Biology subjects

Huang, Y.

Publications and source records attributed to Huang, Y..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Proteomic Identification of Pig Xenoantigens for Clinical Xenotransplantation

Xenotransplantation using genetically engineered pig organs offers a promising solution to the shortage of donor organs for life-saving transplants. However, human preformed antibodies against unknown pig xenoantigens remain a significant barrier to successful xenotransplantation. Current methods for characterizing these antibodies or xenoantigens are limited to cellular-level crossmatch assays. In this study, we developed a novel approach to identify pig xenoantigens, including peptide and glycopeptide epitopes that react with human preformed antibodies. First, human preformed antibodies against xenoantigens were enriched from plasma using immobilized pig kidney proteins. The enriched antibodies were then immobilized and used to isolate pig kidney proteins, peptides, and intact glycopeptides, followed by liquid chromatography-tandem mass spectrometry analysis. This dual-level approach identified 221 peptides corresponding to 153 proteins, with a significant enrichment of plasma membrane and extracellular proteins. Notably, 11 peptides were unique to pig sequences, suggesting their potential role in driving xenogeneic immune responses. Glycoproteomic analysis identified 122 intact glycopeptides, predominantly complex/hybrid glycoforms and Neu5Gc-containing glycans. Our method effectively identifies peptides and intact glycopeptides reactive to human preformed antibodies, providing critical insights for discovering xenoantigens. These findings could guide genetic engineering strategies and enhance recipient candidate screening for xenotransplantation, ultimately increasing the feasibility and success of xenogeneic organ transplantation.

biochemistry↗

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

Click Chemistry-Based Strategy for Modular Ligand Attachment to siRNAs: Toward Extrahepatic RNAi

Efficient extrahepatic delivery of siRNAs remains a major limitation for broadening their therapeutic potential. Using a modular, orthogonal click chemistry platform, we generated 28 siRNA conjugates varying in ligand class, valency, and spatial arrangement. Following systemic administration, fatty acid conjugates - particularly palmitic acid (C16) - outperformed sterol- and phospholipid-based designs in promoting extrahepatic gene silencing, with preferential activity observed in heart and skeletal muscle. Increasing ligand valency through 3',5'-bis-conjugation generally enhanced activity compared to 5-mono conjugation. Nevertheless, bis-C22 conjugates showed increased hepatic activity, suggesting a shift in tissue distribution linked to hydrophobicity. Architectural parameters further modulated outcomes: Branched 5' C16 conjugates, bearing two lipids on one terminus, were markedly less active than their bis counterparts and required short PEG spacers to restore activity. Notably, bis-lipid conjugation strategies that enhanced extrahepatic activity for an siRNA did not translate to an ASO gapmer, underscoring modality-specific constraints. Together, these findings delineate structure-activity relationships and establish bis-fatty-acid conjugation as a robust design principle for achieving extrahepatic RNAi. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/726808v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b7af11org.highwire.dtl.DTLVardef@146b9c6org.highwire.dtl.DTLVardef@4e8948org.highwire.dtl.DTLVardef@16fa07_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

A Comprehensive Computational Model of the Human head for Designing and Optimizing Visual Brain-Machine Interfaces

Brain-machine interfaces (BMIs) for vision restoration require models that accurately simulate the anatomy and electrical properties of visual pathways. However, current models focus only on isolated structures, such as the retina or brain, and overlook surrounding tissues. Here, we present a comprehensive computational model of the human head, incorporating the entire visual pathway--including the eye, optic nerve, and brain--along with critical neighboring tissues such as the orbit, paranasal sinuses, enabling precise simulations. Validation using human and large animal data demonstrated a strong correlation between the simulated and measured electrical potentials. Component elimination analysis revealed that the optimized comprehensive model outperformed simplified versions. The models utility was demonstrated through multiple applications: (1) comparative analysis of electrical neuromodulation technologies for optic neuropathy, revealing the filed intensity limitations of noninvasive approaches and the safety concerns of invasive intraorbital approach; (2) identification of optimal stimulation site, revealing that transnasal stimulation at the optic chiasm outperformed traditional approaches; and (3) in silico design of electrode arrays for optic nerve prosthetics, demonstrating theoretical advantages in invasiveness and visual field coverage compared to existing retinal and cortical prosthetics. This validated and versatile computational resource supports the development of neuromodulation strategies and visual BMI technologies.

bioengineering↗

bioRxiv2026

Nature-inspired nanodiscs for lesion-targeted delivery reprogram macrophages and attenuate established abdominal aortic aneurysms

Abdominal aortic aneurysm (AAA) is a life-threatening vascular disease characterized by chronic inflammation and immune dysregulation, with lesional macrophages playing a pivotal role in disease progression. However, effective and safe delivery of immune modulators to macrophages at the site of AAA remains a major clinical challenge. To address this unmet need, we report a nature-inspired nanodisc platform based on high-density lipoproteins for targeted delivery to lesional macrophages, further engineered with a multi-component targeting strategy incorporating an aneurysm-homing peptide and phosphatidylserine lipids. Nanodiscs encapsulating an anti-inflammatory protein kinase R-like endoplasmic reticulum kinase (PERK) inhibitor remarkably attenuated progression of established AAA in an elastase-induced mouse model. Using a combination of in vivo biodistribution and immune profiling approaches, we demonstrate that nanodisc-assisted PERK inhibitor delivery selectively reprograms the local immune microenvironment and attenuates pathological inflammation in AAA disease models. Notably, a single administration achieves sustained therapeutic efficacy with favorable safety profiles, effectively limiting the progression of established AAA in a clinically relevant setting. This work presents a new avenue of designer nanomedicines for targeted immunomodulation and maybe broadly applicable for a wide range of vascular and immune-mediated pathologies.

bioengineering↗

bioRxiv2026

A Panel of Human Monoclonal Antibodies for Tracking the Antigenic Evolution of Influenza H5N1 Clade 2.3.4.4b

The ongoing panzootic of clade 2.3.4.4b H5N1 influenza has resulted in widespread infection of birds, mammals, and livestock, underscoring the need for tools to interpret its real-time evolution. Here, we describe the isolation and characterization of a panel of 19 human monoclonal antibodies that potently neutralize current isolates. Competition immunoassays and cryo-electron microscopy analyses revealed their collective near-complete epitope coverage of the H5-hemagglutinin surface. Neutralization profiling across multiple historical and contemporary H5 viruses defined their epitope-specific patterns of virus neutralization. One cluster of antibodies potently neutralized only clade 2.3.4.4b viruses, while many others exhibited broadly neutralizing activity against diverse H5N1 clades. Application of this structurally calibrated antibody panel to recent North American human isolates revealed genotype-specific antigenic divergence between lineages that have spread among cattle (B3.13) and poultry (D1.1). Together, the findings of this study establish a structurally grounded antibody reference panel spanning major vulnerable sites of H5 hemagglutinin and provide a toolbox for interpreting emergent mutations, monitoring ongoing antigenic drift, and anticipating the evolutionary trajectory of circulating H5N1 influenza viruses.

microbiology↗

bioRxiv2026

Microbiome-mediated lauric acid turnover contributes to colonization resistance against Ralstonia in the tomato rhizosphere

Host-associated lipids shape plant-microbiome interactions, but their microbiome-mediated turnover and role in pathogen exclusion are not fully understood. In tomato, lauric acid (C12:0) occurs across root-associated compartments, and its abundance at the root-soil interface is linked to soil-dependent differences in rhizosphere community composition and Ralstonia solanacearum colonization. Lauric acid promotes Ralstonia motility and virulence in vitro while inhibiting some beneficial lauric-acid-sensitive taxa, especially Gram-positive antagonists. In disease-suppressive soils, rhizosphere communities enriched in lauric-acid-degrading taxa are associated with lower local lauric acid levels and reduced pathogen colonization, suggesting community-dependent buffering of the rhizosphere lauric acid pool alongside other protective microbiome functions. Soil perturbation, microbiome transplantation, gnotobiotic SynCom reconstruction, and isotope tracing provide convergent evidence that microbiome-mediated lauric acid turnover contributes to colonization resistance and helps explain how rhizosphere lipid chemistry influences pathogen invasion outcomes in tomato, revealing an ecologically grounded protective mechanism for pathogen management.

microbiology↗

bioRxiv2026

Dual-view Guided Context-aware Network for Automated Bone Lesion Segmentation and Quantification in Whole-body SPECT

Whole-body SPECT bone scintigraphy reflects skeletal metabolic activity throughout the body and plays an indispensable role in the screening, treatment evaluation, and prognostic assessment of bone metastases in tumors. However, the automatic detection and segmentation of hypermetabolic bone lesions remain challenging due to low contrast, limited spatial resolution, and complex lesion distributions. In this study, we proposed Bone-Segnet, a dual-view guided automatic segmentation network for hypermetabolic bone lesions that integrated multi-scale feature modeling, global context modeling, and view-conditioned modulation. Pixel-level annotated anterior and posterior whole-body bone scintigraphy images were used for model training and prediction. The proposed network enhanced the recognition of low-contrast and small-scale lesions through small-lesion enhancement and multi-scale contextual modeling. A Transformer module was further introduced to strengthen global feature representation, while cross-view collaborative modeling was achieved by incorporating the complementary characteristics of anterior and posterior imaging. Experimental results demonstrated that the proposed method outperformed existing approaches across multiple evaluation metrics, with the Dice score improving from 0.7440 to 0.8750, indicating a substantial improvement in segmentation performance. Further quantitative analysis based on the segmentation results revealed significant differences among disease types in lesion count, pixel burden, and spatial distribution patterns, reflecting the heterogeneity of disease-related skeletal metabolic activity. Overall, the proposed method improved automatic lesion segmentation performance and enabled quantitative analysis of lesion burden and spatial distribution patterns, providing objective data support for the assessment of related diseases. Index Terms--Whole-body SPECT, bone lesion segmentation, dual-view modeling, quantitative analysis.

bioinformatics↗

bioRxiv2026

Large-scale activity analysis of gut prophages reveals significantly active lineages in the human gut

Prophages dominate the human gut virome and their transition from lysogeny to lytic replication profoundly affects gut microbial community structure. However, the global activity landscape of gut prophages remains largely uncharted. Here, we developed a workflow to assess prophage activity across more than 30,000 gut bacterial isolates cultured in vitro under standard conditions. Using this approach, 4,033 prophages, corresponding to a small fraction (7.5%) of the overall prophage reservoir, were detected as active, indicating a high-lysogeny, low-activity landscape. Prophage activity varied markedly across host taxa, with Bacillota exhibiting notably elevated active rates. Active prophages derived from 183 viral families (vFAMs), 55 of which were assigned as significantly active (SA-vFAMs), including: SA-vFAM7, a novel, hyper-prevalent lineage with expanded host range into Tannerellaceae and substantial intra-lineage diversity; and Candidatus Mubacviridae (SA-vFAM1), a newly defined, hankyphage-inclusive, highly active Mu-like transposable phage family infecting Bacteroidales, characterized by conserved MuA-MuB operons and cross-family host range. Together, this work provides a large-scale, activity-integrated atlas of human gut prophages, demonstrating that prophage activity is driven by both host phylogeny and viral lineage identity, and establishing a foundational resource for understanding prophage ecology.

microbiology↗

bioRxiv2026

ParSeek: Accurate cryo-EM particle picking with a deep learning model trained on synthetic data

Accurate particle picking from noisy cryo-EM micrographs is essential for high-resolution reconstruction. Current deep learning methods rely on manually annotated data, which is labor-intensive, subjective, and limits particle recall under low signal-to-noise ratio (SNR). Here we introduce ParSeek, an automated picker trained entirely on synthetic data without human annotation. Synthetic micrographs are generated by projecting known 3D structures into realistic background patches that reproduce experimental noise. On seven public cryo-EM datasets, ParSeek outperformed Topaz and CryoSegNet on four datasets, achieving the highest F1-score (up to 0.82) and reaching 0.63 on a challenging membrane protein dataset. Density maps from ParSeek-picked particles showed cross-correlation coefficients up to 0.995 with the reference and a minimal resolution difference of 0.1 [A]. ParSeek also overcame severe orientation bias on an influenza dataset, yielding a reasonable reconstruction. Applied to three experimental datasets (an antibody-antigen complex and two GPCRs), ParSeek enabled reconstructions at 5.0 [A], 4.0 [A], and 2.8 [A], respectively. The 2.8 [A] map resolved side-chain densities and ligand flexibility. This study establishes a fully synthetic-data-driven strategy that eliminates manual annotation for training cryo-EM deep-learning models, paving the way for automated, unbiased particle picking.

molecular biology↗

bioRxiv2026

piRNA direct the chromatin reader to its genomic targets

The chromatin reader Rhino (Rhi) is an HP1-paralog and master regulator of piRNA biogenesis in Drosophila that drives the transcription of piRNA precursors. While Rhi binds the heterochromatic mark H3K9me3, this interaction is insufficient to explain its specific occupancy, as Rhi is excluded from the majority of H3K9me3-enriched loci. Instead, we find that Rhi recruitment is orchestrated by several competing pathways, including the piRNA-guided protein Panoramix (Panx) and the DNA-binding protein Kipferl (Kipf). We demonstrate that Panx functions as a piRNA-guided modular scaffold that directs Rhi to its targets through a dual-mode mechanism: its N-terminus recruits the H3K9 methyltransferase machinery, while its C-terminus directly binds the Rhi chromodomain. While H3K9me3 deposition provides a necessary foundation, it acts in synergy with the direct Panx-Rhi physical bridge to drive robust Rhi occupancy. Furthermore, Rhi recruitment is amplified by the cytoplasmic inheritance of maternal piRNAs, forming a transgenerational feed-forward loop. Our results reveal that the piRNA pathway imparts sequence-specificity to the H3K9me3 code, utilizing a direct physical bridge to program the genomic distribution of the chromatin reader Rhi.

molecular biology↗

bioRxiv2026

BRIDGE: A Multi-organ Histo-ST Foundation Model Enables Virtual Spatial Transcriptomics for Enhanced Few-shot Cancer Diagnosis

Recent studies have explored generating virtual spatial transcriptomics (ST) profiles from histological images, offering a promising alternative to laboratory-measured molecular profiling. However, existing approaches predominantly rely on single-organ models and require sub-stantial organ-specific training data, restricting their accuracy under challenging few-shot conditions in clincical practice, where less than 10 slides are available for specific organs or techniques. Here, we present BRIDGE, a multi-organ foundation model pre-trained on over 600,000 paired histology-ST profiles across 13 human organs and three sequencing techniques. By robustly aligning morphological features and genomic information within a shared multi-organ latent space, BRIDGE can leverage common biological knowledge across distinct tissues to enable accurate and generalizable pan-cancer molecular profiling. Without additional organ-specific fine-tuning, BRIDGE accurately predicts the spatial expression of 80 biomarker genes, achieving an average Pearson correlation coefficient (PCC) of 0.474--a 30% improvement over existing state-of-the-art models under three clinically challenging few-shot scenarios. With generated virtual ST, BRIDGE outperforms current state-of-the-art pathology foundation models in predicting cancer survival, achieving an average concordance index (C-index) of 0.724 across six TCGA cohorts. Notably, BRIDGE maintains exceptional performance even in zero-shot scenarios involving three cancer types not seen during its training, achieving an average C-index of 0.717, thereby demonstrating its strong generalization capability that transcends organ- and subtype-specific boundaries. Moreover, BRIDGE-generated virtual spatial transcriptomes match the prognostic accuracy of bulk RNA-seq, highlighting their potential as a spatially informative alternative to laboratory sequencing. In general, BRIDGE represents a data-efficient tool in virtual ST that facilitates biomedical discoveries in clinical few-shot contexts and advances diagnosis of understudied cancers without sufficient samples.

genomics↗

bioRxiv2026

Rewiring c-Myc Transcriptional Activity with an O-GlcNAcylation Targeting Chimera (OGTAC)

c-Myc is a transcription factor that drives tumorigenesis in many cancers. It is notoriously difficult to directly target c-Myc, mainly due to its lack of well-defined druggable pockets. O-linked {beta}-N-acetylglucosamine modification (O-GlcNAcylation) is a post-translational modification (PTM) playing an important role in regulating c-Myc functions in cancer. However, previous studies have primarily relied on global perturbations to investigate c-Myc O-GlcNAcylation, making it difficult to determine its direct functional consequences due to concurrent cellular effects. Here, we report a bifunctional O-GlcNAcylation TArgeting Chimera (OGTAC) molecule, which can induce the proximity of c-Myc and O-GlcNAc transferase (OGT) in living cells, thereby enhancing the O-GlcNAcylation of c-Myc. The c-Myc-targeting OGTAC exhibits anti-proliferation effect against cancer cells. Mapping of c-Myc occupancy on genome indicates that OGTAC rewires c-Myc transcriptional activity and reprograms expression of the downstream oncogene MALAT1, in an O-GlcNAcylation-dependent manner. Overall, OGTAC presents a novel chemically induced proximity (CIP)-based tool to target and rewire c-Myc activity in cancer. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/722559v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@d1c640org.highwire.dtl.DTLVardef@2eb70corg.highwire.dtl.DTLVardef@f38970org.highwire.dtl.DTLVardef@c421c8_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Serine Stabilizes SLC7A11 and Enhances Cystine Influx to Protect Against Acute Pancreatitis

Lethal sterile inflammatory diseases are linked to amino acid metabolism, but the role of serine remains unclear. Here, we show that dysregulated serine metabolism and reduced plasma serine levels correlate with disease severity of acute pancreatitis (AP) in patients and mouse models. Elevating serine levels via exogenous serine supplementation or pancreatic phosphoglycerate dehydrogenase (PHGDH) overexpression mitigates pancreatic injury, whereas a serine deprivation diet or pancreatic PHGDH knockdown exacerbates AP. Serine prevents cell death and oxidative stress in pancreatic acinar cells, human induced pluripotent stem cells-derived pancreatic organoids and mouse pancreatic tissue. Serine enhances cysteine and glutathione biosynthesis primarily by promoting solute carrier family 7 member 11 (SLC7A11)-dependent cystine uptake rather than by serving as a direct substrate. Mechanistically, the E3 ubiquitin ligase NEDD4 mediates ubiquitination and degradation of SLC7A11, whereas serine binds to NEDD4 and thereby inhibits SLC7A11 degradation. Similarly to serine, pharmacological inhibition of NEDD4 alleviates lipid peroxidation and pancreatic injury. These findings identify serine as a critical signaling regulator of SLC7A11 stability and oxidative stress, and provides a new therapeutic strategy for AP and associated sterile inflammatory disorders. HighlightsAcute pancreatitis (AP) is linked to abnormal serine metabolism and serine depletion. Serine prevents cell death in AP acinar cells, human pancreatic organoids and mice. Serine promotes SLC7A11-dependent cystine uptake and glutathione levels in acinar cells. Serine reduces NEDD4-mediated ubiquitination of SLC7A11. In briefSerine protects against cell death and pancreatic injury in acute pancreatitis by stabilizing SLC7A11 through disruption of NEDD4-mediated ubiquitination in acinar cells.

pharmacology and toxicology↗

bioRxiv2026

Prevotella copri alleviates hyperuricemia via uricolysis and gut microbiota reconstruction

Despite the therapeutic promise of uric acid-metabolizing microbes for hyperuricemia, their application is hindered by the scarcity of highly efficacious strains. To identify uric acid-metabolizing microbes, we enriched gut microbiota from cynomolgus monkeys under uric acid-supplemented conditions. This selection increased the abundance of Prevotellaceae_UCG-001 and norank_f_Prevotellaceae, implicating Prevotella species as key uric acid degraders. In vitro validation confirmed efficient uric acid consumption by two new Prevotella isolates and the reference strain Prevotella copri DSM 18205 (P. copri). Oral administration of P. copri in hyperuricemic mice reduced uric acid levels in serum and kidney tissues and alleviated renal fibrosis by suppressing the TGF-{beta}/Smad pathway and downstream fibrogenic genes. Moreover, P. copri administration restored the depleted population of Faecalibaculum, and further assays demonstrated that Faecalibaculum rodentium directly metabolizes uric acid. Collectively, P. copri alleviates hyperuricemia via a dual mechanism: direct uric acid catabolism and enrichment of commensal uric acid-consuming bacteria. These findings establish P. copri as a promising live biotherapeutic agent for hyperuricemia and highlight microbial collaboration as a therapeutic strategy for metabolic diseases.

microbiology↗

bioRxiv2026

Spatially Resolved Microglial State Transitions Govern Strain-Specific Zika Neuropathogenesis

Neurotropic viruses disrupt brain homeostasis through complex interactions among infected cells, resident immune responses, and tissue architecture, yet how these processes unfold across space, time, and cell types, and how viral strain differences shape disease severity, remains poorly understood. Here, we integrate high-resolution spatial transcriptomics with infection-aware cell-type profiling to construct a spatiotemporal atlas of Zika virus (ZIKV) infection in the mouse brain. Comparing Asian and African ZIKV strains across early and late infection stages, we uncover structured, strain-dependent reorganization of immune and structural cell populations that defines discrete infection-associated tissue niches. Microglia undergo region-specific state transitions characterized by both cell-intrinsic antiviral programs and widespread bystander activation, producing tissue-wide immune amplification. In Asian strain infection, we identify disease-associated microglia (DAM) as critical mediators of infection containment: DAM accumulate in regions where viral burden stabilizes, are promoted by Apoe-Trem2 signaling from infected cells and are governed by transcription regulators that restrain inflammatory programs while preserving phagocytic and antiviral functions. In contrast, African strain infection is marked by impaired Apoe-Trem2 signaling, persistent inflammatory microglial activation, and failure of containment. Progressive infection leads to depletion of oligodendrocytes, astrocytes, and neurons, loss of local cellular diversity and disruption of tissue architecture concentrated in somatosensory and motor regions associated with myelination and synaptic programs. These architectural disruptions correlate with severe neurological phenotypes in African strain infection and are preceded by transcriptional dysregulation in infected glial cells, including sustained stress responses, inflammatory signaling, and suppression of myelination and homeostatic pathways. Together, our study establishes a spatially resolved framework linking viral strain-specific microglial states to tissue disorganization and neurological functional impairment, providing mechanistic insight into how neurotropic viruses reshape microenvironments to drive neurological disease.

cell biology↗

bioRxiv2026

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis

Spatial transcriptomics and proteomics map tissue architecture and cellular interactions, but analysis remains limited by programming demands and text-centered AI agents that lack viewer grounding and cross-turn context. We present spatiAlytica, a viewer-centric multimodal interactive agentic system embedded in the Napari viewer that enables non-programmer biologists to perform iterative, hypothesis-driven spatial omics analysis via natural language. spatiAlytica couples viewer-state serialization, agentic memory, biological concept-to-data-field mapping, code generation and debugging, Spatial VQA, and grounded interpretation to support an exploratory analysis and interpretive reasoning workflow. We introduce spatiAlyticaBench, a comprehensive benchmark spanning 222 single-turn spatial analytical coding questions, 178 multi-turn sequential workflow questions, and 7,350 image-grounded reasoning questions. spatiAlytica outperformed strong agentic baselines, while using less time and tokens. Case studies across Kaposis sarcoma, colorectal cancer, and ovarian cancer recapitulated known spatial patterns and uncovered progressive CD8 T-cell dysfunction during KS progression.

bioinformatics↗

bioRxiv2026

A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases

The BRAIN Initiative Cell Atlas Network (BICAN) is generating large-scale multimodal datasets to profile cell types in the human, non-human primate, and mouse brain. The diversity of single-cell and spatial transcriptomic and epigenomic assays, combined with varied experimental contexts, multiple data-generating laboratories and distributed infrastructure, poses substantial challenges for data integration and reuse in BICAN. To address this, we implemented a standards framework that enables layered integration of these data into knowledge-ready products for interoperable brain cell atlases. This framework organizes data based on three progressively structured layers. First, we introduced an assay-agnostic modeling layer that unifies the representation of single-cell and spatial omics data using a common set of biological entities and processes assessed by diverse experimental techniques. Second, we implemented harmonized metadata standards that capture key experimental features linked to biospecimen provenance across heterogeneous tissue sources, species, and preparations, supporting integration and validation while minimizing burden on data contributors. Third, we present an extensible representation for data-driven cell type taxonomies that integrates molecular data with annotations, ontology mappings, and evidence. Together, these contributions represent an end-to-end framework that transforms heterogeneous datasets into structured, interoperable resources that support broad community reuse via mapping algorithms, annotation systems, and visualization platforms. This approach links biospecimen provenance with cell-level outputs and embeds these in a standardized taxonomy format, enabling downstream applications such as cross-dataset integration, reference mapping, and knowledge-driven analysis. More broadly, our work demonstrates a generalizable strategy for enabling an efficient data-to-knowledge pipeline in a large-scale consortium setting.

genomics↗