Biology subjects

Ma, Q.

Publications and source records attributed to Ma, Q..

At least 19 recordsLinked to original sources

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics

Comparative spatial transcriptomics is now routine, yet the number of tissue sections per group is rarely determined by formal power analysis. Power depends jointly on between sample variation and domains recovered by clustering, a combination not represented by existing tools. We present spaCraft, which converts a replicated pilot into endpoint-specific per-group sample-size recommendations. It fits models of spatial expression, domain geometry and composition, then estimates power through a generate, recover, test loop that reestimates domains in every synthetic sample, allowing clustering uncertainty to enter the recommendation. Its differential-expression and composition endpoints are tested on recovered rather than assumed domains, yielding calibrated power rather than detection rates. We applied spaCraft to four cohorts spanning Visium, Stereo-seq, and Visium HD. In held-out validation, three-sample pilots predicted sample-size requirements in independent real samples, supporting the full chain from pilot fitting through domain recovery to endpoint testing. Sample size thereby becomes an explicit, reproducible property of the planned analysis rather than an informal guess.

bioinformatics↗

bioRxiv2026

RBP4-BACH1 Interaction Modulates Transcriptional Regulation of Insulin Signaling Pathway Genes

Retinol-binding protein 4 (RBP4) exhibits diurnal oscillatory pattern and is elevated under conditions of circadian disruption and in type 2 diabetes mellitus, yet the molecular link between RBP4 and impaired glucose metabolism remains elusive. Here, we overexpressed RBP4 in human hepatoma Huh7 cells and performed integrated RNA sequencing (RNA-seq), Co-immunoprecipitation (Co-IP) coupled with mass spectrometry (MS), and Cleavage Under Targets and Tagmentation (CUT&Tag). We identified BACH1 as a direct RBP4-interacting transcription factor that predominantly binds the TGACTCA motif in promoter regions of genes involved in carbon metabolism pathways. Integrative analysis of RNA-seq and CUT&Tag data uncovered 63 direct target genes co-regulated by RBP4 and BACH1, including known circadian and metabolic regulators SLC7A11, PFKFB3, CTCF, NR1D2 and WEE1 as well as novel candidates SF1 and PIN1. These target genes are significantly enriched in insulin receptor signaling and carbohydrate metabolic pathways. Mechanistically, the RBP4-BACH1 axis reprograms glucose metabolism, linking circadian rhythm disturbances to dysregulated glucose homeostasis. Collectively, our findings establish a functional role for RBP4 in connecting circadian disruption to diabetes and highlight RBP4 as a potential therapeutic target.

molecular biology↗

bioRxiv2026

A Principled Statistical Framework for Analyzing Spatial Patterns in Spatially Resolved Multi-Omics

Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for spatially Resolved Multi-omics, for rigorously analyzing spatial patterns in spatial multi-omics research. STORM incorporates a robust and efficient nonparametric test that quantifies local deviations in molecular feature measurements to detect spatial dependence across transcriptomic and proteomic data. It further estimates an interpretable spatial effect size, supports power calculations for both spatial locations and biological replicates, and enables formal group-level comparisons. In several simulated and experimental spatial multi-omics case studies, STORM demonstrates reliable performance in detecting spatial structures while offering quantitative support for study design decisions. Overall, STORM provides a principled statistical framework that unifies spatial hypothesis testing, effect size estimation, power analysis, and experimental design for spatial multi-omics data.

bioinformatics↗

bioRxiv2026

Gene circuit-driven amplified selection enables evolution of fast-growing Escherichia coli

The laboratory Escherichia coli K-12 strain has doubled no faster than [~]20 minutes for decades. This plateau could reflect a biophysical limit or simply the way batch culture selects on growth rate. Here we show it can be broken through amplified selection with a Red Queen gene circuit, which takes advantage of growth rate heterogeneity in monoclonal populations to selectively suppress slow-growing cells and creates a tunable mapping from intrinsic growth rate to survival. After 70 days ([~]1,000 generations) of amplified selection in MG1655+FHr and subsequent removal of the circuit, a top evolved clone (RQ70) reached a maximum specific growth rate of 2.61 h-{superscript 1} in shake-flask culture. This corresponds to a doubling time of 15.9 minutes, to our knowledge the shortest reported for E. coli K-12, against 18.1 minutes for evolved controls and 20.3 minutes for the ancestor. The gain came at the cost of a [~]3-fold increase in lag time, indicating that the 20-minute plateau is a multi-trait optimum under conventional batch selection rather than an absolute constraint. We argue that synthetic gene circuits can therefore reshape the evolutionary process itself, pushing performance beyond apparent physiological limits.

synthetic biology↗

bioRxiv2026

FITdb, an Integrated Functional Immunogenomics and Transcriptomics Database

Genetic screens in immune cells enable the systematic interrogation of gene function at scale, uncovering key regulators of cell functions such as tumor cell killing and persistence. However, existing datasets typically focus on specific biological questions, employ targeted gene panels, are generated under diverse experimental conditions, and are not readily accessible, which together limit their integration and future usability. To address this, we developed the Functional Immunogenomics and Transcriptomics Database (FITdb), a freely accessible resource that harmonizes functional genomics datasets for the study of immune cell biology. FITdb currently integrates 43 independent functional genetics screens, including 32 pooled and 11 single-cell screens, spanning 20, 696 mouse genes and 22, 293 human genes across 199 immune cell types and conditions. All datasets are uniformly re-analyzed to enable cross-study comparisons. FITdb provides intuitive, gene-centric visualizations, detailed exploration of individual screens, and access to sgRNA-level data. Additionally, built-in tools such as "Compare MyGeneSet" and "Compare MyScreen" identify statistically significant overlaps between user-defined gene lists and functional gene sets in FITdb, and enable direct comparison of user-generated screening data with existing datasets, respectively. Together, FITdb provides a comprehensive, user-friendly platform for accelerating the discovery of immune regulatory programs. The database is freely available at https://fitdb.lji.org. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/741304v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@114d978org.highwire.dtl.DTLVardef@1d15d04org.highwire.dtl.DTLVardef@31c2f4org.highwire.dtl.DTLVardef@f63dfe_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid

The retinal pigment epithelium and choroid are critical for supporting the function and maintaining the homeostasis of the outer retina, and their dysfunction underlies a range of inherited and complex ocular diseases. To comprehensively characterize the cellular, transcriptomic, and epigenomic heterogeneity and dynamics within these tissues, we assembled an integrated multi-omics reference atlas comprising 719,813 single-cell/single-nucleus transcriptomes and 234,007 snATAC-seq profiles from 102 ancestrally diverse donors spanning 0 to 99 years of age, including cells from both the macula and periphery. This atlas resolves 48 distinct cell types or states and catalogs 448,567 open chromatin regions. Specifically, we resolved five distinct RPE subpopulations organized along a central-to-peripheral spatial axis, alongside two distinct stress/senescence states. We reconstructed the transcriptomic and epigenetic zonation of endothelial cells and expanded choroidal stromal heterogeneity by characterizing 11 fibroblast and two pericyte types. Age-associated compositional analysis revealed a significant fractional depletion of melanocytes, PI16+ fibroblasts, and venule endothelial cells with age, alongside a modest relative loss of central RPE and a corresponding increase in far-peripheral RPE. Cell-type-specific aging transcriptomics uncovered shared pathways related to inflammatory responses alongside distinct cell-type-specific signatures. Notably, significant age-associated epigenetic changes concentrated in the macula during the transition from early-to-middle adulthood and remained stable into old age, with transcription factors from the AP-1/bZIP family emerging as the dominant enriched motifs. Finally, integrating this atlas with AMD GWAS data provides novel variant-to-gene evidence implicating LIPG and COL4A3 in AMD pathogenesis. Together, this multi-omics atlas serves as both an invaluable community reference and a powerful discovery engine that translates genetic risk signals into localized target cells and candidate mechanisms, laying a foundation for understanding RPE/choroid biology in health and disease.

genomics↗

bioRxiv2026

Human cardiomyocytes with trisomy 21 exhibit heightened susceptibility and immunological response to SARS-CoV-2 infection

Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is associated with significant cardiovascular complications, including myocardial injury and long-term cardiac dysfunction. Individuals with Down syndrome (trisomy 21) exhibit increased susceptibility to severe COVID-19 outcomes, yet the cardiomyocyte-intrinsic mechanisms underlying this vulnerability remain poorly understood. To investigate genotype-specific responses to SARS-CoV-2 infection, we generated induced pluripotent stem cell-derived cardiomyocytes from individuals with trisomy 21 and their euploid, sex-matched biological relatives. Cardiomyocytes were inoculated with SARS-CoV-2, and viral susceptibility was assessed by immunofluorescence. Bulk RNA sequencing was performed under baseline and infected conditions to define transcriptional programs associated with viral response. Trisomy 21 iPSC-CMs exhibited increased susceptibility to SARS-CoV-2 infection, with greater viral protein expression and a higher proportion of infected cardiomyocytes compared to controls. Baseline transcriptomic analysis revealed no significant differences in canonical viral entry factors including ACE2 and TMPRSS2, suggesting that differential susceptibility is not driven by entry receptor availability. Following infection, both trisomy 21 and euploid control groups activated conserved antiviral pathways; however, trisomy 21 cardiomyocytes displayed a markedly amplified transcriptional response, with substantially greater numbers of differentially expressed genes. Upregulated pathways included interferon signaling, NF-{kappa}B activation, cytokine and chemokine signaling, and innate immune responses, while downregulated pathways were enriched for cardiomyocyte structural integrity, calcium handling, and metabolic processes. Notably, inflammatory and cytokine-related transcripts were significantly more elevated in trisomy 21 cells, consistent with an exaggerated immune response. These findings provide mechanistic insight into the increased cardiovascular risk observed in individuals with Down syndrome and highlight dysregulated immune signaling as a potential therapeutic target in this high-risk population.

Cell Biology↗

bioRxiv2026

Genomic hallmarks of parasexual reproduction in three hybrid groups of the human pathogen Cryptococcus neoformans

Hybridization is a major driver of fungal evolution, yet knowledge of the molecular mechanisms underpinning hybridization and its genomic impact remain limited. Here, we analyse 197 Cryptococcus neoformans genomic sequences, including 13 newly sequenced strains, identifying three genetically clustered and distinct hybrid groups (H1, H2 and H3) each with unique parental origins and ecological associations. Using phylogenomics, population structure analyses, and long-read genome assemblies, we identified hybrid genomes with chromosome-wide loss of heterozygosity (LOH), inheritance of large intact parental haplotype blocks and widespread aneuploidy within and across genomes. These patterns are also observed when progeny were generated with spo11{Delta} parents, indicating these features are a result of a meiotic-independent process such as parasex. This is further underscored by discovery of haploid and near-haploid recombinants in both spo11{Delta} mutant progeny and reanalysed wild-type hybrid progeny. We hypothesise these haploid and near-haploid recombinants are generated through ploidy reduction via independent chromosome assortment because of concerted chromosomal loss, which is a key feature of the parasexual cycle. Phenotypic assays demonstrated that several hybrid isolates have diverse growth and virulence patterns, underscoring functional consequences of genome plasticity. Together, our work suggests a non-meiotic reproductive process contributes to shape the genotypic and phenotypic diversity of Cryptococcus neoformans.

genomics↗

bioRxiv2026

DamageFormer: a damage-aware multimodal deep learning framework for DNA lesion identification from nanopore sequencing

BackgroundDNA lesions arise from endogenous metabolism and environmental exposure and are the major drivers of mutagenesis, aging, and cancer development. However, mapping DNA damage at nucleotide resolution remains a technically challenging task. Nanopore sequencing enables direct detection of chemical perturbations through alterations in ionic current signals. Despite this potential, existing computational approaches remain limited in their capacity to generalize across diverse lesion types and to effectively integrate nucleotide sequence context with raw signal information for accurate detection and localization. ResultsWe presented DamageFormer, a multimodal deep learning framework for detection and localization of DNA lesions using native nanopore sequencing data. Central to this framework is LesionBERT, a damage-aware genomic foundation model built upon DNABERT-2 and enhanced with lesion-focused reconstruction objectives to improve representation of chemically modified bases. DamageFormer integrated LesionBERT with a neural signal model through an adaptive gating mechanism, enabling dynamic weighting of sequence context and nanopore signal evidence. The model was trained using a joint objective that combines prediction, localization, and contrastive alignment losses to promote cross-modal coherence and spatial precision. On an oxidative DNA damage benchmark comprising paired sequence and signal data, DamageFormer achieved an AUROC of 0.99997 for lesion detection and a mean absolute localization error of 0.00439, consistently outperforming state-of-the-art baselines. Model interpretation analyses revealed context-dependent modality weighting that adapts to variation in signal quality and sequence ambiguity. The proposed framework further generalizes to chemically distinct guanine lesions not observed during the training process, demonstrating its robustness and transferability to unseen damage types. ConclusionsDamage-aware biological language modeling combined with adaptive multimodal fusion enables accurate and interpretable identification of DNA lesions from nanopore sequencing data. This framework provides a scalable approach for characterizing genome-wide damage landscapes and illustrates how chemical DNA information can be systematically incorporated into genomic language models. The source code and pretrained models of this work are available at: https://github.com/UF-HOBIYin-Lab/DamageFormer.

genomics↗

bioRxiv2026

Targeting cancer-associated cell surface RNAs with oligonucleotide-drug conjugates enables broad antitumor activity

Recent studies have revealed the presence of RNAs on mammalian cell surfaces, yet the linkage of cell surface RNAs (csRNAs) to cellular states and their potential as extracellular accessible drug targets remains unexplored. Here we develop Cell-surface and Intracellular RNAs Co-mapping (CIRCmap), a highly multiplexed in situ profiling approach that simultaneously detects thousands of cell-surface and intracellular RNAs at single-cell resolution. Using CIRCmap, we uncover that csRNA distributions are correlated with cellular states and identified cancer-associated csRNAs. Integrative analysis of cell-surface and intracellular RNAs within the same cells implies that the csRNAs undergo endocytosis and endolysosomal trafficking, which is further supported by perturbation experiments and co-localization visualization. We design oligonucleotide-drug conjugates (ODCs) that target cancer-associated csRNAs for endocytic delivery of cytotoxic payloads selectively to cancer cells. ODCs exhibit broad antitumor activity in cell lines and an in vivo mouse tumor model, opening a promising new avenue for targeted cancer therapy.

cell biology↗

bioRxiv2026

SpaFlow depicts the dynamics of ligand-receptor interaction in spatial transcriptomics data

Spatial transcriptomics (ST) enables the study of cell-cell communication in native tissue context, but current methods for the ligand-receptor interaction (LRI) inference generally rely on static, distance-based assumptions. Here we present SpaFlow, a reaction-diffusion framework that models ligand diffusion, binding, dissociation, production and degradation to infer spatially resolved LRI activity and hotspots from ST data. Across paired 10x Visium and CosMx metastatic renal cell carcinoma datasets, SpaFlow outperformed existing methods in recovering spatially coherent LRIs, with inferred LRI activity showing stronger association with downstream signaling. In hepatocellular carcinoma after neoadjuvant immunotherapy, SpaFlow identified CXCL12-CXCR4 hotspots enriched at immune-rich tumor boundaries in responders. In aging mouse heart, SpaFlow resolved niche-specific pro-fibrotic and senescence-associated signaling, highlighting Postn-Itgav/Itgb5 as an additional pro-fibrotic axis and Angptl2-Pirb as a candidate mediator of inter-niche senescence-related communication. In human idiopathic pulmonary fibrosis lung, SpaFlow localized CXCL12-CXCR4 signaling between adventitial fibroblasts and CD4 T cells, CD8 T cells, and B cells in the fibrotic surrounding regions. Together, SpaFlow provides a physically informed framework for quantifying spatially constrained cell-cell communication and mechanistically interpreting signaling patterns in complex tissues.

bioinformatics↗

bioRxiv2026

Transcriptional and spatial profiling of fibroblasts from human lungs highlights CTHRC1+ cells as fibrogenic signaling hubs in fibrosis

Lung fibroblasts are key regulators of tissue homeostasis and extracellular matrix (ECM) remodeling, and their aberrant activation drives the progressive parenchymal scarring characteristic of idiopathic pulmonary fibrosis (IPF), a fatal disease with limited therapeutic options. Despite their central pathogenic role, lung fibroblasts are difficult to isolate due to their embedded position within the ECM, and standard in vitro culture conditions may lead to the loss of their native functional and transcriptional characteristics, hampering the study of fibroblast behavior in disease. The transcriptional heterogeneity of lung fibroblast subtypes and the extent to which culture-induced alterations diverge from native tissue signatures remain poorly understood. Here, we integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics of lung tissue from IPF patients and age-matched healthy donors with transcriptomic profiling of cultured fibroblasts collected at passages 1 and 6 after isolation using three optimized protocols: whole lung cell suspension (WLCS), negative fraction enrichment, and outgrowth. Tissue-based analysis identified six transcriptionally distinct mesenchymal subtypes: alveolar, adventitial, inflammatory, peribronchial, CTHRC1+ and smooth muscle cell (SMC). The fibroblast subtype CTHRC1+ represented the most transcriptionally activated pro-fibrotic subtype, showing the greatest upregulation of ECM biosynthesis genes, a prominent role in intercellular communication, and preferential enrichment within fibroblastic foci in IPF lung tissue. Pseudotime trajectory analysis supported a directional transcriptional continuum from alveolar and inflammatory fibroblasts toward the CTHRC1+ state, driven by coordinated activation of pro-fibrotic transcription factors, including RUNX2, CREB3L1, and SCX. In vitro culture progressively reshaped fibroblast transcriptional identity relative to native tissue, with increased collagen and matrix metalloproteinase (MMP) expression during passaging, loss of distinct CTHRC1+ fibroblasts, and gain of alveolar fibroblasts displaying pro-fibrotic activation across all isolation protocols. These findings provide a high-resolution transcriptional map of lung fibroblast heterogeneity in IPF and highlight critical limitations of standard in vitro culture systems for recapitulating native fibroblast diversity, with important implications for the development and evaluation of fibroblast-targeted therapeutic strategies in IPF.

molecular biology↗

bioRxiv2026

Multi-component functionalized Bifidobacterium longum hydrogel for multi-target integrated therapy of colitis-associated anxiety and depression

Inflammatory bowel diseases (IBDs) are frequently accompanied by anxiety and depression, largely driven by perturbed gut-brain axis signaling. However, current oral therapies remain constrained by the spatial and functional separation between intestinal inflammation and central nervous system dysfunction. Here, we present a comprehensive gut-brain dual region integrated therapeutic strategy based on functionalized Bifidobacterium longum hydrogel (INPs@BL@Gel), in which baicalin and tyrosine are coordinated with Fe(III) to form infinite coordination polymers (ICPs), coated with inulin, assembled onto Bifidobacterium longum (BL), and subsequently encapsulated within a pH- and matrix metalloproteinase-responsive silk fibroin-gelatin hydrogel. INPs@BL@Gel exhibits high drug-loading, effective gastric protection, inflammation-triggered release, and long-term intestinal colonization. Within the inflamed intestine, BL and components synergistically suppress inflammatory responses, restore gut microbiota homeostasis, and promote intestinal barrier repair through multi-target integrated therapy. Importantly, BL combined with components markedly enhances the production of beneficial neuroactive metabolites such as homovanillic acid and short-chain fatty acids, which integrated regulate neuroinflammation, preserve synaptic function, and facilitate blood-brain barrier repair via the gut-brain axis. In vivo studies demonstrate that INPs@BL@Gel not only exert potent therapeutic efficacy against colitis and effectively alleviate associated depression, but also reshape the gut microbiota and restore barrier integrity, achieving an remarkable comprehensive therapeutic effect. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/710940v1_fig1a.gif" ALT="Figure 11"> View larger version (59K): org.highwire.dtl.DTLVardef@1a831f3org.highwire.dtl.DTLVardef@1676617org.highwire.dtl.DTLVardef@15f99c1org.highwire.dtl.DTLVardef@1677cc4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOScheme 1.C_FLOATNO (a) Schematic diagram of the design and preparation of functionalized Bifidobacterium longum hydrogel. (b) Exploration of the mechanism of INPs@BL@Gel in treating colitis-associated anxiety and depression through a dual-site multi-target synergistic strategy. C_FIG

pathology↗

bioRxiv2026

An autonomous system for multi-objective continuous evolution at scale

Natural evolution is high-dimensional; organisms adapt to many pressures at once, across substrates, environments, and genetic backgrounds. Yet most directed evolution methods flatten this landscape to a single selection axis, hiding tradeoffs, and limiting what can be learned. Phage-assisted continuous evolution (PACE) is uniquely suited for multivariate selection because horizontal gene transfer couples genotype to propagation and allows the same phage lineage to traverse different selection environments. In practice, implementing this at scale has been prohibitive because each selection demands its own host culture, and every culture must be held for days to weeks within a narrow, infectable density window using continuously responsive bioreactors. In this work, TurboPRANCE is presented as an open-source, queueable robotic platform that integrates [~]200 independently controlled turbidostats with 96 parallel PACE lagoons under closed-loop control. Each turbidostat operates as a fully separate unit that can be equilibrated and initiated on its own schedule, enabling asynchronous starts and sustained operation without intervention. Automated media formulation, programmable dosing, on-deck sterilization, and adaptive scheduling coordinate growth control with the changing needs of the robotic workflow, dynamically adjusting dilution and transfer timing around formulation, sampling, and handling steps to keep each culture at consistent infectable densities despite unpredictable method demands. Cultures can be multiplexed and titrated into lagoons at defined ratios, swapped in and out on a schedule, or kept fully separate across experiments, creating a combinatorial space of selection pressures and programs that is effectively unbounded. Additionally, to enable high-throughput evolutionary tracking that scales with TurboPRANCE, Nanopore long-read sequencing was combined with DeepVariant, a deep learning-based variant caller, enabling population-level tracking of evolving variants. The result is a system that generates high-resolution time-resolvable evolutionary trajectories and large parallel datasets spanning diverse selection regimes, yielding dense, multivariate training data to map and engineer complex fitness landscapes at scale. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/709196v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@b0f8daorg.highwire.dtl.DTLVardef@a1d57eorg.highwire.dtl.DTLVardef@c771aeorg.highwire.dtl.DTLVardef@1f8729a_HPS_FORMAT_FIGEXP M_FIG Turbidostat, Phage, and Robotics-Assisted Near Continuous Evolution (TurboPRANCE) In phage-assisted continuous evolution (PACE), biomolecular activity is coupled to pIII expression, linking function to phage propagation. By altering the host strain, distinct selection pressures can be imposed on the same evolving phage population. In TurboPRANCE, [~]200 selection programs can vary over time, including periodic "drift" (mutagenesis), alternation between pressures, or rotational reassignment of host sources, enabling a combinatorial space of selection pressures. C_FIG

bioengineering↗

bioRxiv2026

Single-Cell and Spatial Methods for Multimodal Functional Glycan Profiling in Tissues

Glycans regulate multiple physiological processes, including immune recognition and cancer progression. In disease, altered glycan landscapes are interpreted by human lectins. Functional glycan-lectin interactions are difficult to profile because glycans are not genome-encoded and their changes are poorly captured by existing multimodal methods. We present two platforms, single-cell outlining and transcriptome sequencing (scGOAT-seq) and GlycoScope, which use human lectins to enable functional glycan accessibility into single-cell and spatial multiomic measurements. ScGOAT-seq quantifies lectin-accessible glycan states with gene expression, while GlycoScope enables multiplexed in situ co-detection of glycans and proteins in tissues. Applying these approaches to immune cells, we identify stimulus-specific glycan remodeling and show that distinct Siglec-ligand-defined programs stratify immune activation states not captured by traditional methods; in follicular lymphoma, GlycoScope, resolves spatial glycan programs associated with malignant B cells and localized immune microenvironments. The presented methods provide a general framework for integrating functional glycan accessibility into single-cell and spatial multiomics.

molecular biology↗

bioRxiv2026

A multi-agent platform for assessment and improvement of bioinformatics software documentation

Rapid advances in bioinformatics have transformed biomedical research in areas such as single-cell and spatial omics, digital pathology, and multi-modal data integration, yet software usability and reproducibility have not kept pace with the growing complexity and proliferation of computational tools. Inconsistent, incomplete, or inaccessible documentation remains a pervasive and underappreciated barrier, limiting tool adoption, hindering reproducibility across laboratories, and reducing the long-term impact of computational methods. Here, we introduce BioGuider, a multi-agent platform designed to systematically evaluate and improve documentation quality in bioinformatics software. Rather than treating documentation as ancillary text, BioGuider models it as a first-class, testable object. The platform implements a modular pipeline for documentation collection, assessment, reporting, and optional correction, with specialized agents that emulate real-world user interactions. BioGuider evaluates documentation against standardized, task-oriented criteria spanning installation, configuration, usage, and tutorials, and supports iterative, constraint-aware refinement while preserving code integrity and biological context. We benchmark BioGuider using a controlled error-injection framework that introduces realistic documentation failures across general, biology-specific, and configuration-related categories. Across multiple large language models, BioGuider demonstrates robust error detection and correction, with strong performance maintained under severe documentation degradation. Applying BioGuider to 47 widely used bioinformatics tools, we observe a positive association between documentation quality and citation frequency, highlighting documentation as a previously under-quantified driver of software adoption and scientific impact.

bioinformatics↗

bioRxiv2026

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation. However, direct measurements of DNA methylation, such as whole-genome bisulfite sequencing or DNA methylation arrays, are costly and require substantial DNA input, limiting their scalability for large cohorts and their applicability to emerging modalities such as single-cell and spatially resolved transcriptomics. In this study, motivated by the fact that DNA methylation is fundamentally a metabolic process, we investigate whether sample-wise DNA methylation activity can be inferred directly from transcriptomic profiles of genes involved in methionine and one-carbon metabolism. We show that a compact metabolic model comprising seven core reaction steps and 98 genes accurately predicts total DNA methylation activity across matched transcriptomic and methylation datasets from CCLE, TCGA, GTEx, and an independent single-cell multi-omics data set. Building on this framework, we develop Total DNA Methylation Activity (TDMA), a physics-informed neural network-based score that enables robust estimation of DNA methylation activity from bulk, single-cell, and spatial transcriptomics data. We demonstrate that TDMA captures methylation-dependent transcriptional regulation and identifies genes and pathways under epigenetic control. Applying TDMA to GTEx, we further reveal strong associations between the predicted total methylation activity, chronological aging, and established epigenetic clocks. We also demonstrated that TDMA can serve as a transcriptomics-derived epigenetic clock and highlights age-dependent roles of folate and glutathione metabolism in epigenetic aging. Applying TDMA to single cell and spatial transcriptomics data collected from pancreatic adenocarcinoma (PDAC), we identified that methionine metabolism and DNA methylation regulates T cell cytotoxicity in the tumor microenvironment of PDAC. Together, this work establishes a scalable, modality-agnostic framework for estimating DNA methylation activity from transcriptomics and provides new insights into the metabolic regulation of epigenetic aging.

bioinformatics↗