Abstract
The coexistence of amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the neocortex is linked to neural system failure and cognitive decline in Alzheimer’s disease. However, the underlying neuronal mechanisms are unknown. By employing in vivo two-photon Ca2+ imaging of layer 2/3 cortical neurons in mice expressing human Aβ and tau, we reveal a dramatic tau-dependent suppression of activity and silencing of many neurons, which dominates over Aβ-dependent neuronal hyperactivity. We show that neurofibrillary tangles are neither sufficient nor required for the silencing, which instead is dependent on soluble tau. Surprisingly, although rapidly effective in tau mice, suppression of tau gene expression was much less effective in rescuing neuronal impairments in mice containing both Aβ and tau. Together, our results reveal how Aβ and tau synergize to impair the functional integrity of neural circuits in vivo and suggest a possible cellular explanation contributing to disappointing results from anti-Aβ therapeutic trials.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All data are reported in the main text and supplementary materials, stored at the Massachusetts General Hospital, and are available from the corresponding authors upon reasonable request.
References
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).
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
Arnold, S. E., Hyman, B. T., Flory, J., Damasio, A. R. & Van Hoesen, G. W. The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer’s disease. Cereb. Cortex 1, 103–116 (1991).
Schöll, M. et al. PET imaging of tau deposition in the aging human brain. Neuron 89, 971–982 (2016).
Delacourte, A. et al. The biochemical pathway of neurofibrillary degeneration in aging and Alzheimer’s disease. Neurology 52, 1158–1165 (1999).
Wang, L. et al. Evaluation of tau imaging in staging Alzheimer disease and revealing interactions between β-amyloid and tauopathy. JAMA Neurol. 73, 1070–1077 (2016).
Pontecorvo, M. J. et al. Relationships between flortaucipir PET tau binding and amyloid burden, clinical diagnosis, age and cognition. Brain 140, 748–763 (2017).
Lewis, J. et al. Enhanced neurofibrillary degeneration in transgenic mice expressing mutant tau and APP. Science 293, 1487–1491 (2001).
Götz, J., Chen, F., van Dorpe, J. & Nitsch, R. M. Formation of neurofibrillary tangles in P301l tau transgenic mice induced by Aβ42 fibrils. Science 293, 1491–1495 (2001).
Hurtado, D. E. et al. Aβ accelerates the spatiotemporal progression of tau pathology and augments tau amyloidosis in an Alzheimer mouse model. Am. J. Pathol. 177, 1977–1988 (2010).
Bennett, R. E. et al. Enhanced tau aggregation in the presence of amyloid β. Am. J. Pathol. 187, 1601–1612 (2017).
Jacobs, H. I. L. et al. Structural tract alterations predict downstream tau accumulation in amyloid-positive older individuals. Nat. Neurosci. 21, 424–431 (2018).
Quiroz, Y. T. et al. Association between amyloid and tau accumulation in young adults with autosomal dominant Alzheimer disease. JAMA Neurol. 75, 548–556 (2018).
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).
Kerr, J. N., Greenberg, D. & Helmchen, F. Imaging input and output of neocortical networks in vivo. Proc. Natl. Acad. Sci. USA 102, 14063–14068 (2005).
Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
Busche, M. A. et al. Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer’s disease. Science 321, 1686–1689 (2008).
Grienberger, C. et al. Staged decline of neuronal function in vivo in an animal model of Alzheimer’s disease. Nat. Commun. 3, 774 (2012).
Busche, M. A. et al. Decreased amyloid-β and increased neuronal hyperactivity by immunotherapy in Alzheimer’s models. Nat. Neurosci. 18, 1725–1727 (2015).
Keskin, A. D. et al. BACE inhibition-dependent repair of Alzheimer's pathophysiology. Proc. Natl. Acad. Sci. USA 114, 8631–8636 (2017).
Jackson, R. J. et al. Human tau increases amyloid β plaque size but not amyloid β-mediated synapse loss in a novel mouse model of Alzheimer’s disease. Eur. J. Neurosci. 44, 3056–3066 (2016).
Santacruz, K. et al. Tau suppression in a neurodegenerative mouse model improves memory function. Science 309, 476–481 (2005).
Berger, Z. et al. Accumulation of pathological tau species and memory loss in a conditional model of tauopathy. J. Neurosci. 27, 3650–3662 (2007).
de Calignon, A. et al. Caspase activation precedes and leads to tangles. Nature 464, 1201–1204 (2010).
Angulo, S. L. et al. Tau and amyloid-related pathologies in the entorhinal cortex have divergent effects in the hippocampal circuit. Neurobiol. Dis. 108, 261–276 (2017).
Roberson, E. D. et al. Reducing endogenous tau ameliorates amyloid β-induced deficits in an Alzheimer’s disease mouse model. Science 316, 750–754 (2007).
Ittner, L. M. et al. Dendritic function of tau mediates amyloid-β toxicity in Alzheimer’s disease mouse models. Cell 142, 387–397 (2010).
DeVos, S. L. et al. Antisense reduction of tau in adult mice protects against seizures. J. Neurosci. 33, 12887–12897 (2013).
Zott, B., Busche, M. A., Sperling, R. A. & Konnerth, A. What happens with the circuit in Alzheimer’s disease in mice and humans? Annu. Rev. Neurosci. 41, 277–297 (2018).
Silverman, D. H. et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA 286, 2120–2127 (2001).
Alexander, G. E., Chen, K., Pietrini, P., Rapoport, S. I. & Reiman, E. M. Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am. J. Psychiatry 159, 738–745 (2002).
Greicius, M. D., Srivastava, G., Reiss, A. L. & Menon, V. et al. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. USA 101, 4637–4642 (2004).
Bradley, K. M. et al. Cerebral perfusion SPET correlated with Braak pathological stage in Alzheimer’s disease. Brain 125, 1772–1781 (2002).
Jelic, V. et al. Apolipoprotein E epsilon4 allele decreases functional connectivity in Alzheimer’s disease as measured by EEG coherence. J. Neurol. Neurosurg. Psychiatry 63, 59–65 (1997).
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).
Nelson, P. T. et al. Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J. Neuropathol. Exp. Neurol. 71, 362–381 (2012).
Palop, J. J. & Mucke, L. Network abnormalities and interneuron dysfunction in Alzheimer disease. Nat. Rev. Neurosci. 17, 777–792 (2016).
Dickerson, B. C. et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 65, 404–411 (2005).
Bakker, A. et al. Reduction of hippocampal hyperactivity improves cognition in amnestic mild cognitive impairment. Neuron 74, 467–474 (2012).
Khan, U. A. et al. Molecular drivers and cortical spread of lateral entorhinal cortex dysfunction in preclinical Alzheimer’s disease. Nat. Neurosci. 17, 304–311 (2014).
Hoover, B. R. et al. Tau mislocalization to dendritic spines mediates synaptic dysfunction independently of neurodegeneration. Neuron 68, 1067–1081 (2010).
Menkes-Caspi, N. et al. Pathological tau disrupts ongoing network activity. Neuron 85, 959–966 (2015).
Hatch, R. J., Wei, Y., Xia, D. & Götz, J. Hyperphosphorylated tau causes reduced hippocampal CA1 excitability by relocating the axon initial segment. Acta Neuropathol. 133, 717–730 (2017).
Oddo, S. et al. Reduction of soluble Aβ and tau, but not soluble Aβ alone, ameliorates cognitive decline in transgenic mice with plaques and tangles. J. Biol. Chem. 281, 39413–39423 (2006).
Sydow, A. et al. Tau-induced defects in synaptic plasticity, learning, and memory are reversible in transgenic mice after switching off the toxic Tau mutant. J. Neurosci. 31, 2511–2525 (2011).
Lasagna-Reeves, C. A. et al. Tau oligomers impair memory and induce synaptic and mitochondrial dysfunction in wild-type mice. Mol. Neurodegener. 6, 39 (2011).
Van der Jeugd, A. et al. Cognitive defects are reversible in inducible mice expressing pro-aggregant full-length human Tau. Acta Neuropathol. 123, 787–805 (2012).
Castillo-Carranza, D. L. et al. Passive immunization with Tau oligomer monoclonal antibody reverses tauopathy phenotypes without affecting hyperphosphorylated neurofibrillary tangles. J. Neurosci. 34, 4260–4272 (2014).
Rudinskiy, N. et al. Tau pathology does not affect experience-driven single-neuron and network-wide Arc/Arg3.1 responses. Acta Neuropathol. Commun. 2, 63 (2014).
Kuchibhotla, K. V. et al. Neurofibrillary tangle-bearing neurons are functionally integrated in cortical circuits in vivo. Proc. Natl. Acad. Sci. USA 111, 510–514 (2014).
Jankowsky, J. L. et al. Mutant presenilins specifically elevate the levels of the 42 residue β-amyloid peptide in vivo: evidence for augmentation of a 42-specific gamma secretase. Hum. Mol. Genet. 13, 159–170 (2004).
Mayford, M. et al. Control of memory formation through regulated expression of a CaMKII transgene. Science 274, 1678–1683 (1996).
Froudarakis, E. et al. Population code in mouse V1 facilitates readout of natural scenes through increased sparseness. Nat. Neurosci. 17, 851–857 (2014).
Acknowledgements
We thank A.B. Robbins, D.L. Corjuc, A.D. Roe, and E. Hudry for their excellent technical support, and all members of the Hyman laboratory for providing comments and advice throughout the project. We thank Matthias Staufenbiel for helpful discussions and experimental suggestions. We acknowledge the Genetically-Encoded Neuronal Indicator and Effector (GENIE) project and the Janelia Research Campus of the Howard Hughes Medical Institute and specifically V. Jayaraman, R.A. Kerr, D.S. Kim, L.L. Looger, and K. Svoboda from the GENIE Project, Janelia Research Campus, Howard Hughes Medical Institute for making AAV.Syn.GCaMP6f publicly available. We thank Dr P. Davies for kindly providing the PHF1 and Alz50 antibodies. We thank the following funding agencies for their support: M.A.B. was supported by an EMBO Long-Term Fellowship (grant no. ALTF 590-2016), the Alzheimer Forschung Initiative, and the UK Dementia Research Institute. I.N. was supported by an advanced ERC grant (project RATLAND; grant no. 340063). B.T.H. received support from the Massachusetts Alzheimer’s Disease Research Center (grant no. P50AG005134), the JPB foundation, the National Institutes of Health (grant no. 1R01AG0586741), and the Tau Consortium.
Author information
Authors and Affiliations
Contributions
M.A.B. and B.T.H. designed the study. M.A.B., S.W., S.D., C.C., J.S., N.K., and T.V.K. performed the research. M.A.B., S.W., S.D., C.C., and I.N. analyzed the data. G.A.C. provided the background information regarding mouse breeding. M.A.B. and B.T.H. wrote the manuscript with input from all other authors.
Corresponding authors
Ethics declarations
Competing interests
The 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 Within the 6- to 12-months-old age period, spontaneous cortical activity remains constant with age.
(a–c) Pearson correlations between age (shown in days) and mean neuronal frequencies (a; black), as well as fractions of silent (b, blue) and hyperactive (c, red) neurons from wild-type controls (n = 7 mice). The same data is shown for APP/PS1 (d–f; n = 5 mice), rTg4510 (g–i; n = 9 mice), APP/PS1-rTg4510 (j–l; n = 8 mice), rTg21221 (m–o; n = 6 mice) and APP/PS1-rTg21221 (p–r; n = 5 mice) mice. Each circle represents an individual animal.
Supplementary Figure 2 Similar age distributions within the 6- to 12-months-old as well as the 17- to 24-months-old cohorts of mice.
(a) Within the 6- to 12-months old age group, mean age (shown in days) was not significantly different for all six genotypes (controls, 257 ± 18 days, n = 7 mice; APP/PS1, 257 ± 21 days, n = 5 mice; rTg4510, 257 ± 14 days, n = 9 mice; APP/PS1-rTg4510, 230 ± 14 days, n = 8 mice; rTg21221 271 ± 24 days, n = 6 mice; APP/PS1-rTg21221, 241 ± 21 days, n = 5 mice; F(5,34) = 0.6744, P = 0.6456). (b) Same is shown for the 17- to 24-months old cohort (controls, 609 ± 24 days, n = 5 mice; APP/PS1, 628 ± 40 days, n = 5 mice; rTg4510, 613 ± 57 days, n = 3 mice; rTg21221 657 ± 28 days, n = 6 mice; APP/PS1-rTg21221, 627 ± 37 days, n = 6 mice; F(4,20) = 0.2879, P = 0.8824). Each grey circle represents an individual animal and all error bars reflect mean ± s.e.m; differences between genotypes were assessed using one-way ANOVA.
Supplementary Figure 3 Almost no overlap between GCamP6f and NFTs.
Mouse brain sections from rTg4510 and APP/PS1-rTg4510 mice used for in vivo imaging were labeled for GCaMP6f with an anti-GFP antibody (green, see Methods and Life Sciences Reporting Summary). Neurofibrillary tangles (NFTs) were labeled with PHF1 and Alz50 (red) antibodies and nuclei were labeled with DAPI (blue). Computer Assisted Stereological Toolbox analysis revealed that in rTg4510 mice only 18/1483 cells (1.21 %) were double-positive for GFP and PHF1/Alz50; and that in APP/PS1-rTg4510 mice only 15/1604 cells (0.94 %) were double-positive. Example light microscopy image from an APP/PS1-rTg4510 mouse is shown in this figure to illustrate this result (this experiment was repeated three times with similar results). Scale bars, 100 µm (left panel) and 50 µm (right panel).
Supplementary Figure 4 Total human tau levels are similar in rTg4510 and rTg21221 mice, with and without the APP/PS1 transgene.
(a) Total human tau levels in forebrain homogenates as measured by ELISA for rTg4510 (n = 5 mice) and rTg21221 (n = 7 mice; mean age 10.6 months, t = 1.867 d.f. = 9.99, P = 0.0914). (b) Same is shown for APP/PS1-rTg4510 (n = 4 mice) and APP/PS1-rTg21221 (n = 4 mice; mean age 10.1 months, t = 0.2667 d.f. = 5.026, P = 0.8003). Each circle represents an individual animal and error bars reflect mean ± s.e.m. Differences between groups were assessed by two-sided t-test.
Supplementary Figure 5 Homogeneous human tau expression in the cortex of rTg21221 and rTg4510 mice.
Mouse brain sections from wild-type controls as well as rTg21221 and rTg4510 mice used for in vivo calcium imaging were labeled for total human tau with the antibody Tau13 (red); nuclei were labeled with DAPI (blue). Slices were imaged by light microscopy and representative examples are shown here (antibody concentrations and microscope settings were kept constant for all recordings). As expected, all 3/3 control mice did not show immunoreactivity for human tau (left panel). In contrast, human tau expression was uniformly distributed across the cortex of both rTg21221 and rTg4510 mice (n = 3 for each genotype, middle and right panels). All mice were 10- to 11-months of age at the time of the stainings. Scale bars, 100 µm.
Supplementary Figure 6 Quantification of Aβ and tau burden in APP/PS1-rTg4510 and APP/PS1-rTg21221 mice.
(a) A pan Aβ-antibody (see Methods and Life Sciences Reporting Summary) was used to label plaques in APP/PS1 (n = 2 mice), APP/PS1-rTg4510 (n = 7 mice) and APP/PS1-rTg21221 (n = 6 mice; mean age 10.1 months). There was no obvious difference in the total number of cortical plaques per mm2 between the groups. (b) Quantification of cortical NFTs labeled with PHF1 and Alz50 antibodies in rTg4510 with and without the APP/PS1 transgene (n = 4 mice in each group; mean age 10.0 months) revealed slightly higher NFT numbers in the crosses, in line with a previous report (ref. 11), but at this age this difference was not statistically significant (t = 1.346, d.f. = 5.582, P = 0.2304, two-sided t-test). As expected, we did not detect NFTs in APP/PS1-rTg21221 mice. Each grey circle represents an individual animal and all error bars reflect mean ± s.e.m.
Supplementary Figure 7 Similar functional neuronal impairments in aged, 17- to 24-months-old cohort of mice.
(a–e) Summary histograms showing the frequency distributions of wild-type controls (a, green, n = 1119 neurons in 5 mice), APP/PS1 (b, magenta, n = 656 neurons in 5 mice), rTg4510 (c, light blue, n = 540 neurons in 3 mice), rTg21221 (d, orange, n = 1369 neurons in 6 mice) as well as APP/PS1 x rTg21221 mice (e, yellow-green, n = 1080 neurons in 6 mice). (f) Summary graph of mean frequencies (controls: 1.97 ± 0.21 transients per min, n = 5 mice; APP/PS1: 3.13 ± 0.34 transients per min, n = 5 mice; rTg4510: 0.65 ± 0.12 transients per min, n = 3 mice; rTg21221: 0.94 ± 0.12 transients per min, n = 6 mice, and APP/PS1-rTg21221: 1.10 ± 0.20 transients per min, n = 6 mice; F(4,20) = 19.55, P = 1.10e−6, post hoc comparisons were: P = 1.04e−5 for APP/PS1 vs. rTg4510, P = 4.40e−6 for APP/PS1 vs. rTg21221, P = 1.25e−5 for APP/PS1 vs. APP/PS1-rTg21221, P = 0.0112 for controls vs. APP/PS1, P = 0.0119 for controls vs. rTg4510, P = 0.0200 for controls vs. rTg21221, and not significant for all other correlations). (g) Summary graph representing fractions of hyperactive neurons (controls: 4.6 ± 1.55 %, n = 5 mice; APP/PS1: 17.49 ± 3.66 %, n = 5 mice; rTg4510: 1.52 ± 0.97 %, n = 3 mice; rTg21221: 2.95 ± 0.62 %, n = 6 mice, APP/PS1-rTg21221: 4.30 ± 1.05 %, n = 6 mice; F(4,20) = 10.92, P = 7.32e−5, post hoc multiple comparisons were P = 0.0010 for controls vs. APP/PS1, P = 4.62e−4 for APP/PS1 vs. rTg4510, P = 1.51e−4 for APP/PS1 vs. rTg21221 and P = 4.81e−4 for APP/PS1 vs. APP/PS1-rTg21221, all other comparisons were not significant). (h) Summary graph showing fractions of silent neurons (controls: 12.23 ± 1.85 %, n = 5 mice; APP/PS1: 12.99 ± 2.86 %, n = 5 mice; rTg4510: 56.70 ± 4.37 %, n = 3 mice; rTg21221: 49.95 ± 4.65 %, n = 6 mice, and APP/PS1-rTg21221: 53.57 ± 3.58 %, n = 6 mice; F(4,20) = 35.36, P = 8.32e−9, post hoc comparisons were P = 4.01e−6 for controls vs. rTg4510, P = 2.87e-6 for controls vs. rTg21221, P = 7.11e-7 for controls vs. APP/PS1-rTg21221, P = 5.17e-6 for APP/PS1 vs. rTg4510, P = 3.89e-6 for APP/PS1 vs. rTg21221, P = 9.49e−7 for APP/PS1 vs. APP/PS1-rTg21221, and not significant for all other comparisons). Each solid circle represents an individual animal and all error bars represent mean ± s.e.m. Differences among genotypes were assessed using one-way ANOVA followed by Tukey’s multiple comparisons test, ****P < 0.0001, *P < 0.05. APP/PS1 x rTg4510 were not included in the analysis because imaging was not feasible because of the severe cortical atrophy and high lethality at this age.
Supplementary Figure 8 Within the 17- to 24-months-old age period, spontaneous cortical activity remains constant with age.
(a-c) Pearson correlations between age (shown in days) and mean neuronal frequencies (a, black), as well as fractions of silent (b, blue) and hyperactive (c, red) neurons from wild-type controls (n = 5 mice). The same data is shown for APP/PS1 (d-e; n = 5 mice), rTg4510 (g-i; n = 3 mice), rTg21221 (j-l; n = 6 mice) and APP/PS1-rTg21221 (m-o; n = 6 mice) mice. Each circle represents an individual animal.
Supplementary Figure 9 Abnormal silencing of layer 2/3 cortical neurons is already present in 3- to 4-months-old APP/PS1-rTg4510 mice, prior to overt neuropathology and neurodegeneration.
(a-c) Top, In vivo two-photon fluorescence images of GCaMP6f-expressing (green) layer 2/3 neurons in the parietal cortex and corresponding activity maps from 3- to 4-months old APP/PS1 (a, left panel), rTg4510 (b, middle panel) and APP/PS1-rTg4510 (c, right panel) mice. No plaques were observed at this young age after methoxy-X04 i.p. injections. Bottom, spontaneous Ca2+-transients of neurons indicated in the top panel. Scale bars, 10 µm. (d-g) Summary histograms showing frequency distributions of all recorded neurons in 3- to 4-months-old wild-type controls (d, green, n = 1226 neurons in 5 mice), APP/PS1 (e, magenta, n = 1333 neurons in 8 mice), rTg4510 (f, light blue, n = 1155 neurons in 6 mice) as well as APP/PS1-rTg4510 (g, dark blue, n = 1884 neurons in 8 mice). (h) Mean neuronal frequencies for all 4 genotypes (controls: 1.48 ± 0.09 transients per min, n = 5 mice; APP/PS1: 2.10 ± 0.26 transients per min, n = 8 mice; rTg4510: 1.31 ± 0.25 transients per min, n = 6 mice; APP/PS1-rTg4510: 0.91 ± 0.08 transients per min, n = 8 mice; F(3,23) = 7.11, P = 0.0015, post hoc multiple comparisons were P = 0.0008 for APP/PS1 vs. APP/PS1-rTg4510, P = 0.0468 for APP/PS1 vs. rTg4510, and not significant for all other comparisons). (i) Fractions of hyperactive neurons (controls: 2.81 ± 0.21 %, n = 5 mice; APP/PS1: 6.88 ± 1.56 %, n = 8 mice; rTg4510: 4.15 ± 1.70 %, n = 6 mice; APP/PS1-rTg4510: 2.17 ± 0.53 %, n = 8 mice; F(3,23) = 3.205, P = 0.0420, post hoc multiple comparisons were P = 0.0359 for APP/PS1 vs. APP/PS1-rTg4510, and not significant for the remaining comparisons). (j) Fractions of silent neurons (controls: 25.21 ± 2.48 %, n = 5 mice; APP/PS1: 15.83 ± 3.25 %, n = 8 mice; rTg4510: 33.5 ± 4.18 %, n = 6 mice; APP/PS1-rTg4510: 47.36 ± 3.32 %, n = 8 mice; F(3,23) = 17.29, P = 4.27e−6, post hoc multiple comparisons were P = 2.14e−6 for APP/PS1 vs. APP/PS1-rTg4510, P = 0.0013 for controls vs. APP/PS1-rTg4510, P = 0.0069 for APP/PS1 vs. rTg4510, P = 0.0414 for rTg4510 vs. APP/PS1-rTg4510, and not significant for the remaining comparisons). Each solid circle represents an individual animal, and mean ± s.e.m. is represented by error bars. Differences between genotypes were assessed using one-way ANOVA followed by Tukey’s multiple comparisons test.
Supplementary Figure 10 Age-dependence of spontaneous cortical activity in wild-type control, APP/PS1, rTg4510, and APP/PS1-rTg4510 mice.
(a-c) Mean neuronal frequencies (a) as well as proportions of hyperactive (b) and silent (c) neurons for all genotypes at the three different age groups when recordings where obtained in this study (controls, green, n = 6 mice at 3–4 months, n = 6 mice at 6–12 months, n = 5 mice at 17–24 months; APP/PS1, magenta, n = 8 mice at 3–4 months, n = 5 mice at 6–12 months, n = 5 mice at 17–24 months; rTg4510, light blue, n = 6 mice at 3–4 months, n = 9 mice at 6–12 months, n = 3 mice at 17–24 months; APP/PS1-rTg4510, dark blue, n = 7 mice at 3–4 months, n = 8 mice at 6–12 months). No recordings were obtained in APP/PS1-rTg4510 mice at 17- to 24-months. All error bars indicate mean ± s.e.m.
Supplementary Figure 11 Neuronal silencing was rescued in rTg21221 but not in APP/PS1-rTg21221 mice.
(a,b) Example activity traces illustrating that suppression of tau transgene expression rescues silencing in rTg21221 (a) but not in APP/PS1-rTg21221 mice (b). This experiment was repeated multiple times with similar results (n = 8 rTg21221 mice and n = 5 APP/PS1-rTg21221 mice, see also quantification in Fig. 4).
Supplementary Figure 12 While doxycycline treatment reduced total tau protein levels, there was no clear effect on NFTs.
(a-c) Many Alz50-positive NFTs (green) were still detectable in the cortex of 9- to 10-months old rTg4510 mice after 6 weeks of DOX treatment. 3 representative examples are shown, similar results were obtained in 3 additional mice. Scale bars, 100 µm. (d,e) Total human tau levels as measured by ELISA were significantly reduced in doxycycline (DOX)-treated rTg4510 and APP/PS1-rTg4510 mice compared to naïve genotype controls (rTg4510-control: 6.08 ± 0.80, n = 5 mice; rTg4510-DOX: 2.93 ± 0.28, n = 8 mice; t = 4.418, d.f. = 11, P = 0.0010; APP/PS1-rTg4510-control: 6.443 ± 1.14, n = 4 mice; APP/PS1-rTg4510-DOX: 3.19 ± 0.40, n = 6 mice; t = 3.159, d.f. = 8, P = 0.0134). (f,g) Similar results were obtained in rTg21221 and APP/PS1-rTg21221 mice (rTg21221-control: 8.39 ± 0.95, n = 7 mice; rTg21221-DOX: 3.80 ± 0.45, n = 11 mice; t = 4.892, d.f. = 16, P = 0.0002; APP/PS1-rTg21221-control: 6.085 ± 0.71, n = 4 mice; APP/PS1-rTg21221-DOX: 3.67 ± 0.55, n = 6 mice; t = 2.709, d.f. = 8, P = 0.0267). Each solid circle represents an individual animal, and mean ± s.e.m. is represented by error bars. Differences between groups were assessed by two-sided t-test. All mice were 10- to 11-months of age at the time of the ELISA.
Supplementary Figure 13 Sarkosyl-soluble, but not sarkosyl-insoluble, tau levels were significantly reduced after doxycycline treatment.
(a-c) Western blots of sarkosyl-insoluble (a) and soluble (b) extracts from forebrain homogenates of rTg4510 mice with and without the APP/PS1 transgene, treated for 6 weeks with doxycycline (DOX) or untreated (age of mice was 8.5- to 9.5-months). Same amount of protein was used at the beginning of the extraction and pixel intensity was used to analyze the sarkosyl-insoluble fractions. Sarkosyl-soluble tau levels were normalized to GAPDH (c). (d,e) Quantification of sarkosyl-insoluble tau (rTg4510-controls: 393.5 ± 63.5, n = 2 mice; rTg4510-DOX: 352.00 ± 43.55, n = 3 mice; APP/PS1-rTg4510-controls: 404.80 ± 56.4, n = 4 mice; APP/PS1-rTg4510-DOX: 325.2 ± 1.15, n = 5 mice; t = 1.41, d.f. = 3.00, P = 0.2532). (f,g) Quantification of sarkosyl-soluble tau (rTg4510-controls: 1.67 ± 0.11, n = 2 mice; rTg4510-DOX 1.07 ± 0.10, n = 3 mice; APP/PS1-rTg4510-controls: 1.61 ± 0.06, n = 4 mice; APP/PS1-rTg4510-DOX: 0.83 ± 0.14, n = 5 mice; t = 5.212, d.f. = 5.282, P = 0.0029). Each solid circle represents an individual animal, and mean ± s.e.m. is represented by error bars. Differences between groups were assessed by two-sided t-test, where appropriate.
Supplementary Figure 14 Tau transgene suppression reduces neuronal silencing in 3- to 4-months-old rTg4510 mice but not APP/PS1-rTg4510 mice.
(a) Frequency distributions of all recorded neurons from rTg4510 mice before (black, n = 733 neurons in 4 mice) and after (red, n = 557 neurons in same 4 mice) doxycycline (DOX) treatment. (b) Fractions of silent neurons (baseline: 34.49 ± 0.84 %, DOX: 23.46 ± 1.47 %; n = 4 mice; t = 6.5, d.f. = 4.763, P = 0.0015). (c) Frequency distributions of all recorded neurons from APP/PS1-rTg4510 mice before (n = 1667 neurons in 7 mice) and after (n = 1530 neurons in same 7 mice) DOX treatment. (d) Fractions of silent neurons (baseline: 46.3 ± 3.64 %, DOX: 49.05 ± 2.55 %; n = 7 mice; t = 0.6196, d.f. = 10.73, P = 0.5484). (e,f) Total human tau levels as measured by ELISA were reduced in DOX-treated rTg4510 (e) and APP/PS1-rTg4510 (f) mice compared to naïve genotype controls (rTg4510-control: 17.91 ± 0.86, n = 3 mice; rTg4510-DOX: 4.02 ± 0.57, n = 3 mice; t = 13.43, d.f. = 3.455, P = 0.0004; APP/PS1-rTg4510-control: 16.76 ± 0.29, n = 2 mice; APP/PS1-rTg4510-DOX: 4.41 ± 0.60, n = 8 mice). All mice were 5- to 6- months old at the time of the ELISA. Each solid circle represents an individual animal, and mean ± s.e.m. is represented by error bars. Differences between groups were assessed by two-sided t-test, where appropriate.
Supplementary Figure 15 Doxycycline treatment has no effect on cortical activity in wild-type control mice.
(a) Frequency distributions of all recorded neurons from control mice before (black, left panel, n = 1761 neurons in 7 mice) and after (red, right panel, n = 927 neurons in same 7 mice) 6 weeks of doxycycline (DOX) treatment. (b) Mean frequencies before (black) and after (red) DOX treatment (baseline: 1.52 ± 0.07 transients per min, after DOX: 1.53 ± 0.09 transients per min, n = 7 mice, t = 0.07343, d.f. = 11.17, P = 0.9428). (c) Fractions of hyperactive neurons (baseline: 3.02 ± 0.2 %, after DOX: 3.81 ± 0.62 %, n = 7 mice; t = 1.205, d.f. = 7.204, P = 0.2664). (d) Fractions of silent neurons (baseline: 22.93 ± 2.54 %, after DOX: 23.08 ± 2.81 %, n = 7 mice; t = 0.04155, d.f. = 11.88, P = 0.9675). Each solid circle represents an individual animal, and mean ± s.e.m. is represented by error bars. Differences between groups were assessed by two-sided t-test.
Supplementary information
Rights and permissions
About this article
Cite this article
Busche, M.A., Wegmann, S., Dujardin, S. et al. Tau impairs neural circuits, dominating amyloid-β effects, in Alzheimer models in vivo. Nat Neurosci 22, 57–64 (2019). https://doi.org/10.1038/s41593-018-0289-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-018-0289-8
This article is cited by
-
Linking activity dyshomeostasis and sleep disturbances in Alzheimer disease
Nature Reviews Neuroscience (2024)
-
Xenografted human microglia display diverse transcriptomic states in response to Alzheimer’s disease-related amyloid-β pathology
Nature Neuroscience (2024)
-
Epilepsy and epileptiform activity in late-onset Alzheimer disease: clinical and pathophysiological advances, gaps and conundrums
Nature Reviews Neurology (2024)
-
Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes
Alzheimer's Research & Therapy (2023)
-
Proteostasis failure exacerbates neuronal circuit dysfunction and sleep impairments in Alzheimer’s disease
Molecular Neurodegeneration (2023)