A deep learning framework for automated and generalized synaptic event analysis DOI Creative Commons
Philipp S O’Neill, Martıń Baccino-Calace, Peter Rupprecht

и другие.

eLife, Год журнала: 2024, Номер 13

Опубликована: Июнь 28, 2024

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring events carry fundamental function and plasticity. However, their stochastic nature low signal-to-noise ratio present major challenges for the reliable consistent analysis. Here, we introduce miniML, a supervised deep learning-based method accurate classification automated detection spontaneous events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event methods in terms both precision recall. enables precise quantification electrophysiological recordings. We demonstrate learning approach generalizes easily diverse preparations, different optical recording techniques, across animal species. provides not only comprehensive robust framework automated, reliable, standardized events, but also opens new avenues high-throughput investigations dysfunction.

Язык: Английский

A fast and responsive voltage indicator with enhanced sensitivity for unitary synaptic events DOI
Yukun Hao, Sungmoo Lee, Richard H. Roth

и другие.

Neuron, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

7
Mitochondrial dysfunction drives a neuronal exhaustion phenotype in methylmalonic aciduria DOI Creative Commons
Matthew C.S. Denley, Monique S. Straub,

Giulio Marcionelli

и другие.

Communications Biology, Год журнала: 2025, Номер 8(1)

Опубликована: Март 11, 2025

Abstract Methylmalonic aciduria (MMA) is an inborn error of metabolism resulting in loss function the enzyme methylmalonyl-CoA mutase (MMUT). Despite acute and persistent neurological symptoms, pathogenesis MMA central nervous system poorly understood, which has contributed to a dearth effective brain specific treatments. Here we utilised patient-derived induced pluripotent stem cells vitro differentiation generate human neuronal model MMA. We reveal strong evidence mitochondrial dysfunction caused by deficiency MMUT patient neurons. By employing patch-clamp electrophysiology, targeted metabolomics, bulk transcriptomics, expose altered state excitability, exacerbated application dimethyl-2-oxoglutarate, suggest may be connected metabolic rewiring. Our work provides first driven MMA, through our comprehensive characterisation this paradigmatic model, enables steps identifying therapies.

Язык: Английский

Процитировано

0
A deep learning framework for automated and generalized synaptic event analysis DOI Creative Commons
Philipp S O’Neill, Martıń Baccino-Calace, Peter Rupprecht

и другие.

eLife, Год журнала: 2024, Номер 13

Опубликована: Июнь 28, 2024

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring events carry fundamental function and plasticity. However, their stochastic nature low signal-to-noise ratio present major challenges for the reliable consistent analysis. Here, we introduce miniML, a supervised deep learning-based method accurate classification automated detection spontaneous events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event methods in terms both precision recall. enables precise quantification electrophysiological recordings. We demonstrate learning approach generalizes easily diverse preparations, different optical recording techniques, across animal species. provides not only comprehensive robust framework automated, reliable, standardized events, but also opens new avenues high-throughput investigations dysfunction.

Язык: Английский

Процитировано

3