Alzheimer’s disease induced neurons bearing<i>PSEN1</i>mutations exhibit reduced excitability

Maksour, Simon; Finol-Urdaneta, Rocio K.; Hulme, Amy J.; Cabral-da-Silva, Mauricio Castro; Dias Anastacio, Helena Targa; Balez, Rachelle; Berg, Tracey; Turner, Calista; Sanz Muñoz, Sonia; Engel, Martin; Kalajdzic, Predrag; Lisowski, Leszek; Sidhu, Kuldip; Sachdev, Perminder S.; Dottori, Mirella; Ooi, Lezanne

doi:10.1101/2024.03.22.586207

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…The dataset contains MEA recordings obtained from cortical organoids derived from four different cell lines: two healthy control cell lines (UOWi001-A and UOWi002-A) and two Familial AD patient cell lines (UOWi003-A and UOWi009-A). Both AD patient lines carry mutations in the PSEN1 gene that encodes Presenilin 1, A248E or S290C, as described in Maksour et al 30 and demonstrate alterations in neuronal excitability 30 . Human brain organoids 31 produced from iPSCs based on the protocol by Salick et al 32 and described in Maksour et al 29 were cultured for up to 9 months.…”

Section: Methods Experimental Cortical Organoid-based Mea Datamentioning

confidence: 92%

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Malepathirana

Senanayake

Gautam

et al. 2022

Preprint

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Human-induced pluripotent stem cell derived brain organoids provide models to study the human brain and its diseases. The neuronal activities of these organoids are recorded over time by Multi-electrode Arrays, calling for approaches to analyze their longitudinal variations to study the organoids' electrophysiological maturation. We introduce a pipeline (Il-Vis) that incrementally learns and visualizes a progression trajectory representing the organoids' electrophysiological properties. Unlike a static model that runs at the end of the experiment, Il-Vis generates a progression trajectory thus far at each sampling timepoint. Results on simulated data, for which the true progression trajectories are known, verified Il-Vis's ability to capture and visualize the trajectories accurately and relative to each other. Results on brain organoid data revealed useful insights about organoids' electrophysiological maturation stages and response patterns when exposed to Quinolinic Acid (and its blocking antibody), a metabolite contributing to many neuroinflammatory diseases including Alzheimer's.

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Section: Methods Experimental Cortical Organoid-based Mea Datamentioning

confidence: 92%

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Malepathirana

Senanayake

Gautam

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Visualization of incrementally learned projection trajectories for longitudinal data

Malepathirana,

Senanayake,

Gautam

et al. 2024

Sci Rep

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Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer’s disease, and its blocking antibody. We uncover valuable insights into the organoids’ electrophysiological maturation and response patterns over time under these conditions.

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Familial Alzheimer’s Disease Neurons Bearing Mutations in PSEN1 Display Increased Calcium Responses to AMPA as an Early Calcium Dysregulation Phenotype

Targa Dias Anastacio,

Matosin,

Ooi

2024

Life

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Familial Alzheimer’s disease (FAD) can be caused by mutations in PSEN1 that encode presenilin-1, a component of the gamma-secretase complex that cleaves amyloid precursor protein. Alterations in calcium (Ca2+) homeostasis and glutamate signaling are implicated in the pathogenesis of FAD; however, it has been difficult to assess in humans whether or not these phenotypes are the result of amyloid or tau pathology. This study aimed to assess the early calcium and glutamate phenotypes of FAD by measuring the Ca2+ response of induced pluripotent stem cell (iPSC)-derived neurons bearing PSEN1 mutations to glutamate and the ionotropic glutamate receptor agonists NMDA, AMPA, and kainate compared to isogenic control and healthy lines. The data show that in early neurons, even in the absence of amyloid and tau phenotypes, FAD neurons exhibit increased Ca2+ responses to glutamate and AMPA, but not NMDA or kainate. Together, this suggests that PSEN1 mutations alter Ca2+ and glutamate signaling as an early phenotype of FAD.

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Alzheimer’s disease induced neurons bearingPSEN1mutations exhibit reduced excitability

Cited by 3 publications

References 57 publications

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Visualization of incrementally learned projection trajectories for longitudinal data

Familial Alzheimer’s Disease Neurons Bearing Mutations in PSEN1 Display Increased Calcium Responses to AMPA as an Early Calcium Dysregulation Phenotype

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