Biology subjects

Park, J. H.

Publications and source records attributed to Park, J. H..

At least 19 recordsLinked to original sources

bioRxiv2026

Tamoxifen and Doxorubicin Synergistically Target Estrogen Receptor beta-Mutant p53-p73 Axis, a Therapeutically Actionable Vulnerability in Triple Negative Breast Cancer

Current therapeutic regimens for triple negative breast cancer (TNBC) are not very effective in majority of patients and have serious adverse effects. Tamoxifen (Tam), a selective estrogen receptor modulator (SERM), routinely used to treat estrogen receptor alpha-positive breast cancer, has traditionally not been considered for TNBC therapy because it lacks expression of ER alpha, the canonical target of Tam. Challenging this conventional clinical paradigm, we have demonstrated, using genetic and pharmacological approaches, that Tam synergizes with DNA-damaging agent doxorubicin (Doxo) to increase cell death by both apoptosis and ferroptosis leading to growth inhibition of TNBC CDXs and PDXs expressing mutant p53. Mechanistically, these responses are dependent on ER beta-mutant p53 interaction enhanced by Tam, thereby disrupting p73-mutant p53 complex resulting in reactivation of p73 and transcriptomic reprogramming converging on apoptosis and ferroptosis. These studies demonstrate the potential for repurposing Tam with Doxo as an effective and safe therapeutic strategy to treat molecularly stratified TNBC.

cancer biology↗

bioRxiv2026

Harnessing Escherichia coli motility to engineer bacterial Voronoi patterns

Cell motility drives spatial pattern formation across diverse biological systems. Here, we engineer Escherichia coli motility in semi-solid agar to control Voronoi patterns in two and three dimensions, partitioning space into regions closest to their respective inoculation seeds. Consistent with our reaction-diffusion model, we observed that collisions between expansion fronts generate either biomass depletion (''gaps'') or accumulation (''anti-gaps''), governed by the relative diffusion rates of bacteria and nutrients. By engineering strains with distinct expansion rates and tuneable motility, and by integrating these experimental data into a dynamic Voronoi model, we achieved precise control over pattern geometry. This enabled the generation of gaps with varying widths, curved boundaries, asymmetric structures, seedless regions, and complex composite patterns. Together, these findings establish bacterial Voronoi patterns as a programmable platform for engineering multicellular spatial organization, with potential applications in synthetic biology and materials science.

synthetic biology↗

bioRxiv2026

Semi-automated annotation refinement accelerates cell type identification in brain spatial and single-cell studies

Backgroundsingle-cell and spatial omic techniques have enabled the investigation of cell type specific alterations in biologically complex tissues. In an effort to map cell taxonomies, large atlas-based studies and multi-laboratory consortia have created sets of annotated cell types. However, application of atlas- or database-level knowledge to individual studies is often resource-limited and computational demands scale with the size of both query and reference datasets. ResultsHere, we report a statistical framework for rapid label transfer using summary statistics and user-defined hyperparameters. Semi-Automated Hand Annotation (SAHA)1 allows the user to investigate magnitude, directionality, and statistical significance of matches between unnamed query clusters and reference cell types using either marker-based or marker-free methodologies. By pre-loading the package with summary statistics from the Allen Brain Cell Atlas of the mouse brain, the SAHA R package is capable of rapid cell type comparisons that closely mimic cell typing by integration-based annotation strategies. Furthermore, this flexible package is capable of comparisons across omic modalities, cluster resolutions, and annotations from any study where summary statistics are available. We demonstrate this flexibility by using multiple single-nuclei studies of the mouse cerebellum, mouse cerebral cortex, human cerebral cortex, human peripheral blood mononuclear cells, and one mouse spatial transcriptomic assay. Importantly, this method avoids privacy concerns as it does not require the sharing or deposition of raw data in a web-based tool. ConclusionsAs a result, SAHA offers a non-deterministic annotation reporting structure with automated html reports and summary statistics for transparency in cell typing decisions. Taken together, this scalable framework implemented as a package in R affords increased biological insight into the annotation of single-cell and spatial datasets. SHORT SUMMARYAcri and colleagues present rapid cell type annotation without the need for dataset integration. This paper outlines the utility of the package, SAHA, in annotating neurological datasets.

bioinformatics↗

bioRxiv2026

Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning

Cryogenic electron microscopy (cryo-EM) has become an increasingly important for structure-based drug discovery by enabling characterization of interactions between macromolecules and small-molecule ligands. However, computational interpretation of ligand density remains challenging, particularly when ligand locations are unknown or local map resolution is limited. Existing methods generally require well-resolved macromolecular structures and predefined binding sites, limiting their applicability during early-stage structure determination. To date, no approach has been able to both reliably detect ligand density and subsequently reconstruct ligand atomic structures directly from experimental cryo-EM maps. Here, we present Emap2lig, a two-stage deep learning framework for automated ligand detection and atomic modeling directly from cryo-EM maps. Emap2lig-Find identifies ligand-associated densities and remains effective for maps at resolutions as low as [~]5 [A]. Emap2lig-Build subsequently uses a diffusion-based generative model to build atomic ligand structures. Together, Emap2lig provides a unified framework for ligand discovery and modeling across a broad range of resolutions.

bioinformatics↗

bioRxiv2026

A low-dimensional transcriptional code enables decoding of BMP/TGFβ signaling from single-cell transcriptomes

Cells interpret complex extracellular environments through signaling pathways that compress diverse ligand inputs into a limited set of intracellular mediators. How this information is encoded in transcriptional responses, and whether it can be decoded to infer the original signaling environment, remains unclear. Here, we systematically map BMP/TGF{beta} transcriptional responses using single-cell RNA sequencing across 48 ligand-concentration conditions, generating a multi-ligand dose-response atlas. We find that, for each ligand, transcriptional responses collapse onto a one-dimensional, highly coordinated program in which concentration modulates response amplitude without altering gene identity. Across ligands, we find a small number of distinct, ligand-dependent transcriptional programs. We leverage this structured encoding to define a quantitative perception score that captures pathway activity at single-cell resolution. Finally, we train a machine learning model to decode extracellular ligand concentrations from single-cell transcriptomes. The model further generalizes to in vivo intestinal epithelium, where it reconstructs the spatial BMP gradient profile. Together, these results reveal a low-dimensional transcriptional code in BMP/TGF{beta} signaling that constrains cellular responses and enables quantitative inference of extracellular signaling environments from gene expression.

systems biology↗

bioRxiv2026

Rhizo-PET: A Dedicated PET System for 4D Imaging of Carbon Dynamics in the Rhizosphere

Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant-soil systems. The system achieved a global energy resolution of 11.93 {+/-} 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants (N= 3) over 180 min following 11CO2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments (p <0.001). Hierarchical variability analysis showed that within-plant spatial organization (CVTTP = 0.03) is significantly more stable than inter-plant variation (CVTTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.

plant biology↗

bioRxiv2026

Distinct Spatial Programs of Response versus Resistance in Non-Small Cell Lung Cancer after Neoadjuvant Chemoimmunotherapy

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

cancer biology↗

bioRxiv2026

Cross-Species Translation Enhances the Use of Mouse Models for Translatability and Drug Discovery in Late-Onset Alzheimer's Disease

Alzheimers disease (AD) is a brain disease characterized by deposition of insoluble amyloid-{beta} plaque, intraneuronal neurofibrillary tangles, and cognitive dysfunction. AD can be characterized as early-onset or late-onset based on age and genetic factors. For early-onset, these genetic factors can include amyloid precursor protein (APP), presenilin-1 (PSEN1), and presenilin-2 (PSEN2). For late-onset, these can include apolipoprotein E e4 (APOE4), and the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2). Mouse models incorporating these risk factors provide critical knowledge for studying AD pathology and preclinical studies for drug development. However, these transgenic mice depend on early-onset genetic mutations and are deficient in certain AD features that are present in late-onset. Here, we developed innovative non-linear and feature selection procedures for our cross-species translation framework, Translatable Components Regression (TransComp-R), to identify transcriptomic features in mouse models predictive of human late-onset AD pathobiology. We used the cross-species computational translatability links of TransComp-R to perform computational high-throughput drug screening and identified multiple repurposable drugs for AD treatment that targeted the sleep-wake cycle. We tested these predictions in an orthogonal, prospective cohort of human subjects treated with an orexin receptor antagonist, suvorexant. We correlated conserved protein-level biomarkers from our cross-species transcriptomics model with significant reductions in phosphorylated tau in cerebrospinal fluid collected from humans treated with suvorexant. This study demonstrates the power of computational methods like TransComp-R to enhance the utility of murine disease models for discovering new therapeutic approaches for AD. One Sentence SummaryCross-species translation modeling across different mouse models reveals sleep-relevant drug mechanisms as potentially therapeutic for Alzheimers disease.

systems biology↗

bioRxiv2026

Multiscale confidence quantification for virtual spatial transcriptomics with UTOPIA

Virtual spatial transcriptomics (ST) methods predict gene expression or cell types from histology images, extending molecular readouts beyond the limited regions or samples directly measured by ST platforms. However, the statistical reliability of these predictions remains unclear. Here, we present UTOPIA, a model-agnostic framework for multiscale confidence quantification in virtual ST. UTOPIA assigns statistically calibrated confidence scores to predictions across spatial resolutions and biological granularities, ranging from single genes to metagenes and from specific cell types to broader cell classes. UTOPIA controls false discovery rates for detecting genes, metagenes, or cell types while accounting for local tissue context. We show that prediction confidence depends critically on both spatial resolution and biological granularity, with reliable inference often emerging only at coarser, biologically meaningful scales. Across multiple ST platforms and in both in-sample and out-of-sample settings, UTOPIA enhances interpretability, prevents false biological conclusions, and enables more trustworthy downstream analyses of virtual ST.

genomics↗

bioRxiv2026

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Advances in spatial transcriptomics (ST) have fundamentally transformed our understanding of tissue biology by enabling gene expression profiling within intact spatial contexts and uncovering tissue organization and microenvironmental interactions. However, current high-resolution ST platforms remain constrained by high costs, limited tissue coverage, and technical artifacts, often yielding noisy, sparse, and incomplete data that compromise analytical accuracy, biological interpretation, and clinical utility. To address these challenges, we introduce Pixel2Gene, a deep learning framework that integrates co-registered histology images with ST data to enable histology-guided reconstruction and prediction of spatial gene expression. Pixel2Gene enhances existing expression measurements by denoising low-confidence data and reconstructing coherent expression patterns, while also predicting gene expression in unmeasured tissue regions and new samples lacking direct transcriptomic profiling. We systematically evaluated Pixel2Gene across multiple high-resolution ST platforms, including Visium HD, Xenium, and CosMx, spanning diverse tissue types and disease contexts using downsampling simulations and cross-platform comparisons in clinical samples. Across all settings, Pixel2Gene consistently improved data consistency, mitigated dropout effects, restored biologically meaningful spatial structure, and enabled accurate downstream analyses. By leveraging the scalability and ubiquity of routine histology, Pixel2Gene facilitates comprehensive, cost-effective ST profiling at whole-tissue scale, supporting large cohort studies, translational research, and next-generation biomarker discovery.

genomics↗

bioRxiv2026

AADAT-Driven Metabolic Control of Malate and CoQ10 Shapes Immune Evasion in Triple-Negative Breast Cancer

Compared to other subtypes of breast cancer, triple-negative breast cancers (TNBC) have fewer treatment options and exhibit a worse prognosis. Through integrated transcriptomic, metabolomic, immunohistochemical, spatial, and clinical analyses, we identify the mitochondrial enzyme, -aminoadipate aminotransferase (AADAT) as a previously unrecognized metabolic immune checkpoint in TNBC. AADAT mRNA and protein were significantly upregulated in human TNBC, and high AADAT expression was associated with reduced intra-tumoral CD8 T-cell density and inferior survival. Genetic silencing of AADAT in orthotopic murine TNBC models curtailed primary tumor growth and distant metastasis in a CD8 T-cell-dependent manner, enhanced effector T-cell activation, and sensitized tumors to dual PD-1/CTLA-4 blockade. Mechanistically, unbiased metabolomics showed increased malate levels after AADAT knockdown. Additionally, 4-hydroxyphenylpyruvate, an essential precursor for coenzyme Q10(CoQ10) biosynthesis, decreased following AADAT knockdown, suggesting an impaired mitochondrial electron transport chain. CoQ10 supplementation restored metabolic balance and reversed malate accumulation caused by AADAT knockdown, indicating that AADAT helps maintain CoQ10-supported redox homeostasis, thereby preventing malate buildup and export. Notably, malate addition directly boosted CD8 T-cell oxidative metabolism, increased the NAD/NADH ratio and reactive oxygen species, and augmented TNF- and IFN-{gamma} production. In vivo, malate supplementation in drinking water phenocopied AADAT knockdown, restored the response to paclitaxel plus anti-PD-1 therapy in multiple independent syngeneic TNBC models with de novo or acquired resistance to immunotherapy, reduced tumor burden, and prolonged survival. In patient cohorts, higher spatially clustered intra-tumoral malate is associated with co-localization of functional CD8 T cells, decreased exhausted T-cell neighborhoods, and superior post-chemotherapy outcomes. These data position AADAT as a central metabolic orchestrator of immune escape in TNBC and nominate oral malate as a readily translatable adjuvant to reverse chemo-immunotherapy resistance in TNBC. Statement of SignificanceAADAT defines a metabolic-immune axis driving immune evasion and therapy resistance in triple-negative breast cancer. Blocking AADAT or administering oral malate reactivates CD8 T-cell immunity and sensitizes chemo-immunotherapy-resistant tumors to these agents. These findings uncover a readily translatable metabolic vulnerability with potential to improve outcomes for patients with aggressive breast cancer subtypes.

cancer biology↗

bioRxiv2025

Near-atomic in-situ architecture and membrane-coupled dynamics of the Vibrio cholerae sheathed flagellum

The sheathed flagellum of Vibrio cholerae is a self-assembling membranous organelle that must coordinate axial assembly, sheath biogenesis, and rapid motor rotation. Here, we determine in-situ near-atomic structures of the sheathed flagellar motor inside intact cells. The motor anchors to the outer membrane through lipidated HL-rings without forming a membrane pore, thereby allowing axial assembly to drive sheath formation. Conserved LP-rings act as slide-rotary bushings that permit high-speed rotation within a dynamic envelope yet can constrict to seal the pore upon stress-induced ejection. We further show that stator activation requires a specific PomB-MotX interaction rather than peptidoglycan engagement. Together, these findings reveal how the distinctive architecture and dynamics of the sheathed flagellum promote V. cholerae motility, environmental survival, and persistent colonization of the human gut.

microbiology↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

Intracellularly Coupled Oscillators for Synthetic Biology

Synthetic biology aims to engineer or re-engineer living systems. To achieve increasingly complex functionalities, it is beneficial to use higher-level building blocks. In this study, we focus on oscillators as such building blocks, propose novel oscillator-based circuit designs and model the interactions of intracellularly coupled oscillators. We classify these oscillators on the basis of coupling strength: independent, weakly or strongly, and deeply coupled. We predict a range of fascinating dynamic behaviours to arise in these systems, such as the beat phenomenon, amplitude and frequency modulation, period doubling, higher-period oscillations, chaos, resonance, and synchronization, with the aim of guiding future experimental work in bacterial synthetic biology. Finally, we outline potential applications, including oscillator-based computing that integrates processing and memory functions, offering multistate and nonlinear processing capabilities.

synthetic biology↗

bioRxiv2025

Designing signaling environments to modulate neural progenitor cell differentiation with regulatory network models

During development, combinatorial signals modulate progenitor differentiation to build populations of diverse cell types. Classical deterministic fate-control circuits focus on stable commitment but do not explain how a diverse cell population is generated and tuned by signals. Here, using single-cell RNA-seq profiling of neural progenitor cells (NPCs) upon combinatorial signal inputs, we identify a candidate circuit-level mechanism for controlling cell type compositions through a noise-gated toggle switch. With 40 signaling conditions, we show that combinatorial signal inputs modulate the neuronal-glial population proportions following a simple log-linear model. Regulatory network models built by D-SPIN quantitatively capture cell state distribution shifts induced by signal combinations, and indicate a circuit model for the probabilistic fate specification by a toggle switch with controlled transcriptional heterogeneity. Noise levels drive the system from highly plastic to committed states, allowing the relative level of signal inputs to shift population structures. The model further predicts an early bipotent state expressing lineage-specifying factors of both fates, which we verify in our single-cell profiling experiment and a mouse embryonic visual cortex development dataset. Our work identifies a design principle of noise-driven additive regulation in the logarithmic cell-fate probability space, providing a new strategy for population-level stem cell control.

systems biology↗

bioRxiv2025

Designing smart spatial omics experiments with S2Omics

Spatial omics technologies have transformed biomedical research by enabling high-resolution molecular profiling while preserving the native tissue architecture. These advances provide unprecedented insights into tissue structure and function. However, the high cost and time-intensive nature of spatial omics experiments necessitate careful experimental design, particularly in selecting regions of interest (ROIs) from large tissue sections. Currently, ROI selection is performed manually, which introduces subjectivity, inconsistency, and a lack of reproducibility. Previous studies have shown strong correlations between spatial molecular patterns and histological features, suggesting that readily available and cost-effective histology images can be leveraged to guide spatial omics experiments. Here, we present S2Omics, an end-to-end workflow that automatically selects ROIs from histology images with the goal of maximizing molecular information content in the ROIs. Through comprehensive evaluations across multiple spatial omics platforms and tissue types, we demonstrate that S2Omics enables systematic and reproducible ROI selection and enhances the robustness and impact of downstream biological discovery.

genomics↗

bioRxiv2025

Enhanced cellular and transdermal delivery of the modified chromatin using gH625 cell-penetrating peptide

The transdermal drug delivery system has been used as a noninvasive route for drug administration, but often requires specialized equipment or invasive treatment. To examine recombinant synthetic chromatin as a non-invasive transdermal delivery vehicle, a modified histone H2A fused with a cell-penetrating peptide (gH625) was examined for its utility. Both in vitro porcine skin-based test and in vivo mice studies showed significantly enhanced passive skin penetration of gH625-modified chromatin compared to control groups. The gH625-H2A chromatin was examined as a delivery vehicle of EGF by displaying it at the carboxy terminus of histone H2B to enhance stability and skin penetration. The experimental results showed that the biological activities of EGF-displayed chromatin are comparable to those of the isolated EGF peptide in the HaCa-T cell line. The enhanced skin penetration capabilities of the gH625-H2A-modified chromatin offer great potential for developing non-invasive transdermal delivery of therapeutic proteins or peptides.

synthetic biology↗

bioRxiv2025

Investigating Bio-Nano Interactions of PEGylated Cationic Polyamidoamine (PAMAM) Dendrimers within Synovial Joints

Delivering therapeutics directly to synovial joints to treat osteoarthritis (OA) is challenging due to the dense negatively charged cartilage matrix and rapid turnover of synovial fluid, leading to high clearance rates. Our lab has identified polyamidoamine (PAMAM) dendrimers as optimal nanocarriers to overcome delivery challenges to cartilage due to their positive charge and small size, which enables them to bind to cartilage and diffuse through tissue to deliver therapeutics to chondrocytes. Previously, we have developed and characterized dendrimers functionalized with polyethylene glycol (PEG), demonstrating improved biocompatibility and enhanced transport through cartilage matrix. To improve the design of our therapeutic system, in this next phase of work, we characterized dendrimer-protein interactions or protein coronas that form on dendrimers after being immersed in synovial fluid. We also analyzed how synovial fluid protein coronas affect biological outcomes of dendrimers in synovial joints, specifically uptake in cartilage and internalization by chondrocytes. We identified that protein coronas can reduce dendrimer uptake in cartilage and chondrocytes; however, uptake reduction is mitigated by varying PEG chain length and density. Although protein coronas can be perceived as "biological barriers" to uptake, we demonstrate that dendrimers conjugated with insulin-like growth factor 1 (IGF-1) have better engagement with IGF-1 receptors after being pre-coated with a synovial fluid protein corona. Overall, these studies offer further insight into the mechanisms of how positively charged dendrimers target and transport through cartilage, bridging knowledge gaps between ex vivo and in vivo work. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/668990v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@9298ccorg.highwire.dtl.DTLVardef@6d85aeorg.highwire.dtl.DTLVardef@63838dorg.highwire.dtl.DTLVardef@151ca4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗