Biology subjects

Chen, G.

Publications and source records attributed to Chen, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Endolysosomal damage promotes intraluminal protein condensate formation that limits cytosolic leakage

Endolysosomal membrane damage is a detrimental process in mammalian cells that results in leakage of the luminal contents into the cytosol. However, the nature and extent of the leakage during membrane damage is unknown. Here, we show that endomembrane damage induces the rapid formation of intraluminal condensates in endolysosomes. A subset of resident luminal proteins undergo spatially coordinated condensation upon endomembrane damage. Electron microscopy reveals distinct luminal morphology, and cryo-electron tomography confirms the condensed ultrastructure in their native state. Condensate formation occurs across mechanistically distinct modes of membrane injury and is reversibly dissociated upon lysosomal recovery. Remarkably, these condensates impose a previously unrecognised barrier to endolysosomal escape of therapeutic oligonucleotides. Despite endomembrane damage, luminal oligonucleotide therapeutics are sequestered in damaged endolysosomes through condensate-mediated biophysical immobilisation. Targeting condensate sequestration could represent a novel strategy to improve oligonucleotide-based therapeutics.

cell biology↗

bioRxiv2026

Human-like sequential sound-to-meaning transfer drives artificial speech comprehension

Artificial intelligence has reached a pivotal threshold. Multimodal large models can approach human-level speech comprehension by rapidly transforming sound into meaning. However, whether this process relies on human-like mechanisms remains unknown. Here, we compared the human brain with twelve speech language models (SLMs) using a phonology-semantics confusion paradigm. Stereo-electroencephalography revealed two mechanisms of phonology-to-semantics (P2S) transfer in the human brain: a local sequential transformation within specific neuronal populations, and a global cross-regional hierarchy of P2S representations. Only brain-model alignment in the local sequential manner predicted model performance. Correspondingly, targeted lesioning of local sequential P2S-transfer model units markedly impaired comprehension performance, while activation steering of these units improved performance. In addition, such local sequential P2S-transfer model units were identified across languages. Together, this study establishes local sequential P2S transformation as a fundamental computational principle shared across biological and artificial intelligence, offering a mechanistic bridge for future brain-inspired speech systems.

neuroscience↗

bioRxiv2026

Sparse cortical dynamics reveal flexible condition-dependent spike-order codes

Cortical networks compute with remarkably sparse spiking activity, yet the circuit mechanisms that organize these few spikes into flexible, condition-dependent temporal codes remain poorly understood. Here we combine analyses of large-scale mouse recordings with a data-driven cortical microcircuit model (CMM) of mouse V1. In recordings, V1 activity exhibits condition-dependent spike-order sequences: peak-latency order varies with task outcome, current image identity, and preceding image identity, while remaining stable under split-half and single-trial analyses. After task optimization by backpropagation through time, the CMM reproduces this sequence-level signature and sparse activity more closely than the matched randomly connected RSNN and rate-RNN controls tested here. Ablations indicate that neuronal heterogeneity and distance-dependent local connectivity each reduce rigid sequential activity, with their combination giving the closest match to measured cortical signatures. Low-dimensional trajectory visualizations and model-silencing experiments further identify high-mutual-information early neurons whose removal perturbs task trajectories and decisions. Together, these results identify a biologically grounded computational principle: neuronal diversity and local connectivity help sparse recurrent networks avoid rigid temporal pipelines and support flexible, condition-dependent spike-order computation, providing candidate design principles for SNNs that exploit flexible temporal codes.

neuroscience↗

bioRxiv2026

Near-Infrared-II Chemo-Optogenetics for Deep-Brain Stimulation

Precise activation of ion channels enables fine-tuned control of neuronal excitability, providing a powerful strategy for dissecting and modulating neural circuits. Although optogenetics offers high spatiotemporal neuronal manipulation with cell-type specificity, its reliance on visible light (400-650 nm) limits tissue penetration to millimeter depths, restricting applications in deep-brain stimulation. Here we report a near-infrared-II (NIR-II)-sensitive calcium channel, HaloNeu, created by genetically fusing a circularly permuted HaloTag (cpHaloTag) to the thermo-sensitive transient receptor potential vanilloid 1 (TRPV1) and covalently conjugating NIR-II photothermal nanotransducers (HPN). The HaloNeu enables non-invasive neuromodulation at depths up to 1.0 cm at ultralow laser power ([~]60 mW/cm2) and up to 5.0 cm under the safe exposure limit ([~]1 W/cm2) with 1064 nm laser illumination. Remarkably, HaloNeu maintains stable, on-demand neuron-specific modulation for over two months in vivo, providing sustained activation of ventral tegmental area (VTA) circuits and effective alleviation of Parkinsonian symptoms in mouse models. These results establish HaloNeu as a robust and versatile platform for cell-type-specific, deep-tissue, and chronic neuromodulation, with broad implications for neuroscience and neurotherapeutics.

neuroscience↗

bioRxiv2026

BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer Diagnosis

Recent studies have explored generating virtual spatial transcriptomics (ST) profiles from histological images, offering a promising alternative to laboratory-measured molecular profiling. However, existing approaches predominantly rely on single-organ models and require sub-stantial organ-specific training data, restricting their accuracy under challenging few-shot conditions in clincical practice, where less than 10 slides are available for specific organs or techniques. Here, we present BRIDGE, a multi-organ foundation model pre-trained on over 600,000 paired histology-ST profiles across 13 human organs and three sequencing techniques. By robustly aligning morphological features and genomic information within a shared multi-organ latent space, BRIDGE can leverage common biological knowledge across distinct tissues to enable accurate and generalizable pan-cancer molecular profiling. Without additional organ-specific fine-tuning, BRIDGE accurately predicts the spatial expression of 80 biomarker genes, achieving an average Pearson correlation coefficient (PCC) of 0.474--a 30% improvement over existing state-of-the-art models under three clinically challenging few-shot scenarios. With generated virtual ST, BRIDGE outperforms current state-of-the-art pathology foundation models in predicting cancer survival, achieving an average concordance index (C-index) of 0.724 across six TCGA cohorts. Notably, BRIDGE maintains exceptional performance even in zero-shot scenarios involving three cancer types not seen during its training, achieving an average C-index of 0.717, thereby demonstrating its strong generalization capability that transcends organ- and subtype-specific boundaries. Moreover, BRIDGE-generated virtual spatial transcriptomes match the prognostic accuracy of bulk RNA-seq, highlighting their potential as a spatially informative alternative to laboratory sequencing. In general, BRIDGE represents a data-efficient tool in virtual ST that facilitates biomedical discoveries in clinical few-shot contexts and advances diagnosis of understudied cancers without sufficient samples.

genomics↗

bioRxiv2026

Automated Multimodal Correlative Registration for Organelle-Specific Molecular Imaging

Mapping subcellular drug distribution is essential for understanding trafficking and off-target effects. NanoSIMS enables chemical imaging of labeled therapeutics, but signal interpretation requires ultrastructural correlation with electron microscopy, a manual and laborious process. We present an automated AI-driven pipeline for correlating chemical and ultrastructural images, enabling multiscale, organelle-precise imaging of molecules in cells and tissues. The method integrates bidirectional optical flow, confidence-guided affine transformation, and automated template matching for cross-scale EM alignment. Morphology-rich ion channels (e.g., 32S) estimate transformations that propagate to sparse therapeutic signals (e.g., 79Br, 15N), overcoming low signal-to-noise challenges. We validate this framework across diverse cell and tissue types, tracking oligonucleotide and antibody therapeutics in vitro and in vivo to reveal cell-type- and organelle-specific distribution patterns. This work establishes a generalizable platform for automated multimodal registration and organelle-resolved subcellular pharmacology.

bioinformatics↗

bioRxiv2026

A three-dimensional carbon-nutrient functional balance model explains the formation of Root Economics Space

Root carbon (C)-nutrient functional balance underpins root economic strategies, yet previous models neglected the root-length dimension. Here, we develop a three-dimensional C-nutrient functional balance model for first-order roots that explicitly incorporates root length, and validates it using a global root trait dataset. For subtropical woody species, root cortex volume x root length scaled isometrically with the fourth power of stele diameter (slope{approx}1.0), supporting the model. Herbaceous species from the Qinghai-Tibetan plateau showed a significantly lower slope (0.61), likely due to extreme environmental impacts on transport or metabolism. Woody species preferentially invest in cortical area (thicker roots), supporting mycorrhizal symbiosis, whereas herbaceous species favor root length extension for autonomous soil exploration. By integrating root length, this model provides a novel mechanistic explanation for the formation of both the collaboration and conservation axes within the root economics space, advancing the theoretical framework of root functional strategies.

ecology↗

bioRxiv2026

Generation of High-Affinity Anti-GIPR Antagonist Antibodies with Sustained and Non-rebound Weight Loss in DIO Mice by AlfaBodY

The glucose-dependent insulinotropic polypeptide receptor (GIPR) is an attractive therapeutic target for metabolic disorders, with GIPR antagonism emerging as a promising strategy for obesity and type 2 diabetes. However, developing functional antibodies against GPCRs remains challenging due to their complex architecture and conformational dynamics. Here, we employed AlfaBodY, an iterative active learning platform integrating structural and sequence information, to in silico design human anti-GIPR antibodies. Through four rounds of optimization, we generated antibodies with high binding affinities. Lead candidates AB106-131 (KD 1.2 nM) and AB106-156 (KD 1.7 nM) exhibited 7 to 10-fold higher affinity than 2G10 (KD 12 nM) while maintaining comparable antagonistic activity in a cAMP reporter assay (IC50 4[~]5 nM). In diet-induced obese mice, AB106-156 alone induced weight loss comparable to that of semaglutide ([~] -15%), while preserving lean mass and achieving sustained weight control after treatment withdrawal. Co-administration with the GLP-1 receptor agonist semaglutide produced synergistic weight reduction (-25.4%) and markedly attenuated the fat-mass rebound observed with semaglutide alone. Our results demonstrate that AI-driven design can generate potent anti-GIPR antibodies with favourable in vivo efficacy, supporting further development of GIPR antagonist for obesity and related metabolic disorders. The AlfaBodY platform enables faster development of more efficacious biologic drugs.

pharmacology and toxicology↗

bioRxiv2026

Cellular hydraulics ensures robust endothelial-to-haematopoietic transition

Haematopoietic stem and progenitor cells (HSPCs) emerge from specialised haemogenic endothelial cells (HECs) in the ventral wall of the dorsal aorta (VDA) through a process known as endothelial-to-haematopoietic transition (EHT). During EHT, elongated HECs undergo actomyosin-driven rounding and extrusion, a mechanically demanding transformation that requires cell integrity to be maintained. Here, we identify an osmo-hydraulic volume-regulation mechanism that enables HECs to adapt to and withstand this mechanical challenge. Haemodynamic forces increase Piezo-dependent calcium ion activity in ventral endothelial cells, while Piezo activation promotes HEC swelling. Combined genetic and pharmacological analyses support a pathway in which Piezo-mediated swelling triggers VRAC-dependent osmolyte efflux and Aqp1a.1-mediated water efflux to reduce HEC volume. Disrupting either osmolyte or water efflux causes excessive HEC swelling, compromises HEC integrity and reduces HSPC production. Together, our findings establish osmo-hydraulic volume regulation as a mechanism that preserves cellular robustness during EHT and support a model in which aquaporins act as pressure-relief valves, dissipating intracellular hydrostatic pressure to sustain HEC survival and definitive haematopoiesis.

cell biology↗

bioRxiv2026

Fast and reliable association discovery in large-scale microbiome studies and meta-analyses using PALM

Identifying microbial features associated with various covariates is a long-standing goal in microbiome research. Modern association studies incorporate an ever-increasing number of microbial features, covariates, and datasets from diverse cohorts. However, the complexity of microbiome data challenges analysis, often leading to poor replication of findings. We introduce PALM, a quasi-Poisson regression framework that enables fast and reliable association discovery in large-scale studies and meta-analyses. Extensive, realistic simulations demonstrate PALMs advantages in controlling false discovery rates, boosting power, improving computational efficiency, and preserving cross-study homogeneity of association effects. Three real-world applications at different scales illustrate PALMs utility, underscoring its potential to advance microbiome research.

microbiology↗

bioRxiv2026

SPEND-hSRS imaging of fumarate uncovers mitochondrial metabolic heterogeneity

Mitochondria, acting as the energy powerhouse, biosynthetic center, and reductive equivalent hub of the cell, participate in cellular metabolic activities. However directly imaging mitochondrial chemical content and quantifying metabolic activity in living cells remain challenging. Here, by Self-PErmutation Noise2noise Denoiser enhanced Hyperspectral Stimulated Raman Scattering (SPEND-hSRS) microscopy, we demonstrate fingerprint-region metabolic imaging of fumarate, a key intermediate in the tricarboxylic acid (TCA) cycle, with sub-millimolar sensitivity. In chemotherapy-stressed bladder cancer cells, fumarate imaging revealed two mitochondrial subpopulations with divergent TCA metabolic preferences quantified by ratio metric analysis. Pixel-wise least absolute shrinkage and selection operator (LASSO) spectral unmixing further reconstructs fumarate and lipid maps, uncovering localized fumarate enrichment in protrusions. Extending to CH-window hyperspectral SRS imaging, we uncover the interplay between mitochondria and lipid droplets (LDs) in protrusions, where fatty acid is found to be released from LDs, to fuel the TCA cycle. Together, our work establishes SPEND-hSRS as high-resolution platform for linking fumarate to mitochondrial heterogeneity. Our results provide new insights into how mitochondrial heterogeneity and interaction with LDs drive cancer cell adaptation to stress.

cancer biology↗

bioRxiv2026

STING-STAT3-SOX18 Axis Drives EndMT and Epigenetic Reprogramming in SAVI Lung Fibrosis

A high prevalence of early-onset interstitial lung disease, including pulmonary fibrosis, in pediatric patients with Stimulator of interferon genes (STING)-Associated Vasculopathy with onset in infancy (SAVI) suggests a critical role for the cGAS-STING pathway in the pathogenesis of pulmonary fibrosis. We identified an endothelial-to-mesenchymal transition (EndMT) signature in lesional lung biopsies from SAVI patients, marked by a loss of endothelial and acquisition of mesenchymal markers. Consistently, induced pluripotent stem cell-derived endothelial cells (iECs) from SAVI patients harboring gain-of-function STING1 mutations spontaneously undergo EndMT, a process rescued in isogenic-correction. In endothelial cells, STING activation induces IRF3-independent STAT3 phosphorylation, initiating a SLUG-dependent mesenchymal transcriptional program while repressing SOX18 and an epigenetically-regulated endothelial maintenance network. Our studies define a non-canonical cGAS-STING-STAT3 signaling axis that couples a mesenchymal transcriptional program with epigenetic silencing of an endothelial maintenance program, promoting TGF{beta}-independent STING-mediated EndMT and endothelial dysfunction, and suggesting STING as a therapeutic target for inflammatory pulmonary fibrosis.

cell biology↗

bioRxiv2026

Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function

Mechanistic understanding of the brain requires models constrained by anatomy, physiology, and functional activity. We present a differentiable simulator and a ~67,000-neuron model of mouse primary visual cortex that integrates multimodal data, including electron-microscopy connectomics, multipatch synaptic physiology, cell-type-resolved intrinsic electrophysiology, and large-scale Neuropixels recordings from diverse cell types. End-to-end training completes on a single GPU in ~6.5 hours while preserving biological constraints. Networks trained only on brief drifting-grating responses reproduce cell-type-specific benchmarks and generalize to new contrasts and natural scenes. We uncover heterogeneous cell-type- and tuning-dependent synaptic organization and show that training preferentially sculpts inhibitory connectivity into distinct cohorts that exert outsized control over network activity. Targeted ablations show that removing biological priors on synaptic weight distributions can preserve functional activity yet disrupt emergent wiring rules. The freely shared models and code facilitate differentiable simulations as a computationally practical framework for studying brain circuit function and mechanisms under biological constraints.

neuroscience↗

bioRxiv2026

Structural Basis of Serine Protease Inhibition by Antibodies from Biased Fab Phage-Display Libraries

Biased Fab phage-display libraries were designed to determine whether inhibitory CDR H3 motifs from potent anti-matriptase antibodies could be transferred to target homologous serine proteases. Using reverse-binding and substrate-like H3 motifs from parental clones A11 and E2 as templates, six synthetic libraries with 1010 diversity were constructed. Selection against matriptase identified sixteen inhibitors with sub-100 nM potency, representing 100,000-fold improvement over circularized H3 loops alone. Selection against TMPRSS2, a serine protease implicated in viral entry and prostate cancer with 43% sequence identity to matriptase, yielded binders with micromolar inhibitory potency. Selection against urokinase plasminogen activator (uPA, 35% identity) identified binders that adopted a substrate-like CDR H3 binding mode in our structural models. Across all reference structures, including the separately identified uPA inhibitor AB2 (PDB: 9PYF, deposited with this work), benchmarking of five co-folding methods and rigid-body docking showed that co-folding consistently achieved acceptable to high quality DockQ scores, outperforming traditional docking and capturing the recognition of key active site determinants. Ensemble predictions of mutational binding energy changes ({Delta}{Delta}G) using these models identified key paratope-epitope interactions, with predictions validated through mutagenesis. This work establishes a framework integrating biased antibody libraries with computational structure prediction and analysis for targeting conserved protease epitopes.

molecular biology↗

bioRxiv2026

CEACAM5/6+ Tumor Cells and IL-1β+ Macrophages Drive Resistance to Chemo-immunotherapy in Gastric Cancer

Chemo-immunotherapy is a first-line treatment for advanced gastric cancer, yet response rates remain limited and resistance mechanisms are poorly defined. Here we generate a single-cell atlas of 542,121 cells from 35 patients treated with anti-PD-1 plus chemotherapy, profiling pre- and post-treatment tumors linked to clinical response. Integrating spatial transcriptomics, immunohistochemistry, and bulk RNA sequencing, we identify two temporally distinct resistance programs. Intrinsic resistance in pre-treatment non-responders is marked by enrichment of CEACAM5/6 tumor cells that form immune-excluded spatial niches characterized by macrophage recruitment and CD8 T-cell exhaustion. Acquired resistance in post-treatment non-responders is driven by expansion of IL-1{beta} macrophages, which induces coordinated NF-{kappa}B activation across tumor and stromal compartments, promoting PD-L1 upregulation, epithelial-mesenchymal transition, and chronic inflammation. These findings delineate an evolutionary trajectory of resistance and nominate CEACAM5/6 and IL-1{beta} as predictive biomarkers and therapeutic targets to improve anti-PD-1-based combination strategies.

cancer biology↗

bioRxiv2026

A tunnel microtract organ for T cell progenitor homing is formed by neural crest morphogenesis via Sox10-Cdc42 axis

Thymic colonization by T cell progenitors (TCPs) is essential for adaptive immunity, yet the guiding tissues remain elusive. Here, we unveiled a tunnel microtract (TMT) as an organ indispensable in TCPs homing in both zebrafish and mouse. Disruption of TMT leads to compromised T cell development. Specifically, the zebrafish TMT were positioned bilaterally beneath the fifth branchial levator muscle, connecting the thymus and kidney. They are semi-coiled, non-vascular, non-lymphatic tubes of epithelial signatures. Impressively, Sox10 activates Cdc42 to promote F-actin remodeling in neural crest cells (NCCs), leading to precise elongation and tight packaging of a low-permeability TMT. Remarkably, a homologous CD34 TMT was observed in mice, which bilaterally enveloped the embryonic thymus and extended into the thyroid cartilage. Sox10-Cdc42 signalling functioned recapitulatively in NCC morphogenesis during its construction. These findings establish TMT as an unappreciated NCC-derived organ in TCPs homing, with implications in T cell development and immune disorders.

developmental biology↗

bioRxiv2026

BTK promotes neuroinflammation by interacting with hub genes and modulating microglia following intracerebral hemorrhage

Brutons tyrosine kinase (BTK) has been reported to be important in the inflammatory response in many diseases. However, its role and explicit mechanisms in intracerebral hemorrhage (ICH) remain unclear. Here, we used a mouse ICH model and transcriptomic datasets to explore the effect of BTK on neuroinflammation after ICH. Inhibiting BTK with ibrutinib alleviated ICH-induced neurological deficits and neuroinflammation in mice. After analyzing RNA-sequencing data of ICH and control mice by weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) analysis, we found that Btk was a hub gene in the green dynamic module. Also, 12 hub genes that closely interacted with BTK were identified in the key gene module, all having a critical role in the inflammatory process. Then, single cell RNA-sequencing data analysis showed that microglia were the immune cells that expressed the most BTK in the mouse brain. After dividing microglia in ICH mice into BTK_high and BTK_low groups, GO/KEGG enrichment analyses of differentially expressed genes (DEGs) between these two microglia groups revealed that most of the top 30 enriched pathways were immune-related. Then, gene set enrichment analysis (GSEA) of the BTK_high and BTK_low microglia showed that the expression levels of four anti-inflammatory and phagocytosis-related pathways were significantly lower in the BTK_high microglia than in the BTK_low microglia. Furthermore, gene set variation analysis (GSVA) demonstrated that multiple immune pathways were expressed differentially between the two microglia groups. Also, six microglia polarization scores were calculated, and the results showed that the BTK_high microglia tend to polarize towards M1 and M2b states, while the BTK_high microglia towards M2 (M2a, M2c) states. Finally, intercellular communication analysis was conducted, and BTK was revealed to promote communication between microglia and other immune cells both at the general level and in specific inflammatory pathways. In conclusion, our study showed that BTK is critical in promoting post-ICH neuroinflammation, at least partly by interacting with Btk-related hub genes and modulating microglias immune pathways, polarization, and intercellular communication.

neuroscience↗

bioRxiv2026

Macrophage SLC9A1 Links Endocytic Trafficking to Innate Immune Activation in Myocardial Injury

Excessive innate immune activation drives adverse remodeling after myocardial infarction (MI), yet the upstream mechanisms by which macrophages sense ischemic danger signals remain poorly defined. Here we tested whether macropinocytosis functions as a mediator of post-ischemic inflammation and whether the Na/H exchanger SLC9A1 links membrane ion transport to innate immune activation in the injured heart. Macropinocytosis was robustly activated in infarct-associated macrophages, which are the predominant cell type with the macropinocytotic activity in the injured heart. Pharmacologic inhibition of macropinocytosis with 5-(N-ethyl-N-isopropyl)amiloride (EIPA) improved cardiac function and attenuated post-MI remodeling. EIPA also attenuated cardiac inflammatory responses induced by systemic lipopolysaccharide and Poly(I:C). To define macrophage-intrinsic mechanisms, we generated monocyte- and monocyte-derived macrophage-specific Slc9a1 knockout mice. Genetic deletion of Slc9a1 recapitulated the cardioprotective effects of EIPA and markedly suppressed interferon-stimulated gene programs in infarct-associated macrophages, as revealed by single-cell RNA sequencing. Mechanistically, SLC9A1 promoted endocytic uptake of Poly(I:C) acid and enhanced endosome-dependent inflammatory signaling. Together, these findings identify macrophage macropinocytosis as a regulator of innate immune activation after MI and reveal SLC9A1 as a previously unrecognized link between membrane ion transport and inflammatory signaling in the injured heart. Targeting SLC9A1-dependent membrane trafficking pathways may therefore represent a strategy to limit maladaptive inflammation in ischemic heart disease.

immunology↗