Nature Biomedical Engineering

Timely detection of Barretts esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barretts. However, it depends on pathologists assessment of two slides stained with H&E and the immunohistochemical biomarker TFF3. This resource-intensive clinical workflow limits large-scale screening in the at-risk population. Deep learning can improve screening capacity by partly automating Barretts detection, allowing pathologists to prioritize higher risk cases. We propose a deep learning approach for detecting Barretts from routinely stained H&E slides using diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1,866 patients. We achieve 91.4% and 87.3% AUROCs on discovery and external test datasets for the H&E model, comparable to the TFF3 model. Our proposed semi-automated clinical workflow can reduce pathologists workload to 48% without sacrificing diagnostic performance.

Body surface gastric mapping (BSGM) enables non-invasive measurement of gastric electrophysiology, but the signals are approximately 100 times weaker than cardiac potentials and overlap spectrally with motion artifacts, necessitating labor-intensive manual review that limits clinical scalability. We present an uncertainty-aware deep learning framework combining a signal reconstruction network with a parallel uncertainty estimation network to automate artifact correction in high-resolution BSGM. Models were trained on 2,398 multihour, 64-channel recordings from 27 international clinical sites using weak supervision, a physiology-aware loss function, and uncertainty-gated quality control. In an independent cohort of 127 patients, the system achieved relative reductions of 39% in signal reconstruction error, 9% in total data removed, and 23% in amplitude--movement correlation compared with the industry-standard Wiener filter. Improved signal fidelity altered automated clinical phenotyping in 7% of patients by recovering previously obscured gastric rhythms. Uncertainty-aware deep learning enables reliable automated artifact correction in body-surface gastric mapping, improving signal fidelity and enabling scalable clinical interpretation. The system is FDA-cleared (510(k) K252504) and deployed in clinical practice, demonstrating that data-driven artifact correction can meet regulatory requirements for medical devices and reduce dependence on specialist manual review.

1.Deep brain stimulation is a widely used therapy for Parkinsons disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.

Achieving durable CAR T cell responses against primary brain tumors and metastases requires strategies that enable intracranial control of therapy to overcome the barriers of solid tumor treatment without compromising safety. Here, we show that closed-loop sonothermogenetics enables remote regulation of CAR T cell therapeutic activity through the intact skull. Using MR-guided focused ultrasound with closed-loop temperature feedback, we modulate CAR T cells engineered with a genetically encoded thermal bioswitch to achieve metronomic activation in the brain without lasting adverse effects on healthy brain tissue. In murine models of brain cancer, metronomic production of NKG2D T cell engagers by intratumoral CAR T cells overcomes antigen heterogeneity in breast cancer brain metastasis and myeloid-derived immunosuppression in glioblastoma to drive antitumor responses. Our findings support the use of closed-loop sonothermogenetics for spatial and temporal control of CAR T cell therapies targeting solid brain tumors.

Cell-based therapies are a major emerging category of medicine. The ability of engineered cells to traffic to and function at specific anatomical locations is a major aspect of their performance. However, there is a lack of non-invasive, non-ionizing, cost-accessible methods to track these therapies inside the body and ensure proper function. Here, we establish a platform for in vivo imaging of primary cell therapies using ultrasound - a ubiquitously accessible technology for high-resolution non-invasive imaging. We introduce and optimize a lentiviral delivery system to express acoustic reporter genes based on gas vesicles in primary mammalian cells such as T cells, showing that this results in robust ultrasound contrast. Additionally, we develop genetic circuits making it possible to monitor T cell activation via activity-dependent promoters. We apply this technology to primary human T cells, using it to non-invasively track their accumulation and proliferation as a targeted therapy in a mouse tumor xenograft model and compare it to invasive, terminal measures such as immunohistology. By making it possible to visualize cell-based therapies and their function inside opaque living organs with unprecedented resolution and accessibility, this technology has the potential to significantly accelerate their development and effective use.

Recent advancements in flexible bioelectronics have enabled continuous, long-term stable interrogation and intervention of biological systems. However, effectively utilizing the interrogated data to modulate biological systems to achieve specific biomedical and biological goals remains a challenge. In this study, we introduce an AI-driven bioelectronics system that integrates tissue-like, flexible bioelectronics with cyber learning algorithms to create a long-term, real-time bidirectional bioelectronic interface with optimized adaptive intelligent modulation (BIO-AIM). When integrated with biological systems as an AI-cyborg system, BIO-AIM continuously adapts and optimizes stimulation parameters based on stable cell state mapping, allowing for real-time, closed-loop feedback through tissue-embedded flexible electrode arrays. Applied to human pluripotent stem cell-derived cardiac organoids, BIO-AIM identifies optimized stimulation conditions that accelerate functional maturation. The effectiveness of this approach is validated through enhanced extracellular spike waveforms, increased conduction velocity, and improved sarcomere organization, outperforming both fixed and no stimulation conditions.

Ultrasound imaging and acoustic reporter genes provide unique capabilities for in vivo biological imaging by leveraging ultrasounds ability to visualize opaque tissues with high spatiotemporal resolution. But until now, the expression of acoustic reporter genes - based on gas vesicle (GV) proteins - has been limited to ex vivo-modified cells due to the complexity of the GV gene cluster, precluding valuable in situ applications. Here, we develop a system capable of introducing GV genes directly into native tissues via stoichiometric multi-AAV delivery. We validate this system in the mouse brain, demonstrating well-tolerated in situ gene expression and repeated ultrasound imaging over more than a month in the same animal. Furthermore, by placing GV genes under the control of immediate early gene promoters, we demonstrate the ability to track in vivo gene expression changes arising from elevated neural activity during epileptic seizures. This work connects ultrasound to in situ transcriptional dynamics happening inside the opaque tissues of living creatures.

Accurate polyp segmentation from colonoscopy images is critical for colorectal cancer prevention, yet the generalization of deep learning models under domain shift remains insufficiently explored. We propose Boundary-Explicit Guided Attention U-Net (BEGA-UNet), a boundary-aware segmentation architecture that introduces explicit edge modeling as a structural inductive bias to enhance both segmentation accuracy and cross-domain robustness. The framework integrates three components: an Edge-Guided Module (EGM) with learnable Sobel-initialized operators to capture boundary cues, a Dual-Path Attention (DPA) module that processes channel and spatial attention in parallel, and a Multi-Scale Feature Aggregation (MSFA) module to encode contextual information across multiple receptive fields. Evaluated on the combined Kvasir-SEG and CVC-ClinicDB benchmarks, BEGA-UNet achieves 88.53% Dice and 82.51% IoU, outperforming representative convolutional and transformer-based baselines. More importantly, cross-dataset evaluation demonstrates strong robustness under domain shift, with BEGA-UNet retaining 83.2% of its in-distribution performance--substantially higher than U-Net (64.5%), Attention U-Net (47.5%), and TransUNet (53.1%). In a zero-shot setting on an entirely unseen dataset, the model further maintains 72.6% performance retention. Comprehensive ablation studies indicate that explicit boundary modeling plays a central role in improving generalization, while multi-scale context aggregation further stabilizes performance across domains. Feature distribution analyses support this observation by showing that edge-oriented representations exhibit markedly reduced cross-domain variability compared to appearance-driven features. Overall, BEGA-UNet provides an effective and interpretable solution for robust polyp segmentation, demonstrating that explicit boundary modeling serves as a critical inductive bias for ensuring reliability under clinical domain shifts.

Early cancer detection has the potential to reduce cancer mortality, yet endogenous tumor-shed biomarkers lack sensitivity for early-stage disease. We report OncoSCOUT, a cancer detection strategy using T cells engineered with protease-activatable receptors (PARs) that conditionally recognize tumor cells and release a synthetic biomarker for detection in urine. These PARs comprise masked synthetic Notch receptors in which antigen binding is blocked by a peptide mimotope tethered via a protease-cleavable linker. We demonstrate that requiring both extracellular protease activity and tumor antigen recognition improves spatial specificity and minimizes off-tumor activation of PAR T cells in vivo. To identify tumor-selective PARs, we adoptively transferred a HER2-targeted PAR library displaying [~]160,000 unique 4-mer amino acid linkers and discovered multiple variants significantly enriched in a HER2-positive cancer xenograft model. Using a single customized PAR, we show that OncoSCOUT can detect total tumor burdens as small as 10-30 mm3 with significantly improved sensitivity than the protein biomarker CA 15-3 or a 20-plex circulating tumor DNA (ctDNA) assay.

Monitoring neural population activity at single-cell resolution is essential for driving fundamental research and clinical innovations. However, translating advanced recording techniques from animal models to humans remains a significant challenge. Flexible neural electrodes have recently emerged as powerful tools for large-scale single-unit recordings due to their superior biocompatibility and high recording density. Here, we demonstrate reliable, high-density single-unit recordings during intraoperative procedures in human patients using ultra-Flexible Implantable Neural Electrode (uFINE) arrays. The uFINE array exhibited sufficient mechanical robustness to maintain structural integrity throughout surgical operations. We successfully recorded 616 single units from 10 patients, with up to 135 single units simultaneously recorded. The flexibility of uFINE array minimized signal disturbances from brain pulsations, enabling stable and continuous single-unit detection. Stimulus and response tuning were observed at the level of individual neurons in awake patients. This uFINE-based recording approach offers unique opportunities to investigate human-specific cognitive functions and develop next-generation brain-machine interfaces.

The rapid advances in multi-omics data integration technologies have opened unprecedented avenues for dissecting the mechanisms and accelerating the clinical translation of complex diseases. Nevertheless, the frequent absence of certain modalities, coupled with the inherent heterogeneity and high dimensionality of the data, severely restrict the effectiveness of integrative analysis. To address these challenges, we introduce Entropy-guided Sample-Specific Feature Selection for Incomplete Multi-Omics Learning (ESSFS-IMO), a novel framework that couples instance-wise feature selection with entropy-adaptive optimization and variational representation learning. Concretely, ESSFS-IMO leverages a Gumbel- SoftMax selector parameterized by a neural network to achieve per-sample feature selection, while an entropy-based annealing strategy adaptively controls selector sharpness. The selected features are integrated through an information-bottlenecked variational backbone with variance-weighted fusion, enabling robust classification under arbitrary missing patterns. Extensive experiments on inflammatory bowel disease (IBD) multi-omics datasets demonstrate that ESSFS-IMO consistently outperforms state-of-the-art baselines in terms of accuracy, F1 and AUC.

Human intestinal organoids (HIOs) derived from human pluripotent stem cells co-differentiate both epithelial and mesenchymal lineages in vitro but lack important cell types such as neurons, endothelial cells, and smooth muscle. Here, we report an in vitro method to derive HIOs with epithelium, mesenchyme, enteric neuroglial populations, endothelial cells, and organized smooth muscle in a single differentiation, without the need for co-culture. When transplanted into a murine host, these populations expand and organize to support organoid maturation and function. Functional experiments demonstrate enteric nervous system function, with HIOs undergoing peristaltic-like contractions, suggesting the development of a functional neuromuscular unit. HIOs also form functional vasculature, demonstrated in vitro using microfluidic devices to introduce vascular-like flow, and in vivo following transplantation, where HIO endothelial cells anastomose with host vasculature. Collectively, we report an in vitro model of the human gut that simultaneously co-differentiates epithelial, stromal, endothelial, neural, and organized muscle populations.

Personalized antisense oligonucleotides (ASOs) have achieved positive results in the treatment of rare genetic disease. As clinical sequencing technologies continue to advance, the ability to identify rare disease patients harboring pathogenic genetic variants amenable to this therapeutic strategy will likely improve. Here, we describe a scalable platform for generating patient-derived cellular models and demonstrate that these personalized models can be used for preclinical evaluation of patient-specific ASOs. We establish robust protocols for delivery of ASOs to patient-derived organoid models and confirm reversal of disease-associated phenotypes in cardiac organoids derived from a Duchenne muscular dystrophy (DMD) patient harboring a structural deletion in the dystrophin gene amenable to treatment with existing ASO therapeutics. Furthermore, we design novel patient-specific ASOs for two additional DMD patients (siblings) harboring a deep intronic variant in the dystrophin gene that gives rise to a novel splice acceptor site, incorporation of a cryptic exon, and premature transcript termination. We show that treatment of patient-derived cardiac organoids with patient-specific ASOs results in restoration of DMD expression and reversal of disease-associated phenotypes. The approach outlined here provides the foundation for an expedited path towards the design and preclinical evaluation of personalized ASO therapeutics for a broad range of rare diseases.

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational "digital twins" for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging--a [~]100-fold improvement over manual methods--achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a "physical twin" experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm--combining automated fabrication with domain-specialized generative AI--establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

Bioengineered probiotics enable new opportunities to improve colorectal cancer (CRC) screening, prevention and treatment strategies. Here, we demonstrate the phenomenon of selective, long-term colonization of colorectal adenomas after oral delivery of probiotic E. coli Nissle 1917 (EcN) to a genetically-engineered murine model of CRC predisposition. We show that, after oral administration, adenomas can be monitored over time by recovering EcN from stool. We also demonstrate specific colonization of EcN to solitary neoplastic lesions in an orthotopic murine model of CRC. We then exploit this neoplasia-homing property of EcN to develop early CRC intervention strategies. To detect lesions, we engineer EcN to produce a small molecule, salicylate, and demonstrate that oral delivery of this strain results in significantly increased levels of salicylate in the urine of adenoma-bearing mice, in comparison to healthy controls. We also assess EcN engineered to locally release immunotherapeutics at the neoplastic site. Oral delivery to mice bearing adenomas, reduced adenoma burden by [~]50%, with notable differences in the spatial distribution of T cell populations within diseased and healthy intestinal tissue, suggesting local induction of robust anti-tumor immunity. Together, these results support the use of EcN as an orally-delivered platform to detect disease and treat CRC through its production of screening and therapeutic molecules.

Functional ultrasound imaging (fUSI) is a promising neuroimaging method that infers neural activity by detecting cerebral blood volume changes. It offers high sensitivity and spatial resolution relative to fMRI and is an epidural alternative to electrophysiology for medical and neuroscience applications, including brain-computer interfaces. However, current fUSI methods require hundreds of compounded images and ultrasound pulse emissions, leading to high computational costs, memory demands, and potential probe heating. We propose VARiable Sampling fUSI (VARS-fUSI), the first deep learning fUSI method to allow for different sampling durations and rates during training and inference by using neural operators. VARS-fUSI reconstructs high-quality fUSI images using 10 - 15% of the time or sampling rate needed per image while preserving decodable behavior-correlated signals. Additionally, VARS-fUSI offers efficient finetuning for generalization to new animals and humans. Demonstrated across mouse, monkey, and human data, VARS-fUSI achieves state-of-the-art performance, enhancing imaging efficiency by significantly reducing storage and processing needs.

Chimeric antigen receptor-engineered macrophages (CAR-Ms) hold great promise for solid tumor immunotherapy. The intracellular domains (ICDs) of CARs determine the phenotypic output of therapeutic macrophages but remain largely unexplored. Here, we constructed a CAR library containing 131 unique signaling domains derived from native immune receptors and identified 17 ICDs that enhance macrophage phagocytosis, inflammatory responses, or tumor infiltration in vitro and in vivo. We further developed a scalable 3 barcode technology, CARode, to uniquely label and trace ICD variants within large-scale combinatorial CAR library and applied it to single-cell RNA sequencing and single-cell CAR analysis to assess the synergetic effects of ICD combinations on macrophage activation. Our approach uncovered a novel CD40-LY9-FCRL1 chimeric receptor that modulates the tumor microenvironment and improves solid tumor clearance. In conclusion, our findings demonstrate that pooled screening can accelerate the discovery of complex ICD constructs, providing a powerful platform for engineering macrophage-based immunotherapies.

Synthetic biology enables the engineering of interactions between living medicines to overcome the specific limitations of any singular therapy. One major challenge of tumor-antigen targeting therapies like chimeric antigen receptor (CAR)-T cells is the identification of targetable antigens that are specifically and uniformly expressed on heterogenous solid tumors. In contrast, certain species of bacteria selectively colonize immune-privileged tumor cores and can be readily engineered as antigen-independent platforms for therapeutic delivery. Bridging these approaches, we develop a platform of probiotic-guided CAR-T cells (ProCARs), in which T cells are engineered to sense synthetic antigens (SA) that are produced and released by tumor-colonizing probiotic bacteria. We demonstrate increased CAR-T cell activation and tumor-cell lysis when SAs anchor to components of the extracellular matrix. Moreover, we show that ProCARs are intratumorally activated by probiotically-delivered SAs, receive further stimulation from bacterial TLR agonists, and are safe and effective in multiple xenograft models. This approach repurposes tumor-colonizing bacteria as beacons that guide the activity of engineered T cells, and in turn builds the foundation for communities of living medicines.

Gene expression is a critical component of brain physiology and activity, but monitoring this expression in the living brain represents a significant challenge. Here, we introduce a new paradigm called Recovery of Markers through InSonation (REMIS) for noninvasive measurement of gene expression in the brain with cell-type, spatial, and temporal specificity. Our approach relies on engineered protein markers that are designed to be expressed in neurons and exit into the interstitium. By applying ultrasound to targeted brain regions, these markers are released into the bloodstream, where they can be readily detected using biochemical techniques. REMIS can noninvasively confirm gene delivery and measure endogenous signaling in specific brain sites through a simple insonation and a subsequent blood test. Using REMIS, we successfully measured chemogenetic induction of neuronal activity in ultrasound-tar-geted brain regions. REMIS recovery of markers is reliable and demonstrated improved recovery of markers from the brain into the blood in every tested animal. Overall, our work establishes a noninvasive, spatially-specific means of monitoring gene delivery outcomes and endogenous signaling in mammalian brains, opening up possibilities for brain research and noninvasive monitoring of gene therapies in the brain.

Inflammatory bowel disease (IBD) patients exhibit compromised intestinal barrier function and decreased mucus accumulation, as well as increased inflammation, fibrosis, and cancer risk, with symptoms often being exacerbated in women during pregnancy. Here, we show that these IBD hallmarks can be replicated using human Organ Chips lined by IBD patient-derived colon epithelial cells interfaced with matched fibroblasts cultured under flow. Use of heterotypic tissue recombinants revealed that IBD fibroblasts are the primary drivers of multiple IBD symptoms. Inflammation and fibrosis are accentuated by peristalsis-like motions in IBD Chips and when exposed to pregnancy-associated hormones in female IBD Chips. Carcinogen exposure also increases inflammation, gene mutations, and chromosome duplication in IBD Chips, but not in Healthy Chips. These data enabled by human Organ Chip technology suggest that the intestinal stroma, sex hormones, and peristalsis-associated mechanical deformations play a key role in driving inflammation, fibrosis, and disease progression in male and female IBD patients.