Vaidyanathan, T. | Search bioRxiv

bioRxiv2026

An astrocytic AMPAR-dependent parity signal preserves synaptic strength

Astrocytic processes closely associate with excitatory synapses and express AMPARs, positioning them to sense the same glutamate signals as neurons. Whether a mismatch between neuronal and astrocytic glutamate sensing affects synaptic physiology is unknown. Using AstroHTP, an extension of Drug Acutely Restricted by Tethering (DART) for cell-type-specific pharmacology, we found that acute astrocytic AMPAR inhibition spared neuronal AMPARs and basal transmission yet converted ongoing glutamatergic activity into a remarkably potent form of long-term depression (LTD) in mouse striatal projection neurons (SPNs). Unlike canonical presynaptic LTD, this form of LTD manifests postsynaptically, requires non-ionotropic NMDA receptor signaling, and arises when glutamate transmission occurs without a matching astrocytic signal. Astrocytic glutamate sensing through AMPARs, therefore, acts as a parity signal that verifies transmission and stabilizes active synapses.

neuroscience↗

bioRxiv2018

An event-based paradigm for analyzing fluorescent astrocyte activity uncovers novel single-cell and population-level physiology

Recent work examining astrocytic physiology centers on fluorescence imaging approaches, due to development of sensitive fluorescent indicators and observation of spatiotemporally complex calcium and glutamate activity. However, the field remains hindered in fully characterizing these dynamics, both within single cells and at the population-level, because of the insufficiency of current region-of-interest-based approaches to describe activity that is often spatially unfixed, size-varying, and propagative. Here, we present a paradigm-shifting analytical framework that releases astrocyte biologists from ROI-based tools. Astrocyte Quantitative Analysis (AQuA) software enables users to take an event-based approach to accurately capture and quantify the irregular activity observed in astrocyte imaging datasets. We apply AQuA to a range of ex vivo and in vivo imaging data, and uncover previously undescribed physiological phenomena in each. Since AQuA is data-driven and based on machine learning principles, it can be applied across model organisms, fluorescent indicators, experimental modes, and imaging resolutions and speeds, enabling researchers to elucidate fundamental astrocyte physiology.

neuroscience↗