Biology subjects

Wei, P.

Publications and source records attributed to Wei, P..

At least 19 recordsLinked to original sources

bioRxiv2026

NeuroPilot: Longitudinal intravital neural imaging across scales and modalities through an agentic platform

Longitudinal intravital imaging is essential for understanding how neural populations evolve across behavioral states, yet converting large-scale recordings into biological insight remains a fundamental challenge because neural signals are degraded by noise, non-rigid motion, and heterogeneous recording conditions. Here we introduce NeuroPilot, an agentic framework that transforms neural imaging analysis into a closed-loop, context-aware process, integrating a multimodal large language model with callable computational imaging modules to adaptively coordinate workflows from raw recordings to population-level readouts. Across heterogeneous imaging and experimental regimes, NeuroPilot restores motion-degraded recordings and supports consistent neural signal recovery, detection, and tracking in challenging deep-brain, freely moving, and longitudinal settings. These enable neuronal population tracking across spinal stimulation paradigms, deep-brain imaging, and continuous 24-hour mPFC recordings aligned with spontaneous sleep-wake behavior. By converting heterogeneous intravital recordings into stable population activity maps, NeuroPilot supports behavioral decoding and reveals structured neural dynamics across behavioral states and transitions. NeuroPilot establishes a scalable agentic paradigm for neural imaging analysis, expanding experimental access to the measurement, visualization, and interpretation of neural population activity.

neuroscience↗

bioRxiv2026

Ultrafast and Accurate Selection of High-Quality Protein Complex Models from Large-Scale Prediction

In the post-AlphaFold era, advances in protein complex structure prediction have enabled the generation of thousands of candidate models per target, making the accurate and efficient selection of high-quality models from large candidate pools a critical challenge. Existing model-selection approaches commonly employ estimated model accuracy (EMA) methods, which include single-model and consensus-based methods. The former lack cross-model comparative information, whereas the latter often incur high computational costs from pairwise structural alignments and are influenced by candidate-pool quality. Here, we present DeepUMQA-Selection, a two-stage framework combining single-model accuracy estimators for preliminary selection with Ultrafast Shape Recognition (USR)-based structural alignment for consensus selection. Preliminary selection enriches the candidate pool with high-quality models and reduces subsequent structural comparisons, while USR-based structural alignment enables efficient consensus selection without explicit structural superposition. On the retrospective CASP16 QMODE3 benchmark dataset, DeepUMQA-Selection outperformed all participating methods and predictor-derived confidence metrics, as measured by mean Top-5 weighted penalty. The advantage was particularly pronounced for heteromeric targets, with a 13.9% improvement in model-selection performance relative to the best-performing CASP16 participating method. Furthermore, a confidence-assisted strategy enabled the framework to accommodate larger protein complexes while maintaining superior model-selection performance over competing methods, supporting its scalability. Runtime benchmarking showed that the proposed USR-based structural alignment achieved a substantial 317-fold speedup over US-align for candidate pools containing 1,000 models, demonstrating its computational efficiency for large-scale model selection. Our results demonstrate that DeepUMQA-Selection enables accurate and computationally efficient selection of high-quality protein complex models from large-scale candidate pools.

bioinformatics↗

bioRxiv2026

Mendel, a foundation model of human genetic variation, prioritizes regulatory variants and improves gene expression prediction

Human genome-wide association studies have identified hundreds of thousands of variant trait associations, but interpreting their mechanistic consequences at allele resolution remains a central bottleneck. DNA foundation models pretrained on genomic sequence corpora learn features predictive of regulatory activity, yet their training objectives are dominated by the conserved background common to all individuals, rather than the sparse variant sites at which human genetic variation is concentrated. Here we introduce Mendel, a DNA foundation model trained from scratch on human whole genome sequences using a variant-centric objective that concentrates supervision on allelic events within reference aligned sequence context. On held-out individuals, Mendel attains 0.984 micro-averaged SNV allele-prediction accuracy, compared with 0.311 for Evo 2. Mendel residual difficulty is selectively enriched at cis-eQTL variants, prioritizes fine-mapped causal variants (AUROC 0.731), where Evo 2 residuals performed near chance, and when converted into a cis-expression prior for modelling, improves prediction relative to an unweighted baseline in all 50 GTEx tissues.

genetics↗

bioRxiv2026

An M-learner approach for heterogeneous mediation analysis with high-dimensional omics mediators

Causal mediation analysis is widely used to identify biological pathways linking exposures to outcomes, but most methods assume homogeneous mediation effects across individuals. In high-dimensional omics settings, this assumption can mask important heterogeneity driven by demographic, genetic, or environmental factors. We propose the M-high-learner, a flexible framework for detecting heterogeneous mediation effects with high-dimensional mediators. The method identifies mediators with subgroup-specific indirect effects while distinguishing them from null or homogeneous signals and controlling the type I error rate. It is computationally efficient, scalable, and yields interpretable sub-types. Simulation studies show that the proposed approach achieves high power while maintaining accurate error control. Applications to the Framingham Heart Study and the Multi-Ethnic Study of Atherosclerosis reveal that the mediation role of gene expression in sexs effect on high-density lipoprotein varies across subgroups defined by body mass index and age. Our framework provides a practical tool for uncovering heterogeneous biological mechanisms in high-dimensional genomic studies. Author SummaryBiological processes linking risk factors to disease often differ across individuals, but many existing methods assume these processes are the same for everyone. This can hide important differences between groups. We developed a powerful method to identify when these pathways vary across subgroups using large-scale molecular data. Our approach detects differences in how intermediate biological factors contribute to outcomes in populations defined by characteristics such as age and body mass index. Applying our method to population studies, we found that some biological pathways operate differently across groups, suggesting that key mechanisms may be missed when differences are ignored. Our work provides a tool to better understand how disease-related processes vary across individuals, which may support more targeted and personalized approaches to health research.

bioinformatics↗

bioRxiv2026

Noncanonical Circular RNAs and Potential Functions

Circular RNAs (circRNAs) are ubiquitous in eukaryotes; dysregulated circRNA expression is linked to diseases, including lung cancer. In contrast to canonical circRNAs arising from exon-intron boundaries, noncanonical circRNAs originating within exonic, intronic, and intergenic regions have typically been dismissed as transcriptional noise or technical artifacts. To explore circRNA diversity and appreciate their functions, we developed an algorithm to identify both canonical and noncanonical circRNAs without relying on genome annotation, enabling the identification of circRNAs of all types and in newly sequenced or poorly annotated species. Results from lung cancer cells revealed that noncanonical circRNAs constituted over two-thirds of the circRNA population and were expressed more abundantly than canonical circRNAs, and genes with fewer and shorter exons were hotspots for noncanonical circRNA and circRNA isoform production. Further analyses showed that many noncanonical circRNAs were indeed endogenous circRNAs transcribed within cells rather than experimental artifacts, were potentially translated into proteins or peptides, and were conserved across species. Moreover, we validated 65 noncanonical circRNAs in NCI-H23 cells using multiple bioassays and demonstrated that both exonic and intergenic noncanonical circRNAs influenced cell viability. CircRNA profiles in tumor and tumor-adjacent tissues of lung cancer patients revealed tissue-specific expression and differentially expressed canonical and noncanonical circRNAs from cognate genes involved in cancer-related pathways, indicating their potential clinical relevance. This study confirmed the authenticity of noncanonical circRNAs and provided the first experimental evidence that noncanonical circRNAs influence cancer cell phenotypes. These findings broaden our understanding of circRNA biology, highlighting their widespread genomic distribution, diverse functions, and potential clinical relevance.

molecular biology↗

bioRxiv2026

Lineage specifying transcription factors determine cell function by direct control of metabolism

Execution of a complex cellular function requires the coherent expression of a set of genes that encode the requisite biochemical program. It is believed that this is typically accomplished by employing common transcription factors to activate those genes, thus ensuring coherence. However, many of these programmatic gene sets are expressed at differing levels and in distinct configurations in different cell types. This creates a unique challenge for the logic of gene regulation--precisely tuning the expression of programs across cell types while maintaining coherence. Using the glycolysis pathway as a model of co-expressed genes with highly divergent cell-specific regulation, we developed a CRISPR screening approach to identify trans-acting factors that control RNA abundance. Using this platform, we discovered that lineage-specifying transcription factors directly activate glycolytic gene expression and alter metabolism. We showed that impaired metabolism disrupts lineage-specific activities via loss of specialized biochemical pathways and functions. We hypothesize that lineage-specifying transcription factors directly regulate metabolic gene expression to ensure that cellular metabolism is tuned to the specific demands imparted by specialized cell function.

cell biology↗

bioRxiv2026

Cellular stemness identifies high-risk ductal carcinoma in situ and offers a therapeutic interception opportunity

Ductal carcinoma in situ (DCIS) exhibits substantial heterogeneity in its risk of progression to invasive breast cancer, yet the cellular and molecular determinants of high-risk lesions remain incompletely defined. Using spatially resolved single-cell transcriptomic and epigenomic profiling of 43 patient-derived DCIS and DCIS/invasive ductal carcinoma (IDC) samples, we delineate cellular programs, spatial organization, and epigenetic regulatory mechanisms associated with invasive potential. We identify an epithelial population with stemness features within luminal hormone-responsive (LumHR) cells that progressively expands from benign tissue to DCIS and IDC, and is strongly associated with invasive progression and recurrence-linked transcriptional programs. Spatial mapping reveals discrete DCIS niches enriched for stem-like LumHR cells, characterized by elevated CEACAM6 expression and enhanced ligand-receptor interactions, including CEACAM6-EGFR signaling between epithelial and stromal compartments, including cancer-associated fibroblasts, macrophages (APOC1-positive) and perivascular cells. These niches define a microenvironmental context that supports stemness and invasive potential. Epigenomic analyses implicate FOXA1 as a key regulator of these stem-like transcriptional states. Pharmacologic disruption of FOXA1-regulatory network using LSD1 inhibition suppresses stemness-associated transcriptional programs in vitro and significantly restrains tumor growth in vivo. Collectively, these findings define high-risk DCIS as a stemness-driven disease embedded within specialized microenvironments, and identify associated regulatory networks as candidate biomarkers and therapeutic vulnerabilities.

cancer biology↗

bioRxiv2026

Nanopore event detection in a simple and adaptive way

Nanopore read-out, that is the current signals measured across nanometer-sized openings in dielectric membranes or through natural protein channels, enables the detection, identification and sequencing of individual molecules. The detection can take place by analyzing the events of single biomolecules interacting with the pore. The accuracy in the detection of these single events is key for identification of physicochemical properties of analyte molecules. To this end, we further develop a very simple, fast, almost parameter-free, and adaptable cluster-based event detection (CBED) algorithm that clusters the nanopore signals prior to detecting nanopore events. The algorithm is validated against two other event detection schemes with respect to simplicity and efficiency. For this, nanopore data from four different experiments stemming from different laboratories that vary in the nanopore type, size, and analyte are considered. The comparison is made on the basis of the number of events detected, their quality, and the most important features extracted from nanopore events. Our results underline the higher efficiency and less noise of the CBED detected events for biological nanopore data and the need for an on-the-fly adaptivity of the baseline current for a class of solid-state nanopore data.

bioinformatics↗

bioRxiv2026

Behavioral hierarchy without a hierarchical brain

Behavior is inherently hierarchical. The prevailing view holds that hierarchical behavior arises from the brains hierarchical organization. However, this view is primarily derived from experiments in highly controlled laboratory settings, and it remains unknown whether the same principles apply during natural, freely moving behavior. Here, we show that, under naturalistic and freely moving conditions, hierarchical mouse behavior can emerge from localized cortical dynamics alone, independent of anatomical or functional brain hierarchies. We first established a naturalistic neuroethological platform that enables quantitative characterization of hierarchical behavior alongside its corresponding neural activity. Using this platform, we find that localized cortical dynamics encode multiple levels of behavioral hierarchy. Specifically, low-dimensional neural dynamics encode high-level behavioral composites, whereas higher-dimensional neural dynamics encode low-level behavioral kinematics. Finally, by selectively perturbing high-dimensional components of cortical dynamics using optogenetics, we observe a selective reduction in low-level kinematic behaviors, providing causal evidence for this relationship. Together, these findings challenge a central assumption in systems neuroscience by demonstrating that hierarchical behavior does not require a hierarchically organized brain. Instead, hierarchy emerges from the dimensional organization of localized cortical dynamics, revealing a previously unrecognized principle by which complex behavior can arise from relatively simple neural substrates. This dynamic principle offers a unifying framework for understanding how biological systems achieve behavioral flexibility under natural conditions and suggests new directions for artificial intelligence that rely on adaptive local dynamics rather than increasingly deep architectures.

neuroscience↗

bioRxiv2025

MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome

Mediation analysis is a pivotal tool for elucidating the indirect effect of an environmental factor or treatment on disease through potentially high-dimensional omics data, such as gene expression profiles. However, traditional mediation analysis methods tailored for binary outcomes often rely on the rare disease assumption in logistic regression and provide inadequate measures of total mediation effect when multiple mediators have effects in different directions. In this paper, we develop a MEdiation analysis framework in LOgistic regression for high-Dimensional mediators and a binarY outcome (MELODY). It leverages a second-moment-based measure analogous to the R2 for linear models to quantify the total mediation effect. We also develop a variable selection procedure for high-dimensional data to reduce bias introduced by non-mediators. Our comprehensive simulations demonstrate the superior performance of MELODY in scenarios with non-rare disease binary outcomes and high-dimensional mediators. We apply MELODY to the Framingham Heart Study of over 5000 individuals to analyze the mediation effects of metabolomics and transcriptomics data on the pathways from sex to incident coronary heart disease.

genomics↗

bioRxiv2025

SUGP1 associates Y-box protein to regulate piRNA biogenesis in Bombyx mori

PIWI-interacting RNAs (piRNAs) are critical for transposon silencing and genome integrity, as well as gene expression regulation and antiviral immunity in metazoans, yet the molecular mechanisms governing their biogenesis remain incompletely understood. The participation of the splicing-associated process in piRNA biogenesis has been emphasized in multiple species, but the key factors and mechanisms remain elusive. Here, we identified SUGP1 (SURP and G-Patch Domain Containing 1) in BmE (a unique model cell system with a complete piRNA biogenesis pathway) as a key splicing factor that functions in piRNA biogenesis. Through CRISPR-Cas9-mediated gene knockdown in cultured cells combined with RNA-seq, small RNA-seq, and IP-mass spectrometry (IP-MS), our results reveal that SUGP1 deficiency disrupts piRNA accumulation, alters mature piRNA length distributions, and activates transposon expression. Immunofluorescence and Western blot (WB) analyses further demonstrate that SUGP1 interacts with Y-box protein (YBP), which is key regulators of RNA metabolism. Functional validation in Drosophila SUGP1-RNAi lines highlights evolutionary conserved and species-specific roles of SUGP1 in piRNA maturation. Collectively, our data uncover a dual role for silkworm SUGP1 in coordinating YBP-dependent piRNA biogenesis, thus elucidating a novel mechanistic framework for piRNA pathway regulation. Our work also underscores the silkworm as a unique model for studying non-canonical piRNA biogenesis mechanisms, with implications for treating transposon dysregulation-linked diseases. Author summaryDisruption of piRNA synthesis leads to abnormal consequences such as transposon de-repression, posing a significant threat to genomic stability. It is therefore essential to in-depth analysis of the piRNA biosynthetic mechanism. Previous studies have highlighted the involvement of splicing-related processes in piRNA biosynthesis, yet key factors and mechanisms remain poorly understood. This study employed the silkworm cell system, which possesses a complete piRNA biosynthetic pathway. Utilizing CRISPR-Cas9-mediated gene knockout technology combined with RNA sequencing, small RNA sequencing, and immunoprecipitation-mass spectrometry (IP-MS), we discovered that SUGP1 deficiency disrupts piRNA accumulation, alters the length distribution of mature piRNAs, and activates transposon expression. Furthermore, immunofluorescence and Western blot analyses confirmed an interaction between SUGP1 and Y-box proteins (YBPs), key regulators of RNA metabolism. Functional validation using Drosophila SUGP1-RNAi lines revealed that SUGP1 plays dual roles in piRNA maturation. Our study reveals that the silkworm scissor-related factor SUGP1 has dual functions in coordinating YBP-dependent piRNA biosynthesis.

molecular biology↗

bioRxiv2025

IGP receptors act as redox sensors to mediate broad-spectrum disease resistance induced by cellooligomers

Cys-oxidation is a well-established post-translational regulator of intracellular protein function, yet its role in extracellular receptor-mediated signaling remains poorly understood. Here, we investigate the LRR-Malectin receptor-like kinase IGP1, a key receptor for plant cell wall damage-associated molecular patterns (DAMPs). We demonstrate that IGP1 directly recognizes cellooligomers with a degree of polymerization [≥] 3 via a conserved binding groove in its LRR domain, not the malectin domain as anticipated. Strikingly, a pair of cysteines within the malectin domain functions as a redox sensor, undergoing hydrogen peroxide-induced oxidation that sterically reduces ligand binding affinity and attenuates immune signaling. Phylogenetic analyses reveal that IGP originated after the terrestrialization of plants, and is an evolutionarily conserved redox sensor in land plants. Functional studies in Arabidopsis (Arabidopsis thaliana), wheat (Triticum aestivum), and soybean (Glycine max) confirm that the IGP-cellooligomer module confers broad-spectrum disease resistance. Our work uncovers a unique mechanism where ligand perception and redox sensing are integrated into a single receptor to regulate DAMP-triggered immunity.

plant biology↗

bioRxiv2025

CDK11 activates CDK12 to trigger the elongation of RNA Polymerase II

CDK11 is an essential cyclin-dependent kinase in higher eukaryotes, yet its specific substrates and functional roles have remained elusive. Our findings reveal that CDK11 is functionally analogous to the yeast Bur1 kinase, phosphorylating the repeat region of hSpt5 and the linker domain of Rpb1 in RNA Polymerase II (Pol II). Inhibition of CDK11 results in a significant reduction of active Pol II not only at transcription start sites but also along gene bodies. Further investigation finds that CDK11 is crucial for the phosphorylation and activation of CDK12, which is essential for the elongation of Pol II. Moreover, we find that CDK10 can partially compensate for the function of CDK11. Combining with earlier functional elucidation of CDK9 from us, the consequent roles of CDK7/8, CDK9, CDK10/11, and CDK12/13 in transcription regulation in metazoans are established.

biochemistry↗

bioRxiv2025

A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics

Spatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for understanding complex tissue architectures, such as those found in cancers. Seurat, by far the most popular tool for analyzing ST data, uses the Wilcoxon rank-sum test by default for differential expression analysis. However, as a nonparametric method that disregards spatial correlations, the Wilcoxon test can lead to inflated false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we propose a Generalized Estimating Equations (GEE) framework as a robust solution for differential gene expression analysis in ST. We conducted a comprehensive comparison of the GEE-based tests with existing methods, including the Wilcoxon rank-sum test and z-test. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE test with robust standard error, referred to as the Independent GEE, demonstrated superior Type I error control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed poor calibration of the p-values and potential false positive findings from the Wilcoxon rank-sum test. Our comparative study based on simulations and real data applications suggests that the Independent GEE test is well-suited for ST data, offering more accurate identification of biologically relevant gene expression changes and complementing the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub. Author SummarySpatial transcriptomics (ST) provides unprecedented insights into gene expression patterns while retaining spatial context, making it a valuable tool for studying complex tissue architectures and disease etiology. Seurat, a widely used software tool for analyzing ST data, relies on the Wilcoxon rank-sum test for differential expression analysis. However, this test ignores spatial correlations, leading to inaccurate control of false positive rates and misleading findings. This limitation highlights the need for a more robust statistical approach that effectively incorporates spatial correlations. To this end, we have proposed a Generalized Estimating Equation (GEE) framework as a robust solution for differential gene expression analysis in ST. By appropriately accounting for spatial correlations, extensive simulations showed that the GEE-based test demonstrated superior false positive rate control and comparable power relative to other methods. Applications to ST datasets from breast and prostate cancer showed potential false positive findings from the Wilcoxon rank-sum test. We recommend the GEE method to be a useful complement to the Wilcoxon rank-sum test. We have implemented the proposed method in R package "SpatialGEE", available on GitHub.

bioinformatics↗

bioRxiv2025

CRISPR-based shuttle cloning of 1,397 human genes into UAS vectors

The construction of genome-wide DNA libraries from publicly available resources is essential for leveraging functional genomics to investigate complex biological systems. However, all existing high-throughput cloning methods for transferring DNA fragments between vectors require PCR amplification of the DNA fragments, rendering the construction of genome-wide DNA libraries labor-intensive and time-consuming. By introducing a concept of CRISPRshuttle cassette, we herein present a method named CRISPR-based shuttle cloning (CRISPRshuttle cloning). This method enables the high-throughput transfer of numerous DNA fragments from original plasmids with identical backbones to a different vector background without the need for PCR amplification of the DNA fragments. The procedure comprises two-step test tube reactions followed by bacterial transformation. Using CRISPRshuttle we successfully generated a library of GAL4/UAS-based UAS-ORF plasmids covering 1,397 human genes conserved in Drosophila. This library may serve as a valuable resource for gain-of-function screening in cultured cells and for the creation of a transgenic UAS-ORF library in Drosophila.

molecular biology↗

bioRxiv2025

Estimation of total mediation effect for a binary trait in a case-control study for high-dimensional omics mediators

Mediation analysis helps uncover how exposures impact outcomes through intermediate variables. Traditional mean-based total mediation effect measures may suffer from the cancellation of opposite component-wise effects, and existing methods often lack the power to capture weak effects in high-dimensional mediators. Additionally, most high-dimensional mediation analysis methods have focused on continuous outcomes, with limited attention to binary outcomes, particularly in case-control studies. To fill this gap, we propose an R2 total mediation effect measure within the liability framework that offers a clear and intuitive causal interpretation, provides additional insights beyond the mean-based measures, and is invariant to disease prevalence. We develop a cross-fitted, modified Haseman-Elston regression-based estimation procedure tailored for mediation analysis in case-control studies, which can also be applied to cohort studies. Our estimator remains consistent in the presence of non-mediators and weak effects, as demonstrated in extensive simulations. Theoretical justification for consistency is provided under mild conditions and without requiring exact mediator selection. In a case-control substudy of the Womens Health Initiative involving 2150 individuals, we found that many metabolites were mediators with weak effects in the path from BMI to coronary heart disease, and we estimated that 89% (95% CI: 57%-100%) of the BMI-explained variation in underlying CHD liability is mediated by the measured metabolomics. The proposed estimation procedure is implemented in the R package "r2MedCausal", available on GitHub.

genomics↗

bioRxiv2024

Neural Circuit Underlying Individual differences in Visual Escape Habituation

Emotions, like fear, are internal states enabling organisms to effectively confront environmental threats. When repeatedly exposed to predators, individuals show divergent adaptive responses. However, the neural circuit mechanisms underlying individual differences in to repeated threats remain largely unknown. Here, we identify two distinct types of visual escape--consistent escape (T1) and rapid habituation (T2) -- with unambiguous arousal states to repetitive threat stimuli. We systematically investigate distinct pathways originating from the superior colliculus (SC) and insula that project to the basolateral amygdala (BLA), with relay stations in the mediodorsal thalamus (MD) and ventral tegmental area (VTA), mediating T1 and T2 behavioral types. Additionally, we identify the MD as a critical hub integrating SC and insula inputs, projecting to the BLA and contributing to reduced arousal and attenuated defensive behaviors against looming stimuli. Our findings offer significant insights into the mechanisms of internal states, arousal modulation, and behavioral adaptability. O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/611366v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@164df34org.highwire.dtl.DTLVardef@17a01e6org.highwire.dtl.DTLVardef@112de44org.highwire.dtl.DTLVardef@1be6aea_HPS_FORMAT_FIGEXP M_FIG Graph Abstract C_FIG In briefWe identify two distinct visual escape behaviors--consistent escape (T1) and rapid habituation (T2)--linked to unique SC and insula pathways projecting to the BLA via MD and VTA. The MD acts as a hub integrating sensory inputs to modulate arousal and defensive responses. These findings advance our understanding of the neural mechanisms driving internal states, arousal modulation, and behavioral adaptability. Highlights[bullet] We identify two distinct visual escape behaviors with unambiguous arousal states--consistent escape (T1) and rapid habituation (T2) --to repeated looming stimuli. [bullet]The SC-VTA-BLA pathway mediated T1 behavioral type, while the SC-MD-BLA pathway mediated T2 type. [bullet]MD as a central hub integrating SC and insula inputs to modulate arousal and defensive behavioral adaptation.

neuroscience↗

bioRxiv2024

TUSCAN: Tumor segmentation and classification analysis in spatial transcriptomics

The identification of tumor cells is pivotal to understanding tumor heterogeneity and the tumor microenvironment. Recent advances in spatially resolved transcriptomics (SRT) have revolutionized the way that transcriptomic profiles are characterized and have enabled the simultaneous quantification of transcript locations in intact tissue samples. SRT is a promising alternative method of studying gene expression patterns in spatial domains. Nevertheless, the precise detection of tumor regions within intact tissue remains a great challenge. The common way of identifying tumor cells is via tumor-specific marker gene expression signatures, which is highly dependent on marker accuracy. Another effective approach is through aneuploid copy number events, as most types of cancer exhibit copy number abnormalities. Here, we introduce a novel computational method, called TUSCAN (TUmor Segmentation and Classification ANalysis in spatial transcriptomics), which constructs a spatial copy number variation profile to improve the accuracy of tumor region identification. TUSCAN combines the gene information from SRT data and the hematoxylin-and-eosin-staining image to annotate tumor sections and other benign tissues. We benchmark the performance of TUSCAN and several existing methods through the application to multiple datasets from different SRT platforms. We demonstrate that TUSCAN can effectively delineate tumor regions, with improved accuracy compared to other approaches. Additionally, the output of TUSCAN provides interpretable clonal evolution inferences that may lead to novel insights into disease development and potential druggable targets.

bioinformatics↗