Biology subjects

Liao, S.

Publications and source records attributed to Liao, S..

At least 19 recordsLinked to original sources

bioRxiv2026

CIDER: detecting changes in gene regulatory networks that are associated with changes in phenotype

Changes in gene regulatory networks may drive quantitative traits, or may transmit the effects of one trait, such as blood lipid level, on another, such as cardiovascular health. Yet the standard tools, differential correlation and differential network analysis, compare two discrete groups, while the contexts of interest - circulating lipids, inflammation, and blood glucose - vary continuously; applying them forces dichotomization, discarding within-trait variation. We introduce Continuous Interaction-based Differential Edge Regulation (CIDER), which tests whether a gene regulatory network edge, the relationship between a transcription factor and its target gene, varies with a continuous trait: the target gene's expression is modeled as a function of the TF's expression level, the trait, and their interaction, with the interaction coefficient measuring the trait dependence. To limit multiple testing, CIDER tests only the edges of a reference regulatory network. A generalized additive extension detects interactions that change the shape of the relationship, not only its slope, including forms that cannot be expressed as a difference between two correlations. In simulations it outperformed four two-group methods across sample sizes, effect sizes, and noise levels, with most of its advantage from keeping the trait continuous. In whole-blood transcriptomes from four independent human cohorts across ten quantitative health traits, CIDER identified 63 replicated cases in which a TF's regulation of its target varies with the trait, including coupling of the glucocorticoid-receptor (NR3C1) to the granulocyte colony-stimulating-factor receptor (CSF3R) that strengthens as triglycerides rise, and a pair whose regulation reverses direction across the observed range of C-reactive protein.

bioinformatics↗

bioRxiv2026

HESTIA: Scalable Multimodal Integration of Histology and High-Resolution Spatial Transcriptomics for Robust Spatial Domain Identification

Spatial omics has revolutionized molecular biology by providing invaluable insights into how native tissue microenvironments regulate cellular functions and disease mechanisms. Accurately capturing this structural complexity and decoding the underlying biological processes requires effectively integrating data from multiple modalities. However, transitioning to subcellular resolutions introduces massive data scales and severe transcriptomic sparsity, which challenge current analytical frameworks. To address this, we present HESTIA (Histology-Enhanced Scalable cross-Resolution inTegration for spatial trAnscriptomics), a highly efficient multimodal algorithm designed for identifying spatial domains in large-scale, high-resolution spatial omics data. By circumventing memory-intensive computations, HESTIA efficiently processes massive datasets on which existing algorithms fail due to memory constraints. HESTIA outperforms current multimodal methods in clustering accuracy and spatial continuity, accurately delineating fine structural boundaries. Furthermore, applying HESTIA to large-scale pathological samples successfully dissects clinically relevant intratumoral heterogeneity and maps distinct immune microenvironments in lung and colorectal cancers.

bioinformatics↗

bioRxiv2026

SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

Liquid-liquid phase separation (LLPS) underlies the formation of biomolecular liquid condensates (also referred to membraneless organelles, MLOs), which are essential for spatially organizing various biochemical processes within cells. Proteins that play a key role in driving condensates formation are termed phase-separating proteins (PSPs). Given experimental identification of PSPs remains labor-intensive and time-consuming, multiple computational tools have been developed based on empirical features or deep learning. In this study, we propose SSPSPredictor, a novel multimodal predictive model for PSPs with folded or intrinsically disordered structures, leveraging the fusion of sequence information from a protein language model ESM-2 and structural insights from a graph neural network GVP. Compared with existing tools, SSPSPredictor achieves balanced performance in identifying endogenous PSPs, predicting relative LLPS propensities, and recognizing key regions that drive LLPS. Moreover, SSPSPredictor exhibits good interpretability in identifying driving regions along protein sequences, although no relevant supervision was provided during training. Further predictive analysis of the human proteome using SSPSPredictor reveals that the proportion of intrinsically disordered proteins (IDPs) undergoing LLPS is significantly higher than that of folded proteins. In addition, pathogenic variants, especially those located in disordered regions, exhibit higher LLPS propensity than other mutations, uncovering a link between LLPS and diseases at the amino acid level.

bioinformatics↗

bioRxiv2025

AAV2 Bypasses Direct Endosomal Escape by Using AAVR to Access the Trans-Golgi Network en Route to the Nucleus.

Vectors based on the adeno-associated virus are widely used as delivery methods in gene therapy applications, yet understanding of the mechanisms governing its intracellular trafficking remains incomplete. Traditional models suggest that AAV escapes from endosomes via membrane disruption, but direct evidence for this process are lacking. Here, we show that AAVR, the essential AAV cell entry receptor, functions as a bona fide retromer cargo. Using in vitro reconstitution assays, we demonstrate that AAVRs cytosolic tail is sufficient to engage the SNX3-retromer complex and drive membrane tubulation, a hallmark of retrograde trafficking. In AAVR-knockout HuH-7 cells, AAV2 particles are internalized but fail to reach the trans-Golgi network (TGN) and support transgene expression. Galectin-8 recruitment assays reveal no evidence of endosomal membrane rupture during productive transduction, distinguishing AAV2 from lytic vectors such as lipid nanoparticles. Moreover, VP1u-deficient and PLA2-mutant AAV2 capsids accumulate at the TGN, indicating that VP1u is dispensable for early trafficking but required for post-TGN progression toward productive transduction. These findings challenge the prevailing model of direct endosomal escape and position AAV transduction as a vesicle-guided, receptor-mediated process.

cell biology↗

bioRxiv2025

B Cell-Induced Lymph Node Stromal Remodeling Compromises Neutrophil Response to Secondary Staphylococcus aureus Infection

The lymph node (LN) stromal cells create niches, regulate lymph drainage, and guide the migration, positioning, and activation of immune cells within the LN. Upon stimulation, the stromal cells respond dynamically to accommodate the massive expansion of immune cells in the LN. However, there is still a limited understanding of how niche-associated stromal cell subsets are remodeled and how this remodeling reshapes host immune protection. In an oxazolone (OX)-induced skin inflammation model, we found that LN remodeling weakened the neutrophil response to secondary Staphylococcus aureus (S. aureus) infection in the inflamed LN, whereas depleting B cells rescued the responses. To understand the mechanism, we used single-cell RNA sequencing (scRNAseq) to characterize alterations in fibroblastic reticular cell (FRC) subsets and identified a new FRC subset expressing an intermediate level of CXCL13 (Cxcl13int RCs) in the inflamed LN. Further studies revealed that Cxcl13int RCs replaced Ccl19lo FRCs in the interfollicular zones (IFZs) of the inflamed LN, leading to a compromised conduit network. Depleting B cells preserved the integrity and function of the FRC-conduit network within the IFZ. Finally, the induction of Cxcl13int RCs depended on the B cell-derived lymphotoxin signaling. The induced Cxcl13int RCs were transitional, creating a time window of comprised immune protection during the OX-inflammation. This study elucidated a mechanism underlying LN stromal remodeling and provides proof-of-principle evidence that B cell-stromal interactions may be a novel target to enhance host immunity in diseased conditions.

immunology↗

bioRxiv2025

CellBin:a generalist framework to process spatial omics data to cell level

Spatial omics has rapidly expanded with increasingly diverse imaging modalities, molecular targets, and chip sizes. However, no general framework currently exists to construct cell level matrices that are robust across platforms and omics types. Here we present CellBin, a universal and scalable frame-work that unifies image stitching, cell segmentation, and spot-to-cell mapping for multiple spatial omics technologies. CellBin integrates a multi-field weighted stitching algorithm for large-area images, a family of U-Net-based models trained across diverse staining modalities, and an optimized computational architecture for high-throughput processing. Across five technological platforms and three omics data types, CellBin achieves robust segmentation and accurate single-cell matrix construction, consistently outperforming seven state-of-the-art methods in F1-score, cell size precision, and annotation accuracy. By providing a generalizable, cross-platform solution, CellBin bridges multiple spatial omics, enabling unified, high-resolution cell level analyses across technologies.

bioinformatics↗

bioRxiv2025

Combining Motifs, CRE Activity, And Gene Expression Data Using ML Greatly Improves the Accuracy of Tissue-Specific TF Network Maps

MotivationReconstructing tissue-specific transcription factor (TF) networks remains challenging. TF motif-based methods often lack functional validation, while expression-based methods struggle to distinguish direct binding from indirect regulation. Integration of diverse data types is necessary to accurately prioritize functional targets directly bound by TFs across human tissues. ResultsWe introduce METANet, a supervised ensemble learning framework that combines TF motifs, cis-regulatory element activity, and linear and non-linear expression-derived features to predict TF binding. Applied to 36 human tissues, METANet significantly outperforms established methods in identifying direct, functional targets of TFs validated by ChIP-seq and gene ontology. Furthermore, METANet captures tissue-specific regulation comparable to existing methods, allowing the identification of reproducible gene-trait associations. Availability and ImplementationAll code and network maps are freely available at Zenodo https://doi.org/10.5281/zenodo.17309371. Contactbrent@wustl.edu.

systems biology↗

bioRxiv2025

Distinct cell wall molecular architecture of dimorphic Talaromyces marneffei cells revealed by solid-state NMR spectroscopy

Talaromyces marneffei, causing systemic infections in immunocompromised patients ranging from HIV/AIDS individuals to cancer and transplant recipients, is an increasingly urgent global pathogen. However, the fungus remains underrecognized despite the systemic infection disease talaromycosis caused by this pathogen is associated with high mortality rates. Its pathogenicity depends on a temperature-triggered shift from saprophytic mold (25 {degrees}C) to pathogenic yeast (37 {degrees}C), and the two growth forms display distinct sensitivity to antifungal drugs, which processes involve extensive cell wall structure and components remodeling. To dissect these processes, we use solid-state nuclear magnetic resonance (ssNMR) and other techniques to show that T. marneffei yeast and hyphal cells have distinct cell wall thickness and hydrophobicity, and different assembly of mobile and rigid polymers within the T. marneffei cell wall. The yeast wall was 2.3 times thicker and more hydrated. ssNMR revealed a rigid core of {beta}-1,3-glucans, chitin and chitosan, with {beta}-1,3-glucan rising from 57% in mold to 72% in yeast. Both forms showed tight polysaccharide packing, but only mold exhibited lysine-containing protein interactions with chitin and chitosan. These insights not only map the structural basis of host temperature adaptation and also inform targeted antifungal design in future.

bioinformatics↗

bioRxiv2025

CSRefiner: A lightweight framework for fine-tuning cell segmentation models with small datasets

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in spatial omics technologies have enabled transcriptome profiling at subcellular resolution. By performing cell segmentation on nuclear or membrane staining images, researchers can acquire single cell level spatial gene expression data, which in turn enables subsequent biological interpretation. Although deep learning-based segmentation models achieve high overall accuracy, their performance remains suboptimal for whole-tissue analysis, particularly in ensuring consistent segmentation accuracy across diverse cell populations. Existing fine-tuning approaches often require extensive retraining or are tailored to specific model architectures, limiting their adaptability and scalability in practical settings. To address these challenges, we present CSRefiner, a lightweight and efficient fine-tuning framework for precise whole-tissue single-cell spatial expression analysis. Our approach incorporates support for fine-tuning widely uaed segmentation models in the field of spatial omics, including recent published model Cellpose-SAM, while achieving high accuracy with very limited annotated data. This study demonstrates CSRefiners superior performance across various staining types and its compatibility with multiple mainstream models. Combining operational simplicity with robust accuracy, our framework offers a practical solution for real-world spatial transcriptomics applications.

bioinformatics↗

bioRxiv2025

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Pangenomes are increasingly critical for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here, we report a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated near-gapless genomes representing all seven watermelon species. The super-pangenome captures approximately one million structural variants (SVs), enabling accurate variant genotyping across ~900 watermelon accessions and substantially expanding variant discovery both across and within species. Broader sampling within the pangenome provides insights into genome evolution among watermelon species and sheds light on the origin of cultivated watermelon. SV-inclusive genome-wide association studies enhance trait mapping resolution and identify a copy number variation upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, our findings and genomic resources establish a foundational framework for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crop species.

plant biology↗

bioRxiv2025

Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies

Although aging is a universal event, some individuals are able to achieve extreme longevity. The Long-Life Family Study (LLFS) enrolls participants from families enriched with long-lived individuals, serves as a valuable dataset for studying ageing phenotypes and identify potential intervention targets. We analyzed the association between age at blood draw and 16,284 RNAseq-based blood transcriptomic data from 2,167 LLFS participants with ages ranging from 18 to 107, replicated the results in the Integrative Longevity Omics Study (ILO) dataset of 20,884 RNAseq-based blood transcriptomic data from 419 participants, with ages ranging from 60 to 108, and further compared our findings to a published reference aging signature. We identified 4,227 transcripts increasing and 4,044 transcripts decreasing with age, and enrichment analysis revealed age-related upregulation of inflammatory and senescence-related pathways, and downregulation of MYC and Wnt/{beta}-catenin targets, among others. Further, a subset of transcripts showed age associations unique to the longevity-enriched cohorts (LLFS and ILO). We also identified 314 transcripts significantly associated with mortality risk and found that pro-survival gene sets included NK cell-mediated cytotoxicity and GPCR signaling. Finally, increased transcriptomic age predicted using transcriptomic clock was strongly associated with increased mortality. In summary, this study identified robust transcriptomic signatures of aging and mortality in a longevity-enriched population, highlighting key biological pathways such as immune modulation, inflammation, and senescence. Authors notesThis manuscript has been peer-reviewed and accepted by GeroScience (Springer). This bioRxiv article reflects the published version, incorporating revisions made in response to reviewers comments. The main content, results, and conclusions remain unchanged from the previous version, while the Discussion section has been expanded to further address the functional annotation and clinical relevance of the identified markers. Copy of the acceptance letterThe editors are pleased to inform you that your manuscript, JAAA-D-25-01946R1 entitled "Whole blood transcriptional signatures of age and survival identified in Long Life Family and Integrative Longevity Omics Studies" has been accepted for publication in GeroScience as an Original Article. The editors commend you on your outstanding contribution to the journal. Your manuscript can be published online ahead of print within approximately two weeks.

molecular biology↗

bioRxiv2025

Phylogenomics of Aristolochia subg. Siphisia (Aristolochiaceae) reveals widespread incomplete lineage sorting and supports a novel pollinator-filtering hypothesis

Aristolochia subgenus Siphisia constitutes a monophyletic lineage of predominantly lianescent species, with occasional shrubs or herbs, and is characterized by remarkable diversity in perianth morphology. Members of Siphisia serve as larval hosts for endangered Lepidoptera and are widely used in traditional medicine. Despite its ecological and ethnobotanical significance, Siphisia systematics remains unresolved due to limited genomic resources and insufficient phylogenetic signal across previously sampled loci. Here, we present a phylogenomically informed framework for Siphisia, integrating 46 newly collected accessions and seven public datasets across 44 taxa. Using genome skimming ([~]30x coverage) and HybPiper, we recovered Angiosperms353 nuclear loci, including supercontigs, for phylogenetic reconstruction via concatenation and coalescent approaches. The resulting species trees resolve seven strongly supported monophyletic clades, each defined by distinct biogeographic patterns and morphological synapomorphies. Comparative plastome analyses from de novo assemblies explored quadripartite structure, plastid phylogeny, GC content, and gene synteny. Cytonuclear discordance was concentrated in species-rich Asian clades, while hybridization signals were rare and limited to deep backbone nodes in North American lineages. These results indicate that incomplete lineage sorting, rather than introgression, accounts for most gene-species tree conflicts and likely reflects strong reproductive isolation following speciation. We also revise a previously taxonomically ambiguous complex--the now well-supported A. versicolor species group--based on integrated phylogenomic and morphological evidence. Within this group, we describe five previously unrecognized cryptic species and identify a novel pollination syndrome involving floral adaptations for pollinator filtering. This syndrome may contribute to prezygotic isolation and recent diversification, and it challenges the prevailing assumption that Aristolochia pollination is universally governed by a trapping-release mechanism--suggesting this model may not apply to subg. Siphisia.

evolutionary biology↗

bioRxiv2025

CD163+/Dab2+ Macrophages Alleviate Cardiac Hypertrophy via Nrg2/ErbB4-Mediated Mitochondrial Reprogramming

BackgroundPathological cardiac hypertrophy is a hallmark of numerous cardiovascular diseases, yet effective targeted therapies remain elusive in current clinical practice. Cardiac macrophages contribute to disease progression, but the underlying mechanisms have not been fully elucidated. MethodsSingle-nucleus RNA-sequencing, bulk RNA-sequencing, proteomics, metabolomics, and a mouse model of pressure overload were employed to investigate cardiac remodeling. We identified a macrophage subset co-expressing CD163 and Dab2 and examined its role using immunofluorescence, flow cytometry, and functional assays. We further assessed the Nrg2/ErbB4 signaling axis through genetic and pharmacological modulation. ResultsCD163/Dab2 macrophages were reduced in hypertrophic hearts and positively correlated with Nrg2 expression. These macrophages alleviated cardiomyocyte hypertrophy in vitro, an effect abolished by Nrg2 knockdown. In vivo, recombinant Nrg2 treatment mitigated cardiac hypertrophy, preserved mitochondrial structure, and restored bioenergetics via the ErbB4 receptor. Transcriptomic analyses confirmed enhanced expression of genes involved in mitochondrial oxidative phosphorylation. Furthermore, CD163+/Dab2+ macrophages improved mitochondrial dysfunction by the Nrg2/ErbB4 pathway in vitro. ConclusionsWe identified a CD163/Dab2 macrophage subset that protects against pathological cardiac hypertrophy by promoting mitochondrial function through Nrg2/ErbB4 signaling. This axis may offer a promising therapeutic target for interventions in pathological cardiac hypertrophy.

cell biology↗

bioRxiv2025

Spatial transcriptomics elucidates the central role of cerebral vasculatures in the progression of Japanese encephalitis

The cellular and molecular mechanisms underlying the neuropathology of Japanese encephalitis virus (JEV) remain obscure. Herein, we designed Stereo-seq chips to simultaneously capture the in situ transcriptomes of both the host and JEV, constructing a comprehensive spatiotemporal pathological landscape for Japanese encephalitis (JE). This study reveals the central role of the vascular system in JE pathogenesis, particularly the meninges, which displayed the strongest signal of inflammation and cell death in the JEV-infected brain. The activation of the Ackr1+ endothelial cells, disruption of the blood-brain barrier, the migration of JEV-infected monocytes, secretion of immune factors by the infected cells, and the occurrence of pyroptosis and necroptosis form a positive feedback loop, resulting in an increasing influx of immune cells and tissue damage. JEV infection leads to neurological impairments, which may be attributed to the interaction between viral proteins and host cellular factors such as Stat1, Stat3, Nfkb1, and Sp1. As the vascular system serves as a central receiver and amplifier of inflammatory signals, regulating inflammation within the vascular system is essential in mitigating JE progression.

immunology↗

bioRxiv2025

FISHNET: A Network-based Tool for Analyzing Gene-level P-values to Identify Significant Genes Missed by Standard Methods

FISHNET uses prior biological knowledge, represented as gene interaction networks and gene function annotations, to identify genes that do not meet the genome-wide significance threshold but replicate nonetheless. Its input is gene-level P-values from any source, including omicsWAS, aggregation of GWAS P-values, CRISPR screens, or differential expression analysis. It is based on the idea that genes whose P-values are low due to sampling error are distributed randomly across networks and functions, so genes with suggestive P-values that cluster in densely connected subnetworks and share common functions are less likely to reflect sampling error and more likely to replicate. FISHNET combines network and function analysis with permutation-based P-value thresholds to identify a small set of exceptional genes that we call FISHNET genes. Applied to 11 cardiovascular risk traits, FISHNET identified 19 gene-trait relationships that missed genome-wide significance thresholds but, nonetheless, replicated in an independent cohort. The replication rate of FISHNET genes matched or exceeded that of other genes with similar P-values. FISHNET identified a novel association between RUNX1 expression and HDL that is supported by experimental evidence that RUNX1 promotes white fat browning, which increases HDL cholesterol levels. FISHNET also identified an association between LTB expression and BMI that is supported by experimental evidence that higher LTB expression increases BMI via activation of the LT{beta}R pathway. Both associations failed genome-wide significance thresholds, highlighting FISHNETs ability to uncover meaningful relationships missed by traditional methods. FISHNET software is freely available at https://doi.org/10.5281/zenodo.14765850.

bioinformatics↗

bioRxiv2025

ST-FFPE-mIF: Integrating Spatial Transcriptomics and Multiplex Immunofluorescence in Formalin-Fixed Paraffin-Embedded Tissues Using Stereo-seq

Stereo-seq is the state-of-the-art technique, which allows capturing of spatial transcriptomic data at sub-cellular resolution and in large scale of view. Stereo-seq FFPE technique is recently launched with the capability to obtain spatially resolved gene expression in FFPE tissue sections. However, the omic information is limited to transcriptome. To delineate spatial molecular hierarchy from transcriptome to phenome, we present the protocol that implements nine-plex immunofluorescence staining with detection of spatial transcriptomics in one FFPE tissue section (short for ST-FFPE-mIF). This protocol details the modified biochemistry procedure and also provides analytical framework to achieve single-cell resolution through nuclear or membrane staining-based cell segmentation. We demonstrated the application of ST-FFPE-mIF in colorectal cancer sample to illustrate how ST-FFPE-mIF facilitates a robust analysis of spatially resolved multi-omic data at the single-cell level.

biochemistry↗

bioRxiv2025

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits

We present Gene-Embedded Multi-modal Networks (GEM-Net), a semi-supervised framework for constructing multi-modal networks centered on genes. GEM-Net uses gene-level modules and selectively incorporates heterogeneous omics profiles using a correlated meta-analysis strategy that accounts for scale imbalance, missingness, and intra-modular correlation. Prior to network inference, we developed a harmonized data processing protocol that adjusts each omic layer independently through a shared mathematical workflow involving transformation, dimensionality reduction, and regression-based covariate adjustment. GEM-Net modules were inferred and benchmarked against unsupervised methods using transcriptomic, metabolomic, and lipidomic data from the Long Life Family Study (LLFS), a unique cohort enriched for exceptional familial longevity and health. GEM-Net modules were more diverse and biologically interpretable, with stronger support from protein- protein interactions, transcriptional regulation, and metabolic annotations. Applying GEM-Net to metabolic health in LLFS revealed an axis between the microbiome-derived metabolite N-acetylglycine and immune genes (FCER1A, HDC, CPA3, MS4A2) associated with improved insulin sensitivity and reduced inflammation in healthy older individuals. GEM-Nets offer a reusable reference from a long-lived population and a generalizable framework for multi-omics discovery. https://doi.org/10.5281/zenodo.15003731.

systems biology↗

bioRxiv2024

CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal Models

In recent years, cell segmentation techniques have played a critical role in the analysis of biological images, especially for quantitative studies. Deep learning-based cell segmentation models have demonstrated remarkable performance in segmenting cell and nucleus boundaries, however, they are typically tailored to specific modalities or require manual tuning of hyperparameters, limiting their generalizability to unseen data. Comprehensive datasets that support both the training of universal models and the evaluation of various segmentation techniques are essential for overcoming these limitations and promoting the development of more versatile cell segmentation solutions. Here, we present CellBinDB, a large-scale multimodal annotated dataset established for these purposes. CellBinDB contains more than 1,000 annotated images, each labeled to identify the boundaries of cells or nuclei, including 4,6-Diamidino-2-Phenylindole (DAPI), Single-stranded DNA (ssDNA), Hematoxylin and Eosin (H&E), and Multiplex Immunofluorescence (mIF) staining, covering over 30 normal and diseased tissue types from human and mouse samples. Based on CellBinDB, we benchmarked seven state-of-the-art and widely used cell segmentation technologies/methods, and further analyzed the effects of four cell morphology indicators and image gradient on the segmentation results.

cell biology↗