Biology subjects

Gao, R.

Publications and source records attributed to Gao, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Senescence-associated KRAS upregulation in peripheral T cells links to premature coronary artery disease

Aims: Premature coronary artery disease (PCAD) lacks specific molecular drivers, and the role of immunosenescence is unclear. We investigated whether aging-related gene dysregulation in T cells contributes to PCAD. Methods: We combined bulk transcriptomics of PBMCs from 12 PCAD patients and 21 controls, single-cell RNA sequencing of PBMCs and human atherosclerotic plaques, weighted gene co-expression network analysis, gene perturbation network analysis, and molecular docking. Results: KRAS was identified as a hub gene intersecting PCAD-associated genes and aging-related genes. Single-cell analysis showed KRAS upregulation predominantly in effector CD8+ T cells, which exhibited the highest senescence scores that were further elevated in disease. Network perturbation of KRAS strongly impacted the cell killing pathway. KRAS-high effector CD8+ T cells were detected in coronary and carotid plaques, displaying enhanced cytotoxicity, exhaustion, and senescence features. Additionally, a candidate small molecule was computationally predicted to bind inactive KRAS. Conclusions: Elevated KRAS expression in senescent, cytotoxic CD8+ T cells is associated with PCAD, bridging immunosenescence and premature atherosclerosis. This finding provides a novel biomarker candidate and potential therapeutic entry point, awaiting further functional validation.

bioinformatics↗

bioRxiv2026

High-throughput physics-based enzyme engineering

Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost, leaving the field heavily dependent on expert intuition. Here we present a scalable physics-based framework that combines field-aware machine learning with molecular mechanics to capture enzyme electrostatics at quantum-mechanical accuracy while enabling efficient, atomistic exploration of reaction free-energy landscapes. Coupled with microkinetic modelling, the framework translates molecular free-energy landscapes into catalytic rates and selectivity across competing, multistep reaction pathways. Applied to a newly engineered oxidative amidase (OxiAm), the framework predicts catalytic rate constants with near-experimental accuracy, quantitatively resolves the selectivity between hydrolysis and aminolysis, and generalizes across substrates, mutations and enzyme homologues. Transition-state ensemble analysis further reveals the reaction mechanism and guides the design of enzyme variants for pharmaceutical synthesis. By bringing chemical accuracy and high-throughput sampling to enzyme catalysis, this approach shifts rational design from static, empirical practice toward dynamic, free-energy-driven design, and should accelerate the engineering of biocatalysts.

biophysics↗

bioRxiv2026

Conserved microglial programmes, non-transferable classifiers: boundaries of the single-cell TBI molecular clock

Background: Traumatic brain injury (TBI) pathology evolves over hours to months, and molecular staging could complement clinical and imaging-based assessment. Single-cell transcriptomics captures cell-type-resolved temporal programs, but whether a transcriptional "molecular clock" trained on one injury model can be transferred to others remains unknown. Methods: We compiled four public mouse TBI single-cell/single-nucleus datasets spanning two injury models - controlled cortical impact (GSE277487; CEREBRI) and fluid percussion injury (GSE160763) - plus an independent mild-FPI cohort reserved for validation (GSE247339), totalling 307,991 cells across four pathological stages (control, acute, subacute, chronic). Classifiers were evaluated at cell and sample levels (held-out libraries) against a permutation null, with per-cell-type, strict leave-one-dataset-out (LODO) generalization, glial-only transfer and gene-level SHAP analyses. Results: Within GSE277487, sample-level balanced accuracy reached 0.67 (permutation null 0.32 +/- 0.09; p = 0.001), and the signal was reproduced in the independent mild-FPI cohort (0.40 vs null 0.19 +/- 0.05; p = 0.0010). Cell-level cross-validation overestimated performance by ~0.16, quantifying within-sample leakage. Cross-dataset generalization was poor (strict LODO balanced accuracy 0.20-0.31); the acute immediate-early response was untransferable (recall 0.002) and 80% of chronic (6-month) cells were assigned to the subacute class. Glial-only models raised subacute recall across models (0.91 and 0.75), but precision was indistinguishable from a subacute-only classifier (0.51 vs 0.50; 0.58 vs 0.58) and one-vs-rest AUROC was at or below chance (0.38-0.54) across four classifier families - classifier collapse, not transferable discrimination. The reproducible cross-model component was instead a gene-expression programme: a microglial complement/lysosomal damage programme (Trem2-Apoe axis) was up-regulated across the severe CCI and FPI models (13/13 concordant genes), whereas the immediate-early module was model-specific. Conclusions: A single-cell molecular clock for TBI staging is feasible within datasets once within-sample leakage is controlled, but it does not transfer across datasets as a classifier: apparent cross-model transfer reflected classifier collapse onto the majority class, and the reproducible cross-model component was a microglial complement/lysosomal gene-expression programme. These findings define the boundaries of cross-dataset time inference in TBI and provide evaluation criteria for claims of transferable single-cell classifiers.

neuroscience↗

bioRxiv2026

AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource

Single-cell atlas annotations are routinely treated as ground truth but rarely validated before use. We present AnnoAudit, a marker-based audit protocol that combines four convergent checks - marker scoring, unsupervised clustering, margin-gated module scoring, and applicability-gated pretrained models - into a composite Annotation Contamination Score (ACS), plus an independent-gene discrimination step that distinguishes genuine mis-assignment from ambient-RNA detection artifacts, and a trajectory-correlation fingerprint tracing suspicious signals to their cell type of origin. Applied to CEREBRI (GSE269748), a widely used single-cell TBI atlas (73 citations; 65 citing works; six re-analysis studies, none of which validated the annotations), the audit shows that contamination is systematic: the official "glutamatergic neuron" label is 97.8% non-excitatory by markers, with at least 67.4% of its cells independently confirmed as non-neuronal (microglia-dominated); the GABAergic (46.6%), OPC (66.1%), and astrocyte (37.7%) labels are likewise contaminated by marker-confirmed microglia, whereas the microglial, oligodendrocyte, endothelial, and pericyte labels are largely clean (85-96% self-confirmed). The contamination is state-dependent: astrocyte and OPC labels are largely correct in uninjured controls (76.0% OPC self-marker; 18.5% discrimination-confirmed contamination for the astrocyte label) but collapse in the acute 24-h window (14-16% self-marker; 57-71% microglia), recovering by 6 months - so the annotation failure concentrates precisely in the window of maximal injury response. This temporal structure generates coherent false signals - a biphasic trajectory for 40 of 307 ion-channel genes, a KCNC3-specific OXPHOS signature, and an inverted KCNC3 trajectory at 7 days - whereas the corrected response is a sustained acute KCNC3 up-regulation conserved across three independent datasets and three injury models. Simulation-calibrated ACS is 82.6% for CEREBRI. In a human ALS atlas (GSE330130), an ambient-aware re-analysis shows that the official neuron labels are largely correct (1.8-6.1% contamination after discrimination), confirming that the audit does not flag well-annotated resources, and that marker-only estimates on snRNA data with high ambient RNA must be interpreted with the discrimination step. AnnoAudit needs only the deposited count matrix and a canonical panel; we propose it as a routine quality step for atlas-based re-analysis.

bioinformatics↗

bioRxiv2026

Systemic Nanobubbles Enable Ultrasound-Guided STING Immunotherapy in Breast Cancer

Activation of the STING pathway can induce potent antitumor immunity, but effective delivery of STING agonists to the tumor while limiting systemic exposure remains challenging. We previously developed MUSIC, an ultrasound-guided platform that uses microbubbles (MBs) to deliver the STING agonist 2'3'-cGAMP and locally activate antitumor immunity. However, the vascular confinement of MBs and the need for intratumoral administration limit the potential for systemic tumor targeting. To overcome these limitations, we developed SONATA (Systemic Oncotherapy using Nanobubbles for Acoustically-guided Tumor Activation), which employs nanobubbles (NBs) that are approximately 10-fold smaller than conventional MBs, enabling systemic administration and tumor extravasation. Following NB accumulation within tumors, ultrasound exposure triggers localized cGAMP release, facilitating delivery to targeted CD11b+ antigen-presenting cells (APCs) and STING activation with spatial and temporal control. NBs are composed of the same components as MBs, including phospholipid shells and a perfluorobutane core and are functionalized with anti-CD11b antibodies to target CD11b+ APCs and spermine-modified dextran to stably load cGAMP through nanocomplex formation. Upon ultrasound activation, SONATA induced phosphorylation of STING, TBK1, and IRF3 and increased IFN-{beta} production in bone marrow-derived macrophages. In an orthotopic breast cancer model, intravenously administered SONATA combined with tumor-localized ultrasound significantly inhibited tumor growth compared with controls. Furthermore, SONATA synergized with immune checkpoint blockade prolonged the median survival of tumor-bearing mice. Collectively, these findings establish SONATA as a systemically administered immunotherapy platform that enables ultrasound-guided, spatially controlled STING activation.

bioengineering↗

bioRxiv2026

Modulating hydrogel architecture via cross-linker length for high-resolution tissue imaging and photochemical sectioning

The recent development of Volumetric Imaging via Photochemical Sectioning (VIPS) has enabled nanoscale imaging of whole-mount tissue samples of virtually any size by embedding intact tissue in a photocleavable, superabsorbent hydrogel. However, the efficacy of sample embedding, imaging, and photochemical sectioning is fundamentally governed by the mechanical stiffness, structural stability, and photodegradation kinetics of the photocleavable hydrogel (PC-gel) polymer network. To elucidate the effect of the photosensitive crosslinker design on these critical properties, we synthesized a set of photocleavable crosslinkers (PCs) with varying polyethylene glycol (PEG) backbone lengths and prepared the corresponding PC-gels under a fixed monomer formulation and polymerization condition. We quantified and compared the viscoelastic properties of the formed PC-gels at their swollen states, and found that the crosslinker length markedly reshaped the PC-gel mechanics. In addition, we evaluated the light-triggered degradation of the PC-gels using both wide-field and spatially-controlled illumination. We found that PC-1000, PC-1500, and PC-2000 gels remained comparably photodegradable, all enabling on-demand, spatially confined decrosslinking under such illuminations. These results provide practical guidelines for modulating the crosslinker architecture of PC-gel polymer networks to achieve optimal physicochemical properties for whole-mount tissue imaging using VIPS.

biophysics↗

bioRxiv2026

Immunohistochemistry-compatible gel-assisted mass spectrometry imaging

While lipids are fundamental to cellular signaling and structure, probing native lipid compositions and distributions across complex tissue architectures at single-cell resolution remains challenging due to inherent trade-offs in spatial resolution, sensitivity, and molecular coverage in current spatial lipidomics methods. Here, we report a multimodal imaging approach that seamlessly integrates gold-standard immunohistochemical labeling with gel-assisted mass spectrometry imaging to enable cell-type-specific, single-cell spatial lipidomics with modern instrumentation. Using intact brain tissue as a testbed, we demonstrate in situ measurement of cerebellar Purkinje cells and spatially mapped lipids across timepoints and cerebellar subregions within the pathological landscape of a neurodegenerative, lysosomal storage disorder. With single-cell lipidomic profiling, we delineated spatiotemporally distinct accumulation of specific glycosphingolipids and phospholipids within Purkinje cells and non-Purkinje cells in the diseased brain. Furthermore, unsupervised single-cell lipidomic clustering elucidated disease-progression-and subregion-dependent molecular divergence between healthy and neurodegenerative states within the cerebellum.

neuroscience↗

bioRxiv2026

Scaling laws for group-constrained, subject-specific task fMRI analyses

Functional MRI provides a powerful way to characterize the architecture of the human brain. Yet individual brains often vary in the exact anatomic locations of functionally specific regions; as a result of this variability, traditional random-effects group analyses (RFX) often require unrealistically large sample sizes to converge onto stable results. Here, we explore the scaling patterns for an alternative fMRI analysis framework that accounts for inter-individual topographic variability while still capturing spatial similarities--group-constrained, subject-specific (GcSS) analysis. Using sixteen contrasts from four Human Connectome Project tasks (spanning language, social cognition, motor, and working memory), we first show that the GcSS approach yields much larger effect sizes than traditional RFX analyses. We then investigate the impact of sample size (N=10-450) on the estimation of three key GcSS outputs: (1) group probabilistic maps for the contrast of interest, (2) group-level parcels that serve as anatomical constraints for subsequent functional region of interest (fROI) definitions in individual subjects, and (3) effect sizes of fROI responses to task conditions. For most contrasts, the probabilistic maps show good reliability at N=100 (intraclass correlation, ICC=.75) and excellent reliability at N=200 (ICC=.90). Most group parcels also achieve substantial agreement at N=100 (Dice coefficient, DC=.80). Critically, once robust group parcels are established, the subject-specific portion of the analysis can proceed with much smaller sample sizes: even samples of N=10 participants yield accurate effect size estimates within subject-specific fROIs, detecting 80% of practically meaningful effects (effect size [≥] 0.2); with N=20, this increases to 90%. The scaling patterns we observed held not only in the cortex, but also in the cerebellum. Our results challenge the view that fMRI research requires large samples: once the broad region of interest is established (with N=100 or higher), fROI-based analyses can yield generalizable results with small sample sizes (N=10 or N=20).

neuroscience↗

bioRxiv2026

HLiCA: An integrated cell atlas of the healthy human liver

The human liver is composed of a heterogeneous mix of cell types. How these distinct populations contribute individually and collectively to liver function remains poorly understood. Although single-cell technologies have advanced our understanding of liver biology, individual studies have often been limited by small donor cohorts and inconsistent cell type annotations. Integrating multiple datasets can overcome these challenges and better capture biological variability. We present the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the HLiCA incorporates expert-curated cell annotations refined through community feedback and dedicated cell type annotation meetings. The HLiCA classifies cells into six lineages and expands the cell type resolution to include 47 distinct cell types. Starting from raw sequencing reads, we realigned all data and performed rigorous benchmarking to ensure robust integration across technical and biological variables. Genetic ancestry was inferred for all samples to evaluate the range of ancestral backgrounds represented in the atlas. The expanded cell type annotation enabled identification of previously unrecognized liver cell types, including NRXN1+ stromal cells. Their presence was validated using spatial transcriptomics, which localized NRXN1+ stromal cells to periportal regions. With the number of donors included in the HLiCA we were able to examine cell type specific associations with demographic covariates. In hepatocytes, drug metabolism genes showed differential expression between sexes, and in cholangiocytes, mucus-production genes varied with age. As the largest and most genetically diverse human liver cell atlas to date, the HLiCA provides a comprehensive, well-annotated reference for the field, annotated by expert consensus. This resource will enable deeper interrogation of liver cellular diversity, architecture, and function in the healthy human liver and serve as a reference to understand changes that occur with disease.

genomics↗

bioRxiv2026

Mono-ADP-ribosylation-driven immunosuppression and cross-resistance to therapy through cancer cell intrinsic and extrinsic mechanisms

Mono-ADP-ribosylation (MARylation) is emerging as an important regulator of anti-cancer immunity and immunosuppressive tumor microenvironment (TME). Our previous studies showed that PARP11, one of several enzymes that facilitate MARylation, regulates the activities of intratumoral cytotoxic T lymphocytes (CTLs) and regulatory T cells (Tregs). Here, we demonstrate that stimuli such as adenosine, epinephrine, or glucagon-like peptide-1 (GLP1) induced PARP11 in cancer cells. Upregulation of PARP11 in cancer cells led to PARP11-mediated MARylation, ubiquitination, and accelerated degradation of MHC-I through the autophagy-lysosomal pathway. Induction of PARP11 protected cancer cells from killing by specific CTLs and stimulated tumor growth and progression. Genetic ablation of PARP11 attenuated MHC-I MARylation, ubiquitination, and interaction with autophagy receptors. Pharmacologic inhibition of PARP11 in pancreatic ductal adenocarcinoma (PDAC) cells restored their MHC-I levels, sensitized them to killing by CTLs, inhibited tumor growth, and impeded their initial resistance to chemotherapy and their acquired resistance to targeted therapy with RAS inhibitors. Moreover, inhibition of PARP11 prevented hyperprogressive disease in a mouse melanoma model treated with immune checkpoint inhibitors (ICBs), suggesting that PARP11 is a major therapeutically actionable driver of immunosuppression in tumors. SYNOPSISInduction of PARP11 in the tumor microenvironment mediates immunosuppression. This study reports that PARP11-driven MARylation and ubiquitination of MHC-I in cancer cells drives immune evasion, tumor growth and resistance to therapies.

cancer biology↗

bioRxiv2026

Ultra-efficient High Resolution 3D Reconstruction of Spatial Omics Data with Neural Transcriptomic Field

Biological tissues are inherently three-dimensional (3D) ecosystems where spatial architecture dictates cellular function. While spatial omics technologies have revolutionized molecular profiling, they are largely restricted to isolated two-dimensional (2D) tissue sections. Existing computational methods attempting to reconstruct 3D volumes from sparse slices rely heavily on local slice-to-slice interpolation, struggling to balance high-fidelity reconstruction, noise reduction, and atlas-scale efficiency. Here, we present Neural Transcriptomic Field (NTF), a deep learning framework employing multi-resolution hash-grid encoding and implicit neural representations. Unlike interpolation-based approaches that merely bridge adjacent observations, NTF learns a global, continuous 3D representation of the tissue. By modeling the underlying latent biological patterns, NTF intrinsically decouples true molecular signals from technical artifacts, naturally enabling robust denoising and high-fidelity reconstructions. This global field paradigm shatters traditional scalability limits: NTF achieves up to a 1,000x speedup over existing methods, notably reconstructing a 100-million-cell scale 3D whole-mouse embryo atlas in under 15 minutes. Furthermore, NTF can generate super-resolved volumes from sparse input (e.g., utilizing only 10% of slices) and robustly extrapolating into unseen tissue regions. We demonstrate NTFs versatility across diverse transcriptomic and proteomic datasets, capturing complex spatiotemporal dynamics in Drosophila and mouse embryogenesis, and mapping intra-tumoral functional gradients in human breast cancer. Ultimately, NTF provides an unprecedentedly fast, scalable, and robust computational engine for constructing the next generation of comprehensive 3D tissue atlases.

bioinformatics↗

bioRxiv2026

Self-Organized Neural Integrators in Noisy Spiking Networks

AO_SCPLOWBSTRACTC_SCPLOWNeural integrators convert brief inputs into persistent firing and underlie functions such as working memory, evidence accumulation, and gaze holding. Classical integrator models typically rely on finely tuned recurrent connectivity. Here we identify a biologically plausible route by which randomly connected noisy spiking networks can approximate integration over a finite region of parameter space. Mean-field theory (MFT) reveals surprisingly simple dynamics in such networks, governed by the mean recurrent weight and mean feedforward weight, and shows that linear integration critically depends on noise. We further show that this regime can be reached through a local, reward-modulated two-trace plasticity rule. Comparing the model with new experimental results from a delay-switching tactile decision-making task, we find that it reproduces key features of adaptive ramp-to-threshold cortical dynamics during timing-related learning. The same framework further connects to oculomotor persistence and evidence accumulation, providing a mechanistic realization of single-boundary drift-diffusion dynamics.

neuroscience↗

bioRxiv2026

Defining the Immunomodulatory Determinants of 3-mer Oligonucleotides on TLR7 and TLR8 sensing

Chemical modifications such as 2'-O-methyl (2'-OMe) are central to the efficacy and tolerability of RNA therapeutics. We recently identified that 2'-OMe RNA fragments as short as three nucleotides can exert opposing effects on Toll-like receptor 8 (TLR8) sensing in a motif-dependent manner. This discovery raises important considerations for degradation products of chemically modified RNA therapeutics, which may generate such immunologically active fragments. Here, leveraging their short length, we systematically map how base and sugar modifications within 3-mer oligonucleotides regulate TLR7 and TLR8 responses, and we resolve the structural basis for both TLR8 potentiation and TLR7/8 antagonism by RNA fragments. Building on these insights, we report the development of a dual TLR7/8 inhibitory oligonucleotide with therapeutic potential in autoimmune disease. Together, these findings provide unprecedented resolution of the immunomodulatory properties of oligonucleotide modifications on TLR7/8 and establish 3-mer oligonucleotides as the shortest functional class of RNA therapeutics described to date.

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

ACB1801 enhances tumor immunogenicity by targeting glycolysis/ferroptosis vulnerability and activating STAT1-signaling to overcome anti-PD-1 resistance in MSS colorectal cancer

BackgroundImmune checkpoint blockade (ICB) therapies demonstrate low efficacy in microsatellite stable (MSS) colorectal cancer (CRC) due to an immune-desert tumor microenvironment (TME) characterized by low antigen presentation and limited tumor-infiltrating lymphocytes (TILs). Harmine, a natural small-molecule and its promising derivatives ACB1801 have shown anti-tumor potential in preclinical models; however, their potential to reprogram the TME and overcome ICB resistance in MSS CRC remains unexplored. This study investigates whether and how ACB1801 can reshape TME to sensitize MSS CRC to ICB therapies. MethodsWe used the CT26 MSS colorectal cancer mouse model to evaluate the ability of the harmine derivative ACB1801 to enhance the efficacy of anti-PD-1 therapy. To characterize its mode of action, we performed immune landscape analysis and transcriptomic profiling of both CD45- and CD45+ tumor-derived cells. In parallel, mechanistic studies were conducted in vitro using mouse and human MSS CRC cell lines. ResultsWe demonstrate that the harmine derivative ACB1801 enhances the effectiveness of anti-PD-1 therapy in an MSS CRC mouse model. Combination therapy significantly increased CD8+ T cell infiltration and reduced regulatory T-cell (Treg) density in the TME. Transcriptomic profiling of CRC cells isolated from tumors treated with either anti-PD-1 alone or in combination with ACB1801 revealed significant enrichment of metabolic pathways in the combination group, characterized by reduced glycolysis and enhanced ferroptosis signatures. These findings were supported by in vitro data showing that ACB1801 reduces tumor cell glycolytic activity and promotes ferroptotic vulnerability. Mechanistically, ACB1801 induced STAT1 signaling, promoted CXCL10 release, and enhanced major histocompatibility complex class I (MHC-I)-dependent antigen presentation on tumor cells, thereby increasing tumor susceptibility to anti-PD-1 therapy. ConclusionCollectively, our findings indicate that combination therapy with harmine derivatives and ICBs represents a promising strategy for treating MSS CRC patients.

cancer biology↗

bioRxiv2026

Cost-effective hybrid long- and short-read sequencing enables accurate somatic structural variant detection

Somatic structural variant (SSV) calling typically requires matched normal data. Incorporating relatively inexpensive short-read sequencing not only provides essential germline information but can also replace a substantial portion of long-read sequencing, thereby enabling more cost-effective somatic SV detection. Here, we present SomaSV, a hybrid sequencing framework that integrates 30x tumor long-read data with matched normal data comprising 10x long-read and 30x short-read sequencing. This design achieves high-accuracy somatic SV detection while remaining cost-competitive. Comprehensive benchmarking demonstrates that SomaSV outperforms current state-of-the-art methods by more than 13% in F1 score while reducing sequencing costs by approximately 19%. Moreover, SomaSV identifies clinically relevant cancer-associated genes, including CLDN4 and ROBO2, highlighting its potential for discovering valuable biomarkers to support early cancer screening and diagnosis. The source code for SomaSV can be accessed at https://github.com/eioyuou/SomaSV.

bioinformatics↗

bioRxiv2026

Super-resolved, three-dimensional spatial transcriptomics reveals cell-type and brain-region-specific modulation of key epitranscriptomic switches following adolescent alcohol exposure

Epitranscriptomic mechanisms dynamically regulate neuronal function through gene expression, but their precise roles in neuropsychiatric and neurological disorders remain to be fully elucidated. A major obstacle to advancing such studies is the absence of a methodology for precise, cell-type and brain-region-specific quantification of critical epitranscriptomic regulators under these complex brain conditions. To overcome this challenge, we developed a super-resolved, three-dimensional spatial transcriptomics method to quantify key epitranscriptomic switches in intact brains. Using this method, we quantified the expression of Mettl3, an N6-methyladenosine (m6A) methyltransferase enzyme recently shown to be upregulated in the amygdala after adolescent intermittent ethanol (AIE) exposure in rats. We observed a significant increase in cytoplasmic Mettl3 mRNA in neurons, but not in astrocytes or microglia, within the adult central amygdala and the CA1 and dentate gyrus of hippocampus following AIE. In contrast, no significant changes were observed across neurons, astrocytes, or microglia within the basolateral amygdala or the hippocampal CA3. Additionally, we found both the cytoplasmic density and subcellular localization of Mettl3 mRNA were dependent on the specific cell types and brain subregions examined. These results suggest that AIE increases Mettl3 expression in a highly cell-type-specific and spatially heterogeneous manner, underscoring the necessity of high-resolution spatial transcriptomics methods for studying transcriptomic and epitranscriptomic regulations under neurological conditions. Significance StatementEpitranscriptomics plays a crucial role in neuronal functions by influencing the splicing, stability, and translation of genes. However, the exact role of epitranscriptomic mechanisms, such as m6A RNA methylation, in brain disorders remains unclear, particularly in a cell-type and circuitry-specific manner. Here we developed a super-resolved, three-dimensional spatial transcriptomics method and applied it to a model of alcohol exposure. We found differential cell-type- and brain-region-specific modulation of Mettl3, a key m6A enzymatic switch, across major brain regions following adolescent intermittent ethanol exposure in adulthood. Our findings, coupled with our pipeline, are expected to address existing methodological limitations and knowledge gaps, thereby accelerating brain transcriptomic and epitranscriptomic studies under various psychiatric and neurological conditions.

neuroscience↗