Identification of sensory and motor nerve fascicles by immunofluorescence staining after peripheral nerve injury

Abstract

Background Inappropriate matching of motor and sensory fibers after nerve repair or nerve grafting can lead to failure of nerve recovery. Identification of motor and sensory fibers is important for the development of new approaches that facilitate neural regeneration and the next generation of nerve signal-controlled neuro-prosthetic limbs with sensory feedback technology. Only a few methods have been reported to differentiate sensory and motor nerve fascicles, and the reliability of these techniques is unknown. Immunofluorescence staining is one of the most commonly used methods to distinguish sensory and motor nerve fibers, however, its accuracy remains unknown.

Methods In this study, we aim to determine the efficacy of popular immunofluorescence markers for motor and sensory nerve fibers. We harvested the facial (primarily motor fascicles) and sural (primarily sensory fascicles) nerves in rats, and examined the immunofluorescent staining expressions of motor markers (choline acetyltransferase (ChAT), tyrosine kinase (TrkA)), and sensory markers [neurofilament protein 200 kDa (NF-200), calcitonin gene-related peptide (CGRP) and Transient receptor potential vanillic acid subtype 1 (TRPV1)]. Three methods, including the average area percentage, the mean gray value, and the axon count, were used to quantify the positive expression of nerve markers in the immunofluorescence images.

Results Our results suggest the mean gray value method is the most reliable method. The mean gray value of immunofluorescence in ChAT (63.0 ± 0.76%) and TRKA (47.6 ± 0.43%) on the motor fascicles was significantly higher than that on the sensory fascicles (ChAT: 49.2 ± 0.72%, P < 0.001; and TRKA: 29.1 ± 0.85%, P < 0.001). Additionally, the mean gray values of TRPV1 (51.5 ± 0.83%), NF-200 (61.5 ± 0.62%) and CGRP (37.7 ± 1.22%) on the motor fascicles were significantly lower than that on the sensory fascicles respectively (71.9 ± 2.32%, 69.3 ± 0.46%, and 54.3 ± 1.04%) (P < 0.001). The most accurate cutpoint occurred using CHAT/CRCP ratio, where a value of 0.855 had 100% sensitivity and 100% specificity to identify motor and sensory nerve with an area under the ROC curve of 1.000 (P < 0.001).

Conclusions A combination of ChAT and CGRP is suggested to distinguish motor and sensory nerve fibers.