N Nature Biomedical Engineering · Nov 28, 2025 mRNA engineering of allogeneic mesenchymal stem cells enables coordinated delivery of T cell engagers and immunotherapeutic cues Allogeneic cell therapies can enable off-the-shelf products that address limitations of autologous therapies. Mesenchymal stem cells are a robust allogeneic source, but no bioengineered mesenchymal stem cell-based therapies exist. Here we use mRNA engineering to create an off-the-shelf immunotherapy that we term DC-25. DC-25 consists of a mesenchymal stem cell armed with three designed mRNA constructs encoding CXCR4 to direct migration, a T cell engager specific for B cell maturation antigen to target B cell maturation antigen-expressing plasma cells involved in cancer and autoimmunity, and interleukin-12 to potentiate pro-immune responses. DC-25 allows tunable expression of each gene, supporting a predictable pharmacokinetic profile. In vitro, DC-25 exhibits synergistic killing of target cells, and in a preclinical in vivo myeloma model, this therapy exhibits potent efficacy that surpasses T cell engager protein infusion. In a phase 1 safety study in patients with myeloma, DC-25 appears safe and generates interleukin-12 production after each infusion. This study motivates human cell therapies that exploit mRNA to achieve efficacy through induction of secreted or surface-bound therapeutic elements. Biomedical engineering Cell delivery Immunotherapy Translational research biology mouse experiments
N Nature Biomedical Engineering · Nov 27, 2025 Tumour-specific bioorthogonal synthesis of proteolysis-targeting chimeras and nanoparticles boosts T cell activity Redirecting immune cells or therapeutic molecules to cancer targets without harming healthy tissues remains a major challenge. Here we present a tumour-selective ligation strategy that enables in situ assembly of proximity-based therapies, including proteolysis-targeting chimeras and nanotechnology-based immunotherapy. The system uses a tumour-enriched amino acid mimic to uncage a chemical tag, triggering a rapid and selective bond-forming reaction with a matching tag on a therapeutic module. This decaging-to-ligation chemistry allows precise recruitment of proteins or immune cells at the tumour site. In mouse models, proteolysis-targeting chimeras were synthesized locally at concentrations sufficient for degradation activity, while immune cell-engaging nanoparticles formed only in treated tumours, leading to a 14.8-fold increase in T cell activation. The approach induced strong tumour regression with minimal systemic toxicity. Unlike uncontrolled therapies, which caused marked increases in white blood cell counts, the controlled ligation system showed negligible side effects. This strategy offers a generalizable method for activating therapeutic assemblies in vivo, overcoming key limitations of proximity-mediated cancer treatments. Drug delivery biology mouse experiments
N Nature Biomedical Engineering · Nov 19, 2025 Inferring multi-organ genetic connections using imaging and clinical data through Mendelian randomization Understanding the complex relationships among major clinical outcomes and the interplay among multiple organs remains a considerable challenge. By using imaging phenotypes, we can characterize the functional and structural architecture of major human organs. Mendelian randomization (MR) provides a valuable framework for uncovering robust relationships between phenotypes by leveraging genetic variants as instrumental variables. Here we conduct a systematic multi-organ MR analysis involving 402 imaging traits and 372 clinical outcomes. Our analysis reveals 184 MR associations for 58 diseases and 56 imaging traits across various organs, tissues and systems, including the brain, heart, liver, kidney, lung, pancreas, spleen, adipose tissue and skeletal system. We identify intra-organ MR connections, such as the putative bidirectional genetic links between Alzheimer’s disease and brain function, and interorgan associations, such as heart diseases and brain health. Metabolic disorders, such as diabetes, show genetically rooted putative MR effects across multiple organs. These findings shed light on the genetic links spanning multiple organs, providing targets for future mechanistic follow-up for clinical disease research. Genetics Genetics research biology
N Nature Biomedical Engineering · Nov 18, 2025 Label-free navigation system for grading prostate tumour malignancy in situ via tissue pH and prostate-specific antigen activity Radical prostatectomy is a standard curative approach for high-risk prostate cancer, yet accurately defining tumour margins during surgery remains a major challenge. Intraoperative assessment of prostate tumour malignancy—particularly those with high aggressiveness catalogued in Gleason grade group (GG) ≥ 3—is crucial to prevent positive surgical margins and minimize postoperative complications. Here we develop a surface-enhanced Raman scattering (SERS)-based navigation system for intraoperative localization of high-grade malignant regions by simultaneously accessing tissue acidity and prostate-specific antigen (PSA) enzymatic activity. This system integrates a sampling pen for automated biomarker extraction from tissue surfaces, a nano-imprinted SERS array producing a ratiometric Raman signal in response to acidity and PSA activity, and a two-dimensional deep-learning model for rapid Raman spectral interpretation. We show that the system can intraoperatively identify GG ≥ 3 malignancies in fresh prostate tissues from 144 Chinese patients with an area under the receiver operating characteristic curve of 0.89. This SERS-based navigation system holds strong potential to enhance surgical precision, minimize tumour residue and ultimately improve patient outcomes. Microfluidics Molecular imaging Nanomedicine Prostate Raman spectroscopy biology
N Nature Biomedical Engineering · Nov 17, 2025 HBsAg-tagged tumour vaccine system eliminates solid tumours through virus-specific memory T cells It is challenging for cancer vaccines to identify immunogenic antigens that are specifically and uniformly expressed on heterogeneous solid tumours and that can elicit production of T cells to lyse antigen-positive tumour cells and expand within the immunosuppressive tumour microenvironment. In contrast, microbial antigens are well-defined and robustly immunogenic and can activate specific memory T cells to eliminate microbes within the tumour microenvironment. Inspired by this, we developed a hepatitis B surface antigen (HBsAg)-tagged tumour vaccine system (H-TVAC). H-TVACleverages HBsAg-specific memory T cells from a HBsAg mRNA vaccine to target and lyse HBsAg-tagged tumour cells using the vaccinia virus. This approach also elicits a tumour-specific immune response through epitope spreading by recruiting dendritic cells, thereby eliminating heterogeneous solid tumours. In various preclinical murine models, including the B16-OVA, B16F10, MC38, CT26, 4T1 and H22 hepatocellular carcinoma, as well as a B16F10 bilateral tumour model, H-TVACdemonstrates anti-tumour immune responses, improved survival rates and reduced metastasis and recurrence. Cancer immunotherapy Tumour immunology biology mouse experiments
N Nature Biomedical Engineering · Nov 06, 2025 A vision–language pretrained transformer for versatile clinical respiratory disease applications General artificial intelligence models have unique challenges in clinical practice when applied to diverse modalities and complex clinical tasks. Here we present MedMPT, a versatile, clinically oriented pretrained model tailored for respiratory healthcare, trained on 154,274 pairs of chest computed-tomography scans and radiograph reports. MedMPT adopts self-supervised learning to acquire medical insights and is capable of handling multimodal clinical data and supporting various clinical tasks aligned with clinical workflows. We evaluate the performance of MedMPT on a broad spectrum of chest-related pathological conditions, involving common medical modalities such as computed tomography images, radiology reports, laboratory tests and drug relationship graphs. MedMPT consistently outperforms the state-of-the-art multimodal pretrained models in the medical domain, achieving significant improvements in diverse clinical tasks. Extensive analysis indicates that MedMPT effectively harnesses the potential of medical data, showing both data and parameter efficiency and offering explainable insights for decision-making. MedMPT highlights the potential of multimodal pretrained models in the realm of general-purpose artificial intelligence for clinical practice. Biomedical engineering Machine learning Respiratory tract diseases Machine Learning Clinical Human Respiratory Disease
