Back

High-throughput measurements of neuronal activity in single human iPSC-derived glutamate neurons

Gameiro-Ros, I. M.; Tengolics, A. J.; Prytkova, I.; Kamarajan, C.; Pang, Z. P.; Goate, A. M.; Hart, R. P.; Slesinger, P. A.

2025-04-07 neuroscience
10.1101/2025.04.07.646449 bioRxiv
Show abstract

Induced pluripotent stem cell (iPSC)-derived neurons provide a promising platform for studying neuronal function and modeling central nervous system (CNS) diseases. However, functional analysis of large populations of iPSC-derived neurons has been challenging. Here, we developed a high throughput strategy targeting N-methyl-D-aspartate receptors (NMDA-R) to enhance neuronal activity and reveal functional phenotypes in human iPSC-induced glutamatergic neurons (iGlut). Using a genetically encoded calcium indicator (GCaMP8f), we first demonstrate that using artificial cerebrospinal fluid (ACSF) lacking Mg2+ (Mg2+-free) significantly increases neuronal firing, and that firing is enhanced by a potentiator (glycine) but inhibited by the NMDA-R antagonist AP-V. Similarly, multi-electrode array (MEA) recordings also show robust firing in Mg2+-free ACSF. Lastly, single-cell patch-clamp electrophysiology confirms the high firing rates in Mg2+-free ACSF across multiple iPSC donor lines and also reveals iPSC donor-specific tonic and bursting firing phenotypes. This new methodology provides a scalable, high-throughput method to study neuronal activity in iGlut neurons while preserving single-cell resolution. The strategy also reveals different functional phenotypes, enabling detailed characterization of iGlut neurons in diverse applications such as CNS disease modeling and drug screening. These findings establish a versatile framework for future studies of neuronal network dynamics and individual excitability in iPSC-derived neuronal cultures.

Matching journals

The top 8 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.