AR: An Artificial Intelligence‐Driven Fluorescent Reporter System for Gene Editing Evaluation

Abstract

ABSTRACT Accurate and scalable evaluation of gene‐editing efficiency is crucial for the development of novel editing tools. However, conventional methods are laborious, costly, and low‐throughput. Here, we developed an artificial intelligence (AI)‐driven fluorescent reporter (AR) platform, an integrated system that automates and streamlines the entire evaluation workflow. The AR platform combines an enhanced fluorescent reporter with an AI‐driven analytical pipeline. The dual‐fluorescence reporter system, in which mCherry carrying a PEST domain is paired with GFP, supports Cas variants with diverse PAM requirements and quantifies editing efficiency as the proportion of GFP‐only cells. The platform's AI module, EasyReporter, integrates a deep learning model for automated cell segmentation and quantification with a large language model (LLM)‐based agent for intelligent data interpretation and comprehensive report generation. Editing efficiencies measured by AR correlated with those measured by flow cytometry (Spearman ρ = 0.77) and amplicon sequencing (Spearman ρ = 0.83). The AR platform provides a high‐throughput and automated workflow that accelerates gene‐editing research. In the future, it will be particularly effective for quantifying the efficiency of large‐fragment gene editing, offering a new framework for AI‐empowered biological laboratories.