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Microglial activation and tau propagate jointly across Braak stages

Nature Medicine volume 27, pages 1592–1599 (2021)Cite this article

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Abstract

Compelling experimental evidence suggests that microglial activation is involved in the spread of tau tangles over the neocortex in Alzheimer’s disease (AD). We tested the hypothesis that the spatial propagation of microglial activation and tau accumulation colocalize in a Braak-like pattern in the living human brain. We studied 130 individuals across the aging and AD clinical spectrum with positron emission tomography brain imaging for microglial activation ([11C]PBR28), amyloid-β (Aβ) ([18F]AZD4694) and tau ([18F]MK-6240) pathologies. We further assessed microglial triggering receptor expressed on myeloid cells 2 (TREM2) cerebrospinal fluid (CSF) concentrations and brain gene expression patterns. We found that [11C]PBR28 correlated with CSF soluble TREM2 and showed regional distribution resembling TREM2 gene expression. Network analysis revealed that microglial activation and tau correlated hierarchically with each other following Braak-like stages. Regression analysis revealed that the longitudinal tau propagation pathways depended on the baseline microglia network rather than the tau network circuits. The co-occurrence of Aβ, tau and microglia abnormalities was the strongest predictor of cognitive impairment in our study population. Our findings support a model where an interaction between Aβ and activated microglia sets the pace for tau spread across Braak stages.

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Fig. 1: [11C]PBR28 is associated with TREM2 microglial activation.
Fig. 2: Microglial and tau networks spatially converge to Braak-like stages.
Fig. 3: The patterns of longitudinal tau propagation depend on baseline microglial network circuits.
Fig. 4: Aβ load potentiates microglial activation effects on tau spreading.
Fig. 5: The concomitant presence of Aβ, tau and microglial activation abnormalities is associated with cognitive symptoms.
Fig. 6: Schematic representation of the effect of microglial activation on tau propagation in the presence of Aβ pathology.

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All requests for raw and analyzed data and materials will be promptly reviewed by McGill University to verify if the request is subject to any intellectual property or confidentiality obligations. Anonymized data will be shared upon request from a qualified academic investigator for the purpose of replicating the procedures and results presented in this article. Any data and materials that can be shared will be released via a material transfer agreement. Data are not publicly available due to information that could compromise the privacy of research participants.

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Acknowledgements

This research was supported by the Weston Brain Institute, Canadian Institutes of Health Research (CIHR) (no. MOP-11-51-31 and no. FRN, 152985 (P.R.-N.)), the Alzheimer’s Association (no. NIRG-12-92090 and no. NIRP-12-259245 (P.R.-N.)) and Fonds de Recherche du Québec–Santé (FRQS; Chercheur Boursier, no. 2020-VICO-279314 (P.R.-N.)). T.A.P., S.G. and P.R.-N. are members of the CIHR–Canadian Consortium of Neurodegeneration in Aging (CCNA), Canada Foundation for Innovation, CFI Project 34874. T.A.P. is supported by the Alzheimer’s Association (no. AACSF-20-648075). K.B. is supported by the Swedish Research Council (no. 2017-00915), the Alzheimer’s Drug Discovery Foundation (ADDF) (no. RDAPB-201809-2016615), the Swedish Alzheimer’s Foundation (no. AF-742881), Hjärnfonden (no. FO2017-0243), the Swedish State under the agreement between the Swedish government and the County Councils, ALF agreement (no. ALFGBG-715986) and European Union Joint Program for Neurodegenerative Disorders (no. JPND2019-466-236). H.Z. is a Wallenberg Scholar supported by grants from the Swedish Research Council (no. 2018-02532), European Research Council (no. 681712), Swedish State Support for Clinical Research (no. ALFGBG-720931), the ADDF (no. 201809-2016862) and UK Dementia Research Institute at University College London. The authors thank all study participants and staff of the McGill University Research Centre for Studies in Aging. We thank D. Jolly, A. Kostikov, M. Samoila-Lactatus, K. Ross, M. Boudjemeline and S. Li for assisting with the radiochemistry production. We also thank G. Gagne, C. Mayhew, T. Vinet-Celluci, K. Wan, S. Sbeiti, M. Jin Joung, M. Olmand, R. Nazar, H.-H. Hsiao, R. Bouhachi and A. Aliaga for consenting participants and/or helping with data acquisition. We thank Cerveau Technologies for the use of [18F]MK-6240.

Author information

Authors and Affiliations

  1. Departments of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Tharick A. Pascoal

  2. Departments of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Tharick A. Pascoal

  3. Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada

    Tharick A. Pascoal, Andrea L. Benedet, Min Su Kang, Joseph Therriault, Mira Chamoun, Melissa Savard, Firoza Z. Lussier, Cécile Tissot, Sulantha Mathotaarachchi, Jenna Stevenson, Serge Gauthier & Pedro Rosa-Neto

  4. Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada

    Tharick A. Pascoal, Min Su Kang, Gassan Massarweh, Jean-Paul Soucy & Pedro Rosa-Neto

  5. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Nicholas J. Ashton, Thomas K. Karikari, Michael Schöll, Kaj Blennow & Henrik Zetterberg

  6. Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Institute Clinical Neuroscience Institute, King’s College London, London, UK

    Nicholas J. Ashton

  7. NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley, NHS Foundation, London, UK

    Nicholas J. Ashton

  8. Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium

    Julie Ottoy

  9. LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada

    Julie Ottoy

  10. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

    Michael Schöll

  11. Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

    Michael Schöll & Henrik Zetterberg

  12. Department of Radiology Weill Medical Center Brain Health Imaging Institute, Cornell University, Ithaca, NY, USA

    Mony J. de Leon

  13. Department of Brain Sciences, Imperial College London, Hammersmith Hospital Campus, London, UK

    Paul Edison

  14. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Kaj Blennow & Henrik Zetterberg

  15. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

Authors
  1. Tharick A. Pascoal
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  2. Andrea L. Benedet
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  22. Pedro Rosa-Neto
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Contributions

T.A.P., S.G. and P.R.-N. conceptualized the work. T.A.P., A.L.B., N.J.A. and P.R.-N. contributed to the design of the analyses. T.A.P., A.L.B., M.S.K., J.T., M.C., M. Savard, F.Z.L., C.T., T.K.K., J.O., S.M., J.S., G.M., J.-P.S., M.J.L., P.E. and P.R.-N. contributed to the acquisition, processing and analysis of the neuroimaging data. N.J.A., T.K.K., M. Schöll, K.B. and H.Z. contributed to the analysis of the fluid biomarkers. T.A.P. and P.R.-N. drafted the manuscript. All authors interpreted the data and contributed to revising the manuscript.

Corresponding authors

Correspondence to Tharick A. Pascoal or Pedro Rosa-Neto.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Medicine thanks Michael Heneka and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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

Study flowchart.

Extended Data Fig. 2 Cross-sectional and longitudinal abnormalities in [18F]MK-6240 across clinical groups.

(A) The brain images (cross-sectional analyses) show voxel-wise AUC results obtained from ROC curves between [18F]MK-6240 SUVR values of CU Aβ - versus CU Aβ + , MCI, or AD dementia. [18F]MK-6240 SUVR was increased in CU Aβ + , MCI, and AD dementia individuals in regions comprising early PET Braak-like stages, intermediary Braak stages, and across the whole brain cortex, respectively. Cross-sectional analysis was performed in 64 CU elderly (14 males, mean age = 72 (6)), 28 MCI (17 males, mean age = 73 (9)), and 16 AD dementia (6 males, mean age = 70 (8)). (B) The brain images (longitudinal analyses) show the results of voxel-wise paired t-test comparison between the baseline and follow-up [18F]MK-6240 SUVR images. CU, MCI, and AD dementia showed a more preeminent longitudinal [18F]MK-6240 SUVR increase in early, intermediary, and late Braak regions, respectively. Longitudinal analysis was performed in 34 CU elderly (6 males, mean age = 73 (6)), 13 MCI (9 males, mean age = 74 (6)), and 9 AD dementia (4 males, mean age = 70 (7)). Results survived to false discovery rate correction for multiple comparisons at P < 0.05.

Extended Data Fig. 3

Demographics and key characteristics of the population.

Extended Data Fig. 4 Groups difference in [11C]PBR28 SUVR.

(a) Averaged [11C]PBR28 SUVR maps, overlaid on a structural MRI template, suggest a progressively higher uptake in typical AD-related region in the posterior cingulate/precuneus, inferior parietal, and lateral temporal cortices from CU young (n = 22 (8 males, mean age = 23 (2)) to CU elderly (n = 64 (14 males, mean age = 72 (6)), MCI (n = 28, 17 males, mean age = 73 (9)), and AD dementia (n = 16, 6 males, mean age = 70 (8)) individuals. (b) Voxel-wise AUC maps obtained from ROC curves supported the above-mentioned differences between groups. Voxel-wise AUC also revealed the regions with higher [11C]PBR28 SUVR uptake in CU elderly Aβ + than CU elderly Aβ - and higher uptake in MCI Aβ + than MCI Aβ - (for example, medial temporal, posterior cingulate, and precuneus cortices). Young = cognitively unimpaired young; Elderly = cognitively unimpaired elderly; AD = AD dementia.

Extended Data Fig. 5 Microglial activation positively associates with brain Aβ and tau.

T-statistical parametric maps (false discovery rate corrected for multiple comparison at P < 0.05) overlaid on an MRI template show the results of voxel-wise linear regressions analysis between [11C]PBR28 SUVR and (A) Aβ [18F]AZD4694 SUVR and (B) tau [18F]MK-6240 SUVR. This analysis was performed in CU young (n = 22 (8 males, mean age = 23 (2)), CU elderly (n = 64 (14 males, mean age = 72 (6)), and 44 CI elderly (23 males, mean age = 72 (8)). The scatter plots show the results of two-side Pearson correlations between CSF sTREM2 and (C) CSF Aβ42/40 ratio and (D) CSF p-tau181 levels (CU young (n = 19 (9 males, mean age = 23 (2)), CU elderly (n = 29 (7 males, mean age = 73 (5)), and CI elderly (n = 27, 15 males, mean age = 71 (7)). The error bands denote 95% confidence intervals.

Extended Data Fig. 6 Microglia and tau networks correlations.

The plots show the correlations across PET Braak-like regions used in the network analyses presented in Fig. 2 for (a) [11C]PBR28 and (b) [18F]MK-6240. P values reflect the results of two-sided Pearson’s correlation between PET SUVR values corrected for age, sex, education, APOE ε4 status, and the remaining Braak regions not used in the given correlation. A correlation was interpreted as significant if it survived Bonferroni correction for multiple comparisons (30 tests, P < 0.0017). ** indicates a significant positive correlation; ## indicates a significant negative correlation. This analysis was performed in the elderly population (n = 108, 64 CU elderly (14 males, mean age = 72 (6)), 28 MCI (17 males, mean age = 73 (9)), and 16 AD dementia (6 males, mean age = 70 (8)).

Extended Data Fig. 7 Longitudinal tau propagation network correlations.

The plots show the correlations across PET Braak-like regions used in the longitudinal tau network analysis presented in Fig. 3A. P values reflect the results of two-sided Pearson’s correlation between changes in [18F]MK-6240 SUVR corrected for age, sex, education, APOE ε4 status, and the changes in the remaining Braak regions not used in the given correlation. A correlation was interpreted as significant if it survived Bonferroni correction for multiple comparisons (15 tests, P < 0.0034). ** indicates a significant positive correlation (n = 56, 34 CU (6 males, mean age = 73 (6)), 13 MCI (9 males, mean age = 74 (6)), and 9 AD dementia (4 males, mean age = 70 (7)).

Extended Data Fig. 8 Baseline microglial activation in Braak I region was associated with longitudinal tau accumulation over the neocortex.

The linear regression analysis shows that [11C]PBR28 SUVR value in the transentorhinal cortex (PET Braak-like stage I) was positively associated with 1-year change in tau PET uptake in brain regions comprising PET Braak-like stages II-VI, accounting for age, sex, APOE ε4 carriage status, and global Aβ load. The analysis was performed in 34 CU elderly (6 males, mean age = 73 (6)) and 22 CI elderly (13 males, mean age = 72 (7)). The error bands denote 95% confidence intervals.

Extended Data Fig. 9 The association between CSF sTREM2 and tau PET load recapitulates Braak stages.

The figure shows the results of voxel-wise regressions (false discovery rate corrected for multiple comparisons at P < 0.05) overlaid in a structure template (top) between CSF sTREM2 and [18F]MK-6240 SUVR in (A) CU (n = 48, 15 males, mean age = 53 (25)) and (B) MCI (n = 18, MCI (12 males, mean age = 72 (6)) participants. The bar plots (bottom) represent the percentage of the area showing a significant association between CSF sTREM2 and [18F]MK-6240 across Braak-like stage regions. The bars show that in CU individuals, CSF sTREM2 associates with tau pathology in early Braak stages, whereas in MCI individuals, CSF sTREM2 associates with tau pathology in late Braak stages, supporting a role for microglial activation in the spatial spread of tau in the human brain. No significant association was found in AD dementia patients after correction for multiple comparisons.

Extended Data Fig. 10 Distribution of Aβ, tau, and microglia activation abnormalities across diagnostic groups.

The figure shows the distribution of Aβ [18F]AZD4694, tau [18F]MK-6240, and microglia activation [11C]PBR28 abnormalities (+/-) in clinical groups. The co-occurrence of Aβ (A), tau (T), and microglia activation (MA) abnormalities (A/T/MA) was more prevalent in AD dementia (67%), followed by MCI (27%), CU elderly (4.5%), and CU young (0%). CU young (n = 22 (8 males, mean age = 23 (2)), CU elderly (n = 64 (14 males, mean age = 72 (6)), MCI (n = 28, 17 males, mean age = 73 (9)), and AD dementia (n = 16, 6 males, mean age = 70 (8)).

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Pascoal, T.A., Benedet, A.L., Ashton, N.J. et al. Microglial activation and tau propagate jointly across Braak stages. Nat Med 27, 1592–1599 (2021). https://doi.org/10.1038/s41591-021-01456-w

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