Biology subjects

Gao, Z.

Publications and source records attributed to Gao, Z..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Photo-downregulation of SIRT4 mitigates aging in mice by enhancing H3K9ac via fatty acid metabolism

As organisms age, mitochondrial metabolic activity declines, and disrupted gene expression regulation mediated by histone acetylation induces the emergence of senescent physiological phenotypes in tissues. In this study, we found that periodic exposure to red light significantly increased histone H3 Lys9 acetylation (H3K9ac) levels in the tissues and organs of aged mice. Following red light exposure, silent information regulation factor 4 (SIRT4) protein levels in keratinocytes were notably reduced, whereas glycolysis, fatty acid metabolism, and the tricarboxylic acid (TCA) cycle were significantly activated in keratinocytes. The reduction in mitochondrial SIRT4 levels enhances the acetylation of mitochondrial metabolic proteins, particularly malonyl-CoA decarboxylase (MCD), a potent inhibitor of the key rate-limiting enzyme carnitine palmitoyltransferase 1A (CPT1A) in fatty acid oxidation. This process promotes mitochondrial fatty acid oxidation and TCA cycle. Additionally, the decrease in SIRT4 activates SIRT1 through feedback mechanisms, thereby alleviating its inhibition on PPAR- in senescent keratinocytes and comprehensively activating the expression of genes related to lipid metabolism. This lipid metabolism activation ultimately facilitates the accumulation of acetyl-CoA within keratinocytes, increases H3K9ac levels, and reshapes the expression patterns of senescence-related genes. Eventually, cellular aging is effectively mitigated by the synergistic regulation of metabolism, inflammation, and gene expression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/717004v2_ufig1.gif" ALT="Figure 1"> View larger version (82K): org.highwire.dtl.DTLVardef@ba3865org.highwire.dtl.DTLVardef@1cc9a58org.highwire.dtl.DTLVardef@1a90e7borg.highwire.dtl.DTLVardef@1f5e88f_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical AbstractMechanism of anti-aging action of red light: Red light can reduce SIRT4 signalling in keratinocytes, thereby reactivating lipid metabolism and increasing levels of acetyl-CoA. This promotes histone acetylation, which in turn remodels the expression of age-related inflammatory factors and genes.

cell biology↗

bioRxiv2026

Volumetric Scattering Microscopy

Optical scattering in biological tissues fundamentally limits fluorescence imaging by disrupting spatial and angular information, thereby restricting volumetric visualization. Although hardware-intensive and computational approaches have advanced scattering microscopy, practical three-dimensional imaging through tissue remains constrained by instrumental complexity and axial ambiguity. Here, we present volumetric scattering microscopy (VSM), a scan-free, optical-computational framework for three-dimensional fluorescence imaging in scattering biological media. VSM captures angularly resolved speckle-encoded fluorescence using an aperture-segmented Fourier light-field configuration and reconstructs volumetric structure through adaptive feature-based descattering and joint sub-pupil alignment. This hybrid strategy preserves angular information embedded in scattered light without wavefront measurement or mechanical scanning, while maintaining the simplicity of a standard epi-fluorescence architecture. We demonstrate high-fidelity volumetric reconstruction across phantoms, engineered cellular systems, ex vivo tissues with volumetric muscle loss, and intact Xenopus embryos, achieving preserved spatial resolution, enhanced optical sectioning, and quantitative accuracy under strong scattering conditions. VSM supports large-field, robust volumetric imaging in both layered and fully embedded scattering environments. By transforming scattered light into a structured encoding resource, VSM establishes a scalable pathway toward routine three-dimensional fluorescence imaging in complex biological systems.

bioengineering↗

bioRxiv2026

Latent brain state dynamics predict early amyloid accumulation and cognitive impairment

Amyloid-{beta} (A{beta}) accumulation is a continuous process central to pathological aging that begins decades before cognitive impairment emerges. While subthreshold A{beta} levels have been linked to future decline in cognitive control, the neural mechanisms connecting this early accumulation to its neurocognitive impact are poorly understood. Brain circuit dynamics, which are essential for cognitive function, may offer a sensitive lens into these initial pathological changes. Here, we tested whether brain state dynamics could serve as sensitive markers for cognitive impairment at an early stage of A{beta} burden. Using the Bayesian Switching Dynamic System (BSDS) model, we identified 4 distinct latent brain states from high-temporal-resolution (800 ms) fMRI data acquired from 116 older adults, including 72 cognitively normal (CN) individuals and 44 with mild cognitive impairment (MCI), during an N-back working-memory task. Adopting a dimensional approach, we examined how latent brain state dynamics relate to early amyloid burden, cognitive performance, and clinical symptoms. While A{beta} levels failed to differentiate clinical groups or predict clinical symptoms and task performance, the dynamics of latent brain states proved highly sensitive to both early A{beta} accumulation and cognition. Canonical correlation analysis revealed a significant relationship between brain state dynamics and early A{beta} burden. Furthermore, the temporal properties of brain states were significantly predictive of working memory performance in CN individuals, a relationship that was selectively disrupted in the MCI group. The features of brain dynamics can also successfully predict cognitive impairment. Our findings establish brain state dynamics as sensitive neural markers of initial A{beta} accumulation and early cognitive impairment, offering a new framework for developing predictive models to identify individuals at risk for future cognitive decline.

neuroscience↗

bioRxiv2026

Hydrogel Fiber Endomicroscopy

Multimode fibers enable minimally invasive, high-resolution imaging through ultrathin probes, thereby enhancing diagnostic precision and facilitating real-time monitoring in delicate anatomical regions. In this work, we introduce HYFEN, a hydrogel-based endomicroscopic imaging platform for flexible, biocompatible, and subcellular-scale fluorescence microscopy. HYFEN leverages the unique properties of hydrogel materials, adaptive optics, and pixel-wise image enhancement to address challenges associated with silica-based fibers, including mode scrambling, limited field of view, and mechanical rigidity. The technique achieves precise mode threading, rapid diffraction-limited focusing at kilohertz speeds, and high-fidelity fluorescence signal acquisition with subcellular resolution. Notably, the approach extends fluorescence imaging under enhanced fiber dimensions and bending conditions that are unachievable with conventional modalities. Together, these advances establish HYFEN as a versatile platform for next-generation biointerfacing and minimally invasive imaging across biomedical and clinical settings.

bioengineering↗

bioRxiv2026

Foveated Light-Field Compound Imager

Artificial vision systems hold transformative potential for biomedical imaging, diagnostics, and translational research by emulating and extending the capabilities of biological eyes. However, current techniques often face intrinsic trade-offs between spatial resolution, field of view, and depth perception, particularly in compact, biologically relevant settings. Here, we introduce FOLIC, a foveated light-field compound imaging system, which integrates compound-eye-inspired wide angular coverage and chambered-eye-inspired spatial acuity within a unified multi-aperture concave architecture. FOLIC naturally generates peripheral, blend, and foveated zones from a single capture, enabling seamless, depth-extended, multiscale visualization from wide-field context down to single-cell lateral resolution. We validate FOLIC across diverse fluorescent and non-fluorescent specimens, including cellular phantoms, tissue sections, and small organisms, demonstrating its versatility and scalability for biomedical research and related translational applications. We anticipate FOLIC to offer a biologically informed design blueprint for future artificial vision systems. TeaserA bioinspired system unifies compound and chambered eye principles to achieve wide-field volumetric microscopy.

bioengineering↗

bioRxiv2026

SCALE: Scalable Conditional Atlas-Level Endpoint transport for virtual cell perturbation prediction

Virtual cell models aim to enable in silico experimentation by predicting how cells respond to genetic, chemical, or cytokine perturbations from single-cell measurements. In practice, however, large-scale perturbation prediction remains constrained by three coupled bottlenecks: inefficient training and inference pipelines, unstable modeling in high-dimensional sparse expression space, and evaluation protocols that overemphasize reconstruction-like accuracy while underestimating biological fidelity. In this work we present a specialized large-scale foundation model SCALE for virtual cell perturbation prediction that addresses the above limitations jointly. First, we build a BioNeMo-based training and inference framework that substantially improves data throughput, distributed scalability, and deployment efficiency, yielding 12.51* speedup on pretrain and 1.29* on inference over the prior SOTA pipeline under matched system settings. Second, we formulate perturbation prediction as conditional transport and implement it with a set-aware flow architecture that couples LLaMA-based cellular encoding with endpoint-oriented supervision. This design yields more stable training and stronger recovery of perturbation effects. Third, we evaluate the model on Tahoe-100M using a rigorous cell-level protocol centered on biologically meaningful metrics rather than reconstruction alone. On this benchmark, our model improves PDCorr by 12.02% and DE Overlap by 10.66% over STATE. Together, these results suggest that advancing virtual cells requires not only better generative objectives, but also the co-design of scalable infrastructure, stable transport modeling, and biologically faithful evaluation.

cell biology↗

bioRxiv2026

A generalized life-motion mechanism supports invariant directional coding of local biological kinematics in humans

Humans can detect biological motion (BM) from sparse local kinematic cues, yet whether the visual system encodes directional information in a category-invariant manner remains unresolved. Here we combined a visual adaptation paradigm with computational modelling to reveal direction-sensitive neural mechanisms specialized for local biological kinematics. Adapting to a side-view scrambled point-light walker produced a robust repulsive aftereffect in the perceived direction of an intact human walker near the frontal view. Notably, this aftereffect generalized across different terrestrial vertebrates (pigeon, cat, dog) and across various actions (running, crawling, cycling), demonstrating a high degree of kinematic invariance. Crucially, the effect disappeared when biological kinematics were disrupted (inversion or removal of gravitational acceleration cues) or when the test stimulus was replaced with non-biological object motion. Individuals direction-discrimination abilities were also highly correlated across diverse local BM patterns, indicating a shared underlying mechanism. Drift-diffusion modeling further revealed that adaptation primarily altered the efficiency of sensory evidence accumulation rather than decision-level processes, and individual drift-rate changes strongly predicted the magnitude of perceptual aftereffects. These findings provide compelling evidence for a generalized, direction-sensitive neural system tuned to local biological kinematics, extending the life-motion detector theory and revealing a fundamental principle of biological motion perception. Significance StatementHumans possess an exceptional ability to detect and interpret the movements of living beings--an ability fundamental to survival, social interaction, and adaptive behavior. But how does the visual system extract "life motion" so adeptly amidst the complexity of natural scenes? Using visual adaptation and computational modelling, we provide direct evidence for direction-sensitive neural mechanisms specialized for local biological kinematics. These mechanisms operate robustly across species and actions, and remain selective for natural biological dynamics. Our findings advance the life-motion detector theory by demonstrating that the human visual system encodes directional information in a highly invariant manner, revealing a fundamental computational strategy for detecting animate agents.

neuroscience↗

bioRxiv2026

HitAnno: Atlas-level cell type annotation based on scATAC-seq data via a hierarchical language model

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) has emerged as a core technology for dissecting cellular epigenomic heterogeneity and gene regulatory programs. With the emergence of atlas-level scATAC-seq datasets, cell type annotation increasingly faces challenges arising from unprecedented data scale and increased cell-type diversity, which together place stringent demands on model reliability and robustness. Here, we introduce HitAnno, a hierarchical language model capable of accurate and scalable cell type annotation in atlas-level scATAC-seq data. Leveraging selected cell-type-specific peaks to construct "cell sentences", HitAnno employs a two-level attention mechanism that captures accessibility profiles hierarchically. Extensive evaluations show that HitAnno robustly annotates both major and rare cell types across multiple settings, including intra-dataset, cross-donor and inter-dataset annotation. The hierarchical attention mechanisms of the model reveal co-accessibility patterns among peaks and dependencies across higher-order peak sets, ensuring an interpretable annotation process. Training on a 31-cell-type human atlas, HitAnno can directly annotate new query datasets without retraining and is accessible through an online interface. Our model identifies heterogeneous subgroups within mixed labeled cells from unseen datasets, demonstrating its potential to assist researchers in refining existing cell atlases.

bioinformatics↗

bioRxiv2026

PAVR: High-Resolution Cellular Imaging via a Physics-Aware Volumetric Reconstruction Framework

The rapid convergence of advanced microscopy and deep learning is transforming cell biology by enabling imaging systems in which optical encoding and computational inference are jointly optimized for volumetric information capture and interpretation. However, broadly accessible three-dimensional imaging at high spatiotemporal resolution remains constrained by volumetric reconstruction throughput, susceptibility to artifacts, and the burden of collecting modality-matched training data. Here, we introduce PAVR, a physics-aware light-field imaging platform that integrates single-shot volumetric acquisition with fast, end-to-end volumetric reconstruction. PAVR is trained entirely using in silico system responses, avoiding reliance on external high-resolution ground-truth modalities and enabling sample-independent reconstruction across diverse biological contexts. Using fixed and live mammalian cells, we demonstrate multicolor volumetric imaging of subcellular organelles, three-dimensional tracking of autofluorescent particles, and high-speed visualization of organelle remodeling and interactions. We further extend PAVR to quantify coupled morphological and functional dynamics in beating human induced pluripotent stem cell-derived cardiomyocytes under pharmacological perturbation. Together, PAVR establishes a scalable hardware-software platform for high-throughput volumetric imaging and quantitative analysis of dynamic cellular systems in both basic and translational settings.

bioengineering↗

bioRxiv2026

Geoclimatic oscillations and ancient reciprocal adaptive introgression shape the evolutionary trajectories of threatened Coilia

Geoclimatic oscillations have repeatedly reshaped biodiversity, yet how ancient lineages persist and diversify through profound environmental upheavals remains a central question in evolutionary genomics. By integrating chromosome-level genome assemblies, population genomics, and transcriptomic data across the Coilia species complex, we reconstruct deep evolutionary histories of Coilia along the dynamic East Asian margins. Our analyses reveal divergences dating to the Miocene, coinciding with major tectonic and climatic transitions, including Tibetan Plateau uplift-linked reorganization of the Yangtze drainage toward the East China Sea, and the Middle Miocene Climatic Optimum. Despite long-term separation, we uncover evidence for ancient, reciprocal introgression among Coilia lineages, with gene flow confined to discrete genomic regions rather than distributed genome-wide. These introgressed regions are enriched for loci associated with immune function, vascular development, and osmoregulatory processes, and exhibit population genetic signatures consistent with positive selection. Temporal modelling indicates that this window of adaptive exchange preceded Pleistocene glacial intensification, after which gene flow became effectively restricted. Together, our results suggest that transiently permeable species boundaries historically acted as reservoirs of adaptive variation, shaping evolutionary trajectories and facilitating niche differentiation under repeated geoclimatic oscillations. This study highlights ancient adaptive introgression as an important evolutionary process contributing to the long-term persistence of threatened coastal fishes.

evolutionary biology↗

bioRxiv2026

3D Visualization and Proteomic Analysis of Human Cardiac Transthyretin Amyloidosis Tissue Reveals Microangiopathy and Capillary Occlusion

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSTransthyretin amyloidosis (ATTR) is a progressive, degenerative disease affecting the heart and other organ systems, as well as the peripheral, autonomic, and central nervous systems. Although pharmacological and genetic evidence establishes aggregation as a driver of ATTR pathology, the mechanism by which aggregation compromises post-mitotic tissue function is poorly understood. We utilized bottom-up proteomics on wild-type (WT) human cardiac (WT/WT genotype) and V122I human cardiac (V122I/WT genotype) tissue, combined with tissue clearing technology to create an optically transparent tissue architecture to visualize three-dimensional relationships, to better understand TTR cardiomyopathy (CM). MethodsFlash-frozen 0.5 mm cardiac tissue slices from human subjects with end-stage WT-TTR CM, end-stage V122I CM, and slices from an age-matched human control were used for these experiments. Fibril extraction from diseased tissue followed published protocols. Strong denaturant-mediated proteome tissue extraction on samples from each subject facilitated bottom-up proteomics by using liquid chromatography (LC)-mass spectrometry (MS)/MS. Tissue clearing was performed on 0.5 mm cardiac slices utilizing a lauryl sulfate-based lipid removal strategy. Slices were stained using indirect immunofluorescence with antibodies to protein targets identified by proteomics. We used an antibody to non-native TTR and AmyTracker 480 (an oligothiophene dye that binds to amyloid fibrils) to image TTR deposits. ATTR fibrils were characterized structurally using cryogenic electron microscopy (cryo-EM) followed by helical reconstruction. ResultsProteomic cardiac analysis afforded high spectral counts for transthyretin (TTR) and proteins typically associated with amyloid fibrils, e.g. serum amyloid P (APCS). Fibril and cardiac homogenate proteomics revealed high levels of angiogenic and hemostatic proteins, including those composing the complement and coagulation cascades. 3D imaging revealed loss of normal microvascular architecture in CM samples with regions of hyper- and hypovascularization. Microvascular obstruction by capillary thrombosis was also observed in CM. ATTR fibrils adopted the common spearhead fold and were decorated with collagen VI (COLVI), an extracellular matrix component. ConclusionsWe hypothesize that ATTR CM is a microangiopathy driven by capillary bed thrombo-inflammation and dysregulated angiogenic revascularization. Phenotypic convergence of WT ATTR CM and V122I ATTR CM was observed via proteomics, 3D imaging, and ex vivo fibril characterization by cryo-EM. We provide evidence of capillary thrombosis in ex vivo ATTR CM tissue. Vasodilation and increased capillary permeability expose components of the vascular basement membrane (VBM) to misfolded TTR. These components are known to promote TTR aggregation and stabilize amyloid fibrils in the extracellular space. Congestion of the VBM prevents appropriate revascularization, reducing cardiac exertional capacity over time, leading to heart failure. Our ATTR CM heart tissue proteomics data shows significant overlap with the proteomic profiles of human AD brain tissues, revealing key amyloid, coagulation, complement, and angiogenesis proteins being changed in amyloidoses.

pathology↗

bioRxiv2026

A HaloTag-4R-Tau Pulse-Chase Sensor Reveals Neddylation Inhibition Promotes Degradation of Tau in iNeurons

Tau accumulation is a central driver of neurodegenerative diseases, yet strategies to promote its clearance remain limited. We developed a HaloTag-4R-Tau sensor in human iPSC-derived neurons (iNeurons) that enables sensitive monitoring the kinetics of both lysosomal partitioning and overall cellular turnover of tau. Using this sensor, we screened a small collection of small-molecule modulators of proteostasis network function and identified Neddylation inhibition by Pevonedistat as a robust promoter of soluble tau degradation. Mechanistic analysis including proteomic profiling revealed that Neddylation inhibition hastens HaloTag-Tau clearance via compensatory activation of a proteasome-dependent pathway(s) as well as the autophagy-lysosome pathway. Our findings establish a powerful tool for probing tau homeostasis and highlight Neddylation inhibition as a potential therapeutic approach for enhancing both proteasome and lysosome-mediated tau clearance in tauopathies.

neuroscience↗

bioRxiv2026

Dopamine Accumulation Dynamics Control Time Perception

Accurate timing is fundamental to perception and behavior, and the dopaminergic system plays a key role. According to the dopamine clock hypothesis, elevated dopamine accelerates the interval clock, while reduced levels slow it. However, optogenetic activation of substantia nigra pars compacta (SNc) dopaminergic neurons paradoxically disrupts this model, suggesting greater complexity. To resolve this paradox, we monitored ventral tegmental area (VTA) dopamine activity in mice performing a time perception task. We observed a ramping-to-threshold pattern, where slower ramping corresponded to longer durations. Optogenetic activation accelerated time estimation, whereas inhibition prolonged it. Systemic dopamine elevation had no effect, but reuptake inhibition accelerated timing. Strikingly, prolonged high dopamine levels slowed time estimation, correlating with slower accumulation due to increased reuptake. Our findings challenge the dopamine clock hypothesis, demonstrating that time perception depends not on absolute dopamine levels but on their temporal accumulation dynamics. This revised framework reconciles previous contradictions and highlights the role of dopamine accumulation rates in temporal cognition.

neuroscience↗

bioRxiv2025

Multimodal AI for Single cfDNA Profiling and Cancer Screening

Cell-free DNA (cfDNA) serves as a non-invasive biomarker for cancer detection, but conventional methods face challenges due to the ultra-low abundance of tumor-derived cfDNA (ctDNA) among normal cfDNA. Though nucleosome-bound cfDNA harbors rich epigenomic features that could enable ctDNA identification by single-molecule multi-omics cross-validation, this remains unexplored due to methodological limits. Here, we developed a cfDNA sequencing approach integrating methylation, fragmentomics, and histone modifications at the single-molecule level; together with gene semantics and epigenomic annotations, these modalities were vectorized and fused to represent each cfDNA molecule. We trained a Transformer-based model (cfAI) to profile and evaluate ctDNA likelihood at molecule, gene, and sample levels. cfAI achieved [~]10-fold enrichment of cancer-derived signals over noise and reached 72.6% sensitivity at 93.1% specificity for multi-cancer detection. Our study establishes an innovative framework that overcomes the inherent signal-to-noise limitations of conventional assays and reveals biological features at molecular resolution for cancer detection.

bioinformatics↗

bioRxiv2025

Semi-rational Engineering of Terminal Deoxynucleotidyl Transferase for High-efficiency Enzymatic de Novo DNA Synthesis

The 21st century has witnessed rapid advancements in synthetic biology, with DNA synthesis emerging as a foundational technology. Conventional phosphoramidite-based methods face significant limitations, including short DNA elongation lengths (<300 nt), hazardous chemical waste, and low stepwise incorporation efficiency. Enzymatic DNA synthesis using terminal deoxynucleotidyl transferase (TdT) offers a promising alternative, enabling kilobase-scale assembly with greater efficiency and minimal environmental impact. Here, we identified Bos taurus TdT (BtTdT) through UniProt database mining as a catalytically active scaffold for natural and 3'-modified dNTPs. Comprehensive characterization of BtTdTs enzymatic properties--including pH, temperature, metal ion dependence, and substrate specificity--revealed its optimal conditions. Truncation of the BRCT domain generated variants with enhanced activity compared to wild-type BtTdT. Guided by AlphaFold3-predicted structural models, we engineered a quintuple mutant (M5: Bt15AAR336L/K338G/L397M/E456S/D395G) optimized for 3'-ONH2-dNTP incorporation. M5 exhibited 30-fold activity enhancement relative to the triple mutant M3 (Bt15AAR336L/K338G/L397M) and achieved stepwise incorporation efficiency exceeding 98% in de novo synthesis of 10-nt ssDNA, demonstrating its potential for scalable enzymatic DNA synthesis. This work establishes a rational framework for TdT engineering through rational domain truncation and computational design, showing potential toward industrial-scale enzymatic DNA manufacturing.

synthetic biology↗

bioRxiv2025

Rapid and accurate quantification of viable Listeria monocytogenes with clonal specificity using microfluidic droplet digital PCR based technology

To overcome the technical bottlenecks in the precise quantification and molecular typing of viable foodborne pathogens, this study establishes a microfluidic droplet digital PCR (ddPCR) based method for rapid and accurate detection and quantification of viable Listeria monocytogenes with clonal specificity. In contrast to the time-consuming plate culture methods and unspecific rapid detection methods, the method in this study employs clonal complex (CC) specific primers and probe for strain-specificity and integrate the nucleic acid dye propidium monoazide (PMA) to effectively distinguish viable from dead bacteria. The rapid and precise quantification of viable bacteria is achieved through microdroplet counting. This method does not require DNA extraction, and the entire detection process takes only about 3 hours, with a quantitative detection limit of 3.3x102 CFU/mL, providing strong technical support for the risk monitoring of highly pathogenic specific types of L. monocytogenes.

microbiology↗

bioRxiv2025

Engineering Apical Integrin-binding Cellular Patches to Directed Cell Reprogramming via Mechanical Remodeling

Engineering extracellular microenvironments to control stem cell fate remains a central challenge in regenerative medicine. Here, we develop ECM-mimetic cellular patches formed by the supramolecular assembly of laminin-derived, integrin-binding ligands. The resulting fibrillar networks exhibit well-defined molecular packing and nanoscale ligand distribution, enabling specific engagement of apical integrin {beta}1 on mesenchymal stem cells. This controlled interface converts molecular assembly into hierarchical mechanotransduction, coordinating cytoskeletal remodeling, nuclear deformation, and chromatin reorganization to drive neuronal reprogramming without genetic or chemical induction. Mechanistic studies reveal that the interplay between ligand assembly, spatial orientation, and network stability governs integrin activation and downstream transcriptional regulation These findings demonstrate how molecularly programmed assemblies can transform passive matrices into active, cell-instructive materials. This work establishes a framework for designing supramolecular systems that couple structural hierarchy with mechanotransductive control to direct stem cell fate and advance regenerative material strategies.

bioengineering↗

bioRxiv2025

Agentic Lab: An Agentic-physical AI system for cell and organoid experimentation and manufacturing

Reproducibility in biological research and manufacturing remains constrained by the complexity of multi-step protocols, fragmented data-analysis pipelines, and the intrinsic variability of experimental execution. Here, we present Agentic Lab, an agentic-physical AI platform that unifies large language model and vision language model (LLMs/VLMs)-driven reasoning with real-world laboratory operations. Agentic Lab uses multi-agent orchestration architecture, comprising of specialized subagents for knowledge retrieval, protocol design, multimodal data analysis, and training-free segmentation and representation learning for intrinsically explainable single-cell and organoid phenotyping. These agents operate under the orchestration of a virtual principal investigator MolAgent that is linked to an augmented reality (AR)-based physical AI interface, which can bridge digital reasoning with human physical execution. Agentic Lab perceives real-world experimental activities, provides context-aware instructions, identifying procedural errors in real time for humans to correct, and continuously evolves with its long-term memory database expanding through the accumulation of experimental data logs from human scientists. This interaction allows scientists and AI agents to collaborate and co-evolve dynamically, closing the loop between planning, action, and analysis in the traditional cell and organoid research lifecycle. We demonstrate Agentic Lab in organoid differentiation from human pluripotent stem cells, where it autonomously generates protocols, monitors culture procedures, and identifies subtle morphological heterogeneity linked to growth conditions. The system interprets these phenotypes, grounds them in literature, and proposes targeted instructions for improving differentiation efficiency. By combining multi-agent reasoning with physical laboratory awareness, Agentic Lab transforms experimentation and biomanufacturing from a static workflow into an adaptive, feedback-driven, bidirectional process that integrates agentic AI into the research lifecycle. This framework establishes a foundation for intelligent laboratories that integrate design, execution, and interpretation within a unified agentic-physical system.

bioengineering↗