Automated pixel-based quantification of porcine circovirus 2 genome in formalin-fixed, paraffin-embedded tissues using hybridisation.

INTRODUCTION

Detection of porcine circovirus 2 (PCV2) in lymphoid tissues is essential for diagnostic and research purposes. hybridisation (ISH) enables the localisation of viral genomes in tissue sections but is traditionally assessed visually, which may introduce subjectivity.

METHODS

This study developed an automated pixel classifier to quantify the PCV2 genome using RNAscope ISH technology. Four lymphoid tissues (tonsils and tracheobronchial, mesenteric, and superficial inguinal lymph nodes) from 66 experimentally inoculated pigs were analysed. PCV2 labelling was assessed both visually (scores 0-3) and digitally (percentage of labelled area).

RESULTS

A strong correlation was observed between visual and digital ISH scoring ( = 0.96), allowing the definition of digital thresholds corresponding to visual scores. Among all tissues, TBLN exhibited the highest PCV2 labelling. This tissue was further evaluated by PCV2 quantitative polymerase chain reaction (qPCR), showing a high correlation with digital ISH results ( = 0.85).

DISCUSSION

These findings demonstrate the reliability of digital pathology tools for objective quantification of PCV2 in lymphoid tissues. Automated scoring enhances consistency, reduces observer bias, and improves diagnostic efficiency in PCV2 research and surveillance.