Department of Pharmacology, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China.
Jiangsu Key Laboratory of Neurodegeneration, Department of Pharmacology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
Neurosci Lett. 2024 Jul 27;836:137871. doi: 10.1016/j.neulet.2024.137871. Epub 2024 Jun 8.
Parkinson's disease (PD) entails the progressive loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc), leading to movement-related impairments. Accurate assessment of DA neuron health is vital for research applications. Manual analysis, however, is laborious and subjective. To address this, we introduce TrueTH, a user-friendly and robust pipeline for unbiased quantification of DA neurons. Existing deep learning tools for tyrosine hydroxylase-positive (TH) neuron counting often lack accessibility or require advanced programming skills. TrueTH bridges this gap by offering an open-sourced and user-friendly solution for PD research. We demonstrate TrueTH's performance across various PD rodent models, showcasing its accuracy and ease of use. TrueTH exhibits remarkable resilience to staining variations and extreme conditions, accurately identifying TH neurons even in lightly stained images and distinguishing brain section fragments from neurons. Furthermore, the evaluation of our pipeline's performance in segmenting fluorescence images shows strong correlation with ground truth and outperforms existing models in accuracy. In summary, TrueTH offers a user-friendly interface and is pretrained with a diverse range of images, providing a practical solution for DA neuron quantification in Parkinson's disease research.
帕金森病（PD）涉及黑质致密部（SNc）中多巴胺能（DA）神经元的进行性丧失，导致与运动相关的损伤。准确评估 DA 神经元的健康状况对于研究应用至关重要。然而，手动分析既繁琐又主观。为了解决这个问题，我们引入了 TrueTH，这是一种用户友好且强大的无偏 DA 神经元定量分析流水线。现有的用于酪氨酸羟化酶阳性（TH）神经元计数的深度学习工具往往缺乏可及性或需要高级编程技能。TrueTH 通过为 PD 研究提供开源且用户友好的解决方案来弥补这一差距。我们展示了 TrueTH 在各种 PD 啮齿动物模型中的性能，展示了其准确性和易用性。TrueTH 对染色变化和极端条件具有显著的弹性，即使在染色较轻的图像中也能准确识别 TH 神经元，并能区分脑切片片段和神经元。此外，我们还评估了该流水线在荧光图像分割方面的性能，结果表明与真实数据具有很强的相关性，并且在准确性方面优于现有模型。总之，TrueTH 提供了一个用户友好的界面，并且使用了广泛的图像进行预训练，为 PD 研究中的 DA 神经元定量提供了实用的解决方案。
