Antibody display technologies from phages to cells: translational bottlenecks and AI-enabled opportunities

Beginning with the pioneering hybridoma technology developed in 1975, antibody generation methodologies have advanced substantially, culminating in today's single-cell techniques. Each successive approach contributes unique applications, advantages, and drawbacks that reflect the field's dynamic progress. We highlight the impact of integrating single-cell RNA sequencing (scRNA-seq) with display technologies. This holds potential for the healthcare industry by enabling efficient identification and development of diagnostic and therapeutic antibodies. Monoclonal antibodies (MAbs) produced via each major technology are discussed to illustrate practical outcomes. We have also explored the essential role of glycosylation in maintaining antibody stability and function. Furthermore, we discussed single-cell RNA sequencing (scRNA-seq) that enables high-resolution profiling of immune repertoires and tumour heterogeneity, facilitating the identification of antigen-specific antibodies and rare cell populations. Integration with microfluidics and computational analysis enhances biomarker discovery and cell-specific resolution. These advances support personalised therapies and accelerate next-generation antibody discovery. Finally, we address the emerging integration of machine learning and artificial intelligence in antibody discovery, emphasising recent advances in epitope mapping and predicting three-dimensional protein structures from primary amino acid sequences. Collectively, these developments are poised to revolutionise antibody engineering and expand its impact on therapeutic innovation.