Biology subjects

Zhou, J.

Publications and source records attributed to Zhou, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Localized Chromatin Rewiring Shapes Rhythmic Transcription within a Stable 3D Genome in Rice

How spatial genome architecture coordinates rhythmic gene transcription across scales in plants remains poorly understood. Here, we integrated bridge linker Hi-C (BL-Hi-C), H3K9ac ChIP-seq and RNA-seq at six time points across a 24-h circadian cycle in rice. While global higher-order structure was largely invariant, with 84.5% of A/B compartments and over 95% of topologically associating domains (TADs) conserved across all time points, we identified 557 dynamic compartment transitions coupled to changes in active chromatin marks. Within this stable structural scaffold, 35.8% of TADs containing clustered loci of multiple rhythmic genes with synchronized expression phases. In contrast, fine-scale intradomain contacts and chromatin loops exhibited pronounced temporal plasticity. Chromatin interactions connected rhythmic genes with diverse expression phases. Myb-binding motifs were enriched at chromatin interaction anchors at dawn and early day (ZT0 and ZT4). Together, our findings demonstrate that localized chromatin rewiring occurs within relatively stable architectural frameworks, providing a spatiotemporal context for circadian gene regulation in rice.

genomics↗

bioRxiv2026

Predicting cell-state-specific gene regulation from DNA across species

DNA encodes the cell's phenotype, but the same DNA sequence is interpreted differently in every cell type, developmental stage, and activity state. Existing deep-learning models solve only half of this puzzle: sequence-to-function models read DNA but treat cell state as a fixed label, while single-cell foundation models represent cell state but not sequence. We developed Bolero, a sequence-to-function model whose parameters condition on atlas-scale cell-state embeddings and additional biological variables to predict cell-state-specific chromatin accessibility and transcript abundance from DNA. Trained on 10.7 million cells from 33 datasets across six mammals, Bolero matched or exceeded specialised models and resolved regulatory differences between closely related cell states, including cortical regional and developmental gradients. Extrapolating cell-state embeddings extended Bolero's predictions to cell states that are absent from training. Cell-state conditioning facilitated tracing of the effects of non-coding variants to the cell types in which they act. Using an AP-1 activity score as a further continuous conditioning axis, Bolero predicted activity-regulated chromatin changes without requiring actual stimulation data. Across human cell types, we reconstructed the cooperative grammar of motif identity and spacing underlying predicted AP-1 responses. Bolero further predicted cell-type-specific chromatin accessibility and AP-1 responses in species excluded from training, extending comparative genomics from sequence conservation to predicted functional conservation. Across 84 mammals, we found that predicted AP-1 responses were less conserved than chromatin accessibility at cis-regulatory elements, driven by the human-specific sequence variants on AP-1 and cooperating motifs. By combining reference genomes with reference cell atlases, Bolero allowed genome sequences, either native or mutated, to be queried across cell states, perturbations, and different species for their chromatin and expression signatures.

genomics↗

bioRxiv2026

BioMINTREL expands biological problem solving through cross-disciplinary representations

Biological research agents often rely on familiar analytical methods, limiting the questions explored in existing data. We introduce BioMINTREL, an AI agent that finds methods from other disciplines and connects them to biological measurements. It can draw on relationships among datasets and complementary ideas from several methods to develop new questions and analyses. We evaluate it on BioMDC-Bench, which assesses 228 possible applications of computational methods to biological tasks. Compared with GPT-5.4 mini and Qwen3.6-35B, BioMINTREL improves F1 agreement with human experts by up to 45.3 percentage points and increases coverage of applications supported by studies published in subsequent years by up to 58.8 percentage points. Four case studies on real data go beyond previous analyses to reveal dose-specific variant effects, constraints on spatial cell communication and routes through perturbation-response states, and identify the sampling needed to test whether rare high-state cells predict later lineage size. These results highlight BioMINTREL's potential to expand questions in existing data and suggest directions for subsequent research.

bioinformatics↗

bioRxiv2026

MYC drives endothelial transformation and dedifferentiation in angiosarcoma

MYC amplification is the most common genetic alteration in radiation-associated angiosarcoma and is also present in a subset of de novo primary angiosarcoma, yet its oncogenic role in this vascular malignancy remains unclear. Here, we report that inducible, endothelial-specific MYC overexpression alone was sufficient to drive angiosarcomagenesis in mice. The resulting pulmonary angiosarcoma tumors exhibited impaired endothelial differentiation, indicative of MYC-driven lineage plasticity. Notably, MYC inactivation in dedifferentiated allografts induced growth arrest and restored endothelial gene expression, demonstrating MYC-dependent reversible cellular plasticity. Integrative analyses of MYC ChIP-seq, ATAC-seq, and RNA-seq revealed that overexpressed MYC preferentially occupied highly accessible promoters and activated genes involved in cell growth. Consistent with the murine findings, MYC-amplified human angiosarcomas were enriched for MYC target and cell-cycle programs and depleted of endothelial lineage signatures. MYC overexpression in human endothelial and angiosarcoma cells recapitulated these transcriptional changes, enhanced proliferation, and impaired endothelial tube formation. Together, our results establish MYC as an oncoprotein that promotes endothelial lineage plasticity to drive angiosarcoma initiation and progression.

cancer biology↗

bioRxiv2026

Anti-defense systems of viral communitie vary with genomic traits, host immunity and environmental stress

Abstract: Background Despite the ecological importance of viral anti-defense systems (ADS), yet how viral ADS repertoires vary across microbial ecosystems remains poorly understood. Here, we integrated global virus datasets with metagenomes from heavy metal-contaminated soils and chromium-stress microcosms to investigate ecological determinants of viral ADS. Results We found that ADS repertoires were constrained by viral genome capacity, with most ADS-carrying viruses encoded a single system, whereas multi-ADS occurred mainly in larger genomes. Viral lifestyle partitioned ADS, with temperate viruses enriched in ADS targeting early-stage defenses and virulent viruses preferentially encoding ADS against mid- and late-stage defense. ADS composition also varied by host lineage and across habitats, linking viral ADS repertoires to host immune landscapes and ecosystem-specific viral strategies. Moreover, along the heavy metal contamination gradient, changes in the prevalence of ADS-carrying viruses occurred alongside shifts in prokaryotic defense profiles. In addition, expanded ADS repertoires may further increase dissemination potential of MGE-borne adaptive genes, highlighting viral ADS as a potential contributor to the adaptive evolution of microbial community. Conclusions These findings reveal that viral ADS are shaped by genomic traits, host immunity and environmental stress, with implications for virus-host interaction and microbial adaptive evolution. More broadly, this study extends anti-defense research beyond individual virus-host systems, offering an integrative, community-level perspective to guide future studies testing whether and how virus-prokaryote immune interactions contribute to microbial community dynamics and evolution in changing environments.

microbiology↗

bioRxiv2026

The OxyR-Responsive TacAT Module Promotes Oxidative Stress Adaptation and In Vivo Fitness in K. pneumoniae

During infection, Klebsiella pneumoniae must withstand host-derived oxidative stress not only by detoxifying reactive oxygen species (ROS) but also by repairing oxidative damage to cellular physiology. How these detoxification and stress-adaptive responses are coordinated remains unclear. Here, we identify a plasmid-encoded tacAT locus as an OxyR-responsive regulatory module that supports peroxide tolerance and in vivo fitness in K. pneumoniae. Guided by the conservation of the OxyR DNA-binding domain and its consensus binding motif, we surveyed promoter-proximal regions of the K. pneumoniae CRK3022 chromosome and plasmids for candidate OxyR-responsive loci. This analysis identified tacAT, a putative type II toxin-antitoxin (TA) module, as the only plasmid-associated candidate. OxyR directly bound the tacAT promoter and contributed to tacAT induction during hydrogen peroxide (H?O?) stress. The TacAT complex exhibited DNA-binding and transcriptional regulatory activity, promoting the expression of genes linked to protein quality control and membrane/envelope homeostasis, including clpB, htpG, cadC, bhsA, and marA. Deletion of tacAT compromised survival under lethal peroxide challenge and attenuated bacterial dissemination, tissue pathology, and inflammatory responses in a murine bacteremia model. These findings define an OxyR-TacAT regulatory branch that connects peroxide sensing with stress-adaptive gene expression, revealing how a TA-associated module can be integrated into stress-adaptive programs during host-associated oxidative stress.

microbiology↗

bioRxiv2026

Cell-specific heteroclinic orbits govern interaction dynamics in inertial microfluidics for circulating tumour cell separation

Cancer survival rates increase with earlier diagnosis. However, many cancers are diagnosed only after symptoms develop, in the later stages of cancer progression. We need a diagnostic tool that can detect cancer earlier. Circulating tumour cells (CTCs) are cells that detach from the main tumour and can enter the bloodstream. The capture and analysis of CTCs can provide an early indicator of cancer. However, rarity of CTCs in blood makes their efficient capture challenging. Inertial microfluidics can be utilised for a label-free separation of CTCs from white blood cells (WBCs) by manipulating cells in the microchannel based on cell properties, achieving good separation performance. Inertial microfluidic devices mainly rely on size-based separation. However, residual WBC carryover limits complete separation. Here, we show that cell mechanical heterogeneity plays a large role in the separation process and should be considered in the design of these devices. We simulate the migration dynamics of CTCs and WBCs in a straight microchannel using a 3D lattice-Boltzmann-immersed-boundary-finite-element solver. Our results demonstrate that WBC migration behaviour changes depending on the deformability of a CTC. The size and deformability of the cells have been shown to determine the cell-specific heteroclinic orbits leading to different interaction types, these interactions alter WBC migration resulting in more/less WBC carryover. This work highlights that both cell-specific heteroclinic orbits and single cell migration rates should be considered in the design of inertial microfluidics for separation.

bioengineering↗

bioRxiv2026

Spontaneous thought transitions follow a foraging-like policy on high-order memory patches

When people engage in spontaneous thought under low external constraint, thought content changes continuously and can shift across topics. However, the cognitive computational principles governing these transitions remain unclear, as does whether similar dynamics emerge in artificial language generation. Here, we formalized spontaneous narrative dynamics using a foraging-inspired framework, constructing an agent based on the marginal value theorem (MVT) that balances exploration and exploitation while selecting among informational resources represented in a participant-specific, high-order patch network. Patches were defined as partially overlapping composite structures containing second-order paths and third-order interactions, with their available resources quantified using multilevel structure values. In a multi-theme free spoken narrative task, the model predicted the temporal organization of human patch activation substantially better than random-shuffle baselines, although the strength of model conformity varied across participant-theme samples. Model-predicted patch transitions corresponded to systematic semantic segmentation and reorganization across multiple representational levels. Transition rates were positively associated with the perceived importance and likelihood of the narrated events. These findings were replicated when the modeled memory environment was enriched with additional hub-related narrative resources in a supplemental experiment. Control-model analyses further showed that patch-network topology, resource depletion history, or either order of structure value alone could explain only part of the observed dynamics; the most stable human prediction required foraging-related decisions guided jointly by multilevel structure values. Narratives generated by large language models (LLMs) also showed detectable conformity with the same framework, but the full model captured LLM content less consistently, while simpler control models accounted for a larger proportion of their patch-level dynamics. Together, these findings support a computational account in which patch-level narrative dynamics are organized by value-guided foraging over structured memory resources, while revealing graded differences in the strength and stability of these dynamics across human and artificial language generation.

neuroscience↗

bioRxiv2026

ALS-associated exitron splicing produces UBQLN2 isoforms with distinct properties

X-linked amyotrophic lateral sclerosis (ALS) implicated Ubiquilin 2 (UBQLN2) is expressed from a single exon. Here, we show that human UBQLN2 mRNA is alternatively spliced by virtue of a cryptic exonic intron (exitron) spanning the 5' untranslated region and the 5' end of UBQLN2 coding sequence. Splice-out of this exitron generates a spliced UBQLN2 (UBQLN2-Sp) transcript that is translated from codon M243 to produce an N-terminally truncated UBQLN2 isoform (UBQLN2-M243) with reduced stability, diminished proteasome targeting, and altered aggregation behavior following the introduction of ALS mutations. The RNA-binding proteins SRSF1 and PTBP1 control UBQLN2 splicing through binding to splice donor-proximal motifs, while TDP-43 was implicated as an indirect splicing repressor. UBQLN2 splicing was elevated and inversely correlated with UBQLN2 gene expression in the medial motor cortex of male ALS patients. These findings suggest that alternative splicing regulates UBQLN2 gene dosage and function, which may impact UBQLN2-ALS proteinopathy.

molecular biology↗

bioRxiv2026

siProGenA: Generative siRNA Candidate Construction via Position Proposal and Guide Generation

Small interfering RNAs (siRNAs) are short guide RNAs that recruit the RNA-induced silencing complex (RISC) to complementary target sites on messenger RNAs (mRNAs), triggering Ago2-mediated cleavage and gene silencing. siRNA design requires compact candidate sets that cover a target while preserving efficacy, specificity, and practical sequence constraints. Existing pipelines usually enumerate candidate windows, assign a canonical guide to each window, and then rank preconstructed siRNA--mRNA pairs. This has produced strong pairwise efficacy predictors, but leaves a candidate-construction gap: candidate positions and guide sequences are fixed before the model begins to rank them. We address this gap by decomposing siRNA candidate construction into two generative decisions: where to place candidates within an mRNA segment, and what constrained guide variants to consider at a candidate position. We instantiate this framework as siProGenA, using a Discrete Denoising Diffusion Probabilistic Model (D3PM) for mRNA-conditioned position proposal and a Bayesian Flow Network (BFN) for temperature-controlled guide generation. On 62 positive test segments, the diversity-aware final library reaches Hit@1 = 0.790 and Hit@5 = 0.903. In a measured-site controlled Stage~2 evaluation, seed- and cleavage-preserving variants outscore the canonical complement for 89.8% of measured sites, with supporting gains across additional computational scorers, random-mismatch controls, and biophysical diagnostics. Together, the results support a modular proposal--generation view of siRNA candidate construction for prioritizing compact candidate sets.

bioinformatics↗

bioRxiv2026

A cognitive representation in primary visual cortex modulated by vision

Primary visual cortex (V1) is a critical substrate for mammalian vision. Traditionally, visual inputs are thought to be the main drivers of V1 activity, with internal signals playing a modulatory role. Here we show that this relationship is inverted for a large fraction of V1 neurons. In rats completing a navigation task in darkness, these neurons encoded progress along physically distinct paths with a shared turn structure. Under illumination, visual stimuli gain-modulated this path-invariant activity rather than replacing it with stimulus-driven responses. Path-invariant V1 neurons were also preferentially coordinated with hippocampal ensembles during sharp-wave ripples, linking them to a brain-wide network involved in learning. These findings establish that an internal model of the world can serve as a primary driver of activity in sensory cortex.

neuroscience↗

bioRxiv2026

A pretrained unified model enables cellular functional profile prediction and multi-objective virtual drug screening

Cells are characterized by molecular states, coordinated molecular interactions, regulatory programs, and responses to perturbations. Systematic mapping of these cellular functional profiles across biological contexts remains experimentally costly and fragmented. Here we present InsilicoCell, a pretrained multi-modal, multi-task model that unifies prediction of cellular functional profiles spanning molecular states, molecular interactions, and perturbation-induced responses. Built on a supervised transformer architecture and pretrained on more than 88 million measurements across seven tasks, including drug sensitivity, drug-induced gene expression, and drug-protein binding, InsilicoCell learns a shared representation that links molecular profiles to cellular phenotypes, improves performance over task-specific models, and generalizes to unseen entities, contexts, and conditions. InsilicoCell extends beyond cell line systems to patient, spatial and single-cell settings, and enables multi-objective virtual drug screening. It identifies novel candidate compounds with experimental validation, including c-Myc activity inhibitors, antifibrotic agents and stemness-inducing compounds. Together, InsilicoCell provides a scalable framework for predictive cellular biology and therapeutic discovery.

bioinformatics↗

bioRxiv2026

Synthesis cost is a hidden driver of convergent amino acid composition in plastid ribosomal proteins

Protein evolution is a walk in the evolutionary space directed by mutation and selection. While functional and structural constraints serve as the main determinant of amino acid substitution in most proteins, synthesis cost and mutational bias can also alter the direction and rate of amino acid evolution, especially in systems experiencing relaxed selection. Here, we focused on the highly expressed plastid ribosomal proteins (PRP), which comprise 58 conserved proteins encoded by both plastid and nuclear genomes. Relaxed selection has been repeatedly identified in three distantly related plant lineages, providing a valuable comparative framework to investigate the significance of synthesis cost and mutation. We first demonstrated that the hemiparasitic tribe Cymbarieae (Orobanchaceae) represented a new case where concerted cyto-nuclear rate elevation occurs in their PRP. Further investigation revealed convergent shifts in amino acid composition in all four plant lineages attributable to arginine-to-lysine and methionine-to-isoleucine/valine/leucine substitutions. The replacement residues were biophysically similar but had lower molecular weight and shorter side chains, which significantly destabilized protein folding as demonstrated by protein structure modeling. We found that the composition shifts ran counter to the expectation of mutational bias but were consistent with the expectation of synthesis cost minimization, which is potentially adaptive for highly expressed PRP. Further, cost minimization significantly influenced all conservative substitutions between biophysically similar amino acids but was absent in non-conservative substitutions. We thus propose cost minimization as a secondary selective drive for protein evolution in PRP, unmasked in lineages and sites with relaxed selection on their function.

evolutionary biology↗

bioRxiv2026

Emerging Haemagglutinin Mutations in Bovine-origin H5N1 Influenza Viruses from Humans and Cattle Retain Avian Receptor Binding with Increased Stability

The current H5N1 panzootic has seen an unprecedented host range expansion, including sustained circulation in US dairy cattle, detected in March 2024. By July 2026, infections had been reported on more than 1,150 dairy farms across 19 states. Although the outbreak initially centred in Texas, California has emerged as the principal focus of transmission and accounts for most human infections associated with exposure to infected dairy cattle. Continued transmission in cattle and repeated spillover into humans increase opportunities for acquisition of mammalian-adaptive mutations that could elevate zoonotic and pandemic risk. The haemagglutinin (HA) protein plays a central role in modulating virus receptor binding and airborne transmission. Here, we characterised the receptor-binding and stability phenotypes of HA mutations identified in viruses circulating in Californian dairy cattle. Receptor-binding specificity was assessed using bio-layer interferometry and pseudotype virus entry assays. All tested HA variants maintained a preference for avian-type 2,3-linked sialic acid receptors. We evaluated HA stability using fusion and thermostability assays. All mutants exhibited fusion pH values >5.5, outside the range associated with efficient airborne transmission in humans (pH 5.0-5.5). However, mutations D88G and S94N increased pH stability, with fusion pH values of 5.6 and 5.7, respectively, compared with 5.9 for wild-type HA. Viruses harbouring both mutations displayed increased thermostability. These findings demonstrate that cattle-origin H5N1 viruses retain avian-like receptor specificity despite acquiring mutations that modestly enhance HA stability. Evolution of H5N1 viruses in dairy cattle underscores the importance of genomic and phenotypic surveillance to identify mutations that may increase zoonotic risk.

microbiology↗

bioRxiv2026

Dopamine serves as an independent gain regulator to shape striatal control of movement

Dopamine (DA) drives locomotion by modulating striatal activity in the dorsolateral striatum (DLS). How DA regulates motor function on subsecond timescales remains poorly understood. To address this question, we performed spectrally resolved triple-color fiber photometry, allowing us to simultaneously monitor DA, glutamatergic inputs, and pathway-specific spiny projection neuron (SPN) activity in freely moving mice during spontaneous behaviors as measured by unsupervised behavior quantifications. We showed that DA dynamics were temporally distinct from spontaneous locomotor kinematics or behavioral states. Instead, peri-event DA levels predicted SPN input-output (I/O) efficacy, defined as SPN activity relative to glutamatergic input, with opposite relationships across pathways: higher DA predicted enhanced efficacy in direct-pathway SPNs but reduced efficacy in indirect-pathway SPNs. Paired with unsupervised behavior quantification, elevating extracellular DA with methylphenidate, a DA re-uptake inhibitor, shifted direct-pathway SPNs toward higher I/O gain and indirect-pathway SPNs toward lower neuronal activity outputs, while biasing behavior toward selected mobile and turning states. Reserpine administration, which mediates DA vesicular depletion, resulted in the opposite efficacy shifts and increased occupancy of immobile states. Together, these findings support that DA does not simply encode spontaneous movement, but acts as a pathway-specific gain controller that dynamically tunes the transformation of glutamatergic input into SPN output to bias locomotor-state transitions in vivo.

neuroscience↗

bioRxiv2026

Handwritten Digit classification with neural cultures is influenced by neural architecture, network dynamics, and decoding methods

As silicon-based computing approaches fundamental physical limits, neurocomputing offers an energy-efficient alternative by leveraging the intrinsic non-linear dynamics of biological systems. To harness these dynamics, it is vital to understand the structure-function relationship governing how neural cultures process complex spatio-temporal information and how to appropriately decode the resulting neural electrophysiological activity. We investigated this utilizing a closed-loop electrophysiology platform, the CL1, to implement reservoir computing in human iPSC-derived neuronal networks. To systematically evaluate the variables driving neurocomputational capacity, we explored how cellular composition (cortical vs. hippocampal lineages), and the physical architecture (unstructured monolayers, 3D neural organoids, and modular networks confined by microfluidic devices) influenced electrophysiological properties and interacted with different decoding methodologies. Using a spatio-temporal version of a handwritten digit pattern recognition task (MNIST), we analyzed how these biological and analytical factors influenced classification accuracy. To ensure robust interpretation this required us to first demonstrated that reservoir computing decoding methods require strict artifact control and trial-based cross-validation to distinguish network computation from artifactual signal separability or temporal data leakage. Applying this validated frequency-domain pipeline, we suggest a clear functional hierarchy where structural modularity acts as a vital functional regularizer. Modular cortical cultures significantly outperformed unconstrained monolayers and organoids on MNIST. Furthermore, decoding frequency information from raw signals proved superior to typical time-bin decoding implementations. These findings establish that maximizing the computational potential of Synthetic Biological Intelligence, while avoiding false positives, requires a synergistic optimization of cellular identity, structural governance, and rigorous decoding logic. In doing so, this work provides a critical base establishing the criteria under which to evaluate neurocomputing implementations.

neuroscience↗

bioRxiv2026

3D printing and deep learning enable holistic and dynamic analyses of tens of thousands of parasites infecting hundreds of genotypes

Host-parasite interactions are dynamic systems, where parasites usually outnumber hosts by one or more orders of magnitude. However, our understanding is often limited by the assessment of parts of the host, at arbitrary time points, and/or aggregate parasite responses. Here we combined custom-built 3D-printed hardware and deep-learning-based algorithms to enable holistic (i.e. all infecting individuals on the whole plant), spatio-temporal, and parasite-centric analyses of plant-parasitism by nematodes from timelapse videos of infection over months. In so doing, we tracked the dynamic growth and development of all individual parasites, at the organismal level, for thousands of hosts across hundreds of genotypes of Arabidopsis thaliana. Categorising traits into the static (i.e. in an acquired image at a given time point) and dynamic (i.e. phenotypic changes over time), we revealed a greater extent of host-genetic control of parasite traits, and new physiological limits of the species under these conditions. Using this capability, we identify Quantitative Trait Loci (QTL) in the host plant associated with 18 phenotypic traits in the parasite as a resource for the community. Finally, we leverage the large and diverse dataset to understand fundamental features of the parasite, independent of host genotype, revealing aspects of the life cycle which are pseudo-deterministic as well as local, deleterious interactions between co-infecting parasites. Given that plant-parasitic nematodes cause an estimated $100 billion in agricultural damages per year, these insights are contextualised in a global challenge driven by plant-parasitic nematodes.

pathology↗

bioRxiv2026

Integrative spatial profiling of 3D genome organization and gene expression in tissue

The interplay between 3D genome architecture and transcriptional activity is fundamental to gene regulation. However, existing methodologies cannot simultaneously measure these modalities within intact tissues, limiting our understanding of how genome organization coordinates transcriptional programs across diverse cell types and spatial microenvironments. Here, we introduce Spatial Hi-C-RNA, a spatial multi-omics technology that enables the genome-wide co-mapping of chromatin conformation and transcriptome directly from the same tissue section at near- single-cell resolution. Applied to the mouse embryo and adult brains, Spatial Hi-C-RNA generated high-resolution tissue maps revealing that chromatin organization and gene expression jointly define spatially coherent domains aligned with histological structures. While concordant features were observed across modalities, distinct domain patterns also emerged, indicating that chromatin structure and transcription each contribute complementary layers of spatial regulation. We further demonstrated the robustness and biological insight of Spatial Hi-C-RNA in human melanoma, where both modalities delineated tumor boundaries and microenvironmental niches. Notably, chromatin maps revealed fine-scale tumor subdomains undetectable by transcriptomic profiling alone, highlighting the added resolution provided by spatial chromatin architecture. Integrated analysis revealed that multiscale 3D genome features, from A/B compartments and topologically associating domains to chromatin loops, are closely coupled with domain- and cell-type-specific transcriptional programs. In addition, Spatial Hi-C-RNA resolves spatiotemporal dynamics underlying embryonic lineage specification and tumor progression. Together, these capabilities extend the spatial omics landscape beyond transcriptome and epigenome profiling to the level of chromatin organization, establishing an integrative framework for understanding tissue biology across development and disease.

genomics↗