Detection of multiplex exosomal miRNAs for clinically accurate diagnosis of Alzheimer’s disease using label-free plasmonic biosensor based on DNA-Assembled advanced plasmonic architecture

2022 ◽  
Vol 199 ◽  
pp. 113864
Sojin Song ◽  
Jong Uk Lee ◽  
Myeong Jin Jeon ◽  
Soohyun Kim ◽  
Sang Jun Sim
2020 ◽  
Vol 8 (1) ◽  
Dominik Röhr ◽  
Baayla D. C. Boon ◽  
Martin Schuler ◽  
Kristin Kremer ◽  
Jeroen J. M. Hoozemans ◽  

AbstractThe neuropathology of Alzheimer’s disease (AD) is characterized by hyperphosphorylated tau neurofibrillary tangles (NFTs) and amyloid-beta (Aβ) plaques. Aβ plaques are hypothesized to follow a development sequence starting with diffuse plaques, which evolve into more compact plaques and finally mature into the classic cored plaque type. A better molecular understanding of Aβ pathology is crucial, as the role of Aβ plaques in AD pathogenesis is under debate. Here, we studied the deposition and fibrillation of Aβ in different plaque types with label-free infrared and Raman imaging. Fourier-transform infrared (FTIR) and Raman imaging was performed on native snap-frozen brain tissue sections from AD cases and non-demented control cases. Subsequently, the scanned tissue was stained against Aβ and annotated for the different plaque types by an AD neuropathology expert. In total, 160 plaques (68 diffuse, 32 compact, and 60 classic cored plaques) were imaged with FTIR and the results of selected plaques were verified with Raman imaging. In diffuse plaques, we detect evidence of short antiparallel β-sheets, suggesting the presence of Aβ oligomers. Aβ fibrillation significantly increases alongside the proposed plaque development sequence. In classic cored plaques, we spatially resolve cores containing predominantly large parallel β-sheets, indicating Aβ fibrils. Combining label-free vibrational imaging and immunohistochemistry on brain tissue samples of AD and non-demented cases provides novel insight into the spatial distribution of the Aβ conformations in different plaque types. This way, we reconstruct the development process of Aβ plaques in human brain tissue, provide insight into Aβ fibrillation in the brain, and support the plaque development hypothesis.

Proteomes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Lenora Higginbotham ◽  
Eric Dammer ◽  
Duc Duong ◽  
Erica Modeste ◽  
Thomas Montine ◽  

Previous systems-based proteomic approaches have characterized alterations in protein co-expression networks of unfractionated asymptomatic (AsymAD) and symptomatic Alzheimer’s disease (AD) brains. However, it remains unclear how sample fractionation and sub-proteomic analysis influences the organization of these protein networks and their relationship to clinicopathological traits of disease. In this proof-of-concept study, we performed a systems-based sub-proteomic analysis of membrane-enriched post-mortem brain samples from pathology-free control, AsymAD, and AD brains (n = 6 per group). Label-free mass spectrometry based on peptide ion intensity was used to quantify the 18 membrane-enriched fractions. Differential expression and weighted protein co-expression network analysis (WPCNA) were then used to identify and characterize modules of co-expressed proteins most significantly altered between the groups. We identified a total of 27 modules of co-expressed membrane-associated proteins. In contrast to the unfractionated proteome, these networks did not map strongly to cell-type specific markers. Instead, these modules were principally organized by their associations with a wide variety of membrane-bound compartments and organelles. Of these, the mitochondrion was associated with the greatest number of modules, followed by modules linked to the cell surface compartment. In addition, we resolved networks with strong associations to the endoplasmic reticulum, Golgi apparatus, and other membrane-bound organelles. A total of 14 of the 27 modules demonstrated significant correlations with clinical and pathological AD phenotypes. These results revealed that the proteins within individual compartments feature a heterogeneous array of AD-associated expression patterns, particularly during the preclinical stages of disease. In conclusion, this systems-based analysis of the membrane-associated AsymAD brain proteome yielded a unique network organization highly linked to cellular compartmentalization. Further study of this membrane-associated proteome may reveal novel insight into the complex pathways governing the earliest stages of disease.

Diagnostics ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 913
Le Minh Tu Phan ◽  
Thi Xoan Hoang ◽  
Thuy Anh Thu Vo ◽  
Jae Young Kim ◽  
Sang-Myung Lee ◽  

Emerging nanomaterials providing benefits in sensitivity, specificity and cost-effectiveness are being widely investigated for biosensors in the application of Alzheimer’s disease (AD) diagnosis. Core biomarkers amyloid-beta (Aβ) and Tau have been considered as key neuropathological hallmarks of AD. However, they did not sufficiently reflect clinical severity and therapeutic response, proving the difficulty of the Aβ- and Tau-targeting therapies in clinical trials. In recent years, there has still been a shortage of sensors for non-Aβ-Tau pathophysiological biomarkers that serve as advanced reporters for the early diagnosis of AD, predict AD progression, and monitor the treatment response. Nanomaterial-based sensors measuring multiple non-Aβ-Tau biomarkers could improve the capacity of AD progression characterization and supervised treatment, facilitating the comprehensive management of AD. This is the first review to principally represent current nanobiosensors for non-Aβ-Tau biomarker and that strategically deliberates future perspectives on the merit of non-Aβ-Tau biomarkers, in combination with Aβ and Tau, for the accurate diagnosis and prognosis of AD.

2002 ◽  
Vol 8 (4) ◽  
pp. 596-597
Edith V. Sullivan

Alzheimer's disease—occurring upward of 15% of individuals age 65 and older—is the most prevalent age-related dementia. Since the late 1970s, neuropsychologists have been instrumental in identifying patterns of sparing and impairment of cognitive, sensory, and motor functions and rates of declines in selective functions. Anyone who has engaged in longitudinal study of AD and anyone of that large segment of the population with relatives suffering with AD has witnessed first-hand the relentless, irreversible demise of function and ultimate loss of dignity characteristic of AD's course. The approach of Scinto and Daffner's edited book, Early Diagnosis of Alzheimer's Disease, avoids rehashing the already established descriptions of AD and provides firm, scientific rationale for the meaningfulness of early and accurate diagnosis of AD despite its current dire prognosis and lack of effective medical treatment.

2019 ◽  
Vol 18 ◽  
pp. 153601211986907 ◽  
Ian R. Duffy ◽  
Amanda J. Boyle ◽  
Neil Vasdev

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital biomarkers within positron emission tomography (PET) imaging have emerged. Interest in the use of artificial intelligence in PET imaging for the study of neurodegenerative diseases and oncology stems from the potential for such techniques to streamline decision support for physicians providing early and accurate diagnosis and allowing personalized treatment regimens. In this review, the use of ML to improve PET image acquisition and reconstruction is presented, along with an overview of its applications in the analysis of PET images for the study of Alzheimer's disease and oncology.

Nanoscale ◽  
2014 ◽  
Vol 6 (7) ◽  
pp. 3561-3565 ◽  
Sung Sik Lee ◽  
Luke P. Lee

We utilize nanoplasmonic optical imaging as the noninvasive and label-free method in order to monitorin vitroamyloid fibrogenesis in real-time, which is considered as the primary pathological mechanism of Alzheimer's disease.

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