Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 505 records · Page 28Linked to original sources

bioRxiv2025

Pericellular oxygen dynamics in human cardiac fibroblasts and iPSC-cardiomyocytes in high-throughput plates: insights from experiments and modeling

Adequate oxygen supply is crucial for proper cellular function. The emergence of high-throughput (HT) expansion of human stem-cell-derived cells and HT in vitro cellular assays for drug testing necessitate monitoring and understanding of the oxygenation conditions, yet virtually no data exists for such settings. For metabolically active cells like cardiomyocytes, variations in oxygenation may significantly impact their maturation and function; conversely, electromechanical activity can drive oxygen demands. We used HT label-free optical measurements and computational modeling to gain insights about oxygen availability (peri-cellular oxygen dynamics) in syncytia of human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CM) and human cardiac fibroblasts (cFB) grown in glass-bottom 96-well plates under static conditions. Our experimental results highlight the critical role of cell density and solution height (oxygen delivery path) in peri-cellular oxygen dynamics. The developed 3D reaction-diffusion model with Michaelis-Menten kinetics, trained on the obtained comprehensive data set, revealed that time-variant maximum oxygen consumption rate, Vmax, is needed to faithfully capture the complex peri-cellular oxygen dynamics in the excitable hiPSC-CMs, but not in the cFB. For the latter, accounting for cell proliferation was needed. Interestingly, we found both hypoxic (< 2%) and hyperoxic (> 7%) conditions can easily emerge in these standard HT plates in static culture and that peri-cellular oxygen dynamics evolves with days in culture. Our results and the developed computational model can directly be used to optimize cardiac cell growth in HT plates to achieve desired physiological conditions, important in cellular assays for cardiotoxicity, drug development, personalized medicine and heart regeneration applications.

bioengineering↗

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

Rewiring protein sequence and structure generative models to enhance protein stability prediction

Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models-a protein language model and an inverse folding model-and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stability predictions with exceptional generalization to unseen proteins and mutations. Beyond stability prediction, SPURS enables broad applications in protein informatics, including zero-shot identification of functional residues, improved low-N protein fitness prediction, and systematic dissection of stability-pathogenicity for human diseases. Together, these capabilities establish SPURS as a versatile tool for advancing protein stability prediction and protein engineering at scale.

bioinformatics↗

bioRxiv2025

HGCPep: A Hypergraph-based Deep Learning Model for Enhancing Representation of Peptide Features in Cancer-associated ncPEPs Identification

The emergence of non-coding RNA-encoded small peptides (ncPEPs) has sparked significant interest within the realm of cancer immunotherapy, owing to their potential as valuable therapeutic targets and biomarkers. The identification and characterization of cancer-associated ncPEPs assume a crucial role in propelling cancer research forward and augmenting our comprehension of immune-related processes. However, prevailing methods for cancer-associated ncPEPs identification primarily rely on sequence order, neglecting the latent relationships between peptides. As widely acknowledged in the central dogma, peptides are translated from RNA via a many-to-many mapping. In this study, we capitalize on the translation relationship and introduce a hypergraph-based model called HGCPep to enhance the representation of peptide features. Specifically, RNA is regarded as a hyperedge connecting the peptides that are translated from it. Our experimental results validate that the HGCPep approach, leveraging the strengths of hypergraph and convolutional neural networks, outperforms alternative methodologies in the recognition of ncPEPs. Additionally, employing a reduction tool, we visualize the results of multi-label classification within a 2D latent space, shedding light on how multi-label classification task influence the representation of peptides. The dataset and source code of our proposed method can be found via https://github.com/Longwt123/HGCPep_Github.

bioinformatics↗

bioRxiv2025

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

ImportanceMedical reporting guidelines are significant in improving the transparency, quality, and integrity of medical research, particularly in randomized clinical trials; adherence to these guidelines supports research interpretability and has direct implications for downstream applications, such as patient treatment. However, with over 600 distinct reporting guidelines, manual assessments are often time-consuming and labor-intensive. ObjectiveTo evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. Design, Setting, and ParticipantsThis study used large language models to design a retrieval augmentation generation architecture and collected published journal articles as training and validation sets to optimize prompts within the framework and comprehensively evaluate the performance of the framework. Medical reporting experiments were collected from 50 randomized controlled trials without the intervention of AI tools and 41 randomized controlled trials with the intervention of AI tools. Main Outcomes and MeasuresFor effective evaluation of the performance of this tool, a classification accuracy metric (Reported/Not Reported) defined as the number of correct judgments divided by all judgments and a content consistent score metric defined as the number of contents retrieved by the tool that are the same as those retrieved by researchers divided by the total number of judgments were calculated. ResultsThe RAPID tool uses the widely used Word document and Portable Document Format as an input file. Fifty published journal articles without the intervention of AI tools and 41 published journal articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. For these two reporting guidelines, a training set of 5 articles was selected from each dataset to refine the prompts used in the tools for CONSORT and CONSORT-AI reporting checklist. The validation set of the remaining articles was used to assess the performance of the RAPID. The RAPID tool used the Word document and Portable Document Format of the articles as input files. A RAPID graphical user interface was built using JavaScript and Vue. Conclusions and RelevanceThe RAPID tool is designed to assist in the reporting of various types of trials. RAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID may effectively save time and improve working efficiency for different user groups, for example, 1) simplifying the submission process and improving report quality by verifying manuscript completeness for medical authors; 2) facilitating evaluation of report quality for medical researchers; 3) expediting manuscript distribution for medical editors; and 4) identifying reporting deficiencies and providing deeper insights for review comments for reviewers. Key PointsO_ST_ABSQuestionC_ST_ABSCan large language model tools automatically generate medical reporting checklists for manuscripts of different types of clinic trials? FindingsAn automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, RAPID, was developed. RAPID was fully evaluated on all items across two separate datasets related to the Consolidated Standards of Reporting Trials (CONSORT) and CONSORT-AI reporting guidelines. In the first dataset corresponding to CONSORT, RAPID achieved an average accuracy of 92.11%, while in the second dataset associated with CONSORT - AI, it reached an average accuracy of 83.81%. Additionally, RAPID is highly scalable. It can be easily and smoothly adapted to different medical reporting guidelines without transfer learning on a large dataset. MeaningRAPID can effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.

bioinformatics↗

bioRxiv2025

Coro1a promotes the multivesicular body and plasma membrane fusion by facilitating PKM2-mediated SNAP-23 phosphorylation

During the formation of endosomal pathway-dependent extracellular vesicles (EVs), intraluminal vesicles in multivesicular bodies (MVBs) fuse with the plasma membrane (PM), releasing intraluminal vesicles to produce exosomes. Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex, membrane fusion effector, is essential in this process. However, which SNARE complex is involved in MVB and PM fusion and how its assembly is regulated remain elusive. We have demonstrated that neddylation of Coro1a inhibits EV secretion by inducing Rab7-mediated degradation of MVBs. Here, we find that Coro1a increases EV biogenesis by promoting the assembly of SNARE complex STX-12-SNAP-23-VAMP-7 in a neddylation-independent manner. Coro1a activates PKM2 to enhance SNAP-23 phosphorylation in the presence of neddylation inhibitor MLN4924, which drives SNAP-23 to recruit more STX-12 and VAMP7, leading to increased formation of the STX-12-SNAP-23-VAMP-7 complex. Correspondingly, Coro1a-induced EV biogenesis is eliminated by PKM2 inhibitor or SNAP-23 silencing independent of neddylation. Reduced EVs from tumors due to Coro1a knockout cannot be observed in MLN4924-treated tumor mice with PKM2 inhibitor treatment either. Furthermore, Coro1a increases in tumor tissues from lung tumor patients with tumor progression, and high Coro1a indicates unfavorable survival of tumor patients, suggesting that increased Coro1a-mediated enhanced EV production from tumors accelerates tumor progression. Altogether, our data demonstrate that Coro1a facilitates EV biogenesis by promoting the assembly of STX-12-SNAP-23-VAMP-7 complex during MVB and PM fusion independent of neddylation, thereby being involved in the progression of EV-related diseases.

cell biology↗

bioRxiv2025

Phytoplankton recruitment of specific microbial assemblages and phylosymbiotic patterns

Phytoplankton and bacteria represent two major pillars of the carbon cycle in marine ecosystems. While their interactions are known to be tightly linked, the specific mechanisms underlying these interactions remain largely unexplored. Evidence of host-specific microbial assemblages could serve as a foundation for studies of detailed host-microbe interactions, yet such research remains limited in phytoplankton. Here, we not only investigate the microbial assemblages of six phytoplankton species, including multiple strains of dinoflagellates and diatoms, but also samples of phytoplankton blooms from the field. Our results reveal the presence of host-specific microbial assemblages in phytoplankton, with members of the core microbial lineages (MCGs) playing pivotal roles in shaping host-specific microbial assemblages and contributing to network structures. Deterministic processes, particularly host genotypes, were the dominant factors shaping microbial assemblages, overriding environmental influences. Consequently, microbial composition reflected the evolutionary relationships of the host species, demonstrating phylosymbiotic patterns. These findings suggest that studying MCGs will be a crucial foundation for investigating specific phytoplankton-microbe interactions and highlight the ecological and evolutionary importance of host-microbial specificity in phytoplankton.

ecology↗

bioRxiv2025

Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression

BackgroundPancreatic ductal adenocarcinoma (PDAC) features a complex tumor microenvironment (TME) that significantly influences patient outcomes. Understanding the TMEs cellular composition and interactions is crucial for identifying therapeutic targets to improve treatment. MethodsWe performed an integrative analysis combining 88 single-cell RNA sequencing (scRNA-seq) samples with 187,520 cells, 20 Visium spatial transcriptomics (ST-seq) samples with 67,933 spots, and 1383 bulk RNA-seq samples and 2 Xenium high-resolution ST-seq samples with 307,679 cells, to delineate and characterize distinct subpopulations of fibroblasts, macrophages, T/NK cells, and B/plasma cells within the PDAC microenvironment. Correlations among major cell types across 12 PDAC datasets were assessed through gene set variation analysis (GSVA). Kaplan-Meier survival analysis was used to evaluate the prognostic significance of cell subtypes. Furthermore, multiplex immunohistochemistry (mIHC) and the Xenium platform was utilized to validate cellular interactions in human PDAC tissues at both protein and RNA levels. ResultsSix fibroblast subtypes and eight macrophage/monocyte subtypes were identified. POSTN high fibroblasts and SPP1 high macrophages highly infiltrated tumor tissues and were associated with poor prognosis. Most immune cell subtypes mediate adverse prognoses, except for CCL4 high CD8+ T EFF cells and IGHG1 high plasma cells, which are linked to favorable outcomes. ST-seq revealed spatial colocalization of POSTN high Fibro and SPP1 high Macro cells, as well as CCL4 high CD8+ T EFF and IGHG1 high plasma cells. These findings were corroborated by mIHC and validated using Xenium spatial transcriptomics with single-cell resolution, which confirmed the expression and spatial proximity of these markers at both the gene and protein levels. ConclusionThis integrative analysis of the PDAC TME underscores the prognostic importance of POSTN high fibroblasts and SPP1 high macrophages, while also highlighting the protective roles of CCL4 high CD8+ T EFF cells and IGHG1 high plasma cells. These insights could improve PDAC treatment strategies.

cancer biology↗

bioRxiv2025

Axonal Mechanotransduction Drives Cytoskeletal Responses to Physiological Mechanical Forces

Axons experience strong mechanical forces due to animal movement. While these forces serve as sensory cues in mechanosensory neurons, their impact on other neuron types remains poorly defined. Here, we uncover signaling that controls an axonal cytoskeletal response to external physiological forces and plays a key role in axonal integrity. Live imaging of microtubules at single-polymer resolution in a C. elegans motor neuron reveals local oscillatory movements that fine-tune polymer positioning. Combining cell-specific chemogenetic silencing with targeted degradation alleles to distinguish neuron-intrinsic from extrinsic regulators of these movements, we find that they are driven by muscle contractions and require the mechanosensitive protein Talin, the small GTPase RhoA, and actomyosin activity in the axon. Genetic perturbation of the axons ability to buffer tension by disrupting the spectrin-based membrane-associated skeleton leads to RhoA hyperactivation, actomyosin relocalization to foci at microtubule ends, and converts local oscillations into processive bidirectional movements. This results in large gaps between microtubules, disrupting coverage of the axon and leading to its breakage and degeneration. Notably, hyperpolarizing muscle or degrading components of the mechanotransduction signaling pathway in the axon rescues cytoskeletal defects in spectrin-deficient axons. These results identify mechanisms of an axonal cytoskeletal response to physiological forces and highlight the importance of force-buffering and mechanotransduction signaling for axonal integrity.

neuroscience↗

bioRxiv2025

In harmony? A scoping review of methods to combine multiple 16S amplicon data sets

Robust evidence on relationships between the human microbiome and health are critical for understanding and improving the human condition. However, there is little information about methodological approaches to combine and analyze multiple microbiome data sets. To address this gap, we conducted a scoping review of studies that combine sequencing data from multiple data sets to understand the study objectives, data sources and selection, feature -table assembly methods, and analyses. References were identified through a systematic search of literature published between 2011 and 2022. Our final review included 60 articles. Despite the wide-spread use of the word "meta-analysis," we found that only 24 studies used a systematic process to select their data sets, suggesting multiple meanings of the term within the field. While more than two thirds of studies used at least one publicly available data source, 19 had to request data from the original authors. Most studies (60%) combined data sets from multiple disjoint hypervariable regions. The number of hypervariable regions combined was not associated with the table construction method, but feature table construction method and the number of hypervariable regions influenced analytical resolution. Our results suggest the microbiome community needs to examine the use of terminology and analytic approaches for combining data sets; that additional work is needed to explore the impact of data source on bias in combined studies; and invite an independent evaluation of the methods used for feature table construction across disjoint regions.

microbiology↗

bioRxiv2025

Beyond RNA Structure Alone: Complex-Aware Feature Fusion for Tertiary Structure-based RNA Design

Tertiary structure-based RNA design plays a crucial role in synthetic biology and therapeutics. While existing methods have explored structure-to-sequence mappings, they focus solely on RNA structures and overlook the role of complex-level information, which is crucial for effective RNA design. To address this limitation, we propose tthe Complex-Aware tertiary structure-based RNA Design model, CARD, that integrates complex-level information to enhance tertiary structure-based RNA sequence design. To be specific, our method incorporates protein features extracted by protein language model (e.g., ESM-2), enabling the design model to generate more accurate and complex relevant sequences. Considering the biological complexity of protein-RNA interactions, we introduce a distance-aware filtering for local features from protein representation. Furthermore, we design a high-affinity design framework that combines our CARD with an affinity evaluation model. In this framework, candidate RNA sequences are generated and rigorously screened based on affinity and structural alignment to produce high-affinity RNA sequences. Extensive experiments demonstrate the effectiveness of our method with an improvement of 5.6% compared with base model without our complex-aware feature integration. A concrete case study for 2LBS further validates the superiority of our CARD.

bioinformatics↗

bioRxiv2025

Genomic Insights into the Demographic History and Local Adaptation of Wild Boars Across Eurasia

Wild boars exhibit genetic and phenotypic diversity shaped by migrations and local adaptations. Its expansion across Eurasia, especially in central Asia, remains underexplored. Here, we present newly sequenced whole-genome data of 47 wild boars from Eastern Asia, Central Asia, and Europe, combined with 49 existing genomes, creating a comprehensive dataset of 96 individuals. Our analyses show that Asian wild boars and Southeast Asian Suids split [~]3.6 Mya, with Central Asian and Southern Chinese ancestors diverging [~]1.8 Mya. The split between Central Asian and European-Near East ancestors occurred [~]0.9 Mya, followed by a European-Near East divergence [~]0.6 Mya. We identify signatures of local adaptation in Central Asian populations, including two positively selected variants in LPIN1, associated with cold adaptation, and a missense mutation in ALPK2, linked to meat traits. These findings provide insights into wild boar dispersal and adaptation, and shed light on domestic pigs breeding.

evolutionary biology↗

bioRxiv2025

Leopard Cats Occupied Human Settlements in China for 3,500 years before the Arrival of Domestic cats in 600-900 CE around the Tang Dynasty

The earliest cats in human settlements in China were not domestic cats (Felis catus), but native leopard cats (Prionailurus bengalensis). To trace when and how domestic cats arrived in East Asia, we analyzed 22 feline bones from 14 sites across China spanning 5,000 years. Nuclear and mitochondrial genomes revealed that leopard cats began occupying anthropogenic scenes around 5,400 years ago and last appeared in 150 CE. Following several centuries gap of archeological feline remains, the first known domestic cat (706-883 CE) in China was identified in Shaanxi during the Tang Dynasty. Genomic analysis suggested a white or partially white coat and a link to a contemporaneous domestic cat from Kazakhstan, indicating a likely dispersal route via the Silk Road. The two felids once independently occupied ancient anthropogenic environments in China but followed divergent paths and reached different destinations in human-animal interactions.

evolutionary biology↗

bioRxiv2025

p.D372H: A novel SCN5A mutation associated with Brugada syndrome

BackgroundBrugada syndrome (BrS) is a genetic cardiac arrhythmia disorder inherited in an autosomal dominant manner, characterized by ST-segment elevation in the right precordial leads (V1-V3) on electrocardiograms (ECGs). This syndrome predominantly affects young individuals with structurally normal hearts and significantly increases the risk of ventricular arrhythmias and sudden cardiac death (SCD). The most common genotype found among BrS patients is caused by mutations in the SCN5A gene, which lead to a loss of function of the cardiac sodium (Na+) channel (Nav1.5) by different mechanisms. MethodsPlasmids containing SCN5A were constructed using PCR and site-directed mutagenesis to create the D372H mutation. HEK293 cells were cultured and transfected with the wild-type and mutant constructs. Patch-clamp recordings assessed sodium current characteristics. Confocal microscopy visualized channel localization. Quantitative RT-PCR analyzed mRNA expression levels, while Western blot evaluated protein expression using specific antibodies. We identified a novel missense mutation, D372H, in the SCN5A gene associated with Brugada syndrome. Functional assays in HEK293 cells expressing the D372H mutant revealed a near-complete loss of sodium currents. Subsequent experiments with co-transfection of WT and D372H plasmids demonstrated that co-expression led to a significant reduction in current density in WT-expressing cells (P < 0.05). The D372H mutation also resulted in a hyperpolarizing shift of approximately 20 mV in the voltage dependence of inactivation, while activation and recovery kinetics remained unaffected. Additionally, confocal microscopy showed reduced membrane localization of the D372H mutant, with a significant decrease in protein expression levels confirmed by Western blot and RT-qPCR analyses. ConclusionIn summary, our findings indicate that the D372H mutation in the Nav1.5 sodium channel leads to significant reductions in sodium current density, altered channel expression, and impaired membrane localization. These changes contribute to the pathophysiology of Brugada syndrome by disrupting cardiac action potential dynamics.

genetics↗

bioRxiv2025

WDR2 regulates the orphan kinesin KIN-G to promote hook complex and Golgi biogenesis in Trypanosoma brucei

The flagellum in Trypanosoma brucei plays crucial roles in cell locomotion, cell morphogenesis, and cell division, and its inheritance depends on the faithful duplication of multiple flagellum-associated structures. One of such cytoskeletal structures is a hairpin-like structure termed the hook complex composed of a fishhook-like structure and a centrin arm structure, whose cellular functions remain poorly understood. We recently identified KIN-G, an orphan kinesin that promotes hook complex and Golgi biogenesis. Here we report a WD40 repeats-containing protein named WDR2, which interacts with and regulates KIN-G. WDR2 co-localizes with KIN-G at the centrin arm, and knockdown of WDR2 disrupts hook complex integrity and morphology, inhibits flagellum attachment zone elongation and flagellum positioning, and eventually arrests cytokinesis. Knockdown of WDR2 also disrupts the maturation of the centrin arm-associated Golgi, thereby impairing Golgi biogenesis. WDR2 interacts with KIN-G via its N-terminal unknown motifs, the middle domain containing a coiled coil and a PB1 motif, and the C-terminal WD40 domain, and targets KIN-G to its subcellular location. These results uncover a regulatory role for WDR2 in recruiting KIN-G to regulate hook complex and Golgi biogenesis, thereby impacting flagellum inheritance and cell division plane positioning.

cell biology↗

bioRxiv2025

Mouse Cortical Cellular Diversification Through Lineage Progression of Radial Glia

Cortical radial glia (RGs) sequentially generate pyramidal neurons (PyNs) and glia. In this study, we investigated the cell intrinsic programs underlying cortical cellular diversification using time-series scRNA-seq and snATAC-seq on purified mouse cortical progenitors across embryonic and postnatal stages. Our data revealed that RGs transition from early to late over time, sequentially producing intermediate neuronal progenitors (INPs) and intermediate glial progenitors (IGPs). While INPs expand exclusively to generate PyNs, IGPs progress from young to old, sequentially producing cortical astrocytes, oligodendrocytes, and olfactory bulb interneurons. We constructed comprehensive molecular maps that reflect cell lineage progression. In particularly, we found that chromatin accessibility drives cortical cellular diversification by restricting broadly expressed transcription factors to specific stages and cell types. Developmental changes in chromatin accessibility confine Lhx2-induced neurogenesis to early-stage RGs, leading to the loss of neurogenic competence and the acquisition of gliogenic competence as corticogenesis progresses.

neuroscience↗

bioRxiv2025

Unsupervised multi-animal tracking for quantitative ethology

Quantitative ethology necessitates accurate tracking of animal locomotion, especially for population-level analyses involving multiple individuals. However, current methods rely on laborious annotations for supervised training and have restricted performance in challenging conditions. Here we present an unsupervised deep-learning method for multi-animal tracking (UDMT) that achieves state-of-the-art performance without requiring human annotations. By synergizing a bidirectional closed-loop tracking strategy, a spatiotemporal transformer network, and three sophisticatedly designed modules for localization refining, bidirectional ID correction, and automatic parameter tuning, UDMT can track multiple animals accurately in various challenging conditions, such as crowding, occlusion, rapid motion, low contrast, and cross-species experiments. We demonstrate the versatility of UDMT on five different kinds of model animals, including mice, rats, Drosophila, C. elegans, and Betta splendens. Combined with a head-mounted miniaturized microscope, we illustrate the power of UDMT for neuroethological interrogations to decipher the correlations between animal locomotion and neural activity. UDMT will facilitate advancements in ethology by providing a high-performance, annotation-free, and accessible tool for multi-animal tracking.

animal behavior and cognition↗

bioRxiv2025

Unveiling Cognitive Processes in Mice through Reinforcement Learning Models in the FST and TST

Simple behavioral tests like the forced swim test (FST) and tail suspension test (TST) are widely used to assess depression-like behaviors in rodents, primarily measuring immobility time. However, this approach oversimplifies behavioral readouts and overlooks the cognitive processes driving behavior, leaving the relationship between increased immobility and cognitive biases unclear. Here, we developed the SwimStruggleTracker (SST) to extract fine-grained behavioral trajectories and integrate computational modeling to methodically analyze behavior. Our findings reveal that behavior in the FST and TST follows reinforcement learning principles involving learning, consequence perception, and decision-making. Notably, the cognitive processes underlying behavior differ between the two tests, challenging the assumption that they are interchangeable for cross-validation. Regression analyses identify distinct behavior phases: early behavior is primarily influenced by learning-related factors, while later stages are more affected by consequence sensitivity. These findings suggest traditional analyses focusing final minutes may underestimate the role of learning and overemphasize consequence sensitivity. MotivationThe forced swim test (FST) and tail suspension test (TST) are among the most widely used paradigms for assessing depression-like behaviors in rodents. Yet, traditional analyses typically quantify only immobility during the final minutes, discarding rich temporal structure in the data and hindering efforts to uncover the cognitive mechanisms underlying these behaviors. To address this gap, we developed an automated tool that captures behavioral trajectories with fine temporal resolution and integrates computational modeling to dissect the cognitive processes driving behavior. Using this approach, we demonstrate that the FST and TST engage overlapping but partially distinct cognitive processes, and that the dominant cognitive components shift across different stages of the tests. HighlightsO_LISwimStruggleTracker (SST) accurately rejects passive movements, such as pendulum-like motion. C_LIO_LIReinforcement learning models capture the behavioral dynamics of mice in the FST and TST. C_LIO_LIDistinct winning models indicate that the FST and TST engage partially dissociable cognitive processes. C_LIO_LILearning factors dominate early stages, whereas consequence-sensitivity factors dominate later stages. C_LI

animal behavior and cognition↗