Biology subjects

Li, J.

Publications and source records attributed to Li, J..

At least 289 records · Page 16Linked to original sources

bioRxiv2026

NRF2 pathway activation and SPP1⁺TREM2⁺ macrophages drive chemoradiotherapy resistance in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is among the most aggressive cancers, with low rates of durable response to chemoradiotherapy and limited therapeutic options for relapsed disease. To uncover mechanisms of treatment resistance and relapse, we performed comprehensive multi-omics profiling of >100 pre-treatment and post-relapse ESCC tumors from a prospective clinical trial (NCT04694391), integrating whole-exome/genome sequencing, bulk RNA-sequencing, single-cell RNA sequencing, and spatial transcriptomics. We identify somatic alterations in NFE2L2/KEAP1 in nearly 40% of relapsed patients, which are associated with upregulation of NRF2 signaling targets in resistant tumors and cell line models. At single-cell and spatial resolution, relapsed tumors are enriched for NRF2-activated epithelial cells that physically co-localize with immunosuppressive SPP1TREM2 macrophages. This co-localization suggests a synergistic interaction between NRF2-driven tumor programs and macrophage-mediated immune suppression that promotes relapse after chemoradiotherapy. Our findings nominate NFE2L2/KEAP1 mutations as predictive biomarkers for patient stratification and highlight therapeutic targeting of NRF2 signaling and SPP1TREM2 macrophages as rational strategies to overcome resistance in ESCC.

cancer biology↗

bioRxiv2026

Super-resolution single-cell spatial atlas of plant de novo regeneration

Plant de novo regeneration, a capacity absent in animals, relies on stem-cell-niche formation rather than pre-existing reservoirs. Growth hormones such as auxin and cytokinin, together with numerous genetic regulators, participate in regeneration, but the architectural principles coordinating extensive cellular reprogramming at the individual-cell scale remain elusive. Using super-resolution multimodal spatial transcriptomics to 1.16 million cells, we tracked tomato regeneration across temporal intervals from wounding to organogenesis. Datasets are available at http://www.single-cell-spatial.com.

cell biology↗

bioRxiv2026

ATLAS: Graph-based 3D RNA Motif Library Incorporating non-Watson-Crick Interactions

The structures of RNA exhibit recurrent patterns, which are defined as motifs. Motifs are crucial for the biological functions of RNA molecules. The 3D RNA motif library includes 3D RNA structures, providing a resource for motif-based RNA 3D structure prediction and design. We built the Advanced Template Library for Assembly and Structure (ATLAS) that features graph representations of RNA structures, atomic 3D structures from the Protein Data Bank (PDB), and an isomorphism graph searching algorithm. ATLAS includes nucleotide-level graph representations of RNA motifs and corresponding PDB IDs from the PDB and 3D atomic structures retrieved from the PDB. We provide the web service to search for and download RNA motifs and user-defined structures, supporting non-WC interactions. Graph representations include two types of graphs: motifs with non-Watson-Crick (non-WC) and without non-WC. Compared to existing motif libraries, ATLAS includes graph representations with non-Watson-Crick interactions and pseudoknots and integrates the latest RNA structures from the PDB. We also develop a measure of similarity between RNAs based on shared motifs. We proposed a physics-inspired, time-dependent mechanistic model for RNA evolution, whose steady-state solution fits the global distribution of RNA structural similarity.

biophysics↗

bioRxiv2026

Genetic Architecture of Addiction-Relevant Behaviors in Outbred Sprague-Dawley Rats Reveals Loci for Anxiety-Like and Nociceptive Traits

Studies have shown that substance use liability is associated with novelty seeking, anxiety-like behavior, and pain sensitivity. We examined whether common genetic variation in outbred Sprague-Dawley rats explained variation in behavioral measures from three assays with established links to substance use: locomotor response to a novel environment, elevated plus maze, and tail flick. We estimated single-nucleotide polymorphism heritability and performed genome-wide association analyses using permutation-derived significance thresholds (N=534-654 rats across traits). Heritability estimates ranged from 0.14-0.38 across eleven traits. Three independent loci were identified: chromosome 1 for elevated plus maze open-arm behavior (=0.05), chromosome 14 for elevated plus maze immobility (=0.10), and chromosome 17 for tail flick latency (=0.05). Candidate genes included Slc18a2, Gfra1, and Pdzd8 (chromosome 1); Rel and Bcl11a (chromosome 14); and Eci2 and Eci3 (chromosome 17). We compared these loci with our genome wide association study of a F2 intercross of selectively bred high- and low-responder rats, originally derived from Sprague-Dawleys, that model individual differences in externalizing and internalizing behavior. The current loci are distinct from the ones identified in the bred lines. This difference likely reflects selection history in the high- and low-responder F2s, which focused on facets of exploratory locomotion, while loci for anxiety and pain sensitivity traits were identified in the outbreds. This highlights the benefit of using both outbred and selectively bred rats to probe causal variants contributing to individual differences in substance use liability. The current outbred findings implicate monoaminergic signaling, transcriptional control, and lipid metabolism as testable mechanisms for addiction-relevant behaviors.

genetics↗

bioRxiv2026

Postnatal conversion of methylcytosine to hydroxymethylcytosine reconfigures the human neuronal epigenome

Healthy brain development requires a coordinated process of postnatal cellular maturation throughout the first two decades of life that transforms neuronal morphology, connectivity, physiology, and gene expression. The maturation and stable maintenance of neuron identity is driven, in part, by large-scale reconfiguration of the neuronal DNA methylome. Neurons have uniquely high levels of 5-hydroxy-methyl-cytosine (hmC) compared to other cell types, yet the relative contributions of 5hmC and 5-methyl-cytosine (mC) remain unknown because most experimental assays do not distinguish these marks. We measured mC and hmC using bisulfite- and oxidative-bisulfite sequencing in excitatory and inhibitory neurons, along with mRNA and histone modifications, from the prefrontal cortex of 103 human donors, ranging from 38 days to 77 years of age. Up to half of all CG dinucleotides convert from mC to hmC in a gradual process extending throughout the first decade of life, dramatically reshaping the neuronal methylome. Asymmetric enrichment of hmC on the sense strand of actively transcribed genes increases in a linear, clock-like fashion throughout the lifespan, indicating a mechanistic link between transcription and hmC. We found that sex differences in X-linked DNA methylation in the human brain are primarily driven by hmCG rather than mCG, suggesting an important role for hmC in X-chromosome inactivation (XCI) and escape gene expression. We found key changes in 5hmC at dynamic cis-regulatory elements marked by changing cell type-specific levels of active and repressive histone modifications. Collectively, our findings reveal the dynamic trajectory of hmC in human neurons across the lifespan and highlight the association of DNA hydroxymethylation with transcription, chromatin state, and sex-specific gene regulation.

neuroscience↗

bioRxiv2026

A Non-Alchemical Absolute Binding Free Energy Framework for Small Molecule Drugs

A fully physical, non-alchemical framework is presented for absolute binding free energy (ABFE) calculations for protein-ligand complexes. Incorporation of a regularization potential eliminates unphysical artifacts and provides several key advantages: no endpoint catastrophes, fast convergence, robust performance for charged and neutral ligands, and rapid upfront verification. Validated on 30 diverse systems, the method improves predictive accuracy by 15.6% and numerical stability by 17.1% over leading alchemical approaches. This non-alchemical ABFE framework can be a potentially accurate and robust tool, carrying a potential for future computer aided small-molecule drug design.

biophysics↗

bioRxiv2026

Circumventing the synthesizability problem in generative molecular design

Generative structure-based drug design (SBDD) models have shown great promise to accelerate our ability to discover novel drug candidates. However, these models have been criticized for producing compounds that are not very synthesizable, and therefore not practically applicable to drug design. In this work, we propose a way to circumvent the synthesizability issue by introducing a model-guided virtual screening (MGVS) pipeline which pairs SBDD models with efficient chemical similarity search methods to identify synthesizable analogs of generated compounds in existing ultra-large compound databases. Using this approach, we demonstrate that synthesizable analogs of generated compounds with equivalent or better docking scores and similar predicted binding poses can be reliably identified across a wide range of protein targets. We find that MGVS outperforms standard virtual ligand screening (VLS), consistently yielding at least a 25x improvement in screening efficiency across three different SBDD models. As drug-like chemical spaces continue to grow and standard VLS methods focused on exhaustive screening become increasingly impractical, approaches like MGVS that effectively narrow the search space will become critical for advancing drug discovery.

bioinformatics↗

bioRxiv2026

Regulation of the Balance between Concentric and Eccentric Cardiac Hypertrophy by a CDC14A-KMT5A Signaling Pathway

BackgroundDepending upon the type of pathological stress, the heart undergoes concentric or eccentric remodeling. This structural change is associated with diastolic and/or systolic ventricular dysfunction reflecting differentially altered cardiomyocyte morphology, ultrastructure, metabolism, contractility, and survival, as well as interstitial myocardial fibrosis. Despite an association of both concentric and eccentric remodeling with heart failure and sudden death, the molecular mechanisms resulting in abnormal cardiac geometry remain poorly understood. A better understanding of the basic mechanisms conferring these contrasting forms of remodeling should inform novel approaches to preserve normal cardiac structure and function in cardiovascular disease. The protein phosphatase Cell Division Cycle 14A (CDC14A) and its substrate the lysine methyltransferase KMT5A are identified herein as key regulators of the balance between concentric and eccentric pathological cardiac remodeling. MethodsThe regulation of adult rat ventricular myocyte morphology by CDC14A and KMT5A was studied in vitro following gain and loss of function by expression of wild-type and mutant proteins and RNA interference (RNAi). Epigenomic regulation by KMT5A was studied by mapping histone 4 lysine 20 mono-methylation (H4K20me1) modified chromatin sites and correlating them with gene transcription. Regulation of pathological cardiac remodeling in vivo was demonstrated by CDC14A and KMT5A RNAi using adeno-associated virus (AAV) mediated cardiomyocyte-specific small hairpin RNA (shRNA) expression in mice. ResultsCDC14A inhibited the growth in width of cultured adult myocytes stimulated by -adrenergic receptor activation or by serum response factor. KMT5A was downregulated by CDC14A in cardiomyocytes and was required for myocyte growth in width. -adrenergic stimulation of KMT5A-dependent H4K20 mono-methylation across transcription units correlated with regulation of gene transcription. Accordingly, AAV-expressed KMT5A shRNA induced eccentric remodeling and cardiac dysfunction in wild-type mice. Conversely, expression of Cdc14A shRNA improved systolic function and cardiac structure and inhibited pathological gene expression in the Tpm1 E54K mouse with Dilated Cardiomyopathy. ConclusionsCDC14A-KMT5A-dependent epigenomic regulation of gene transcription constitutes a molecular switch that determines concentric versus eccentric cardiac remodeling. These findings identify CDC14A as a potential therapeutic target for the treatment of dilated cardiomyopathy and other forms of heart failure with reduced ejection fraction. Clinical PerspectiveO_ST_ABSWhat is newC_ST_ABSO_LIA function is identified for the first time for the protein phosphatase CDC14A in the heart, regulation of cardiomyocyte morphology and overall cardiac geometry in pathological cardiac remodeling. C_LIO_LIThe lysine methyltransferase KMT5A is shown to mediate the effects of CDC14A in the adult cardiomyocyte by regulating H4K20 mono-methylation, such that reduced KMT5A expression promotes a phenotype resembling Dilated Cardiomyopathy. C_LIO_LIH4K20me1 epigenomic modification is identified as a regulator of cardiac structure and function. C_LI Clinical implicationsO_LICDC14A loss of function experimentation in vivo, resulting in improved cardiac structure and function in a mouse model of Dilated Cardiomyopathy, suggests that CDC14A is a novel therapeutic target for heart failure with reduced ejection fraction. C_LI

cell biology↗

bioRxiv2026

Joint Learning of Drug-Drug Combination and Drug-DrugInteraction via Coupled Tensor-Tensor Factorization with SideInformation

Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning framework for the simultaneous prediction of effective drug combinations and drug-drug interactions, based on coupled tensor-tensor factorization. Specifically, we model drug combination therapies and DDI by representing drug-drug-disease associations and drug-drug interaction profiles as coupled three-way tensors. To address the challenges of data incompleteness and sparsity, the proposed model integrates auxiliary drug similarity information, such as chemical structure similarities, drug-specific side effects, drug target profiles, and drug inhibition data on cancer cell lines, within a multi-view learning frame-work. For optimization, we adopt a modified Alternating Direction Method of Multipliers (ADMM) algorithm that ensures convergence while enforcing non-negativity constraints. In addition to standard tensor completion tasks, we further evaluate the proposed method under a more realistic new-drug prediction setting, where all interactions involving a previously unseen drug are withheld. This scenario closely aligns with real-world applications, in which reliable predictions for emerging or under-studied compounds are essential. We evaluate the proposed method on a comprehensive dataset compiled from multiple sources, including DrugBank, CDCDB, SIDER, and PubChem. Our experiments show that SI-ADMM maintains robust performance and achieves the best results comparing to other tensor factorization approaches, with or without auxiliary information, particularly in the new-drug prediction setting. The implementation of our method is publicly available at: https://github.com/Xiaoge-Zhang/SI-ADMM.

bioinformatics↗

bioRxiv2026

Unwinding of RNA G-quadruplexes induces mouse and human totipotency

Totipotent stem cells (TotiSCs) have significant application prospects in regenerative medicine and assisted reproductive technologies. The mRNAs of totipotency-related genes are rich in G-quadruplex (G4) structures, while the G4 density of pluripotency-related genes mRNAs is relatively low, suggesting a potential functional link between RNA G4s and the totipotent state. Nevertheless, how G4s are involved in the establishment or maintenance of mouse and human totipotency remains poorly understood. Here, we demonstrate that FC3, a small-molecule RNA G4 unwinder, enables efficient induction and maintenance of mouse and human TotiSCs (mG4TotiSCs and hG4TotiSCs). These FC3-induced TotiSCs closely recapitulate the molecular features of 2-cell-stage blastomeres via transcriptomic and epigenomic profiling. Functionally, both mG4TotiSCs and hG4TotiSCs exhibit totipotent capacity: they differentiate into embryonic and extraembryonic lineages in vitro and in chimeric embryos; moreover, they autonomously self-organize into blastocyst-like structures without exogenous signalling. Mechanistically, we identify that Gata2 mRNA, a G4-containing transcript, is essential for FC3-mediated reprogramming. Genetic disruption of Gata2 would abolish totipotency induction. Collectively, RNA G4 unwinding is a new regulatory axis governing totipotency acquisition and maintenance, providing insight into the functional interplay between nucleic acid secondary structures and cell fate plasticity.

cell biology↗

bioRxiv2026

CodonRL: Multi-Objective Codon Sequence Optimization Using Demonstration-Guided Reinforcement Learning

Optimizing synonymous codon sequences to improve translation efficiency, RNA stability, and compositional properties is challenging because the search space grows exponentially with protein length and objectives interact through long range RNA structure. Dynamic programming-based methods can provide strong solutions for fixed objective combinations but are difficult to extend to additional constraints. Deep generative models require large-scale, high-quality mRNA sequence datasets for training, limiting applicability when such data are scarce. Reinforcement learning naturally handles sequential decision-making but faces challenges in codon optimization due to delayed rewards, large action spaces, and expensive structural evaluation. We present CodonRL, a reinforcement learning framework that learns a structural prior for mRNA design from efficient folding feedback and demonstration-guided replay, and then enables user-controlled multi-objective trade-offs during inference. CodonRL uses LinearFold for fast intermediate reward computation during training and ViennaRNA for final evaluation, warms up learning with expert sequences to accelerate convergence for global structure objectives, and introduces milestone-based intermediate rewards to address delayed feedback in long range optimization. On a benchmark of 55 human proteins, CodonRL outperforms GEMORNA, a state-of-the-art codon optimization method, across multiple metrics, achieving 9.5% higher codon adaptation index (CAI), 25.4 kcal/mol more favorable minimum free energy (MFE), and 3.4% lower uridine content on average, while improving codon stabilization coefficient (CSC) in over 90% of benchmark proteins under matched constraints. These gains translate into designs that are predicted to be more efficiently translated, more structurally stable, and less immunogenic, while supporting continuous objective reweighting at inference time.

bioinformatics↗

bioRxiv2026

Enhancing ML-based binder design with high-throughput screening: a comparison of mRNA and yeast display technologies

Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein binders with minimal manual input. While computational design capacity has scaled rapidly, experimental validation methods have lagged, creating a bottleneck in binder discovery pipelines. Here, we apply mRNA display to screen an ML-designed miniprotein binder library and directly compare its performance with the more widely used yeast surface display platform using a single shared DNA library. We screened 2,009 designs targeting the platelet receptor TLT-1 and 3,159 designs targeting the immune receptor B7-H3 across both platforms. While both selection methods reliably identified functional binders, we found that mRNA display preferentially enriched binders with slower dissociation rates. In addition, mRNA display achieved higher library coverage than yeast display, likely rescuing functional designs that are penalized in a cell-based expression system. Biophysical characterization of selected binders from both platforms revealed strong binding affinities and high thermal stabilities. These results showcase the power of integrating ML-based computational design tools with rapid in vitro selection technologies, providing a scalable framework for therapeutic miniprotein discovery. IMPORTANCEMiniprotein binders offer major advantages as next-generation therapeutics, including small size, high stability, and efficient production. In this work, we conduct a side-by-side comparison of mRNA and yeast display as platforms for high-throughput evaluation of de novo miniprotein binders. The binders generated here serve as starting points for therapeutics targeting TLT-1 or B7-H3, two clinically relevant molecules.

bioengineering↗

bioRxiv2026

ChatDIA: A zero-shot large language model workflow for targeted analysis of data-independent acquisition mass spectrometry data

Data-independent acquisition (DIA) proteomics enables reproducible, large-scale protein identification and quantification but remains challenging to analyze due to highly complex MS/MS spectra and chromatographic interference, particularly in low signal-to-noise single-cell proteomics. Here, we introduce ChatDIA, a zero-shot large language model (LLM)-based workflow for targeted DIA analysis that operates through an explicit reasoning-based decision framework. ChatDIA performs automated peptide identification and supports natural-language interaction with DIA data. Unlike purpose-built DIA software that relies on domain-specific models, ChatDIA employs general-purpose LLMs in a zero-shot setting to reason directly over extracted ion chromatograms and generate human-interpretable rationales for each decision. On an expert-annotated Streptococcus pyogenes DIA benchmark dataset, ChatDIA achieved 96.9% accuracy, matching the domain-specific state-of-the-art software DIA-NN (95.5%). In a challenging single-cell HEK-293T DIA proteomics dataset, ChatDIA further demonstrated excellent performance, achieving a lower risk-coverage area under the curve than DIA-NN (0.06 vs. 0.12) and identifying 38.75% and 46.25% of library peptides at 1% and 5% false discovery rate, respectively, compared with 16.25% and 48% for DIA-NN. Together, these results demonstrate that zero-shot LLM reasoning can competitively automate core targeted DIA decision-making while providing transparent, inspectable rationales that enable conversational, interactive validation and data exploration in noisy proteomics applications. More broadly, ChatDIA illustrates how reasoning-based AI systems can move beyond prediction to generate evidence-based decisions, offering a foundation for more general and interpretable computational analysis in proteomics.

bioinformatics↗

bioRxiv2026

Development of KASP Molecular Markers and Construction of Fingerprinting for Cowpea (Vigna unguiculata (L.) Walp) unguiculata Based on ddRAD-Seq

Cowpea (Vigna unguiculata (L.) Walp.) is a globally important legume crop. However, the scarcity of efficient molecular markers has hindered molecular breeding efforts and the protection of plant breeders rights. In this study, we employed double-digest restriction-site associated DNA sequencing (ddRAD-seq) to characterize the genetic diversity of 19 core cowpea germplasm accessions. We generated 791,621 SNPs, from which 13,469 high-quality SNPs were filtered. Population structure and phylogenetic analyses revealed that these accessions cluster into three distinct groups. To facilitate cost-effective and rapid genotyping, we developed a panel of KASP (Kompetitive Allele Specific PCR) markers. Through rigorous screening for polymorphism and stability, we identified six core KASP markers located in exonic regions. These six markers alone were sufficient to discriminate all 19 accessions. Based on these core markers, we constructed a unique DNA fingerprinting profile and assigned specific QR codes for each accession. This study demonstrates that extracting core KASP markers from ddRAD-seq data is a powerful strategy for germplasm identification. The developed fingerprinting system provides a robust, low-cost tool for seed purity testing, variety authentication, and marker-assisted selection in cowpea breeding programs.

plant biology↗

bioRxiv2026

A structurally unique effector shared between Verticillium dahliae and Fusarium oxysporum is involved in cotton defoliation and virulence on other hosts

Defoliating (D) strains of the vascular wilt fungus Verticillium dahliae cause severe yield losses in cotton and olive worldwide, yet the genetic basis underlying this pathotype has remained unknown. Here, we combined comparative genomics, functional genetics, structural analysis, and phylogenomics to uncover the molecular determinant of defoliation. We identified a small, D pathotype-specific genomic region encoding two duplicated secreted effector genes. Simultaneous deletion of both gene copies abolished pathogenicity and defoliation in cotton, olive, Nicotiana benthamiana, and Arabidopsis thaliana, whereas single deletions reduced virulence and genetic complementation restored disease symptoms. Conversely, expression of the D effector in non-defoliating strains was sufficient to induce defoliation. Moreover, or exogenous application of the purified protein, induced wilting and defoliation as well. Structural analyses revealed that D homologs share a conserved but previously uncharacterized protein fold and are distributed across Verticillium and Fusarium species, exhibiting functional diversification and host-specific activity. Phylogenomic and genomic context analyses indicate repeated horizontal transfer events mediated by giant transposable elements known as Starships. Together, our findings identify the D effector as a central pathogenicity factor that drives defoliation and virulence, and demonstrate how Starship-mediated horizontal gene transfer shapes the emergence and dissemination of an agriculturally devastating fungal trait.

plant biology↗

bioRxiv2026

Elimination of intramuscular immunoglobulin accumulation alleviates Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is a devastating neuromuscular disorder due to loss of dystrophin, a cytoskeletal protein critical for muscle integrity and functionality. Despite recent therapeutic advances, there remains a significant unmet need for more effective and accessible therapeutics. Here, we discovered an early accumulation of immunoglobulin G (IgG) in the sarcolemma, which exacerbates tissue inflammation and disease progression. The IgG accumulation primarily resulted from ectopic localization of Fc{gamma}R1, a high-affinity IgG Fc receptor, on dystrophin-deficient myofibers. In two independent murine models, eliminating IgG accumulation via B cell depletion provided sustained benefit to alleviate DMD progression. Our findings uncover a novel disease accelerator in DMD and demonstrate the potential to target this mechanism as therapeutics for broader population of patients with DMD.

immunology↗

bioRxiv2026

Investigation of Regulation and Binding Patterns of the Human Cathelicidin Peptide LL-37 in Complexation with Nucleic Acids, and its Impact on Neutrophil Extracellular Traps

The human cathelicidin host defense peptide LL-37 forms complexes with nucleic acids that can have either beneficial or detrimental health effects. We suggest that these differential impacts are directly connected to dsDNA binding by LL-37 and to complex formation between protomers. Here, we show using phage {lambda} DNA that LL-37 binds non-specifically to dsDNA, condensing it, followed by complex formation between LL-37 peptides. We find that complex formation is concentration-dependent, with low LL-37 amounts yielding loosely aggregated DNA structures, while higher LL-37 concentrations lead to well-defined, disc-like structures of about 150 nm in diameter. The condensation of the nucleic acids, which causes a loss of the characteristic B-DNA features, results from interactions of the phosphodiester backbone with protonated amino acid side chains of the peptide at physiological pH, predominantly in A-T rich sequences of the nucleic acid. However, in our studies, electrostatic interactions did not appear to be the driving force for complexation, but rather we found the -helical structure of the peptide with its amphipathic and hydrophobic surfaces to be essential. Further, we show that LL-37 also interacts with nucleic acids from neutrophil extracellular traps (NETs) in a concentration-dependent way, causing a reduction in NET aggregate area, which may offer new biophysical insights into diseases such as systemic lupus erythematosus (SLE), which involve slower-than-normal NET clearance. Our results indicate the key importance of LL-37 expression levels for regulation of the innate immune system for optimal human health, since the relative amounts of expressed LL-37 present to interact with extracellular DNA will determine the extent to which the DNA can be condensed, which in turn will affect the ability of the body to clear the NETs before they can cause inflammatory conditions.

biophysics↗

bioRxiv2026

An Integrated Pipeline for Cell-Type Annotation, Metabolic Profiling, and Spatial Communication Analysis in the Liver using Spatial Transcriptomics

The liver acts as a central metabolic hub, integrating systemic signals through a spatially organized pattern known as zonation, driven by the coordinated activity of diverse cell types including hepatocytes, stellate cells, Kupffer cells, endothelial cells, and immune populations. Spatial transcriptomics (ST) enables the profiling of thousands of cells with spatial resolution in a single experiment, facilitating the identification of novel gene markers, cell types, cellular states, and tissue neighborhoods across diverse tissues and organisms. By simultaneously capturing transcriptional and spatial heterogeneity, ST has become a powerful tool for understanding cellular and tissue biology. Given its advantages, there is growing demand for applying ST to uncover novel biological insights in the liver under various physiological and pathological conditions including obesity, diabetes, and metabolic dysfunction-associated steatotic liver disease (MASLD). However, to date no comprehensive and practical protocols currently exist for analyzing ST data specifically in the context of liver metabolism. Herein, we present a systematic and detailed protocol for ST data analysis using liver tissues from MASLD mouse models. This guide offers practical support for metabolic based researchers without advanced expertise in coding, mathematics and statistics enabling single-cell RNA-seq referencing for deconvolution-based annotation, curated liver cell type markers for manual annotation, and a GMT file of metabolic gene sets and flux balance analysis to analyze liver metabolic activity. This framework and integrated computational resources for decoding metabolic reprogramming and cellular heterogeneity will empower researchers to uncover novel biological pathways regulating liver metabolism in health and disease.

bioinformatics↗