Methodological Advances in Transcriptomics and Metabolomics for Assessing Crop Stress Resilience

Climate change poses a serious threat to global agriculture, biodiversity, and food security, underscoring the need to develop crops with enhanced resilience to abiotic stresses. Methodological advancements in transcriptomics and metabolomics have revolutionized the assessment of crop stress resilience, providing comprehensive and high-resolution insights into plant responses at the molecular and biochemical levels. Transcriptomics enables detailed profiling of gene expression patterns and regulatory networks activated under stress conditions, whereas metabolomics offers comprehensive profiling of metabolites involved in stress adaptation, signaling, and cellular homeostasis. Recent innovations in high-throughput sequencing, long-read transcriptomics, and advanced mass spectrometry techniques have expanded analytical sensitivity, specificity, and throughput. This review critically examines the latest methodological developments in transcriptomics and metabolomics, emphasizing their synergistic potential in decoding plant stress resilience. In addition, we discuss key challenges in cross-omics data integration, including computational complexity, standardization, and environmental variability, and highlight emerging solutions such as spatial omics, AI-assisted analytics, and high-throughput phenotyping. By utilizing these cutting-edge methodologies, researchers can enhance predictive modeling, accelerate stress-resilient crop breeding programs, and contribute to the development of climate-smart agriculture, ultimately supporting global food security. With advanced technologies, researchers can better understand complex regulatory networks, identify resilience-associated biomarkers, and accelerate the development of climate-resilient crops. Climate-resilient crops can be developed by understanding complex regulatory networks and identifying resilience-associated biomarkers. Ultimately, integrative omics approaches will play a crucial role in supporting sustainable agriculture and global food security. Integrating transcriptomics and metabolomics with AI-based analytics offers new precision tools for evaluating crop stress.