The changing landscape of gene expression analysis

Gene expression analysis has evolved substantially over the past 25 years, from early transcript surveys using expressed sequence tags and microarrays to RNA sequencing, and more recently to single-cell and spatial transcriptomics. These successive waves have expanded measurement scale and resolution, enabling systematic discovery of transcriptional programmes, inference of gene regulatory networks, and increasingly direct links between transcriptomic insight and therapeutic strategies that modulate gene expression. In this Perspective, we synthesize major methodological milestones with bibliometric trends in leading bioinformatics journals to describe four revolutions that redefined gene expression analysis. We also map widely used computational tools onto a common timeline by analysing 70 78 831 open-access full-text articles, illustrating how enduring statistical frameworks coexist with rapidly growing end-to-end analysis ecosystems. We highlight current challenges and emerging directions in core bioinformatics approaches for gene expression analysis. Looking ahead, we argue that the next era will be defined less by generating new datasets and more by organizing, searching, and reusing transcriptomic and multimodal information at scale. We propose three future directions: consortium-scale searchable transcriptomic knowledgebases, foundation models for gene expression analysis, and programmable regulatory design for engineered control of gene expression. The landscape of gene expression analysis is shifting from descriptive measurement towards queryable, predictive, and programmable gene expression biology.