Department of Neurology, University Hospital of Würzburg, 97080, Würzburg, Germany.
Sci Rep. 2024 Nov 18;14(1):28496. doi: 10.1038/s41598-024-79271-9.
Assessing localization of the transient receptor potential vanilloid-1 (TRPV1) in skin nerve fibers is crucial for understanding its role in peripheral neuropathy and pain. However, information on the specificity and sensitivity of TRPV1 antibodies used for immunofluorescence (IF) on human skin is currently lacking. To find a reliable TRPV1 antibody and IF protocol, we explored antibody candidates from different manufacturers, used rat DRG sections and human skin samples for screening and human TRPV1-expressing HEK293 cells for further validation. Final specificity assessment was done on human skin samples. Additionally, we developed two automated image analysis methods: a Python-based deep-learning approach and a Fiji-based machine-learning approach. These methods involve training a model or classifier for nerve fibers based on pre-annotations and utilize a nerve fiber mask to filter and count TRPV1 immunoreactive puncta and TRPV1 fluorescence intensity on nerve fibers. Both automated analysis methods effectively distinguished TRPV1 signals on nerve fibers from those in keratinocytes, demonstrating high reliability as evidenced by excellent intraclass correlation coefficient (ICC) values exceeding 0.75. This method holds the potential to uncover alterations in TRPV1 associated with neuropathic pain conditions, using a minimally invasive approach.
评估瞬时受体电位香草酸 1（TRPV1）在皮肤神经纤维中的定位对于理解其在外周神经病变和疼痛中的作用至关重要。然而，目前缺乏用于免疫荧光（IF）的 TRPV1 抗体的特异性和敏感性的信息。为了找到可靠的 TRPV1 抗体和 IF 方案，我们探索了来自不同制造商的抗体候选物，使用大鼠背根神经节（DRG）切片和人皮肤样本进行筛选，并进一步在表达人 TRPV1 的 HEK293 细胞中进行验证。最终在人皮肤样本上进行了特异性评估。此外，我们开发了两种自动化图像分析方法：基于 Python 的深度学习方法和基于 Fiji 的机器学习方法。这些方法涉及基于预注释来训练用于神经纤维的模型或分类器，并利用神经纤维掩模来过滤和计算 TRPV1 免疫反应性斑点和神经纤维上的 TRPV1 荧光强度。这两种自动化分析方法都能够有效地将神经纤维上的 TRPV1 信号与角质形成细胞中的信号区分开来，证明了其可靠性，因为内部一致性系数（ICC）值超过 0.75。该方法有可能通过微创方法揭示与神经病理性疼痛状况相关的 TRPV1 改变。
