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Cerebral amyloid angiopathy is associated with glymphatic transport reduction and time-delayed solute drainage along the neck arteries

Nature Aging volume 2, pages 214–223 (2022)Cite this article

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An Author Correction to this article was published on 24 November 2023

A Publisher Correction to this article was published on 19 April 2022

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Abstract

Cerebral amyloid angiopathy (CAA) is a common disease in older adults that contributes to dementia1,2,3. In CAA, amyloid beta (Aβ) is deposited along either capillaries (type 1) or vessel walls (type 2)4, with the underlying pathophysiology incompletely understood5. Here, we developed imaging and analysis tools based on regularized optimal mass transport (rOMT) theory6,7 to characterize cerebrospinal fluid (CSF) flow dynamics and glymphatic transport in a transgenic CAA type 1 rat model. We discovered that, in CAA, CSF moves more rapidly along the periarterial spaces that serve as influx routes to the glymphatic system. The observation of high-speed CSF flow currents in CAA was unexpected given the build-up of microvascular Aβ. However, velocity flux vector analysis revealed that CSF currents in CAA are partly diverted away from the brain, resulting in overall decreased glymphatic transport. Imaging at the neck showed that drainage to the deep cervical lymph nodes occurs along the carotid arteries and is time delayed in CAA, implying that upstream connections to the meningeal lymphatics were altered. Based on our findings we propose that, in CAA, both glymphatic transport and lymphatic drainage are compromised and that both systems represent therapeutic targets for treatment of CAA-related cognitive decline and dementia.

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Fig. 1: CSF flow speed becomes hyperdynamic with severe CAA.
Fig. 2: CSF flow currents partly divert away from the glymphatic system in CAA type 1.
Fig. 3: Dynamic MRI for tracking cervical lymph node drainage from the CNS in normal rats.
Fig. 4: Drainage to the cervical lymph nodes is sustained but time delayed in rTg-DI rats.

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Data availability

Statistical source data files depicting the quantification values mentioned in the text or plotted in graphs shown in Figs. 1 and 2 and Extended Data Figs. 3, 5 and 6 are available in the online version of this paper. rOMT processed speed map and Péclet map datasets generated from WT and rTg-DI rats analyzed in the current study are available at https://zenodo.org/record/5809664#.Yczwyy2ZNBw

Code availability

The rOMT code used for analysis of DCE-MRI data is available at https://zenodo.org/record/5809635#.YczwqS2ZNBw. Custom codes used for preprocessing of DCE-MRI datasets for glymphatic analysis are available at https://zenodo.org/record/5809482#.YczwgC2ZNBw

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Acknowledgements

We thank P. Brown (Magnetic Resonance Research Center) at Yale University for coil development and support. We also thank L. Zhao for editing our manuscript. This work was supported by a grant from the National Institutes of Health/National Institute on Aging (no. AG053991 to H.B., A.T. and W.E.V.N.), Cure Alzheimer’s Fund and A.T. acknowledges support from AFOSR grant FA955-20-1-0029.

Author information

Author notes

  1. These authors contributed equally: Xinan Chen, Xiaodan Liu.

  2. These authors jointly supervised this work: Allen Tannenbaum, William E. Van Nostrand, Helene Benveniste.

Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA

    Xinan Chen & Allen Tannenbaum

  2. Department of Anesthesiology, Yale School of Medicine, New Haven, CT, USA

    Xiaodan Liu, Sunil Koundal, Brittany Monte, Maysam Pedram, Hedok Lee & Helene Benveniste

  3. Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Rena Elkin

  4. George and Anne Ryan Institute for Neuroscience and the Department of Biomedical and Pharmaceutical Sciences, University of Rhode Island, Kingston, RI, USA

    Xiaoyue Zhu, Feng Xu & William E. Van Nostrand

  5. Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA

    Feng Dai

  6. Center for Brain Immunology and Glia, Department of Pathology and Immunology, Washington University, St. Louis, MO, USA

    Jonathan Kipnis

  7. Department of Computer Science, Stony Brook University, Stony Brook, NY, USA

    Allen Tannenbaum

  8. Department of Biomedical Engineering, Yale School of Medicine New Haven, New Haven, CT, USA

    Helene Benveniste

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Contributions

X.C., R.E. and A.T. designed all the computational fluid dynamics algorithms based on regularized optimal mass transport, performed all rOMT analysis of the data and wrote the rOMT Supplementary Methods. X.L. and S.K. performed all brain glymphatic MRI experiments and morphometric analysis of brain data. S.K. and H.B. designed, performed and analyzed all MRI experiments on lymph node drainage. X.Z., B.M. and F.X. performed immunohistochemistry and assisted with blinded data analysis and quantifications. F.D. assisted with statistical design and data analysis. M.P. assisted with analysis of LN time activity data. H.L. assisted with quantitative MRI data analysis and helped design and perform initial glymphatic whole-brain experiments. J.K. provided intellectual contribution and interpretation of lymph node data, and participated in manuscript writing. A.T. designed the mathematical rOMT framework, provided intellectual contribution, oversaw rOMT data analysis and contributed to writing the manuscript. W.E.V.N. designed the biological components of the experiments with H.B., created the rTg-DI rat model and supplied this strain and WT littermates for the study; and provided intellectual contributions and participated in manuscript writing. H.B. designed all experiments, oversaw data analysis and interpretation and wrote the manuscript.

Corresponding author

Correspondence to Helene Benveniste.

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Competing interests

H.B. received research support from PureTech. J.K. is a member of the scientific advisory group for PureTech. The remaining authors declare no competing interests.

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Nature Aging thanks Steve Greenberg, Bryn Martin, Douglas Kelley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Progressive accumulation of microvascular amyloid and astrocytes in rTg-DI rats.

Brain sections from ventral hippocampus from (a) 3months (M), (b) 6 M and (c) 12 M wild-type rats and age-matched rTg-DI rats (d-f). The brain sections were labeled with Amylo-Glo to detect fibrillar amyloid (blue), rabbit polyclonal antibody to detect cerebral microvessels (red), and goat polyclonal antibody to GFAP to identify astrocytes (green). Scale bars = 50 µm. Note that an increased number of perivascular astrocytes is evident in rTg-DI rats as early as 3 M. This experiment was independently repeated twice with similar results.

Extended Data Fig. 2 Increased perivascular microglia in rTg-DI rats.

Brain sections from ventral hippocampus of (a) 3-month (M), (b) 6 M and (c) 12 wild-type rats and age-matched rTg-DI rats (d-f). The brain sections were labeled with Amylo-Glo to detect fibrillar amyloid (blue), rabbit polyclonal antibody to detect cerebral microvessels (red), and goat polyclonal antibody to Iba-1 to identify microglia (green). Scale bars = 50 µm. Note that increased number of microglia cells are evident in rTg-DI rats as early as 3 M. This experiment was independently repeated twice with similar results.

Extended Data Fig. 3 CSF and tissue volume changes across age and strain.

a Graph with quantification of CSF compartment volumes of the 3-month, (M) 6 M and 12 M WT (light blue bars) cohort and corresponding rTg-DI rat cohorts (blue bars). Each dot above the bar represents the value obtained from one rat. Note: WT n = 9, 10, 8 at 3,6 and 12 months; rTg-DI n = 9, 9, 10 at 3, 6, and 12 months, respectively from 3 independent experiments. Data are mean ± s.e.m. Statistical analysis with two-way ANOVA with independent variables including strain (rTg-DI vs WT rats), time (age: 3, 6, 12 M) and the time x strain interaction were fit to compare the mean differences of different outcomes between rTg-DI and WT rats, between different time points within each strain of rats. A p-value of less than 0.05 was chosen to indicate statistical significance and no adjustment of multiple testing was considered. **p-value = 0.004. b Graph with quantification tissue compartment volumes of the 3 M 6 M and 12 M WT (light blue bars) and rTg-DI rat (blue bars) cohorts. Each dot above the bar represents the value obtained from one rat. Note: WT n = 9, 10, 8 at 3,6 and 12 months; rTg-DI n = 9, 9, 10 at 3, 6, and 12 months, respectively from 3 independent experiments. Data are mean ± s.e.m. Statistical analysis same as in b. *p-value = 0.038, **p-value = 0.021, ***p-value = 0.015.

Extended Data Fig. 4 Significantly greater spatial distribution of glymphatic transport observed in WT compared to rTg-DI rats.

a Spatially normalized population averaged color coded speed maps of 12-month (M) old WT (N = 8) and 12 M rTg-DI (N = 10) rats are shown overlaid onto population averaged proton density weighted anatomical MRI brain templates. b For the 12 M WT (N = 8) and 12 M rTg-DI (N = 10) cohorts, statistical parametric maps (color coded for p-values) were calculated at p-value < 0.05 and overlaid onto the MRI brain images to display anatomical areas with significantly more speed in WT rats in comparison to rTg-DI rats or the reverse comparison. Note that the p-value map is uncorrected via the false-discovery rate procedure. Scale bars = 2 mm. Anatomical levels of the axially displayed anatomical templates are given by their nearest Bregma distance. L-HypoT = left hypothalamus; Thal = thalamus; vHip = ventral hippocampus; GN = geniculate nucleus; R-Ctx = retro-splenial cortex; O-Ctx = Occipital cortex. Scale bar = 3 mm.

Extended Data Fig. 5 Heart rate changes across age and strains.

a Graph with quantification of the mean heart rate recorded of the anesthetized rats during MRI imaging from 3-month (M) 6 M and 12 M WT (light blue bars) and age-matched rTg-DI rats (blue bars). Each dot above the bar represents the mean heart rate recorded over the 2–3 h imaging period from one rat. Data are mean ± s.e.m. Note: WT n = 9, 10, 8 animals examined at 3,6 and 12 M, respectively, as independent experiments; rTg-DI n = 9, 9, 10 animals examined at 3,6 and 12 M, respectively, as independent experiments. Statistical analysis with two-way ANOVA with independent variables including strain (rTg-DI vs WT rats), time (age: 3, 6, 12 M) and the time x strain interaction were fit to compare the mean differences of different outcomes between rTg-DI and WT rats, between different time points within each strain of rats. A p-value of less than 0.05 was chosen to indicate statistical significance and no adjustment of multiple testing was considered. *p-value = 0.030, **p-value = 0.013, ****p-value < 0.0001.

Extended Data Fig. 6 Perivascular AQP4 polarization of capillaries is impaired with evolving CAA.

Changes in APQ4 localization was evaluated in WT and CAA rats by immunofluorescence. a-c: Representative slices at the level of the ventral hippocampus from 3 M (a), 6 M (b) and 12 M (c) WT rats showing strong perivascular AQP4 expression and localization across all age cohorts. d-f Corresponding brain slices at the level from age-matched rTg-DI rats demonstrating that the localization of perivascular AQP4 changes with evolving CAA pathology and is down-regulated in relation to the vasculature resulting in higher tissue ‘background’ AQP4 expression in 6 M (e) and 12 M (f) rTg-DI rats in comparison to 3 M rTg-DI rats (D). Scale bar = 500 μ g-i: Graphs of quantification of AQP4 expression in perivascular domains surrounding capillaries in WT and rTg-DI rats. At 3 M there are no differences in perivascular AQP4 expression across the strains (g), however, at 6 M and 12 M the polarization index is decreased in rTg-DI compared to WT inferring more dispersed expression away from the capillary (h, i). Each dot represents the polarization from one capillary in the ventral hippocampus, with n = 20 capillaries/rat and n = 4 for 3 M and 6 M groups and 20capillaries/rat and n = 5 for the 12 M group from three independent experiments. Horizontal bars indicate mean ± s.e.m.; two-tailed Mann-Whitney U test. *p-value < 0.05, ****p-value < 0.0001.

Extended Data Fig. 7 Drainage from CNS to cervical lymph nodes in normal SD rats.

a Graphs of time signal changes in individual right-sided (dashed blue lines) and left-sided (blue lines) deep cervical lymph nodes (dcLN) derived from independent experiments of n = 6 normal Sprague Dawley (SD) rats. dcLN data from one rat was excluded to excessive vascular motion artefacts. Blue line indicates the mean peak time. b Corresponding graphs of time signal changes in individual right-sided (dashed magenta lines) and left-sided (magenta lines) parotid lymph nodes from the same cohort of normal SD rats. c Corresponding graphs of the time signal changes observed in the submandibular cervical lymph nodes (average of 2-3 nodes/rat) from the same cohort of normal SD rats. d-f: Velocity flux vectors – color coded for magnitude – from three different SD rats overlaid onto anatomical masks of the carotid arteries and dcLN showing the direction of solute drainage along the external carotid artery and within the carotid bifurcation towards the dcLN (black boxes). Scale bars = 1 mm.

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Chen, X., Liu, X., Koundal, S. et al. Cerebral amyloid angiopathy is associated with glymphatic transport reduction and time-delayed solute drainage along the neck arteries. Nat Aging 2, 214–223 (2022). https://doi.org/10.1038/s43587-022-00181-4

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