There is an urgent need for predictive in vitro models to improve disease modeling and drug target identification and validation, especially for neurological disorders. Cerebral organoids, as alternative methods to in vivo studies, appear now as powerful tools to decipher complex biological processes thanks to their ability to recapitulate many features of the human brain. Combining these innovative models with microfluidic technologies, referred to as brain organoids-on-chips, allows us to model the microenvironment of several neuronal cell types in 3D. Thus, this platform opens new avenues to create a relevant in vitro approach for preclinical applications in neuroscience. The transfer to the pharmaceutical industry in drug discovery stages and the adoption of this approach by the scientific community requires the proposition of innovative microphysiological systems allowing the generation of reproducible cerebral organoids of high quality in terms of structural and functional maturation, and compatibility with automation processes and high-throughput screening. In this review, we will focus on the promising advantages of cerebral organoids for disease modeling and how their combination with microfluidic systems can enhance the reproducibility and quality of these in vitro models. Then, we will finish by explaining why brain organoids-on-chips could be considered promising platforms for pharmacological applications.

Neurodegenerative diseases, including Alzheimer's disease (AD), present significant diagnostic challenges due to overlapping symptoms and the invasive, time-consuming, and costly nature of current diagnostic methods. While AD remains the only neurodegenerative disorder for which biomarkers in cerebrospinal fluid (CSF), such as amyloid beta peptide (Aβ), are available for clinical diagnosis, similar tools are lacking for other neurodegenerative conditions. This diagnostic gap hinders timely and accurate differential diagnoses, limiting patient access to appropriate clinical trials and therapeutic interventions. In this study, we developed a compartmentalized microfluidic platform to facilitate differential diagnosis of neurodegenerative diseases by providing an initial screening tool to guide patients toward targeted clinical pathways. Using CSF samples from AD patients with confirmed diagnoses, we showed a proof of concept to distinguish between non neurodegenerative (NN) and Alzheimer's samples. Human glutamatergic neurons derived from induced pluripotent stem cells (iPSCs) were exposed to synthetic Aβ oligomers (AβO) and patient CSF to assess their effects on neuronal network activity. Neuronal responses were recorded via microelectrode array (MEA) before and after treatments, with tetrodotoxin (TTX) serving as a control for validating modulation of the neuronal network. Our findings demonstrated that key electrophysiological metrics extracted from MEA recordings can tend to differentiate AD from non-neurodegenerative CSF samples. This standardized platform not only provides a robust approach for AD biomarker validation but also offers a foundation for broader differential diagnosis of neurodegenerative diseases. By enabling more accurate patient stratification, this tool could have the potential to direct patients toward appropriate clinical trials, enabling the diagnosis of a broader range of neurodegenerative diseases. This approach has the potential to expand the patient population included in research and accelerate the development of new therapeutic strategies.

Alzheimer’s disease (AD) is characterized by the accumulation of aggregated amyloid beta peptide (Aβ) leading to progressive neuronal loss and dysfunction. Current AD’s diagnosis involves biomarkers assays in cerebrospinal fluid (CSF) as Aβ to validate the diagnosis. However, these methods are time-consuming, expensive, and can result in inaccurate diagnoses by not accounting for differential diagnose. To overcome these challenges, researchers are exploring new technologies for detecting AD biomarkers in biological fluids, though progress is hindered by an incomplete understanding of AD mechanisms and CSF composition. In this study, we used a standardized microfluidic platform to investigate the effects of synthetic Aβ peptides and cerebrospinal fluid (CSF) from AD and healthy patients on neuronal functional activity. First, human neurons derived from induced pluripotent stem cells (iPSCs) were characterized. Then, to modulate the functional activity of neurons, tetrodotoxin (TTX), a specific sodium channel blocker, was used as a control for inhibiting neuronal activity. Subsequently, glutamatergic neurons were chronically exposed to AβO and patients’ CSF. MEA recordings were performed before and after the treatments to assess changes in network activity. Our results demonstrated that extracting key electrophysiological metrics allows for discrimination between healthy and AD CSF samples. This system could offer the potential for differential diagnosis and development of personalized therapeutic strategies.