Biology subjects

Tian, L.

Publications and source records attributed to Tian, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Dual-Channel Event Microscopy for Ultrafast Biological Imaging

Many fundamental biological processes--spanning immune-tumor interactions, neuronal signaling, and microvascular flow--exhibit fast, multiscale dynamics among diverse cell types within three-dimensional tissue environments. Capturing such activity requires imaging systems that simultaneously achieve high temporal resolution, multicolor capability, and volumetric coverage over large fields of view (FOVs). However, existing modalities remain limited by trade-offs among imaging speed, spectral capacity, depth of field (DOF), and spatial resolution. Here, we present Dual-Channel Event Microscopy (DEM), which integrates digital micromirror device (DMD)-based pulsed illumination, extended-DOF optics, and event-based sensing for ultrafast, dual-channel volumetric imaging across a 2.3 x 1.3 mm2 FOV with an effective 200 {micro}m DOF. Using dual-color fluorescent phantoms and microsphere flow assays, DEM achieves accurate spectral separation and reconstruction of rapid motion at kilohertz frame rates. In vivo, DEM enables simultaneous visualization of neutrophils and premalignant tumors in freely swimming zebrafish. In immobilized specimens, it provides robust, sensor-level optical sectioning near the heart, suppressing diffuse background to reveal fine vascular networks and active blood circulation into and out of the cardiac chambers. DEM further enables quantitative mapping of blood-flow dynamics in the zebrafish tail, resolving arterial-venous differences and capturing heartbeat-driven oscillations that reflect cardiac pumping with high temporal fidelity. By uniting ultrafast acquisition, dual-channel capability, volumetric coverage, and intrinsic optical sectioning within a single event-driven architecture, DEM offers a powerful platform for visualizing rapid multicellular interactions and physiological dynamics in living systems.

bioengineering↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2025

Differential colocalization of cell-type pairs in single-cell spatial omics with DC-SPOMIC

MotivationSpatial omics studies compare cell-cell organization across samples, but most methods model between-sample variability while treating sample-level spatial estimates as error-free. Overlooking within-sample uncertainty can distort inference in heterogeneous cohorts, motivating methods that explicitly quantify and propagate this uncertainty into cohort-level analyses. ResultsWe present PANORAMIC, a hierarchical framework for spatial colocalization analysis that uses edge-corrected neighborhood enrichment to estimate local cell-type colocalization, spatial bootstrapping to quantify within-sample uncertainty, and multilevel random-effects meta-analysis to propagate this uncertainty across samples, patients, and conditions. In simulations, PANORAMIC improved recovery of within-sample uncertainty and between-sample heterogeneity relative to naive estimators across diverse spatial settings and progressive data degradation. Applied to a colorectal cancer tissue microarray profiled by multiplexed immunofluorescence imaging, PANORAMIC identified stronger B- and T-cell colocalization in tumors with Crohns-like reaction than in tumors with diffuse inflammatory infiltration, together with tighter higher-order immune organization consistent with immune aggregates. These results show that propagating within-sample spatial uncertainty can improve cohort-level inference in spatial omics studies. Availability and ImplementationPANORAMIC is released as an open-source R package at https://github.com/plevritis-lab/panoramic. Contact[sylvia.plevritis@stanford.edu]

bioinformatics↗

bioRxiv2025

Toward Precision Detection of Pyrazinamide Resistance: Critical Concentration Assessment and Rapid Molecular Method Validation

This study aimed to evaluate the diagnostic performance of broth microdilution (BMD) and fluorescence PCR melting curve analysis (MeltPro MTB/PZA assay) for detecting pyrazinamide (PZA) resistance in rifampicin-resistant tuberculosis (RR-TB).RR-TB strains isolated from patients in TB prevention and control in stitutions and designated hospitals in Beijing from January to December 2009 were analyzed. PZA susceptibility was assessed using BMD, MeltPro MTB/PZ A assay (targeting pncA mutations), and whole-genome sequencing (WGS). The sensitivity, specificity, agreement rate, and Kappa value of BMD (at critical c oncentrations (CCs) of 100 g/mL and 200 g/mL)and MeltPro MTB/PZA assa y were evaluated using WGS as the reference standard. At CCs of 100 g/mL and 200 g/mL, BMD showed resistance rates of 63.6% (70/110) and 45.5% (50/110), respectively. Compared to WGS, BMD demonstrated sensitivities of 96 .1% and 92.2%, specificities of 64.4% and 94.9%, with {kappa} values of 0.590 and 0.872, respectively. The MeltPro assay detected resistance in 44.5% (53/119) of strains, with 89.7% sensitivity, 98.4% specificity, and {kappa} value of 0.882. The MeltPro MTB/PZA assay demonstrates high diagnostic accuracy for PZA resista nce screening in RR-TB patients, supporting its utility as a frontline detection t ool. BMD at 200 g/mL showed better concordance with WGS. Additionally, f ive novel pncA mutations (Ile5Thr, Leu27Gln, Ser67stop, Pro69Arg, Trp119Ser) identified in this study expand the catalog of known resistance-associated vari ants and offer new targets for mechanistic investigations. IMPORTANCEThis study evaluates the diagnostic performance of broth micr odilution and a rapid molecular assay in detecting pyrazinamide resistance in R R-TB, providing insight into reliable testing approaches and resistance profiles.

microbiology↗

bioRxiv2025

NicheScope: Identifying Multicellular Niches and Niche-Regulated Cell States in Spatial Transcriptomics

The functional state of a cell is intrinsically linked to its local microenvironment, or cell niche, a complex milieu formed by multiple interacting cell types. Deciphering how these multicellular niches regulate cell states is fundamental to understanding tissue biology and disease mechanisms, yet remains challenging with current computational approaches. Here, we present NicheScope, a computational framework for transcriptome-wide identification and characterization of Multicellular Niches (MCNs) and their corresponding Niche-Regulated Cell States (NRCSs) from spatial transcriptomics data. NicheScope operates on the principle that a cells transcriptional state is associated with its local multicellular composition. It employs a robust statistical approach to jointly model neighborhood composition and transcriptome-wide gene expression, enabling the simultaneous discovery of MCNs, defined by specific combinations of neighboring cell types, and their associated NRCSs, characterized by distinct gene programs. We demonstrate NicheScopes power and versatility across diverse tissues and platforms, including lymph node, lung adenocarcinoma, and head and neck cancer. NicheScope reproducibly dissected established tissue structures in lymph nodes across tissue regions and platforms, uncovered clinically relevant tumor cell-associated MCNs in lung adenocarcinoma, and revealed shared and condition-specific MCNs in primary and metastatic tumors. Our results establish NicheScope as a powerful, robust, scalable, and interpretable framework for dissecting the spatial and functional organization of complex tissues, providing new insights into multicellular coordination in health and disease.

bioinformatics↗

bioRxiv2025

The competition between splicing and 3' processing shapes the human transcriptome

Eukaryotic pre-mRNA processing steps, including splicing and 3' processing, are tightly coordinated, yet the underlying mechanisms remain incompletely understood. U1 snRNP has been proposed to inhibit 3' processing at intronic polyadenylation (IPA) sites through a splicing-independent mechanism termed telescripting. In contrast, we discovered that disrupting splicing--by targeting various key components such as U1 snRNP, U2 snRNP, U2AF, and SF3b--activates 3' processing at thousands of IPA sites. Notably, splicing inhibition, especially of U1 snRNP, induced widespread premature transcription termination within gene bodies through both IPA-coupled and -independent mechanisms. Different splicing factors activated overlapping and distinct sets of IPA sites, reflecting their specific contributions to transcription and spliceosome function. Conversely, inhibition of 3' processing enhanced splicing globally. These findings support a model in which splicing and 3' processing are competing processes that intersect with transcription to shape the transcriptome landscape.

molecular biology↗

bioRxiv2025

LENG8 mediates RNA nuclear retention and degradation in eukaryotes

In eukaryotes, incompletely processed and misprocessed mRNAs as well as numerous noncoding RNAs are retained in the nucleus and often degraded. However, the mechanisms for this critical quality control pathway remain poorly understood. Here we identify LENG8 as a conserved RNA nuclear retention factor. We showed that LENG8 is recruited to pre-mRNAs by splicing factors, including the U1 snRNP. LENG8 binds to PCID2 and SEM1 to form the REX (Repressor of EXport) complex, which is conserved from yeast to human, and causes RNA nuclear retention by acting as a dominant negative factor for the essential mRNA export factor TREX-2. LENG8 depletion leads to the leakage of misprocessed mRNAs, including intronically polyadenylated and intron-retained mRNAs, as well as noncoding RNAs into the cytoplasm. Finally, LENG8 promotes RNA degradation by recruiting PAXT and the RNA exosome. Thus our study revealed a conserved quality control mechanism for eukaryotic gene expression that ensures only fully and correctly processed RNAs are exported from the nucleus.

molecular biology↗

bioRxiv2025

An improved SIFT algorithm based method for detecting of tumor tissue movement in respiratory motion

BackgroundThis paper intends to make use of the excellent performance of the SIFT algorithm to accurately calculate the displacement of tumor tissues during respiratory movement. Material and methodFirst, we moved phantoms to up,down,right,left respectively to simulate the different directions of movement during respiratory. And rotated phantom to left and right respectively by the angle of 5,10,30,45. Then, used SIFT and cross-correlation algorithms to search the phantoms. Last, use the t-test to check the differences between different directions. ResulThe t-test result proved that there was no significant difference in the detection of moving phantom in all directions. There was no statistical difference between the SIFT of the moving motif and the cross-correlation. The detection results of rotating phantom indicated that the SIFT test results could be used to detect distortion. ConclusionSIFT algorithm can be used to calculate the distortion caused by the thoracic tissue during respiratory movement.

biophysics↗

bioRxiv2025

AtlasAgent: Vision language model and Agent-guided Framework for Evaluation of Atlas-scale Single-cell Integration

As single-cell omics transitions into the era of AI-virtual cells (AIVC), where large-scale single-cell data integration becomes prevalent, the computational demands of integration evaluation emerge as critical scalability bottlenecks. Traditional integration evaluation pipelines, requiring metrics like k-nearest-neighbor batch effect test (kBET) and Local Inverse Simpsons Index (iLISI) employed by state-of-the-art scIB method, often demand large computational resources and long runtimes, making them infeasible for large scale integration studies. Herein, we present AtlasAgent, the first vision-language model (VLM)-powered and AI agent framework to accelerate atlas-scale integration evaluation at unprecedented speed and scale. We systematically evaluate batch correction quality, biological signal preservation and overcorrection risks using chain-of-thought reasoning in conjunction with few-shot and zero-shot prompting strategies. AtlasAgent completes evaluation within 32 seconds, in contrast to scIBs runtime of 5.55 hours in GPU, while identifying the scIB-determint best integration methods within the top-3 in 88.3% of the time, lowering evaluation time from hours to seconds while preserving alignment with domain expert reasoning. AtlasAgent pioneers the use of VLMs to realize scalable and rapid integration evaluation at atlas scale.

bioinformatics↗

bioRxiv2025

Subtype-Specific Roles of Nigrostriatal Dopaminergic Neurons in Motor and Associative Learning

Nigrostriatal dopaminergic neurons (DANs) in the substantia nigra pars compacta (SNc) comprise distinct subtypes defined by unique gene expression profiles and anatomical characteristics. However, their specific contributions to motor and non-motor functions remain elusive. Using Calbindin 1 (Calb1) and Aldehyde dehydrogenase 1a1 (Aldh1a1) as molecular markers, we investigated the functions of these nigrostriatal DAN subtypes in mice. Through intersectional genetics and chemogenetic manipulation, we selectively inhibited Calb1+ or Aldh1a1+ DANs by stereotactically delivering an adeno-associated viral vector (AAV-CreOn-FlpOn-hM4Di-P2A-mCherry) into the SNc of ThFlp; Calb1IRESCre or ThFlp; Aldh1a1CreERT2 double knock-in (KI) mice. This approach enabled subtype-specific neuronal inhibition via designer receptors exclusively activated by designer drugs (DREADD). Following DREADD ligand administration, both Calb1+ and Aldh1a1+ DAN-inhibited mice exhibited significant reduction in voluntary movement and impaired motor skill learning, demonstrating their essential roles in motor function. However, only Calb1+ DAN inhibition affected early associative-learning behavior, suggesting a unique role in reinforcement learning. These findings establish Calb1+ and Aldh1a1+ nigrostriatal DANs as key regulators of movement and motor learning, with Calb1+ neurons additionally modulating reward-based associative learning. This study advances our understanding of the functional heterogeneity of nigrostriatal DAN subtypes and identifies potential therapeutic targets for addressing motor and non-motor deficits in Parkinsons disease.

neuroscience↗

bioRxiv2025

A single binge ethanol exposure is apoptotic within hours across neurodevelopment and partially regulated by the Myt1l gene.

Ethanol rapidly produces widespread neuronal apoptosis during early development, but this susceptibility declines as the brain matures. In previous research, we found Myt1l (a proneuronal transcription factor) mutations can cause precocious differentiation, neuronal immaturity, and transcriptomic alterations, including many in apoptotic regulators. Therefore, we used a recently developed Myt1l haploinsufficient mouse model to examine this genes effects on ethanol-induced apoptosis across different developmental stages. We discovered that haploinsufficiency can moderately influence vulnerability to ethanol in a complex, age- and cell type-specific manner: apoptosis was reduced on P7, increased P21, but unaffected on P60. Remarkably, we also discovered the previously unrecognized ability of a single binge of ethanol to rapidly increase apoptosis within six hours in early adolescent and adult wild-type mice occurring in microglia and the newborn granule neurons in the hippocampus. This suggests apoptosis is an underappreciated contributor to ethanols neuropathology at older ages and, translated to human use, occurs far more frequently than previously recognized.

neuroscience↗

bioRxiv2025

Targeting Regnase-1 unleashes CAR T cell antitumor activity for osteosarcoma and creates a proinflammatory tumor microenvironment

Negative regulators of T cell function represent promising targets to enhance the intrinsic antitumor activity of CAR T cells against solid tumors. However, the endogenous immune ecosystem in solid tumors often represents an immunosuppressive therapeutic barrier to CAR T cell therapy, and it is currently unknown whether deletion of negative regulators in CAR T cells reshapes the endogenous immune landscape. To address this knowledge gap, we developed CAR T cells targeting B7-H3 in immune-competent osteosarcoma models and evaluated the intrinsic and extrinsic effects of deleting a potent negative regulator called Regnase-1 (Reg-1). Deletion of Reg-1 not only improved the effector function of B7-H3-CAR T cells but also endowed them with the ability to create a proinflammatory landscape characterized by an influx of IFN{gamma}-producing endogenous T cells and NK cells and a reduction of inhibitory myeloid cells, including M2 macrophages. Thus, deleting negative regulators in CAR T cells enforces a non-cell-autonomous state by creating a proinflammatory tumor microenvironment.

immunology↗

bioRxiv2025

Age-informed, attention-based weakly supervised learning for neuropathological image assessment

Chronic Traumatic Encephalopathy (CTE) and other neurodegenerative disorders (NDs) pose diagnostic challenges due to their diffuse and subtle pathological changes. Traditional diagnostic methods relying on manual histopathological slide inspection are labor-intensive and prone to variability, often missing subtle structural alterations. This study introduces an age-informed, attention-based multiple instance learning (MIL) pipeline to predict AT8 density, a key marker of p-tau aggregation in CTE. Using Luxol Fast Blue and Hematoxylin & Eosin (LH&E) stained images, our model identifies critical pathological regions and generates interpretable attention maps highlighting structural changes linked to tau pathology. Incorporating patient age enhances predictive accuracy and contextual understanding, addressing agings confounding effects. We also develop quantitative evaluation procedures for foundation models (FMs), assessing attention map smoothness, faithfulness, and robustness to perturbations like stain variability and noise. These benchmarks facilitate informed FM selection and optimization for neuropathological tasks. By enabling scalable, automated whole-slide image (WSI) analysis, our approach advances digital neuropathology, supporting earlier and more precise ND diagnoses and uncovering subtle markers with potential applications in clinical imaging.

bioinformatics↗

bioRxiv2025

Gene drive dynamics in plants: the role of seedbanks

Gene drives offer revolutionary potential for the management of problematic plant populations, such as invasive weeds and herbicide-resistant species, by rapidly spreading desired genetic alterations. Two recent studies have provided the first experimental demonstrations of engineered CRISPR gene drive systems in plants (CAIN and ClvR). However, the successful application of such systems in the field will critically depend on an accurate understanding of plant-specific life-history traits, especially seed dormancy, a ubiquitous yet frequently overlooked eco-evolutionary force. In this study, we develop the first comprehensive modeling framework for gene drives in plant populations that incorporates a persistent soil seedbank. We show how the presence of a seedbank can significantly slow gene drive spread but also reduce the genetic load required to achieve population elimination. Furthermore, we show that seedbanks substantially increase the required introduction frequency of threshold-dependent gene drives, which could prevent establishment in some cases, yet also provide an intrinsic biosafety mechanism for confining a highly efficient drive to a target population. Our study highlights the need to incorporate seedbank dynamics into gene drive strategies to ensure realistic predictions and successful field applications.

evolutionary biology↗

bioRxiv2025

The landscape of structural variation in pediatric cancer

Structural variants (SVs) account for over 60% of the driver variants in pediatric cancer, and in many cases act as the cancer initiating event. To study SVs from a pan-cancer perspective, we analyzed 1,616 pediatric cancer genomes in 16 major cancer types of hematological malignancies (n = 908), brain tumors (n = 183), and solid tumors (n = 525) and compared their profiles to those of 2,203 adult cancers. The SV burden varied [~]100-fold across pediatric cancer types and demonstrated an 8-to 16-fold reduction compared to adult brain and solid tumors but was comparable in pediatric versus adult hematological malignancies. Recurrent SV hotspots occurred uniquely in pediatric acute lymphoblastic leukemias (ALLs) in proximity to RAG-mediated recombination signal sequences (RSS) and disrupted multiple immune-related loci as well as 69 genes, which often involved cryptic RSS sites. By contrast, such hotspots affected only immune-related loci but not driver genes in adult lymphoid cancers. Eight SV signatures extracted from the cohort had varying distributions across cancer types, with clustered translocations reflecting templated insertions in osteosarcoma, and medium-sized deletions (10 kb to 1 Mb) enriched in cancers with RAG-mediated deletions. Intra-patient evolutionary analysis in 13 patients with multiple spatiotemporally distinct samples revealed that RAG-mediated recombination in leukemia and complex rearrangements in solid tumors occurred both early in disease initiation and continuously during later diversification, contributing to clonal heterogeneity. Finally, we found that both driver genes and fragile sites were the two genomic regions most frequently disrupted by SVs. The unique and diverse SV landscapes that emerged from this comprehensive analysis expand the scope of RSS-mediated mutagenesis in pediatric ALL and will be a valuable resource for guiding future functional studies and the design of clinical genomic testing in pediatric cancer.

cancer biology↗

bioRxiv2025

stPipe: A flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

bioinformatics↗

bioRxiv2025

Stress Granule Formation Drives ZBP1-dependent Necroptosis in Non-obstructive Azoospermia and Aging testes

Male infertility and age-related reproductive decline remain major unmet medical challenges, with limited understanding of the underlying mechanisms. Here, we identify a stress granule-driven necroptosis pathway involving ZBP1 and RIPK3 as a central driver of non-obstructive azoospermia (NOA), a severe form of male infertility marked by loss of spermatogenesis. We show that heat stress or environmental insults activate eIF2 kinases, inducing stress granules that recruit ZBP1 and RIPK3 to form a signaling complex. This leads to RIPK3 activation, MLKL phosphorylation, and necroptotic death of spermatogonia and Sertoli cells. Genetic ablation of Zbp1 or Ripk3 protects mice from heat-induced testicular atrophy, highlighting their essential role in testicular cell death. Notably, this same necroptosis pathway is also activated in aged human testes, suggesting a shared mechanism driving both male infertility and age-related testicular degeneration.

molecular biology↗

bioRxiv2025

Unveiling Gene Modules at Atlas Scale through Hierarchical Clustering of Single-Cell Data

A major challenge in scRNAseq analysis is how to recover the biologically meaningful cell ontology tree and conserved gene modules across datasets. Data integration and batch-effect correction have been the key to effectively analyze multiple datasets, but often fail to disentangle cell states in heterogeneous samples, such as in cancer and the immune system. Here we present super single cell clustering (SuperSCC), a novel algorithm that utilizes machine-learning models to discover cell identities and gene modules from multiple datasets without the need of data integration. Of note, SuperSCC can be implemented both in cell lineage and cell state level, thereby building the hierarchy of cell programs with specific cell identity and gene modules. Such information has the great potential to identify the shared rare populations across datasets regardless of batch effect and benefits label transfer for mapping cell labels from reference to query. We used SuperSCC to perform atlas level data analysis on more than 90 datasets and build a cell state map of complex tissue in healthy and diseased stages, such as human lung. We show that SuperSCC outperforms existing approaches in identifying cellular context, has better annotation accuracy, and outlines gene modules that indicate conserved immune cell status in lung microenvironments.

bioinformatics↗