Comparative transcriptomics is a field that explores how gene activity changes across different organisms and tissues, helping scientists understand how evolution shapes the diversity of life. Researchers at the Universitat Pompeu Fabra discuss how new technologies are pushing this field forward. The authors describe how RNA sequencing initially focused on bulk tissue and organ samples, but single-cell and spatial transcriptomics now allow scientists to study gene activity at the level of individual cell types.

Overview of this review: schematic representation of the prevailing standards in comparative transcriptomic studies, including RNA-seq technologies (left), evolutionary scales (middle), and modeling frameworks (right), as applied in the past (top) and present/future (bottom).

Another important trend is expanding research beyond classic model organisms like mice and fruit flies to include a much wider range of species. This broader scope helps reveal both the uniqueness and the shared biology of life across evolutionary branches. Finally, the growth of large transcriptomic datasets has created opportunities for machine learning to model how genes are expressed, reconstruct cell type histories, and even predict RNA activity directly from DNA sequences. Together, these advances are helping researchers better connect evolutionary biology with biomedical applications.