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Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease

Nature Neuroscience volume 24, pages 276–287 (2021)Cite this article

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Abstract

Alzheimer’s disease (AD) is characterized by the selective vulnerability of specific neuronal populations, the molecular signatures of which are largely unknown. To identify and characterize selectively vulnerable neuronal populations, we used single-nucleus RNA sequencing to profile the caudal entorhinal cortex and the superior frontal gyrus—brain regions where neurofibrillary inclusions and neuronal loss occur early and late in AD, respectively—from postmortem brains spanning the progression of AD-type tau neurofibrillary pathology. We identified RORB as a marker of selectively vulnerable excitatory neurons in the entorhinal cortex and subsequently validated their depletion and selective susceptibility to neurofibrillary inclusions during disease progression using quantitative neuropathological methods. We also discovered an astrocyte subpopulation, likely representing reactive astrocytes, characterized by decreased expression of genes involved in homeostatic functions. Our characterization of selectively vulnerable neurons in AD paves the way for future mechanistic studies of selective vulnerability and potential therapeutic strategies for enhancing neuronal resilience.

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Fig. 1: AD progression differentially affects the cell-type composition of the EC and SFG.
Fig. 2: RORB-expressing excitatory neuron subpopulations in the EC are selectively vulnerable.
Fig. 3: Immunofluorescence of the EC validates selective vulnerability of RORB-expressing excitatory neurons.
Fig. 4: Inhibitory neuron subpopulations do not consistently show differences in resilience or vulnerability to AD progression.
Fig. 5: GFAPhigh astrocytes show signs of dysfunction in glutamate homeostasis and synaptic support.

Data availability

The raw snRNA-seq sequencing data and unfiltered UMI count matrices are available on the Gene Expression Omnibus (GEO) under accession code GSE147528. Single-cell data after quality control is available for download at Synapse.org under the Synapse ID syn21788402. Post-quality-control data can also be explored interactively through the CellXGene platform at https://kampmannlab.ucsf.edu/ad-brain. Data from Mathys et al.14 was downloaded from Synapse under Synapse ID syn18485175.

Code availability

We provide the full bioinformatics pipeline for the analysis of snRNA-seq data in this paper at https://kampmannlab.ucsf.edu/ad-brain-analysis.

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Acknowledgements

We thank A. Pisco, A. Maynard, S. Darmanis and the MACA team at the Chan Zuckerberg Biohub for advice on analysis 10X library preparation and reagents. We thank members of the Kampmann laboratory (A. Samelson, X. Guo, R. Tian and B. Rooney) for feedback on the manuscript. This work was supported by National Institutes of Health (NIH) awards F30 AG066418 (to K.L.), K08 AG052648 (to S.S.), R56 AG057528 (to M.K. and L.T.G.), K24 AG053435 (to L.T.G.), U54 NS100717 (to L.T.G. and M

Author information

Author notes

  1. These authors contributed equally: Kun Leng, Emmy Li.

Authors and Affiliations

  1. Institute for Neurodegenerative Disease, University of California, San Francisco, San Francisco, CA, USA

    Kun Leng, Emmy Li & Martin Kampmann

  2. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Kun Leng, Emmy Li, Rene Sit, Michelle Tan, Norma Neff & Martin Kampmann

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

    Kun Leng & Emmy Li

  4. Medical Scientist Training Program, University of California, San Francisco, San Francisco, CA, USA

    Kun Leng

  5. Memory and Aging Center, Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA

    Rana Eser, Antonia Piergies, Song Hua Li, Alexander J. Ehrenberg, William W. Seeley, Salvatore Spina & Lea T. Grinberg

  6. Department of Neurology, Universidade de São Paulo, Faculdade de Medicina, São Paulo, Brazil

    Roberta Diehl Rodriguez

  7. Department of Pathology, Universidade de São Paulo, Faculdade de Medicina, São Paulo, Brazil

    Claudia Kimie Suemoto, Renata Elaine Paraizo Leite, Carlos A. Pasqualucci, Helmut Heinsen & Lea T. Grinberg

  8. Division of Geriatrics, Department of Clinical Medicine, Universidade de São Paulo, Faculdade de Medicina, São Paulo, Brazil

    Claudia Kimie Suemoto

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

    William W. Seeley

  10. Department of Psychiatry, University of Würzburg, Würzburg, Germany

    Helmut Heinsen

  11. Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, USA

    Lea T. Grinberg

  12. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Martin Kampmann

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  1. Kun Leng
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Contributions

K.L., E.L., L.T.G. and M.K. conceptualized and led the overall project. K.L., L.T.G. and M.K. wrote the manuscript, with input from all co-authors. K.L. analyzed snRNA-Seq data and visualized results. E.L. generated snRNA-Seq data, with support from R.S., M.T., and N.N. R.D.R., C.K.S., R.E.P.L., A.E., C.A.P. W.W.S., and S.S. contributed to neuropathological data generation and analysis, R.E., A.P. and H.H. contributed to neuropathological data analysis, and S.H.L. contributed to neuropathological method development.

Corresponding authors

Correspondence to Lea T. Grinberg or Martin Kampmann.

Ethics declarations

Ethics Declarations

This project was approved the ethical committee of the University of Sao Paulo (for tissue transfer) and deemed non-human subject research by UCSF.

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Asgeir Kobro-Flatmoen, Menno Witter, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Data quality and initial clustering without cross-sample alignment.

a-b, Mean number of genes (a) or UMIs (b) detected per cell across individual samples for major cell types identified in each dataset. Grubman et al.15 did not resolve excitatory neurons from inhibitory neurons. Pericytes were identified only in Mathys et al.14 Cell type abbreviations: Exc – excitatory neurons, Oligo – oligodendrocytes, Astro – astrocytes, Inh – inhibitory neurons, OPC – oligodendrocyte precursor cells, Micro – microglia, Endo – endothelial cells, Per – pericytes. c-d, tSNE projection of cells from the EC (c) and SFG (d) clustered without first performing cross-sample alignment, colored by individual of origin (center) or cluster assignment (outer). e-f, Heatmap and hierarchical clustering of clusters and cluster marker expression (top subpanels); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Expression of cell type markers (bottom subpanels).

Extended Data Fig. 2 Expression of selected EC excitatory neuron subpopulation markers and pathway enrichment analysis of differentially expressed genes in selectively vulnerable EC excitatory neuron subpopulations.

a, Expression heatmap of genes that are specifically expressed by four or fewer EC excitatory neuron subpopulations; “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). b-d, Enrichment analysis against Gene Ontology Cellular Component terms or Reactome Pathways (b,d) and functional association network analysis (c,e; see Methods) of genes with higher (b-c) or lower expression (d-e) in RORB+ vulnerable EC excitatory neurons, with selected terms highlighted by color. In panels c and e, genes with stronger associations are connected by thicker lines, and genes without known associations are not shown.

Extended Data Fig. 3 Differential expression analysis across Braak stages for EC excitatory neuron subpopulations.

a-b, Number of differentially expressed genes in EC excitatory neuron subpopulations with higher (a) or lower (b) expression in Braak stage 6 vs. Braak stage 0. c-f, Enrichment analysis against Gene Ontology Cellular Component terms (c-d) or Reactome Pathways (e-f) of differentially expressed genes in EC excitatory neuron subpopulations with higher (c,e) or lower (d,f) expression in Braak stage 6 vs. Braak stage 0.

Extended Data Fig. 4 Alignment of EC and SFG maps homologous excitatory neuron subpopulations.

a, tSNE projection of excitatory neurons from the EC and SFG in the joint alignment space, colored by subpopulation identity (top), individual of origin (middle), or brain region (bottom). b, Heatmap and hierarchical clustering of subpopulations and subpopulation marker expression (top subpanel); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations across Braak stages (second and third subpanels); for each brain region, statistical significance of differences in relative abundance across Braak stages (Braak 0 n = 3, Braak 2 n = 4, Braak 6 n = 3, where n is the number of individuals sampled) was determined by beta regression and adjusted for multiple comparisons (see Methods). Expression heatmap of EC layer-specific genes identified from Ramsden et al.22 (fourth subpanel). Expression heatmap of neocortical layer-specific genes from Lake et al.12 (fifth subpanel). Expression of selectively vulnerable EC excitatory neuron subpopulation markers by excitatory neurons in the EC (sixth subpanel) or SFG (bottom subpanel). Significant beta regression P values (adjusted for multiple testing) are shown in a table at the bottom of the panel. c, Sankey diagram connecting subpopulation identity of excitatory neurons in the EC alignment space and the SFG alignment space to subpopulation identity in the EC+SFG alignment space. The links connecting EC:Exc.s2 and EC:Exc.s4 to SFG:Exc.s2 and SFG:Exc.s4, respectively, are highlighted.

Extended Data Fig. 5 Cross-sample alignment of excitatory neurons from Mathys et al. recapitulates selective vulnerability in a RORB-expressing subpopulation.

a, tSNE projection of excitatory neurons from Mathys et al.14 in the alignment space, colored by subpopulation identity (top) or individual of origin (bottom). b, Heatmap and hierarchical clustering of subpopulations and subpopulation marker expression (top subpanel); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations in in AD cases vs. controls, separated by sex (second and third subpanels); for each sex, statistical significance of differences in relative abundance between AD cases vs. controls (cases n = 12, controls n=12, where n is the number of individuals sampled) was determined by beta regression and adjusted for multiple comparisons (see Methods). Expression heatmap of neocortical layer-specific genes from Lake et al.12 (fourth subpanel). Expression of selectively vulnerable EC excitatory neuron subpopulation markers (bottom subpanel). c, Heatmap of Pearson correlation between the gene expression profiles of excitatory neuron subpopulations from the EC vs. those from the prefrontal cortex in Mathys et al.14.

Extended Data Fig. 6 Delineation of the EC for each case used in immunofluorescence validation.

a, The borders of the caudal EC delineated on sections stained with hematoxylin and eosin (H&E) for all 26 cases used in immunofluorescence validation (Table 1). b, Borders of the EC were determined with the aid of 400 um thick serial coronal sections of whole-brain hemispheres stained with gallocyanin (see Methods). Each H&E section (left) along with its corresponding immunofluorescence image (middle) was aligned to the most approximate gallocyanin section (right), in which the the dissecans layers (diss-1, diss-2, and diss-ext) characteristic of the caudal EC were easier to visualize. This was then used to guide delineation of the EC on the H&E and immunofluorescence sections. For more details on the cytoarchitectonic definitions used to define the caudal EC, please consult Heinsen et al.19.

Extended Data Fig. 7 Inhibitory neurons from Mathys et al. also do not show differences in resilience or vulnerability to AD.

a, tSNE projection of inhibitory neurons from Mathys et al.14 in the alignment space, colored by subpopulation identity (top) or individual of origin (bottom). b, Heatmap and hierarchical clustering of subpopulations and subpopulation markers (top subpanel); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations in in AD cases vs. controls, separated by sex (second and third subpanels); for each sex, statistical significance of differences in relative abundance between AD cases vs. controls (cases n=12, controls n=12, where n is the number of individuals sampled) was determined by beta regression and adjusted for multiple comparisons (see Methods). Expression heatmap of inhibitory neuron subtype markers from Lake et al.12 (bottom subpanel).

Extended Data Fig. 8 Subclustering of microglia does not sufficiently resolve disease associated microglia signature.

a-c, tSNE projection of astrocytes from the EC (a), SFG (b), and Mathys et al.14 (c) in their respective alignment spaces, colored by subpopulation identity (left) or individual of origin (right). d-f, Heatmap and hierarchical clustering of subpopulations and subpopulation marker expression (top subpanels); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations (middle subpanels) across Braak stages in the EC and SFG (for each brain region, Braak 0 n=3, Braak 2 n=4, Braak 6 n=3, where n is the number of individuals sampled) or between AD cases vs. controls in Mathys et al.14 (for each sex, cases n =12, controls n = 12, where n is the number of individuals sampled); statistical significance of differences in relative abundance was determined by beta regression and adjusted for multiple comparisons (see Methods). Expression of disease associated microglia markers, with median expression level marked by line (bottom subpanels).

Extended Data Fig. 9 Subclustering of oligodendrocytes identifies subpopulations with higher expression of AD-associated oligodendrocyte markers from Mathys et al.

a-c, tSNE projection of oligodendrocytes from the EC (a), SFG (b), and Mathys et al.14 (c) in their respective alignment spaces, colored by subpopulation identity (left) or individual of origin (right). d-f, Heatmap and hierarchical clustering of subpopulations and subpopulation marker expression (top subpanels); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations (middle subpanels) across Braak stages in the EC and SFG (for each brain region, Braak 0 n=3, Braak 2 n=4, Braak 6 n=3, where n is the number of individuals sampled) or between AD cases vs. controls in Mathys et al.14 (for each sex, cases n =12, controls n = 12, where n is the number of individuals sampled); statistical significance of differences in relative abundance was determined by beta regression and adjusted for multiple comparisons (see Methods). Relative expression of AD-associated oligodendrocyte subpopulation markers from Mathys et al.14 (bottom subpanels).

Extended Data Fig. 10 Astrocyte subpopulations with high GFAP expression from Mathys et al. are highly similar to those from the EC and SFG.

a, tSNE projection of astrocytes from Mathys et al.14 in the alignment subspace, colored by subpopulation identity (top) or individual of origin (bottom). b, Heatmap and hierarchical clustering of subpopulations and subpopulation marker expression (top subpanel); “High” and “Low” relative expression reflect above- and below-average expression, respectively (see Methods). Relative abundance of subpopulations in in AD cases vs. controls, separated by sex (middle subpanels); for each sex, statistical significance of differences in relative abundance between AD cases vs. controls (cases n=12, controls n=12, where n is the number of individuals sampled) was determined by beta regression and adjusted for multiple comparisons (see Methods). Expression of genes associated with reactive astrocytes, with median expression level marked by line (bottom subpanel). c, Enrichment analysis of overlap between differentially expressed genes in astrocytes with high GFAP expression from Mathys et al.14 vs. differentially expressed genes in astrocytes with high GFAP expression from the EC and SFG; the number of genes in each gene set and the number of overlapping genes are shown in parentheses, and the hypergeometric test p-values are shown without parentheses.

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Leng, K., Li, E., Eser, R. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat Neurosci 24, 276–287 (2021). https://doi.org/10.1038/s41593-020-00764-7

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