The engineering of a 3D lymphatic collecting vessel has been of recent interest due to the lack of treatments for lymphatic valve disruptions. While oscillatory shear stress has been shown to regulate lymphatic collecting vessel morphogenesis, its effect in the context of physiologically relevant extracellular matrix remains elusive. In this study, we used a microfluidic device to elucidate the effect of oscillatory shear stress on human lymphatic endothelial cells cultured on dopamine-modified hyaluronic acid or fibronectin. Here, we show that hyaluronic acid can synergistically enhance mechanoresponsive morphological changes, activate cytoskeleton localization, induce nuclear expression of key mechanosensitive markers (PROX1 and FOXC2), amplify mechanotransduction pathways (VE-CAD and mTORC1), and downregulate capillary lymphatic markers (PDPN and LYVE-1). These results highlight the potential of hyaluronic acid-based materials for the generation of 2D and 3D lymphatic collecting vessel models.
Artificial intelligence (AI)-based methods are increasingly critical in therapeutic peptide discovery but have been concentrated in certain applications, such as the development of antimicrobial peptides. Therapeutic peptide discovery in contexts such as metabolism, endocrinology, and tissue regeneration remains challenging due to a paucity of available indication-specific training data. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying candidate oligopeptides for various metabolism-related contexts. Leveraging the intrinsic relationships between disease indication, biological processes, and molecular function, Deepeptide identifies oligopeptides associated with disease-related processes as lead candidates. Deepeptide was applied in five representative disease-related contexts: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity, with most of them showing comparable potency to the benchmark therapeutic agents. These findings highlight the potential utility and generalizability of Deepeptide for oligopeptide lead discovery across metabolism-related contexts.
Mapping neuronal connectivity is essential for understanding the structure and function of neural circuits. While high-throughput, cost-effective methods using barcoded rabies viruses provide valuable cellular-level insights, they are limited by the spatial resolution of barcode sequencing. To provide a fluorescent in situ hybridization (FISH)-compatible alternative for spatial barcode readout, we developed the CASS (combination of artificial short sequences) barcode, an error-robust and in situ-hybridization-detectable tool. By combining CASS barcoding with rabies virus monosynaptic tracing, we enable in situ connectome mapping through FISH-based barcode decoding. Using this approach, we identified 1,532 synaptic pairs connected to neurons in the primary visual cortex with spatial precision across three mice, demonstrating its efficiency and scalability. This method provides a FISH-based approach for identifying neuronal connectivity with spatial context and may facilitate future integration of connectivity mapping with molecular profiling, thereby advancing our understanding of neural circuits.
Micronuclei (MN) are structures containing small DNA fragments that arise from mitotic errors or failed DNA repair and serve as markers of genome instability. MN are typically quantified manually or with threshold methods, which can be tedious and inaccurate, leading to variable success and throughput. By employing a two-phase labeling approach that uses polygon and brush segmentation, along with SAM2-based refinement, we developed a high-quality MN segmentation tool. Data augmentation capturing heterogeneity in image quality and color diversity enabled us to train a generalizable Mask region-based convolutional neural network (Mask-RCNN) model optimized for small-object detection, achieving state-of-the-art performance in MN detection. Finally, we applied our model to immunofluorescence data from cell lines exposed to DNA damage conditions to gain biological insights into MN dynamics and their role in genome instability. In summary, this work establishes an accessible resource for systematically studying genome instability with greater fidelity and sensitivity, enabling previously unresolved insights into damage biology.
Current de-extinction efforts center on editing the genomes of extant species to express key traits that evolved in closely related extinct species. These complex projects require advances in genetic engineering pipelines coupled with animal model production as an approach to test hypotheses about genotype and phenotype associations. Here, we establish a multiplex genome engineering pipeline capable of introducing up to eleven genomic modifications across seven different genes simultaneously in viable founder mice. Our workflows achieved high editing efficiencies spanning three genome-editing modalities: Cas9 knockout, Cas9-mediated homology-dependent repair (HDR), and cytosine base editing and included editing of zygotes and embryonic stem cells. As a proof of concept, we produced six mouse models with modifications in genes involved in hair development and lipid metabolism, and the resulting mice displayed predicted hair phenotypes including curly, textured coats, and gold hair. This study advances methods of rapid establishment of complex genetic models, with a wide array of applications.
Accurate spectral unmixing is a critical step for flow cytometry data analysis and requires a single stain control for every fluorescent parameter used in an experiment. Currently, compensation/unmixing particles are often used for making single stain controls when a target protein is of low abundance or a cell type is of low frequency. However, compensation/unmixing particles introduce incongruencies in emission spectra, compared to cells, resulting in spectral unmixing or compensation errors. To enable the use of cells regardless of the abundance of target proteins or immune cell type, we generated a bispecific antibody that links a human anti-CD45 and mouse anti-immunoglobulin G (IgG) variable region. We refer to this bispecific tool as CaptureBody (CB) and highlight the benefits of its final nanobody-based design. We provide all sequences and methods necessary for the in-house expression of a CaptureBody in order to disseminate their use for spectral flow cytometry experiments.
DNA ploidy is an important predictor of tumor behavior and prognosis, and its accurate estimation is essential for robust genomic analysis in translational cancer research and diagnostics. However, the most common in silico methods for ploidy estimation using next-generation-sequencing-based copy-number aberration (CNA)-calling algorithms are often inaccurate due to inherent ambiguity in fitting ploidy solutions. This study evaluates the accuracy of state-of-the-art CNA callers using whole-genome sequencing by comparing their ploidy estimates with gold-standard ploidy measurements derived by flow cytometry (FC). We demonstrate that CNA callers are up to 38% inaccurate in cancers with complex genomes, which impacts the accurate estimation of copy number of cancer genes and could have clinical implications and impacts on inferences of tumor evolution. Critically, flow-cytometry-based calibration of CNA callers yields highly accurate ploidy estimates (ρPearson = 0.92, p < 0.001), providing a robust solution to the substantial inaccuracies that compromise clinical decision-making and evolutionary inference in complex cancers.
Chronic inflammatory bowel diseases (IBDs), including Crohn's disease, are characterized by relapsing-remitting intestinal inflammation associated with epithelial barrier dysfunction. Existing preclinical models of IBD have limited capacity to quantitatively capture dynamic epithelial damage and recovery, which hinders the translation of research findings into new therapeutic approaches. Here, we establish a scalable, microarrayed 3D human intestinal organoid platform enabling high-throughput, single-organoid-resolution phenotyping of cytokine-induced epithelial injury and recovery. By integrating time-resolved morphological imaging, transcriptomics, permeability assays, and the intestinal organoid recovery score (IORS), we quantify injury-recovery trajectories and identify responder organoids following preventive or therapeutic intervention. CytoMix-challenged organoids display epithelial inflammatory programs with transcriptional overlap to Crohn's disease biopsy signatures. Dexamethasone, nicotinamide, and β-hydroxybutyrate improved epithelial recovery, with dexamethasone and nicotinamide modulating distinct inflammatory and barrier-associated pathways. Collectively, this single-organoid-resolution approach establishes a standardized platform for modeling epithelial inflammatory damage and recovery, enabling phenotype-driven precision medicine applications in gastrointestinal disease.
Mycotoxins pose severe threats to food safety and public health, especially with synergistic toxicity from co-contamination. Herein, a deep learning-driven two-dimensional (2D)-encoded single microbead imaging decoding platform is developed for homogeneous analysis of four mycotoxins. The aptamer-recognition-triggered DNAzyme walker-hybridization chain reaction (Dz-HCR) cascade design enables direct fluorescent signal lighting and amplification on microbeads, avoiding complex separation and signal probe preparation. By leveraging two different-sized microbeads and two fluorophores modified at HCR hairpins, an ingenious color-size 2D-encoding strategy is established for high-throughput analysis. The YOLOv11 deep learning model enables rapid, accurate decoding and analysis of fluorescence images with multi-dimensional information, improving data processing efficiency. This platform exhibits high sensitivity (<1 pg/mL), wide linear ranges, and excellent specificity. The desirable spiked recovery (85.6%-114.0%) in maize and plant-based meat analogs and the consistency with HPLC-MS/MS confirm practical applicability. This method provides a promising strategy for high-throughput mycotoxin detection in food safety monitoring.
Very rare cells often have outsized physiologic functions. However, their low abundance presents a technical challenge for studying their function. Here, we generate a murine model, which enables the profiling of secreted proteomes (secretomes) from physiologically relevant low-abundance cells in complex tissues, without the use of viral vectors. We then deploy this model to profile the secretomes of two rare innate immune cell types: Kupffer macrophages from whole liver and the rare airway microfold (M) cells in cultures of differentiated airway epithelia. The method we have developed is characterized by remarkable sensitivity, capturing cell-specific proteins that were not captured in single-cell RNA sequencing (scRNA-seq) atlases. We have made this genetic secretome mouse model available to the community to empower future studies of cell-to-cell communication, secreted protein function, and biomarker discovery for any given cell type in vitro or in vivo.
Neural organoids provide tractable models of human brain development, function, and disease, but adeno-associated viral vector (AAV)-based gene delivery is limited by inefficient transduction and insufficient comparative evaluation of the natural and engineered capsids. Here, we evaluated seven AAV capsids (AAV-2, -6, -9, -DJ, -2-retro, -PHP.eB, and -Cap-Mac) for transduction efficiency, cell type tropism, cytotoxicity, and functional integrity in human cortical and thalamic organoids. AAV-Cap-Mac demonstrated the highest transduction efficiency, with broad cellular tropism, but caused cytotoxicity and decreased neuronal activity. By contrast, AAV-DJ yielded efficient neuronal transduction, with preserved calcium dynamics and minimal cytotoxicity, which enabled rabies virus-based circuit tracing and all-optical physiology. Our findings reveal a capsid-dependent trade-off between transduction efficiency and functional integrity and identify AAV-DJ as an optimal serotype for robust gene delivery and physiological interrogation in organoid-based neuroscience. Combining AAV-DJ with emerging molecular and circuit-level technologies in organoids should facilitate mechanistic and translational studies of the human brain.
Biological systems often exhibit intermediate molecular states under perturbation, showing changes that align with baseline condition or perturbed state. Capturing these complex patterns is critical for understanding molecular resilience and maladaptive persistence. We introduce SwitchClass, a label-switch classification framework that distinguishes features whose intermediate-state profiles align with baseline or perturbed condition. By training a dual classifier with inverted labels, SwitchClass computes a directional importance score (δ), which quantifies each feature's alignment across biological states. Applied to colorectal cancer proteomics, SwitchClass reveals proteins that normalize after therapy and those remaining dysregulated, uncovering partial molecular recovery. In dietary perturbation and reversal phosphoproteomics, it uncovers the phosphorylation sites linked to incomplete restoration of insulin signaling. In single-cell transcriptomes from COVID-19 patients with varying severities, it identifies cell-type-specific transcripts marking resolution or persistence of inflammatory activity. Together, these analyses demonstrate SwitchClass as an interpretable framework for mapping directional molecular changes in systems with intermediate states.
Volume electron microscopy (VEM) enables nanometer-resolution three-dimensional (3D) visualization of biological specimens via serial sectioning and imaging. Owing to limitations of downstream analysis, VEM datasets are often acquired at slow speeds and high resolutions, thereby limiting achievable imaging throughput. By systematically searching for optimal VEM acquisition conditions, we find that sufficient spatial resolution effectively counteracts high image noise in preserving 3D structural information. To further verify that denoising is more effective in restoring volumetric datasets than axial interpolation, we compared machine learning-based methods, including a newly developed 3D context-based denoising model, through various tasks on VEM datasets acquired simultaneously. Our volumetric approach not only outperforms other baseline methods in faithful feature recovery but also facilitates robust serial block-face cutting down to 20 nm by allowing fast imaging. This work provides both an optimized acquisition strategy and volumetric denoising methods as actionable guidelines for maximizing VEM throughput.
An in vitro model of human liver with functional integrity would advance liver research and enable personalized therapeutics. A microfluidic liver-on-a-chip can generate a functional hepatobiliary network using human liver cells and recapitulate blood circulation and bile excretion. However, many current microfluidic liver models lack a biliary component and cannot fully recapitulate the excretory function of the human liver. Here, we develop a microfluidic liver-on-a-chip incorporating functional endothelial cells, hepatocytes, and cholangiocytes derived from a single source of human mesenchymal stem cells. This stem cell-derived liver-on-a-chip reconstructs a simplified liver unit, including a sinusoid, a hepatocyte compartment, and a bile duct supported by barrier integrity and fluid flow. It reproduces integrated organ-level responses to xenobiotics, including hepatic uptake and biliary excretion, and models ischemic liver injury with therapeutic cell recruitment. This technology expands personalized organ-level modeling and holds strong potential for studying tissue development and advancing targeted therapies for liver injury.
Gene amplification plays a critical role in evolution and disease and is widely utilized to overexpress valuable gene products in biotechnology. To broaden these applications, we previously developed break-induced replication (BIR)-mediated tandem repeat expansion (BITREx), a method utilizing Cas9 nickase (nCas9) to amplify genetic sequences by driving tandem array expansion through ectopic BIR. Since BITREx efficiency depends on the guide RNA (gRNA) recruiting nCas9 to the array's flanking regions, here we develop a plasmid-based reporter system in budding yeast for the rapid identification of high-performing gRNAs. Furthermore, we introduce BITREx 2.0, a dual-nicking strategy that targets both sides of the gene array. We demonstrate that BITREx 2.0 is effective for both natural and synthetic arrays, enhancing expansion efficiency by up to an order of magnitude compared to the original single-nicking format. These advancements significantly broaden the applicability and efficiency of nCas9-mediated gene amplification across diverse biological and biotechnological contexts.
Classical type 1 dendritic cells (cDC1s) are crucial to anti-tumor immunity by cross-presenting tumor antigens and priming cytotoxic CD8+ T lymphocytes (CTLs). Licensing via CD4+ T cell help endows cDC1s with enhanced cross-presentation and CTL-priming capacity, making them a promising tool to improve adoptive T cell therapies. However, the scarcity of primary human cDC1s limits their in-depth translational investigation and application. Here, we describe a method to efficiently generate DCs in vitro from CD34+c-KIT+ progenitors in non-mobilized peripheral blood. This DC population is enriched for cDC1-like cells that respond to CD4+ T cell help by upregulation of key molecules involved in antigen cross-presentation and T cell costimulation. Upon CD4+ T cell-mediated licensing, the progenitor-derived DC promotes tumor-specific CTL priming and facilitates detection of rare tumor antigen-specific CD8+ T cells in blood and tumor tissues. This DC culture platform incorporating CD4+ T cell help provides a scalable system for immunomonitoring and optimizing adoptive T cell therapies in cancer.
The complexity of the eukaryotic cell cycle complicates experiment design and data interpretation, limiting our understanding of how cells coordinate cell cycle processes. Traditional perturbation methods, including knockouts, deletions, and arrest-inducing chemicals, are limited by compensatory feedback interactions or pleiotropic side effects. Inducible synthetic systems offer greater specificity but often rely on external inducers, making rapid reversibility difficult. Here, we developed OPTO-Cln2, an optogenetic tool for light-controlled and reversible regulation of G1 progression in budding yeast. Using time-lapse microscopy, we show that OPTO-Cln2-strains rapidly switch between normal and altered G1 progression. Combining OPTO-Cln2 with a readout of TORC1 and PKA activity, we find that oscillatory signaling dynamics is coordinated with G1 progression. Finally, we show that OPTO-Cln2 enables at least two cycles of synchronous arrest and release in liquid cultures. This system provides a powerful approach for studying cell cycle dynamics and the coordination of cell growth with division.
Quantitative analysis of corticospinal tract (CST) sprouting after injury requires reliable labeling of long-range axons and fine collateral branches. Conventional biotinylated dextran amine (BDA) tracing has limited sensitivity and requires additional surgeries, while some viral-based approaches, although robust, rely on extensive tissue processing and signal amplification. Here, we describe a streamlined adeno-associated virus (AAV)-based workflow for CST sprouting analysis using TurboRFP that enables robust labeling of descending CST axons, including sprouting fibers after unilateral pyramidotomy. This approach allows direct visualization of fine CST axons without immunostaining or signal amplification, simplifying tissue processing and reducing experimental variability. Using a standardized workflow, we enable consistent CST labeling and reproducible quantification of CST remodeling. In addition, compatibility with co-delivery of other AAVs enables simultaneous circuit tracing and genetic manipulation within the same neuronal population. This workflow provides a practical platform that lowers technical barriers to axon repair research and potentially improves reproducibility across laboratories.
Adipose tissue innervation plays a critical role in regulating energy homeostasis and metabolic functions, yet targeted gene delivery to these peripheral nerves has remained challenging. We systematically evaluated both naturally occurring and engineered adeno-associated virus (AAV) capsids for their ability to transduce nerve fibers in the adipose tissue of mice. We compared seven AAVs (AAV1, AAVrg, AAV5, AAV9, AAV-PHP.S, AAV-MaCPNS1, and AAV-MaCPNS2) in C57BL/6J mice and identified AAV-PHP.S as highly efficient for transducing nerves within inguinal subcutaneous white adipose tissue (ing-scWAT). Titration studies further optimized intra-adipose delivery to minimize off-target expression while selectively targeting adipose tissue nerves. Building on this optimization, we employed Cre-dependent AAVs to selectively target Nav1.8+ sensory nerves in ing-scWAT, enabling Tetbow-based multicolor axon labeling and delivery of a chemogenetic effector. Together, these findings establish conditions for AAV transgene delivery to adipose nerves, providing opportunities for mechanistic studies and the development of therapies for neuropathic disorders.
