Data-Driven Therapeutics Using Single-Cell Technologies

This review article discusses the advancements in spatially resolved single-cell technologies and their integration with generative artificial intelligence (AI) for therapeutic development. It outlines how recent innovations in multi-omics have transformed our ability to analyze cellular states, providing insights into the molecular landscapes of human tissues in health and disease. The article highlights the challenges posed by high-dimensional data and the role of generative AI, particularly variational autoencoders and transformer-based models, in addressing these challenges. By effectively identifying complex patterns and reducing data dimensionality, these AI methodologies enhance our understanding of autoimmune diseases. The review also details the evolution of single-cell multi-omics assays, the potential for precision medicine, and the future directions for leveraging these technologies to improve diagnostics and treatment strategies. The authors emphasize the importance of combining spatial and single-cell data with advanced AI methodologies to achieve personalized medicine outcomes.