Pain syndromes are often accompanied by complex molecular and cellular changes in dorsal root ganglia (DRG). However, the evaluation of cellular plasticity in the DRG is often performed by heuristic manual analysis of a small number of representative microscopy image fields. In this study, we introduce a deep learning–based strategy for objective and unbiased analysis of neurons and satellite glial cells (SGCs) in the DRG. To validate the approach experimentally, we examined serial sections of the rat DRG after spared nerve injury (SNI) or sham surgery. Sections were stained for neurofilament, glial fibrillary acidic protein (GFAP), and glutamine synthetase (GS) and imaged using high-resolution large-field (tile) microscopy. After training of deep learning models on consensus information of different experts, thousands of image features in DRG sections were analyzed. We used known (GFAP upregulation), controversial (neuronal loss), and novel (SGC phenotype switch) changes to evaluate the method. In our data, the number of DRG neurons was similar 14 d after SNI vs sham. In GFAP-positive subareas, the percentage of neurons in proximity to GFAP-positive cells increased after SNI. In contrast, GS-positive signals, and the percentage of neurons in proximity to GS-positive SGCs decreased after SNI. Changes in GS and GFAP levels could be linked to specific DRG neuron subgroups of different size. Hence, we could not detect gliosis but plasticity changes in the SGC marker expression. Our objective analysis of DRG tissue after peripheral nerve injury shows cellular plasticity responses of SGCs in the whole DRG but neither injury-induced neuronal death nor gliosis.

疼痛综合征通常伴随着背根神经节（DRG）复杂的分子和细胞变化。然而，DRG 中细胞可塑性的评估通常是通过对少量代表性显微镜图像场进行启发式手动分析来进行的。在这项研究中，我们引入了一种基于深度学习的策略，用于对 DRG 中的神经元和卫星胶质细胞 (SGC) 进行客观和公正的分析。为了通过实验验证该方法，我们在幸免神经损伤（SNI）或假手术后检查了大鼠 DRG 的连续切片。对切​​片进行神经丝、神经胶质原纤维酸性蛋白 (GFAP) 和谷氨酰胺合成酶 (GS) 染色，并使用高分辨率大视野（平铺）显微镜进行成像。在对不同专家的共识信息进行深度学习模型训练后，分析了 DRG 部分中的数千个图像特征。我们使用已知的（GFAP 上调）、有争议的（神经元丢失）和新颖的（SGC 表型转换）变化来评估该方法。在我们的数据中，SNI 与假手术后 14 天的 DRG 神经元数量相似。在 GFAP 阳性分区中，SNI 后靠近 GFAP 阳性细胞的神经元百分比增加。相反，SNI 后 GS 阳性信号和接近 GS 阳性 SGC 的神经元百分比减少。GS 和 GFAP 水平的变化可能与不同大小的特定 DRG 神经元亚群有关。因此，我们无法检测到神经胶质增生，而是检测到 SGC 标记表达的可塑性变化。我们对周围神经损伤后 DRG 组织的客观分析显示，整个 DRG 中 SGC 的细胞可塑性反应，但既没有损伤引起的神经元死亡，也没有神经胶质增生。