Biology subjects

Wen, H.

Publications and source records attributed to Wen, H..

At least 19 recordsLinked to original sources

bioRxiv2026

DeSPA uncovers genes with cross-state spatial pattern remodeling and coordinated spatial programs across multi-slice spatial transcriptomics atlases

Spatial transcriptomics increasingly generates multi-slice atlases spanning developmental, regenerative, and disease-associated states. Identifying genes with spatial pattern remodeling across these states is essential for understanding tissue reorganization and the underlying gene programs, yet few existing methods systematically detect such changes while accounting for variation among replicate slices. Here we introduce DeSPA, a statistical framework that models gene-specific spatial variation within each slice using a linear mixed model, tests for cross-state remodeling by aggregating evidence across replicate slices, and organizes identified genes into interpretable spatial co-expression modules that reveal coordinated spatial programs. Simulations show that DeSPA achieves high power while controlling the false discovery rate under inter-slice heterogeneity. In mouse embryogenesis, it recovers programs linked to extracellular matrix organization, muscle formation, neural development, and erythrocyte development. In diabetic testis, it reveals disruption of a late-spermatid spatial program consistent with known spermatogenic dysfunction, while maintaining stable gene rankings under coordinate rotation. In axolotl brain, regeneration preserves a developmental-like biosynthetic backbone but reorganizes it into regeneration-specific modules. Across two mouse intracerebral hemorrhage models, DeSPA resolves a conserved spatial injury program alongside model-specific synaptic and tissue-response programs. Together, these findings establish DeSPA as a scalable and interpretable framework for detecting spatial pattern remodeling and resolving coordinated spatial programs across diverse biological states.

bioinformatics↗

bioRxiv2026

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

bioinformatics↗

bioRxiv2026

Sex-related structural alterations across common epilepsies: a worldwide ENIGMA study

Epilepsy is characterized by widespread structural brain alterations extending beyond the epileptic zone, involving both cortical and subcortical regions. Importantly, the clinical manifestation of epilepsy, including seizure types, psychiatric comorbidities, and treatment responses, has been shown to differ between sexes. However, sex differences in structural alterations in epilepsy have been seldomly reported in neuroimaging studies, partly due to limited sample sizes and single-center designs. Here, we systematically investigated sex differences in common epilepsies and their related clinical variables using structural neuroimaging biomarkers in an international multi-center cohort of 1,253 epilepsy patients and 1,077 healthy controls. We studied cortical thickness and subcortical volume in two types of epilepsy: temporal lobe epilepsy (TLE) and genetic generalized epilepsy (GGE). Both male and female patients with TLE showed widespread cortical and subcortical thinning compared with controls. In GGE, when compared separately to controls, male patients showed only subtle structural alterations, whereas female patients exhibited more widespread structural alterations. Sex-stratified analyses revealed some variation in the extent and distribution of cortical thickness and subcortical volume alterations between male and female patients in both epilepsy cohorts. Yet, we did not find significant sex-by-diagnosis interaction effects in TLE and GGE. Similarly, no significant interaction effects were observed between sex and age of onset or disease duration in either patient group. Overall, although we observed some differences in regional cortical thickness and subcortical volume between male and female patients with epilepsy, we did not find significant sex-by-diagnosis interactions. Our findings indicate that sex differences in behavioral and clinical outcomes of epilepsy may involve biological or functional processes that require further investigation.

neuroscience↗

bioRxiv2026

The in vivo inhibitory function of the MHC-I alpha3 domain/CD8alpha interaction

The interaction between the Major Histocompatibility Complex Class I (MHC-I) 3 domain and CD8 has classically been viewed as a positive coreceptor interaction that stabilizes TCR signaling during antigen recognition. However, its physiological function in mature peripheral CD8+ T cells in vivo remains incompletely understood. Here, we identify the MHC-I 3 domain-CD8 interaction as a previously unrecognized inhibitory pathway that tonically restrains peripheral CD8+ T-cell activation and maintains T-cell tolerance in vivo. Antibody-mediated disruption of the MHC-I 3 domain-CD8 interaction induced spontaneous activation of peripheral CD8+ T cells without impairing their survival, lowered the threshold for antigen-induced activation, and enhanced responsiveness to cognate peptide stimulation. In peptide-induced OT-I T-cell anergy models, blockade of either H-2Db or H-2Kb 3 domain interactions with CD8 prevented the induction of anergy and restored responsiveness of previously anergic T cells. Notably, blockade of the H-2Kb 3 domain enhanced OT-I responses despite simultaneously disrupting the classical positive coreceptor interaction within the TCR-peptide-MHC complex, indicating that tonic inhibitory signaling mediated by the MHC-I 3 domain predominates under these conditions. Together, these findings redefine the classical MHC-I-CD8 interaction as a bidirectional pathway that not only supports antigen recognition but also imposes tonic inhibitory control over peripheral CD8+ T cells. These results identify the MHC-I 3 domain-CD8 axis as a potential target for reversing T-cell tolerance and enhancing antitumor or antiviral immunity.

immunology↗

bioRxiv2026

Age Dependent Immunopathology Drives Pneumonia-Associated Cardiac Dysfunction During Streptococcus pneumoniae Infection

Aging is a major risk factor for severe Streptococcus pneumoniae (Spn) infection and pneumonia-associated - major adverse cardiac events (PA-MACE), yet underlying mechanisms remain unclear. Using young and aged murine models, we show that aging exacerbates bacterial burden, mortality, and cardiac dysfunction following Spn infection, associated with impaired macrophage bacterial killing. Single-cell RNA sequencing infected hearts revealed extensive age-dependent remodeling across immune and stromal compartments. Aged mice exhibited heightened pro-inflammatory myeloid responses, with increased neutrophil infiltration characterized by elevated S100A8/9 and LCN2 and reduced antimicrobial programs. Macrophages displayed defective efferocytosis, including disruption of the GAS6-AXL axis. Aging also drove expansion of an infection-responsive fibroblast population with inflammatory signatures and reduced extracellular matrix gene expression. These changes were linked to oxidative stress and impaired glucose oxidation. Notably, anti-inflammatory treatment rescued cardiac dysfunction, implicating excessive inflammation as a central driver of PA-MACE. Together, these findings define mechanisms linking aging to pneumococcal cardiac complications and identify potential therapeutic targets.

immunology↗

bioRxiv2026

MTB-KB: A Curated Knowledgebase of Mycobacterium tuberculosis Related Studies

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), has regained its position as the worlds leading killer among infectious diseases. Despite extensive research progress across epidemiology, diagnosis, drug development, treatment regimens, vaccines, drug resistance, virulence factors, and immune mechanisms, MTB-related knowledge remains fragmented across thousands of publications, limiting its effective use. To address this gap, we present MTB-KB, a literature-curated knowledgebase that systematically integrates high-impact findings from eight major sections of TB research. The current release contains 75,170 associations from 1,246 publications, covering 18,439 entities standardized using authoritative databases and WHO-endorsed classifications. A central feature is the interactive knowledge graph, which links cross-section associations to reveal and infer MTB-host interactions, treatment strategies, and vaccine development opportunities. MTB-KB also provides a user-friendly interface with browsing, advanced search, and statistical visualization. Overall, by consolidating dispersed MTB knowledge into a structured and accessible platform, MTB-KB provides a valuable resource for researchers, clinicians, and policymakers, supporting both basic and clinical TB research, enabling evidence-based TB prevention, diagnosis, and treatment, and contributing to global elimination efforts. MTB-KB is accessible at https://ngdc.cncb.ac.cn/mtbkb/.

bioinformatics↗

bioRxiv2026

Generating and navigating single cell dynamics via a geodesic bridge between nonlinear transcriptional and linear latent manifolds

Time-series single-cell RNA sequencing (scRNA-seq) captures cellular processes as sparse and unpaired snapshots, limiting our ability not only to reconstruct continuous cell state transitions, but also to navigate between states in a controlled and interpretable manner. Here we present GeoBridge, a framework modeling cellular dynamics as geodesic trajectories on the transcriptional manifold based on isometric geodesic theory. By learning the geodesic bridge mapping, the method theoretically and computationally transforms time-varying nonlinear transcriptional geodesics (original nonlinear manifold) into constant-velocity straight-line geodesics (latent linear manifold). In the learned geodesic space, continuous interpolation becomes biologically meaningful, enabling reconstruction of unobserved intermediate states and efficient navigation between distinct cellular phenotypes at a single-cell resolution. By mapping interpolated trajectories back to the original gene expression space, GeoBridge recovers smooth transcriptional programs that are robust to noise and snapshot sparsity. Leveraging the derived geodesic potentials, GeoBridge further infers pseudo-temporal trajectories with superior fidelity compared to mainstream approaches from single-snapshot scRNA-seq data without temporal annotation, and directly identifies genes that drive progression along transition paths. We demonstrate the utility of GeoBridge across diverse systems, where GeoBridge resolves EMT-MET progression in cancer stem cells and identifies stage-specific modules as well as the branching cell-fate dynamics in human pluripotent stem cells with higher reconstruction accuracy than state-of-art methods. More importantly, GeoBridge supports single-cell fate navigation in multi-target hematopoietic lineages, allowing neutrophil-biased cells to be virtually guided toward mast-cell fates along biologically plausible paths. Together, GeoBridge establishes a principled method that transforms sparse single-cell measurements into a continuous, controllable landscape for the reconstruction, navigation and manipulation of cellular state transitions.

bioinformatics↗

bioRxiv2026

Metabolic adaptation to maternal hyperglycemia via ACLY-dependent acetyl-CoA production drives epigenetic remodeling and dysregulated placental development

AbstractsGestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy that is paradoxically associated with both fetal overgrowth and fetal growth restriction (FGR). While maternal hyperglycemia is widely presumed to drive macrosomia through excessive nutrient supply, the mechanisms underlying FGR remain poorly understood. Here, using a mouse model that recapitulates the small-for-gestational-age (SGA) phenotype observed in human GDM pregnancies, we identify placental underdevelopment as a principal driver of FGR. Despite systemic nutrient abundance, hyperglycemic placentas exhibit reduced mass and an increased fetal-to-placental weight ratio, indicative of placental insufficiency. Mechanistically, maternal hyperglycemia induces anabolic metabolic rewiring while suppressing oxidative phosphorylation (OXPHOS), accompanied by upregulation and nuclear redistribution of ATP-citrate lyase (ACLY). ACLY converts glucose-derived carbon into acetyl-CoA in the cytosol and nucleus, thereby coupling glycolytic flux to lipid and hexosamine biosynthesis as well as to global histone hyperacetylation. This hyperacetylation-associated epigenetic reprogramming activates metabolic, innate immune, and inflammatory gene programs while repressing pro-proliferative and anti-apoptotic pathways. Consequently, placental growth is compromised despite nutrient excess. Importantly, activation of the ACLY-acetyl-CoA axis and global histone hyperacetylation is consistently observed in human GDM placentas across diverse birth outcomes, suggesting a conserved metabolic-epigenetic adaptation to maternal hyperglycemia. Together, these findings identify ACLY-dependent acetyl-CoA production as a central metabolic node linking maternal hyperglycemia to chromatin remodeling and placental development control, thereby reshaping fetal growth trajectories.

developmental biology↗

bioRxiv2026

SpatioCAD: Context-aware graph diffusion model for pinpointing spatially variable genes in heterogeneous tissues

Spatial transcriptomics enables comprehensive characterization of tissue architecture, and the identification of spatially variable genes (SVGs) is a critical step for defining region-specific molecular markers and uncovering spatially regulated mechanisms across diverse biological contexts. However, most existing methods for SVG detection overlook cell density variations, a major confounding factor in complex tissues such as tumors, where heterogeneous cellularity frequently introduces false-positive calls. Here we present SpatioCAD, a computational framework that explicitly decouples genuine spatial expression patterns from confounding effects driven by cellularity. SpatioCAD leverages and extends a graph diffusion model to simulate expression propagation under cell-density-aware conditions, thereby ensuring unbiased detection of SVGs across all expression levels. Systematic evaluations on simulated datasets demonstrate its superior statistical power and specificity. Applied to breast cancer, lung cancer, and glioma datasets, SpatioCAD identifies functionally diverse SVGs, including low-abundance transcripts with established roles in tumor progression, while also recapitulates biologically meaningful tissue architecture features.

bioinformatics↗

bioRxiv2026

Longitudinal modality prediction learns gene regulatory patterns: insights from a single-cell competition

Simultaneous measurement of chromatin, transcriptomic, and proteomic features in single cells opens new avenues for modeling interactions between molecular layers during dynamic biological processes. Predicting one modality from another - such as inferring gene expression from chromatin profiles or protein abundance from RNA - has the potential to reveal regulatory relationships and enhance downstream analyses. However, conventional approaches for predicting gene regulation have largely failed and method development in modality prediction for regulatory inference has been limited. To explore effective modeling strategies and stimulate innovation, we generated a purpose-built longitudinal multimodal benchmarking dataset that captures early hematopoietic differentiation and organized the largest single-cell data competition to date, receiving over 27,000 submissions from 1,602 competitors worldwide. In our extensive analysis of the competition results, we demonstrated that top-performing approaches outperform state-of-the-art methods, and uncover how best-performing models captured biologically meaningful regulatory relationships between modalities. With ablation studies of the winning models, we identified feature-engineering strategies, model architectures and cross-validation schemes that are crucial for outstanding performance, and provide simplified, reproducible, light-weight code for state-of-the-art models. Together, the benchmark and analyses serve as an evaluation standard and guide future method development, including recently emerging foundation models, to advance our understanding of regulatory interactions in longitudinal, multimodal single-cell data.

bioinformatics↗

bioRxiv2026

Multi-agent reasoning enables predictive design of living materials

Artificial intelligence is increasingly used to accelerate scientific discovery, but most successful frameworks operate within well-defined molecular, protein or materials spaces. Living materials present a more formidable computational problem because functions emerge from context dependent coupling among cells, matrices, fabrication processes and evaluation conditions. Here we introduce LiveMat, a multi-agent reasoning framework that transforms unstructured literature into a computable design space for living materials. LiveMat standardizes 34,215 living material records, integrating 16,769 microorganism and 17,446 polymer entries into a knowledge graph linking living components, abiotic matrices, functional outputs, evaluation contexts and performance metrics. Benchmarking across five large language models shows that living material reasoning is limited mainly by cross-domain feature integration rather than coarse classification. LiveMat overcomes this limitation through constraint decomposition, provenance-aware extraction, consistency checking and expert-anchored ranking. In a prospective wound-healing task, it prioritizes a four-component design with state-of-the-art in vivo performance, establishing a scalable infrastructure for interpretable, evidence-grounded living material discovery.

biochemistry↗

bioRxiv2026

DIA-CLIP: a universal representation learning framework for zero-shot DIA proteomics

Data-independent acquisition mass spectrometry has increasingly emerged as a cornerstone for characterizing highly heterogeneous biological systems, such as single-cell proteomics, metaproteomics, and spatial proteomics, offering unparalleled identification depth and quantification reproducibility. Current DIA analysis frameworks, however, require semi-supervised training within each run for peptide-spectrum match (PSM) re-scoring, which is prone to overfitting and lacks generalizability across diverse species and experimental conditions. Here, we present {pi}DIA-CLIP, a generalized framework shifting the DIA analysis strategy from semi-supervised training to zero-shot cross-modal representation learning through integrating dual-encoder contrastive learning and encoder-decoder architectures to establish a unified, high-precision representation for spectral features and peptides. Notably, the generalized zero-shot nature of {pi}DIA-CLIP facilitates an inference-only architecture, streamlining the analysis to achieve exceptional computational efficiency. Extensive evaluations across five distinct benchmarks demonstrate that {pi}DIA-CLIP consistently outperforms existing tools, yielding an up to 44.6% increase in protein identification alongside a reduction in entrapment identifications reaching a maximal 52.5%. Furthermore, the enhanced identification depth facilitates the discovery of novel biomarkers and the elucidation of intricate cellular mechanisms.

bioinformatics↗

bioRxiv2026

Causal evidence for a shared mechanism linking language and tool use via the putamen

Both human language and tool use--two hallmark capacities of human cognition--depend on organizing discrete elements, i.e., symbols and actions, into highly constrained structured sequences to achieve a functional goal. However, the neural mechanism linking these capacities is unclear. We combined brain lesion analysis, developmental contrast, and functional neuroimaging to test whether the basal ganglia play a causal role in their shared capacity. In 100 adults with focal brain injury, damage to the putamen disrupted both sentence processing and tool use, with impairments specifically explained by reduced goal-dependent sequence integrity for both tasks. Further comparing populations with typical and deprived early language experience (congenitally deaf adults with vs. without early sign language exposure), we found that early language acquisition was associated with improved tool-use performance and strengthened putaminal responses to such goal dependency, which mediated the relationship between sentence sequence integrity and tool behavior. Together, these results identify the putamen as a key neural substrate supporting goal-dependent sequence integrity across language and action, and show how early language experience shapes this conserved control system. One-Sentence SummaryLanguage and tool use share a putamen-based mechanism that supports goal-dependent sequence integrity, and this mechanism is strengthened by early language experience.

neuroscience↗

bioRxiv2026

Prophylactic Lipoxin A4 Attenuates Clostridioides difficile Infection by Augmenting Epithelial Barrier and Resolving Inflammation

Clostridioides difficile infection (CDI) is a leading healthcare-associated diarrhea with high recurrence rates, partially due to antibiotic-induced dysbiosis and dysregulated host inflammation. Specialized pro-resolving mediators (SPMs), such as Lipoxin A4 (LXA4), offer promise in controlling excessive inflammation and promoting tissue repair, yet their role in CDI remains unexplored. Here, we developed a compact, gas-tight gut-on-a-chip (GOC) system that reconciles the anaerobic requirements of C. difficile with the oxic needs of human intestinal epithelium, enabling physiologically relevant co-culture within a standard incubator. A CDI in vitro model was established based on this GOC system. Using the model, we demonstrated that prophylactic administration of LXA4 significantly preserved epithelial barrier integrity, attenuated pro-inflammatory cytokine secretion (IL-8 and IFN-{gamma}), and reduced bacterial colonization. Transcriptomic analysis revealed that LXA4 pretreatment upregulated genes involved in cell junction organization while downregulated immune activation pathways. These protective effects were validated in a murine CDI model, where LXA4 pretreatment reduced weight loss, pathological damage, and fecal bacterial burden. Furthermore, prophylactic administration of LXA4 synergized with vancomycin treatment further enhanced antibiotic efficacy while allowing a 50% dose reduction without compromising therapeutic outcomes. Our study establishes a robust approach for CDI research and highlights the prophylactic and adjuvant potential of inflammation-resolving strategies, offering a novel approach to mitigate CDI incidence and improve treatment outcomes.

bioengineering↗

bioRxiv2026

A novel anaerobic n-Hexadecane hydroxylation pathway in Thermalkanevorax longiformis gen. nov., sp. nov. isolated from a deep-sea hydrothermal vent

Hydrothermal systems contain a wide spectrum of alkanes derived from biological sources and geological processes. However, despite their widespread occurrence in hydrothermal environments, the capacity and mechanisms of anaerobic alkane degradation by members of the phylum Bacillota remain poorly understood. In this study, we isolated a bacterial strain (designated strain L01) from the Lost City hydrothermal field (LCHF) by adding crude oil for enrichment culture. Strain L01 can grow anaerobically at 55 {degrees}C and pH 7.0 in medium supplemented with n-hexadecane, indicating its capability for anaerobic alkane degradation. Phylogenetic analysis based on the 16S rRNA gene sequence (93.11% similarity to its closest described relative) and average amino acid identity (AAI, 66.27-66.58%) revealed that strain L01 represents a novel genus within the family Symbiobacteriaceae, for which the name Thermalkanevorax longiformis gen. nov., sp. nov. is proposed. Cells of strain L01 are elongated rods measuring approximately 10-20 m in length and [~]0.2 m in diameter. Integrated genomic, transcriptomic, and metabolomic analyses revealed a previously unrecognized anaerobic n-hexadecane utilization pathway, involving initial alkane activation by AhyA homologous enzymes followed by stepwise oxidation and {beta}-oxidation. Together, these findings provide new insights into anaerobic alkane metabolism by hydrothermal bacteria, expanding current understanding of microbial hydrocarbon degradation in extreme marine environments. IMPORTANCEAlthough alkanes have be reported degradation by microorganisms through both aerobic and anaerobic metabolic pathways in hydrothermal ecosystems, the mechanisms underlying anaerobic alkane degradation remain incompletely understood, and the metabolic strategies employed by hydrothermal microorganisms are still poorly characterized. In this study, a novel genus bacterium Thermalkanevorax longiformis within the Symbiobacteriaceae family was isolated from the LCHF and identified an unrecognized C1-position n-hexadecane hydroxylation of AhyA homologous proteins at the anaerobic condition. These findings not only provide fundamental insights into the metabolic strategies of hydrothermal bacteria but also expand our understanding of microbial hydrocarbon degradation in extreme marine environments, offering a potential genomic resource for biotechnological applications in hydrocarbon remediation.

microbiology↗

bioRxiv2025

Ensemble-DeepSets: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Immunosenescence increases susceptibility to infection and reduces vaccine responsiveness, yet bulk transcriptomic clocks obscure the cellular heterogeneity underlying this process. Here, we present IDEAL-Age, an interpretable deep learning framework that operates directly on single-cell PBMC transcriptomes. Benchmarking against 31 methods across independent cohorts demonstrates superior predictive performance. The frameworks interpretability uncovers linear and non-linear transcriptomic dynamics that reveal phase-specific physiological transitions, and identifies pro-youthful or pro-aging cellular contributions. Application to systemic lupus erythematosus (SLE) reveals accelerated immunological aging driven by interferon-associated monocyte shifts. IDEAL-Age establishes a high-resolution computational framework for deciphering systemic immune aging.

bioinformatics↗

bioRxiv2025

DNASE1L3 surveils mitochondrial DNA on the surface of distinct mammalian cells

The extracellular space is a critical environment for discriminating self versus non-self nucleic acids and initiating the appropriate immune responses through signaling cascades to relay information about extracellular nucleic acids. Here, we provide evidence that oxidized mitochondrial DNA is tethered to the surface of select mammalian cells through cell surface proteins and heparan sulfate proteoglycans. We demonstrate that cell surface DNA accumulates in large clusters that partially overlap with domains enriched in RNA binding proteins. Finally, we show that human and murine B cell surfaces contain DNA that can be cleared by the secreted nuclease DNASE1L3, and that patients with a DNASE1L3 missense variant associated with increased risk for autoimmune disease harbor increased levels of surface DNA on B and T cells. Taken together, this work expands the scope of cell surface nucleic acid biology and provides a mechanistic link between cell surface molecules and DNA targeting in autoimmune disease.

cell biology↗

bioRxiv2025

Albumin-STING Nanocomplex Reprograms HSPCs to Antitumor Neutrophils with Enhanced MHC I Antigen Presentation for Cancer Immunotherapy

Tumor-associated immunosuppressive neutrophils, termed myeloid-derived suppressor cells (MDSCs), compromise cancer immunotherapy. They can be pathologically programmed as early the hematopoietic stem and progenitor cell (HSPC) stage by suppressing interferon signaling. Reprogramming HSPCs toward antitumor neutrophils through the stimulator of interferon genes (STING) activation offers a promising therapeutic strategy. Here, we demonstrate that an albumin-STING nanoagonist (Nano ZSA-51D) reprograms HSPCs to generate antitumor neutrophils, enhancing MHC I-mediated CD8 T cell immunity. Nano ZSA-51D activates STING-interferon signaling in HSPCs, promoting their expansion and differentiation toward granulocyte-monocyte progenitors via STING-NF-{kappa}B-IL-6 signaling. It further reprograms neutrophils into CD14ICAM-1+ subset through STING-NF-{kappa}B-TNF- signaling, enhancing tumor infiltration. These neutrophils upregulate interferon signaling and MHC I antigen presentation, boosting tumor-specific CD8 T cell responses. Adoptive transfer of Nano ZSA-51D-reprogrammed neutrophils with -PD1 therapy achieves complete colon tumor remission. Our findings provide a novel strategy to reprogram HSPCs toward antitumor neutrophils and highlight the potential of early interventions at HSPC stage to rewire neutrophil fate for cancer immunotherapy.

immunology↗