Biology subjects

Fan, J.

Publications and source records attributed to Fan, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

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

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

cell biology↗

bioRxiv2024

Altered AP-1, RUNX, and EGR chromatin dynamics drive fibrotic lung disease

Pulmonary fibrosis, including systemic sclerosis-associated interstitial lung disease (SSc-ILD), involves myofibroblasts and SPP1hi macrophages as drivers of fibrosis. Single-cell RNA sequencing has delineated fibroblast and macrophages transcriptomes, but limited insight into transcriptional control of profibrotic gene programs. To address this challenge, we analyzed multiomic snATAC/snRNA-seq on explanted SSc-ILD and donor control lungs. The neural network tool ChromBPNet inferred increased TF binding at single base pair resolution to profibrotic genes, including CTHRC1 and ADAM12, in fibroblasts and SPP1 and CCL18 in macrophages. The novel algorithm HALO confirmed AP-1, RUNX, and EGR TF activity controlling profibrotic gene programs and established TF-regulatory element-gene networks. This TF action atlas provides comprehensive insights into the transcriptional regulation of fibroblasts and macrophages in healthy and fibrotic human lungs.

systems biology↗

bioRxiv2024

Bridging the Gap in Immunotherapy Prediction: The AGAE Score as a Pan Cancer Biomarker for Immune Checkpoint Inhibitor Response

BackgroundImmune checkpoint inhibitor (ICI) therapy efficacy varies among cancer patients, necessitating precise predictive biomarkers for optimized treatment strategies. MethodsWe developed the Adaptive best subset selection algorithm and Genetic algorithm Aided Ensemble learning (AGAE) score through multi-cohort transcriptomic analysis of ICI-treated patients. The AGAE score incorporated gene-pairing, Adaptive Best Subset Selection for feature optimization, and a Genetic Algorithm for optimal basic learner identification. We explored correlations between AGAE score and immune microenvironments using multi-omics data. Potential targets were screened using 17 CRISPR datasets and validated through in vitro and in vivo experiments. ResultsThe AGAE score demonstrated robust predictive power for ICI therapy outcomes, with lower scores correlating with enhanced treatment response. The AGAE score outperformed published signatures and conventional biomarkers. Lower AGAE scores were associated with increased immune cell infiltration, higher immunogenicity, and enhanced antitumor immune activity. The CEP55 was identified as a potential key target driving immune evasion through AGAE scoring and CRISPR screening. Experimental validation showed CEP55 downregulation attenuated tumor cell malignancy and augmented ICI therapy efficacy by modulating T cell responses. ConclusionsThe AGAE score was a potent predictor of ICI therapy efficacy, facilitating refined patient stratification. CEP55s role in the tumor microenvironments immune response highlights its potential as a therapeutic target. Targeted interventions against CEP55 may improve immunotherapy precision.

cancer biology↗

bioRxiv2024

GATA1-deficient human pluripotent stem cells generate neutrophils with improved antifungal immunity that is mediated by the integrin CD18

Neutrophils are critical for host defense against fungi. However, the short life span and lack of genetic tractability of primary human neutrophils has limited in vitro analysis of neutrophil-fungal interactions. Human induced pluripotent stem cell (iPSC)-derived neutrophils (iNeutrophils) are a genetically tractable alternative to primary human neutrophils. Here, we show that deletion of the transcription factor GATA1 from human iPSCs results in iNeutrophils with improved antifungal activity against Aspergillus fumigatus. GATA1 knockout (KO) iNeutrophils have increased maturation, antifungal pattern recognition receptor expression and more readily execute neutrophil effector functions compared to wild-type iNeutrophils. iNeutrophils also show a shift in their metabolism following stimulation with fungal {beta}-glucan, including an upregulation of the pentose phosphate pathway (PPP), similar to primary human neutrophils in vitro. Furthermore, we show that deletion of the integrin CD18 attenuates the ability of GATA1-KO iNeutrophils to kill A. fumigatus but is not necessary for the upregulation of PPP. Collectively, these findings support iNeutrophils as a robust system to study human neutrophil antifungal immunity and has identified specific roles for CD18 in the defense response. Author SummaryNeutrophils are important first responders to fungal infections, and understanding their antifungal functions is essential to better elucidating disease dynamics. Primary human neutrophils are short lived and do not permit genetic manipulation, limiting their use to study neutrophil-fungal interactions in vitro. Human induced pluripotent stem cell (iPSC)-derived neutrophils (iNeutrophils) are a genetically tractable alternative to primary human neutrophils for in vitro analyses. In this report we show that GATA1-deficient iPSCs generate neutrophils (iNeutrophils) that are more mature than wild-type iNeutrophils and display increased antifungal activity against the human fungal pathogen Aspergillus fumigatus. We also show that GATA1-deficient iNeutrophils have increased expression of antifungal receptors than wild-type cells and shift their metabolism and execute neutrophil antifungal functions at levels comparable to primary human neutrophils. Deletion of the integrin CD18 blocks the ability of GATA1-deficient iNeutrophils to kill and control the growth of A. fumigatus, demonstrating an important role for this integrin in iNeutrophil antifungal activity. Collectively, these findings support the use of iNeutrophils as a model to study neutrophil antifungal immunity.

microbiology↗

bioRxiv2024

scatterbar: an R package for visualizing proportional data across spatially resolved coordinates

SummaryDisplaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to difficulties in interpretation. Increasing the visual saliency of such data visualizations can help viewers more accurately identify proportional trends and compare proportional differences across spatial locations. We developed scatterbar, an open-source R package that extends ggplot2, to visualize proportional data across many spatially resolved coordinates using scatter stacked bar plots. We apply scatterbar to visualize deconvolved cell-type proportions from a spatial transcriptomics dataset of the adult mouse brain to demonstrate how scatter stacked bar plots can enhance the distinguishability of proportional distributions compared to scatter pie plots. Availability and implementationScatterbar is available at https://github.com/JEFworks-Lab/scatterbar with additional documentation and tutorials at https://jef.works/scatterbar/.

bioinformatics↗

bioRxiv2024

CellClear: Enhancing Single-cell RNA Data Quality via Biologically-Informed Ambient RNA Correction

Ambient RNA, caused by the pool of mRNA molecules released from lysed or dead cells in samples, poses persistent challenges to single-cell RNA sequencing data analysis, and leads to potential problems such as inaccurate classification of cell types and downstream functional analyses. Here we propose CellClear to estimate and remove the ambient RNA expression programs in single-cell RNA sequencing data. CellClear demonstrates significant increase in the accuracy of ambient gene expression correction comparing with state-of-art methods, without distorting native information of individual cell types.

bioinformatics↗

bioRxiv2024

Single cell autofluorescence imaging reveals immediate metabolic shifts of neutrophils with activation across biological systems

Neutrophils, the most abundant leukocytes in human peripheral circulation, are crucial for the innate immune response. They are typically quiescent but rapidly activate in response to infection and inflammation, performing diverse functions such as oxidative burst, phagocytosis, and NETosis, which require significant metabolic adaptation. Deeper insights into such metabolic changes will help identify regulation of neutrophil functions in health and diseases. Due to their short lifespan and associated technical challenges, the metabolic processes of neutrophils are not completely understood. This study uses optical metabolic imaging (OMI), which entails optical redox ratio and fluorescence lifetime imaging microscopy of intrinsic metabolic coenzymes NAD(P)H and FAD to assess the metabolic state of single neutrophils. Primary human neutrophils were imaged in vitro under a variety of activation conditions and metabolic pathway inhibitors, while metabolic and functional changes were confirmed with mass spectrometry, oxidative burst, and NETosis measurements. Our findings show that neutrophils undergo rapid metabolic remodeling to a reduced redox state indicated by changes in NAD(P)H lifetime and optical redox ratio, with a shift to an oxidized redox state during activation. Additionally, single cell OMI analysis reveals a heterogeneous metabolic response across neutrophils and human donors to live pathogen infection (Pseudomonas aeruginosa and Toxoplasma gondii). Finally, consistent OMI changes with activation were confirmed between in vitro human and in vivo zebrafish larvae neutrophils. This study demonstrates the potential of OMI as a versatile tool for studying neutrophil metabolism and underscores its use across different biological systems, offering insights into neutrophil metabolic activity and function at a single cell level.

immunology↗

bioRxiv2024

Overcrowding Drives Tumor Invasion by Inducing Nanotopographical Transition of Plasma Membrane

During the progression from epithelial neoplasms to invasive carcinoma, cells are subjected to prolonged confinement. However, the response of cancer cells to such mild yet sustained compressive pressure during the initial stages of tumor invasion remain poorly understood. Here, using a spontaneous crowding model to recapitulate the progressive compressive stress caused by cell proliferation, we demonstrated that prolonged crowding alone is sufficient to induce the acquisition of an invasive phenotype and associated gene expression patterns in cancer cells. This invasiveness persisted even after cells were removed from the crowded environment, a phenomenon mediated by mechanomemory. By combining genetic manipulations, mechanical modeling, and biophysical measurements, we revealed that the disaggregation of membrane domains--driven by a nanoscale smooth-corrugated topography transition of plasma membranes induced by Laplace pressure under crowded conditions--is essential for initiating cancer cell invasion. Inhibiting membrane domains disaggregation through membrane-to-cortex-attachment effectively suppresses cancer cell invasion in both cellular crowding models and mouse xenograft models. This study underscores the critical role of tissue-scale mechanics in regulating the biophysics of mechanosensitive membrane domains during the early stages of tumor invasion.

cancer biology↗

bioRxiv2024

Where the patterns are: repetition-aware compression for colored de Bruijn graphs

We describe lossless compressed data structures for the colored de Bruijn graph (or, c-dBG). Given a collection of reference sequences, a c-dBG can be essentially regarded as a map from k-mers to their color sets. The color set of a k-mer is the set of all identifiers, or colors, of the references that contain the k-mer. While these maps find countless applications in computational biology (e.g., basic query, reading mapping, abundance estimation, etc.), their memory usage represents a serious challenge for large-scale sequence indexing. Our solutions leverage on the intrinsic repetitiveness of the color sets when indexing large collections of related genomes. Hence, the described algorithms factorize the color sets into patterns that repeat across the entire collection and represent these patterns once, instead of redundantly replicating their representation as would happen if the sets were encoded as atomic lists of integers. Experimental results across a range of datasets and query workloads show that these representations substantially improve over the space effectiveness of the best previous solutions (sometimes, even dramatically, yielding indexes that are smaller by an order of magnitude). Despite the space reduction, these indexes only moderately impact the efficiency of the queries compared to the fastest indexes. SoftwareThe implementation of the indexes used for all experiments in this work is written in C++17 and is available at https://github.com/jermp/fulgor.

bioinformatics↗

bioRxiv2024

Down regulation of Engase in Caenorhabditis elegans may improve its stresses adaptivity

Endo-beta-N-acetylglucosaminidase (ENGASE) is one of the key enzymes involved in the regulation of structure and function of glycoproteins. It is conserved from prokaryotic to eukaryotic cells. Although their activities in vitro and applications have been well studied, the biological function of ENGASE remains to be illustrated. In this study, we analyzed the molecular and physiological function of Engase from Caenorhabditis elegans homolog eng-1(CeEngase). We found that CeEngase knockout or knockdown increased the environmental stresses adaptability, such as heat stress and osmotic stress. Preliminary glycomics analysis showed that the basement membrane proteins of extracellular matrix may be the main targets of CeENGASE. In addition, CeENGASE may selectively prefer to N2H7 glycans on glycoproteins. In conclusion, our data illustrated that the defection and/or down regulation of CeEngase may provide a beneficially adaptation for stresses.

biochemistry↗

bioRxiv2024

CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells

The rapid evolution of single-cell sequencing technologies has facilitated precise transcriptomics profiling at the single-cell level, shedding light on the intricate heterogeneity within cellular populations. Despite these advances, the inherent diversity of cells and data challenges such as noise, batch effects, and sparsity, underscores the pressing need for a unified model to learn and represent cellular states effectively. Single-cell Large Language Models (LLMs) have been crafted to bridge this gap yet exhibit limited performance on human cells. This short-fall may stem from the confounding effects of training data from diverse species, partly because of limited cells for the single species. Here, we have compiled a dataset of approximately 100 million human cells sequenced by multiple technolo-gies from human single-cell datasets with various file types deposited in public databases and websites. Leveraging these extensive data cohorts, we developed CellFM, a robust single-cell foundation model with an impressive 800 million parameters, marking an eight-fold increase over the current largest single-species model. To ensure the training of CellFM on the MindSpore AI framework from Huawei, we have integrated RetNet, a Transformer architecture variant with lin-ear complexity for a balance between efficiency and performance, serving as the backbone of our model. Our comprehensive experiments have shown that CellFM outperforms existing models across diverse applications, such as cell annotation, perturbation prediction, and gene function prediction.

bioinformatics↗

bioRxiv2024

Accurate Conformation Sampling via Protein Structural Diffusion

Accurately sampling of protein conformations is pivotal for advances in biology and medicine. Although there have been tremendous progress in protein structure prediction in recent years due to deep learning, models that can predict the different stable conformations of proteins with high accuracy and structural validity are still lacking. Here, we introduce UFConf, a cutting-edge approach designed for robust sampling of diverse protein conformations based solely on amino acid sequences. This method transforms AlphaFold2 into a diffusion model by implementing a conformation-based diffusion process and adapting the architecture to process diffused inputs effectively. To counteract the inherent conformational bias in the Protein Data Bank, we developed a novel hierarchical reweighting protocol based on structural clustering. Our evaluations demonstrate that UFConf out-performs existing methods in terms of successful sampling and structural validity. The comparisons with long time molecular dynamics show that UFConf can overcome the energy barrier existing in molecular dynamics simulations and perform more efficient sampling. Furthermore, We showcase UFConfs utility in drug discovery through its application in neural protein-ligand docking. In a blind test, it accurately predicted a novel protein-ligand complex, underscoring its potential to impact real-world biological research. Additionally, we present other modes of sampling using UFConf, including partial sampling with fixed motif, langevin dynamics and structural interpolation.

bioinformatics↗

bioRxiv2024

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.

bioengineering↗

bioRxiv2024

Prominent involvement of acetylcholine in shaping stable olfactory representation across the Drosophila brain

Despite the vital role of neuromodulation in the neural system, the specific spatiotemporal dynamics of neuromodulators and their interactions with neuronal activities in vivo are still unclear, hampering our understanding of their information representation and functional contributions systemically. To address this problem, we employed two-photon synthetic aperture microscopy (2pSAM) to record long-term neuronal and neuromodulatory olfactory responses across the Drosophila brain at high speed. Our results revealed distinct response properties, global information propagation, functional connectivity, and odor identity representation among neuronal, cholinergic, and serotoninergic dynamics across multiple brain regions. We discovered the compensation between neuronal activity and cholinergic dynamics, both in the odor identity representation across the brain and the functional connectivity network structures of specific brain regions. Moreover, employing low-dimensional manifold and functional connectivity network analyses, we characterized the stable representation of cholinergic dynamics over a long term. Collectively, our unbiased and comprehensive investigation unveiled the prominent involvement of acetylcholine (ACh) in shaping olfactory representation across the brain, underscoring the inadequacy of solely considering neuronal activities when examining information representation of the brain.

neuroscience↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2024

Srs2 binding to PCNA and its sumoylation contribute to RPA antagonism during the DNA damage response

Activation of the DNA damage checkpoint upon genotoxin treatment induces a multitude of cellular changes to cope with genome stress. After prolonged genotoxin treatment, the checkpoint can be downregulated to allow cell cycle and growth resumption. In yeast, downregulation of the DNA damage checkpoint requires the Srs2 DNA helicase, which removes the ssDNA binding complex RPA and the associated Mec1 checkpoint kinase from DNA, thus dampening Mec1-mediated checkpoint. However, it is unclear whether the anti-checkpoint role of Srs2 is temporally and spatially regulated to allow timely checkpoint termination while preventing superfluous RPA removal. Here we address this question by examining regulatory elements of Srs2, such as its phosphorylation, sumoylation, and protein-interaction sites. Our genetic analyses and checkpoint level assessment suggest that the RPA countering role of Srs2 is promoted by Srs2 binding to PCNA, which recruits Srs2 to a subset of ssDNA containing regions. RPA antagonism is further fostered by Srs2 sumoylation, which we found depending on the Srs2-PCNA interaction and Mec1, and peaking after Mec1 activity reaches maximal levels. These data suggest that Srs2 recruitment to PCNA proximal ssDNA-RPA filaments followed by its sumoylation can promote checkpoint recovery, whereas Srs2 action is minimized at regions with no proximal PCNA to permit RPA-mediated ssDNA protection.

molecular biology↗

bioRxiv2024

Integrative single-cell analysis of neural stem/progenitor cells reveals epigenetically dysregulated interferon response in progressive multiple sclerosis

Progressive multiple sclerosis (PMS) is characterized by a primary smouldering pathological disease process associated with a superimposed inflammatory activity. Cellular and molecular processes sustaining the pathobiology of PMS remain to be identified. We previously discovered senescence signatures in neural stem/progenitor cells (NSCs) from people with PMS. Applying direct reprogramming to generate directly induced NSCs (iNSCs) from somatic fibroblasts, we retain epigenetic information and observe hypomethylation of genes associated with lipid metabolic processes and IFN signalling only in PMS lines. Single-cell/nucleus transcriptomic and epigenetic profiling reveal an inflammatory, senescent-like, IFN-responsive radial glia (RG)-like cell subcluster mainly in PMS iNSCs that is driven by IFN-associated transcription factors. Lastly, we identify a population of senescent, IFN-responsive, disease-associated RG-like cells (DARGs) in the PMS brain that share pseudotime trajectories with iNSCs in vitro. We describe the existence of a non-neurogenic, dysfunctional DARG population that has the potential to fuel smouldering inflammation in PMS.

neuroscience↗

bioRxiv2024

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

MotivationSpatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. ResultsTo enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce resource requirements while maintaining high performance. We further integrate SEraster with existing analysis tools to characterize cell-type spatial cooccurrence. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as cooccurring cell-types that recapitulate expected organ structures. Availability and implementationSource code is available on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster.

bioinformatics↗