Abstract:

With the accelerating commercialization of genetically modified （GM） and gene-edited （GE） crops within global agricultural trade， the accurate identification of potential unintended effects （UEs） in biotechnology-derived breeding products has become a central issue in biosafety assessment. For a long duration， the evaluation system has relied largely on the principle of "substantial equivalence"， wherein targeted detection technologies have played a significant role in assessing conventional physicochemical indicators. However， traditional methods struggle to capture complex molecular variations. Moreover， conventional "bulk multi-omics" analysis tends to mask cellular heterogeneity， failing to detect rare variants and their intricate microenvironmental interactions at the microscopic level. This review summarized recent advances in the application of multi-omics technologies for evaluating unintended effects in biotech breeding products. It focused on the potential of emerging technologies such as single-cell omics and spatial omics in deciphering tissue-specific unintended effects. Furthermore， the article provided an in-depth analysis of how non-targeted multi-omics—encompassing genomics， transcriptomics， proteomics， and metabolomics—enable the comprehensive profiling of multi-layered molecular variations through full-spectrum scanning. Finally， the article explored the challenges and prospects of integrated multi-omics data analysis and artificial intelligence （AI） in future biosafety regulatory frameworks. Therefore， a systems biology strategy integrating multi-dimensional omics data， combined with machine learning and artificial intelligence， represents a crucial trend toward achieving comprehensive and precise food safety and unintended effects assessment of biotech breeding products.

Key words: biotechnology-derived breeding products, unintended effects, multi-omics, single-cell omics, spatial omics, biosafety assessment