Back

Neurons as biosensors for discriminating neurological disorders in a brain-on-chip platform: Application to Alzheimer s Disease using patient CSF

Miny, L.; Rontard, J.; Allouche, A.; Violle, N.; Dubuisson, L.; Batut, A.; Ponomarenko, A.; Talbi, R.; Gautier, H.; Maisonneuve, B.; Roux, S.; Larramendy, F.; Honegger, T.; Quadrio, I.

2024-08-26 neuroscience
10.1101/2024.08.23.609425 bioRxiv
Show abstract

Alzheimers disease (AD) is characterized by the accumulation of aggregated amyloid beta peptide (A{beta}) leading to progressive neuronal loss and dysfunction. Current ADs diagnosis involves biomarkers assays in cerebrospinal fluid (CSF) as A{beta} to validate the diagnosis. However, these methods are time-consuming, expensive, and can result in inaccurate diagnoses by not accounting for differential diagnose. To overcome these challenges, researchers are exploring new technologies for detecting AD biomarkers in biological fluids, though progress is hindered by an incomplete understanding of AD mechanisms and CSF composition. In this study, we used a standardized microfluidic platform to investigate the effects of synthetic A{beta} peptides and cerebrospinal fluid (CSF) from AD and healthy patients on neuronal functional activity. First, human neurons derived from induced pluripotent stem cells (iPSCs) were characterized. Then, to modulate the functional activity of neurons, tetrodotoxin (TTX), a specific sodium channel blocker, was used as a control for inhibiting neuronal activity. Subsequently, glutamatergic neurons were chronically exposed to A{beta}O and patients CSF. MEA recordings were performed before and after the treatments to assess changes in network activity. Our results demonstrated that extracting key electrophysiological metrics allows for discrimination between healthy and AD CSF samples. This system could offer the potential for differential diagnosis and development of personalized therapeutic strategies.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.