Biology subjects

Li, Y.

Publications and source records attributed to Li, Y..

At least 307 records · Page 17Linked to original sources

bioRxiv2026

Biological characterization and host-phage interaction of a novel Pectobacterium phage

Taro (Colocasia esculenta (L.) Schott) is an important vegetable and food crop in China, but in recent years, soft rot disease has frequently occurred during its cultivation and production. This disease damages the underground corms and petiole bases of taro, causing decay in the affected parts and emitting a foul odor, leading to wilting and lodging of the entire plant. This has resulted in significant economic losses to taro production in China, along with food safety issues and ecological problems caused by excessive pesticide use, making it urgent to find a green and efficient control method. Due to its specificity and environmental safety, phage therapy exhibits advantages that chemical pesticides cannot match, representing a promising alternative to chemical pesticides for controlling pathogenic bacteria. In the preliminary work of this study, a bacterial strain was isolated from taro soft rot in Shaoguan, Guangdong, and initially identified as Pectobacterium colocasium ZXC0623. Using this strain as the host bacterium, a Pectobacterium phage was screened and named QJphage. We analyzed its physicochemical properties and obtained its biological characteristics, including optimal titer, optimal infection latency period, optimal infection multiplicity, optimal storage solvent, and resistance to ultraviolet light, pH, and chloroform. Through homologous alignment analysis, eight tail fiber proteins encoded in the QJphage genome were predicted as putative receptor-binding proteins (RBPs). To validate this prediction, the corresponding genes were cloned downstream of the egfp gene via homologous recombination, and the resulting recombinant plasmids were transformed into a prokaryotic host to express EGFP-tagged tail fiber fusion proteins. Fluorescence detection and confocal laser scanning microscopy confirmed that the protein encoded by ORF04 functions as the RBP. Furthermore, lipopolysaccharide (LPS) was knocked out in the host strain P. colocasium ZXC0623. Both{Delta} LPS1 and{Delta} LPS2 mutants formed smaller plaques compared to the wild-type strain, and the{Delta} LPS1 mutant additionally exhibited a significant reduction in plaque number, indicating that LPS serves as a receptor involved in QJphage adsorption. Finally, transcriptomic analysis during the latent period of infection focused on 20 genes predicted to be associated with phage-host receptor binding and anti-phage immune systems. The results revealed that pilin proteins act as potential reversible adsorption receptors for QJphage, while the host strain ZXC0623 also possesses a diverse repertoire of anti-phage defense systems. Collectively, QJphage exhibits stable physicochemical properties, a well-defined LPS-dependent infection mechanism, and a host with diverse defense systems, providing a foundation for the control of taro soft rot and future phage-related research. ImportancePhage therapy has emerged as a highly effective biocontrol strategy against Pectobacterium, with its specificity making it particularly valuable. A critical aspect of this approach is the identification of phage receptors. The initial step in the phage life cycle involves adsorption to the bacterial host, beginning with reversible contact followed by irreversible binding between phage receptor-binding proteins and specific bacterial surface receptors. Potential receptors include glycolipids in the Gram-negative outer membrane, capsular polysaccharides, and various membrane proteins or appendages. In this study, we first characterized the physicochemical properties of the isolated QJphage. Through integrated transcriptomic and whole-genome analyses, we demonstrated that the LPS of Pectobacterium specifically interact with the tail fiber proteins of QJphage. This research provides the first evidence revealing the molecular mechanism of interaction between Pectobacterium and its phage, establishing a foundation for developing phage-based control strategies against soft rot diseases.

microbiology↗

bioRxiv2026

A Head and Neck Cancer Patient-Specific Microphysiological System for Predicting Response to Chemoradiation

Head and neck cancer (HNC) is the 6th most common malignancy worldwide. 60% of patients present with advanced disease and approximately 50% of patients recur following primary treatment. Chemoradiation remains a standard of care for most patients. However, clinicians lack functional tools to predict which patients will respond to chemoradiation prior to treatment and current models, including organoids and animal model systems, fail to capture either full complexity or patient-to-patient heterogeneity of the individual HNC tumor and microenvironment (TME). Here, we have developed, characterized, and tested a patient-specific microphysiological system (MPS) that reconstructs the HNC TME in a vascularized 3D environment. This MPS was constructed from malignant cells, fibroblasts, and immune cells from a patients surgically resected tumor, seeded within a 3D hydrogel with molded endothelial lumens. Single-cell RNA sequencing confirmed that the MPS preserved 12 transcriptionally distinct cell populations found in matched native tissue. The platform recapitulated tumor hypoxia, with a 12-fold increase in hypoxic marker expression that altered radiation response, consistent with clinical HNC biology. Compartment-resolved imaging revealed distinct treatment dynamics in tumor, stromal, and vascular regions, and individual patients exhibited divergent responses to chemoradiation in spheroid morphology, cell viability, and migration. We found the slope of spheroid area change with treatment tracked with tumor recurrence, suggesting this metric could serve as a functional predictor of therapeutic response.

bioengineering↗

bioRxiv2026

Advancing ab initio genome annotation with OrionGeno

The rapid expansion of eukaryotic genome sequencing has created an urgent demand for accurate and scalable genome annotation. Existing ab initio methods often struggle to reconstruct complex gene architectures and generalize across distant lineages, limiting their use for large-scale annotation. Here we present OrionGeno, a phylogeny-aware deep learning model for end-to-end eukaryotic genome annotation. OrionGeno integrates phylogenetic context, long-range sequence modeling and joint prediction of gene structures and repetitive elements to annotate exons, introns, untranslated regions and repeats directly from genomic sequences. Applied to chromosome-level eukaryotic genomes from NCBI that lack annotations, OrionGeno generates annotations for more than 5,300 genomes, substantially expanding public annotation resources. Across diverse eukaryotic lineages, OrionGeno outperforms state-of-the-art methods at the exon, gene, protein-sequence, and protein-structural levels. It also identifies candidate protein-coding loci absent from reference protein-coding annotations in well-curated genomes. Together with a web platform and integrated annotation database, OrionGeno provides a scalable and accessible framework for translating genome assemblies into functional biological resources and supporting large-scale biodiversity initiatives such as the Earth BioGenome Project.

bioinformatics↗

bioRxiv2026

Engineering Dual-Target Chimeric Lysins for Synergistic Eradication of Porphyromonas gingivalis

Periodontitis is a chronic inflammatory disease driven by complex subgingival multispecies biofilms, in which Porphyromonas gingivalis plays a central role in coordinating microbial interactions. Together with host-associated factors, these microbial communities create dual constraints that limit antimicrobial efficacy. In this study, we engineered a library of recombinant lysins by fusing membrane-destabilizing peptides to the periodontal pathogen-derived lysin LysPd078 and identified four optimized variants (PlyPd06, PlyPd19, PlyPd27, and PlyPd44) with enhanced salt tolerance, environmental stability, and bactericidal activity. In a clinically derived polymicrobial oral biofilm, PlyPd44 at only 25 g/mL eradicated 96.6% of P. gingivalis. In a humanized oral microbiota mouse model of periodontitis, selected PlyPds significantly reduced inflammation and inhibited alveolar bone loss compared with LysPd078 and minocycline. Collectively, these results establish membrane-destabilizing peptide-enabled lysins as a promising platform for developing microenvironment-adapted precision antimicrobials for periodontitis.

microbiology↗

bioRxiv2026

mChIP-seq for Multiplex and Multifactorial Epigenomic Profiling Uncovers Cancer-specific Histone Features in Cellular and Circulating Nucleosomes

Epigenomic profiling facilitates access to investigate regulatory roles of histone marks in a type-specific cell, and serves as a critical path for discovering noninvasive epigenetic models in cell-free nucleosomes. Here, we present mChIP-seq, an epigenomic profiling technology that is compatible with both cell and cell-free samples for synchronously profiling multifactorial epigenetic landscapes on multiple samples. Combining sample indexing in a single reaction with a pool-and-split strategy for immunoprecipitation, mChIP-seq enhances efficiency and reduces cost. Using mChIP-seq, we profiled H2A.Z and 10 histone modifications in cell lines representing 9 cancer types. Integrative analyses further revealed an atypical association of H2A.Z and H3K4me3 at promoter regions in cancer. Based on mChIP-seq, we developed cf-mChIP-seq for circulating nucleosomes, which requires as little as 25 l of plasma per profile. Profiling 38 plasma samples for H2A.Z, H3K4me3, H3K27ac, and H3K9me3 with cf-mChIP-seq revealed distinct histone mark-associated cfDNA fragment patterns in breast cancer versus healthy control, highlighting the potential of cf-mChIP-seq to expand liquid biopsy methodologies. These results demonstrate that mChIP-seq is a widely applicable technology for large-scale epigenomic profiling of nucleosomes in cellular or cell-free forms.

genomics↗

bioRxiv2026

An Isoform-Centric, Structure-Aware Framework for Protein Function Prediction and Evaluation, Instantiated in 3DisoDeepPF

Understanding and accurately predicting function across protein isoforms has been a long-standing challenge with profound implications for both biological and translational research. However, most functional annotations and benchmarks remain tied to genes or reference protein sequences, leaving limited support for distinguishing protein isoforms from the same gene whose sequence, structure and domain composition may lead to different functions. To address this gap, we developed an isoform-centric framework for protein function and domain annotation that integrates a dense graph of sequence and structure similarity with protein features. We implemented this framework as 3DisoDeepPF, a graph-based multimodal model, and applied it to a breast cancer isoform atlas. Across canonical benchmarks and evaluations at isoform resolution, 3DisoDeepPF showed strong performance in predicting GO terms and Pfam domains and remained robust in tests with homology control. It further captured changes in Pfam domain composition among isoforms from the same gene, including reference-relative domain gain and loss. An evidence tracing module links predicted labels to supporting proteins in the graph, as illustrated by the calcium and integrin binding protein 1 (CIB1). Together, this study provides a framework informed by protein structure for function and domain annotation at isoform resolution, converting human protein isoform diversity into traceable functional evidence for cancer atlas interpretation.

cancer biology↗

bioRxiv2026

Hypermorphic TaARF4 shapes wheat architecture by repressing SPL-mediated developmental timing

Integrating spatial morphogens with temporal developmental clocks is fundamental to optimizing plant architecture and crop yield, yet their molecular interface remains elusive. Here, we characterize Branched shoot 1 (Bsh1), a semi-dominant wheat mutant exhibiting non-canonical upper-aerial branching and aberrant spike development, caused by a T265I substitution in the DNA-binding domain of the auxin response factor TaARF4-A2. Unlike flanking mutations governing protein stability in basal land plants, this central substitution uniquely converts TaARF4-A2 into an auxin-insensitive hypermorphic repressor. The mutant protein disrupts auxin-cytokinin homeostasis and markedly enhances the native repression of SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factors, the core timers of developmental phase transitions. This spatial and temporal uncoupling unleashes upper axillary buds from dormancy, promoting aerial branching and diverting resources from reproductive development. Our findings demonstrate how a conserved spatial morphogen effector was evolutionarily rewired in polyploid wheat to dictate the temporal SPL clock, orchestrating species-specific shoot architectural innovation.

plant biology↗

bioRxiv2026

NbRD21 protease controls receptor kinase homeostasis in Nicotiana benthamiana

RD21-like proteases are papain-like cysteine proteases with a C-terminal granulin domain that are abundant and ubiquitous in angiosperms and have often been implicated in immunity. We previously found that the activity of RD21 in Nicotiana benthamiana (NbRD21) is suppressed during infection with Pseudomonas syringae. Here, we studied the role of NbRD21 in immunity and proteome processing. NbRD21 was disrupted by genome editing and rd21 mutants were subjected to disease assays and shot-gun proteomics. Dipeptide substrate zLR-AMC was used in protease assays and agroinfiltration was used to transiently express NbRD21 and candidate substrates. Genome edited lines lacking NbRD21 develop normally but have drastically reduced zLRase activity and are significantly more susceptible to P. syringae. Shot gun proteomics revealed an increased accumulation of [~]20 diverse receptor-like kinases (RLKs) in untreated rd21 knockout lines, but their transcript levels are unaltered when compared to wild-type plants. 35S-driven GFP-tagged RLKs accumulate more upon transient expression in rd21 plants than in wild- type plants. These data indicate that NbRD21 post-translationally controls RLK homeostasis, either by directly degrading RLKs, or indirectly by regulating endocytic RLK recycling.

plant biology↗

bioRxiv2026

Connections across regional glymphatic clearance, neural activity and amyloid-β deposition in cortex

Neural activity inevitably produces waste, which promotes neurodegeneration with topographic features. The glymphatic system is important for waste clearance. However, the spatial characteristics of glymphatic clearance across cortex and whether it interplays with neural activity in contribution to amyloidosis in human remain unexplored. Here, by intrathecal administration of gadolinium-based contrast agents, glymphatic influx and clearance patterns across cortex in 96 participants are depicted via Glymphatic MRI. Analyses integrating post-mortem transcriptomic profiles from Allen Human Brain Atlas indicate that, genes related with excitatory and inhibitory neurons, and pathways engaging in synaptic function were enriched in regions with faster glymphatic clearance. FALFF was calculated from resting-state fMRI to represent neural activity. At the regional level, based on a subgroup with rs-fMRI (N = 15), regional glymphatic clearance was positively coupled with spontaneous neural activity. Mismatch index, reflecting decoupling between spontaneous neural activity and glymphatic clearance function, turned out to be positively associated with regional severity of amyloidosis using open-source 11C-PiB dataset. Together, this study for the first time demonstrates the intricate interplays between neural activity and glymphatic dynamics from transcriptional to physiological level. The mismatch between these two processes may serve as an undescribed comprehensive mechanism promoting regional vulnerability to proteopathy and subsequent neurodegeneration in cortex.

neuroscience↗

bioRxiv2026

H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics Profiling

Spatial omics technologies have revolutionized the molecular profiling of tissues but remain constrained by high costs and limited scalability. While hematoxylin and eosin (H&E) staining is ubiquitous, it lacks molecular specificity. Here, we present H2O (Histopathology to Omics), a generalist AI framework that bridges the modality gap between histopathology and spatial multi-omics, enabling the direct inference of spatial transcriptomics (ST) and proteomics (SP) landscapes from routine H&E images. H2O integrates Vision Transformers (ViT) with Large Language Models (LLM) via contrastive learning to align histological morphology with semantic molecular knowledge. This cross-modal approach allows the model to incorporate spatial expression profiles into histological pattern recognition, effectively decoding the molecular heterogeneity underlying tissue morphology. Trained on a pan-tissue dataset of 1.3 million paired H&E-spatial patches across 25 organs and cancer types, H2O predicts spatial omics expression from histology with high concordance to sequenced measurements and consistently outperforms state-of-the-art models across three cancer benchmarks. Notably, H2O recovers the MIF-CD74/CD44 signaling axis directly from H&E images, highlighting its capacity to infer biologically meaningful cell-cell communication without molecular profiling. Applying on three additional public cohorts covering fetal and paediatric thymus tissues, human metastatic lymph node, and breast cancer, encompassing human development, 3D spatial frameworks, and integrative multi-omics, H2O yields biologically concordant insights, demonstrating superior accuracy, robustness, and generalizability across real-world applications in diverse scenarios. H2O converts routine histopathology into a portal for spatially resolved multi-omics profiling by computationally generating transcriptomic and proteomic landscapes, thereby enhancing tissue phenotyping and enabling scalable, integrative tissue-atlas construction.

bioinformatics↗

bioRxiv2026

scConcept enables concept-level exploration of single-cell transcriptomic data

Interpreting high-dimensional single-cell transcriptomic data remains challenging, as existing methods rely on latent representations or prior knowledge that require extensive post hoc analysis to derive biologically meaningful insights. Topic models provide interpretable gene-level signals but often produce redundant and coarse-grained programs that are difficult to translate into coherent biological concepts. While recent foundation models and large language models (LLMs) show promise, they are not readily applicable to large-scale single-cell data or fail to provide structured, cell-level interpretations. Here we present scConcept, a framework that introduces concept-level representation by transforming gene-level topic representations into structured, human-interpretable biological concepts. By integrating neural topic modeling with LLMs, scConcept distills fragmented gene programs into semantically coherent concepts defined by a biological label, description, and gene set, and quantitatively maps them back to individual cells. Across 16 single-cell datasets, scConcept improves clustering performance by 27.1% and interpretability by 50.7% over state-of-the-art methods. These concept-level representations enable interpretable cell-state annotation and capture gene programs that generalize across datasets. In cancer applications, scConcept identifies clinically relevant programs associated with tumor progression and patient survival, and links them to candidate therapeutic targets. Together, scConcept establishes concept-level representation as a general and scalable abstraction for interpretable single-cell analysis.

bioinformatics↗

bioRxiv2026

CellChem: Cellular transcriptional responses reshape molecular representation space for efficient and multi-scale drug discovery

Despite decades of progress in computational drug discovery, deep learning-based molecular representation models remain largely structure-centric, assuming that chemical similarity approximates functional similarity. However, drug effects in cells are shaped not only by chemical similarity but also by molecular interactions in the cellular context. To capture this complexity, we introduce CellChem, a cellular-chemical representation-learning framework that learns cell-guided molecular representations of small-molecule actions within cells. By incorporating large-scale cellular transcriptional profiles during pretraining, CellChem reshapes molecular representation space from structure-centric to a balanced integration of structure and function. The learned CellChem molecular representations exhibit biologically meaningful geometric organization, such that distances between molecules encode not only structural similarity but also similarity in the cellular responses they elicit, independent of downstream tasks. Using downstream derivative models such as cell-guided compound-protein interaction prediction and drug-induced transcriptional response profile generation, CellChem supports highly efficient, multi-scale drug discovery, achieving significantly better performance than traditional models.

molecular biology↗

bioRxiv2026

Normobaric hypoxia alters the transcriptional response of healthy human skeletal muscles to a single session of high-intensity interval exercise

Given its well-documented effects on human physiology, hypoxia has garnered increasing interest for its potential to enhance specific adaptations to exercise. However, the molecular response of skeletal muscle to exercise under normobaric hypoxia remains poorly understood. To address this gap in knowledge, ten healthy young males completed a crossover study in which exercise in hypoxia was compared to exercise in normoxia matched by either absolute or relative intensity. This design allowed us to identify shared transcriptomic responses across all three conditions, as well as changes that were specific to exercise intensity or hypoxic exposure. Skeletal muscle biopsies were collected before, immediately after, and at 3 and 24 hours following each exercise session, with RNA sequencing performed to assess changes in gene expression. Following exercise, a greater number of differentially expressed genes were observed in hypoxia compared to normoxia at 24 h post-exercise. This hypoxia-specific response involved the downregulation of multiple mitochondrial pathways and appears to be regulated by a transcriptional network comprising both positive and negative regulators of HIF-1 activity. These findings highlight the ability of normobaric hypoxia to influence exercise-induced gene expression and suggests that it may promote distinct molecular adaptations in skeletal muscle following longer-term training.

physiology↗

bioRxiv2026

MSAgent: An Evidence Grounded Agentic Framework for LLM-driven Scientific Exploration in Mass Spectrometry-based Metabolomics

Mass spectrometry (MS) is a cornerstone high-throughput technology for molecular discovery, yet the reliable elucidation of chemical structures remains a formidable, expert-dependent bottleneck. Currently, achieving a reliable molecular identification from raw mass spectra necessitates a manual assembly--a labor-intensive ordeal of heuristic reasoning and the tedious integration of siloed computational tools, perpetuating a profound throughput gap between rapid data acquisition and the glacial pace of structural annotation. Here we present MSAgent, an autonomous agentic framework that bridges the gap between computational automation and expert intuition by emulating the cognitive logic of human specialists. By orchestrating a MSToolbox of over 50 domain-specific tools via Large Language Models (LLMs), MSAgent dynamically unifies the analytical pipeline into a scalable, evidence-grounded workflow, allowing for intent-aware planning, cross-resources outputs synthesis, and visual mechanistic interpretation within traceable reasoning chains and evidence-backed analytical reports. We evaluated MSAgent across multiple open benchmarks, including the established community challenges - Critical Assessment of Small Molecule Identification (CASMI) 2016/2022, CANOPUS, and LLM-oriented test cases. On CASMI, MSAgent consistently boosts retrieval performance by over 10% MRR across diverse benchmarks while ensuring high reliability--improving or preserving ranks in 95% of cases. For more challenging molecular de novo tasks on CANOPUS, MSAgent builds upon the outputs of baseline models with consistent refinement, yielding over a 40% average gain in Tanimoto similarity for ground-truth recovery. In addition, MSAgent demonstrates remarkable advantages in eliminating the hallucination phenomenon over LLMs without domain tool support, producing better-calibrated confidence (Pearson r = 0.438 vs -0.219 for gpt-4o). It improves exact-match rate by 38.8% over gpt-4o in candidate discrimination tasks, and achieved a 64% success rate in recommending high-quality candidate structures with Tanimoto similarity more than 0.7, where gpt-4o predominantly selected candidates with similarity below 0.3. Our work enables high-throughput mass spectrometry data to be analyzed in an intent-driven and automated manner, lowering the analysis barrier for no-expert to deliver molecular identification result with transparent analytical process, and accelerating discovery in metabolism and related fields by bridging the gap between experimental data acquisition and computational interpretation.

bioinformatics↗

bioRxiv2026

A new species of Rana from Anhui, China (Anura, Ranidae)

The diversity of the brown frog genus Rana may be underestimated as the high similarity of morphological characters. A new species belonging to the genus Rana is delineated based on eight specimens obtained from the Tianma National Nature Reserve, Jinzhai County, Luan City, Anhui Province, China. The phylogenetic analysis based on three mitochondrial genes (12S, ND2, and Cyt b) and one nuclear gene (BDNF) showed that the new species formed an independent clade closely related to R. culainensis and received strong support. In addition, morphological differentiation confirmed the phylogenetic results, and both supported the validity of a new species (Rana tianmaensis sp. nov.) in the R. japonica species group. The discovery of this new species enhances peoples understanding of the biodiversity of Rana and can provide important foundational data for scientific decision-making on protected area construction, ecological conservation, and species diversity. With the inclusion of newly described species in this study, the distribution of Rana genus in China now includes 31 recognized species.

zoology↗

bioRxiv2026

A comprehensive DNA methylation atlas for the Chinese population through nanopore long-read sequencing of 106 individuals

DNA methylation constitutes the primary epigenetic language mediating organismal phenotypic plasticity. Establishing a cohort-level genomic methylation landscape featuring wide geographical diversity is fundamental for dissecting its genetic and environmental attributes. Leveraging nanopore sequencings strength in genome-methylome co-sequencing, we generated a whole-genome, haplotype-resolved methylation atlas for 106 individuals from 19 provinces across China. The atlas identified 27,609,354 CpG sites genome-wide, with notably more informed gene proximal regions and CpG islands compared to whole-genome bisulfite sequencing. Detailed analyses revealed genomic structural variants as a pervasive covariate of DNA methylation, with a remarkable 2-fold compensation effect found in genome-wide heterozygous deletions. On the other hand, habitat altitude is found to be a strong environmental determinant of DNA methylation. We established a quantitative relationship between altitude and methylation states and identified a gene set strictly responsive to altitude differences, revealing epigenetically regulated genes such as PRDM16, EPHB2 and WNT7A. The methylation atlas provides a reference resource to facilitate further explorations into human epigenetics.

genomics↗

bioRxiv2026

Dual Pathways of Extracellular ATP Action in Cancer Cells: Purinergic Signaling Driven Senescence and Macropinocytic ATP Internalization

BackgroundOpportunistic nutrient uptake is a hallmark of cancer metabolism. Cancer cells upregulate macropinocytosis to acquire extracellular nutrients to support growth and stress adaptation. We previously showed that extracellular ATP (eATP) is internalized by macropinocytosis and promotes multiple cancer phenotypes. Here, we tested whether eATP uptake is prevalent across cancers and whether eATP also induces senescence through purinergic receptor (PR) signaling. MethodsIntracellular ATP (iATP) levels were measured following eATP exposure across multiple cancer cell lines. eATP internalization was visualized in vitro and in vivo using a non-hydrolyzable fluorescent ATP analog together with high-molecular-weight dextran as a macropinocytosis marker. Senescence was quantified using three SA-{beta}-galactosidase assays and flow cytometry. Pharmacologic inhibitors of macropinocytosis and purinergic receptors were used to define pathway dependence. Combination treatments with the glucose transporter inhibitor DRB18 and the senolytic navitoclax were evaluated for antiproliferative effects. ResultseATP produced dose- and time-dependent increases in iATP across diverse cancer cell types. Imaging demonstrated widespread macropinocytic internalization of ATP in vitro and in tumor xenografts. eATP induced senescence in NSCLC cells, confirmed by multiple {beta}-gal assays and flow cytometry. PR inhibition significantly reduced senescence, whereas macropinocytosis inhibition had minimal effect on senescence induction. ConclusionseATP acts through dual pathways in cancer cells: macropinocytic internalization that elevates iATP and PR signaling that drives senescence. Targeting metabolic uptake together with senolytic therapy may offer a novel anticancer strategy.

cancer biology↗

bioRxiv2026

Foundation cell segmentation models performance on live microscopy and spatial-omics data

Accurate cell segmentation is an essential step for quantitative analysis of biological imaging data. Recent advances in deep learning have led to the development of generalist segmentation models that perform robustly across multiple imaging modalities, including label-free phase contrast, fluorescence cell culture, and multiplexed fluorescence tissue imaging. However, systematic comparisons of these models at the level of downstream biological analysis remain limited. To address this gap, we evaluated several recent segmentation models, including Cellpose cyto3, Cellpose-SAM, {micro}SAM, and CellSAM, on phase contrast and fluorescence cell culture images. In addition, Mesmer and InstanSeg were included for benchmarking on multiplexed fluorescence tissue images generated using CO-Detection by IndEXing (CODEX). We found that Cellpose-SAM achieved strong performance on phase contrast images, while SAM-based models consistently performed well on fluorescence cell culture data. In contrast, no single model consistently outperformed others on CODEX datasets. Instead, each model exhibited distinct strengths and limitations, which led to differences in downstream analyses, including clustering and cell type identification. Together, our study emphasizes the importance of selecting segmentation models based on dataset characteristics and analytical goals, rather than relying on a single universal approach.

bioinformatics↗