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Neuronal ApoE upregulates MHC-I expression to drive selective neurodegeneration in Alzheimer’s disease

Nature Neuroscience volume 24, pages 786–798 (2021)Cite this article

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Abstract

Selective neurodegeneration is a critical causal factor in Alzheimer’s disease (AD); however, the mechanisms that lead some neurons to perish, whereas others remain resilient, are unknown. We sought potential drivers of this selective vulnerability using single-nucleus RNA sequencing and discovered that ApoE expression level is a substantial driver of neuronal variability. Strikingly, neuronal expression of ApoE—which has a robust genetic linkage to AD—correlated strongly, on a cell-by-cell basis, with immune response pathways in neurons in the brains of wild-type mice, human ApoE knock-in mice and humans with or without AD. Elimination or over-expression of neuronal ApoE revealed a causal relationship among ApoE expression, neuronal MHC-I expression, tau pathology and neurodegeneration. Functional reduction of MHC-I ameliorated tau pathology in ApoE4-expressing primary neurons and in mouse hippocampi expressing pathological tau. These findings suggest a mechanism linking neuronal ApoE expression to MHC-I expression and, subsequently, to tau pathology and selective neurodegeneration.

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Fig. 1: PCA reveals the most prominent sources of cell-by-cell variation within each cell type in human ApoE-KI mouse hippocampus.
Fig. 2: The top correlates of neuronal ApoE expression in human ApoE-KI mouse hippocampus are enriched for cellular stress and immune response pathways.
Fig. 3: Neuronal ApoE expression correlates with cellular stress and immune response pathways in brains of persons with MCI or AD.
Fig. 4: Neuron-specific knockout of the APOE gene protects from ApoE4-induced neuronal, synaptic and hippocampal volume loss in aged ApoE-KI mice (15 months).
Fig. 5: The proportion of ApoE-expression-high neuronal cells tracks disease progression in patients with MCI or AD.
Fig. 6: Neuron-specific knockout of APOE reduces expression of genes encoding MHC pathway proteins, especially MHC-I, in hippocampal neurons in ApoE-KI mice and in primary neurons of WT mice.
Fig. 7: Reducing or eliminating functional MHC-I decreases or rescues AD-related tau pathologies in ApoE4-overexpressing or WT mouse neurons.
Fig. 8: B2M-KO protects from p-tau pathology in a tau-P301S overexpression mouse model.

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Data availability

Mouse snRNA-seq data generated in association with this study are available in the Gene Expression Omnibus under accession number GSE167497. Source data associated with Figs. 4 and 6–8, as well as Extended Data Fig. 9, are available in the Supplementary Information.

snRNA-seq data from the ROSMAP study referenced in Fig. 3, as well as in Extended Data Figs. 5 and 8, are available on the Rush Alzheimer’s Disease Center Research Resource Sharing Hub at https://www.radc.rush.edu/docs/omics.htm (snRNA-seq PFC) or at Synapse (https://www.synapse.org/#!Synapse:syn18485175) under accession number syn18485175. The ROSMAP metadata can be accessed at https://www.synapse.org/#!Synapse:syn3157322.

Data from the Allen Institute for Brain Science, referenced in Extended Data Fig. 6a–d, along with data visualization tools and associated metadata, are available at https://portal.brain-map.org/atlases-and-data/rnaseq.

Single-cell sequencing data from neurotypical human brain, referenced in Extended Data Fig. 6e–g, are available in the Gene Expression Omnibus database: www.ncbi.nlm.nih.gov/geo (accession no. GSE67835).

The Kyoto Encyclopedia of Genes and Genomes Pathways database is available at https://www.genome.jp/kegg/pathway.html. Source data are provided with this paper.

Code availability

All codes generated during this study are available upon reasonable request from the corresponding authors.

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Acknowledgements

This work was partially supported by grants R01AG048017, RF1AG055421 and R01AG055682 to Y. Huang from the National Institutes of Health (NIH). The results published here are, in part, based on data obtained from the AMP-AD Knowledge Portal accessed at https://doi.org/10.7303/syn2580853. We are grateful to the participants and data contributors in the Religious Order Study, the Memory and Aging Project (ROSMAP). ROSMAP data were provided by the Rush Alzheimer’s Disease Center at Rush University Medical Center. ROSMAP data collection was supported through funding by National Institute on Aging grants P30AG10161, R01AG15819, R01AG17917, R01AG36836, U01AG32984, U01AG46152 and U01061356; the Illinois Department of Public Health; and the Translational Genomics Research Institute. The Gladstone Flow Cytometry Core FACSAria cell sorter is supported by NIH S10 RR028962 and the James B. Pendleton Charitable Trust. We thank E. Chow and the staff at the UCSF Center for Advanced Technology Core for advice and support with RNA sequencing; N. Carli, J. McGuire, P.-L. So and K. Pollard of the Gladstone Genomics Core for advice on sample preparation and sequencing; N. Raman of the Gladstone Flow Cytometry Core for sorting the nuclei; S. Belunek and W. Maguire of Gladstone Information Technology for server support; J. Roudabush and V. Viray for contract support; S. Oduah and L. Hagimori for purchasing; and T. Pak for editorial assistance.

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Authors and Affiliations

  1. Gladstone Institute of Neurological Disease, San Francisco, CA, USA

    Kelly A. Zalocusky, Ramsey Najm, Alice L. Taubes, Yanxia Hao, Seo Yeon Yoon, Nicole Koutsodendris, Maxine R. Nelson, Antara Rao, Jason Bant, Dah-eun J. Amornkul, Qin Xu, Alice An, Olga Cisne-Thomson & Yadong Huang

  2. Gladstone Center for Translational Advancement, San Francisco, CA, USA

    Kelly A. Zalocusky, Yanxia Hao, Dah-eun J. Amornkul, Qin Xu, Olga Cisne-Thomson & Yadong Huang

  3. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Kelly A. Zalocusky, Jason Bant & Yadong Huang

  4. Developmental and Stem Cell Biology Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Ramsey Najm, Nicole Koutsodendris, Antara Rao & Yadong Huang

  5. Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Alice L. Taubes, Maxine R. Nelson & Yadong Huang

  6. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

  7. Department of Pathology, University of California, San Francisco, San Francisco, CA, USA

    Yadong Huang

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  1. Kelly A. Zalocusky
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Contributions

K.A.Z and Y. Huang designed and coordinated the study and wrote the manuscript. K.A.Z. carried out most studies and data analysis. R.N. conducted immunohistochemical studies. A.L.T. and Y. Hao dissected mouse hippocampi, isolated cell nuclei and prepared samples for RNA sequencing. S.Y.Y. managed mouse lines and contributed to mouse hippocampal virus injection, brain collections, immunohistochemistry and image collection. N.K., M.R.N., A.R., D.J.A., Q.X., A.A. and O.C.T. contributed to biochemical and immunohistochemical studies and data analysis. J.B. contributed to mouse hippocampal virus injection. D.A.B. provided clinical, pathological and snRNA-seq data from human brains of the ROSMAP cohort and critically reviewed the manuscript. Y. Huang supervised the project.

Corresponding authors

Correspondence to Kelly A. Zalocusky or Yadong Huang.

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Competing interests

Y. Huang is a co-founder and scientific advisory board member of E-Scape Bio, GABAeron and Mederon Bio. All other authors declare no competing financial interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Cell cluster identification and quality control measures in snRNA-seq analysis of apoE-KI mice.

a, Feature plots of imputed expression of marker genes for major cell type clusters, as well as matched whole-brain and hippocampal expression of that marker gene in the Allen Institute for Brain Science Mouse ISH Atlas28. b, Violin plot depicting marker genes for larger cell classes (such as Syn1 for neurons) as well as marker genes for individual clusters, such as C1ql2 for dentate gyrus granule cells, Pdgfra for OPCs, and Folr1 for choroid plexus. c, tSNE plots of all the nuclei broken out by apoE genotype (columns) and mouse age (rows) showing a lack of batch effect by sample and representation of all major cell types in both genotypes at all ages. d, Quality control measures: number of UMIs, number of genes, and percent mitochondrial reads from each cluster.

Extended Data Fig. 2 ApoE correlation with the first two PCs is not driven by age, genotype, cell type markers, or quality control markers.

a–d, PCA plots demonstrating that the correlation between apoE gene expression and the first 2 principal components (PC1 and PC2) across neuronal cell types is not driven by measures of quality control or read depth, such as number of UMIs, number of genes, or percent mitochondrial reads. e–h, Neither is the apoE expression gradient driven by apoE genotype or mouse age. i–l, Additionally, this apoE expression gradient is not explained by differences in cell type marker expression, such as Syn1 for neurons or Aqp4 for astrocytes (i–l), indicating that the apoE-expression-high cells are not misclassified neuron/astrocyte doublets.

Extended Data Fig. 3 ApoE and pathway correlations are highly similar across apoE genotype and age.

a,b, Heatmaps showing apoE and pathway correlation across cell types for the top 10 apoE-correlated pathways for each neuronal subtype, broken out by apoE genotype and mouse age, demonstrating a strong conservation of apoE and pathway relationships across apoE genotypes and ages.

Extended Data Fig. 4 Principal components analysis (PCA) of snRNA-seq data reveals the most prominent sources of cell-by-cell variation within each neuronal type in wildtype (WT) mouse cortex and the top correlates of neuronal apoE expression are enriched for cellular stress and immune response pathways in WT mouse cortical neurons.

a, Clustering using the Seurat package revealed 16 distinct cellular populations in WT mouse cortex where neurons were purposefully enriched34. Marker gene analysis led to the identification of 15 neuronal clusters and one cluster of oligodendrocytes. b, ApoE expression across cell types, demonstrating expression of apoE across neuronal types. c, Heatmap illustrating the correlation between apoE expression and KEGG pathway expression scores for the top 10 apoE expression-correlated pathways from each subset of neurons. d, Network visualization of the proportion of shared genes amongst the pathways represented in c. There are two main modules of inter-related pathways. The blue module is related to neurodegenerative disease and includes the Alzheimer disease, Huntington disease, and Parkinson disease pathways. The orange module, consisting of ten apoE-correlated pathways, relates to immune response. e, In Cluster Ex. 1 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = 0.86, p = 1.7 × 10−284) and PC2 (Pearson’s correlation coefficient; r = 0.47, p = 2.5 × 10−54). f, In Cluster Ex.3 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = −0.84, p = 2.5 × 10−121) and PC2 (Pearson’s correlation coefficient; r = 0.24, p = 8.8 × 10−8). g, In Cluster Ex.5 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = −0.98, p = 5.1 × 10−251) and PC2 (Pearson’s correlation coefficient; r = 0.13, p = 0.01). h, In Cluster Ex. 6 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = 0.44, p = 8.0 × 10−17) and PC2 (Pearson’s correlation coefficient; r = 0.88, p = 4.6 × 10−108). i, In Cluster Ex. 7 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = 0.94, p = 2.1 × 10−143) and PC2 (Pearson’s correlation coefficient; r = −0.32, p = 1.8 × 10−8). j, In Cluster Ex. 8 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = 0.96, p = 3.5 × 10−160). k, In Cluster Ex. 9 cells, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = −0.88, p = 3.2 × 10−90) and PC2 (Pearson’s correlation coefficient; r = −0.43, p = 5.4 × 10−14). l, In SST Interneurons, apoE expression is strongly correlated with PC1 (Pearson’s correlation coefficient; r = −0.51, p = 1.5 ×10−18) and PC2 (Pearson’s correlation coefficient; r = −0.81, p = 8.7 × 10−62).

Extended Data Fig. 5 Principal components analysis (PCA) reveals the most prominent sources of cell-by-cell variation across neuronal types in the ROSMAP dataset.

Across multiple human neuronal cell types, apoE expression levels correlate with the first two PCs. a, In Cluster 4 Excitatory neurons (n = 3574), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = 0.33, p = 2 × 10−91) and PC2 (Pearson’s correlation coefficient; r = 0.52, p = 3 × 10−255). b, In Cluster 8 Excitatory neurons (n = 2482), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = −0.36, p = 2 × 10−80) and PC2 (Pearson’s correlation coefficient; r = 0.54, p = 1 × 10−191). c, In Cluster 11 Excitatory neurons (n = 1492), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = 0.27, p = 7 × 10−26) and PC2 (Pearson’s correlation coefficient; r = −0.48, p = 4 × 10−86). d, In Cluster 7 Inhibitory neurons (n = 2537), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = −0.45, p = 2 × 10−127) and PC2 (Pearson’s correlation coefficient; r = −0.12, p = 1 × 10−10). e, In Cluster 12 Inhibitory neurons (n = 1425), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = −0.40, p = 4 × 10−55) and PC2 (Pearson’s correlation coefficient; r = 0.67, p = 3 × 10−186). f, In Cluster 15 Inhibitory neurons (n = 897), apoE expression is correlated with PC1 (Pearson’s correlation coefficient; r = 0.54, p = 2 × 10−70) and PC2 (Pearson’s correlation coefficient; r = −0.67, p = 7 × 10−120).

Extended Data Fig. 6 Relationships of neuronal apoE and cellular stress and immune response pathways are replicated in additional human brain snRNA-seq datasets.

a, Clustering of a human brain dataset by cell type (https://portal.brain-map.org/atlases-and-data/rnaseq). b, ApoE expression across cell types, demonstrating expression of apoE across neuronal types. c, Heatmap illustrating the correlation between apoE expression and KEGG pathway expression scores for the top 10 apoE expression-correlated pathways from each subset of neurons. d, Network plot illustrating the proportion of shared genes amongst apoE expression-correlated pathways shared between human and mouse. Edge width represents proportion of shared genes. There are two main modules of inter-related pathways. One (blue) module is related to neurodegenerative disease and includes the Alzheimer disease and Huntington disease. The other (orange) module, consisting of eight apoE-correlated pathways, is related to immune response. e, Clustering of another human dataset by cell type36. f, Heatmap illustrating the correlation between apoE expression and KEGG pathway expression scores for the top 10 apoE expression-correlated pathways from each subset of neurons. g, Network plot illustrating the proportion of shared genes amongst apoE expression-correlated pathways. Edge width represents proportion of shared genes. There are two main modules of inter-related pathways. The larger (green) module is related to cellular metabolism. The other (orange) module, consisting of six apoE-correlated pathways, is related to immune response.

Extended Data Fig. 7 Cell cluster identification and apoE expression in the combined set of apoE-KI and apoE-KI/Syn-Cre data.

a, Feature plots of marker genes for major cell types in the combined apoE-KI and apoE-KI/Syn-Cre cell clustering. b, Histograms of apoE expression levels in the combined apoE-KI and apoE-KI/Syn-Cre cohort, showing that even the low levels of apoE expression measured in apoE-KI neurons are true expression, fully separated from the noise levels in apoE-KI/Syn-Cre neurons.

Extended Data Fig. 8 Neuronal expression of apoE predicts neuronal expression of MHC-I genes, and neuronal expression of MHC-I genes predicts Tau tangle, but not β-amyloid, pathology across patients with MCI or AD.

a, Linear regression coefficients (± 95% confidence intervals) for age, sex, apoE4 genotype, clinical diagnosis (MCI or AD relative to control) and average apoE expression level in neurons in predicting the expression of MHC-I genes and B2M gene in neurons of patients from the ROSMAP snRNA-seq cohort depicted in Fig. 3. b, Linear regression coefficients (± 95% confidence intervals) for age, sex, apoE4 genotype, clinical diagnosis (MCI or AD relative to control), and MHC-I genes and B2M gene expression in predicting tau tangle pathology in patients from the ROSMAP snRNA-seq cohort depicted in Fig. 3. c, Linear regression coefficients (± 95% confidence intervals) for age, sex, apoE4 genotype, clinical diagnosis (MCI or AD relative to control), and MHC-I genes and B2M gene expression in predicting β-amyloid pathology in participants from the ROSMAP snRNA-seq cohort depicted in Fig. 3.

Extended Data Fig. 9 Quantification of B2M protein in B2M-shRNA-treated NSE-E4+/+ mouse primary neurons and B2M-KO mouse primary neurons.

a, Western blot of B2M and TUJ1 protein in NSE-E4+/+ mouse primary neurons treated with Lenti-B2M-shRNA or Lenti-scrambled-shRNA control. Data are representative of two primary neuron culture experiments. b, Quantification of B2M/TUJ1 ratio from western blots. B2M protein is significantly reduced in B2M-shRNA-treated neurons as compared to control shRNA-treated neurons (two-sided t-test, p = 0.017, n = 7 per group). c, Western blot of B2M and TUJ1 protein in lysates of WT and B2M-KO mouse primary neurons, showing elimination of B2M in the B2M-KO neurons. The experiment was performed once.

Source data

Extended Data Fig. 10 Model of apoE upregulation of MHC-I driving Tau pathology and selective neuronal and synaptic degeneration/loss.

In response to various cellular stressors during aging, increase in neuronal apoE expression, as a molecular switch, triggers aberrant upregulation of neuronal MHC-I, driving Tau pathology and the selective destruction of individual synapses and neurons, potentially (as a hypothesis) by reactive microglia and/or, MHC’s classical partner, CD8+ T-cells. In the AD context, apoE4 exacerbates this process.

Supplementary information

Reporting Summary

Supplementary Table 1.

Marker genes for all clusters in all snRNA-seq datasets in Figs. 1, 3 and 6. Includes marker genes for every cluster of cells in the apoE-KI and apoE-KI/Syn-Cre mouse hippocampus as well as in the human MCI/AD prefrontal cortex dataset.

Supplementary Table 2.

Pearson’s correlation coefficient (r) and associated P values for all apoE–pathway correlations represented in Figs. 2 and 3. P values associated with Fig. 4. log fold change and P values associated with Fig. 6h. Includes the statistics associated with the heat maps displayed as Figs. 2a, 3c and 6h and the time courses in Fig. 4e–h.

Supplementary Table 3.

Demographic information related to the human MCI and AD donors represented in Fig. 3. Describes the demographic and basic pathological information of the 48 donors from the ROSMAP cohort included in Fig. 3. AD, Alzheimer’s disease; braaksc, braak score; ceradsc, cerad score; cogdx, cognitive diagnosis; dx, diagnosis; educ, education; gpath, global pathology; mmse, mini-mental state exam; msex, male sex; nft, neurofibrillary tangles (silver stain); tangles, tau tangles (AT8).

Supplementary Table 4.

Demographic information related to the human donors represented in Extended Data Fig. 6a–d. Describes the demographic and basic pathological information from the eight donors in the Allen Brain Atlas Brain Map snRNA-seq data included in Extended Data Fig. 6a–d of this manuscript. C, Caucasian; COD, cause of death; F, female; H, Hispanic; I Iraqi descent; M, male; MTG, middle temporal gyrus; N, neurosurgical; N/A, not applicable; N/Av, not available; P, postmortem; PMI, postmortem interval; RIN, RNA integrity number.

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Source Data Fig. 4

Statistical source data for Fig. 4i,k,l,m.

Source Data Fig. 6

Statistical source data for Fig. 6i,l,k,l,m,n.

Source Data Fig. 6

Western blot source data for Fig. 6i,j.

Source Data Fig. 7

Statistical source data for Fig. 7b,c,d,e,f,h,i,j.

Source Data Fig. 8

Statistical source data for Fig. 8b,c.

Source Data Extended Data Fig. 9

Statistical source data for Extended Data Fig. 9b.

Source Data Extended Data Fig. 9

Western blot source data for Extended Data Fig. 9a,c.

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Zalocusky, K.A., Najm, R., Taubes, A.L. et al. Neuronal ApoE upregulates MHC-I expression to drive selective neurodegeneration in Alzheimer’s disease. Nat Neurosci 24, 786–798 (2021). https://doi.org/10.1038/s41593-021-00851-3

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