Three-Dimensional Chromatin Architecture Landscape of Aging and Alzheimer's Disease

2020 ◽  
Author(s):  
Guofeng Meng ◽  
Jialan Huang ◽  
Dong Lu ◽  
Zhenzhen Zhao ◽  
Feng Yu ◽  
...  
Author(s):  
Mark Ellisman ◽  
Maryann Martone ◽  
Gabriel Soto ◽  
Eleizer Masliah ◽  
David Hessler ◽  
...  

Structurally-oriented biologists examine cells, tissues, organelles and macromolecules in order to gain insight into cellular and molecular physiology by relating structure to function. The understanding of these structures can be greatly enhanced by the use of techniques for the visualization and quantitative analysis of three-dimensional structure. Three projects from current research activities will be presented in order to illustrate both the present capabilities of computer aided techniques as well as their limitations and future possibilities.The first project concerns the three-dimensional reconstruction of the neuritic plaques found in the brains of patients with Alzheimer's disease. We have developed a software package “Synu” for investigation of 3D data sets which has been used in conjunction with laser confocal light microscopy to study the structure of the neuritic plaque. Tissue sections of autopsy samples from patients with Alzheimer's disease were double-labeled for tau, a cytoskeletal marker for abnormal neurites, and synaptophysin, a marker of presynaptic terminals.


2021 ◽  
pp. 1-20
Author(s):  
Yang Yu ◽  
Yang Gao ◽  
Bengt Winblad ◽  
Lars Tjernberg ◽  
Sophia Schedin Weiss

Background: Processing of the amyloid-β protein precursor (AβPP) is neurophysiologically important due to the resulting fragments that regulate synapse biology, as well as potentially harmful due to generation of the 42 amino acid long amyloid β-peptide (Aβ 42), which is a key player in Alzheimer’s disease. Objective: Our aim was to clarify the subcellular locations of the amyloidogenic AβPP processing in primary neurons, including the intracellular pools of the immediate substrate, AβPP C-terminal fragment (APP-CTF) and the product (Aβ 42). To overcome the difficulties of resolving these compartments due to their small size, we used super-resolution microscopy. Methods: Mouse primary hippocampal neurons were immunolabelled and imaged by stimulated emission depletion (STED) microscopy, including three-dimensional, three-channel imaging and image analyses. Results: The first (β-secretase) and second (γ-secretase) cleavages of AβPP were localized to functionally and distally distinct compartments. The β-secretase cleavage was observed in early endosomes, where we were able to show that the liberated N- and C-terminal fragments were sorted into distinct vesicles budding from the early endosomes in soma. Lack of colocalization of Aβ 42 and APP-CTF in soma suggested that γ-secretase cleavage occurs in neurites. Indeed, APP-CTF was, in line with Aβ 42 in our previous study, enriched in the presynapse but absent from the postsynapse. In contrast, full-length AβPP was not detected in either the pre- or the postsynaptic side of the synapse. Furthermore, we observed that endogenously produced and endocytosed Aβ 42 were localized in different compartments. Conclusion: These findings provide critical super-resolved insight into amyloidogenic AβPP processing in primary neurons.


2021 ◽  
Vol 19 (11) ◽  
pp. 126-140
Author(s):  
Zahraa S. Aaraji ◽  
Hawraa H. Abbas

Neuroimaging data analysis has attracted a great deal of attention with respect to the accurate diagnosis of Alzheimer’s disease (AD). Magnetic Resonance Imaging (MRI) scanners have thus been commonly used to study AD-related brain structural variations, providing images that demonstrate both morphometric and anatomical changes in the human brain. Deep learning algorithms have already been effectively exploited in other medical image processing applications to identify features and recognise patterns for many diseases that affect the brain and other organs; this paper extends on this to describe a novel computer aided software pipeline for the classification and early diagnosis of AD. The proposed method uses two types of three-dimensional Convolutional Neural Networks (3D CNN) to facilitate brain MRI data analysis and automatic feature extraction and classification, so that pre-processing and post-processing are utilised to normalise the MRI data and facilitate pattern recognition. The experimental results show that the proposed approach achieves 97.5%, 82.5%, and 83.75% accuracy in terms of binary classification AD vs. cognitively normal (CN), CN vs. mild cognitive impairment (MCI) and MCI vs. AD, respectively, as well as 85% accuracy for multi class-classification, based on publicly available data sets from the Alzheimer’s disease Neuroimaging Initiative (ADNI).


2017 ◽  
Vol 13 (7S_Part_15) ◽  
pp. P725-P726
Author(s):  
Takashi Kikuchi ◽  
Takahiro Mori ◽  
Shinsuke Kojima ◽  
Kenji Wada Isoe ◽  
Yumi Umeda Kameyama ◽  
...  

1993 ◽  
Vol 23 (3) ◽  
pp. 623-629 ◽  
Author(s):  
H. Förstl ◽  
A. Burns ◽  
R. Levy ◽  
N. Cairns

SynopsisThe performance on four drawing tasks was studied in a sample of patients with verified Alzheimer's disease in order to examine the relationship of ‘constructional apraxia’ to neuropathological changes in the parietal lobe and in other brain areas. Twenty-three patients were able to attempt to copy pentagons, a spiral and a three-dimensional drawing of a house, 22 patients were able to draw a clock-face spontaneously. The results were rank-ordered by two independent raters. The values obtained in the different drawing tasks were correlated significantly with each other, with global estimates of cognitive performance (CAMCOG, Mini-Mental State), with a shorter duration of illness, higher brain weight (in the subsample of female patients), higher counts of large neurons in the parahippocampal gyrus and hippocampus, but not in the parietal lobe. This suggests that there is no specific relationship between ‘constructional apraxia’ and neuropathological changes in the parietal lobes of patients with advanced Alzheimer's disease, but that there is a correlation between widespread brain changes and several neuropsychological deficits, one of them being drawing disability.


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