Abstract
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.