Brain virtual histology with X-ray phase-contrast tomography Part II: 3D morphologies of amyloid-β plaques in Alzheimer’s disease models

Abstract

While numerous transgenic mouse strains have been produced to model the formation of amyloid-β (Aβ) plaques in the brain, efficient methods for whole-brain 3D analysis of Aβ deposits are lacking. Moreover, standard immunohistochemistry performed on brain slices precludes any shape analysis of Aβ plaques. The present study shows how in-line (propagation-based) X-ray phase-contrast tomography (XPCT) combined with ethanol-induced brain sample dehydration enables hippocampus-wide detection and morphometric analysis of Aβ plaques. Performed in three distinct Alzheimer mouse strains, the proposed workflow identified differences in signal intensity and 3D shape parameters: 3xTg displayed a different type of Aβ plaques, with a larger volume and area, greater elongation, flatness and mean breadth, and more intense average signal than J20 and APP/PS1. As a label-free non-destructive technique, XPCT can be combined with standard immunohistochemistry. XPCT virtual histology could thus become instrumental in quantifying the 3D spreading and the morphological impact of seeding when studying prion-like properties of Aβ aggregates in animal models of Alzheimer's disease. This is Part II of a series of two articles reporting the value of in-line XPCT for virtual histology of the brain; Part I shows how in-line XPCT enables 3D myelin mapping in the whole rodent brain and in human autopsy brain tissue.

Brain virtual histology with X-ray phase-contrast tomography Part II: 3D morphologies of amyloid-β plaques in Alzheimer’s disease models

“…For APP/PS1, "Minimum" was used instead of "Maximum" after the application of a 3D Laplacian-of-Gaussian (Mexican Hat) filter [23], as suggested by Astolfo et al [10]. A step-by-step guide to perform all image processing, "Amyloid-β XPCT Workflow", is publicly available (DOI: 10.5281/zenodo.4584752) [24].…”

Brain virtual histology with X-ray phase-contrast tomography Part I: whole-brain myelin mapping in white-matter injury models

“…This was achieved in the current project through a public-private partnership. In part II of this series of articles, we develop and share a pipeline designed with open-source tools (https://zenodo.org/record/4584753) to segment amyloid-β plaques and extract 3D morphometric parameters in different animal models of Alzheimer's disease (12). The development of such end-user-friendly workflows is crucial to allow widespread use of XPCT in the neuroscience community.…”

“…In-line XPCT might thus become a novel gold-standard for investigating white-matter injury in the intact brain ex vivo. This is Part I of a series of two articles reporting the value of in-line XPCT for virtual histology of the brain; Part II shows how in-line XPCT enables whole-brain 3D morphometric analysis of amyloid-β (Aβ) plaques (12). Sixteen rodents (Janvier, France) were used (Table 1).…”

Machine Learning Quantification of Amyloid Deposits in Histological Images of Ligamentum Flavum

“…The use of this algorithm arose from basic science research where it has been used in studies to count colonies and track cells, analyze stained mouse muscle sections, and recognize amyloid plaques in a mouse model of Alzheimer’s disease. 14 , 15 , 16 In clinical research, this method has been emerging in various uses, such as to analyze human CT scans and to delineate boundaries of breast carcinoma in human tissue. 17 , 18 Here, we describe the first use of the TWS machine learning algorithm to quantify amyloid deposits in histological samples of ligamentum flavum from patients with spinal stenosis.…”