bioRxivNeurosciencePreprint
Tunable neuronal microenvironments drive distinct functional phenotypes in human iPSCs-derived dopaminergic neurons
Bahaa DaouiD, Lara RodrigueziD, Paola SartoriiD, Gisela C. LuqueiD, Maurizio PratoiD, Núria AlegretiD, Sonia Alonso‐MartíniD
Abstract
Neuronal heterogeneity is a defining feature of complex neural circuits, where local differences in firing patterns, activity levels, and temporal dynamics shape information processing and emergent network activity. In adult neurons, this heterogeneity arises from a variety of known and unknown factors including the extracellular environment. Although neurons have been cultured on three-dimensional substrates, the effect of the microenvironments on their firing activity remains poorly understood. Here, we have synthesized two chitosan hydrogel systems seeded with human iPSCs-derived dopaminergic neurons as tunable platforms to control neuronal microenvironments. Both systems were formulated with the ability to incorporate carbon nanotubes (CNT), thus promoting neural interfacing. Calcium imaging combined with computational single-cell analysis demonstrated that supramolecular organization, hydration state, and general physicochemical properties differentially bias neuronal firing dynamics and synchrony leading to the emergence of distinct activity phenotypes despite identical cellular origin. These activity profiles were clustered through K-means and assigned to specific phenotypes including bursting irregular neurons, regular network contributors, or less active/quiescent neurons. Furthermore, CNTs incorporation enhanced local hydrogel compaction, resulting in unique active neuronal phenotypes, highlighting the potential of CNTs to modulate local cellular microenvironments. These findings establish tunable biomaterials as microenvironment contenders for controlling neuronal network state while giving insights on the interplay of different cues in promoting neuronal heterogeneity and functional phenotype relevant to neurodevelopment, neurodegeneration, and disease modelling.
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