Biology subjects

He, Z.

Publications and source records attributed to He, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

tBid mediated genetic ablation of connective tissue cells reveal their key regulatory function during limb regeneration in axolotls

Limb regeneration in adulthood is a fascinating phenomenon unique to salamanders, requiring precise coordination and interaction of various cell types. Connective tissue (CT) constitutes a major component of the limb and serves as the primary reservoir of positional information during regeneration. However, the direct evidence and the extent of CT in positional contribution remain limited. Here we develop an inducible Cre-LoxP-mediated tBid cell ablation system in axolotls and demonstrate efficient elimination of targeted muscle tissue and muscle stem cells through transient electroporation or genetic approach. Ablation of CT cells at early stages of regeneration results in delayed regeneration and loss of proximal limb segments (e.g., upper and lower limb), with minimal impact on hand differentiation. CT ablation during development yields similar defects as observed during regeneration, indicating an essential role for CT cells in determining the positional identity of developing and regenerating limb structures. Further scRNA-seq reveal a progressive proximal-to-distal transition among CT cells and identify distinct CT subtypes that contribute to proximal and distal segments. CT ablation results in reduction of the differentiated CT1--a subpopulation that maintains proximal identity throughout regeneration, and a delay of distal transformation in the proliferative CT1--a major CT subtypes showing progenitor property. These cellular shifts likely lead to the observed morphological deficits in regenerating limbs after CT ablation. Moreover, differentiated CT1 cells enhanced their interactions with surrounding cell types after CT ablation, to modulate adaptive proliferation in other populations, e.g., muscle stem cells. Our work establishes a tBid-based ablation strategy for functional studies of CT and other cell types in axolotls, and provides direct evidence demonstrating pivotal roles of CT cells in guiding positional memory and segmental patterning during organ regeneration.

molecular biology↗

bioRxiv2025

Multi-Timescale Neural Adaptation Failure as a Mechanistic Signature of Major Depression

Major depression (MD) is usually characterized in terms of momentary abnormalities, such as negative bias or emotional blunting, derived from paradigms that measure only the first response to a stimulus. Yet depressive experience in daily life is repetitive and cumulative. Here we test the hypothesis that a failure of neural adaptation across repetitions is a core feature of MD. Using a repeated negative-stimulus paradigm, we quantified adaptation at micro (within-trial) and macro (across-session) scales via electroencephalography in 28 MD patients and 34 healthy controls, measuring both conventional event-related potentials and large-scale traveling-wave propagation. MD patients exhibited pervasive maladaptation: neural rigidity at the micro scale (absent repetition suppression) and progressive regulatory depletion at the macro scale (escalating emotional reactivity with declining cognitive control). Across analyses, adaptation-based metrics revealed larger group differences than single-shot indices, and classifiers built on adaptation features--especially traveling-wave dynamics--outperformed single-shot models (task AUC = 0.83 vs 0.78) and generalized to two independent datasets (up to AUC = 0.91). These findings challenge conventional symptom-based characterizations of MD, establishing adaptation failure--not initial reactivity--as the mechanistically primary pathological signature. This reframing provides a unified mechanistic explanation for rumination and cognitive inflexibility while offering a standardized, low-cost biomarker requiring minimal electrodes ([≤]8 channels), enabling large-scale screening in resource-limited settings.

neuroscience↗

bioRxiv2025

DISCO-seq: 3D single-cell transcriptomics of intact biological systems

Single-cell transcriptomics has transformed tissue analysis, yet current methods struggle to integrate whole-tissue 3D architecture. Conventional techniques restrict molecular profiling to pre-selected 2D sections, losing systemic context and introducing anatomical bias by sampling less than 0.001% of a whole organism. To overcome these challenges, we developed DISCO-seq, a tissue-clearing chemistry that enables superior RNA accessibility compared to fresh or fixed tissues. DISCO-seq integrates whole-organ or organism 3D imaging with both untargeted and targeted transcriptomics, yielding high-quality RNA from cleared tissues comparable to standard samples. We demonstrate its versatility by investigating tumor heterogeneity in a syngeneic glioblastoma mouse model, using 3D imaging to identify spatially distinct microenvironments and characterize their unique transcriptomic signatures. Moreover, DISCO-seq enabled unbiased, whole-body mapping of SARS-CoV-2 S1 protein deposition in mice, followed by transcriptomic profiling of spatially defined niches. By bridgingmesoscale 3D imaging with single-cell transcriptomics, DISCO-seq establishes a paradigm for anatomically contextualized, hypothesis-free tissue interrogation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/693352v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@758ee5org.highwire.dtl.DTLVardef@1f862c6org.highwire.dtl.DTLVardef@1cf3b9org.highwire.dtl.DTLVardef@c5082e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISCO-seq integrates RNA-preserving tissue-clearing chemistry with whole-organ or organism 3D imaging, enabling anatomically unbiased single-cell transcriptomics. C_LIO_LIDISCO-seq yields RNA quality and transcriptome profiles equivalent to those obtained from matched fresh or fixed tissues. C_LIO_LIDISCO-seq identifies discrete glioblastoma microenvironments and defines their transcriptomic states within the intact brain. C_LIO_LIDISCO-seq enables whole-body mapping of SARS-CoV-2 S1 and uncovers region-specific immune and metabolic responses across anatomical niches. C_LI Supplementary movies can be seen at: http://discotechnologies.org/DISCO-seq/

systems biology↗

bioRxiv2025

Identification of SASP-associated biomarkers and regulatory mechanisms in diabetic foot ulcers based on transcriptomics and experimental validation

BackgroundDiabetic foot ulcers (DFU) constitute a major complication arising from diabetes mellitus. Emerging research findings have underscored the pivotal contribution of cellular senescence to the pathophysiological development of wounds exhibiting delayed healing; nevertheless, the molecular regulatory networks governing this process in DFU pathogenesis remain incompletely elucidated MethodsDFU-related transcriptomic datasets were acquired from public databases. Biomarkers were identified through machine learning algorithms combined with expression validation. To evaluate the diagnostic predictive capacity of identified biomarkers for DFU, a nomogram prediction model was developed and validated. Further analyses included Gene Set Enrichment Analysis (GSEA), molecular network regulation, immune infiltration profiling, and drug prediction. Subsequently, the expression profiles of these biomarkers were quantitatively assessed through reverse transcription quantitative PCR (RT-qPCR) methodology. ResultsFour feature genes were identified using machine learning. Among them, CXCR2 and SIGLEC7 passed expression validation and were significantly up-regulated in DFU groups across two datasets (p < 0.05), confirming their potential as biomarkers. A nomogram constructed based on these two biomarkers demonstrated high discriminative accuracy and good calibration (decision curve net benefit > 0, AUC = 0.99). GSEA indicated that CXCR2 and SIGLEC7 were both enriched in the cytokine-cytokine receptor interaction pathway as well as other functionally associated pathways. Molecular regulatory network analysis indicated that both CXCR2 and SIGLEC7 were predicted to be regulated by transcription factors (TFs) such as SOX2. Immune infiltration analysis showed a significant strong positive correlation between CXCR2 and activated mast cells (correlation coefficient (cor) = 0.72, p < 0.001), and between SIGLEC7 and macrophages M0 (cor = 0.71, p < 0.001). Drug prediction identified 12 drugs targeting CXCR2 (e.g., Indoprofen) and one drug targeting SIGLEC7 (N-acetyl-alpha-D-glucosamine). Validation through Reverse Transcription quantitative Polymerase Chain Reaction (RT-qPCR) revealed that CXCR2 and SIGLEC7 expression levels were markedly increased in the DFU group, showing statistically significant differences (p<0.05), which aligned with the bioinformatics analysis outcomes ConclusionThis study identified CXCR2 and SIGLEC7 as SASPRG-related biomarkers in DFU through bioinformatic analysis, providing new theoretical support and a basis for early diagnosis and treatment of DFU.

genomics↗

bioRxiv2025

Cryo-EM structures of anti Z-DNA antibodies in complex with antigen reveal distinct recognition modes of a left-handed geometry

Double-stranded nucleic acids can undergo transitions from canonical B/A-forms to alternate left-handed Z-DNA/Z-RNA (Z-NAs). Z-NAs are implicated in processes such as neuroinflammation in Alzheimers disease, Lupus Erythematosus, microbial biofilms, and type I interferon-mediated human pathologies. Since endogenous Z-NA sensors like the Z domain can induce B-to-Z transitions, monoclonal antibodies (mAbs) Z-D11 and Z22 have been regarded as conformation-specific tools to confirm Z-NA in situ, although high-resolution structural information is missing. Here, we employed single-particle cryo-electron microscopy to solve structures of Z-D11 and Z22 bound to synthetic d(CG)6 12mer Z-DNA duplex. Both mAbs form filamentous trimers around the Z-DNA axis, further stabilized by Fab-Fab interactions. The mAbs achieve specificity through extensive contacts to both Z-form backbone strands and the exposed guanine/cytosine bases in the major groove. This mode of recognition is dictated by shape complementarity rather than sequence specificity, sensing the alternating syn/anti backbone torsions and the phosphate zig-zag geometry unique to Z-DNA. Our data also suggest that these mAbs are not inducing B-to-Z transitions under normal physiological conditions. Finally, comparison to other double-stranded NA-binding mAbs defines a similar structural logic adapted to different helical geometry recognition patterns, thus providing a framework for engineering highly specific nucleic acid probes.

biochemistry↗

bioRxiv2025

AI recognizes convergent somatic hypermutation signatures to allow the discovery of variant-resilient broadly neutralizing antibodies

Elucidating the mechanisms by which broadly neutralizing antibodies (bnAbs) develop to confer durable immunity in humans is pivotal for the rational design of next-generation vaccines and therapeutics. VH3-53/3-66-encoded public antibodies are widely elicited in the population after the COVID-19 pandemic. Here, we isolated 15 VH3-53/3-66-encoded bnAbs from elite neutralizers who experienced sequential SARS-CoV-2 Omicron breakthrough infections. These bnAbs exhibit exceptional potency and breadth against circulating variants and, as members of a long-lived public antibody lineage, likely contribute to durable humoral protection. Genetic analysis of VH3-53/3-66-encoded bnAbs reveals a convergent somatic hypermutation pattern at seven positions in and around the CDR loops that emerges with repeated viral exposures and distinguishes them from non-bnAbs. Grafting these combined mutations onto clinically escaped antibodies broadens their breadth and restores neutralization activity. Structural analysis shows that these convergent mutations cooperatively remodel CDRs to bind and tolerate virus receptor-binding domain mutations. Using an AI-based antibody language model trained on data linking somatic hypermutations to binding affinity and neutralization, the model is able to identify mutational patterns predictive of breadth, enabling the discovery of a rare bnAb with protective activity against the latest variants from early-pandemic antibody repertoires. This demonstrates the feasibility of establishing an AI-empowered pipeline to identify mutation-tolerant bnAbs from early-pandemic antibody repertoires to fight fast-evolving newly introduced viruses.

immunology↗

bioRxiv2025

PULSAR: a Foundation Model for Multi-scale and Multicellular Biology

Biology emerges from interactions across physical scales, where molecular interactions drive cellular states, which in turn orchestrate multicellular tissue functions that collectively define health and disease. However, current computational models are often constrained to single scales in isolation, failing to integrate the biology that emerges from lower to higher levels [1]. Here we present PULSAR (Patient Understanding Leveraging Single-cell universAl Representation), a multi-scale and multicellular foundation model architecture that explicitly enables information flow from genes to cells to multicellular systems. Applied to the human peripheral immune system, PULSAR extracts a unified donor representation that supports rapid disease classification, biomarker prediction, and forecasting of future clinical events, such as Rheumatoid arthritis onset. As a generative model, PULSAR enables the simulation of cytokine perturbation response across physical resolutions, while its interpretability reveals the key cell types driving disease. Overall, PULSAR opens new avenues for precision medicine by enabling computational reasoning that connects molecular biology to clinical phenotypes.

bioinformatics↗

bioRxiv2025

Unified Generation of Regionalized Neural Organoids from Single-Lumen Neuroepithelium

Human brain development begins with the formation of the neural tube, a neuroepithelium organized around a single continuous lumen that is patterned to specify distinct brain regions. Human pluripotent stem cell (hPSC)-derived neural organoids offer powerful models to study this process and its disruption in disease. However, most existing protocols rely on stochastic self-organization of hPSC aggregates, leading to high variability in tissue architecture and cell type composition, which limits reproducibility and fidelity to natural development. This variability has also hindered the broader adoption of brain organoid technology in translational and pharmaceutical research, where robustness and standardization are critical. Here, we present a scalable and reproducible platform that uses uniform, single-lumen neuroepithelium (SLN) as a standardized starting point for generating diverse, regionally specified neural organoids. SLNs form with high efficiency and reproducibility, can be patterned along dorso-ventral and anterior-posterior axes, and mature in suspension culture into organoids representing forebrain, midbrain, hindbrain, and neural retina. This unified and scalable approach provides a reproducible foundation for modeling human brain development, offering broad translational potential for mechanistic studies, disease modeling, and drug discovery.

developmental biology↗

bioRxiv2025

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By using self-supervised learning (SSL), we discover and quantify the full range of histopathological appearances in a disease, and associate them with clinicopathological ground truths such as prognosis. We used this approach to discover under-appreciated morphologies of lung adenocarcinoma (LUAD), using a highly characterised resected tumour cohort of over 4000 slides from over 1000 patients. By constructing an authoritative lexicon of recurrent LUAD appearances, we ab initio discovered several stromal morphologies strongly predictive of outcome. With multimodal data integration and external dataset validation, we propose that epithelial discohesion is lethal, but only in the context of immunologically cold stroma. Both these morphological features are independent of current prognostic schema. Crucially, we describe these features in the context of real-world diagnostic histopathology, giving them immediate clinical translatability. Statement of SignificanceHistopathology relies heavily on epithelial morphology, often neglecting the stroma. We use self-supervised AI to identify under-appreciated morphologies in LUAD linked to poor outcome and validate these observations in an external cohort, demonstrating the utility of self-supervised AI as a powerful biological discovery tool.

cancer biology↗

bioRxiv2025

Ca2+ signature-dependent control of auxin sensitivity in Arabidopsis

Plants must continually balance growth with arrest, especially under stress. Auxin signaling acts as a central regulatory hub in this process, yet the mechanisms that dynamically tune early auxin signalling in real time remain unknown. Here, we used the light-gated, Ca2+-permeable Channelrhodopsin 2 variant XXM2.0 to optogenetically impose defined Ca2+ signatures on Arabidopsis root cells. Repetitive light activation triggered cytosolic Ca2+ signals that in turn suppressed auxin-induced membrane depolarization and Ca2+ transients. Moreover, prolonged optogenetic Ca2+ stimulation affects auxin-responsive transcriptional reprogramming. As phenotypic output, reversible inhibition of root growth by suppressing cell division and elongation was observed. We further identify a candidate CaM7-CNGC14 module that likely mediates Ca2+-dependent gating of early auxin signalling. Our study thus introduces a synthetic biology approach to decompose calcium-auxin crosstalk in plant cells, and demonstrates that optogenetically imposed cytosolic Ca2+ signals act as dynamic regulators of auxin susceptibility in roots.

plant biology↗

bioRxiv2025

Born to Condense: Polysomes Drive Co-Translational Condensation of Biomolecular Condensate Proteins

Biomolecular condensates formed by protein liquid-liquid phase separation (LLPS) are ubiquitous in cells and play crucial roles in cellular regulation. While the physics and functions of LLPS are well studied, its interplay with protein synthesis - translation - remains largely unexplored. Here we introduce a theoretical framework for Co-Translational Condensation (CTC), in which nascent protein chains of polysomes - multiple ribosomes on one mRNA - interact with condensates, localizing translation to condensate surfaces. Using coarse-grained simulations, we show that protein domain architecture dictates the thermodynamics of CTC, consistent with a Langmuir adsorption model. Bioinformatic analysis of more than 7,500 proteins reveals that most condensate-associated proteins have architectures favoring CTC, with strong interaction regions of nascent chains exposed on polysomes. At the dynamical level, simulation and reaction-diffusion modeling reveal that CTC is kinetically feasible within typical polysome lifetimes, either through large polysomes nucleating new condensates or via diffusion to pre-existing condensates. As a case study, we demonstrate that CTC enhances post-translational modifications by minimizing unmodified intermediates. More broadly, we anticipate CTC may also influence protein folding, misfolding, and signal-integration latency. Together, our results establish CTC as a general mechanism coupling translation with phase separation, with broad implications for protein evolution, cellular organization, and synthetic biology.

biophysics↗

bioRxiv2025

Distilling Direct Effects via Conditional Differential Gene Expression Analysis

Differential gene expression (DGE) analysis is foundational for interpreting RNA sequencing data, but it conflates direct biological effects with correlations propagated through gene co-expression. Across three RNA sequencing datasets (including a genome-scale perturb-seq experiment), we find that only a small fraction of differentially expressed genes have direct effects on the trait of interest, while the majority are undirected or passengers whose associations are mediated through other genes. To distinguish direct effect genes, we introduce conditional differential gene expression (CDGE) analysis, a framework that tests for conditional rather than marginal association between each gene and the trait of interest. Implemented via the GhostKnockoff procedure with lasso regression, CDGE delivers false discovery rate control, operates on summary statistics from existing DGE pipelines, and accommodates batch effects. The genes identified by CDGE mediate the effects of most other differentially expressed genes and show stronger enrichment for known protein-protein interactions and biological pathways than DGE-identified genes. These results suggest that the field has been systematically over-interpreting DGE outputs, and that distinguishing direct from mediated effects is essential for prioritizing genes for functional follow-up and therapeutic development.

bioinformatics↗

bioRxiv2025

scPhoenix: A Contrastive Learning-based Framework with Aux-Core Feature Disentanglement Enhances Sparse Cellular Multi-omics Translation

Recent advances in single-cell multi-omics co-assays and spatiotemporal sequencing technologies have provided unprecedented opportunities for systematically characterizing cellular heterogeneity. However, severe sparsity and pronounced spatial heterogeneity--hallmark features of complex diseases and tumor microenvironment--remain major obstacles in deciphering cellular multi-omics data. Here, we present scPhoenix, a contrastive learning-based framework for single-cell cross-modality translation. scPhoenix adopts a two-stage Aux-Core strategy to disentangle modality-specific feature extraction from cross-modality feature interaction. Across diverse datasets, it preserves cellular heterogeneity during translation and demonstrates significant advantages for data with high sparsity. In addition, the framework integrates a contrastive learning framework with five effective data augmentation methods tailored to single-cell data. Moreover, scPhoenixs design supports extensions to unpaired data training and spatial multi-omics translation, enabling robust performance in scenarios with high spatial heterogeneity. scPhoenix is freely available at https://github.com/liwz-lab/scPhoenix.

bioinformatics↗

bioRxiv2025

SlATG8f enhances tomato thermotolerance and fruit quality via autophagy and HS pathways

Plant responses to high-temperature stress involve dynamic and complex changes in physiology, biochemistry, and gene expression. Autophagy, a highly conserved intracellular degradation system, facilitates the removal and recycling of damaged cytoplasmic components such as proteins and organelles. It plays an essential role in plant growth, development, and adaptation to various stresses. Although the functions of many autophagy-related genes under high-temperature stress have been characterized, the role of SlATG8f, a key member of the SlATG8 family in tomato, remains poorly understood. In this study, we generated SlATG8f-Overexpressing tomato lines using the recombinant vector pBWA(V)HS. Using quantitative reverse transcription polymerase chain reaction (qRT-PCR), we analyzed physiological indices and expression levels of ATG8 family members and heat shock protein-related genes in fruits of wild-type (WT) and SlATG8f-overexpressing plants across four developmental stages under high-temperature stress. Our results indicate that overexpression of SlATG8f upregulates the expression of autophagy-related and heat shock protein genes, promotes early fruit ripening, and improves fruit quality under high-temperature stress. These findings reveal a regulatory role for SlATG8f in maintaining tomato fruit quality under heat stress and provide a theoretical foundation for tomato variety improvement.

plant biology↗

bioRxiv2025

Polyclonal origins of human premalignant colorectal lesions

Cancer is generally thought to be caused by expansion of a single mutant cell1. However, analyses of early colorectal cancer lesions suggest that tumors may instead originate from multiple, genetically distinct cell populations2,3. Detecting polyclonal tumor initiation is challenging in patients, as it requires profiling early-stage lesions before clonal sweeps obscure diversity. To investigate this, we analyzed normal colorectal mucosa, benign and dysplastic premalignant polyps, and malignant adenocarcinomas (123 samples) from six individuals with familial adenomatous polyposis (FAP). Individuals with FAP have a germline heterozygous APC mutation, predisposing them to colorectal cancer and numerous premalignant polyps by early adulthood4. Whole-genome and/or whole-exome sequencing revealed that many premalignant polyps--40% with benign histology and 28% with dysplasia--were composed of multiple genetic lineages that diverged early, consistent with polyclonal origins. This conclusion was reinforced by whole-genome sequencing of single crypts from multiple polyps in additional patients which showed limited sharing of mutations among crypts within the same lesion. In some cases, multiple distinct APC mutations co-existed in different lineages of a single polyp, consistent with polyclonality. These findings reshape our understanding of early neoplastic events, demonstrating that tumor initiation can arise from the convergence of diverse mutant clones. They also suggest that cell-intrinsic growth advantages alone may not fully explain tumor initiation, highlighting the importance of microenvironmental and tissue-level factors in early cancer evolution.

cancer biology↗

bioRxiv2025

Adaptive mutations at lysine residues of PRRSV nsp12 enable evasion of host proteasomal and selective autophagic degradation

Ubiquitin signaling in viral infections can either enhance immune responses and degrade viral proteins or be exploited by viruses to target host antiviral factors. Porcine reproductive and respiratory syndrome virus (PRRSV) poses a significant threat to the pig industry, with its non-structural proteins (nsps) critical for virulence and replication. Here, we investigated the role of ubiquitination in the stability of PRRSV-encoded nsps and found that nsp12 was specifically degraded via the ubiquitin-proteasome system. Mechanistically, nsp12 underwent K48- and K63-linked polyubiquitination at lysine residues 89, 91, 127, and 130. RNF114 served as an E3 ubiquitin ligase for nsp12, with its enzymatic activity essential for both nsp12 degradation and viral replication. Additionally, nsp12 underwent ubiquitin-dependent selective autophagy through receptor-mediated recognition, wherein NBR1, SQSTM1, and NDP52 bridged its interaction with LC3 for autophagic degradation. Evolutionary analyses revealed that PRRSV nsp12 acquired non-lysine residues at positions 89, 127, and 130 during viral adaptation. Correspondingly, recombinant PRRSV strains carrying the K91/127/130R mutations within nsp12 exhibited enhanced replication, while a revertant strain with the R89K mutation in nsp12 showed attenuated infectivity. Mass spectrometry analysis further identified significant enrichment of ubiquitination-related modifications among nsp12-interacting proteins. These findings provide valuable insights for anti-PRRSV drug design and highlight the challenge posed by adaptive mutations in viral proteins to the swine industry. Author SummaryUbiquitination plays crucial roles in both proteasomal degradation and selective autophagy during viral infections, yet its impact on the stability of porcine reproductive and respiratory syndrome virus (PRRSV) nonstructural proteins (nsps) remains unexplored. Here, we found that nsp12 was targeted for proteasomal degradation through K48/K63-linked polyubiquitination at lysine residues 89, 91, 127, and 130, which requires the involvement of E3 ubiquitin ligase RNF114. Furthermore, nsp12 underwent receptor-mediated selective autophagic degradation through the action of NBR1, SQSTM1, and NDP52. Using reverse genetics technology, mutations of lysine to arginine in PRRSV nsp12 enhanced viral replication, whereas the reverse mutations reduced its infectivity. Our findings demonstrate that PRRSV evades host degradation by acquiring adaptive mutations within nsp12, which counteract ubiquitin-dependent clearance thus enhancing viral fitness. This novel mechanism illustrates a key viral immune evasion strategy and underlines the challenge in controlling PRRSV.

microbiology↗

bioRxiv2025

Cellular dynamics and epidermal specialization during Arabidopsis floral development at single-cell resolution

Cellular specialization underlies the functional diversification of floral organs, yet the regulatory networks driving epidermal differentiation remain poorly understood. Here, we constructed a single-nucleus transcriptomic atlas of developing Arabidopsis thaliana flowers by profiling over 70,000 nuclei. For annotation of single-cell identities and differentiation status, we additionally generated tissue- and stage-specific transcriptomic datasets using nine fluorescent marker lines spanning four floral developmental stages. Guided by this integrative approach, we focused specifically on the floral epidermis at single-cell resolution and resolved 22 transcriptionally distinct epidermal cell populations. Gene regulatory network analyses identified key transcription factors essential for epidermal differentiation, highlighting MYB16 as a central regulator within the petal epidermis. Developmental trajectory and chromatin-binding analyses demonstrated that MYB16 dynamically orchestrates gene expression programs involved in epidermal identity establishment, cuticle biosynthesis, and stress resilience. Moreover, we demonstrate MYB16 interacts with DRMY1, a regulator of organ growth robustness. Complementation assays confirmed the non-redundant role of MYB16 in epidermal cell fate specification, distinct from other MYB transcription factors. This study provides a comprehensive cellular and regulatory framework for floral epidermal specialization, advancing our understanding of how temporal transcriptional dynamics integrate with spatial cell fate decisions during flower development.

molecular biology↗

bioRxiv2025

SAMBA: A Segment Anything Model-based tool for semi-automated Behavioural Analysis of Drosophila and other model organisms

Quantitative behavioural analysis is a powerful approach for linking genotype to phenotype, but many existing tools require specialised hardware, extensive preprocessing, or coding expertise. We present SAMBA (Segment Anything Model for Behavioural Analysis), an open-access, Google Colab-based pipeline that harnesses the Segment Anything Model 2 (SAM2) for accurate, semi-automated tracking without thresholding or background subtraction. With minimal user input, SAMBA extracts movement parameters, detects behavioural states, and supports batch processing. Validating SAMBA in a Drosophila melanogaster model of 3-hydroxyisobutyryl-CoA hydrolase (HIBCH) deficiency revealed impaired locomotion, reduced speed, and altered decision-making, highlighting its ability to capture nuanced phenotypes in neurometabolic disease. We further demonstrate adaptability to adult Drosophila and larval zebrafish, underscoring its cross-species utility. By combining foundation-model segmentation with an accessible interface, SAMBA lowers technical barriers to high-throughput behavioural phenotyping and is readily extendable to diverse model organisms, life stages, and experimental paradigms. This flexibility positions SAMBA as a valuable platform for accelerating disease mechanism studies, genetic screens, and preclinical testing. SUMMARY STATEMENTWe present an easy-to-use, open-access tool designed for Drosophila larval movement analysis, and readily customised for other applications, enabling rapid, scalable assessment of organism behaviour without specialised equipment or coding expertise.

animal behavior and cognition↗