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Detection of the RNA-Editing Enzyme ADARB1 in Primary Auditory Neurons and Anti-ADARB1 Immunofluorescence as a Means for Automated Counting

Journal
The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society (Q1)
Published
18 October 2026
Study design
Unclassified
Evidence level
Level 5, Expert Opinion (CEBM 5)
Authors
Garner C Fincher, Punam Thapa, Steven C Gressett, Bradley J Walters
PMID
42892742
DOI
10.1369/00221554261490630

Why clinicians should know about it

  • Picked for Histology (paper of the day, 20 October 2026): ADARB1 immunofluorescence for neuronal counting

Abstract

Spiral ganglion neurons (SGNs), the primary auditory afferents in the inner ear, are vulnerable to degeneration from neuropathies, concussion, and aging, among other causes. Accurate quantification of SGNs is critical for assessing degeneration and therapeutic interventions, but manual counting is arduous due to dense cellular crowding and unreliable nuclear markers. Because SGNs receive input from GluA2-containing AMPA receptors, the transcripts for which must be RNA-edited for calcium impermeability, we hypothesized that SGNs express high levels of the RNA-editing enzyme ADARB1. Here, we confirm enriched expression of Adarb1 transcripts in SGNs and show that anti-ADARB1 antibodies robustly label the nuclei of type I and type II SGNs from young and aged mice, adult macaques, and adult humans. Neuronal versus glial specificity was confirmed using antibodies against neurofilament heavy chain (NFH), human antigen D (HuD), GATA binding protein 3 (GATA3), and SRY-box 2 (SOX2). A blinded investigator manually counted mouse SGNs via NFH, and these were compared to automated counts of ADARB1-positive nuclei using the analyze particles function in ImageJ. A concordance correlation coefficient and Bland-Altman analysis demonstrated strong agreement between the manual and automated counts. Together, these results suggest broad utility of ADARB1 immunolabeling for automated counts of SGNs across species.

Abstract as published, via PubMed.

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For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.