A neuropathologic feature of brain aging: multi-lumen vascular profiles

Abstract

Cerebrovascular pathologies other than frank infarctions are commonly seen in aged brains. Here, we focus on multi-lumen vascular profiles (MVPs), which are characterized by multiple vessel lumens enclosed in a single vascular channel. Little information exists on the prevalence, risk factors, and co-pathologies of MVPs. Therefore, we used samples and data from the University of Kentucky Alzheimer's Disease Research Center (n = 91), the University of Kentucky Pathology Department (n = 31), and the University of Pittsburgh Pathology Department (n = 4) to study MVPs. Age at death was correlated with MVP density in the frontal neocortex, Brodmann Area 9 (r = 0.51; p < 0.0001). Exploratory analyses were performed to evaluate the association between conventional vascular risk factors (e.g., hypertension, diabetes), cardiovascular diseases (e.g., heart attack, arrhythmia), and cerebrovascular disease (e.g., stroke); the only nominal association with MVP density was a self-reported history of brain trauma (Prevalence Ratio = 2.1; 95 CI 1.1-3.9, before correcting for multiple comparisons). No specific associations were detected between neuropathological (e.g., brain arteriolosclerosis) or genetic (e.g., APOE) variables and MVP density. Using a tissue clearing method called SeeDB, we provide 3-dimensional images of MVPs in brain tissue. We conclude that MVPs are an age-related brain pathology and more work is required to identify their clinical-pathological correlation and associated risk factors.

Keywords: Endothelial; SVD; Senescence; Small vessel disease; TBI; VCID; VTE.

Fig. 1

Multi-lumen vascular profiles (MVPs). MVPs are vascular beds consisting of ≥ 3 lumens enclosed in a perivascular space on a cross-sectional view. A, B Photomicrographs of hematoxylin-and-eosin stained blood vessels within the grey matter of frontal tissues. A What we assume to be a normal arteriole (due to its structure and size) from a 42 year-old female. B What we describe as a MVP with at least 3, lumens, some of which contain red blood cells, from a 96 year-old female. C, D Photomicrographs of alpha smooth muscle actin (α-SMA) stained MVPs in cross-section. C shows an MVP with at least 4 lumens of similar size from a 91 year-old female. D shows a MVP with at least 13 lumens of varying size from a 89 year-old male. E–G) Photomicrographs of CD34 stained MVPs. E, F show MVPs with at least 10 lumens in cross-section from a 89 year-old male. G shows a MVP cut in a longitudinal direction from a 91 year-old female. Scale bars: a-f = 50 µm, g = 100 µm

Fig. 2

Schematic of MVP quantification. The photograph is of a CD34 stained tissue from the frontal cortex (Broadmann area 9) of an 84 year-old female. Using Aperio ScanScope digital slide scanner accompanying image analysis software, the grey matter area was outlined (green-black line). Next, the grey matter area was manually scanned for MVPs which were marked by a counter (pink crosses). The total # of MVPs and the grey matter area for each case were used to calculate an MVP density. Scale bar = 5 mm

Fig. 3

Relationship between age at death and MVP density (counts per 10⁷ microns²). Cases from the UKPD and the UK-ADRC cohort (blue dots) were combined in order to determine the association between age at death and MVP density. A scatter plot was used to show each case’s MVP density with corresponding age at death. Using a Spearman’s rho test, the correlation between age at death and MVP density was 0.60 with a p-value of < 0.0001. Cases with severe CAA (red dots) from the UK-ADRC and 2 with CTE (purple dots) and aged-matched controls (yellow dots) were also included. Comb. combined, U. university, CAA cerebral amyloid angiopathy, CTE chronic traumatic encephalopathy, MVP multi-lumen vascular profiles

Fig. 4

UK-ADRC case with highest MVP density. A, B Photographs showing the gross anatomy of the basal ganglia from a 89 year-old male. B Inset of (A) with red arrows indicate holes in the basal ganglia. C, D Photomicrographs of hematoxylin-and-eosin stained tissue sections from the putamen of a 89 year-old male. C shows regions of infarct tissue. D shows what we presume to be a cerebral microaneurysm (Charcot-Bouchard aneurysm). Scale bars: a = 2 mm, b = 2 cm, c = 4 mm, d = 500 µm

Fig. 5

3-D Visualization of MVPs using SeeDB Method. Photomicrographs show the 3D branching of MVPs within the frontal cortex of a 89 year-old male. Panel A shows the raw data, and the image in panel B was surface-rendered and isolated from other non-vascular objects using Imaris for improved visibility of the MVPs. Scale bar = 100 mm

Fig. 6

Multiplexed staining of glia in relation to vascular profiles and analysis of Vascular Phenotypes A sequential multiplexed staining and analysis, known as QUIVER, was employed on human FFPE tissue. The procedure started with the staining for CD34 to visualize multiple vascular profiles (MVP) and single vascular profiles (SVP) in the same case (A). This step utilized a permanent chromogen to preserve the staining throughout each subsequent round. Subsequent staining rounds were performed for ferritin (B, F), GFAP (C, G), and IBA1 (D, H), sequentially, using a removable chromogen. Post-deconvolution of single-channel IHC images, merged pseudo-fluorescent images were generated for MVP (I, J) and SVP (K, L). Cell count data was produced using the object colocalization algorithm in the HALO software, and digital markup (J and L). The color labels correspond with the representative color in the markup image and merged image. Scale bars = 50 μm

Fig. 7

Digital analysis of glial populations near vascular structures. To explore the glial populations' relationship with vascular structures, a digital analysis method was used. First, we identified each vascular structure using CD34 staining. Next, the vascular space outline was created, forming a digital concentric layer at 50 μm intervals. With Halo software, we generated automated tissue analysis markups for cell quantification via the object colocalization algorithm and staining area determination through the area fractionator algorithm, as the inset illustrates. We obtained cell counts and area of positive staining for IBA1 (A, B), GFAP (C, D), and ferritin (E, F), respectively. The gray lines are mean values for each person. With the dashed lines for the ADNC cases and the solid lines for the LATE-NC cases. The color lines show the mean ± SD for call the cases. Analysis of the overall staining area revealed similar trends to the cell counts, with the most striking difference noted between MVP and SVP in the IBA1-positive cells associated with the blood vessel. The results presented are the mean per case for 20 MVPs and 20 SVPs quantified each time. Scale bars = 25 μm