Nature Biomedical Engineering最新文献

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.

In patients with pancreatic ductal adenocarcinoma (PDAC), intratumoural and intertumoural heterogeneity increases chemoresistance and mortality rates. However, such morphological and phenotypic diversities are not typically captured by organoid models of PDAC. Here we show that branched organoids embedded in collagen gels can recapitulate the phenotypic landscape seen in murine and human PDAC, that the pronounced molecular and morphological intratumoural and intertumoural heterogeneity of organoids is governed by defined transcriptional programmes (notably, epithelial-to-mesenchymal plasticity), and that different organoid phenotypes represent distinct tumour-cell states with unique biological features in vivo. We also show that phenotype-specific therapeutic vulnerabilities and modes of treatment-induced phenotype reprogramming can be captured in phenotypic heterogeneity maps. Our methodology and analyses of tumour-cell heterogeneity in PDAC may guide the development of phenotype-targeted treatment strategies.

To infer intent, brain–computer interfaces must extract features that accurately estimate neural activity. However, the degradation of signal quality over time hinders the use of feature-engineering techniques to recover functional information. By using neural data recorded from electrode arrays implanted in the cortices of three human participants, here we show that a convolutional neural network can be used to map electrical signals to neural features by jointly optimizing feature extraction and decoding under the constraint that all the electrodes must use the same neural-network parameters. In all three participants, the neural network led to offline and online performance improvements in a cursor-control task across all metrics, outperforming the rate of threshold crossings and wavelet decomposition of the broadband neural data (among other feature-extraction techniques). We also show that the trained neural network can be used without modification for new datasets, brain areas and participants.

Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatGPT embeddings of genes based on the literature. We used GPT-3.5 to generate gene embeddings from text descriptions of individual genes and to then generate single-cell embeddings by averaging the gene embeddings weighted by each gene’s expression level. We also created a sentence embedding for each cell by using only the gene names ordered by their expression level. On many downstream tasks used to evaluate pretrained single-cell embedding models—particularly, tasks of gene-property and cell-type classifications—our model, which we named GenePT, achieved comparable or better performance than models pretrained from gene-expression profiles of millions of cells. GenePT shows that large-language-model embeddings of the literature provide a simple and effective path to encoding single-cell biological knowledge.

Tactile feedback from brain-controlled bionic hands can be partially restored via intracortical microstimulation (ICMS) of the primary somatosensory cortex. In ICMS, the location of percepts depends on the electrode’s location and the percept intensity depends on the stimulation frequency and amplitude. Sensors on a bionic hand can thus be linked to somatotopically appropriate electrodes, and the contact force of each sensor can be used to determine the amplitude of a stimulus. Here we report a systematic investigation of the localization and intensity of ICMS-evoked percepts in three participants with cervical spinal cord injury. A retrospective analysis of projected fields showed that they were typically composed of a focal hotspot with diffuse borders, arrayed somatotopically in keeping with their underlying receptive fields and stable throughout the duration of the study. When testing the participants’ ability to rapidly localize a single ICMS presentation, individual electrodes typically evoked only weak sensations, making object localization and discrimination difficult. However, overlapping projected fields from multiple electrodes produced more localizable and intense sensations and allowed for a more precise use of a bionic hand.