Biology subjects

Yan, J.

Publications and source records attributed to Yan, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Cellfm-datasets: A Unified Data Infrastructure for Single-Cell and Spatial Transcriptomics Foundation Model Pretraining

Large-scale cell foundation models are increasingly limited not only by model architecture, but also by the data infrastructure required to repeatedly sample sparse transcriptomic profiles from out-of-core cohorts. AnnData/H5AD has become a standard exchange format for single-cell and spatial omics analysis, yet its HDF5-backed layout is not designed for high-frequency random mini-batch loading under multi-worker and distributed pretraining. We present Cellfm-datasets, a data infrastructure artifact that converts H5AD cohorts into a self-describing compressed sparse row (CSR) memmap layout and exposes the resulting corpus through Hugging Face Dataset and IterableDataset interfaces. The artifact stores a shared gene vocabulary, per-sample metadata, optional spatial coordinates, observation metadata, manifests, and checksums, and reconstructs sparse cell or group records at runtime without dense expansion. A unified sampling abstraction supports random-cell groups, manifest-defined biological regions, and coordinate-based spatial blocks, with deterministic sharding across distributed ranks and data-loader workers. Spatial demonstrations on P14 mouse brain transcriptomics sections illustrate region- and block-level sampling over real anatomical structures. In controlled benchmarks on a public heterogeneous ModelScope scRNA-seq subset, Cellfm-datasets reached 60,571 {+/-} 1,734 samples/s in single-core random loading, scaled to approximately 160,000 samples/s with eight workers, and maintained near-constant process-private memory while reading up to one million cells. By moving sparse single-cell and spatial corpora from model-specific loader code into reusable, validated, and framework-native dataset artifacts, this design may reduce the engineering burden of reproducible cell foundation model pretraining and make repeated training runs, model comparisons, and mixed-modality data reuse easier to standardize. Code availabilityhttps://github.com/PangJiangShuan/cellfm-datasets

bioinformatics↗

bioRxiv2026

Integration of zebrafish pineal transcriptomes reveals cell type-specific timing

The teleost pineal gland is an eye-like photoreceptive organ with a central role in the circadian clock system, primarily through its melatonin-producing photoreceptor cells. However, the functional molecular interactions between pineal photoreceptors, accessory cells predicted to support photoreceptor function, and projecting neurons remain incompletely understood. Here, we integrated single-cell zebrafish pineal transcriptomes with bulk circadian and light-response pineal transcriptomes. Combined analysis of two single-cell datasets identified novel photoreceptor and neuronal subtypes, including parietopsin-expressing cone-like cells and neurons expressing markers of neuronal maturation. Integration with the light-response dataset revealed light inhibition of photoreceptor opsin genes. Integration with circadian transcriptomes from wildtype fish and fish expressing the clock-disrupting dominant-negative CLOCK ({Delta}CLK) in pineal photoreceptors revealed cell-type-specific rhythmicity. Despite comparable expression of {Delta}CLK, photoreceptor subtypes differed in sensitivity to rhythm disruption, with rod-like cells (rods) most severely affected. In neurons, despite the absence of {Delta}CLK expression, rhythm disruption was comparable to that of rods. Moreover, rhythmic neuronal markers and rhythmic photoreceptor markers exhibited a similar circadian pattern, peaking mainly during the early night. These observations suggest that clock function in neurons depend on photoreceptor output. In contrast, accessory cell rhythmic markers were relatively resistant to {Delta}CLK disruption and peaked predominantly around subjective dawn, consistent with partially autonomous clock function. To facilitate comparative analysis of gene expression, rhythmicity and light responsiveness across pineal cell types, we developed the Zebrafish Pineal Transcriptomics Viewer. Our findings reveal a temporally structured and functionally heterogeneous organization of the zebrafish pineal gland.

neuroscience↗

bioRxiv2026

Predicting P-glycoprotein Substrate Status Using a Pretrained Graph Neural Network: A TDC Benchmark Study

P-glycoprotein (Pgp/ABCB1) is a critical efflux transporter that significantly impacts drug bioavailability and multidrug resistance. Accurate prediction of Pgp substrate status is essential for early-stage drug discovery. In this study, we evaluate a pretrained Graph Iso-morphism Network (GIN) with attribute masking on the Pgp_Broccatelli benchmark from the Therapeutics Data Commons (TDC). Our approach fine-tunes a GIN encoder pretrained on approximately 2 million molecules using a self-supervised attribute masking strategy, followed by a multilayer perceptron (MLP) classification head. On the TDC benchmark, our model achieves an AUROC of 0.937 {+/-} 0.004 across five independent runs, ranking second on the leaderboard, as of May 2026. We further compare this approach against an XGBoost baseline using Morgan fingerprints (AUROC 0.912 {+/-} 0.007), demonstrating the advantage of graph-based molecular representations with transfer learning for small-dataset ADMET prediction tasks.

bioinformatics↗

bioRxiv2026

PRISMA: A tensor-based framework for deconstructing the genetic architecture of complex diseases, with application to diabetic retinopathy

Complex-disease GWAS compress heterogeneous, tissue-dependent genetic effects into global locus-level statistics, masking local regulatory routes through which polygenic risk contributes to disease architecture. Here we introduce PRISMA (Polygenic Risk Integration via Summary-statistics Multi-tissue Array-decomposition), a summary-statistics framework for quantifying tissue-resolved genetic heterogeneity hidden within aggregate GWAS signals. PRISMA integrates GWAS and multi-tissue cis-eQTL data using graph Laplacian-regularized block-wise factorization, preserving local linkage disequilibrium topology during decomposition. Applied to diabetic retinopathy, PRISMA deconvolved aggregated polygenic risk into three tissue-biased genetic axes corresponding to vascular-metabolic, systemic immune-inflammatory, and retina-specific neurodegenerative trajectories. The framework prioritized 549 axis-associated targets, including 403 below the conventional genome-wide significance threshold, and showed higher tissue-regulatory resolution than PCA, NMF, and K-means. A height GWAS negative-control analysis supported trait-dependent regulatory reprioritization within a shared eQTL-mappable framework. Independent single-cell transcriptomic analyses supported axis-specific enrichment across fibrovascular, immune, and retinal compartments. Exploratory vitreous humor proteomic and metabolomic profiling further nominated candidate molecular correlates of downstream convergence. PRISMA reframes complex-disease GWAS from aggregate locus discovery toward quantitative, LD-aware mapping of tissue-resolved genetic trajectories.

genomics↗

bioRxiv2026

Cobamide-Dependent Dichloromethane Fermentation by Dehalobacter Reveals a Hidden Acetogenic Route for Organohalide Biotransformation

Halogenated one-carbon (C1) compounds, such as dichloromethane (DCM), drive critical fluxes in global carbon and halogen cycles. While the genus Dehalobacter is canonically defined by obligate organohalide respiration, its physiological and ecological roles in anaerobic C1 metabolism have remained fundamentally ambiguous. Here, we document a paradigm-shifting metabolic capacity within a sediment-derived microbial consortium: the autonomous fermentation of DCM by a novel population, Candidatus Dehalobacter formatiformans strain J1. Over successive transfers, strain J1 outcompeted co-existing Dehalobacterium formicoaceticum to become the overwhelmingly dominant population (>80% relative abundance), converting DCM stoichiometrically to acetate and formate (4:1) without auxiliary substrates. Genome-resolved metagenomics revealed that strain J1 couples a distinct mec gene cassette--mediating methyl-transfer reactions during DCM activation--to a complete Wood-Ljungdahl pathway for efficient C1 assimilation. Crucially, strain J1 lacks the complete genetic repertoire for de novo cobamide biosynthesis. Physiological validation confirmed that this fermentative pathway is strictly dependent on exogenous cobamides, exposing a profound reliance on community cross-feeding. These findings reveal an unexpected acetogenic lifestyle within Dehalobacter, a lineage historically viewed as comprising obligate organohalide-respiring bacteria. More broadly, this work identifies cobamide-dependent methyl-transfer metabolism as an ecological control on anaerobic DCM fermentation and expands the known roles of Dehalobacter in carbon-halogen cycling in anoxic environments.

microbiology↗

bioRxiv2026

Decoding Immune Reconstitution Failure in People Living with HIV through Single-cell Genomics

Immune non-responders (INRs), a subset of people living with HIV (PLWH), fail to achieve full immune reconstitution and remain at increased risk of morbidity, mortality, and non-AIDS-related illnesses. As the mechanisms underlying this impaired immune recovery remain poorly understood, we performed single-cell multi-omics profiling on peripheral blood mononuclear cells from 43 INRs, 47 immune responders and 53 healthy donors. Our dataset comprises 2,744,009 transcriptomes and 1,226,658 chromatin-accessibility profiles across 58 identified immune cell types. INRs exhibited markedly elevated inflammatory signaling, increased apoptotic activity, and dysregulation of immune-activating ligand-receptor interactions. Additionally, we identified 2,996 interaction cis-eGenes and 5,938 cis-caPeaks, and validated key cell type-specific cis-xQTL effects associated with immune reconstitution failure. Furthermore, we identified 3,551 sc-eQTLs and 872 cell-state interaction eQTLs (ieQTLs) in CD4+ T cells. A genotype-specific upregulation of OAS3 in INRs, likely regulated by STAT1, was identified based on these results. We developed scPRISM (single cell Predictive Reconstitution Immune Status Model), a multi-modal framework that integrates scRNA-seq and scATAC-seq data to perform the dual tasks of differentiating disease states and predicting gene expression from chromatin accessibility, thereby deciphering the cell type-specific cis-regulatory basis of the disease. Our study delineates the transcriptional and epigenomic landscape of immune dysregulation in INRs and provides new genetic insights into HIV-associated immune reconstitution failure. These findings offer a foundation for developing biomarkers and precision therapies to restore immune function in PLWH.

genetics↗

bioRxiv2026

Prediction of Transcription Factor DNA Binding Affinity with High-Throughput Kd Measurements and Deep Learning

Transcription factors (TFs) regulate gene expression through specific interactions with genomic DNA. While TF binding motifs from public databases describe sequence preferences, quantifying genome-wide affinity (Kd) is highly desirable for a more accurate thermodynamic description. Here, we report ivtFOODIE (in vitro FOOtprinting with DeamInasE), an assay that leverages deaminase-mediated cytosine-to-uracil conversion to measure Kd values for a given TF across accessible genomic regions from human cells. By pre-training on TF binding sites from JASPAR and fine-tuning with our ivtFOODIE data from 46 TFs representing 13 different DNA-binding domains (DBDs), we developed Seq2Kd, a deep learning model capable of predicting a TFs absolute binding affinity on DNA sequences. Seq2Kd enables de novo motif discovery of [~]500 previously uncharacterized human TFs and reveals the effects of genetic variation both in TF-coding regions and DNA-binding sites on gene expression and disease susceptibility. By correlating predicted affinity changes with the sign and magnitude of expression quantitative trait locus (eQTL) effects, we stratified TFs into activator-like and repressor-like groups. Compared to clinically benign variants, pathogenic single-nucleotide variants (SNVs) within regulatory and protein-coding regions show significantly larger predicted shifts in Kd. We provide an interactive web portal, the ENcyclopedia of Transcription-factor Interactions with Regulatory Elements (ENTIRE), which integrates the Seq2Kd model with the ivtFOODIE dataset. This resource offers thermodynamic prediction for TF-DNA interactions for functional genomics and human disease.

biophysics↗

bioRxiv2026

Structure-Property-Performance Engineering of Hydrogel Depots for Long-Acting Peptide Delivery

Controlled release systems for subcutaneous peptide delivery often exhibit a pronounced initial burst release followed by inadequate maintenance of therapeutic exposure, limiting depot lifetime and increasing pharmacokinetic variability. Here, we engineer a dynamic, injectable hydrogel depot technology for months-long delivery of lipidated peptides. Using semaglutide as a model, we establish a modular formulation framework integrating: (i) formulation-driven tuning of depot mechanics to control release kinetics, (ii) cargo complexation strategies leveraging hydrophobic and multivalent ion-mediated interactions, and (iii) oxidative stabilization through sacrificial antioxidant excipients. We evaluated depot performance by rheology, in vitro cargo release, and in vivo pharmacokinetic and pharmacodynamic studies in rodents. Optimized formulations sustained semaglutide exposure for over six weeks from a single administration with two-fold reduction in peak-to-trough exposure and comparable total bioavailability relative to daily dosing, resulting in improved glucose control, weight regulation, and preservation of pancreatic islet content. These results suggest potential for quarterly dosing in humans. Together, this work establishes integrated and generalizable structure-property-performance relationships that account for cargo-matrix and cargo-excipient interactions across burst, diffusion, and erosion regimes to inform a practical formulation framework for engineering long-acting depots for sustained peptide delivery.

bioengineering↗

bioRxiv2026

BiomniBench: Process-level Evaluation of LLM Agents for Real-world Biomedical Research

LLM agents now perform real biomedical research, but evaluating them rigorously is hard. Outcome-only benchmarks fail in two ways. First, a correct final answer can come from memorization, reward hacking, or wrong reasoning that produces the right number by chance. Second, valid alternative analyses are marked wrong simply because they differ from the reference. We introduce BiomniBench, a process-level evaluation framework that scores the full agent trajectory against expert-designed, task-specific rubrics. Our first release, BiomniBench-DA, contains 100 data-analysis tasks across 17 task types, 5 disease areas, and a general-biology category, each based on a paper from journals such as Nature, Cell, and Science and co-developed with an original author or a domain expert. Benchmarking frontier and open-weight models across four agent harnesses reveals three findings. Frontier and open-weight bases cluster within a few points of each other, with substantial headroom for all models. The agent harness shifts scores by more than the gap between successive model generations. Agents reliably ground claims in real sources yet consistently fall short on method selection, biological interpretation, and scientific reasoning. BiomniBench is the first process-level benchmark for LLM agents in biomedical research, providing the dimension-level diagnostics that outcome scoring cannot. Datasethuggingface.co/datasets/phylobio/BiomniBench-DA

bioinformatics↗

bioRxiv2026

Disease-guided functional gene mapping across species reveals translational correspondences beyond sequence orthology

Selecting the correct mouse gene to model a human disease phenotype is critical for translational research, yet sequence-based orthology fails when genes have been lost, duplicated, or functionally rewired between species. Here we present BRIDGE (Biological Rank Integration for Disease Gene Equivalence), a framework that identifies functional mouse equivalents of human disease genes without sequence input. BRIDGE integrates 3.37 million disease-gene associations, biological pathways, and Gene Ontology annotations into a unified heterogeneous graph (94,897 nodes, [~]8.3 million edges), encoded by a heterogeneous graph transformer with fused Gromov-Wasserstein alignment and multi-strategy reciprocal rank fusion. On two sequence-independent benchmarks, BRIDGE achieves Recall@5 of 61.8-66.7%, compared with 0.0-20.1% for Ensembl Compara. We validate BRIDGE through case studies including neutrophil pathway rewiring (CXCL8[->]Cxcl1/2/5), acute-phase divergence (CRP[->]Apcs), and immune checkpoint substitution (LILRB2[->]Pirb), and demonstrate complementarity with sequence methods in drug-translation analysis. Prospective validation of 30 novel predictions against three independent data modalities (tissue expression, cell-type expression, and phenotype concordance) shows that BRIDGE picks are favoured in 64 of 65 orthogonal tests (sign test P = 3.6 x 10-{superscript 1}) and significantly outperform all tested baselines including Ensembl Compara, BLAST RBH, and ESM-2. BRIDGE provides a benchmarked framework for functional cross-species gene mapping in disease-model design.

bioinformatics↗

bioRxiv2026

Talin controls the spatial distribution of vinculin tension in focal adhesions

Cells transmit force between the extracellular matrix and the actin cytoskeleton through integrin adhesion complexes centred on talin and vinculin. Vinculin binds talin through -helical vinculin-binding sites (VBS) that are exposed when talin rod domains unfold under force. Dissecting the significance of this interaction has relied heavily on the A50I mutation in vinculin, which has been widely used as a talin-binding-null mutant. Here we show that although the A50I mutation abolishes binding to the -catenin VBS, it retains nanomolar-affinity binding to multiple talin VBS. We therefore designed an improved mutant, I12K/A50I, that eliminates this residual talin binding. Biochemical assays and single-molecule stretching experiments demonstrate that I12K/A50I VD1 fails to bind talin even when VBS are exposed by force. Using vinculin tension and conformation sensors, we show that talin binding is required for efficient recruitment of vinculin to focal adhesions and for establishing spatial gradients of vinculin tension. However, vinculin can still experience mechanical load in the absence of talin binding. These results demonstrate that A50I is not a talin-binding-null and reveal that, while talin is not required for vinculin loading, it is essential for organising the spatial distribution of mechanical load within adhesion complexes.

cell biology↗

bioRxiv2026

Whole-body 3D kinematics of freely behaving Drosophila

Understanding how nervous systems generate coordinated movement requires precise measurement of body kinematics during natural behavior. The fruit fly, Drosophila, is a model organism with sophisticated behavior and well-studied neural circuits, but tracking fly movements in 3D remains challenging because of their teeny bodies, rapid movements, and frequent self-occlusions. Here we present a pipeline for markerless, full-body 3D pose estimation of fly terrestrial behavior, combining seven synchronized high-speed cameras to capture whole-body kinematics at 800 frames per second. We trained a hybrid 2D/3D deep learning model to track 50 keypoints, then refined them to produce anatomically feasible kinematic trajectories through a retargeting process that solved an inverse kinematics problem constrained by a biomechanical body model. Analysis of 3D kinematics revealed that flies perform grounded running across their full speed range, without transitioning between discrete gaits. Using multi-animal tracking, we found that courting males coordinate both wings during song and modulate body pitch to track the female's vertical position. Our open-source pipeline and large 3D kinematic dataset of fly behavior provide a foundation for neuromechanical modeling and mechanistic studies of motor control in a genetically tractable model organism.

neuroscience↗

bioRxiv2026

Physics-informed self-supervised learning enables spectra-free multiplexed imaging on standard fluorescence microscopes

Multiplexed fluorescence imaging is limited by spectral overlap and the small number of excitation or emission channels available on standard microscopes, restricting most laboratories to low-plex imaging. Here we introduce physics-informed spectra-free multiplexed imaging (PhySMI), a self-supervised framework for underdetermined spectral unmixing that enables highly multiplexed imaging without dense spectral measurements after training. By embedding the spectral forward-mixing process into a self-consistent architecture, PhySMI recovers physically plausible source decompositions from unlabeled data without paired ground-truth labels while suppressing stochastic acquisition noise. PhySMI resolves five subcellular structures from only three excitation channels, overcoming the conventional channel-number limit while preserving spectral fidelity and minimizing crosstalk (<0.5%). The framework also generalizes across imaging systems, enabling zero-shot deployment on standard fluorescence microscopes. In live cells, PhySMI enables fast five-color imaging of dynamic multi-organelle interactions with improved temporal resolution and reduced photobleaching and phototoxicity relative to conventional spectral imaging. These results establish a general strategy for physics-informed learning in underdetermined imaging inverse problems and represent a step toward a general-purpose framework for highly multiplexed fluorescence imaging on standard microscopy platforms.

biophysics↗

bioRxiv2026

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

bioinformatics↗

bioRxiv2026

mChIP-seq for Multiplex and Multifactorial Epigenomic Profiling Uncovers Cancer-specific Histone Features in Cellular and Circulating Nucleosomes

Epigenomic profiling facilitates access to investigate regulatory roles of histone marks in a type-specific cell, and serves as a critical path for discovering noninvasive epigenetic models in cell-free nucleosomes. Here, we present mChIP-seq, an epigenomic profiling technology that is compatible with both cell and cell-free samples for synchronously profiling multifactorial epigenetic landscapes on multiple samples. Combining sample indexing in a single reaction with a pool-and-split strategy for immunoprecipitation, mChIP-seq enhances efficiency and reduces cost. Using mChIP-seq, we profiled H2A.Z and 10 histone modifications in cell lines representing 9 cancer types. Integrative analyses further revealed an atypical association of H2A.Z and H3K4me3 at promoter regions in cancer. Based on mChIP-seq, we developed cf-mChIP-seq for circulating nucleosomes, which requires as little as 25 l of plasma per profile. Profiling 38 plasma samples for H2A.Z, H3K4me3, H3K27ac, and H3K9me3 with cf-mChIP-seq revealed distinct histone mark-associated cfDNA fragment patterns in breast cancer versus healthy control, highlighting the potential of cf-mChIP-seq to expand liquid biopsy methodologies. These results demonstrate that mChIP-seq is a widely applicable technology for large-scale epigenomic profiling of nucleosomes in cellular or cell-free forms.

genomics↗

bioRxiv2026

Repetition-controllable gain-managed nonlinear fiber amplifier enables ultrashort, multiphoton imaging with reduced photodamage

1.We report a repetition-controllable gain-managed nonlinear fiber amplifier (GMNA) that delivers near-infrared 50-fs pulses with pulse energies up to 150 nJ and a widely tunable repetition rate from 1-20 MHz, while maintaining stable pulse quality across the full range. Using this source, we demonstrate label-free multiphoton imaging--including metabolic autofluorescence (2PF/3PF), second/third-harmonic generation, and Simultaneous Label-free Autofluorescence Multiharmonic (SLAM) microscopy imaging--across live cells, human lung spheroids, and hard tissues. We further assess the impact of laser repetition rate on photodamage at fixed pulse energy, supported by preliminary measurements indicating lower damage at lower repetition rate. Collectively, the compact architecture and repetition-rate agility of the GMNA enable real-time optimization of imaging speed, depth, and sample safety for advanced biological microscopy.

biophysics↗

bioRxiv2026

Bacteria-mimetic bioadhesives with multivalent mucoadhesion and drug-compatible delivery

The ability to adhere to mucus-lined tissues underpins a range of biomedical devices and therapies. However, many existing strategies rely on covalent bonding chemistries and can be unstable, cytotoxic, or incompatible with therapeutics. Here, we present a bacteria-mimetic bioadhesion strategy inspired by Vibrio cholerae. A short Bap1-derived adhesion peptide is grafted onto chitosan to strengthen mucus interactions through multivalent, cooperative secondary bonding, while preserving pH-triggered interfacial bridging behavior. Bacterial peptide grafting significantly increases adhesion energy on porcine intestine, and when paired with a tough hydrogel matrix achieves adhesion energies >400 J/m2 without forming covalent bonds to tissue. Confocal imaging reveals deep tissue penetration ([~]80 m) with markedly enhanced mucin binding and no loss of cytocompatibility. Ex vivo intestinal delivery and in vitro drug release tests demonstrate improved drug transport and tissue exposure compared to carbodiimide-mediated covalent bonding strategy. These findings establish a bacteria-mimetic bioadhesion strategy for tissue repair and drug delivery. Novelty StatementBioadhesive designs have drawn inspiration from nature such as mussel-inspired catechol chemistry and gecko-inspired dry adhesion. In contrast, bacterial adhesion mechanisms, despite enabling robust colonization of mucus-lined tissues under demanding conditions, remain largely overlooked. This work introduces a bacteria-mimetic bioadhesion strategy that selectively repurposes a short, non-pathogenic peptide derived from a Vibrio cholerae adhesin to enhance bioadhesion through multivalent, cooperative physical interactions rather than covalent bonding. This strategy significantly toughens adhesion even on chitosan, a polymer already rich in adhesive functional groups. By decoupling bacterial adhesion function from pathogenic risk, this study establishes bacterial adhesion peptides as a safe, modular, and mechanistically distinct foundation for next generation bioadhesives with improved drug compatibility.

bioengineering↗

bioRxiv2026

Light-sheet excitation-encoded volumetric spectroscopy for fast multiplexed imaging and quantitative physicochemical mapping in cells

Understanding how cellular organization relates to function requires imaging approaches that resolve structural and physicochemical information throughout three-dimensional volumes. Here we introduce light-sheet excitation spectroscopic microscopy (LS-ExSM), a method that captures full excitation spectra with [~]10 nm spectral resolution at each voxel for multiplexed imaging and quantitative spectroscopic readout. Excitation-wavelength encoding makes this spectroscopic readout compatible with single-objective wide-field light-sheet imaging while maintaining high spectral fidelity. Combined with sparse volumetric sampling and deep-learning-based reconstruction, LS-ExSM achieves high-speed volumetric imaging while preserving structural and spectral information. We demonstrate six-color volumetric imaging of live cells at near-second temporal resolution with minimal crosstalk, enabling visualization and quantitative analysis of dynamic organelle interactions in three dimensions. We further apply LS-ExSM to three-dimensional mapping of lipid-droplet polarity at near-diffraction-limited resolution. Together, these results establish excitation-encoded volumetric spectroscopy as a route to whole-cell imaging of subcellular organization and physicochemical state.

cell biology↗