Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A tau homeostasis signature is linked with the cellular and regional vulnerability of excitatory neurons to tau pathology

Nature Neuroscience volume 22, pages 47–56 (2019)Cite this article

Abstract

Excitatory neurons are preferentially impaired in early Alzheimer’s disease but the pathways contributing to their relative vulnerability remain largely unknown. Here we report that pathological tau accumulation takes place predominantly in excitatory neurons compared to inhibitory neurons, not only in the entorhinal cortex, a brain region affected in early Alzheimer’s disease, but also in areas affected later by the disease. By analyzing RNA transcripts from single-nucleus RNA datasets, we identified a specific tau homeostasis signature of genes differentially expressed in excitatory compared to inhibitory neurons. One of the genes, BCL2-associated athanogene 3 (BAG3), a facilitator of autophagy, was identified as a hub, or master regulator, gene. We verified that reducing BAG3 levels in primary neurons exacerbated pathological tau accumulation, whereas BAG3 overexpression attenuated it. These results define a tau homeostasis signature that underlies the cellular and regional vulnerability of excitatory neurons to tau pathology.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Excitatory and inhibitory neurons are differentially vulnerable to tau pathology in primary and secondary affected regions of EC-tau mice.
Fig. 2: EX and IN human neurons are differentially vulnerable to tau pathology in primary affected regions of AD brain.
Fig. 3: Single-nucleus RNA-seq analysis reveals a specific tau homeostasis signature in EX neurons in human brains.
Fig. 4: Single-nucleus RNA-seq analysis shows high levels of tau aggregation protectors in glia.
Fig. 5: Co-expression network analysis of the subproteomes relevant to tau homeostasis.
Fig. 6: Validation by single-molecule fluorescence in situ hybridization of the localization and mRNA expression levels of representative tau homeostasis signature genes in human EC and prefrontal cortex.
Fig. 7: Modulating the expression of one of the tau aggregation protectors, BAG3, affects tau accumulation in primary cortical neurons.

Data availability

The data used to generate the results that support the findings of this study are available from the corresponding authors upon reasonable request.

References

  1. Šimić, G. et al. Tau protein hyperphosphorylation and aggregation in Alzheimer’s disease and other tauopathies, and possible neuroprotective strategies. Biomolecules 6, 6 (2016).

    Article  Google Scholar 

  2. Dugger, B. N. & Dickson, D. W. Pathology of neurodegenerative diseases. Cold Spring Harb. Perspect. Biol. 9, a028035 (2017).

    Article  Google Scholar 

  3. Spina, S. et al. The tauopathy associated with mutation+3 in intron 10 of Tau: characterization of the MSTD family. Brain 131, 72–89 (2008).

    Article  Google Scholar 

  4. Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    Article  CAS  Google Scholar 

  5. Davies, P. & Maloney, A. J. Selective loss of central cholinergic neurons in Alzheimer’s disease. Lancet 2, 1403 (1976).

    Article  CAS  Google Scholar 

  6. Morrison, B. M., Hof, P. R. & Morrison, J. H. Determinants of neuronal vulnerability in neurodegenerative diseases. Ann. Neurol. 44(Suppl 1), S32–S44 (1998).

    Article  CAS  Google Scholar 

  7. Götz, J., Schonrock, N., Vissel, B. & Ittner, L. M. Alzheimer’s disease selective vulnerability and modeling in transgenic mice. J. Alzheimers Dis. 18, 243–251 (2009).

    Article  Google Scholar 

  8. Riascos, D. et al. Age-related loss of calcium buffering and selective neuronal vulnerability in Alzheimer’s disease. Acta Neuropathol. 122, 565–576 (2011).

    Article  CAS  Google Scholar 

  9. Ahmadian, S. S. et al. Loss of calbindin-D28K is associated with the full range of tangle pathology within basal forebrain cholinergic neurons in Alzheimer’s disease. Neurobiol. Aging 36, 3163–3170 (2015).

    Article  CAS  Google Scholar 

  10. Hof, P. R. et al. Age-related distribution of neuropathologic changes in the cerebral cortex of patients with Down’s syndrome. Quantitative regional analysis and comparison with Alzheimer’s disease. Arch. Neurol. 52, 379–391 (1995).

    Article  CAS  Google Scholar 

  11. Seeley, W. W. Selective functional, regional, and neuronal vulnerability in frontotemporal dementia. Curr. Opin. Neurol. 21, 701–707 (2008).

    Article  Google Scholar 

  12. Hyman, B. T., Van Hoesen, G. W., Damasio, A. R. & Barnes, C. L. Alzheimer’s disease: cell-specific pathology isolates the hippocampal formation. Science 225, 1168–1170 (1984).

    Article  CAS  Google Scholar 

  13. Mattson, M. P. & Magnus, T. Ageing and neuronal vulnerability. Nat. Rev. Neurosci. 7, 278–294 (2006).

    Article  CAS  Google Scholar 

  14. Saxena, S. & Caroni, P. Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron 71, 35–48 (2011).

    Article  CAS  Google Scholar 

  15. Roselli, F. & Caroni, P. From intrinsic firing properties to selective neuronal vulnerability in neurodegenerative diseases. Neuron 85, 901–910 (2015).

    Article  CAS  Google Scholar 

  16. Labbadia, J. & Morimoto, R. I. The biology of proteostasis in aging and disease. Annu. Rev. Biochem. 84, 435–464 (2015).

    Article  CAS  Google Scholar 

  17. Taylor, R. C. & Dillin, A. Aging as an event of proteostasis collapse. Cold Spring Harb. Perspect. Biol. 3, a004440 (2011).

    Article  Google Scholar 

  18. Kaushik, S. & Cuervo, A. M. Proteostasis and aging. Nat. Med. 21, 1406–1415 (2015).

    Article  CAS  Google Scholar 

  19. Freer, R. et al. A protein homeostasis signature in healthy brains recapitulates tissue vulnerability to Alzheimer’s disease. Sci. Adv. 2, e1600947 (2016).

    Article  Google Scholar 

  20. Liu, L. et al. Trans-synaptic spread of tau pathology in vivo. PLoS One 7, e31302 (2012).

    Article  CAS  Google Scholar 

  21. Lei, Z., Brizzee, C. & Johnson, G. V. BAG3 facilitates the clearance of endogenous tau in primary neurons. Neurobiol. Aging 36, 241–248 (2015).

    Article  CAS  Google Scholar 

  22. Seidel, K. et al. The HSPB8-BAG3 chaperone complex is upregulated in astrocytes in the human brain affected by protein aggregation diseases. Neuropathol. Appl. Neurobiol. 38, 39–53 (2012).

    Article  CAS  Google Scholar 

  23. Wegmann, S. et al. Removing endogenous tau does not prevent tau propagation yet reduces its neurotoxicity. EMBO J. 34, 3028–3041 (2015).

    Article  CAS  Google Scholar 

  24. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  Google Scholar 

  25. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-Seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  Google Scholar 

  26. Ciryam, P. et al. A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation. Proc. Natl Acad. Sci. USA 113, 4753–4758 (2016).

    Article  CAS  Google Scholar 

  27. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).

    Article  Google Scholar 

  28. Fu, H., Hardy, J. & Duff, K. E. Selective vulnerability in neurodegenerative diseases. Nat. Neurosci. 21, 1350–1358 (2018).

    Article  CAS  Google Scholar 

  29. de Calignon, A. et al. Propagation of tau pathology in a model of early Alzheimer’s disease. Neuron 73, 685–697 (2012).

    Article  Google Scholar 

  30. Fu, H. et al. 3D visualization of the temporal and spatial spread of tau pathology reveals extensive sites of tau accumulation associated with neuronal loss and recognition memory deficit in aged tau transgenic mice. PLoS One 11, e0159463 (2016).

    Article  Google Scholar 

  31. Ferrer, I. et al. Glial and neuronal tau pathology in tauopathies: characterization of disease-specific phenotypes and tau pathology progression. J. Neuropathol. Exp. Neurol. 73, 81–97 (2014).

    Article  CAS  Google Scholar 

  32. Kahlson, M. A. & Colodner, K. J. Glial tau pathology in tauopathies: functional consequences. J. Exp. Neurosci. 9(Suppl 2), 43–50 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. Leyns, C. E. G. & Holtzman, D. M. Glial contributions to neurodegeneration in tauopathies. Mol. Neurodegener. 12, 50 (2017).

    Article  Google Scholar 

  34. Brehme, M. et al. A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. Cell Rep. 9, 1135–1150 (2014).

  35. Baranczak, A. & Kelly, J. W. A current pharmacologic agent versus the promise of next generation therapeutics to ameliorate protein misfolding and/or aggregation diseases. Curr. Opin. Chem. Biol. 32, 10–21 (2016).

    Article  CAS  Google Scholar 

  36. Balch, W. E., Morimoto, R. I., Dillin, A. & Kelly, J. W. Adapting proteostasis for disease intervention. Science 319, 916–919 (2008).

    Article  CAS  Google Scholar 

  37. Seubert, P. et al. Detection of phosphorylated Ser262 in fetal tau, adult tau, and paired helical filament tau. J. Biol. Chem. 270, 18917–18922 (1995).

    Article  CAS  Google Scholar 

  38. Sanders, D. W. et al. Distinct tau prion strains propagate in cells and mice and define different tauopathies. Neuron 82, 1271–1288 (2014).

    Article  CAS  Google Scholar 

  39. Fu, H. et al. Tau pathology induces excitatory neuron loss, grid cell dysfunction, and spatial memory deficits reminiscent of early Alzheimer’s disease. Neuron 93, 533–541.e5 (2017).

    Article  CAS  Google Scholar 

  40. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  Google Scholar 

  41. Ji, C., Steimle, B. L., Bailey, D. K. & Kosman, D. J. The ferroxidase hephaestin but not amyloid precursor protein is required for ferroportin-supported iron efflux in primary hippocampal neurons. Cell. Mol. Neurobiol. 38, 941–954 (2018).

    Article  CAS  Google Scholar 

  42. Tosto, G. et al. F-box/LRR-repeat protein 7 is genetically associated with Alzheimer’s disease. Ann. Clin. Transl. Neurol 2, 810–820 (2015).

    Article  CAS  Google Scholar 

  43. Wu, J. W. et al. Small misfolded Tau species are internalized via bulk endocytosis and anterogradely and retrogradely transported in neurons. J. Biol. Chem. 288, 1856–1870 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank M. Diamond (UT Southwestern Medical Center) for providing the Tau RD-P301S-YFP lentivirus and DS9 clone cell line; K. Zhang and B. Lake (UCSD) and A. Regev, N. Habib, I. Avraham-Davidi, and A. Basu (Broad Institute of MIT and Harvard) for sharing their single-nucleus RNA-seq datasets; L. Honig, J.P. Vonsattel, A. Teich, and E. Cortés (New York Brain Bank, Columbia University Medical Center), the NIH NeuroBrainBank at the University of Maryland Brain and Tissue Bank as well as T. Beach and G. Serrano (Banner Sun Health Research Institute Brain and Body Donation Program) for providing human de-identified brain tissue and for helpful discussions on brain regions and immunostaining techniques; P. Davies (The Feinstein Institute for Medical Research) for providing the MC1 and PHF1 tau antibodies; P. Dolan (Prothena) for providing the 12E8 tau antibody; C. Pröschel (University of Rochester Medical Center) for providing psPAX2 and VSVG; ACDBio for troubleshooting on RNAscope FISH; and W.H. Yu and C.L. Clelland for discussing the results. This work was funded by: NIH/NIA AG056673 (H.F.), Alzheimer’s Association AARF-17-505009 (H.F.), NIH/NINDS NS074874 (K.E.D.), NIH/NIA AG056151 (K.E.D) and by the BrightFocus Foundation, the Rainwater Foundation/Tau Consortium and the Cure Alzheimer’s Fund (K.E.D). The Banner Sun Health Research Institute Brain and Body Donation Program is supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the National Institute on Aging (P30 AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson’s Disease Consortium), and the Michael J. Fox Foundation for Parkinson’s Research.

Author information

Author notes

  1. Paula V. M. Cauhy

    Present address: Federal University of Uberlândia, Uberlândia, Brazil

Authors and Affiliations

  1. Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA

    Hongjun Fu, Yoshikazu Nakano, Nancy C. Hernandez Villegas, Paula V. M. Cauhy, Benjamin A. Lassus, Shuo Chen, Stephanie L. Fowler, Helen Y. Figueroa, Edward D. Huey & Karen E. Duff

  2. Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA

    Hongjun Fu & Karen E. Duff

  3. Department of Neuroscience, Chronic Brain Injury, Discovery Themes, The Ohio State University, Columbus, OH, USA

    Hongjun Fu

  4. Centre for Misfolding Diseases, Department of Chemistry, University of Cambridge, Cambridge, UK

    Andrea Possenti, Rosie Freer & Michele Vendruscolo

  5. Department of Anesthesiology, University of Rochester, Rochester, NY, USA

    Maoping Tang & Gail V. W. Johnson

  6. Departments of Psychiatry and Neurology, Columbia University, New York, NY, USA

    Edward D. Huey

  7. Division of Integrative Neuroscience, New York State Psychiatric Institute, New York, NY, USA

    Karen E. Duff

Authors
  1. Hongjun Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Possenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosie Freer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yoshikazu Nakano
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nancy C. Hernandez Villegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maoping Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Paula V. M. Cauhy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Benjamin A. Lassus
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shuo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stephanie L. Fowler
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Helen Y. Figueroa
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Edward D. Huey
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gail V. W. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Michele Vendruscolo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Karen E. Duff
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.E.D., H.F., and M.V. jointly designed and supervised the study, discussed the results, and wrote the paper. H.F. designed and performed experiments and analyzed the data. A.P., R.F., and M.V. analyzed single-nucleus RNA-seq datasets. A.P. discussed the results and wrote parts of the paper. Y.N., N.C.H.V., M.T., P.V.M.C., B.A.L., S.L.F., S.C., and H.Y.F. provided technical assistance. E.D.H. provided critical input into the paper. G.V.W.J. designed and supplied all the BAG3-related viruses. All authors discussed the results and contributed to the manuscript.

Corresponding authors

Correspondence to Hongjun Fu, Michele Vendruscolo or Karen E. Duff.

Ethics declarations

Competing interests

K.E.D. is on the board of directors and SAB of Ceracuity LLC. All other authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Tau pathology co-localizes with EX, but not with IN neurons in secondary affected regions in EC-tau mice.

(a-d) Representative images of MC1+ tau staining co-localized with TBR1+ and SATB2+ EX neurons, but not PVALB+, SST+ or CALB2+ IN neurons, in the PRH (a) and NC (b) of EC-tau mice at 22 months (n = 6 animals, 2 sections each animal), and in the PRH (c) and NC (d) of EC-tau mice at 30+ months (n = 5-6 animals, 2 sections each animal). Scale bar, 20 µm.

Supplementary Figure 2 Tau pathology co-localizes with EX, but not with IN neurons or glial cells in human AD brain.

(a) Representative images of MC1+ tau staining co-localized with TBR1+ and SATB2+ EX neurons, but not PVALB+, SST+ or CALB2+ IN neurons in the prefrontal cortex (BA9) of human AD brain at Braak stage V-VI. (b, c) Representative images of AT8+ tau staining did not co-localize with IBA-1+ microglia (b) or GFAP+ astrocytes (c) in the EC of human AD brain at different Braak stages. Scale bars, 20 µm (a) and 10 µm (b and c). Three independent experiments were repeated with similar results.

Supplementary Figure 3 Tau pathology detected by specific phospho-tau antibodies co-localizes with EX, but not IN, neurons in EC-tau mice and in human AD brain.

(a) Representative images of phospho-tau staining (AT8+, PHF1+, pS422+) co-localized with SATB2+ EX neurons, but not GAD1+ IN neurons in the MEC of EC-Tau mice at 22 months. (b) Representative images of phospho-tau staining (AT8+, PHF1+, AT100+) co-localized with SATB2+ EX neurons, but not GAD1+ IN neurons in the BA9 of human AD at Braak stage V-VI. Scale bar, 20 µm. Three independent experiments were repeated with similar results.

Supplementary Figure 4 Null model for seven different subproteomes between EX and IN neurons.

The random distribution of the mean values (orange histogram) computed on each sample, the normal fit (solid orange line), and the observed value (dashed red vertical line) are reported in each panel (see Methods for details). Next to each observed value, the probability computed with the cumulative distribution obtained from the fit and evaluated at the observed mean is reported. (a-g) Null models from the SNS dataset; (h-n) Null models from the DroNc-Seq dataset. Here nsns and ndrnc represent the sample sizes corresponding to SNS and DroNc-seq datasets, respectively. (a, h) EX markers:): nsns=ndrnc=2; (b, i) tau: nsns=ndrnc=1; c, j) promoters (tau aggregation promoters): nsns=ndrnc=6; (d, k) MS (metastable subproteome): nsns=162, ndrnc=179; (e, l) tangles (tangle co-aggregators): nsns=57, ndrnc=68; f, m) protectors (tau aggregation protectors): nsns=ndrnc=6; (g, n) IN markers: nsns=ndrnc=3. A p-value is computed as the probability to have a value more extreme than the observed one (one-tailed).

Supplementary Figure 5 Null model for five different subproteomes in EX neurons between early- and late-affected brain regions.

The statistical significance of the results is studied by creating a null model for each subproteome under scrutiny (see Supplementary Figure 4). (a-e) Null models from the SNS dataset (BA21+22+10+41 vs BA17); (f-j) Null models from the DroNc-Seq dataset (HP vs PFC). (a, f) protectors; (b, g) promoters; (c, h) MS; (d, i) tangles; (e, j) tau. A p-value is computed as the probability to have a value more extreme than the observed one (one-tailed). Sample sizes are the same as Supplementary Figure 4.

Supplementary Figure 6 Comparison of the differential expression of the subproteomes for the two independent single-nucleus RNA-seq datasets.

Each point in the scatterplot indicates a Δ score in the DroNc-Seq dataset (x-axis) and the SNS dataset (y-axis) shown in Fig. 3. Data are presented as mean ± SEM. Sample sizes are the same as Supplementary Figure 4.

Supplementary Figure 7 BAG3 protein is differentially regulated in neurons in human non-AD and AD cases.

(a) Representative immunofluorescent images of the co-staining of IN neuronal marker (GAD1, purple), pan neuronal marker (NeuN, green), and BAG3 (red) in the BA9 of human AD (Braak stage V/VI) brain. The white arrow represents the IN neurons (GAD1+/NeuN+), while the white arrowhead indicates the putative EX neurons (GAD1-/NeuN+). The letter “g” stands for glial cells. The nuclei (blue) were counterstained with Hoechest3342. Three independent experiments were repeated with similar results. Scale bar, 20 µm. (b) Comparison of the mean intensity of BAG3 in individual neurons in the BA9 regions (n = 3 human brains, 20 GAD1-/NeuN+ and 20 GAD1+/NeuN+ neurons from each case). Data are presented as mean ± SEM. The statistical significance was assessed by the two-tailed nonparametric Mann-Whitney test. *** P < 0.0001 vs non-AD and/or AD EX neurons (The Mann-Whitney U is 1551, 714, 531, and 130, respectively).

Supplementary Figure 8 Null model for seven different subproteomes between microglia and EX or IN neurons.

The statistical significance of the results is studied by creating a null model for each subproteome under scrutiny (see Supplementary Figure 4). (a, c, e, g) Null models for MG (microglia) vs EX (excitatory) neurons; (b, d, f, h) Null models for MG (microglia) vs IN (inhibitory) neurons. (a, b) tau; (c, d) promoters; (e, f) tangles; (g, h) protectors. A p-value is computed as the probability to have a value more extreme than the observed one (one-tailed). Sample sizes are the same as Supplementary Figure 4.

Supplementary Figure 9 Null model for seven different subproteomes between astrocytes and EX or IN neurons.

The statistical significance of the results is studied by creating a null model for each subproteome under scrutiny (see Supplementary Figure 4). (a, c, e, g) Null models for ASC (astrocytes) vs EX (excitatory) neurons; (b, d, f, h) Null models for ASC (astrocytes) vs IN (inhibitory) neurons. (a, b) tau; (c, d) promoters; (e, f) tangles; (g, h) protectors. A p-value is computed as the probability to have a value more extreme than the observed one (one-tailed). Sample sizes are the same as Supplementary Figure 4.

Supplementary Figure 10 Null model for seven different subproteomes between oligodendrocytes and EX or IN neurons.

The statistical significance of the results is studied by creating a null model for each subproteome under scrutiny (see Supplementary Figure 4). (a, c, e, g) Null models for ODC (oligodendrocytes) vs EX (excitatory) neurons; (b, d, f, h) Null models for ODC (oligodendrocytes) vs IN (inhibitory) neurons. (a, b) tau; (c, d) promoters; (e, f) tangles; (g, h) protectors. A p-value is computed as the probability to have a value more extreme than the observed one (one-tailed). Sample sizes are the same as Supplementary Figure 4.

Supplementary Figure 11 Full length of the western blot image.

Original western blot images of Fig. 7a in primary cortical neurons transduced with lentivirus expressing scrambled BAG3 or shBAG3, or overexpressing BAG3 (OE) as described in Methods. BAG3 is the target protein detected by primary rabbit anti-BAG3 antibody. GAPDH is a housekeeping protein used as the loading control.

Supplementary information

Supplementary Figures 1–11

Supplementary Figures 1–11

Reporting Summary

Supplementary Table 1

Single-nucleus RNA-seq datasets analysis in neurons.

Supplementary Table 2

Single-nucleus RNA-seq datasets analysis in glia.

Supplementary Table 3

Weighted gene co-expression network analysis of the subproteomes relevant to tau homeostasis.

Supplementary Table 4

Supplementary Table 4.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, H., Possenti, A., Freer, R. et al. A tau homeostasis signature is linked with the cellular and regional vulnerability of excitatory neurons to tau pathology. Nat Neurosci 22, 47–56 (2019). https://doi.org/10.1038/s41593-018-0298-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0298-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing