Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.

Circular RNA (circRNA) is a candidate for next-generation messenger RNA therapeutics owing to its remarkable stability. Here we describe trans-splicing-based methods for the synthesis of circRNAs over 8,000 nucleotides. The methods are independent of bacterial sequences, outperform the permuted intron-exon method and allow for the incorporation of RNA modifications. The resulting unmodified circRNAs, which incorporate sequences from human 28S ribosomal RNA, display low immunogenicity and are translated more efficiently than permuted intron-exon-derived circRNAs. Additionally, by using viral internal ribosomal entry sites for rolling circle translation, we show that ribosomes can efficiently read through highly structured internal ribosomal entry sites, enhancing the efficiency of rolling circle translation by over 7,000-fold with respect to previous constructs. The efficient and reliable production of circRNA may facilitate its therapeutic use.

Allogeneic chimaeric antigen receptor T cells (allo-CAR T cells) derived from healthy donors could provide rapid access to standardized and affordable batches of therapeutic cells if their rejection by the host's immune system is avoided. Here, by means of an in vivo genome-wide CRISPR knockout screen, we show that the deletion of Fas or B2m in allo- T cells increases their survival in immunocompetent mice. Human B2M- allo-CAR T cells become highly sensitive to rejection mediated by natural killer (NK) cells, whereas FAS- CAR T cells expressing normal levels of human leukocyte antigen I remain resistant to NK cells. CD3- FAS- CAR T cells outperformed CD3- B2M- CAR T cells in the control of leukaemia growth in mice under allogeneic pressure by T cells and NK cells. The partial protection of CD3- FAS- allo-CAR T cells from cellular rejection may improve the efficacy of allogeneic cellular therapies in patients with cancer.

In mammalian cells, signalling pathways orchestrate cellular growth, differentiation and survival, as well as many other processes that are essential for the proper functioning of cells. Here we describe cutting-edge genetic-engineering technologies for the rewiring of signalling networks in mammalian cells. Specifically, we describe the recombination of native pathway components, cross-kingdom pathway transplantation, and the development of de novo signalling within cells and organelles. We also discuss how, by designing signalling pathways, mammalian cells can acquire new properties, such as the capacity for photosynthesis, the ability to detect cancer and senescent cell markers or to synthesize hormones or metabolites in response to chemical or physical stimuli. We also review the applications of mammalian cells in biocomputing. Technologies for engineering signalling pathways in mammalian cells are advancing basic cellular biology, biomedical research and drug discovery.

The paucity of high-quality medical imaging datasets could be mitigated by machine learning models that generate compositionally diverse images that faithfully represent medical concepts and pathologies. However, large vision-language models are trained on natural images, and the diversity distribution of the generated images substantially differs from that of medical images. Moreover, medical language involves specific and semantically rich vocabulary. Here we describe a domain-adaptation strategy for large vision-language models that overcomes distributional shifts. Specifically, by leveraging publicly available datasets of chest X-ray images and the corresponding radiology reports, we adapted a latent diffusion model pre-trained on pairs of natural images and text descriptors to generate diverse and visually plausible synthetic chest X-ray images (as confirmed by board-certified radiologists) whose appearance can be controlled with free-form medical text prompts. The domain-adaptation strategy for the text-conditioned synthesis of medical images can be used to augment training datasets and is a viable alternative to the sharing of real medical images for model training and fine-tuning.