Artificial Intelligence-Enabled Single-Cell Resolution and Spatial Plant Multi-omics

The ability to investigate the complex cellular landscapes and regulatory systems within plants is made possible by single-cell and spatial omics methods, which are transforming the field of plant biology. The study of cell types and interactions in their natural surroundings is made easier by the preservation of geographical information provided by spatial transcriptomics, while single-cell RNA sequencing has exposed transcriptional heterogeneities. Modern developments, including spatially improved-resolution omics-sequencing, combines these techniques to get over the drawbacks of conventional methods and offer high-resolution insights into plant transcriptomes. Notwithstanding the potential of these technologies, there are still obstacles to overcome, such as difficulties with tissue dissection and data integration. Multi-omics applications in medicinal plants demonstrate the extensive implications of these technologies in basic and applied plant research, as well as the possibility of bio-manufacturing natural products. This chapter provides insights into the influence of artificial intelligence in single-cell and spatial plant multi-omics in providing the detailed output of data interpretation.