Biology subjects

Yao, J.

Publications and source records attributed to Yao, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Endosperm secretion remodels the maternal seed coat

In flowering plants and mammals, maternal tissues must accommodate the growth of the offspring they enclose. The maternal seed coat expands around the developing embryo and endosperm. Endosperm pressure and hormonal signals coordinate seed-coat development1,2, but whether the endosperm directly promotes maternal cell elongation remains unclear. Here we show that the coenocytic Arabidopsis endosperm directly supplies cell-wall-loosening proteins to maternal seed-coat walls. The seed-size-regulating HAIKU-WRKY10 pathway activates an endosperm secretory programme enriched in cell-wall-associated proteins. These proteins spread through a shared extracellular domain across multiple inner seed-coat layers, revealing an extensive functional maternal-filial interface through which the endosperm reaches maternal cells it does not directly contact. Among these proteins, three expansins are necessary and sufficient for maternal cell elongation and seed growth. The endosperm therefore promotes its own growth by enabling expansion of the maternal tissues that enclose it. Paternal DNA methylation and maternal Polycomb activity jointly regulate this programme, with Polycomb also required for timely shutdown of expansin expression. These findings link parental epigenetic states to maternal tissue expansion through proteins supplied by the offspring. This cross-generational control of growth parallels epigenetically regulated signaling from mammalian extraembryonic tissues3, suggesting a shared strategy for adapting the maternal environment to offspring development.

plant biology↗

bioRxiv2026

Intelligent SEEQPI enables reconstruction of inhibitory neuronal nuclei and dry-mass mapping deep in the living mouse brain

Multiphoton fluorescence microscopy enables deep brain imaging with subcellular resolution and molecular specificity, but limited photon budgets constrained by tissue heating and nonlinear damage restrict temporal resolution of structural imaging in deeper regions. Moreover, imaging multiple molecularly defined cell populations requires additional fluorescent labels, while spectral bandwidth and crosstalk constrain multiplexed detection. Here we develop an intelligent imaging framework that uses label-free Scattering-Enabled Epi-Quantitative Phase Imaging (SEEQPI) to infer molecularly specific information deep in vivo. Using paired SEEQPI and three-photon fluorescence images, we developed a Feature-wise Linear Modulation U-Net with generative adversarial learning to reconstruct inhibitory-neuron nuclei. The network accurately predicts nuclear distributions and maintains performance when training and validation volumes are separated axially by up to 6 m, enabling sparse fluorescence supervision. Cross-animal training further improves predictions in sparsely sampled target animals. Inferred nuclear maps enable dry-mass density estimation, demonstrating molecularly specific, label-free quantitative imaging deep within the living brain.

bioengineering↗

bioRxiv2026

Root responses to mechanical heterogeneity are shaped by pore geometry and root-soil biomechanics

Mechanical limitations to root growth occur when pressure exerted by turgor within root cells is counteracted by excessive resistance of the surrounding soil as well as by that of the cell walls. In a natural soil, however, heterogeneity allows roots to find paths of least resistance, though the governing principles behind this process have yet to be identified. Here, we combined artificial substrates, high-throughput live imaging, advanced image analysis, and mechanical modelling to uncover the principles governing the ability of a root to penetrate or deflect from zones of high mechanical impedance within a heterogeneous substrate. Our findings show that root responses to substrate heterogeneity can be accurately predicted from the pore-matrix geometry when both the mechanical properties of the substrate and those of the roots are known. Consequently, a crucial step toward understanding the physical limitations to root growth may lie in a detailed characterisation of soil mechanical properties.

plant biology↗

bioRxiv2026

ALBIS: simulation of multi-resolution and multi-dimensional spatial transcriptomics data

The rapid adoption of spatial transcriptomics (ST) technologies has been accompanied by substantial growth in computational methods, creating an increasing need for systematic and reliable benchmarking, particularly as analyses increasingly extend from individual tissue sections to three-dimensional (3D) tissue reconstruction. Existing benchmarks often rely on a small and similar set of experimentally-derived datasets because they offer a reliable ground truth label, which is hard to find in practice. Simulated or synthetic data provide an alternative, yet most existing simulators either adapt single-cell RNA-sequencing models to spatial coordinates or generate individual sections in a two-dimensional (2D) space at predefined resolutions. More recent approaches can simulate 2D and 3D tissues, but generally do not derive multiple spatial resolutions from the same molecular realization. Here, we introduce ALBIS (A muLti-resolution Biological In-silico Simulator for spatial transcriptomics), a statistically grounded and highly customizable framework that generates synthetic ST data from an in silico 3D tissue. ALBIS defines spatially structured cell populations with corresponding markers for gene expression, then initializes individual mRNA molecules in 3D space to create cell-level information with a ground truth 3D coordinate for each molecule and cell. From this common realization, ALBIS cuts the 3D tissue into multiple 2D tissue sections with cell-, bin-, and spot-level measurements, enabling direct comparison across spatial resolutions. Properties of the simulated data, including spatial domains, cell type composition, gene-expression effects, batch effects, measurement resolution, and tissue scale, can be systematically varied, enabling rigorous stress-testing of computational methods under known and increasingly challenging conditions.

bioinformatics↗

bioRxiv2026

Alignment-Aware Genomic Coordinate Encoding for Long-Range DNA Modeling

Existing genomic foundation models (GFMs) are adept at capturing local, sequential dependencies in DNA. However, their architectures often struggle to efficiently model gene regulation features, such as sparse motifs and distal regulatory elements that operate beyond linear proximity. Moreover, standard positional encodings fail to fully exploit the reference genome coordinate system. Therefore, we present USM-AA, an alignment-aware genomic foundation model that incorporates reference genome coordinates through a non-contiguous positional encoding, Alignment-Aware Rotary Position Embedding, and an alignment embedding module encoding chromosome, strand, and Compact Idiosyncratic Gapped Alignment Report (CIGAR) information. To improve long-range sequence modeling, USM-AA leverages a reverse-complement masked learning strategy. Pretrained on alignment-aware genomic sequences spanning cross-species evolution and human population variation, USM-AA consistently outperforms existing models across multiple downstream tasks, highlighting the importance of alignment-aware representations for genomic sequence modeling.

genomics↗

bioRxiv2026

SpatialOmicsLab: an integrated research environment for AI co-scientists in spatial transcriptomics

Spatial profiling is moving from method development into a central platform for biological discovery and clinical translation, but analyzing such complex data has become the bottleneck of scientific discovery. Translating spatial transcriptomic measurements into biological insight requires reproducible workflows that integrate diverse analytical methods. Although language-model agents can plan and execute multistep analyses, fragmented data and software ecosystems hinder their use as reliable research assistants. In this study, we introduce SpatialOmicsLab, an integrated research environment for spatial transcriptomics comprising 113 curated computational tools, 63 reference datasets and 4,614 task specifications. We also introduce ST-Coscientist, an AI co-scientist that uses this environment to assemble, execute and refine spatial transcriptomic analyses. Within SpatialOmicsLab, ST-Coscientist outperformed expert-led workflows and other scientific agents across six benchmark settings spanning spatial clustering, cell-type deconvolution and spatially variable gene detection. It constructed alignment-aware workflows for three-dimensional MERFISH data and addressed biological questions by selecting and analysing relevant reference datasets. Procedural memory increased the success rate of integrating and validating additional analytical tools from 48% to 95%. Together, SpatialOmicsLab and ST-Coscientist establish an extensible ecosystem for reproducible spatial transcriptomic research, enabling AI co-scientists to address evolving biological questions with an expanding analytical repertoire.

bioinformatics↗

bioRxiv2026

CHAIR an antigen-resolved antibody-reactome resource for healthy adult immune aging

Antigen-recognition histories accumulate across adult life, but existing human aging resources largely emphasize host-state measurements rather than antigen-resolved humoral memory. We present CHAIR, the Chinese Healthy Aging Immune Resource, which integrates self-antigen, human virome and toxin/virulence-factor PhIP-seq with routine clinical measurements and T/B/NK phenotyping in clinically screened adults aged 20-94 years. The resource framework includes 1,008 unique PhIP-seq samples, 995 clinical records, 977 T/B/NK records and 853 participants with all three layers. Across modeled spaces comprising 23,584 self-antigen targets, 210 virome targets and 1,258 virulence-factor source targets, most measured antibody-reactivity architecture was consistent with broad preservation within domains, whereas age- and sex-associated signals were concentrated in focal targets and modules. Together, CHAIR provides a traceable, antigen-resolved reference framework based on clinically screened adults for investigating focal humoral remodeling across healthy adult aging.

immunology↗

bioRxiv2026

Three dimensional reconstruction of the human nucleus accumbens reveals topographic organization and molecular heterogeneity of D1-islands across the anterior posterior axis

Spatial transcriptomics has transformed molecular characterization of the human brain, but most studies profile individual two-dimensional tissue sections and therefore cannot capture molecular organization across 3D neuroanatomical axes. Here, we developed a spatial genomics framework to reconstruct the 3D cellular architecture of the human nucleus accumbens (NAc) by densely sampling and aligning serial sections across its anterior-posterior (AP) extent. Combining single-cell Xenium profiling with near-transcriptome-wide VisiumHD, we mapped cell type composition and topography across AP, mediolateral and dorsoventral axes. Neuronal populations varied in relative abundance across the AP axis while maintaining characteristic spatial topographies. These differences were particularly pronounced among D1-islands, a topographically organized structure within the NAc in which transcriptionally distinct subpopulations of DRD1-expressing medium spiny neurons (MSNs) aggregate into discrete cellular formations. D1-islands comprised two molecularly distinct spatial domains with conserved counterparts across species. Both domains varied in relative abundance across the AP axis, but demonstrated distinct 3D distribution; one was present throughout the NAc, whereas the other, corresponding to islands of Calleja (ICj), was more restricted to intermediate-to-posterior levels. Local spatial analyses revealed region- and AP-dependent cellular relationships, including segregation of the two island populations despite their anatomical proximity. Together, these findings establish a framework for three dimensional spatial genomics of human brain tissue and reveal molecular and cellular organization not captured by individual tissue sections.

neuroscience↗

bioRxiv2026

An Artificial Intelligence Model for Longitudinal Assessment of TCR Repertoires in SARS-CoV-2 Vaccine Recipients

T cells play a crucial role in reducing disease severity during SARS-CoV-2 infection and in shaping long-term immune memory. However, the precise molecular immune responses, particularly involving T-cell receptor (TCR) repertoire changes after full vaccination, and the use of TCR analysis to evaluate vaccine efficacy, remain incompletely understood. In this study, we developed the DeepAir-Cov19 model (AUC=0.94), a large language model tailored to identify SARS-CoV-2-specific TCRs, and observed significant differences in the TCR profiles between antibody-negative and antibody-positive populations before and after vaccination, indicating that the immune status of pre-vaccine recipients can directly assess the efficacy of vaccination. Notably, SARS-CoV-2-specific TCRs expanded to peak levels after the second dose and remained detectable in most subjects up to 10 months post-vaccination. Meanwhile, we also identified three specific V genes, 20 V-J combinations, and 3 epitopes associated with these responses. Finally, by leveraging vaccine-specific TCRs as novel biomarkers, we developed a vaccine efficacy model that predicts antibody levels with a mean AUC of 0.96. These findings underscore the accuracy of the large model in predicting SARS-CoV-2-specific TCRs and reveal a strong correlation between SARS-CoV-2-specific TCR responses and antibody levels. This highlights the complementary and synergistic roles of T cells and antibodies in providing vaccine-mediated protection. Our results offer valuable insights into the longitudinal dynamics of SARS-CoV-2-specific TCRs and illustrate the potential for developing potency evaluation models based on these TCR insights.

bioinformatics↗

bioRxiv2026

Whole-body Super-resolution Functional and Molecular Imaging with Panoramic Photoacoustic-Ultrasound Tomography

Photoacoustic (PA) and ultrasound (US) imaging provide complementary molecular, functional, and anatomical contrasts. Here, we present a panoramic PA-US imaging platform that integrates multispectral PA computed tomography (PACT) along with reflection-mode and transmission-mode US imaging through a single shared full-ring ultrasound array. We employ an ultrafast planewave transmission scheme in reflection-mode US for power Doppler (PWD) imaging and ultrasound localization microscopy (ULM). Additionally, we use the transmission-mode US to reconstruct a spatially resolved speed of sound (SoS) map that corrects both PA and US reconstruction. Such correction sharpens the resolution of PACT, suppresses the artifacts of PWD, and improves microbubble localization of ULM. Elevational scanning further enables whole-body volumetric imaging with co-registered PA and US contrasts. The integrated system maps photoswitchable DrBphP1-expressing tumors alongside their blood perfusion and oxygenation environment. Applying the platform to monitor unilateral renal ischemia-reperfusion injury, we report that microvascular perfusion and renal oxygenation recover at different rates. Collectively, we demonstrate that the integrated PA-US imaging platform provides a unified framework for multiparametric study of anatomy, perfusion, microvascular flow, oxygenation, and molecular activities.

bioengineering↗

bioRxiv2026

Dissecting the TMEM132A-EGFR Dependency to Unlock Translational Therapeutic Opportunities for Pan-Solid Tumor

Solid tumors remain refractory to conventional treatments, yet cell surface proteins, by virtue of their extracellular accessibility and critical roles in tumor signaling, represent an attractive class of targets for precision-targeted therapy. Here, we report that TMEM132A is an essential and previously unrecognized pan-cancer target. TMEM132A interacts directly with EGFR and stabilizes its expression, thereby tethering EGFR at the plasma membrane and sustaining constitutive activation of lipid synthesis. Mechanistically, the TMEM132A-EGFR axis promotes lipogenesis by facilitating SREBP nuclear translocation, which in turn upregulates ACLY and ACSS2 expression to drive acetyl-CoA production and downstream lipid biosynthesis, ultimately disrupting lipid droplet homeostasis. To therapeutically target this axis, we developed a nanobody, LFNanoT132A#3, which effectively blocks the TMEM132A-EGFR interaction, abrogates downstream signaling activation, and potently inhibits proliferation across multiple solid tumor types. Notably, LFNanoT132A also exerts robust antitumor activity against H1975 xenografts, a model resistant to first- and second- generation EGFR inhibitors, underscoring its potential to overcome conventional drug resistance. Our findings establish TMEM132A#3 as a critical node in membrane-tethered oncogenic signaling and metabolic rewiring, and position LFNanoT132A#3 as a promising therapeutic candidate for precision cancer therapy.

cancer biology↗

bioRxiv2026

Constrained Laser-Induced Cavitation for Miniaturized Treatment of Deep Vein Thrombosis

Chronic, mechanically resilient thrombi remain difficult to remove rapidly and safely using existing therapies, which are limited by slow treatment speeds, reduced efficacy against aged clots and risks associated with embolic debris. Here we introduce Constrained Laser-Induced Cavitation (CLIC), a novel approach that confines laser-induced cavitation bubble generation and collapse within a miniaturized waveguide to enhance thrombolysis. Optimized CLIC removed retracted clots at a mass-loss rate of 393.5 mg/min, [~]40-fold higher than reported state-of-the-art sonothrombolysis under similar conditions. Systematic variation of channel length and laser parameters showed that CLIC efficacy depends strongly on treatment geometry and cavitation dynamics. Post-treatment analysis revealed cylindrical channels consistent with clot removal dominated by fluid jetting and suction-driven evacuation, with cavitation shockwaves likely contributing a secondary role. Debris fragment measurements remained predominantly below a 1 mm embolic-risk threshold, consistent with a promising embolic safety profile. These findings establish CLIC as a viable strategy for rapid thrombolysis of chronic, mechanically resistant thrombi.

bioengineering↗

bioRxiv2026

Peripheral Nerve Stimulation Optimized Pulses for EPI (POPE) allows high-resolution fMRI

PurposeRecent improvements in MRI gradient design and amplifiers, advanced MRI scanners are now routinely utilizing slew rates of several hundred T/m/s. However, full gradient performance cannot be exploited in high-resolution EPI due to peripheral nerve stimulation (PNS) limits. We aim to characterize and mitigate these PNS constraints using a simple sequence modification: PNS-optimized EPI gradient pulse shapes. MethodsPNS-Optimized Pulses for EPI (POPE): we selectively reduce the slew rate of gradient pulses at periods of high predicted PNS spikes, while leaving the rest of the waveform unchanged. PNS sensation was evaluated. ResultsPOPE allows 7%-35% faster imaging of EPI protocols resolutions of 1mm-0.3mm resolutions without exceeding predicted PNS. With such improvements, POPE allows robust 0.3 mm isotropic fMRI protocols that would have exceeded safety limits without it. ConclusionPOPE facilitates locally precise fMRI activation mapping on clinical 7T scanners at spatial resolution that were previously unattainable due to PNS limitations.

neuroscience↗

bioRxiv2026

Compact red-shifted near-infrared fluorescent proteins enable deep-tissue SWIR imaging with in vivo optical clearing.

Compact fluorescent proteins (FPs) with red-shifted emission are needed for deep-tissue short-wavelength infrared (SWIR) imaging. We engineered a GAF domain from the JSC1 cyanobacteriochrome of thermophilic Leptolyngbya sp. into three monomeric, biliverdin-binding FPs of 19.1 kDa: miRFP729nano, miRFP732nano and miRFP735nano, with excitation/emission maxima of 714/729, 716/732 and 719/735 nm, respectively. Their off-peak fluorescence beyond 1,000 nm was several-fold higher than that of miRFP718nano previously used for SWIR imaging. miRFP732nano functioned as a fusion tag, a component of target-stabilized nanobodies, and a reporter of NF-{kappa}B and AP-1 transcriptional activities. It enabled single-laser, dual-color three-photon imaging with EGFP to depths of [~]950 m in cortex and [~]300 m in spinal cord. In mice, miRFP732nano supported SWIR imaging of skeletal muscle, inflammatory signaling and intracellular targets. Combining SWIR detection with biocompatible 4-aminoantipyrine-based in vivo tissue clearing enhanced signal and image sharpness. These red-shifted NIR FPs expand the genetically encoded toolkit for deep-tissue imaging.

bioengineering↗

bioRxiv2026

Sequence-based modeling of plant epigenomes reveals cell-type-specific cis-regulatory grammar

How cis-regulatory sequences and their genetic variation govern chromatin accessibility and gene expression to establish plant cell identities remains incompletely understood, despite their fundamental roles in development, environmental responses, and phenotypic diversity. Here we present PEAgent, a framework for training, evaluating and interpreting deep-learning models that predict single-cell chromatin accessibility directly from DNA sequence, packaged in an interactive web portal and toolkit. Models were trained on single-cell chromatin-accessibility atlases of soybean, maize and rice, together spanning over 355,000 cells and 320 cell types and [~]150 million years of evolution. We unraveled a lexicon of 243 cell-type-resolved regulatory patterns, half of them composite, with TCP and bHLH showing the greatest influence and strongest conservation across species. Co-occurrence and in silico synergy analyses, explicitly modeling motif orientation and spacing, revealed two distinct cooperative modes acting at short and nucleosome-scale distances. We further showed that model predictions distinguish grass-conserved from rice-specific regulatory sequences far more accurately than sequence conservation scores alone, and validated the models predicted variant effects against cell-type-level chromatin accessible QTLs. PEAgent provides a foundational resource for decoding cell-type-specific plant cis-regulatory logic and interpreting noncoding variation in plants. HighlightsO_LIPEAgent predicts single-cell chromatin accessibility directly from DNA sequence in soybean, rice and maize. C_LIO_LINearly half of cell-type-resolved patterns are composite, with TCP and bHLH motifs most influential and conserved across species. C_LIO_LICo-occurrence and synergy analysis reveals distinct cooperative grammar at short and nucleosome-scale distances. C_LIO_LIPEAgent distinguishes grass-conserved from lineage-specific regulatory sequences better than sequence conservation scores alone. C_LI

genetics↗

bioRxiv2026

Spatial Topology Reveals Biologically Distinct Recurrent Motifs in Colorectal Cancer

Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multicellular tissue architectures shared across patients, and typically depend on predefined cell-type annotations or compartment definitions. We developed STORM (Spatial Topology analysis of Recurrent Motifs), an unsupervised graph-attention variational autoencoder that learns recurrent spatial motifs directly from cell-level graph structure and molecular profiles without cell-type labels, manual annotation, or predefined compartments. We applied to 32 Xenium sections from 16 patients with paired early-onset (EOCRC) and average-onset (AOCRC) colorectal cancer, STORM identified 10 recurrent motifs that self-organized into tumor-parenchymal, stromal, and immune macro-compartments. Among these, the Desmoplastic Fibrotic Barrier (DFB) motif, a CAF- and ECM-rich boundary architecture, was associated with restricted CD8+ T-cell geodesic access to tumor cores independently of CD8+ abundance, as demonstrated by abundance-normalized neighborhood enrichment statistics and within-sample mixed-effects models. EOCRC selectively amplified this barrier-exclusion architecture, exhibiting tighter tumor parenchyma, denser DFB shells, and a DFB-specific ECM activation program that yielded an age-specific prognostic signature in TCGA-COAD. Translation of motif macro-classes to H&E images via a Vision Transformer classifier produced an image-derived DFB-barrier composite that predicted overall survival in advanced-stage TCGA colorectal cancer. STORM provides an annotation-free framework for discovering recurrent spatial motifs and identifies a fibroblast barrier architecture whose topological association with immune exclusion is independent of effector abundance, amplified in early-onset disease, and translatable to a deployable pathology-based prognostic biomarker.

bioinformatics↗

bioRxiv2026

Dynamics of ML-based Morphological Features Indicate a Shear Stress-Dependent Bifurcation of hiPSC-Derived Endothelial Cell States

Cell states are increasingly conceptualized as attractors of high-dimensional dynamical systems, yet quantitative approaches for integrating phenotypic information into this framework remain limited. Here, we take an image-based approach that combines unsupervised machine learning (ML) with timelapse imaging to extract and characterize the temporal dynamics of morphological features. Using a cell line with endogenously tagged VE-cadherin, we acquired brightfield and fluorescence timelapse images of human induced pluripotent stem cell-derived endothelial cell (hiPSC-EC) monolayers, which adopt distinct phenotypes at two different magnitudes of shear stress in terms of their morphology, behavior, and VE-cadherin organization. To quantify these phenotypic cell states without segmentation, we trained a diffusion autoencoder to predict VE-cadherin signal from brightfield images. We identified interpretable ML-based features representing cell orientation, elongation, and local density. Treating these variables as dimensions of a morphological state space, we estimated a data-driven vector field and found that the two observed phenotypic cell states correspond to stable fixed points of the inferred dynamical system. Mapping measured cell migration coherence onto this space further distinguished the states. Imaging cells across intermediate shear stresses revealed a regime of bistability in which both states coexist, indicating that the shear-stress-dependent transition between endothelial cell states occurs as a bifurcation of the inferred dynamical system. Finally, we applied this method to study an N-terminal truncation of VE-cadherin, finding that mutated cells preserve alignment and coherent migration, but exhibit altered morphology and increased migration speed. This work demonstrates the applicability of a dynamical systems approach to quantitatively characterize morphological aspects of cell state from interpretable ML-based features.

biophysics↗

bioRxiv2026

Device-embedded accelerometry complements neural signals for tracking parkinsonian motor states

Adaptive deep brain stimulation (aDBS) relies on physiological biomarkers to infer motor state and guide therapeutic stimulation in Parkinsons disease. However, neural biomarkers may themselves be altered by stimulation, potentially limiting their utility for closed-loop control. We address this limitation by testing whether DBS device-embedded accelerometers can accurately track Parkinsonian motor state across stimulation conditions. We analysed over 1,900 hours of chronic recordings of subthalamic nucleus (STN), sensorimotor cortical and device-embedded accelerometry signals acquired before and during continuous STN stimulation, alongside continuous wearable assessments of bradykinesia and dyskinesia. Across stimulation conditions, accelerometry-derived features robustly tracked motor symptom severity and outperformed neural features for symptom decoding. Mechanistically, total STN beta power - a widely used biomarker for aDBS - proved less informative because it conflates periodic and aperiodic neural processes with opposing relationships to motor state. Under active stimulation, periodic beta activity showed reduced coupling to symptom severity, whereas STN aperiodic activity, cortical periodic activity and cortico-subthalamic coherence remained comparatively stable. Together, these findings demonstrate that neural and behavioural biomarkers exhibit differential robustness during deep brain stimulation and identify device-embedded accelerometry as a robust behavioural biomarker of motor state, motivating its use in next-generation adaptive DBS systems.

bioengineering↗