructure Center (RCIC) at the University of California, Irvine (UCI). For ROSMAP, study data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago. Data collection was supported through funding by National Institute on Aging grants P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG36836, U01AG32984 and U01AG46152, the Illinois Department of Public Health and the Translational Genomics Research Institute. The results presented here are in whole or in part based on data obtained from the AMP-AD Knowledge Portal (https://doi.org/10.7303/syn2580853).

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Author notes

  1. These authors contributed equally: Samuel Morabito, Emily Miyoshi, Neethu Michael.

Authors and Affiliations

  1. Mathematical, Computational and Systems Biology (MCSB) Program, University of California, Irvine, CA, USA

    Samuel Morabito

  2. Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, CA, USA

    Samuel Morabito, Emily Miyoshi, Neethu Michael, Saba Shahin & Vivek Swarup

  3. Department of Neurobiology and Behavior, University of California, Irvine, CA, USA

    Emily Miyoshi, Neethu Michael, Saba Shahin, Alessandra Cadete Martini, Elizabeth Head, Justine Silva, Kelsey Leavy, Mari Perez-Rosendahl & Vivek Swarup

  4. Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA

    Alessandra Cadete Martini, Elizabeth Head & Mari Perez-Rosendahl

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Contributions

S.M., E.M., N.M. and V.S. planned and directed all experiments and analyses and wrote the manuscript with assistance from all authors. N.M., E.M. and V.S. performed all dissections and sequencing experiments, and S.M., E.M. and V.S. performed all bioinformatic analyses. A.C.M., E.H., J.S., K.L., M.P.-R., E.M., N.M. and V.S. reviewed clinical information. E.M. performed RNAscope validation, and N.M., S.S. and A.C.M. performed immunofluorescence/immunohistochemistry.

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Correspondence to Vivek Swarup.

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Peer review information Nature Genetics thanks Goncalo Castelo-Branco, Alexandra Grubman and Yin Shen for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Batch correction of snATAC-seq, snRNA-seq, and merged datasets.

a, snRNA-seq UMAPs before (left) and after iNMF batch correction (right), colored by sequencing batch. b, snATAC-seq UMAPs before (left) and after MNN batch correction (right), colored by sequencing batch. c, Dot plot of iNMF metagene expression in each snRNA-seq cluster. d, snRNA-seq UMAPs colored by metagene expression of selected iNMF metagenes. e, Dot plots showing the iNMF loading for the top 30 genes for the same metagenes in d.

Extended Data Fig. 2 Cell-type immunostaining and in situ hybridization.

a–d, Representative immunofluorescence images from postmortem human brain tissue from control and late-stage AD cases for Iba-1 (a), GFAP (b) MAP2 (c), and 6E10 (d). e, Quantification of 6E10-positive amyloid plaques. n = 3 cognitively healthy controls, 3 late-stage AD. Data is presented as the average of three different sections per sample. Linear mixed-effects model **** p < 0.0001. Box boundaries and line correspond to the interquartile range (IQR) and median respectively. Whiskers extend to the lowest or highest data points that are no further than 1.5 times the IQR from the box boundaries. f, Representative immunofluorescence images from postmortem human brain tissue from control and late-stage AD cases for OLIG2 with PDGFRA co-labeling. g, h, Representative RNAscope images from postmortem human brain tissue from control and late-stage AD cases for CNP (g) and PLP1 (h) with DAPI counterstain.

Extended Data Fig. 3 Comparison of gene expression and gene activity.

a, Scatter plot comparing average gene activity from snATAC-seq and average gene expression from snRNA-seq by each major cell-type, with accompanying Pearson correlation statistics and linear regression lines. b, Donut chart showing the percent of genes with high chromatin accessibility and low gene expression in grey for each major cell-type. High chromatin accessibility was defined as genes in the top 20% of gene activity, while low gene expression was defined as genes in the bottom 20% of gene expression. Percent of all other genes are colored by the cell-type.

Extended Data Fig. 4 NEAT1 validation and neuronal TFs.

a, b, Representative RNAscope images from postmortem human brain tissue for NEAT1 and AQP4 staining (a) and NEAT1 and MOG staining (b) with DAPI nuclear counterstain. c, Boxplots showing quantification of NEAT1 puncta per AQP4 + astrocyte as in a. n = 4 cognitively healthy controls, 5 late-stage AD. d, Boxplots showing quantification of NEAT1 puncta per MOG + oligodendrocyte as in b. n = 4 cognitively healthy controls, 4 late-stage AD. Data is represented as the mean of four equally sized regions per sample. Linear mixed-effects model e, Tn5 bias subtracted TF footprinting for JUN by snATAC-seq neuron cluster (top) and by AD diagnosis (bottom), with TF binding motif logo above and Tn5 bias insertions below. f, Left: Co-embedding UMAP colored by JUN motif variability (top) and JUN target gene score (bottom). Right: Violin plots of JUN motif variability (top) and JUN target gene score (bottom) in excitatory neuron clusters, split by diagnosis. Wilcoxon test (ns: p > 0.05, *: p < = 0.05, **: p < = 0.01, ***: p < = 0.001, ****: p < = 0.0001). g, Tn5 bias subtracted TF footprinting for EGR1 by snATAC-seq neuron cluster (top) and by AD diagnosis (bottom), as in e. h, Left: Co-embedding UMAP colored by EGR1 motif variability (top) and EGR1 target gene score (bottom). Right: Violin plots of EGR1 motif variability (top) and EGR1 target gene score (bottom) in excitatory neuron clusters, split by diagnosis, as in f. i, Violin plots of SREBF1 motif variability in oligodendrocyte snATAC-seq clusters, as in f. j, Violin plots of SREBF1 gene expression in oligodendrocyte snRNA-seq clusters, as in i. For boxplots, box boundaries and line correspond to the interquartile range (IQR) and median respectively. Whiskers extend to the lowest or highest data points that are no further than 1.5 times the IQR from the box boundaries.

Source data

Extended Data Fig. 5 Schematics of analyses.

a, Schematic diagram linking cCREs to target genes and downstream analysis. First, we identify co-accessible chromatin peaks in each cell-type for control and late-stage AD. Second, we identify pairs of co-accessible peaks where one peak overlaps a gene promoter and correlate the expression of that gene with the chromatin accessibility of the other peak. Third, NMF is used to group gl-cCREs into functional modules. b, Schematic of construction of TF regulatory networks for each cell-type. c, Schematic representation of scWGCNA analysis, including iNMF integration with the Mathys et al. 2019 dataset, metacell aggregation, construction of co-expression networks, and downstream analysis of gene modules.

Extended Data Fig. 6 Pseudotime trajectory analysis to identify dysregulated TFs and gene expression in glia.

a, Line plot showing the RVAgene training loss at each epoch for oligodendrocyte (ODC), microglia (MG), and astrocyte (ASC) RVAE models. b, c, d, Heatmaps showing TF motif variability smoothed using loess regression and scaled to minimum and maximum values for TFs up- and down-regulated in AD as well as cell-type marker TFs along the oligodendrocyte trajectory (b), microglia trajectory (c), and astrocyte trajectory (d). TFs are ordered by trajectory rank (point in trajectory where of 75% maximum value is reached). e, f, g, Dot plot showing the enrichR combined score for the top enriched GO terms in oligodendrocyte (e), microglia (f), and astrocyte (g) t-DEGs.

Extended Data Fig. 7 Metacell aggregation and SREBP.

a, Heatmap showing the enrichment of cell-type marker genes in standard WGCNA modules constructed from our snRNA-seq data. b, Schematic showing generation of 30,218 metacells from the integrated transcriptomic dataset of 132,106 nuclei from our snRNA-seq and Mathys et al. c, d, e, Heatmap showing enrichment of oligodendrocyte (c), microglia (d), and astrocyte (e) scWGCNA modules constructed with 12 metacells, 25 metacells, 100 metacells, and 200 metacells in the scWGCNA modules constructed with 50 metacells, as shown in Fig. 7 and Supplementary Fisg. 15 and 16. f, SREBP protein-protein interaction (PPI) network. Green circle denotes proteins involved in ribosome processing and transcription pathway, cyan circle for mTOR pathway, and red circle for lipid processing pathway. g, Left: Representative immunohistochemistry images from postmortem human brain tissue for SREBP with nuclear counterstain. Right: Quantification of SREBP staining. n = 4 pathological controls, 3 late-stage AD. Data is represented as the mean of four equally sized regions per sample. Scale bar represents 100 μm. Linear mixed-effects model ** p < 0.01. Box boundaries and line correspond to the interquartile range (IQR) and median respectively. Whiskers extend to the lowest or highest data points that are no further than 1.5 times the IQR from the box boundaries.

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Extended Data Fig. 8 iNMF integration of snRNA-seq with Mathys et al. snRNA-seq.

a, Schematic representation of iNMF integration of snRNA-seq with Mathys et al. snRNA-seq. UMAP plots are colored by cell-type assignments. b, Dot plot of iNMF metagene expression in each cell-type, split by dataset of origin. c, UMAP plots of the integrated dataset colored by selected iNMF metagenes. d, Dot plots showing the iNMF loading for the top 30 genes for the same metagenes in c.

Extended Data Fig. 9 scWGCNA in microglia and astrocytes.

a, Signed correlation of astrocyte modules to AD diagnosis. b, c, d, Co-expression plots for modules AM1 (b), AM2 (c), and AM5 (d). e, GO term enrichment of astrocyte modules. f, Heatmaps showing row-normalized Seurat module scores of astrocyte modules in snRNA-seq (left) and snATAC-seq (right) astrocyte clusters. g, Signed correlation of microglia co-expression modules with AD diagnosis. h, i, j, Co-expression plots for modules MM1 (h), MM2 (i), and MM4 (j). k, GO term enrichment of microglia modules. l, Heatmaps showing row-normalized Seurat module scores of microglia modules in snRNA-seq (left) and snATAC-seq (right) microglia clusters.

Extended Data Fig. 10 scWGCNA in neurons.

a, Signed correlation of excitatory neuron modules to AD diagnosis. b, c, d, e, Co-expression plots for modules EM1 (b), EM2 (c), EM5 (d), and EM7 (e). f, GO term enrichment of excitatory neuron modules. g, Heatmaps showing row-normalized Seurat module scores of excitatory neuron modules in snRNA-seq (left) and snATAC-seq (right) excitatory neuron clusters. h, Signed correlation of inhibitory neuron modules to AD diagnosis. i, j, k, l, m, n, Co-expression plots for modules IM1 (i), IM2 (j), IM3 (k), IM4 (l), IM5 (m), and IM6 (n). o, GO term enrichment of inhibitory neuron modules. p, Heatmaps showing row-normalized Seurat module scores of inhibitory neuron modules in snRNA-seq (left) and snATAC-seq (right) inhibitory neuron clusters.

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Morabito, S., Miyoshi, E., Michael, N. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet 53, 1143–1155 (2021). https://doi.org/10.1038/s41588-021-00894-z

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