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. Author manuscript; available in PMC: 2022 Nov 28.
Published in final edited form as: Neurobiol Aging. 2021 Apr 30;105:115–128. doi: 10.1016/j.neurobiolaging.2021.04.019

Microvascular Degeneration Occurs Before Plaque Onset and Progresses with Age in 3xTg AD Mice

Dominic D Quintana 1, Yamini Anantula 1, Jorge A Garcia 1, Elizabeth B Engler-Chiurazzi 1, Saumyendra N Sarkar 1, Deborah R Corbin 1, Candice M Brown 1, James W Simpkins 1
PMCID: PMC9703920  NIHMSID: NIHMS1709857  PMID: 34062487

Abstract

Heart disease and vascular disease positively correlate with the incidence of Alzheimer’s disease (AD). Although there is ostensible involvement of dysfunctional cerebrovasculature in AD pathophysiology, the characterization of the specific changes and development of vascular injury during AD remains unclear. In the present study, we established a time-course for the structural changes and degeneration of the angioarchitecture in AD. We used cerebrovascular corrosion cast and μCT imaging to evaluate the geometry, topology, and complexity of the angioarchitecture in the brain of wild type and 3xTg AD mice. We hypothesized that changes to the microvasculature occur early during the disease, and these early identifiable aberrations would be more prominent in the brain subregions implicated in the cognitive decline of AD. Whole-brain analysis of the angioarchitecture indicated early morphological abnormalities and degeneration of microvascular networks in 3xTg AD mice. Our analysis of the hippocampus and cortical subregions revealed microvascular degeneration with onset and progression that was subregion dependent.

Keywords: Alzheimer’s disease, Cerebrovascular degeneration, Microvessel, Aging, Corrosion cast, MicroCT

Introduction

Alzheimer’s disease (AD) is the most common form of dementia and is characterized by progressive neurodegeneration and cognitive decline. Histopathological examination of the postmortem brain from AD patients demonstrates parenchymal deposition of amyloid-β (Aβ)-plaques and neurofibrillary tangles formed by hyper-phosphorylated tau, both of which are neuropathological hallmarks of AD (Castellani et al., 2010; Querfurth and LaFerla, 2010). The contribution of Aβ deposition to the development and progression of AD has been a central focus of AD research. Unfortunately, though many novel drugs targeting Aβ have entered clinical trials, none have been successful at altering the trajectory of the disease.

Comorbidities of the cerebrovasculature are frequently reported in AD, as many as 92% of patients diagnosed with AD also demonstrate ischemic lesions of the white matter that resemble arteriosclerosis of small vessels (Rosenberg et al., 2016). It is well established that regional hypoperfusion, hypometabolism, and blood-brain barrier disruption are common pathological manifestations in patients with AD (Brundel et al., 2012a, 2012b; de la Torre, 2004). Aβ is a potent vasoconstrictor (Thomas et al., 1996) and its production has been associated with impaired endothelium-dependent regulation of cortical microcirculation and aberrant functional hyperemia (Iadecola et al., 1999; Niwa et al., 2000). In rodents, transient exposure to brain ischemia results in increased neuronal tau, amyloid precursor protein (APP) expression, and deposition of Aβ in the hippocampus and cortex (Bailey et al., 2004; Iadecola, 2004). In addition, patients diagnosed with AD are at an increased risk for developing hemorrhagic stroke, cerebral microinfarctions, spontaneous cerebral emboli, and microhemorrhages (Brundel et al., 2012a; Chi et al., 2013; Purandare and Burns, 2009; Tolppanen et al., 2013). Evidence from both clinical and animal studies has demonstrated that cerebrovascular impairment precedes the onset of neurodegeneration and progresses with age-related cognitive decline and with dementia (de la Torre, 2010; Fischer et al., 1990; Iturria-Medina et al., 2016; Jellinger, 2010; Kalaria, 2010; Kalaria and Hedera, 1995), suggesting that vascular dysfunction to participate in a causative manner in the emergence of AD.

Remarkably, 90% of patients with AD demonstrate cerebral amyloid angiopathy (CAA), a neuropathological disease characterized by the deposition of amyloid-β on the walls of cerebral vasculature (DeSimone et al., 2017; Han et al., 2015; Janson, 2015; Vinters, 1987). Aβ deposition on the basement membrane of blood vessels promotes local inflammation, and the activation of vascular endothelial cells, perivascular microglia, pericytes, and astrocytes (Bailey et al., 2004; Farkas et al., 2001; Vinters and Farag, 2003), which contribute to the degradation of the vascular wall. In addition, Aβ accumulation at blood vessels causes vascular smooth muscle cell (vSMC) degeneration (Park et al., 2014; Van Nostrand et al., 2001) and pericyte loss (Park et al., 2014), which together with the weakened blood vessel wall can increase the risk for hemorrhagic stroke that is common in patients with AD (Vinters and Farag, 2003).

Despite demonstrations of strong evidence linking various pathology of the cerebrovasculature with AD etiology, the precise timeline and extent of cerebrovascular changes remain unclear, representing a critical barrier to research progress in the field. Data collected using an innovative whole-brain cerebrovascular casting approach demonstrated that 3xTg AD mice display a disruption of the cerebral angioarchitecture early during the disease process and preceded the established time that is known for AD neuropathological hallmarks such as Aβ deposition.

Methods

Animal Usage

Procedures performed in this study that involved the use of laboratory animals were carried out in accordance with the National Institutes of Health guide for the care and use of laboratory animals (NIH Publication No. 8023, revised 1978) and in compliance with the ARRIVE guidelines. For the purposes of this study, we used the male B6; 129-Tg (APPSwe, tauP301L) 1Lfa Psen1tm1Mpm/Mmjax [RRID:MMRRC_034830-JAX] (3xTg AD) mouse model of Alzheimer’s disease and the B6129SF2/J (wild type, WT) mouse strains from the Mutant Mouse Resource and Research Center (MMRRC) at The Jackson Laboratory. To study the effects of aging, experiments were performed on these mice at 3-months (WT n = 4, 3xTg AD n = 5), 6-months (WT n = 7, 3xTg AD n = 5), 12-months (WT n = 5, 3xTg AD n = 4), and 24-months (WT n = 5, 3xTg AD n = 4) of age. Power analysis for ANOVA designs indicated a sample size of 4 mice per group (power = 0.999) for an effect size of Δ = 1.25. Mice were maintained under a light/dark cycle (12 h : 12 h) with food and water available ad libitum at West Virginia University vivarium. Please refer to ‘Experimental Design and Analysis’ section for additional information regarding group sizes.

Vascular Corrosion Casts

For a detail description of the procedure used to prepare cerebrovascular corrosion casts, please refer to our previous report (Quintana et al., 2019). Briefly, mice were deeply anesthetized with 4% isoflurane diffused into a 30% oxygen and 70% nitrogen mixture. After confirming anesthetization via tail pinch, mice received an intraperitoneal injection of 25U heparin in 250 μl saline intravenous solution. Mice were then transcardially perfused at 160 mmHg with 0.01M phosphate buffered saline (PBS) containing 25U/mL of heparin followed by 4% paraformaldehyde (PFA) in 0.01M PBS at physiological pH. The polyurethane (PU4ii, VasQtec) cast solution was prepared by adding 5 g of polyurethane resin with 3 g of methyl ethyl ketone containing approximately 10 mg of blue pigment. Shortly before perfusion of the cast resin, 0.8 g of polyurethane hardener was added to the solution then immediately perfused into mice at 160 mmHg. Resin perfused mice were kept at room temperature for 4 hours to facilitate the hardening of the polyurethane resin.

Vascular Corrosion Cast Processing

Perfused mice were decapitated, and the skin was removed from the skull with dissecting scissors. The isolated skull was decalcified by immersion in 20 mL of 8% formic acid diluted in Milli-Q water for 5 h at 37 °C. After decalcification, the skull was rinsed with distilled water then immersed in 20 mL of potassium hydroxide solution diluted in Milli-Q water for 4 h at 37 °C. The skull was rinsed with distilled water and the brain carefully extracted with a pair of iris scissors and forceps. The extracted brain was macerated by immersion into 20 mL of 8% potassium hydroxide diluted in Milli-Q water at 37 °C overnight. Macerated tissue was removed from the cerebrovascular cast by several washes with Milli-Q water, until only the casted vessels remained. At this point, the clean cerebrovascular cast was lyophilized with a benchtop freeze dryer system (Labconco) operating at −54 °C and a vacuum pressure of 0.0025 mBar. To enhance the radio opacity of the cerebrovascular casts, 6 mL solution of 2% osmium tetroxide diluted in Milli-Q water was embedded onto the polyurethane cast by immersion overnight at 4 °C. Osmium embedded casts were mounted with cyanoacrylate adhesive on custom-made hexagonal pedestals cut from Plexiglas.

Micro-computerized Tomographic Imaging

Tomographic images of the vascular network of the whole brain were acquired using a SkyScan 1272 μCT (Bruker) operating at 35 kV and 200 μA with no filter and a frame size of 4032-by-2686. Acquisition parameters were as follows, a pixel size of 2 μm, and exposure settings that produce a maximum transmission of 35–40%, minimum of 80–90%, and an average of 65–75% through the sample. Tomographic images of each vascular cast were acquired over the total 360° of the sample at a step size of 0.05° and averaged by 5 images per rotation step. Before further processing, each dataset was corrected for misalignments, beam hardening, and ring artifacts then converted into a coronal image series. Each acquisition routine produced 7200 projection images and 4032 images in coronal series.

Anatomical Selection of Volume of Interest

Datasets containing the whole brain vasculature in coronal series were accessed on CTan (Bruker) to isolate the volume of interest (VOI). Guided by the Allen Mouse Brain Atlas (Lein et al., 2007), VOIs over the medial orbital prefrontal cortex (MO PFC), somatosensory cortex (SS CTX), cingulate cortex (CC), entorhinal cortex (ENT CTX), dentate gyrus (DG) and hippocampal CA1 and CA3 were located and selected by tracing. The traced regions were made in square selections of approximately 1.5 mm and dynamically interpolated in the Z direction for 1.5 mm. These selected VOIs were converted into a BMP formatted series and saved as pre-processed VOIs.

Image Processing and Optimization for Volume of Interest

For a detailed description of VOI dataset optimization please refer to our previous report (Quintana et al., 2019). Briefly, VOI datasets were optimized for further image analysis using ImageJ (NIH). Each VOI was cropped in three-dimensions to 500 × 500 × 500 (MO PFC, SS CTX, ENT CTX, CA1, and CA3) or 400 × 400 × 400 (CC and DG) then rotated so that a preselected landmark was co-registered by rigid transformation across all datasets. For each dataset, the image series was duplicated and saved as TIFF formatted images. With the 3D/2D skeletonization plugin on ImageJ, the first duplicated dataset was used to produce centerlines of the vascular structures. The second duplicated dataset was filtered by maximum pixel intensity of 160 to produce a dataset with pixel intensities 160 and below. Finally, both image series containing the vessel centerlines and capped pixel intensity were merged so that the final dataset contains vessel structures composed of pixel intensities 160 and below and centerlines of vessels at an intensity of 255.

Vascular Network Analysis

Datasets were analyzed with Imaris (Bitplane) software. Vascular networks were identified and quantified with the Imaris Filament Trace function using the automatic filament and path detection algorithm. Filament trace approximates the diameter of blood vessels in a volumetric data set by calculating the diameter of the largest sphere that fits within the cross-sectional area of a vessel segment. We used the Loops algorithm and automatic threshold during the tracing procedure for an accurate identification of circular pathways between vessel segments. Traced networks were automatically re-centered to the centerlines of the vascular structures that were inserted into the volumetric dataset in each VOI. The identified vascular networks were manually inspected for errors that occurred during the tracing process before measurements were generated from the mapped vessel network(s). These measurements included vessel diameter, length, volume, area, tortuosity, branch level, and angle in degrees.

Experimental Design and Statistical Analysis

Power analysis for ANOVA designs indicated a sample size of 4 mice per group (power = 0.999) for an effect size of Δ = 1.25. To study the effects of aging and disease on the cerebrovasculature over the whole-brain, analyses were performed on mice at 3-months (WT, n = 4; 3xTg AD, n = 4), 6-months (WT, n = 3; 3xTg AD, n = 4), 12-months (WT, n = 4; 3xTg AD, n = 3), and 24-months (WT, n = 4; 3xTg AD, n = 4) of age. Analyses of the cerebrovasculature in specific brain regions were performed on mice at 3-months (WT, n = 4; 3xTg AD, n = 5), 6-months (WT, n = 7; 3xTg AD, n = 5), 12-months (WT, n = 5; 3xTg AD, n = 4), and 24-months (WT, n = 5; 3xTg AD, n = 4). All values are presented as mean ± SEM for all data provided. All statistical analyses were performed on GraphPad Prism 8.4.2. The planned comparison for all data were the effects of genotype at each age. Before performing planned comparisons, 2-way ANOVA analyses of Genotype by Age were used to qualify the data from each of the experimental measurements. Details of each 2-way ANOVA, including degrees of freedom, F-values, and p-values are reported in the Results section of the text. To compare means, significant 2-way ANOVAs were probed for effects of Genotype with planned student’s t-test at each age. All student’s t-tests are two-tailed unless specified. Analysis of trend was performed with linear regression analysis. Details of each linear regression analysis, including R2-values and p-values are reported in the Results section of the text. Values of p < 0.05 were considered as significant.

Results

We have reported previously a procedure for the acquisition and analysis of the entire brain cerebrovascular system at a microvessel-relevant resolution, to provide a detailed characterization of the morphological and topological properties of vessels and their networks (Quintana et al., 2019). In the present study, we use vascular corrosion casts to acquire data from complete networks of cerebral blood vessels in a state that preserves native connectivity. We performed an in-depth analysis of the cerebrovasculature in WT (n = 21) and 3xTg AD (n = 18) mice by assessing the function, morphology, topology, and complexity of vascular networks over the entire brain then focused on prominent brain regions critically affected during the progression of AD (Figure 1). Indeed, geometric characterization of the vasculature has provided valuable insight for identifying fundamental, yet broad changes to the cerebrovasculature during age and AD. However, these broad changes may only be detectable when the pathology of the cerebrovascular system is severe. It is likely that early changes to the organization of the vascular network cause dysregulated functional connectivity before the onset of detectable changes to broad parametric properties such as volume density or cumulative length. It is firmly accepted that with the progression of AD, the development of vascular dysfunction is at increased risk (Santos et al., 2017). It is unknown whether the 3xTg AD mouse line manifests functional deficits and degeneration of the vasculature that is similar to human patients with the disease.

Figure 1. The cerebrovasculature in 3xTg AD mice develops pathology.

Figure 1.

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Reconstructed cerebrovascular corrosion cast in three-dimensions, color-coded by vessel diameter of (A) whole brain, (B-E) cortex, and (F) hippocampus. Color-coded (cooler colors = small diameter / warmer colors = larger diameter) penetrating arteriole demonstrating (B) normal morphology compared to a (C and D) tortuous vessel from a 3xTg AD mouse. Color-coded (cooler colors = small diameter / warmer colors = larger diameter) vasculature demonstrating (D) arteriolar aneurysm and (E and F) capillary aneurysm from 3xTg AD mice.

The mice used in this study were 3.0 ± 0.5, 6.0 ± 0.3, 12 ± 0.6, and 24 ± 0.6 months of age. The average body temperature for WT mice was 35.5 ± 0.7, 36.6 ± 0.2, 35.8 ± 0.1, and 35.6 ± 0.1 °C at 3-, 6-, 12-, and 24-months of age, respectively. Average body temperature of 3xTg AD mice was 36.1 ± 0.1, 36.3 ± 0.4, 35.2 ± 0.2, and 35.7 ± 0.1 °C at 3-, 6-, 12-, and 24-months, respectively. The average body weight for WT mice was 29.7 ± 0.6, 27.0 ± 0.4, 30.7 ± 1.8, and 39.4 ± 4.8 grams at 3-, 6-, 12-, and 24-months, respectively. Average body weight for 3xTg AD mice was 29.7 ± 0.5, 30.7 ± 0.4, 34.1 ± 0.7, and 30.2 ± 2.3 grams at 3-, 6-, 12-, and 24-months, respectively.

Whole-brain analysis revealed early structural changes to the angioarchitecture occur before the onset of vascular degeneration in 3xTg AD mice

Our first goal was to characterize the extent of vascular disruption over the whole brain with age and the progression of AD. To evaluate the evolution of the structural changes to the angioarchitecture, we calculated the overall means of the geometric properties over all vessels of the brain. Morphometric analysis of the vasculature over the entire brain indicated that the average vessel diameter at 3-months (t(7) = 1.897, p = 0.05, t-test) and 6-months (t(6) = 2.310, p = 0.03, t-test), but not at 12- (t(6) = 0.135, p = 0.4, t-test) and 24- (t(7) = 1.234, p = 0.13, t-test) months were statistically different between the two genotypes (Figure 2A). A change to the average vessel diameter may indicate a compensatory adaptation of the vascular network, likely caused by either chronic injury to vessels or via disrupted angiogenesis.

Figure 2. Brain-wide vascular degeneration in 3xTg AD mice that progresses with age.

Figure 2.

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Bar graph(s) (mean ± SEM) depicting (A) average vessel distance, (B) number of vessel segments, (C) total surface area, (D) total vascular volume, (E) intervessel distance, (F) number of junctions, (G) network redundancy, and (H) fractal dimension from 3- (WT, n = 4; 3xTg AD, n = 4), 6- (WT, n = 3; 3xTg AD, n = 4), 12- (WT, n = 4; 3xTg AD, n = 3), and 24- (WT, n = 4; 3xTg AD, n = 4) month old mice. Line graph(s) (mean ± SEM) depicting total vascular volume as a function of average vessel diameter from (I) 3- (WT, n = 3; 3xTg AD, n = 4), (J) 6- (WT, n = 3; 3xTg AD, n = 4), (K) 12- (WT, n = 4; 3xTg AD, n = 3), and (L) 24- (WT, n = 4; 3xTg AD, n = 4) month old mice. Bar graph(s) (mean ± SEM) depicting expanded data denoted by the box inserts (M-P). To compare means, significant 2-way ANOVAs were probed for effects of Genotype with planned student’s t-test at each age or vessel diameter (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Arborization of the vasculature is achieved by the growth of new vessel segments. With the growth of new segments, vascular density increases, enhancing the exchange of waste and nutrients with the surrounding tissue. Therefore, we quantified the total number of vessel segments (F(3,22) = 17.53, p < 0.0001, 2-way ANOVA) of the whole brain and found early changes to the number of segments in 3xTg AD mice that began at 6-months of age. At 6-months of age, the cerebrovascular network in 3xTg AD mice was composed of a greater number of vessel segments (t(6) = 2.476, p = 0.02, t-test) than in age-matched WT mice (Figure 2B). The total number of vessel segments in 3xTg AD mice remained increased (t(6) = 2.222, p = 0.04, t-test) at 12-months of age. Interestingly, at 24-months of age, the total number of vessel segments in 3xTg AD mice was substantially lower (t(7) = 6.806, p = 0.0005, t-test) than the number of segments recorded in age-matched WT mice (Figure 2B).

An essential physical property of blood vessels is the surface area they possess. The total surface area a vascular network possesses is directly proportional to the efficacy of solute exchange between the parenchyma and peripheral circulation. Regression analysis of the total surface area (F(3,22) = 4.882, p = 0.009, 2-way ANOVA) of all vessels revealed a linear trend that decreased with age in 3xTg AD mice (R2 = 0.55, p = 0.0013), whereas the linear trend in WT mice increases with age (R2 = 0.29, p = 0.036). The total surface area of vessels was most affected at 24-months in 3xTg AD mice. At this age, the complete cerebrovascular network provided a total surface area that was substantially less (t(7) = 3.867, p = 0.0008, t-test) in 3xTg AD mice than in age-matched WT mice (Figure 2C).

Given these findings, since the majority of the surface area is provided by the microvasculature (Gould et al., 2017), we interpret these data to suggest that the age-dependent changes to the cerebrovasculature of 3xTg AD mice are largely driven by microvascular degeneration. In order to determine the extent of vascular degeneration in 3xTg AD mice, we measured the total vascular volume of the entire brain with age. We found no early differences in the total volume occupied by vessels between the genotypes (Figure 2D). However, a regression analysis of these changes revealed an age-dependent reduction of volume in 3xTg AD mice (Figure 2D). In contrast, WT mice demonstrated a pattern of vessel volume that increased with age. When we compared means between genotypes, the total volume of vessels (F(3,22) = 4.621, p = 0.01, 2-way ANOVA) was concordant at 3- (t(7) = 0.84, p = 0.35, t-test), 6- (t(6) = 0.59, p = 0.14, t-test), and 12- (t(6) = 0.29, p = 0.15, t-test) months of age in 3xTg AD and age-matched WT mice. At 24-months of age, 3xTg AD mice demonstrated a marked reduction of the total volume occupied by vessels (t(7) = 3.84, p = 0.0009, t-test) compared to age-matched WT mice (Figure 2D). These data may indicate that early structural changes to the organization of the cerebrovasculature occur before the onset of large-scale changes, which may result in vascular degeneration.

Therefore, we measured the average intervessel distance in three-dimensions as a proxy to assess the adequacy of vascularization. We found intervessel distance (F(3,22) = 28.34, p = 0.0001, 2-way ANOVA) to be a profoundly compromised property of the angioarchitecture in all ages of 3xTg AD mice (Figure 2E). At 3-months of age, the average distance between vessels was larger (t(7) = 6.40, p < 0.0001, t-test) in 3xTg AD mice than in WT mice. Increased intervessel distance in 3xTg AD mice was also evident at 6-months (t(6) = 7.13, p < 0.0001, t-test) and to 12-months (t(6) = 4.01, p = 0.0006, t-test) of age compared to age-matched WT mice. Interestingly, we found that by 24-months, intervessel distance (t(7) = 4.86, p < 0.0001, t-test) was reduced in 3xTg AD mice than WT mice (Figure 2E). Regression analysis revealed that intervessel distance is inversely associated with age in 3xTg AD mice (R2 = 0.64, p = 0.003) while positively associated with age in WT mice (R2 = 0.82, p < 0.0001). These data indicate an early deficit of vessel density that persists to later stages of progression in 3xTg AD mice.

Topological analysis of the vascular network over the whole brain revealed disrupted network organization in 3xTg AD mice that progresses with age

Since morphometric analyses of the angioarchitecture provided evidence of early structural changes to the vasculature that preceded loss of vessels, we assessed the topology of the vascular network over the entire brain. We used a topological analysis to evaluate the organization and complexity of the vascular network with age. We first examined the connectedness of the vascular network. Consistent with the literature (Gross, 2006; Sheth and Liebeskind, 2014), we observed a dense network of vessels with elevated pathway redundancy in both WT and 3xTg AD mice at all ages.

We used an algorithm to calculate the Euler number and pathway redundancy of the vascular network in three-dimensions. The Euler number is calculated based on the classification of several morphological events: islands, bridges, and holes. We first quantified the number of connections in the vascular network by counting vessel-to-vessel junctions (F(3,22) = 10.96, p = 0.0002, 2-way ANOVA) within the entire brain. At 6-months of age, the number of vessel junctions in 3xTg AD mice were greater (t(6) = 2.72, p = 0.05, t-test) than the number of junctions in WT mice. While we observed no difference in the number of connections at 12-months (t(6) = 0.74, p = 0.46, t-test) between genotypes, by 24-months of age, the total number of connections in 3xTg AD mice were lower (t(7) = 5.61, p < 0.0001, t-test) than those in WT mice (Figure 2F).

The cerebrovascular network provides a profound number of redundant pathways for the delivery of blood to any given region. These redundant pathways maintain perfusion by providing an alternate route to communicate shunted blood to a particular region. These collateral pathways for blood flow lend to the resilience of the network to maintain adequate blood delivery during an event of a pathway communicating blood becomes compromised. We next evaluated the redundancy of pathways (F(3,22) = 3.694, p = 0.02, 2-way ANOVA) within the vascular network by calculating the connectivity value and compared this across genotype. The connectivity value is topologically defined as the maximum number of cuts that can be performed on a network before splitting into two isolated subnetworks (Nyengaard, 1999). Regression analysis of pathway redundancy indicated an inverse correlation (R2 = 0.32, p = 0.02) with age in 3xTg AD mice. In contrast, pathway redundancy in WT mice correlated positively (R2 = 0.17, p = 0.15) with age. Connectivity analysis indicated an elevated (t(7) = 2.37, p = 0.03, t-test) number of redundant pathways in 3xTg AD mice at 3-months of age. These pathways remained elevated (t(6) = 2.25, p = 0.05, t-test) in 3xTg AD mice relative to WT mice at 6-months of age. Interestingly, these differences were absent at 12-months (t(6) = 1.37, p = 0.18, t-test) and 24-months (t(7) = 1.27, p = 0.21, t-test) of age (Figure 2G).

We next evaluated the complexity of the vascular network with age and progression of AD. We used Kolmogorov box counting method for fractal dimensions (Quintana et al., 2019) and found that the complexity of the vascular network progressively declines with the progression of age in 3xTg AD mice whereas in WT mice partially increases with age. The fractal dimension of a vascular structure quantitatively evaluates its branching patterns and their space-filling ability by assessing the relationship between its branches of like hierarchical order and the number of levels the network branches. Analysis of fractal dimension value (F(3,22) = 10.53, p = 0.0002, 2-way ANOVA) indicated no difference between the two genotypes at 3-months of age. However, at 6-months of age, the complexity of the vascular network of 3xTg AD mice was greater (t(6) = 2.96, p = 0.007, t-test) than of age-matched WT mice. Following 6-months, the vascular network complexity of 3xTg AD mice begins to trend downward with age (R2 = 0.79, p = 0.0002) whereas in WT mice, network complexity trends upward with age (R2 = 0.39, p = 0.05) (Figure 2H). By 24-months of age, network complexity in 3xTg AD mice was severely compromised (t(7) = 4.42, p = 0.0002, t-test) when compared to age-matched WT mice (Figure 2H).

Age and disease related changes to vessel hierarchy over the whole brain occur in 3xTg AD and WT mice

Specific differences at the level of vessel hierarchy may exist between age and genotype. We generated thickness maps of all vessels of the brain to classify and analyze vascular volume as a function of vessel diameter. Such that, the sum volume is calculated for all vessels of each successive diameter. We found that the distribution of vessel volume in 3xTg AD mice demonstrated large changes that occurred with age and the progression of the disease (Figure 2I – L). The distribution of vessels as a function of diameter depicted that the largest changes between genotypes per age was accounted for by vessels with an average diameter between 2 – 40μm (Figure 2I–L). When we compared the mean volume per vessel diameter between the two genotypes at each age we found that vessels with an average diameter between 2 – 20μm appeared to decrease with age in 3xTg AD mice whereas in WT mice, these vessels increased with age (Figure 2M–P). Interestingly, 3xTg AD mice demonstrated an early increase (F(7,40) = 6.0, p < 0.0001, 2-way ANOVA) of vessels with an average diameter between 2 – 20μm at 3-months of age. This observation was also seen at 6-months of age (F(7,40) = 10.97, p < 0.0001, 2-way ANOVA), relative to age matched WT mice (Figure 2M and N). However, by 12-months of age, vessels with an average diameter between 2 – 20μm was not significantly different (F(7,40) = 1.99, p = 0.08, 2-way ANOVA) between the two genotypes and remained indifferent (F(7,40) = 1.49, p = 0.19, 2-way ANOVA) by 24-months of age (Figure 2O and P). Furthermore, larger vessels with an average diameter between 20 – 40μm were reduced at later ages in 3xTg AD mice. Although we found no difference in the population of larger vessels at 3- and 6-months of age, we observed reduced vessels with an average diameter with 20 – 40μm by 24-months of age in 3xTg AD mice relative to WT mice.

These data indicate that the cerebrovascular network in 3xTg AD mice undergo an early vascularization by 3-months of age then begin to progressively decline by 12-months of age (Figure 3). To better demonstrate these changes with age, we measured the total volume of capillaries and non-capillaries for both genotypes at each age. Regression analysis revealed an age-associated decline (R2 = 0.53, p = 0.002) of the total capillary volume in the brain of 3xTg AD mice, whereas in WT mice, the total capillary volume increased with age (R2 = 0.47, p = 0.006). When we compared means across genotype per age group (F(3,21) = 6.41, p = 0.003, 2-way ANOVA), we found that by 3-months the total volume of capillaries was increased (t(6) = 3.84, p = 0.01, t-test) in 3xTg AD mice compared to age-matched WT mice (Figure 3A). The total capillary volume remained elevated (t(6) = 3.70, p = 0.01, t-test) by 6-months in 3xTg AD mice, relative to age-matched WT mice (Figure 3A). By 12-months of age, the total volume of capillaries in 3xTg AD mice did not differ (t(6) = 1.70, p = 0.14, t-test) from that in age-matched WT mice. At 24-months of age the total volume of capillaries was lower (t(7) = 2.37, p = 0.05, t-test) in 3xTg AD mice compared to age-matched WT mice (Figure 3A).

Figure 3. Topological analysis of the cerebrovasculature of the entire brain describes changes that progress with age in 3xTg AD mice.

Figure 3.

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Reconstructed cerebrovascular corrosion cast, color-coded by vessel diameter from a 3-month and 12-month WT and 3xTg AD mouse. (A) The total volume of capillaries and (B) non-capillaries in WT and 3xTg AD mice at 3- (WT, n = 3; 3xTg AD, n = 4), 6- (WT, n = 3; 3xTg AD, n = 4), 12- (WT, n = 4; 3xTg AD, n = 3), and 24- (WT, n = 4; 3xTg AD, n = 4) months of age. To compare means, significant 2-way ANOVAs were probed for effects of Genotype with planned student’s t-test at each age (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Similarly, regression analysis of the total volume of non-capillaries revealed an age-associated decline (R2 = 0.76, p < 0.0001) in 3xTg AD mice, whereas in WT mice, the total volume of non-capillaries increased with age (R2 = 0.15, p = 0.15). When we compared means across genotype at each age group (F(3,21) = 6.29, p = 0.003, 2-way ANOVA), we found no significant difference at 3- (t(6) = 1.79, p = 0.06, t-test) and 6-months (t(6) = 0.17, p = 0.43, t-test) months of age (Figure 3B). However, at 12-months of age, the total volume of non-capillaries was reduced (t(6) = 1.98, p = 0.05, t-test) compared to WT mice and continued to decline (t(7) = 3.14, p = 0.009, t-test) by 24-months of age (Figure 3B).

Cerebrovasculature of the hippocampal formation during ageing and the progression of AD

We next evaluated the angioarchitecture of the hippocampal formation by measuring the composition and topology of the vascular network in order to characterize regional pathology to the vasculature network of the hippocampus and to establish a time-course for the onset and progression of vascular disruption in 3xTg AD mice.

The hippocampus develops age-dependent neuropathy in 3xTg AD mice (Billings et al., 2005; España et al., 2010; Giménez-Llort et al., 2007; Oddo et al., 2003b, 2003a) and is the first region of the brain to develop Aβ-plaque (Oddo et al., 2003b, 2003a). Furthermore, the hippocampus is a major region of the brain for the deposition of Aβ in hAPP-J20, APP/PS1, and 3xTg AD mice (Oddo et al., 2003a, 2003b; Whitesall et al., 2004). To characterize the region-specific and age-associated, disease-related pathology to the vascular network of the hippocampus, we analyzed the angioarchitecture in subfield(s) CA1, CA3 and DG of the hippocampus proper and the ENT CTX of the hippocampal formation in 3xTg AD and WT mice.

Our initial assessment consisted of morphometric analyses of all vessels to evaluate the breadth of vessel branching (vessel segment count and number of vessel-to-vessel junctions), regional vascularization (total vessel volume and vessel surface area), and network efficacy (intervessel distance). We found that changes to the angioarchitecture in the hippocampus occurs early, at 3-months of age (Figure 4), prior to the development of Aβ-plaques in 3xTg AD mice (Oddo et al., 2003a, 2003b; Whitesall et al., 2004). Morphometry of the vasculature at the age of 3-months indicated that the earliest changes to the angioarchitecture consisted of a significantly reduced number of vessel segments in the CA1 (t(8) = 2.65, p = 0.01, t-test) and CA3 (t(8) = 2.05, p = 0.005, t-test) hippocampal subfield(s) and a significantly reduced number of vessel-to-vessel junctions in the CA1 (t(8) = 2.77, p = 0.01, t-test), CA3 (t(8) = 2.32, p = 0.02, t-test), and DG (t(8) = 2.25, p = 0.05, t-test) subfield(s) (Figure 4A–H). However, these differences diminished by 6-months and throughout age, whereas the total volume occupied by all vessels (Figure 4I–L) and the total surface area of all vessels (Figure 4M–P) significantly reduced, while the intervessel distance (Figure 4Q–T) significantly increased by 6-months and progressed with age. Furthermore, we found that the angioarchitecture of the ENT CTX to be weakly affected with age in 3xTg AD mice. Thus, we interpret these measurements to suggest that early deficits in vessel branching occur before the onset of age-associated progression of vessel loss at 6-months of age.

Figure 4. Vascular network analysis of the hippocampus indicates early and age-dependent changes to the vasculature in 3xTg AD mice.

Figure 4.

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Bar graph(s) (mean ± SEM) depicting (A-D) number of vessel segments, (E-H) number of vessel junctions, (I-L) total vascular volume, (M-P) total vascular surface area, and (Q-T) intervessel distance from 3- (WT, n = 4; 3xTg AD, n = 5), 6- (WT, n = 7; 3xTg AD, n = 5), 12- (WT, n = 5; 3xTg AD, n = 5), and 24- (WT, n = 6; 3xTg AD, n = 3) month WT and 3xTg AD mice. To compare means, significant 2-way ANOVAs within each brain region were probed for effects of Genotype with planned student’s t-test at each age (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Age and disease-related changes specific to vessel hierarchy occur in the hippocampus of 3xTg AD mice

We next sought to extrapolate on the differences obtained by morphometric and geometric analysis of the vasculature to determine if these genotypic differences were a result of changes to vessels of a specific hierarchy. Therefore, by mapping the total length of vessels as a function of average vessel diameter, we searched for genotypic differences in the summated length of vessels per vessel diameter. We observed in 3xTg AD mice at 3-months, a major deficit of microvessels in the CA1 (Figure 5C), CA3 (Figure 5G), and DG (Figure 5M) subfield(s) but not in the ENT CTX (Figure 5Q). By 6-months, microvascular deficits worsened in the CA1 (Figure 5D) and persisted in the CA3 (Figure 5H) subregion(s) in 3xTg AD mice. In contrast, vessels with diameters larger than capillary caliber were considerably elevated in the CA1 (Figure 5C), CA3 (Figure 5D), and DG (Figure 5M) in AD mice at 3-months, then became concordant between genotypes in the CA1 (Figure 5D) and CA3 (Figure 5H) but not DG (Figure 5N) subfield(s) by 6-months of age. By this time, microvessels begin to decline and larger vessels increase in the ENT CTX in 3xTg AD mice (Figure 5R).

Figure 5. Microvascular degeneration in the hippocampus occurs early and progresses with age in a hippocampal subregion specific manner.

Figure 5.

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Reconstructed vascular network, color-coded by vessel diameter (cooler color = small / warmer color = large) from hippocampal subregions (A) CA1, (B) CA3, (K) DG, (L) and entorhinal cortex from WT and 3xTg AD mice. Grouped bar graph(s) (mean ± SEM) depicting the total length of vessels as a function of average vessel diameter in hippocampal subregion(s) (C-F) CA1, (G-J) CA3, (M-P) DG, and (Q-T) entorhinal cortex from 3- (WT, n = 4; 3xTg AD, n = 5), 6- (WT, n = 7; 3xTg AD, n = 5), 12- (WT, n = 5; 3xTg AD, n = 5), and 24- (WT, n = 5; 3xTg AD, n = 3) month old WT and 3xTg AD mice. To compare means, significant 2-way ANOVAs within each age group and in each region were probed for effects of Genotype with planned student’s t-test at each vessel diameter (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Although, capillary deficits in the CA1, CA3, and DG seem to be most severe at 3- and 6 -months of age, the deficits persist throughout age in 3xTg AD mice. Peculiar to us, is our observation of increased length of larger vessels that were seemingly transitorily affected with age in all of the measured hippocampal subregions. Nonetheless, since the data revealed profound changes to microvessels and only minor transitory changes to larger vessels we conclude that in the hippocampus of 3xTg AD mice, microvessels are the major population of vessels affected at both early and late stages of the disease progression and that these vessels account for many of the genotypic differences of the vascular network we observed via morphometric and geometric analyses.

Changes to the cortical vasculature in 3xTg AD mice is age-associated and region-dependent

We next examined the cortical vasculature via morphometric and geometric analyses to determine the time-course of the onset and progression of vascular disruption. In order to make these assessments, we measured the intensity of vessel branching, regional vascularization, and network efficacy in the MO PFC, SS CTX, and CC in 3xTg AD and WT mice. Deposits of extracellular Aβ in the MO PFC begin to appear at 6-months of age in layers 4 and 5 and becomes severe by 12-months (Oddo et al., 2003b, 2003a). Our analyses revealed that the vascular network of the CC was the only cortical region to be affected at 3-months of age in 3xTg AD mice. Similar to that observed in the hippocampus, the number of vessel segments (Figure 6C) and number of vessel junctions (Figure 6F) were significantly reduced in 3xTg AD mice compared to age-matched WT mice. However, these genotypic differences diminished by 6-months and throughout age in the CC. We found no differences to the number of vessel segments and the number of junctions in the MO PFC (Figure 6A) and SS CTX (Figure 6B) at 3-months, however by 6-months there were significantly fewer vessel segments and junctions in the MO PFC but not in the SS CTX of 3xTg AD mice. Compromised vascular volume was initially observed at 6-months in the MO PFC (Figure 6G) and CC (Figure 6I) and again at 12-months of age but only in the CC. In fact, at 6-months of age, the total surface area of all vessels and the intervessel distance was significantly reduced in all cortical regions evaluated. At 12-months of age, the surface area of all vessels in the MO PFC (Figure 6J) and CC (Figure 6L) was significantly reduced in 3xTg AD mice. Similarly, the intervessel distance at the MO PFC (Figure 6M), SS CTX (Figure 6N), and CC (Figure 6O) of 3xTg AD mice was significantly higher at 6-months, an effect that largely persisted at later ages.

Figure 6. Vascular network analysis of the cortex indicates early and age-dependent changes to the vasculature in 3xTg AD mice.

Figure 6.

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Bar graph(s) (mean ± SEM) depicting (A-C) number of vessel segments, (D-F) number of vessel junctions, (G-I) total vascular volume, (J-L) total vascular surface area, and (M-O) intervessel distance from 3- (WT, n = 4; 3xTg AD, n = 5), 6- (WT, n = 7; 3xTg AD, n = 5), 12- (WT, n = 5; 3xTg AD, n = 5), and 24- (WT, n = 5; 3xTg AD, n = 4) month WT and 3xTg AD mice. To compare means, significant 2-way ANOVAs within each brain region were probed for effects of Genotype with planned student’s t-test at each age (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Early deficits of cortical microvessels progress with age in 3xTg AD mice

We next extrapolated on these differences by evaluating the changes that occur to vessels of specific hierarchy. Using local thickness maps of vessels, we calculated the total length of vessels as a function of average vessel diameter. We found that the earliest changes to the vasculature occur in the SS CTX (Figure 7C) and CC (Figure 7L) at the age of 3-months. In both of these regions, 3xTg AD mice demonstrated a deficit of microvessels that had an average diameter less than 5 μm and a significant increase of vessels with a diameter greater than 7 μm (Figure 7C and L). At 6-months, microvessels with an average diameter of 3 and 4 μm were significantly reduced in all three cortical regions (Figure 7D, H and M). However, at this age, larger vessels in the MO PFC (Figure 7D) and CC (Figure 7M) were reduced in 3xTg AD compared to WT mice. At 12-months of age, microvessel deficits were observed only in the SS CTX (Figure 7E) and MO PFC (Figure 7I). Interestingly, larger vessels were increased in the MO PFC (Figure 7I) while reduced in the SS CTX (Figure 7E) and CC (Figure 7N) in 3xTg AD mice. By 24-months of age, microvessel deficits were observed only in the MO PFC (Figure 7J) of 3xTg AD mice. Whereas, deficits of larger vessels were observed in the MO PFC (Figure 7I) and CC (Figure 7O) of 3xTg AD mice at 24-months of age.

Figure 7. Microvascular degeneration in the cortex occurs early and progresses with age in a subregion specific manner.

Figure 7.

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Reconstructed vascular network color-coded by vessel diameter (cooler color = small / warmer color = large) from cortical subregions (A) somatosensory cortex (SS CTX), (B) medial orbital prefrontal cortex (MO PFC), and (K) cingulate cortex from WT and 3xTg AD mice. Grouped bar graph(s) (mean ± SEM) depicting the total length of vessels as a function of average vessel diameter in cortical subregion(s) (C-F) SS CTX, (G-J) MO PFC, and (L-O) cingulate cortex from 3- (WT, n = 4; 3xTg AD, n = 5), 6- (WT, n = 7; 3xTg AD, n = 5), 12- (WT, n = 5; 3xTg AD, n = 5), and 24- (WT, n = 5; 3xTg AD, n = 4) month old WT and 3xTg AD mice. To compare means, significant 2-way ANOVAs within each age group and in each region were probed for effects of Genotype with planned student’s t-test at each vessel diameter (*, p < 0.05; **, p < 0.01; ***, p < 0.001).

Discussion

Although numerous neuroimaging studies in patients with AD confirm a reduction in CBF during early and late stages of AD, it remains unclear whether cerebrovascular hypoperfusion is a cause or consequence of AD. Pathological changes to the structure of the microvasculature have been shown to be more prominent in demented than in non-demented individuals (Brown et al., 2009). Further evaluation of the pathological changes that occur to the cerebrovasculature during AD offers a promising approach to identify useful biomarkers for the detection of AD at early stages.

In the present study, our goal was to characterize the changes to the cerebrovasculature in 3xTg AD mice by establishing the time-course for vascular degeneration with age and the progression of AD. We hypothesized that the degeneration of the microvasculature is a major component of the pathophysiology of AD. We addressed this hypothesis by evaluating the microvasculature of the whole brain and in key brain regions with age and the progression of AD in 3xTg AD mice.

Vascular risk factors for hypoperfusion can cause hemodynamic changes to the brain microvasculature that can result in cognitive impairment by preventing optimal delivery of oxygen and glucose to the brain (Duron and Hanon, 2008). Furthermore, previous studies conducted with AD patients suggest that hypoxia increases the levels of Aβ in the vasculature of the brain (Peers et al., 2009). It is believed that Aβ exerts its toxicity to the vasculature by dysregulating intracellular calcium homeostasis, which can lead to the dysfunction of many cellular processes (Quintana et al., 2020).

Reconstruction of cerebrovascular corrosion casts in three-dimension demonstrated visual evidence of chronic cerebrovascular injury in 3xTg AD mice including, tortuous vessels in the cortex (Figure 1B and C) and aneurysms of capillaries and arterioles (Figure 1D–F). Our observed vessel tortuosity confirms prior reports demonstrating similar dysmorphic tortuous vessels in the cortex of AD transgenic mice (Dorr et al., 2012). Progressive weakening of the structural elements of the vascular wall is responsible for both the formation of aneurysms and tortuous vessels (Dobrin et al., 1988). Elastin is an important extracellular matrix protein of the vessel wall and its degradation can compromise the structural integrity of the vascular wall (Dobrin and Canfield, 1984). Elastin deficiency has been implicated in the formation of tortuous vessels in patients with arterial tortuosity syndrome and in transgenic mice (Carta et al., 2009; Coucke et al., 2006; Nakamura et al., 2002; Taarnhøj et al., 2008; Yanagisawa et al., 2002). Elastolytic enzymes have been noted to be elevated in the aneurysmal wall, including neutrophil elastase and members of the matrix metalloprotease (MMP) class. It is likely that the deposition of Aβ on the wall of cerebral blood vessels causes damage to the extracellular matrix and degeneration of the vessel wall. Noteworthy, mechanical stress to the vessel wall and oxidative stress can cause the activation of vascular smooth muscle cells (vSMC) resulting in a phenotype shift from a contractile state into a biosynthetic state (Ye et al., 2014). Activated vSMC directly participate in the destabilization of the vascular wall via the production of matrix-degrading proteases such as MMPs (Bond et al., 1998; Fabunmi et al., 1996). Activation of vSMC may provide a potential mechanism for the Aβ-induced damage to cerebral blood vessels and to the formation of aneurysms and tortuous vessels.

Cerebrovascular hypoperfusion causes metabolic stress and degeneration of the brain parenchyma (Quintana et al., 2018). As cerebrovascular hypoperfusion progresses, reactive oxygen species (ROS) are produced and accumulate within mitochondria (Clanton, 2007; Halestrap and Pasdois, 2009). Concomitantly, hypoperfusion results in ATP depletion, causing high intracellular Na+ concentration, which compromises the Na+/Ca+ antiporter resulting in the accumulation of intracellular Ca+ (Halestrap and Pasdois, 2009). Increased intracellular Ca+ concentration together with elevated levels of mitochondrial ROS induce the formation of the mitochondrial membrane permeability transition pore (mPTP), facilitating the pathway for necrotic cell death (Halestrap, 2010; Loor et al., 2011).

Geometric analysis of the angioarchitecture revealed early changes to the cerebrovasculature that indicated morphological response to chronic injury. Interestingly, these early changes occurred at 3-months of age, and included alterations to the average vessel diameter, intervessel distance, and pathway redundancy of the vascular network, suggesting an alteration to the network composition. This contrasted with the changes to the angioarchitecture that occurred following 6-months of age. In 3xTg AD mice at later ages, whole brain analyses of the angioarchitecture demonstrated changes that indicated degeneration of the cerebrovasculature.

Consistent with the literature, whole brain analyses of vessel volume as a function of average vessel diameter revealed smaller vessels to be most affected by age in 3xTg AD mice, indicating that the majority of the changes to the angioarchitecture are accounted for by changes to the microvasculature.

When we assessed the region-specific changes to the hippocampal and cortical vasculature with age, we found early differences to the number of vessel segments and vessel-to-vessel junctions at 3-months of age. The most severely affected region of the hippocampus was the CA1, DG, CA3, and then ENT CTX, in that order. Similar to our whole brain analyses, changes to the hippocampal vasculature in 3xTg AD mice indicated network adaptations to chronic injury that occurs early and progresses with age. Hippocampal vasculature began to demonstrate evidence of vascular degeneration at 6-month that progressed with age in 3xTg AD mice. The hippocampus is among the first region of the brain to accumulate Aβ plaque in patients and mice with Alzheimer’s disease. In 3xTg AD mice, the hippocampus develops sparse Aβ plaques by 6-months of age and by 12-months plaque deposition is dense (Oddo et al., 2003a). We found that cerebrovascular network changes were disproportionately contributed by alterations to the microvasculature. Our analysis of the distribution of vessels by hierarchy as a function of average vessel diameter indicated that changes to the microvascular network occurred with age and differed between genotype. These data indicate that vascular damage in the hippocampus occurs before the onset of plaque deposition in 3xTg AD mice.

Hypoperfusion of the frontal cortex is implicated in causing the unawareness of cognitive deficits in patients with AD (Amanzio et al., 2011). Likewise, hypoperfusion of the cingulate cortex is associated with the impaired orientation for time in AD patients (Yamashita et al., 2019). Region specific analyses of the cortical vasculature indicated it to be less affected than regions of the hippocampus. The deposition of Aβ plaques in cortical regions is first detected at 12-months of age in 3xTg AD mice (Belfiore et al., 2019). We found the angioarchitecture of the MO PFC to be the most severely affected cortical region. At 3-months, the vasculature of the CC and not the MO PFC and SS CTX, demonstrated a reduced number of vessel segments and a lower count of vessel-to-vessel junctions. At 6-months of age, the MO PFC contained a reduced number of segments, vessel junctions, total vessel volume, surface area, and intervessel distance. Whereas, the angioarchitecture of the SS CTX and CC provided a reduced surface area of vessels and a greater distance between vessels relative to age-matched WT mice. Interestingly, at 12- and 24-months of age, an increased intervessel distance was the only measured parameter to be affected and in all cortical regions assessed.

We have previously demonstrated in cultured cerebrovascular endothelial cells that Aβ induces cytotoxicity via a mechanism that involves a calcium-driven upregulation of mitochondrial oxygen consumption that resulted in excessive production of mitochondrial superoxide (Quintana et al., 2020). During the presymptomatic stage of AD, early cerebrovascular injury is likely caused by relatively high levels of soluble Aβ, which mediates vascular damage by increasing mitochondrial superoxide production in vascular endothelial cells. Region-specific production of soluble Aβ is likely the cause of intersubregional differences we observed from the angioarchitecture in mice with the progression of age and disease.

During the early presymptomatic stage of AD, cerebrovascular injury and degeneration occurs gradually. However, as cerebrovascular injury and degeneration occurs, the vascular-mediated clearance of Aβ is diminished. This critical period likely marks the transition from the presymptomatic stage of AD to the symptomatic stage. Since approximately 85% of Aβ is cleared from the brain through the vasculature, a loss of efficiency in this process likely results in a rapid increase of Aβ in the brain parenchyma, initiating the onset of Aβ-plaque development. At this stage, when Aβ in the brain parenchyma increases rapidly, injury to the cerebrovasculature increases in parallel. Following this stage of injury, cerebrovascular degeneration progresses with age and AD, and in doing so forms a self-propagating positive feedback loop driving the degenerative changes in AD.

Overall, our data demonstrate that the cerebrovasculature of 3xTg AD mice is affected early during aging and is characterized by morphological and structural adaptations that are likely caused by chronic injury to vessels. These data demonstrate early changes to the angioarchitecture that may predict cerebrovascular degeneration that occurs later in the disease. Currently there exists no therapeutic strategies that target early events for the treatment of AD. An ideal strategy to mitigate the initial degradative injury to vessels would be from a therapeutic mechanism(s) that functions in the protection and/or repair of the cerebrovasculature. For example, attenuation of pathology-associated cell signaling which mediated cell death can increase the threshold of stimulus required to induce programmed cell death, effectively protecting the cerebrovasculature and preventing the decline of critical vascular functions, such as Aβ clearance. Another possibility is the use of the anti-Aβ immunotherapy, which has largely been unsuccessful in clinical trials. The data presented in this study may provide evidence that the most therapeutically effective window for immunotherapy would exist early, preventing the initial vascular damage caused by Aβ on the walls of blood vessels. This is important because many therapeutic strategies that have been developed have all been designed to address events that occur in late stages of the disease, when most irreparable damage has already been caused. Maintenance of a healthy functioning cerebrovascular system via early intervention may provide more Aβ clearance throughput at late stages than the immunotherapy that began at late stage AD. However, at late stages of AD, the ideal therapeutic strategy would be one that increases endogenous Aβ clearance through the cerebrovasculature via a therapeutic mechanism that induces angiogenesis to revascularize the cerebral tissue, which would enhance Aβ clearance by facilitating the innate transport of Aβ from the parenchyma and into peripheral circulation.

Highlights.

  • 3xTg AD mice develop tortuous arterioles and both arteriolar and capillary aneurysms.

  • Both capillary and non-capillary vessels progressively decline with age in 3xTg AD mice.

  • Hippocampal subregion-dependent changes to microvessels with age in 3xTg AD mice.

  • Cortical subregion-dependent changes to the microvasculature with age in 3xTg AD mice.

Significance Statement.

Cerebrovascular hypoperfusion and hypometabolism are fundamental neuropathological features of Alzheimer’s disease. This study demonstrates in a mouse model of AD, age-related changes to the angioarchitecture over the whole-brain that occurred early, before the onset of Aβ-plaque deposition and neurofibrillary tangles. Changes to the cerebrovasculature dramatically affected the microvasculature over the whole-brain and in key brain regions that are implicated in behavioral and cognitive deficits in AD. These results revealed a critical period of cerebrovascular dysfunction and degeneration during early presymptomatic stages of AD development. These findings provide a basis for development of novel therapeutic strategies targeting early cerebrovascular changes and may provide a prophylactic strategy for AD and indicate novel early biomarkers of the vasculature for diagnosis of the disease.

Acknowledgements

This work was supported by the NIH grants P20 GM109098, P01 AG027956, U54 GM104942, T32 AG052375, K01 NS081014, and K01 MH11734. Imaging experiments and image analyses were performed in the West Virginia University Microscope Imaging Facility, which has been supported by the Mary Babb Randolph Cancer Center and NIH grants P20 RR016440, P30 RR032138/GM103488 and P20 RR016477.

Footnotes

Author Atatement

DDQ designed studies, conducted studies and composed the manuscript. YA, JAG and DRC conducted studies. SNS, EBEC and CMB revised the manuscript. JWS designed studies and revised the manuscript.

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The authors declare no competing financial interests.

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