Biology subjects

Luo, R.

Publications and source records attributed to Luo, R..

At least 19 recordsLinked to original sources

bioRxiv2026

Somatic haplotype reconstruction and variant recalibration from tumor-only long-read sequencing

Separating somatic from germline variants and reconstructing somatic haplotypes are the two central problems of tumor-only cancer genome analysis. Long reads carry the linkage needed to solve both, but chromosome-scale loss of heterozygosity (LOH) and an unknown degree of normal-cell admixture blur the distinction between somatic and germline haplotypes. Here we present LongPhase-TO, the first method to reconstruct somatic haplotypes from a tumor sample alone. Rather than mapping somatic variants onto germline haplotypes, LongPhase-TO co-phases germline and somatic alleles in a unified graph, in which LOH and tumor DNA fraction are resolved internally from heterozygosity depletion and haplotype imbalance rather than a copy-number and ploidy model. Across eight datasets from six cancer cell lines, LongPhase-TO increased haplotype block N50 by a median of 2.9-fold relative to germline phasers. It also consistently improved somatic single-nucleotide variant (SNV) and indel calls from ClairS-TO and DeepSomatic-TO, raising mean F1 from 0.55 to 0.62 and 0.65 for SNVs and from 0.19 to 0.23 for indels, with the largest gains at low tumor DNA fraction. Across breast, melanoma and lung cancer cell lines, LongPhase-TO improves the accuracy of existing somatic callers and reconstructs megabase-scale somatic haplotypes.

bioinformatics↗

bioRxiv2026

Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction

The hexagonal organization of epithelial cells represents a fundamental feature of normal tissue architecture, reflecting the precise spatial coordination that underlies healthy biological structure. Disruptions to this organization--manifesting as spatially aberrant spots with abnormal gene expression and misplaced positioning--are closely associated with disease initiation and progression. Here, we introduce SPADE, a computational framework that integrates single-cell RNA sequencing and spatial transcriptomics data to quantitatively characterize and detect spatial aberrancy. SPADE leverages a variational autoencoder coupled with Gaussian mixture modeling for cell-type embedding and spatial deconvolution, and incorporates conformal prediction to enable uncertainty-calibrated identification of aberrant spots. Through extensive validation, SPADE demonstrates superior performance in identifying biologically meaningful aberrant spots.

bioinformatics↗

bioRxiv2026

Client-server interfaces enable efficient agent-driven variant calling

BackgroundLarge language model (LLM) agents increasingly automate bioinformatics analyses, but most existing bioinformatics tools were built for standalone use by human experts. An agent driving such a tool must reason about its installation, configuration, and execution from documentation for human, spending many turns, tokens, and tool calls per result. How a method is exposed to an agent can therefore matter as much as the method itself. By designing agentic interfaces for these tools, agent can reduce such overhead and improve the reliability of agent-driven analyses. FindingsTo test this design, we re-architected Clair3, a widely used deep-learning-based long-read variant caller, into a client-server system, Clair3-Connect. The client performs all genomics related processing and holds the identifiable data. The server runs only neural-network inference, and the client sends only feature tensors to the server, while sample identifiers and genomic context remain on the client. The client exposes schema-defined agent-facing tools that an agent invokes through single structured calls. On an APOE diplotyping task, all 60 agent runs were correct. The agentic tools used 12K tokens in 3 turns, 6.8 to 14 times fewer tokens than the shell-driven baselines (81K-163K tokens), at about a quarter the wall-clock time and far more stably (4% versus 35% token usage variation). Dropping the pileup and phasing stages to keep the client light left SNP F1 within 0.1-0.3 points of standard Clair3 by 50x coverage, while mutual TLS and AES-256-GCM encryption added 7.2% to end-to-end runtime. ConclusionsRecasting an established algorithm as developer-built, agentic tools behind a secure client-server boundary makes it more efficient, reliable, and easier to deploy for an LLM agent than a third-party wrapper, which cannot recover the defaults and conventions only its developers know. Agentic interfaces should be a first-class deliverable of bioinformatics tool development.

bioinformatics↗

bioRxiv2026

A Pro-Regenerative Petroleum Jelly-Based, Copper-Doped Bioactive Glass Ointment for Impaired Wound Healing in Metabolic Syndrome

Chronic wound healing is often impaired in conditions such as metabolic syndrome, requiring effective therapeutic interventions to promote tissue regeneration and repair. In this study, we evaluated the wound healing potential of petroleum jelly (P Jelly)-based bioactive glass ointments (PBGCu) with varying copper concentrations (0, 1, and 3 wt%) in both in vitro and in vivo models of wound healing. PBGCu formulations demonstrated high biocompatibility with human dermal fibroblasts (HDF) and human umbilical vein endothelial cells (HUVEC). Additionally, PBGCu ointments exhibited strong antibacterial activity against Staphylococcus aureus, suggesting their utility for the care of chronic wounds. In both metabolic syndrome mouse and pig models, PBGCu3-treated wounds showed significantly faster wound closure, enhanced epithelial regeneration, and increased dermal thickness compared to saline and P Jelly controls. Histological analysis also revealed 50% increased vascularization (p < 0.0001) and a 90% reduction in scar formation (p < 0.0001) in PBGCu3-treated wounds. These findings show that PBGCu formulations, especially at 3 wt% copper concentration, significantly improve wound healing by promoting epithelial regeneration, dermal tissue formation, and vascularization, while also offering antibacterial protection. The sustained Cu2+ ions release from PBGCu ointments provides long-term support for tissue regeneration, positioning this ointment composition as a promising therapeutic tool for chronic wound management. Future studies will focus on elucidating the underlying mechanisms and evaluating the therapeutic efficacy of PBGCu formulations in infected wounds. HighlightsO_LIDeveloped a Petroleum Jelly-based copper-doped bioactive glass ointment (PBGCu) enabling sustained and controlled Cu{superscript 2} ion release. C_LIO_LIPBGCu significantly accelerated wound closure and improved epithelial and dermal tissue regeneration. C_LIO_LIPBGCu enhanced hair follicle regeneration and tissue remodeling in full-thickness wounds. C_LIO_LIValidated therapeutic efficacy in both mouse and pig models that support translational relevance. C_LIO_LIOffers a simple, low-cost, and clinically adaptable topical formulation for metabolic syndrome-related wound complications. C_LI

bioengineering↗

bioRxiv2026

Somatic variant detection in normal tissues from single-cell sequencing data

A crucial advantage of single-cell sequencing (SCS) is its ability to identify somatic variants in individual cells, enabling phylogenetic analysis of cellular populations within bulk tissues. While identifying somatic variants in tumor tissues via SCS has become a common practice, doing so in normal tissues remains challenging due to the rarity of somatic variants in normal cells. To evaluate the feasibility of somatic variant calling from widely available single-nucleus RNA-seq (snRNA-seq) and single-nucleus ATAC-seq (snATAC-seq) data, we profiled a Cell-line mix of six HapMap samples prepared by the SMaHT consortium using 10x Genomics 5 snRNA-seq (12k cells with 36k mean reads per cell) and snATAC-seq (11k cells with 14k median high-quality fragments per cell) for variant calling. PacBio long-read whole genome sequencing (WGS) data (109x) generated from individual cell lines were used as ground truth. Two computational tools, Monopogen and SComatic, were used for somatic variant calling from the SCS data. Monopogen achieved single nucleotide variant (SNV) detection accuracies of 93.30% in the snRNA-seq and 99.64% in the snATAC-seq data, both of which outperformed SComatic (74.35% and 94.29%, respectively). Monopogen also consistently detected somatic SNVs at cellular fractions as low as 0.5% (2.54% in snRNA and 0.81% in snATAC) in individual samples. Notably, snATAC-seq exhibited higher genomic coverage breadth and larger number of variants detected than snRNA-seq. While the SCS data have lower overall genome coverage than that of the bulk WGS, the single-cell level variant resolution allows Monopogen to assign variants to their cells of origin with over 80% accuracy in both RNA and ATAC modalities, thereby facilitating studies of clonal evolution and cell-type-specific mutagenesis. Other benchmarking methods were also evaluated (DeepVariant, Cellsnp-lite and Mutect2) for comparison. In conclusion, our study demonstrated the feasibility of performing reliable single-cell somatic mutation calling in a cell-line mixture and discussed the strengths and limitations of current computational methods when applied to normal tissues.

bioinformatics↗

bioRxiv2026

HOPE: Interpretable Histology Analysis with Spatial Omics-Derived Signatures for Precision Oncology

Hematoxylin and eosin (H&E) stained images are fundamental clinical tools for disease assessment. However, even with advanced computational models, their prognostic capabilities remain limited. Spatial omics characterizes tumor microenvironments (TME) in detail yet remains clinically inaccessible due to cost and complexity. In this study, we present HOPE, a lightweight framework that learns TME signatures from paired H&E and spatial omics data during training, then applies these to H&E alone at inference. Leveraging H&E foundation models, HOPE consistently outperforms identical architectures trained without spatial omics guidance across cancer types and cohorts. It further generates interpretable annotations of TME signature on H&E regions, stratifying patients into biologically coherent groups with different prognostic outcomes. HOPE establishes a practical route to translate high-content spatial omics discoveries into scalable, clinically deployable tools.

bioinformatics↗

bioRxiv2026

OpenBase: a universal framework for high-accuracy single-molecule detection of diverse non-canonical DNA bases using nanopore sequencing

Nanopore sequencing holds great potential for the direct detection of non-canonical DNA bases from electrical signals, yet current approaches remain limited to a few classical epigenetic marks. Here we present OpenBase, an open and universal framework that standardizes training-data generation and deep learning based-modeling for single-molecule identification of diverse non-canonical DNA bases. OpenBase transforms nanopore sequencing into a broadly accessible platform for exploring the chemical diversity of DNA.

bioinformatics↗

bioRxiv2026

sPRR signaling in macrophages via the AT1R/Yap/Taz axis to induce renal fibrosis

BackgroundWithin the kidney, (pro) renin receptor (PRR) is abundantly expressed in the collecting duct (CD) and modulate physiological and pathophysiological processes. We have recently reported that activation of CD PRR mediates obstructive renal fibrosis in a mouse model of unilateral ureteral obstruction (UUO). The current study addresses the underlying mechanisms by examining the profibrotic pathway mediated by soluble PRR (sPRR)-dependent alternative macrophage activation. MethodsWe performed UUO or sham surgery on mice with CD-specific deletion of PRR (CD PRR KO) and floxed controls. After 7 days, we assessed fibrosis-related parameters, inflammatory cytokines, M1/M2 macrophage markers, other gene expression markers of kidney injury, and the concentration of plasma sPRR. We also administered vehicle or site-1 protease (S1P) inhibitor PF-429242 (PF) to C57BL/6 mice with UUO to determine the role of sPRR. Experiments were performed in vitro to examine the mechanism of sPRR-His-mediated macrophage M2 polarization and activation of potential target genes in bone-marrow-derived macrophages (BMDMs). ResultsCompared with the floxed control, CD PRR KO decreased macrophage accumulation, M2 polarization, and Yap/Taz expression while improving renal fibrosis and suppressing plasma sPRR levels following UUO. In BMDMs, sPRR-His treatment promoted macrophage M2 polarization, fibrosis, and Yap/Taz expression, which was dependent on angiotensin type 1 receptor (AT1R). ConclusionCD PRR-derived sPRR acts via ATR to promote macrophage M2 polarization and stimulates the AT1R/Yap/Taz axis, which leads to renal fibrosis during UUO.

pathology↗

bioRxiv2026

Inflammatory memory disables a DDR1 degradation checkpoint to enable pancreatic cancer growth

Inflammation and stroma remodeling regulate pancreatic ductal adenocarcinoma (PDAC), but how or if these cues are integrated at the molecular level remains unclear. Here, we identify a metabolic checkpoint that controls the stability of the collagen receptor DDR1, and subsequent tumorigenesis. We show that defective Col-I remodeling deprives PDAC cells of the high affinity DDR1 ligand, [3/4]Col-I, resulting in reduced ATP and activation of AMPK. AMPK phosphorylates DDR1 at T519, promoting its recognition by the E3 ubiquitin ligase adaptor FBXW2 and subsequent degradation. Importantly, this degradation pathway can be disabled by inflammatory signaling. Exposure to inflammatory cytokines induces methylation-dependent silencing of FBXW2, which establishes an inflammatory memory that preserves DDR1 stability, enabling sustained ligand-triggered receptor oligomerization and downstream NF-{kappa}B-NRF2 signaling even in restrictive stromal environments. Together, these findings identify regulated receptor turnover as a mechanism through which stromal architecture, metabolic state, and inflammatory memory are integrated to control PDAC progression.

cancer biology↗

bioRxiv2026

An interferon independent innate immune response to double stranded RNA in embryonic stem cells

In vertebrate early embryos and embryonic stem cells, the interferon (IFN)-centered double-stranded RNA (dsRNA) sensing and signaling pathway is markedly suppressed, implying the existence of an alternative repertoire of dsRNA sensors in these stages. We recently reported that dsRNA treatment triggers translation inhibition using Prkra as a sensor in zebrafish and mouse early embryos. Independently, here we show that dsRNA stimulates the expression of a subset of interferon-stimulated genes (ISGs) in the absence of IFN production, establishing a defensive state in mouse embryonic stem cells (mESCs). Upon dsRNA stimulation, the multifunctional DExD/H-box RNA helicase Dhx9 is recruited into dsRNA-induced condensates, where it promotes the recruitment and functional suppression of the Mdm2/Cul4A ubiquitin ligase machinery by excluding their substrate adaptor Ddb1, thereby stabilizing p53 and Stat1. Dhx9, p53 and Stat1 then cooperate to stimulate an ISG response. This signaling is important for the defense against ZIKV infection in mESCs as well as other dsRNA stresses. Such a cascade is conserved in human ESCs, while in zebrafish embryos, p53 but not Stat1 is required for the transcriptional response. Our findings define an immediate, cell-autonomous innate immune pathway operating in ESCs and vertebrate embryos.

immunology↗

bioRxiv2026

A Genetically Engineered Human Organoid Model Reveals Distinct Genetic and Epigenetic Barriers of Lineage Plasticity in Early PDAC Transformation

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro, validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

cancer biology↗

bioRxiv2026

FourC: identifying significant and differential contacts in 1D chromatin conformation data

4C-seq is a cost-effective 3C-based assay that measures the interactions between a single genomic element and all other genomic elements. However, 4C-seq data remains semi-quantitative because it cannot be deduplicated without UMIs. To address this, we developed an open source method, FourC, based on a Bayesian Bernoulli regression model, that overcomes the duplication problem and models spatial patterns with Gaussian processes to identify significantly enriched and differential contacts. We demonstrate the utility of FourC on 4C-seq data that profiles the local chromatin structure at key genes necessary for pancreatic differentiation and under CRISPR perturbation of enhancers.

bioinformatics↗

bioRxiv2026

Leveraging ONT move table values for signal aware variant calling

Oxford Nanopore Technologies (ONT) sequencing enables long-range haplotype phasing and contiguous genome assembly but still exhibits elevated error rates that challenge small variant calling, particularly for insertions and deletions (Indels). While raw electrical signals contain rich information, existing signal-aware methods require computationally intensive processing of large signal files. Here, we present Clair3 v2, a method that leverages the ONT move table--a lightweight byproduct of basecalling that maps signal events to nucleotide positions--to improve variant calling accuracy. Clair3 v2 builds upon Clair3 and integrates signal-level dwelling time to significantly enhance variant calling performance. We also propose a genome position based circular buffer to incorporate dwelling time with minimal computational overhead. Benchmarking across six Genome in a Bottle samples demonstrates substantial improvements in variant calling accuracy. With HAC basecalling, Clair3 v2 achieves a mean SNP F1-score of 97.69% at 10x depth (compared to 96.45% for baseline Clair3), and Indel F1 scores improved from 64.27% to 76.70%, while gains persisted at higher depths. The benefits were most pronounced for longer Indels and in complex genomic regions, where Indel F1 scores in long homopolymer regions improved from 14.3% to 45.2%. Benchmark results across various basecalling modes, samples, and coverage settings outperformed Clair3 baselines and other methods, including DeepVariant and Dorado Variant, and demonstrate the significant benefits of Clair3 v2. Furthermore, Clair3 v2 incurs negligible runtime compared to standard Clair3, making it practical for routine use.

bioinformatics↗

bioRxiv2026

Coacervate Droplets as a Liquefier for the Solid-to-Liquid Transition of RNA Aggregates

Biomolecular condensates are functionally gated by their material properties. While RNA is a primary structural scaffold, its sequence-specific interactions can aberrantly drive condensates into dysfunctional solid aggregates. Yet, actively reversing this solidification to restore function remains a fundamental challenge, impeding progress in synthetic biology and therapeutics interventions. Here, we establish that complex coacervates can be engineered as liquefiers to actively remodel solid RNA-peptide aggregates into functional liquid droplets. Integrating systematic experiments with all-atom simulations, we decode a multiscale mechanism: coacervate infiltration at the micrometer scale mediates molecular buffering at the nanometer scale, which fluidizes the crosslinked network to drive macroscopic liquefaction. This capability is governed by a design rule, where coacervates formed by small-molecular anions exhibit optimal efficacy. We deploy this principle to functionally rescue silenced RNA within a model of pathologically solidified ribonucleoprotein assemblies. Our work provides a general framework for the active, compositional control of biomolecular phase behavior, with direct implications for managing pathological aggregation and engineering functional condensates in synthetic and living systems.

biophysics↗

bioRxiv2025

A stem cell knockout village reveals lineage rewiring and a non-canonical islet cell fate in monogenic diabetes

Genetics studies have identified a core set of regulators essential for pancreatic {beta} cell development, many of which are mutated in monogenic diabetes. However, how these mutations alter developmental trajectories to produce pathological cell states remains elusive. Here we introduce a knockout village framework that enables longitudinal scRNA-seq profiling of 79 human pluripotent stem cell mutant lines targeting 30 developmental regulators, including 15 diabetes genes, across five islet differentiation stages. We show that loss of lineage regulators impairs {beta} cell formation in a stage-specific manner and rewires developmental trajectories towards competing lineages. Notably, several monogenic diabetes gene mutations drive a shift from {beta} cells to enterochromaffin (EC)-like cells, a recently recognized non-canonical islet cell fate. These EC-like cells exhibit incomplete activation of hormone regulation programs, along with elevated neuron signatures. Leveraging the diversity of cell fate outcomes across mutants, we predicted and experimentally validated ISL1 as a key downstream effector of PDX1 and PAX6 that safeguards {beta} cell identity against an EC-like fate. Together, our findings reveal cell fate rewiring as a widespread, previously underappreciated pathological mechanism in monogenic diabetes and establish a scalable platform for uncovering developmental vulnerabilities in human genetic disorders.

developmental biology↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2025

LongPhase-S: purity estimation and variant recalibration with somatic haplotying for long-read sequencing

Accurate detection of somatic variants is crucial for precision oncology, and long-read sequencing offers unprecedented advantages in resolving complex cancer genomes. However, most long-read somatic callers rely on phasing built for a diploid genome, an assumption violated by various contamination, subclonal heterogeneity, and aneuploidy in tumors. We present LongPhase-S, a novel method that jointly reconstructs somatic haplotypes, infers tumor purity, and recalibrates somatic variants in a purity-aware manner for paired tumor-normal long-read sequencing. By anchoring each somatic read to a parental germline lin-eage, LongPhase-S provides a phase-resolved view in which germline and somatic reads are disentangled across the genome. Building on somatic haplotyping, LongPhase-S trains a phase-aware purity estimator that outperformed existing methods. Using eight benchmark datasets comprising six cancer cell lines, including breast, melanoma, and lung cancers, LongPhase-S boosted the accuracy of state-of-the-art somatic callers wuth the estimated purity and somatic haplotypes. Specifically, mean F1 scores increased by 4.5% and 7.1% for single-nucleotide variants and insertions and deletions with ClairS, and by 1.2% and 0.5% with DeepSomatic. Collectively, these results showed that somatic haplotyping is a critical yet missing piece in existing somatic callers, which enables purity-aware and phase-resolved variant interpretation in heterogneous tumors.

bioinformatics↗

bioRxiv2025

Clair-Mosaic: A deep-learning method for long-read mosaic small variant calling

Mosaic variants, defined as postzygotic mutations occurring during an organisms development from zygote to adult, play critical roles in developmental biology, aging, and diseases such as cancer and neurological disorders. However, their accurate detection remains challenging due to low abundance in the genome and low variant allelic fractions (VAF). While current mosaic variant callers are primarily designed for short-read sequencing, no method is available for long-read sequencing, which can generate tens of kilobase-long reads to cover complex genomic regions inaccessible to short reads. To fill the gap, we present Clair-Mosaic, a deep-learning-based method for detecting mosaic small variants from long-read data. Clair-Mosaic was trained on hundreds of millions synthetic variants encompassing diverse read coverages and allelic fractions, enabling it to detect low-VAF mosaic variants with high sensitivity in paired-sample and single-sample modes. In addition to neural network prediction, Clair-Mosaic distinguishes genuine mosaic variants from sequencing artifacts by leveraging their inherent haplotype relationship in phased long reads. Furthermore, a Bayesian mosaic-germline discriminator is introduced to distinguish mosaic variants from germline variants. It also employs multiple post-calling filters, including a mosaic variant database and multiple germline population resources, to tag common germline and mosaic variants. Comprehensive benchmarking on synthetic datasets and real samples demonstrated Clair-Mosaics outstanding performance in ONT and PacBio. Clair-Mosaic is also applicable to short-read data and outperforms methods like MosaicHunter, MosaicForecast, DeepMosaic, and DeepSomatic. Clair-Mosaic is open-source and available at https://github.com/HKU-BAL/Clair-Mosaic.

bioinformatics↗