Miny Louise, Rontard Jessica, Allouche Ahmad, Violle Nicolas, Dubuisson Louise, Batut Aurélie, Ponomarenko Alexandre, Talbi Rania, Gautier Hélène, Maisonneuve Benoît G C, Roux Serge, Larramendy Florian, Honegger Thibault, Quadrio Isabelle
NETRI, Lyon, France.
Lyon Neurosciences Research Center, BIORAN Team, 59 Boulevard Pinel Bron, CNRS UMR 5292, INSERM U1028, 69500, Lyon, France.
Sci Rep. 2025 Aug 13;15(1):29738. doi: 10.1038/s41598-025-97186-x.
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.
神经退行性疾病,包括阿尔茨海默病(AD),由于症状重叠以及当前诊断方法具有侵入性、耗时且成本高昂的特点,带来了重大的诊断挑战。虽然AD仍然是唯一一种可利用脑脊液(CSF)中的生物标志物(如淀粉样β肽(Aβ))进行临床诊断的神经退行性疾病,但其他神经退行性疾病缺乏类似的诊断工具。这种诊断差距阻碍了及时、准确的鉴别诊断,限制了患者参与适当临床试验和治疗干预的机会。在本研究中,我们开发了一种分区微流控平台,通过提供一种初始筛查工具来引导患者走向有针对性的临床路径,以促进神经退行性疾病的鉴别诊断。使用确诊为AD患者的脑脊液样本,我们展示了区分非神经退行性(NN)样本和阿尔茨海默病样本的概念验证。将源自诱导多能干细胞(iPSC)的人谷氨酸能神经元暴露于合成Aβ寡聚体(AβO)和患者脑脊液中,以评估它们对神经网络活动的影响。在处理前后通过微电极阵列(MEA)记录神经元反应,用河豚毒素(TTX)作为验证神经网络调节的对照。我们的研究结果表明,从MEA记录中提取的关键电生理指标往往能够区分AD与非神经退行性脑脊液样本。这个标准化平台不仅为AD生物标志物验证提供了一种可靠的方法,也为更广泛地鉴别诊断神经退行性疾病奠定了基础。通过实现更准确的患者分层,该工具有可能引导患者参与适当的临床试验,从而能够诊断更广泛的神经退行性疾病。这种方法有可能扩大纳入研究的患者群体,并加速新治疗策略的开发。