Tau molecular diversity contributes to clinical heterogeneity in Alzheimer’s disease

Abstract

Alzheimer’s disease (AD) causes unrelenting, progressive cognitive impairments, but its course is heterogeneous, with a broad range of rates of cognitive decline1. The spread of tau aggregates (neurofibrillary tangles) across the cerebral cortex parallels symptom severity2,3. We hypothesized that the kinetics of tau spread may vary if the properties of the propagating tau proteins vary across individuals. We carried out biochemical, biophysical, MS and both cell- and animal-based-bioactivity assays to characterize tau in 32 patients with AD. We found striking patient-to-patient heterogeneity in the hyperphosphorylated species of soluble, oligomeric, seed-competent tau. Tau seeding activity correlates with the aggressiveness of the clinical disease, and some post-translational modification (PTM) sites appear to be associated with both enhanced seeding activity and worse clinical outcomes, whereas others are not. These data suggest that different individuals with ‘typical’ AD may have distinct biochemical features of tau. These data are consistent with the possibility that individuals with AD, much like people with cancer, may have multiple molecular drivers of an otherwise common phenotype, and emphasize the potential for personalized therapeutic approaches for slowing clinical progression of AD.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Heterogeneity of clinical progression and age of onset in Alzheimer’s disease.
Fig. 2: Heterogeneity of tau seeding in the human brain.
Fig. 3: Tau seeding relies on an oligomeric form of tau.
Fig. 4: Tau seeding is associated with rate of disease progression and intensity of tau phosphorylation.

Data availability

All requests for raw and analyzed data and materials are promptly reviewed by the Partners Healthcare innovation department to verify whether the request is subject to any intellectual property or confidentiality obligations. Patient-related data not included in the paper may be subject to patient confidentiality. Any data and materials that can be shared will be released via a Material Transfer Agreement upon reasonable request to the corresponding author.

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE52 partner repository with the dataset identifier PXD018855. Source data are provided with this paper.

References

  1. 1.

    Komarova, N. L. & Thalhauser, C. J. High degree of heterogeneity in Alzheimer’s disease progression patterns. PLoS Comput. Biol. 7, e1002251 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Giannakopoulos, P. et al. Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology 60, 1495–1500 (2003).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Arriagada, P. V., Growdon, J. H., Hedley-Whyte, E. T. & Hyman, B. T. Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology 42, 631–639 (1992).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Arakhamia, T. et al. Posttranslational modifications mediate the structural diversity of tauopathy strains. Cell 180, 633–644 e612 (2020).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Falcon, B. et al. Novel tau filament fold in chronic traumatic encephalopathy encloses hydrophobic molecules. Nature 568, 420–423 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Zhang, W. et al. Novel tau filament fold in corticobasal degeneration. Nature 580, 283–287 (2020).

  7. 7.

    Kovacs, G. G. Invited review: neuropathology of tauopathies: principles and practice. Neuropathol. Appl. Neurobiol. 41, 3–23 (2015).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Falcon, B. et al. Structures of filaments from Pick’s disease reveal a novel tau protein fold. Nature 561, 137–140 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Holmes, B. B. et al. Proteopathic tau seeding predicts tauopathy in vivo. Proc. Natl Acad. Sci. USA 111, E4376–E4385 (2014).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Colin, M. et al. From the prion-like propagation hypothesis to therapeutic strategies of anti-tau immunotherapy. Acta Neuropathol. 139, 3–25 (2020).

    CAS  PubMed  Article  Google Scholar 

  12. 12.

    Dujardin, S. et al. Different tau species lead to heterogeneous tau pathology propagation and misfolding. Acta Neuropathol. Commun. 6, 132 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Dujardin, S. & Hyman, B. T. Tau prion-like propagation: state of the art and current challenges. Adv. Exp. Med. Biol. 1184, 305–325 (2019).

    PubMed  Article  Google Scholar 

  14. 14.

    Falcon, B. et al. Conformation determines the seeding potencies of native and recombinant tau aggregates. J. Biol. Chem. 290, 1049–1065 (2015).

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Clavaguera, F. et al. Brain homogenates from human tauopathies induce tau inclusions in mouse brain. Proc. Natl Acad. Sci. USA 110, 9535–9540 (2013).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

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

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Harper, J. D. & Lansbury, P. T. Jr. Models of amyloid seeding in Alzheimer’s disease and scrapie: mechanistic truths and physiological consequences of the time-dependent solubility of amyloid proteins. Annu. Rev. Biochem. 66, 385–407 (1997).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Andorfer, C. et al. Hyperphosphorylation and aggregation of tau in mice expressing normal human tau isoforms. J. Neurochem. 86, 582–590 (2003).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Boluda, S. et al. Differential induction and spread of tau pathology in young PS19 tau transgenic mice following intracerebral injections of pathological tau from Alzheimer’s disease or corticobasal degeneration brains. Acta Neuropathol. 129, 221–237 (2015).

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    He, Z. et al. Amyloid-β plaques enhance Alzheimer’s brain tau-seeded pathologies by facilitating neuritic plaque tau aggregation. Nat. Med. 24, 29–38 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. 21.

    Iba, M. et al. Synthetic tau fibrils mediate transmission of neurofibrillary tangles in a transgenic mouse model of Alzheimer’s-like tauopathy. J. Neurosci. 33, 1024–1037 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Bielefeld-Sevigny, M. AlphaLISA immunoassay platform—the ‘no-wash’ high-throughput alternative to ELISA. Assay Drug Dev. Technol. 7, 90–92 (2009).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Usenovic, M. et al. Internalized tau oligomers cause neurodegeneration by inducing accumulation of pathogenic tau in human neurons derived from induced pluripotent stem cells. J. Neurosci. 35, 14234–14250 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Takeda, S. et al. Neuronal uptake and propagation of a rare phosphorylated high-molecular-weight tau derived from Alzheimer’s disease brain. Nat. Commun. 6, 8490 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Lasagna-Reeves, C. A. et al. Alzheimer brain-derived tau oligomers propagate pathology from endogenous tau. Sci. Rep. 2, 700 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  26. 26.

    Bennett, R. E. et al. Enhanced tau aggregation in the presence of amyloid beta. Am. J. Pathol. 187, 1601–1612 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    DeVos, S. L. et al. Tau reduction in the presence of amyloid-β prevents tau pathology and neuronal death in vivo. Brain 141, 2194–2212 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Wang, J. Z., Grundke-Iqbal, I. & Iqbal, K. Kinases and phosphatases and tau sites involved in Alzheimer neurofibrillary degeneration. Eur. J. Neurosci. 25, 59–68 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Shi, Y. et al. ApoE4 markedly exacerbates tau-mediated neurodegeneration in a mouse model of tauopathy. Nature 549, 523–527 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Calafate, S., Flavin, W., Verstreken, P. & Moechars, D. Loss of Bin1 promotes the propagation of tau pathology. Cell Rep. 17, 931–940 (2016).

    CAS  PubMed  Article  Google Scholar 

  31. 31.

    Courade, J. P. et al. Epitope determines efficacy of therapeutic anti-tau antibodies in a functional assay with human Alzheimer tau. Acta Neuropathol. 136, 729–745 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Nobuhara, C. K. et al. Tau antibody targeting pathological species blocks neuronal uptake and interneuron propagation of tau in vitro. Am. J. Pathol. 187, 1399–1412 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Yanamandra, K. et al. Anti-tau antibodies that block tau aggregate seeding in vitro markedly decrease pathology and improve cognition in vivo. Neuron 80, 402–414 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Fitzpatrick, A. W. P. et al. Cryo-EM structures of tau filaments from Alzheimer’s disease. Nature 547, 185–190 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Kiris, E. et al. Combinatorial Tau pseudophosphorylation: markedly different regulatory effects on microtubule assembly and dynamic instability than the sum of the individual parts. J. Biol. Chem. 286, 14257–14270 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Shahani, N., Subramaniam, S., Wolf, T., Tackenberg, C. & Brandt, R. Tau aggregation and progressive neuronal degeneration in the absence of changes in spine density and morphology after targeted expression of Alzheimer’s disease-relevant tau constructs in organotypic hippocampal slices. J. Neurosci. 26, 6103–6114 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Planel, E. et al. Anesthesia-induced hyperphosphorylation detaches 3-repeat tau from microtubules without affecting their stability in vivo. J. Neurosci. 28, 12798–12807 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Schneider, A., Biernat, J., von Bergen, M., Mandelkow, E. & Mandelkow, E. M. Phosphorylation that detaches tau protein from microtubules (Ser262, Ser214) also protects it against aggregation into Alzheimer paired helical filaments. Biochemistry 38, 3549–3558 (1999).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Singh, T. J. et al. Calcium/calmodulin-dependent protein kinase II phosphorylates tau at Ser-262 but only partially inhibits its binding to microtubules. FEBS Lett. 387, 145–148 (1996).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Iqbal, K. et al. Subgroups of Alzheimer’s disease based on cerebrospinal fluid molecular markers. Ann. Neurol. 58, 748–757 (2005).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Serrano-Pozo, A. et al. Examination of the clinicopathologic continuum of Alzheimer disease in the autopsy cohort of the National Alzheimer Coordinating Center. J. Neuropathol. Exp. Neurol. 72, 1182–1192 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    DeVos, S. L. et al. Synaptic tau seeding precedes tau pathology in human Alzheimer’s disease brain. Front. Neurosci. 12, 267 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Hyman, B. T. et al. National Institute on Aging—Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 8, 1–13 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Montine, T. J. et al. National Institute on Aging—Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol. 123, 1–11 (2012).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Arganda-Carreras, I. et al. Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Serrano-Pozo, A. et al. Reactive glia not only associates with plaques but also parallels tangles in Alzheimer’s disease. Am. J. Pathol. 179, 1373–1384 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Wisniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Muller, T. & Winter, D. Systematic evaluation of protein reduction and alkylation reveals massive unspecific side effects by iodine-containing reagents. Mol. Cell Proteomics 16, 1173–1187 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Schilling, B. et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol. Cell Proteomics 11, 202–214 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Gomathinayagam, S. et al. In vivo anti-tumor efficacy of afucosylated anti-CS1 monoclonal antibody produced in glycoengineered Pichia pastoris. J. Biotechnol. 208, 13–21 (2015).

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Wang, Y. et al. Rapid alteration of protein phosphorylation during postmortem: implication in the study of protein phosphorylation. Sci. Rep. 5, 15709 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Aoyagi, A. et al. Abeta and tau prion-like activities decline with longevity in the Alzheimer’s disease human brain. Sci. Transl. Med. 11, eaat8462 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Duyckaerts, C., Colle, M. A., Dessi, F., Piette, F. & Hauw, J. J. Progression of Alzheimer histopathological changes. Acta Neurol. Belg. 98, 180–185 (1998).

    CAS  PubMed  Google Scholar 

  56. 56.

    Ingelsson, M. et al. Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 62, 925–931 (2004).

    CAS  PubMed  Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by a research agreement to Massachusetts General Hospital from Merck and by the NIH/NIA—5P50AG005134-35 (B.T.H.), 1RF1AG059789 (B.T.H.), 1RF1AG058674 (B.T.H.), 1P30AG062421 (B.T.H.), 1K08AG064039 (A.S.-P.). The authors also want to thank additional funding sources: Alzheimer’s Association (2018-AARF-591935 (S.D.), AACSF-19-617308 (A.L.)), the Martin L. and Sylvia Seevak Hoffman Fellowship for Alzheimer’s Research (S.D.), the Tau Consortium (B.T.H.), the Cure Alzheimer’s Fund (R.E.T.), the JPB Foundation (B.T.H. and R.E.T.), and the Swiss National Science Foundation (P2ELP3_184403 (A.L.)). We thank J. A. Gonzalez for helping with brain sampling, M. C. Potter for input in early phases of the work, P. Davies (Albert Einstein College of Medicine, New York City) for generously providing with PHF1 antibody and M. Diamond (UT Southwertern, Dallas, Texas) for the generous gift of the TauRD-P301S-CFP/YFP cells.

Author information

Affiliations

Authors

Contributions

S.D., F.E., M.E.K. and B.T.H. conceived the study and designed the experiments. S.D., C.C., A.L., A.R.F., T.V.K., P.B., A.V., M.B.D.L.S., N.K., D.L.C., B.T.C., P.M.D., B.D.M., K.M., D.J.-G., R.C., K.A., R.M. and A.S.-P. performed experiments and analyzed data. J.A.S. designed the MS experiments. L.B.C. helped with the statistical analysis. R.E.T. designed the whole-exome sequencing procedure. A.S.-P., D.H.O. and M.P.F. provided clinical advice and critical input on the manuscript. The manuscript was written by S.D. and B.T.H. with input from all of the authors.

Corresponding author

Correspondence to Bradley T. Hyman.

Ethics declarations

Competing interests

This work was supported in part by a research agreement to Massachusetts General Hospital from Merck & Co. D.J.-G, R.C., R.M., K.A., F.E., and M.E.K. are/were full time employees of Merck & Co. Inc. during the course of the work.

Additional information

Peer review information Jerome Staal was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team

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

Extended data

Extended Data Fig. 1 Kinetics of tau seeding in FRET biosensor assay relates to Fig. 2d.

a, Tau seeding was quantified by live imaging over 72 h of FRET biosensor cells exposed to PBS extracts of the 32 human AD brains. This process is divided into three phases: a nucleation/lag phase followed by the exponential polymerization/elongation phase and ending by a plateau phase. Samples were normalized to total tau levels before being added to the seeding assay and the number of aggregates obtained was normalized to both positive and negative controls. A sigmoidal, 4PL, X is log(concentration) nonlinear regression was applied before plotting the data b, Tau seeding dose response was investigated in two of these AD brain extracts. Tau seeding was quantified with 8 ng of total tau in the sample (dose used for Fig. 2d and in panel A) but also with 0.08 ng, 0.8 ng, 4 ng, 16 ng and 40 ng to demonstrate the dose dependence of the plateau phase and the ceiling effect of the assay. c, Statistically significant two-tailed Spearman’s rank nonparametric correlation between the plateau value for each of the 32 AD brain extracts measured in this manner and the seeding value obtained on the FRET biosensor assay by flow cytometry (Fig. 2b). d, Statistically significant two-tailed Spearman’s rank nonparametric correlation between the values of the plateau and of the slope for the 32 AD brains. Source data

Extended Data Fig. 2 hTau seeding assay cell viability and association with FRET biosensor assay.

a, Timecourse of insoluble AT8 aggregates appearance in the hTau primary neuron seeding assay 1 hour, 1 day, 2 days and 7 days post incubation with the AD brain extracts. The right panel only represents the time points from 1 h to 2 days post incubation with the brain extracts. Error bars represents Standard deviations to the mean. n = 10 human subjects. Quantification of immunolabelling with NeuN (b) and MAP2 (c) in hTau mouse primary neurons incubated with 9 different AD brain extracts from our study’s cohort show a general viability of neuronal cells after brain extract incubation beside a possible toxicity with subject #32 brain extract. n = 4 independent experiments, error bars represent standard deviations to the mean d, Quantification of AT8 hyperphosphorylated tau staining on 9 human subjects from Fig. 2g. n = 4 independent experiments. Error bars represents Standard deviations to the mean. The color gradient scale bar relates to seeding quantities obtained in Fig. 2b. e, Statistically significant two-tailed Spearman’s rank nonparametric correlation between the value of seeding activity obtained in Fig. 2b and the AT8 signal intensity obtained with the hTau primary neuron seeding assay for 9 subjects AD brain extracts. f, Tau seeding in a mouse model of tauopathy- Relates to Fig. 2h,i. Two-month-old P301S transgenic mice were stereotactically injected with human AD brain PBS extracts from 10 human AD subject. Mice were euthanized 2 months later, and their brains processed for AT8 immunohistochemistry to assess the in vivo seeding potential of human AD brain extracts. n = 5 animals per human subject. Bar graph depicts the stereological quantification of the number of AT8-positive neurons in the cortex and hippocampus. The color gradient scale bar relates to seeding quantities obtained in Fig. 2b. Error bars represents Standard deviations to the mean. Source data

Extended Data Fig. 3 Hyperphosphorylation is closely associated with tau seeding.

Brain extracts from the 32 AD subject were quantified for two epitopes of tau using alphaLISA (AT8 (a) and PHF6 (b)). Prior to this assay, samples were normalized for total protein amount as obtained using a BCA assay. Background Corrected Relative Light Unit (RLU) are plotted here. The color gradient scale bar relates to seeding quantities obtained in Fig. 2b. Both phospho-epitopes show a positive statistically significant association with tau seeding using a two-tailed Spearman’s rank non parametric correlation. SEC fractions from 9 AD brain extracts grouped into high seeders (red, n = 3), moderate seeders (green, n = 3) and low seeders (blue, n = 3) were tested for the presence of epitopes of tau hyperphosphorylation by alphaLISA (AT8 (d) and PHF6 (c)). Error bars represent the standard deviation to the mean. Showing the enriched presence of these epitopes in HMW fractions, especially in high and moderate seeders. e, HMW tau species quantified from the SDD-AGE (bin1-6, see Fig. 3b) were correlated with oligomeric tau levels from the alphaLISA showing a significant two-tailed Spearman’s rank nonparametric correlation. The r coefficient and p value are indicated on the plot. n = 14 individual subjects. Source data

Extended Data Fig. 4 Proteinase K digestion Western blots.

Relates to Fig. 3f–i. 12 AD brain extracts from our study’s cohort were incubated with increasing doses of proteinase K and run on a Western blot in order to investigate differential stabilities of tau species. Antibodies recognizing total tau proteins as well as hyperphosphorylated tau proteins were used as detection antibodies. This experiment was repeated two times with similar results.

Extended Data Fig. 5 Correlation of postmortem interval and longevity versus intensities of phosphorylation.

Intensity of phosphorylation of phospho-sites T181, S198/S199/S202, T217, T231, T231&S235, S262, S400/T403/S404 (ordinate) were correlated with postmortem interval53 (a) or age at death54 (b) (abscissa). n = 31 individual subjects. Two-tailed Spearman’s rank nonparametric correlation tests were used, and r coefficient and p values are indicated on the tables. c, Some phospho-epitopes do not correlate with seeding- Intensity of phosphorylation of phospho-sites T181, T217 and T231 (ordinate) were correlated with tau seeding activity (abscissa). n = 31 individual subjects. Two-tailed Spearman’s rank nonparametric correlation test was used, and r coefficient and p value are indicated on the plots. Source data

Extended Data Fig. 6 Tau seeding activity correlates with tau and GFAP-immunoreactive burdens.

Immunohistochemical staining of tau (indicative of NFTs, neuropil threads and plaque-associated neuritic dystrophies) a, Amyloid-β (indicative of Aβ plaques) c, GFAP + reactive astrocytes e, and CD68 + phagocytic microglia g, formalin-fixed paraffin-embedded sections from the frontal association cortex (BA8/9) of the 32 AD subjects and their respective burden quantifications (b,d,f,h). The color gradient scale bar relates to seeding activities obtained in Fig. 2b. Both the tau burden and the GFAP burden show a statistically significant positive association with tau seeding using a two-tailed Spearman’s rank nonparametric correlation. i, Cortical thickness measured as a proxy for neurodenegeration45 in the same sections did not significantly correlated with tau seeding activity using a two-tailed Spearman’s rank nonparametric correlation. n = 32 individual human subjects. Source data

Extended Data Fig. 7 Age of disease onset correlates with tau seeding activity but not with intensity of tau phosphorylation.

a, Tau seeding (on the abscissa) as quantified in Fig. 2b negatively correlates with age of onset. n = 32 individual subjects. b, Intensities of phosphorylation of different phospho-sites (ordinate) were positively or negatively correlated with age of onset (abscissa). n = 31 individual subjects. Two-sided Spearman’s rank nonparametric correlation test was used and r coefficient and p value are indicated on the plots. Source data

Extended Data Fig. 8 Some phospho-epitopes do not correlate with rate of disease clinical progression.

Intensities of phosphorylation of phospho-sites T181, T217 and T231 (ordinate) were correlated with rate of clinical disease progression as indicated by the slope of the linearized CDR-SOB score trajectories (abscissa). Two-sided Spearman’s rank nonparametric correlation test was used and r coefficient and p value are indicated on the plots. n = 31 individual human subjects. Source data

Extended Data Fig. 9 Rate of clinical disease progression and age of symptom onset correlate with tau burden, oligomeric tau and phosphorylated tau levels.

The relationship between age of onset and rate of progression undoubtedly has many contributors, hence it is not surprising that some relationships are not evident statistically in a relatively small sample that was not selected to examine this question. As expected and previously established2,3,55,56, Rate of disease clinical progression as indicated by the slope of the linearized CDR-SOB score trajectories and age of symptom onset as quantified in Fig. 1b, c significantly correlates with tau burden from Supplementary Fig. 8a,b (respectively a, and b) but also oligomeric tau levels from Fig. 3a (respectively c, and d, p = 0.057) and 2 epitopes of tau hyperphosphorylation: PHF6, from Supplementary Fig. 4b (respectively e, and f, p = 0.051) and AT8 from Supplementary Fig. 4a (respectively g, and h) in the 32 subjects of this study’s cohort54. i, As recently described and probably not independent of the age of onset, tau seeding correlates with age at death54. Correlations were carried out using a two-sided Spearman’s rank nonparametric correlation, r coefficient and p values are indicated on the plots. n = 32 individual human subjects. j, Analysis of tau seeding activity by APOE genotype showed a statistically significant difference with higher seeding activity in APOEε4/ε4 subjects (n = 5) compared to APOEε3/ε4 (n = 20, p = 0.0013) and APOEε4 non-carriers (n = 8, p = 0.0072). Groups were compared using a one-way ANOVA with a Tukey’s multiple comparison post-test. Error bars represent standard deviation to the mean. Source data

Extended Data Fig. 10 Reduction of tau seeding by antibodies is epitope and subject-to-subject dependent.

a, Schematic representation of the paradigm of tau seeding reduction via immunodepletion. Antibodies were coupled with magnetic beads and incubated with AD brain extract. Beads/antibodies/antigens complexes were depleted and the supernatant placed on the FRET biosensor seeding assay. FRET was quantified by flow cytometry. b, Schematic representation of tau protein with alternative exons 2 (yellow), 3 (green) and 10 (red) as well as the repeated regions of the microtubule binding domains (black). Antibodies used in this study are indicated below. Green antibodies (Tau12, HT7 and Tau46) target the total protein when red antibodies (AT270, AT8, pS262 and PHF1) target phospho-epitopes known to be associated with AD tau pathology. c, Antibody-mediated reduction of tau seeding across 15 AD subjects from our study’s cohort (left column). Antibodies are organized in columns. IgG serve as negative control for seeding reduction. Percentage of seeding reduction and standard deviation are indicated for each individual/antibody association. The color code of seeding reduction is indicated in the lower panel.

Supplementary information

41591_2020_938_MOESM3_ESM.avi

Kinetic of tau seeding in FRET-biosensor assay. Tau seeding was quantified by fluorescent live imaging over 72 h in the FRET-biosensor assay cells exposed to PBS extracts of a human AD brain. Representative video of the appearance of aggregates is shown in the left panel, along with our image-analysis model (center panel) separating background (white), cells (gray) and aggregates (black). Quantification and plotting overtime are shown in the right panel. This experiment was repeated at least 3 times with similar results.

Supplementary Information

Supplementary Tables 1–3 and Figs. 1 and 2.

Reporting Summary

Supplementary Video 1

Kinetic of tau seeding in FRET-biosensor assay. Tau seeding was quantified by fluorescent live imaging over 72 h in the FRET-biosensor assay cells exposed to PBS extracts of a human AD brain. Representative video of the appearance of aggregates is shown in the left panel, along with our image-analysis model (center panel) separating background (white), cells (gray) and aggregates (black). Quantification and plotting overtime are shown in the right panel. This experiment was repeated at least 3 times with similar results.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dujardin, S., Commins, C., Lathuiliere, A. et al. Tau molecular diversity contributes to clinical heterogeneity in Alzheimer’s disease. Nat Med (2020). https://doi.org/10.1038/s41591-020-0938-9

Download citation