Biology subjects

Jia, S.

Publications and source records attributed to Jia, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Quanta microscopy enables fluorescence imaging at the native bit-plane rate

Live-cell fluorescence microscopy remains constrained by limited photon budgets and exposure-integrated detection, imposing a fundamental trade-off between temporal sampling and image quality. Here we present quanta microscopy (QM), an optical-computational framework that integrates high-speed single-photon acquisition with self-supervised spatiotemporal reconstruction. QM transforms highly sparse binary photon-detection maps, or bit planes, into multibit grayscale fluorescence image sequences, generating one output frame per bit plane at the native 50 kHz sensor cadence without temporal binning. We demonstrate QM across biological specimens spanning diverse spatial and temporal characteristics, including subcellular organelles with distinct morphologies, rapid contractile dynamics in beating hiPSC-derived cardiomyocytes, and electrically stimulated calcium-response transients in primary cortical neurons. By uniting quanta-level acquisition with data-driven image formation, QM broadens the scope of live-cell fluorescence microscopy for quantitative analysis of rapid cellular physiology under photon-limited conditions.

bioengineering↗

bioRxiv2026

Multiscale, Survey-and-Target Optical Super-Resolution Histology

Super-resolution microscopy reveals fine subcellular tissue architecture, yet extending this capability to histological screening requires balancing spatial resolution with imaging area, axial access, throughput, and instrumental complexity. Here we present multiscale optical super-resolution histology (MOSH), an integrated optical-computational platform that combines tissue-scale, extended-depth-of-focus super-resolution surveys with coordinate-guided three-dimensional interrogation of selected targets. MOSH leverages stationary multifocal excitation, continuous sample translation, tunable axial refocusing, and parallelized reconstruction within a shared spatial-coordinate framework in a conventional epi-fluorescence setting. This survey-and-target strategy preserves tissue-scale context, directs volumetric acquisition to selected subcellular structures, and generates virtual H&E images that retain resolution-enhanced structural information. Across cultured cells, cytological preparations, conventional tissue sections, and intact unsectioned biopsies, MOSH achieved approximately twofold resolution enhancement, continuous lateral coverage beyond the native field of view, and tunable axial survey ranges up to 20 m. Bridging tissue-scale histological screening and targeted, quantitative nanoscale interrogation, MOSH establishes a scalable framework for multiscale analysis of biological and clinical specimens.

bioengineering↗

bioRxiv2026

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.

neuroscience↗

bioRxiv2026

A native single-transcript TnpB architecture enables efficient virus-induced genome editing and visual screening of heritable progeny harboring phenotypically silent edits

TnpB are ultra-compact RNA-guided nucleases, yet most reported systems require separate expression of the nuclease and cognate reRNA, eroding their size advantage and restricting viral delivery in plants. Here we demonstrate that ISDra2 TnpB harnesses its native overlapping architecture in which the reRNA sequence resides within the 3' coding region of TnpB, processing its own mRNA to generate functional reRNA and enabling single-transcript genome editing in plants. Only a 7-bp conserved motif followed by a 16-20 bp spacer directly downstream of the coding sequence suffices for editing, requiring no exogenous ribozymes or independent guide promoters. This ultra-compact design enables simultaneous packaging of the editing system and an NbPDS silencing module into a single TRV vector, achieving robust editing in systemic Nicotiana benthamiana tissues and establishing a dual VIGE-VIGS strategy for direct visual selection of heritable, transgene-free edited progeny via distinct albino phenotypes, reducing laborious large-scale genotyping even for targets lacking inherent visible traits.

plant biology↗

bioRxiv2026

Multimodal 3D light-field and laser-speckle endoscopy

Minimally invasive surgery is a powerful technique that enables operations deep within the body while minimizing patient trauma and recovery time. Optical endoscopes are key to providing intraoperative vision but still face challenges due to the loss of essential senses, including depth perception and tactile feedback for tissue evaluation. Thus, it is critical to develop endoscopic imaging technologies that can augment operators with critical information. In this work, we explore a prototype multimodal 3D imaging endoscope that integrates volumetric light-field imaging with laser-speckle contrast imaging to simultaneously capture 3D structure and blood-flow information in a clinically relevant form factor.

bioengineering↗

bioRxiv2026

vDeepInsight: an injective three-dimensional voxel carrier for tabular-feature neighborhood learning

DeepInsight-style methods make tabular feature relationships accessible to convolutional networks by placing each feature at a fixed position on an image carrier. An open design question is how the carrier geometry should be constructed when feature neighborhoods themselves carry part of the signal. We introduce vDeepInsight, an injective three-dimensional (3D) voxel carrier that preserves feature neighborhoods more faithfully than matched two-dimensional (2D) carriers while keeping a one-to-one mapping from each feature to a single voxel. Genes are embedded with t-SNE or UMAP, assigned one-to-one to a sparse voxel grid by linear-sum assignment, and processed by a submanifold sparse 3D convolutional network. We evaluate the carrier on gene expression through four linked analyses. First, representation-quality metrics show that 3D layouts reduce gene-neighborhood distortion relative to matched 2D layouts before any model is trained. Second, controlled synthetic tasks show that a sparse 3D convolution can exploit this preserved locality, but only when the supervised signal is constructed to depend on co-located genes and the receptive field spans adjacent voxels. Third, on real omics tasks the 3D carrier matches or exceeds tuned tabular baselines and consistently exceeds matched 2D carriers; the margin is small on marker-type classification, where individual genes already carry much of the label (tissue, lineage and cancer-type classification), and larger on program-type tasks, where the target depends on coordinated, pathway-level multi-gene activity (drug-response regression, TCGA immunogenomic-context regression and mechanism-of-action classification). Fourth, because the assignment is injective, voxel attribution maps directly back to genes, enabling gene-level attribution and pathway-level functional interpretation without voxel-to-gene deconvolution. Overall, the added carrier dimension improves the fidelity of feature-neighborhood representation and translates this improvement into prediction gains that are largest when the signal is distributed across local gene programs rather than dominated by individual marker genes.

bioinformatics↗

bioRxiv2026

Incorporation of single-neuron projectome-based connectivity motifs enhances the cortex-specific performance of artificial neural networks

The organizational principles of natural neural networks could inspire the new architecture design of artificial neural networks (ANNs). Analysis of single-neuron connectomes of mouse brains revealed distinct profiles of three-node connectivity motifs in various cortical areas and hippocampal formation. A connectome-informed neural network algorithm ("CINA") was developed to incorporate natural connectivity motifs into ANN algorithms represented by recurrent neural network (RNN) and transformer-based large language model (LLM). We found that incorporation of the average profile of cortical motifs improved the RNNs performance in noise-resistant categorization and motor learning benchmark tasks, as compared with RNNs with random connectivity. Notably, incorporating cortex-specific motifs further elevated the RNNs performance in tasks related to the cortical function, and this effect was enhanced by artificially increasing the bias in the motif profile. Similar experimental results were verified on an LLM using Motif-Transformer for natural language question answering and brain-signal decoding tasks. Graph-theoretic analyses showed that incorporating natural motifs drove the emergence of modular and small-world properties in ANNs. Together, we demonstrated not only connectome-inspired optimization of ANN architecture but also functional significance of specific motif profiles in various cortices.

neuroscience↗

bioRxiv2026

Entanglement-governed protein networks enable mechanically adaptive artificial skin for transplantation-scale skin replacement

Artificial skin substitutes that simultaneously achieve mechanical robustness, regenerative bioactivity, and transplantation-scale tissue integration remain challenging to engineer. Here we report a mechanically adaptive bilayer artificial skin based on entanglement-mediated protein networks. By integrating protein chain entanglement, flexible molecular linkers, and photo-triggered intermolecular crosslinking, we establish a hierarchically organized protein matrix with enhanced toughness, structural adaptability, and regenerative compatibility. Spatial biofunctionalization further enables integration of an antibacterial Zn{superscript 2}-coordinated epidermal layer and a regenerative CLP-EGF-functionalized dermal layer within a unified construct. The engineered skin promotes cellular proliferation through PI3K-AKT-mTOR activation, exhibits sustained antibacterial activity, and supports large-area full-thickness skin replacement covering approximately 40% of the dorsal skin surface in mice. The construct further accelerates diabetic wound repair and extracellular matrix remodeling in vivo. These findings establish entanglement-mediated protein engineering as a strategy for mechanically adaptive regenerative biomaterials and provide a platform for transplantation-scale skin regeneration.

bioengineering↗

bioRxiv2026

HYPER-Net: Physics-Conditioned Self-Supervised Reconstruction for Fourier Light-Field Microscopy

The rapid convergence of optical innovation and machine intelligence is reshaping biological imaging by enabling platforms that jointly advance image formation and computational reconstruction for highspeed, high-resolution volumetric microscopy. However, broadly accessible three-dimensional imaging at high spatiotemporal resolution remains limited by the reliance of existing supervised methods on large modality-matched training datasets, the computational burden of conventional iterative reconstruction, and sensitivity to optical mismatch arising from small deviations in the spatial-angular point spread functions. Here, we introduce HYPER-Net, a physics-conditioned self-supervised framework for Fourier light-field microscopy that integrates scan-free volumetric acquisition with fast, robust three-dimensional reconstruction. HYPER-Net incorporates experiment-specific point-spread functions into the learning process in two complementary roles: as the forward operator that enforces measurement consistency and as a conditioning signal that adaptively modulates intermediate feature representations. This design reduces reliance on paired experimental ground-truth volumes, improves robustness to system variation, and enables generalizable reconstruction across diverse biological contexts. Using human colon organoids, embryonic Xenopus laevis hearts, hiPSC-derived cardiac spheroids, and freely moving Caenorhabditis elegans, we demonstrate high-fidelity volumetric imaging of tissue morphology, cardiac function, calcium-contraction coupling, and locomotion-associated neural and muscular dynamics. These results position HYPER-Net as a versatile framework for rapid volumetric imaging and quantitative analysis of dynamic biological systems across basic research and biomedical applications.

bioengineering↗

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

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

Imaging Intrinsic Stochastic Magnetic Fluctuations in Living Cells

SignificanceThis work establishes a probabilistic magnetometry framework for detecting weak stochastic magnetic fluctuations at the nanoscale, which provides the first quantitative access to intrinsic magnetic activity in living cells. Weak stochastic magnetic fluctuations at nanoscale are difficult to quantify. In living cells, ionic transport and molecular currents generate electromagnetic activity whose magnetic component is likewise nanoscale, weak, stochastic, and rapidly varying and has therefore remained experimentally inaccessible. Here we introduce Bio-Spin Probabilistic Inference (BISPIN), a digital statistical framework that can quantify weak, stochastic magnetic fluctuations at the nanoscale. Using threshold-resolved signals from enhanced nitrogen-vacancy quantum sensors, BISPIN converts unstable analog magnetic readouts into statistically convergent digital observables and infers fluctuation strength through probabilistic modeling, enabling robust quantification under random sensor orientations and biological heterogeneity within the experimental bandwidth. Applied to living cells, this approach distinguishes live from fixed cells, resolves agonist-induced activation, and maps subcellular variations in magnetic fluctuation strength. By providing the first quantitative access to intrinsic stochastic magnetic fluctuations in living cells, this work establishes a probabilistic magnetometry framework for cellular electrodynamics and opens a new magnetic dimension of cellular phenotyping for bio-spin omics.

biophysics↗

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

M&uumlllerian duct maintenance at the cranial region is promoted by Gata2

The paradigm of sexual differentiation holds that female embryos retain Mullerian ducts (the precursor to female reproductive tract) by default in the absence of anti-Mullerian hormone (AMH). However, whether Mullerian duct maintenance requires active signaling has remained unclear. Here, we discover that the deletion of mesenchymal Gata2 induces selective regression of the cranial Mullerian duct (future oviduct). This regression is not driven by ectopic AMH signaling, but rather by the loss of region-specific NRG1 signaling. In contrast, the caudal Mullerian duct (future uterus) is retained and displays disrupted epithelial differentiation upon Gata2 deletion in either the mesenchymal or epithelial compartments. Our findings reveal a region-specific, GATA2-dependent program that actively maintains the cranial Mullerian duct, reshaping our understanding of female reproductive tract development.

developmental biology↗

bioRxiv2026

Cellular transcriptomics reveals evolutionary adaptation and rumination of vertebrate stomachs

Stomach, an essential digestive organ in vertebrates, has undergone significant changes in its morphology, chamber numbers, and physiology, in response to dietary diversity. However, its cellular and molecular basis underlying evolutionary adaptation remains largely uncharacterized. Here we report a single-cell and spatial transcriptomic atlas of stomachs from 23 vertebrate species with diverse feeding habits (e.g., omnivores, carnivores, and herbivores) and gastric architectures, including monogastric (e.g., primates), two- (e.g., aves), three- (e.g., camel) and four-chambered stomachs (e.g., sheep). We reveal conservation and divergence in cell-type composition, developmental trajectory, evolutionary origins and forces, spatial distribution, gene regulatory network and effect, metabolic signature and disease susceptibility associated with the feeding habits and stomach chamber. The diversification of plant based diets, particularly the expansion of coarse fibrous plants that required enhanced mechanical processing, driving the rapid evolution of specific cells and associated genes. Ruminants have evolved chamber-specific cell types with enhanced expressions of relevant functional genes (e.g., KRT6A in spinous cells of forestomach, LUC7L in SMCs of abomasum, and TSPYL4 in abomasal enteroendocrine cells), which were validated by fluorescence in situ hybridization and spatial transcriptomic profiling. The three cell-specific expressed genes showed significant effects on cell proliferation and migration by RNA interference to knock down their expressions. In particular, the knockdown of LUC7L in SMCs promotes a transition from a contractile to a synthetic phenotype, and Luc7l knock-out in mice delays gastric emptying and impairs gastric motility, demonstrating that this gene is essential for the coordinated multi-chambered gastric motility that underlies rumination in ruminants. These findings elucidate the cellular and molecular adaptations underlying the evolution of stomach coupled with the emergence of diet land plants. Our results provide potential cellular and gene targets for engineering monogastric animals to acquire functional rumination digestive capabilities as well as for medication of gastric mobility.

evolutionary biology↗

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

An agentic framework turns patient-sourced records into a multimodal map of ALS heterogeneity

ALS progression is multidimensional, yet fragmented records and scalar outcomes obscure how patients move through disease states and how those states relate to molecular variation. MEDSTREM converts patient-held medical-record images into standardised longitudinal data, enabling bottom-up cohort construction. Using MEDSTREM-structured records from more than 8,000 AskHelpU participants together with PRO-ACT and Answer ALS, we developed DynaALS, the ALS Disease Dynamics Atlas. DynaALS represents ALS as a dynamic patient-state manifold that captures distinct directions of deterioration and patient movement between them over time. DynaALS retrieved population-referenced future states and decoded them into multidimensional clinical profiles without requiring patient-specific longitudinal histories. Motor-neuron RNA and chromatin profiles linked DynaALS states to developmental and regulatory programs, while neuromuscular-organoid single-cell multi-omics converged on a neural-developmental Netrin-DCC signalling axis across interacting cell types. By coupling MEDSTREM-enabled data construction to dynamic state modelling, DynaALS establishes a transferable patient-state engine for predictive and biologically interpretable disease models.

bioinformatics↗

bioRxiv2026

Histone 3 lysine 9 dimethylation by the G9a-GLP heterodimer requires intranucleosomal product reading

Repressive histone methyltransferases carry a catalytic ("write") domain and a separate domain specialized for recognizing ("reading") the reaction product. This read-write configuration acts as a positive feedback mechanism for epigenetic maintenance and the growth of repressive chromatin domains. Feedback exhibits as catalytic stimulation and is understood to act towards a proximal (trans) nucleosome. Whether this stimulation affects a specific methylation transition and whether it is restricted to trans-stimulation remains opaque. Here, we dissect the positive feedback in the heterodimeric histone 3 lysine 9 (H3K9) mono- and dimethlyase G9a-GLP, which carries two catalytic SET and two product-reading Ankyrin repeat (ANK) domains. We find that reading by both ANK domains is required for H3K9 di-, but not monomethylation on nucleosomes and for tight binding to them. As this read-writing occurs on dilute mononucleosomes, we propose that intranucleosomal feedback occurs for G9a-GLP. Swapping the ANK domains results in loss of dimethylation while maintaining nucleosome binding, indicating catalytic coupling of nucleosome methylation intermediates to reading. Crosslinking mass spectrometry reveals specific G9a surfaces that contact nucleosomal methylation intermediates. Structural approaches reveal how these surfaces position the G9a ANK domain on the methylation-intermediate nucleosome and stabilize G9a-GLP on chromatin during the reaction.

biochemistry↗