Biology subjects

Park, J.

Publications and source records attributed to Park, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Activity-dependent lipid droplet biogenesis and turnover regulate synaptic integrity

Lipid droplets (LDs) are conserved organelles that buffer lipid storage and stress, yet their dynamics and functions in neurons remain largely unknown. Here, we report activity-dependent dynamics of neuronal LDs, visualized by a novel, genetically encoded LD reporter (termed LipiDew), in both cultured neurons and mouse motor cortex. Using LipiDew, we found that various paradigms of neuronal activation induced predominant and transient formation of LDs in neurites. Disruption of autophagic LD degradation (lipophagy) resulted in abnormal lipid accumulation in dendritic spines and shafts, promoted recruitment of synaptic scaffolding proteins to LDs, and altered intracellular calcium kinetics in neurons. In addition, mice with neuron-specific genetic impairment of lipophagy showed motor function defects. Together, these findings identify activity-dependent LD formation and lipophagic clearance in neuronal compartments as a crucial regulatory mechanism of synaptic integrity and neuronal function.

neuroscience↗

bioRxiv2026

Value and spatial preferences guide habitual reaching and manual object selection in primates

Habits are automatic actions shaped by prior experience, enabling efficient and fast responses in stable environments. While habitual gaze rapidly directs the eyes toward valuable objects, the mechanisms underlying habitual manual choice for reaching and grasping remain unclear. Here we show that macaque monkeys develop habitual manual choices toward previously rewarded objects through multi-day object-value learning, driven by learned value and spatial preferences. This reaching habit persisted without reward and showed shorter latencies than in value-deliberative tasks, consistent with automatic control. Regression analyses further revealed that manual choice was guided by learned object values, whereas visual salience had no effect, unlike gaze habits. Instead, intrinsic spatial preferences continued to bias reaching behavior even after long-term value learning. These findings demonstrate that habitual manual choice arises from the integration of spatial preferences and long-term value memory, defining a distinct mechanism of automatic behavior beyond habitual gaze.

animal behavior and cognition↗

bioRxiv2026

High-resolution image-projection fluorescence lifetime imaging microscopy

Fluorescence lifetime imaging microscopy (FLIM) provides molecular contrast that is largely independent of fluorophore concentration, yet it remains constrained by a persistent trade-off among acquisition speed, photon dose, and detector complexity. To address this challenge, we developed image-projection fluorescence lifetime imaging microscopy (IP-FLIM), an integrated optical and computational platform that enables high-resolution, component-resolved lifetime imaging using only a linear single-photon avalanche diode array. We validate IP-FLIM using fluorescent microbeads and bovine pulmonary artery endothelial cells, demonstrating up to 22.3x improvement in contrast-to-noise ratio and 72.3% reduction in background noise over conventional filtered back-projection reconstruction. By combining wide-field projection acquisition with computational k-space reconstruction, IP-FLIM provides a scalable route to fast, high-resolution multiplex lifetime imaging.

bioengineering↗

bioRxiv2026

Metastatic Osteosarcoma is Characterized by Loss of Osteoblastic Lineage Fidelity

Osteosarcoma (OS) is a rare and aggressive bone cancer with limited therapeutic progress in several decades. To characterize the transcriptional diversity of malignant cell states in OS, we analyzed single-nucleus RNA sequencing data from 24 tumors, including both primary and metastatic lesions. We identified a canonical osteoblastic cell state that predominated in primary tumor cells, but was dampened in metastases. Integrating tumor data with data from embryonic skeletal development and an experimental osteoblast differentiation model revealed that metastatic OS cells resemble poorly specified mesenchyme enriched for non-osseous programs. Clonal populations in metastatic samples acquired mesenchymal cell states distinct from paired primary tumors. Similar cell state shifts were evident in post-treatment primary tumors relative to paired pre-treatment samples. Our results suggest that localized post-treatment and metastatic OS malignant cells lose osteoblastic lineage fidelity, which may represent a unifying axis of disease evolution and reveal new therapeutic vulnerabilities.

Cancer Biology↗

bioRxiv2026

Evaluating agentic AI for biological discovery in autonomous and copilot settings

Advances in large language models (LLMs)-based artificial intelligence (AI) agents have improved their ability to execute structured analytical workflows, including standard bioinformatic pipelines for biological discovery. However, computational biology rarely consists of deterministic pipeline execution alone. Biological datasets are heterogeneous and noisy, and meaningful discovery often requires open-ended hypothesis generation and iterative reasoning over multimodal evidence. These challenges are particularly evident in multi-omic studies, where paired molecular modalities and heterogeneous clinical contexts create both opportunities and obstacles for discovery. The extent to which emerging agentic AI systems can support or automate this mode of scientific discovery remains poorly understood. Here, we systematically evaluated the capabilities and limitations of agentic AI for biological discovery using multi-omic single cell datasets spanning 11 cancer types. We developed the Multistep Multimodal Multiomic Agentic (M3A) Framework to support LLM-driven reasoning over persistent multimodal data states and to capture agentic reasoning behavior in autonomous and human-AI copilot settings. Using this framework, we assessed AI agents across complementary tasks, including autonomous cell-type annotation, generation of falsifiable biological hypotheses from gene programs, and copilot experiments testing the effect of human involvement and domain expertise. We found that current AI agents are effective at broad, systemic exploration of complex data, whereas domain experts remain critical for methodological guidance and biological synthesis across analyses. Together, our results delineate the current potential and boundaries of agentic AI in computational biology, and establish a framework for evaluating AI systems designed to support biological discovery.

cancer biology↗

bioRxiv2026

Comparative Proteomics Across Tissues and Crop Agroecosystems Reveals Agricultural Stressor Responses in the Western Honey Bee

Maintaining honey bee health in crop production systems is increasingly difficult because worker bees encounter multiple chemical and biological pressures from pesticides and pathogens. How these field-realistic pressures affect molecular physiology across functionally distinct tissues remains poorly understood. Here, we tested whether tissue-resolved proteomics could separate stable tissue-specific patterns from crop-associated molecular changes. To do this, we profiled abdomen, gut, and head proteomes from honey bees collected across four Canadian crop ecosystems over two consecutive years, and integrated these data with pesticide-residue and pathogen-load measurements. Proteomic variation was structured by both tissue identity and crop environment. Tissue-specific proteomic profiles were characterized across samples, whereas crop-associated effects were detected in both years and were stronger in 2021, the second year of the study. Tissue-specific enrichment and network analyses linked the abdomen to lipid catabolism and ubiquitin-proteasome proteostasis, the gut to central carbon metabolism, membrane transport, vesicle trafficking, and cytoskeletal organization, and the head to neurosensory and mitochondrial functions, together with amino-sugar metabolism and vesicle-associated quality-control modules. Among the measured pesticide residues, boscalid was the most reproducible chemical correlate of proteomic variation, with the strongest signal in the gut. Cross-year validation associated boscalid exposure with reduced abundance of gut proteins involved in mitochondrial metabolism, protein quality control, vesicle trafficking, nutrient transport, and biosynthetic pathways. Additionally, integrated proteome-transcriptome-microbiome factor analysis further identified gut-centered components associated with measured stressor variables and linked protein-level variation to coordinated transcriptomic and microbial shifts. Independent-year validation showed that compact crop-associated protein signatures detected in 2020 were also present in 2021. Together, these results show that honey bee tissues maintain stable proteomic identities while showing tissue- and year-specific responses to pesticide and pathogen pressures encountered in crop ecosystems. The gut proteome may specifically provide a sensitive molecular indicator of pesticide-associated perturbation under field conditions.

bioinformatics↗

bioRxiv2026

Reproducible and shareable bioinformatics pipelines from natural-language prompts

Large language models (LLMs) are increasingly used to generate bioinformatics pipelines and to carry out analyses from natural-language prompts. However, the resulting analyses are often difficult to reproduce across sessions, owing to the non-deterministic nature of LLM-driven conversations and heterogeneity of local execution environments, and cannot run on remote high-performance computing (HPC) servers or be shared and reused. We present Autopipe, a platform that guides any Model Context Protocol (MCP) - compatible LLM to produce, execute, and publish source-preserved, re-executable containerized pipelines. Autopipe enables users to execute bioinformatics pipelines on any on-premises remote servers - supported by comprehensive setup documentation aimed at researchers without prior server-administration experience - and to visualize results through an extensible web-based viewer. The Autopipe platform comprises four components: a desktop application with an embedded MCP server for pipeline management and remote execution, an online registry for pipeline and plugin discovery, a web-based result viewer, and a CLI tool for customizing viewer plugins. Autopipe turns conversational analysis into re-executable and shareable workflows. Autopipe is freely available at https://autopipe.org/.

bioinformatics↗

bioRxiv2026

Antibody maturation increases rigidity in protein-contacting regions and flexibility at glycan interfaces

Antibody design is a challenging task that could be improved by understanding the conformational changes accompanying affinity maturation. Antibody maturation is a critical immune system process by which antibodies gain mutations that improve affinity and specificity for antigen targets. However, how these mutations change antibody dynamics, specifically whether antibodies become more rigid as they mature, remains a contested topic. Using adaptive sampling molecular dynamics simulations and over 8.5 milliseconds of Folding@home simulations of seven lineages, we discover that affinity maturation selectively tunes the dynamics of antibody paratope regions depending on whether they contact protein residues or glycans. We find that antibody regions that contact glycans evolve to become more flexible and, consistent with other studies, antibody regions that contact protein residues on the antigen become rigid. This pattern holds regardless of whether one includes the constant region in the simulations, indicating that the computational cost of all-atom antibody MD simulations can be reduced by half without sacrificing the accuracy of the variable regions dynamics. We expect the principles identified in this study will enable precise, dynamics-based engineering of high-affinity antibodies and to inform immunogen design against challenging, glycan-shielded viral targets such as HIV. Significance StatementAntibodies are currently one of the most powerful therapeutics available. Our bodies evolve antibodies to bind a target (called an antigen) through a process called maturation. Understanding how antibody dynamics change during maturation would lead to improved antibody design strategies. Through extensive all-atom molecular dynamics simulations of multiple antibody lineages, we demonstrate that the dynamics accompanying affinity maturation are dependent on the antigen binding site. Antibody regions that contact protein residues on the antigen become rigid while regions that contact glycans gain flexibility with maturation. These findings can be leveraged for improved, dynamics-based antibody design.

biophysics↗

bioRxiv2026

Oral exposure to Perfluorooctanoic acid disrupts the microbiota-gut-liver axis and enhances the severity of chemically induced colitis in mice.

Inflammatory bowel diseases (IBD) affect millions of patients worldwide and impair quality of life. Although genetic and environmental factors are known to disrupt the gastrointestinal (GI) epithelial barrier and increase susceptibility to IBD, the precise contribution of specific environmental exposures remains unclear. Per- and polyfluoroalkyl substances (PFAS), or "forever chemicals," are widely used in consumer products and contaminate food and water sources, resulting in chronic oral exposure worldwide. Perfluorooctanoic acid (PFOA), a common PFAS, has been epidemiologically associated with the development of IBD, particularly in older adults. Here, we assessed the effects of oral PFOA exposure on the GI tract, liver, and susceptibility to colitis. C57BL/6 mice were exposed to PFOA (0.1 mg/kg or 1.0 mg/kg) beginning at weaning (post-natal day [P]21) for a time course of 4 or 8 weeks. GI physiology/pathology (Ussing chambers; histology), expression of pro-inflammatory cytokines (qPCR), microbiota composition (16S sequencing), bile acids production (qPCR; LC/MS), and liver pathology (histology) were assessed. Colitis susceptibility was evaluated in genetically predisposed (IL10 knockout) mice, and in induced (dextran sodium sulfate [DSS]) mouse models following PFOA exposure (8 weeks at 1.0 mg/kg). Oral PFOA exposure increased intestinal permeability, mildly increased cytokine expression, altered gut microbiota composition, disrupted liver and serum bile acids, and caused hepatic hypertrophy at higher doses and longer exposure. Although PFOA did not increase disease susceptibility in genetically predisposed Il10 KO mice, it significantly worsened DSS-induced colitis, but only in male mice. Together, these findings demonstrate that early-life PFOA exposure disrupts the gut-liver axis and may contribute to colitis development in a sex dependent manner.

pharmacology and toxicology↗

bioRxiv2026

Vagus nerve stimulation limits colonic inflammation through distinct neuroimmune circuitry shaped by inflammatory history

Bidirectional communication between the nervous and immune systems has been demonstrated to limit or enhance immune cell function across organ systems and conditions. Although these neuroimmune circuits can become activated as an anti-inflammatory reflex, vagus nerve stimulation (VNS) reduces inflammation in models of endotoxemia, rheumatoid arthritis, and intestinal inflammation. In the spleen during endotoxemia, VNS activates a "cholinergic anti-inflammatory pathway" (CAIP), whereby choline acetyltransferase (ChAT)-expressing T cells release acetylcholine to reduce macrophage activation. VNS can also drive CAIP-independent pathways to reduce inflammation in the spleen and intestinal tract, although the circuitry modulating colonic inflammation remains underexplored. Here, we demonstrate that left cervical VNS reduces acute LPS-induced inflammation, evidenced by reduced Tnfa expression in colon and spleen and decreased circulating TNF. In the colon, these protective effects required efferent but not afferent VNS and were independent of ChAT+ T cells, IL10, {beta}-adrenergic signaling, and colonic sympathetic innervation. Critically, the ability of VNS to modulate colonic inflammation depended on prior tissue-specific inflammation. Mice recovering from DSS colitis, despite near-complete histological recovery, were refractory to the protective effects of VNS in the colon. This lack of efficacy in the colon was not reflected in measures of inflammation in the spleen or serum, highlighting the need for target-organ-specific monitoring. This loss of efficacy after colonic inflammation was transient, with restoration occurring upon complete recovery. These findings demonstrate that VNS efficacy in colonic inflammation depends on circuitry distinct from canonical systemic anti-inflammatory pathways, and that tissue responsiveness is shaped by anatomical site and inflammatory history. Key PointsO_LIElectrical stimulation of the left cervical vagus nerve reduces LPS-induced inflammation in the mouse colon. C_LIO_LIThis colonic anti-inflammatory effect requires vagal efferents but not afferent signaling. C_LIO_LIUnlike canonical splenic anti-inflammatory pathways, the colonic response does not require ChAT+ T cells, IL-10, {beta}-adrenergic signaling, or local sympathetic innervation. C_LIO_LIRecent DSS colitis abolishes colonic responsiveness to VNS despite preserved splenic and systemic anti-inflammatory effects. C_LIO_LIRecovery of VNS anti-inflammatory efficacy after colitis shows that neuroimmune responsiveness in the colon is dynamically shaped by inflammatory history. C_LI

immunology↗

bioRxiv2026

Vision-Based Genomic Model for Copy Number Variant Pathogenicity Prediction

Copy number variants (CNVs) are a major class of structural genomic alterations underlying rare disease, including neurodevelopmental delay and intellectual disability, yet predicting their pathogenicity remains challenging. Existing methods reduce CNVs to region-level numerical features, discarding the positional structure and cross-track patterns that expert clinical reviewers use to interpret genomic evidence. To address this, we introduce TO_SCPLOWESSERACTC_SCPLOW for CNV, a track-based spatial representation for CNV pathogenicity prediction, which represents each variant as a base-pair-resolution multi-track image and models spatial genomic patterns across annotation tracks while preserving positional structure and cross-track dependencies. Trained on a chromosome-level hold-out split of the ClinVar dataset, TO_SCPLOWESSERACTC_SCPLOW outperforms prior methods on held-out and curated noncoding benchmarks, improving AUROC by up to 0.10 over the state-of-the-art baseline. On the independent DECIPHER cohort, the model demonstrates generalizability by maintaining the highest AUROC and the highest F1 score across baselines. Furthermore, our model localizes pathogenic signals to clinically meaningful genomic subregions, providing track-annotated evidence that supports practical clinical interpretation.

bioinformatics↗

bioRxiv2026

FERONIA limits jasmonic acid overaccumulation and oxidative stress to enable plant survival at elevated temperatures

Plants, as sessile organisms, must continually adapt to fluctuating temperatures to ensure survival. The plasma membrane-localized receptor-like kinase FERONIA (FER) coordinates diverse physiological processes and responses to various biotic and abiotic stresses. However, the role of FER in plant adaptation to elevated temperatures remains largely unexplored. Here, we report that FER is indispensable for plant thermotolerance. We found that fer loss-of-function mutants exhibit impaired thermomorphogenic growth and are hypersensitive to mild heat stress, displaying extensive oxidative stress-mediated cell death at elevated temperatures. Combined genetic and molecular analyses revealed that these temperature-sensitive defects in fer mutants are caused by an overaccumulation of jasmonic acid (JA), which subsequently triggers excessive production of reactive oxygen species. Furthermore, we show that this aberrant JA accumulation and oxidative stress are attributable to impaired FER-mediated regulation of turgor-dependent cell wall tensile stress. Taken together, our results suggest that FER-mediated cell wall tensile stress regulation serves as a critical mechanism to prevent aberrant JA accumulation and oxidative stress at elevated temperatures, thereby enabling plants to adapt to and survive under high-temperature conditions.

plant biology↗

bioRxiv2026

Genomic analysis of BCG unresponsive non-muscle-invasive bladder cancer identifies drivers of sensitivity to intravesical Gemcitabine/Docetaxel

Background and ObjectivesIntravesical gemcitabine/docetaxel (Gem/Doce) is an effective therapy for Bacillus Calmette- Guerin (BCG)-unresponsive non-muscle-invasive bladder cancer (NMIBC), achieving 50% complete responses at 2 years. However, the genomic determinants underlying response and resistance to Gem/Doce remain poorly defined. Our objective was to define the mutational landscape of BCG-unresponsive NMIBC and nominate genomic features associated with response or resistance Gem/Doce. MethodsPatients with BCG-unresponsive NMIBC treated with Gem/Doce were classified as responders (recurrence-free survival [RFS] >12 months) or non-responders (RFS <12 months). Whole-exome sequencing was performed on tumors prior to Gem/Doce treatment (n=23). Single nucleotide variants were identified and annotated using a Cancer Genome Analysis pipeline. Copy number alterations were inferred with ABSOLUTE, and clonal architecture was reconstructed using PhylogicNDT. Key Findings and LimitationsResponders demonstrated significantly prolonged time to high-grade recurrence (3.5 vs 42 months, p<0.001) and cystectomy compared with non-responders (9.5 months vs not reached; p<0.001). Non-responders exhibited higher tumor mutational burden (13.66 vs 8.71; p=0.02) and more frequent whole-genome doubling (2/2 non-responders vs 0/1 responders; p=0.33). Phylogenetic analyses revealed clonal BAP1 and subclonal BRCA2 mutations in responders, whereas non-responders harbored clonal FGFR3 mutations. Limitations include small sample size and retrospective design. Conclusions and Clinical ImplicationsDistinct genomic features underlie differential response to Gem/Doce in BCG-unresponsive NMIBC. In responders, alterations in DNA repair pathways (e.g., BRCA2) may sensitize tumors to chemotherapy, while non-responders with FGFR3 mutations may benefit from alternative targeted strategies. These findings warrant validation in larger cohorts and support the development of biomarker-driven clinical trials. Patient summaryIn this report we analyzed bladder tumors and found that some tumors respond well to treatment because they have defects in repairing DNA, making them more vulnerable to chemotherapy. In contrast, tumors that do not respond to chemotherapy harbor different genetic changes that help them survive and grow. These findings may help physicians choose more effective and personalized treatments in the future.

genomics↗

bioRxiv2026

Genomic, Clinical, and Spatial Predictors of Durable Response to BRAF/MEK Inhibition in BRAF-Mutant Melanoma

BRAF-targeted therapy (BRAFi/MEKi) and immune checkpoint blockade (anti-PD-1/anti-CTLA-4) have transformed the treatment of BRAF-mutant metastatic melanoma. While most patients who respond to targeted therapy eventually progress, a subset derives durable benefit, and biomarkers to identify this subset would inform optimal treatment selection. In this study, we analyzed pre-treatment tumor samples from a clinically annotated cohort of 155 patients with BRAF-mutant metastatic melanoma treated with first-line BRAFi/MEKi and followed for up to five years. We stratified patients into durable responders (PFS [≥] 24 months) and rapid progressors (PFS < 6 months with progression) and found that a global metric of tumor genomic heterogeneity, rather than individual gene alterations, distinguished these groups. Combining genomic heterogeneity with baseline tumor burden (e.g., lactate dehydrogenase (LDH) or radiographic lesion dimensions), we developed a parsimonious model that predicted durable responders with high precision and specificity. Notably, the analogous population of patients treated instead with immunotherapy were not durable responders, suggesting that the selected predictors of durable responders are targeted therapy specific. Spatial profiling of a subset of pre-treatment biopsies (n = 47) demonstrated that high intratumoral, but not peritumoral, CD8+ T-cell infiltration correlated with prolonged survival on BRAF-targeted therapy and served as an independent predictive factor when considered with genomic heterogeneity and features of clinical tumor burden. Together, these findings highlight the distinct baseline intrinsic and extrinsic features underlying durable response to BRAF-targeted therapy and support their potential implication in guiding treatment selection for patients with BRAF-mutant metastatic melanoma. One-Sentence SummaryIntegrated clinical, tumor genomic, and immune microenvironmental features predict durable responses to BRAF-targeted therapy.

cancer biology↗

bioRxiv2026

Explainable prediction and simulation of complex system dynamics through networks of manifolds

Complex systems such as brains and other interacting biological and physical processes are difficult to represent because they evolve across many variables, scales, and nonlinear interactions. To capture these multivariate, multiscale interactions we have developed Generative Manifold Networks (GMNs) a machine learning framework consisting of a network of linked dynamical systems. The network is discovered by an interaction function which can focus on causality, shared information, nonlinearity or other metric. Network nodes are low-dimensional data-driven state-space manifolds with generator functions accommodating multiscale dynamics. In contrast to many machine learning approaches GMNs have no latent or randomly initialized variables providing transparent explainability. GMNs generate short term dynamics of chaos on par with echo state networks while outperforming them in long term generation of chaos and neural dynamics, but with a markedly reduced number of dimensions and without sensitive dependence on reservoir parameters or random states. As a result of their holistic, multiscale representation GMNs can learn the complete dynamics of a complex system. We further show that GMNs are universal approximators. GMNs are demonstrated on chaotic dynamics, neural and behavioral recordings of the fruit fly and domestic rat with comparisons to echo state networks and crossformer - a time series transformer. SignificanceA major challenge in machine learning is to model complex systems accurately without losing interpretability. Many methods that succeed in prediction rely on latent variables obscuring mechanistic insight and complicating experimental testing. Generative manifold networks (GMN) construct a network of low-dimensional functional manifolds directly from observed variables with no latent or randomly initialized variables: the model remains transparent and experimentally testable. We prove that GMN are universal approximators showing that high representational power can be achieved without sacrificing explainability. GMN therefore provides a general framework for prediction and simulation in neuroscience and complex systems where unraveling the links between variables in an experimentally testable manner is as important as forecasting their behavior.

neuroscience↗

bioRxiv2026

Acoustofluidic Active Flow Sculpting Enables Dynamic, Reconfigurable Cross-Sectional Patterning

Soft materials created with flow lithography exhibit distinct functionality depending on the shape and material composition of the precursor fluids, enabling applications ranging from tissue engineering to anti-counterfeiting. However, current manufacturing techniques largely rely on static nozzle geometries or passive hydrodynamic focusing, which commit to a fixed structure during fabrication and limit the ability to actively reconfigure material architecture in continuous flow. Here, we introduce ActiSculpt, an acoustofluidic platform that replaces physical constructions with dynamic, programmable, non-invasive elements to configure the continuous flow. By exploiting the interplay between laminar stability and acoustic streaming, we decouple deterministic fluid deformation from chaotic mixing, enabling the precise sculpting of cross-sectional flow structures. We demonstrate the high-throughput generation of a diverse library of hydrogel particles with continuously tunable moments of inertia. We further demonstrate continuous fiber fabrication in which acoustic parameters are varied in real time, using temporal control to encode structural variation along the fiber's length. Finally, we translate this platform into a motion-controlled 3D printer, establishing printability of fully-curable and half-curable fiber configurations and confirming that acoustically programmed cross-sections, along with their capacity for multi-fiber structural assembly, are preserved in the printed output. The result is a platform that overcomes the one-device, one-geometry constraint of existing techniques, enabling not only on-demand reconfiguration between fabrication runs but real-time control of material architecture within a single continuous flow, extending from bench-scale characterization to an operating additive manufacturing process. This spatiotemporal control establishes a new design axis for engineering soft materials with programmable mechanical and functional properties.

bioengineering↗

bioRxiv2026

Morphological fingerprints enable machine learning based inference of neuroblastoma cell states without transcriptomics

Inference of cancer cell states is essential for understanding oncogenic mechanisms and predicting clinical outcomes, yet current reliance on transcriptomic profiling limits scalability and real-time monitoring. Here, we show that cell morphology provides a low-dimensional, observable representation of cellular identity and its dynamics. Using neuroblastoma (NB) as a model system, we establish a machine learning- morphology profiling framework that infers adrenergic (ADRN) and mesenchymal (MES) cell states directly from high-dimensional morphological fingerprints without reliance on transcriptomic measurements. By benchmarking against single-cell RNA sequencing (scRNA-seq), we demonstrate that morphology-defined states closely align with transcriptomic profiles at single-cell resolution. We further show that cell state transitions are represented as continuous trajectories within a morphology-defined state space. Perturbations targeting distinct regulatory layers, including ROCK signaling and epigenetic regulation via EZH2, drive convergent trajectories along a shared phenotypic axis. Together, these results establish cell morphology as a scalable and non-destructive readout of cell state with machine learning providing a unified framework for high-throughput phenotyping and real-time tracking of cancer cell state plasticity.

cell biology↗

bioRxiv2026

3D Printed Bioelectronic Scaffolds for Impedance-based Cytotoxicity Monitoring of In Vitro Cancer Models

Cancer is a significant contributor to global mortality and places a substantial burden on healthcare systems, underscoring the need for improved strategies for developing and evaluating new therapies. Electrochemical impedance monitoring of in vitro cancer models is a promising technique for evaluating treatment effectiveness, particularly for evaluating how well a drug may kill cancer cells. This approach is advantageous over conventional end-point assays because it is non-destructive, label-free, and can provide temporal information on cell behavior and drug kinetics. However, traditional impedance devices are limited in that they do not support three-dimensional cell culture that has become standard in cancer studies. Typical devices are planar substrates that support monolayer culture, which has been shown to overestimate drug effectiveness. In this work, we propose 3D printed bioelectronic scaffold devices that provide 3D cancer cell culture while functioning as an on-chip readout for monitoring changes in cell characteristics via impedance. We describe device development and demonstrate reproducible fabrication, stable electrochemical properties, cell detection by impedance, and proof-of-concept monitoring of cytotoxicity in response to a chemotherapeutic drug. Overall, this technology offers a promising platform that could be further developed for compound screening as part of drug development or precision medicine.

bioengineering↗