A Simple Nonviral Method to Generate Human Induced Pluripotent Stem Cells Using SMAR DNA Vectors.

Induced pluripotent stem cells (iPSCs) are a powerful tool for biomedical research, but their production presents challenges and safety concerns. Yamanaka and Takahashi revolutionised the field by demonstrating that somatic cells could be reprogrammed into pluripotent cells by overexpressing four key factors for a sufficient time. iPSCs are typically generated using viruses or virus-based methods, which have drawbacks such as vector persistence, risk of insertional mutagenesis, and oncogenesis. The application of less harmful nonviral vectors is limited as conventional plasmids cannot deliver the levels or duration of the factors necessary from a single transfection. Hence, plasmids that are most often used for reprogramming employ the potentially oncogenic Epstein-Barr nuclear antigen 1 (EBNA-1) system to ensure adequate levels and persistence of expression. In this study, we explored the use of nonviral SMAR DNA vectors to reprogram human fibroblasts into iPSCs. We show for the first time that iPSCs can be generated using nonviral plasmids without the use of EBNA-1 and that these DNA vectors can provide sufficient expression to induce pluripotency. We describe an optimised reprogramming protocol using these vectors that can produce high-quality iPSCs with comparable pluripotency and cellular function to those generated with viruses or EBNA-1 vectors.

BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database.

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.