Neurocircuits in the human brain govern complex behavior and involve connections from many different neuronal subtypes from different brain regions. Recent advances in stem cell biology have enabled the derivation of patient-specific human neuronal cells of various subtypes for the study of neuronal function and disease pathology. Nevertheless, one persistent challenge using these human-derived neurons is the ability to reconstruct models of human brain circuitry. To overcome this obstacle, we have developed a compartmentalized microfluidic device, which allows for spatial separation of cell bodies of different human-derived neuronal subtypes (excitatory, inhibitory and dopaminergic) but is permissive to the spreading of projecting processes. Induced neurons (iNs) cultured in the device expressed pan-neuronal markers and subtype specific markers. Morphologically, we demonstrate defined synaptic contacts between selected neuronal subtypes by synapsin staining. Functionally, we show that excitatory neuronal stimulation evoked excitatory postsynaptic current responses in the neurons cultured in a separate chamber.
Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describe Intellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore, Intellicount is integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process.
Neuropsychiatric disorders have traditionally been difficult to study due to the complexity of the human brain and limited availability of human tissue. Induced pluripotent stem (iPS) cells provide a promising avenue to further our understanding of human disease mechanisms, but traditional 2D cell cultures can only provide a limited view of the neural circuits. To better model complex brain neurocircuitry, compartmentalized culturing systems and 3D organoids have been developed. Early compartmentalized devices demonstrated how neuronal cell bodies can be isolated both physically and chemically from neurites. Soft lithographic approaches have advanced this approach and offer the tools to construct novel model platforms, enabling circuit-level studies of disease, which can accelerate mechanistic studies and drug candidate screening. In this review, we describe some of the common technologies used to develop such systems and discuss how these lithographic techniques have been utilized to advance our understanding of neuropsychiatric disease. Finally, we address other in vitro model platforms such as 3D culture systems and organoids and compare these models with compartmentalized models and ask important questions regarding how we can further harness induced pluripotent stem cells in these engineered culture systems for the development of improved in vitro models. This article is protected by copyright. All rights reserved.
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