Biology subjects

Dong, W.

Publications and source records attributed to Dong, W..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Robust analytical methods for bis(monoacylglycero)phosphate profiling in health and disease

Bis(monoacylglycero)phosphate (BMP), a distinct anionic phospholipid predominantly found in late endosomes and lysosomes, plays a pivotal role in supporting lysosomal functions and maintaining metabolic homeostasis. Its impaired function is associated with an array of disorders, notably neurodegenerative diseases. However, the identification and quantitation of BMP remains difficult due to its structural similarity to isomer phosphatidylglycerol (PG), thus necessitating robust analytical methods for accurate and reliable BMP profiling. In this study, we present comprehensive liquid chromatography - tandem mass spectrometry (MS2) methodologies for the precise and systematic analysis of BMP species in biological samples. We detail LC/MS methods for both an untargeted Orbitrap mass spectrometer and a targeted triple quadrupole (QQQ) mass spectrometer. We utilize differences in polarity and structure to annotate BMPs and PGs based on retention time and positive mode MS2 fragmentation patterns, respectively. Further, we propose a new approach for overcoming common challenges in BMP profiling by leveraging the newly discovered biochemical function of CLN5 as the BMP synthase. Since genetic ablation of CLN5 leads to specific depletion of BMPs but not PGs, we use lipid extracts from CLN5 knockout (KO) and wild-type (WT) cells as biological standards to confidently annotate BMPs as targets with significantly low BMP Identification Index (BMPII), defined as BMPII = CLN5 KO / WT. We additionally propose the BMP enrichment score (BMPES) as a secondary validation metric, defined as lysosomal abundance of BMP / whole-cell abundance. Altogether, this approach constitutes a robust method for BMP profiling and annotation, furthering research into health and disease.

biochemistry↗

bioRxiv2025

SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs

BackgroundSpatial transcriptomics has revolutionized our ability to characterize tissues and diseases by contextualizing gene expression with spatial organization. Current spatial transcriptomics pipelines require researchers to use a variety of models and tools to explore spatial domains. However, few methods provide researchers with a way to jointly analyze spatial data from both annotation-free and annotation-guided perspectives using consistent inductive biases and levels of interpretability. A single framework with consistent inductive biases ensures coherence and transferability across tasks, reducing the risks of conflicting assumptions. ResultsWe propose the Spatial Topic Model (SpaTM), a topic-modelling framework capable of annotation-guided and annotation-free analysis of spatial transcriptomics data. SpaTM can be used to learn gene programs that represent histology-based annotations while providing researchers with the ability to infer spatial domains with an annotation-free approach if manual annotations are limited or noisy. Our benchmarking experiments reveal SpaTMs competitiveness at spatial label prediction and clustering when compared to state-of-the-art methods. We also demonstrate SpaTMs interpretability with its use of topic mixtures to represent cell states and transcriptional programs in dorsolateral prefrontal cortex and ductal carcinoma samples and how its intuitive framework facilitates the integration of annotation-guided and annotation-free analyses of spatial data with downstream analyses. Finally, we demonstrate how SpaTM can be used to extend the analysis of large-scale snRNA-seq atlases with the inference of cell proximity and spatial annotations in human brains with Major Depressive Disorder. ConclusionsSpaTM provides researchers with a unified analysis framework for spatial transcriptomics data. By enabling competitive performance in a variety of tasks, SpaTM helps researchers undertake biologically-driven analyses through the identification of interpretable and biologically informed gene programs.

bioinformatics↗

bioRxiv2025

scGraphETM: Graph-Based Deep Learning Approach for Unraveling Cell Type-Specific Gene Regulatory Networks from Single-Cell Multi-Omics Data

Multi-omics sequencing technologies can jointly measure transcriptome and chromatin accessibility at the single-cell resolution. This enables inference of gene regulatory networks (GRNs) at the cellular level, thereby elucidating high-resolution differential GRNs associated with diseases. However, existing methods lack interpretability and scalability. We present single-cell Gene Regulatory Inference with Prior knowledge (scGRIP), a graph variational autoencoder with 3 technical contributions. First, we treat transcription factors (TF), target genes (TG), and regulatory elements (RE) as nodes and their potential TF-RE and RE-TG interactions as edges using a prior cis-regulatory knowledge graph. Second, we tokenize the single-cell chromatin accessibility and gene expression with a shared codebook to compute cell-specific feature embedding. Third, we incorporate a GraphSHAP technique to infer GRN edge attribution at the single-cell level. scGRIP, evaluated against existing regulatory inference frameworks such as LINGER and scGLUE, demonstrates that integrating prior regulatory knowledge yields regulatory interaction features that improve discrimination of cell types and disease conditions. In particular, scGRIP identified coordinated activation of immune signaling, amyloid-related responses, and downstream target gene programs. The inferred GRNs for Alzheimers disease were supported by functional enrichment analyses and independent reference dataset.

genomics↗

bioRxiv2024

Attention field as a cognitive-behavioral marker for demarcating internet- but not smoking-addiction from reward

Attentional effect (AE), attention profile (AP), and attention field (AF) have been studied extensively, however, their roles in addiction and demarcating addiction from rewards remain unclear. Using a modified Posner-paradigm with two types of pre-rewarded-cues (addiction-related and addiction-unrelated) and four groups (smoking-dependents, internet-dependents, and respective HCs), we found that both AEs and APs were independent of either cue type or group, while AFs were interactively modulated by the two. AFs of addiction-related cues were narrower than those of addiction-unrelated cues for internet-dependents, but not for either smoking-dependents or HCs; AFs of internet-dependents (not smoking-dependents) were narrower than those of HCs for addiction-related cues, but not for addiction-unrelated cues. Significantly, internet-dependents reduced AFs can be simulated by the divisive-normalization computation, both of which closely tracked their addictive severities. Our findings identify a cognitive-behavioral marker for demarcating internet-addiction from rewarding, arguing against the notion that internet-addiction, or, more generally, non-substance-addiction, is ill-posed.

neuroscience↗

bioRxiv2024

Elastase mediated white matter damage in cerebral small vessel disease: Microglia - neutrophils pas de deux

Cerebral small vessel disease (CSVD) leads to an extensive white matter damage associated with cognitive decline, yet the underlying damaging mechanisms remain incompletely understood. Here we established a positive correlation between plasma levels of serine proteinase elastase ELANE and periventricular white matter hyperintensity (PV-WMH) in a cohort of CSVD patients. In a CSVD murine model induced by bilateral carotid artery stenosis (BCAS), upregulated ELANE was detected both in microglia and peripheral blood neutrophils. Genetic ELANE deficiency significantly alleviated oligodendrocyte loss, thereby reducing white matter lesions (WMLs) as well as ameliorating sensorimotor and cognitive impairments in BCAS mice. In vitro studies demonstrated that ELANE triggered time-dependent and dose-dependent oligodendrocyte lineage cell death. Bone marrow transplantation showed that ELANE from microglia and peripheral blood both contributed to WML development and BCAS-induced neurological deficits. Mechanistically, ELANE, accumulated by oligodendrocytes, cleaved the phosphodiesterase domain of 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase). Pharmacological inhibition of ELANE with Sivelestat reduced oligodendrocyte loss and WMLs leading to the restoration of white matter integrity and neurological improvements in BCAS mice. In post-mortem brain specimens of CSVD patients ELANE accumulated within WMLs being predominantly localized in microglia (and hence defined as microglial ELANE) rather than in the brain-infiltrating neutrophils. We therefore posit microglial ELANE as an instigator of whiter matter injury in CSVD and suggest its potential therapeutic relevance.

neuroscience↗

bioRxiv2024

Genome-wide Mapping Eukaryotic Open Chromatin Regions with Double-Stranded DNA Deaminase

A variety of methods such as ATAC-seq and CUT&Tag have been developed for probing chromatin states. However, enzymatic cleavage of DNA in these methods hinders their multi-omics integration with other DNA profiling techniques. Here, by extending our use of dsDNA deaminase (FOOtprinting with DeamInasE, FOODIE), we introduce two new methods for mapping Chromatin Open region by DeamInasE (ChrODIE) and histone modification by ANtibody-associated DeamInasE (ANDIE), respectively. Unlike cleavage-based methods, both ChrODIE and ANDIE leave genomic DNA after deamination unfragmented and amplifiable. This offers the potential for simultaneous or downstream integration with other genomic profiling methods, such as mapping 3D genome interactions.

genomics↗

bioRxiv2024

Neurons and astrocytes have distinct organelle signatures and responses to stress

Neurons and astrocytes play critical yet divergent roles in brain physiology and neurological conditions. Intracellular organelles are integral to cellular function. However, an in-depth characterization of organelles in live brain cells has not been performed. Here, we used multispectral imaging to simultaneously visualize six organelles - endoplasmic reticulum (ER), lysosomes, mitochondria, peroxisomes, Golgi, and lipid droplets - in live primary rodent neurons and astrocytes. We generated a dataset of 173 Z-stack and 99 time-lapse images accompanied by quantitative analysis of 1418 metrics (the "organelle signature"). Comparative analysis revealed clear cell-type specificity in organelle morphology and interactions. Neurons were characterized by prominent mitochondrial composition and interactions, while astrocytes contained more lysosomes and lipid droplet interactions. Additionally, neurons displayed a more robust organelle response than astrocytes to acute oxidative or ER stress. Our data provide a systems-level characterization of neuron and astrocyte organelles that can be a reference for understanding cell- type-specific physiology and disease.

cell biology↗

bioRxiv2024

Cell-Type Resolved Protein Atlas of Brain Lysosomes Identifies SLC45A1-Associated Disease as a Lysosomal Disorder

Mutations in lysosomal genes cause neurodegeneration and neurological lysosomal storage disorders (LSDs). Despite their essential role in brain homeostasis, the cell-type-specific composition and function of lysosomes remain poorly understood. Here, we report a quantitative protein atlas of the lysosome from mouse neurons, astrocytes, oligodendrocytes, and microglia. We identify dozens of novel lysosomal proteins and reveal the diversity of the lysosomal composition across brain cell types. Notably, we discovered SLC45A1, mutations in which cause a monogenic neurological disease, as a neuron-specific lysosomal protein. Loss of SLC45A1 causes lysosomal dysfunction in vitro and in vivo. Mechanistically, SLC45A1 plays a dual role in lysosomal sugar transport and stabilization of V1 subunits of the V-ATPase. SLC45A1 deficiency depletes the V1 subunits, elevates lysosomal pH, and disrupts iron homeostasis causing mitochondrial dysfunction. Altogether, our work redefines SLC45A1-associated disease as a LSD and establishes a comprehensive map to study lysosome biology at cell-type resolution in the brain and its implications for neurodegeneration.

cell biology↗

bioRxiv2024

Circulatory proteins shape microglia state and boost phagocytosis

Microglia, the brains immune cells, are highly responsive to their local environment. Given that circulatory proteins can enter the brain, we asked whether microglia are responsive to such proteins. Here, we identify a stable population of microglia specialized to take up circulatory proteins in a region-specific manner under physiological conditions; human hematopoietic stem cell-derived microglia replacing endogenous microglia in chimeric mice show similar regional specialization. Plasma-positive microglia are characterized by prominent expression of genes related to innate immunity and antigen presentation and exhibit high metabolic and phagocytic activity. This activity is dependent, in part, on microglial uptake and accumulation of circulatory Apolipoprotein AI (ApoA-I). Our findings thus identify a new model of communication between brain and periphery through specialized microglia.

neuroscience↗

bioRxiv2024

HBIcloud: An Integrative Multi-Omics Analysis Platform

As biological datasets have grown exponentially in size and complexity, there has been an increasing need for integrative tools that can handle diverse data types and facilitate comprehensive analyses. Traditional methods often require significant computational expertise, creating barriers for many researchers. HBIcloud is a comprehensive online platform designed to facilitate multi-omics data analysis by integrating a wide array of tools across genomics, transcriptomics, proteomics, metabolomics, phenomics, and multi-omics integration. Developed to address the growing complexity and volume of biological data, HBIcloud provides researchers with a powerful and user-friendly resource for conducting sophisticated analyses without the need for extensive programming skills. With a total of 94 tools, the platform offers standardized workflows, extensive parameter options, and rich documentation, catering to the diverse needs of the scientific community. The research behind HBIcloud aimed to create a centralized, user-friendly platform that simplifies the analytical process, enabling researchers to focus on scientific discovery rather than technical challenges. By integrating a wide array of tools and offering extensive support and documentation, HBIcloud addresses the critical need for standardized, reproducible workflows in multi-omics research. This paper presents a detailed overview of HBIcloud, highlighting its development background, key features, and its significant contribution to advancing multi-omics research. Furthermore, we discuss the future prospects of HBIcloud, including planned enhancements and its potential for high citation impact within the scientific community. By providing a robust and versatile platform, HBIcloud aims to accelerate discovery and innovation in the field of multi-omics, fostering collaborative research and expanding the boundaries of biological understanding. The official website of HBIcloud is https://bioinformatics.hainanu.edu.cn/HBIcloud/.

bioinformatics↗

bioRxiv2024

MicroWorldOmics:A "One step for All" desktop application for comprehensive analysis in microbiome and virome and exploring "Dark Matter" from MicroWorld

A large amount of high-throughput sequencing data generated in ecology, medicine, and pharmacy has increasingly raised the complexity of data analysis and interpretation. However, the microbiome and virome field still lacks a convenient and programming-free desktop application for the comprehensive analysis in microbiome and virome data, particularly in virome analysis and Dark Matter exploration. Therefore, a plugin development mode desktop service, MicroWorldOmics, is proposed with a convenient and one-stop pipeline for life sciences and biomedical fields. Plugin development allows users to analyze data in parallel and interactively, while these tasks usually require advanced bioinformatician to achieve. MicroWorldOmics is a software widely used in microbiome and virome, containing 90 sub-applications, including four main functions: epidemiological analysis, metagenomic/amplicons and virome comprehensive analysis, and exploring Dark matter. More than 80 Python modules and 600 R packages are invoked in all aspects of bioinformatics analysis, statistical analysis, deep learning, and visualization. At the same time, the software supports multiple input and output results, such as GFF3, FASTA, CSV, PNG, JPG, JSON, and TXT, etc. Besides, to improve the efficiency for users, MicroWorldOmics supports being deployed in three mainstream systems: Windows, Linux, and Mac and also sets the demo/example data for users to test the benchmark easily. In summary, the development of MicroWorldOmics greatly facilitates the process of analyzing microbiome and virome data for researchers who have no programming foundation and are devoted to life science and biomedicine data analysis.

genomics↗

bioRxiv2024

Human transcription factor combinations mapped by genome-wide footprinting with deaminase

An individuals somatic cells have the same genome but exhibit cell-type-specific transcriptome regulated by a combination of transcription factors (TFs) for each gene. Mapping of TF sites on the human genome is critically important for understanding functional genomics. Here we report a novel technique to measure human TFs binding sites genome-wide with single-base resolution by footprinting with deaminase (FOODIE). Single-molecule sequencing reads from thousands of cells after in situ deamination yielded site-specific TF binding fractions and the cooperativity among adjacent TFs. In a human lymphoblastoid cell line, we found that genes in a correlated gene module (CGM) share TF(s) in their cis-regulatory elements to participate a particular biological function. Finally, single-cell resolved experiments (scFOODIE) allow cell-type-specific TF footprinting in heterogeneous brain tissues.

genomics↗

bioRxiv2024

Tagless LysoIP method for molecular profiling of lysosomal content in clinical samples

Lysosomes are implicated in a wide spectrum of human diseases including monogenic lysosomal storage disorders (LSDs), age-associated neurodegeneration and cancer. Profiling lysosomal content using tag-based lysosomal immunoprecipitation (LysoTagIP) in cell and animal models allowed major discoveries in the field, however studying lysosomal dysfunction in human patients remains challenging. Here, we report the development of the "tagless LysoIP method" to enable rapid enrichment of lysosomes, via immunoprecipitation, using the endogenous integral lysosomal membrane protein TMEM192, directly from clinical samples and human cell lines (e.g. induced Pluripotent Stem Cell (iPSCs) derived neurons). Isolated lysosomes are intact and suitable for subsequent multimodal omics analyses. To validate our approach, we employed the tagless LysoIP to enrich lysosomes from peripheral blood mononuclear cells (PBMCs) derived from fresh blood from patients with CLN3 disease, a neurodegenerative LSD. Metabolic profiling of isolated lysosomes showed massive accumulation of glycerophosphodiesters (GPDs) in patients lysosomes. Interestingly, a patient with a milder phenotype and genotype displayed lower accumulation of lysosomal GPDs, consistent with their potential role as disease biomarkers. Altogether, the tagless LysoIP provides a framework to study native lysosomes from patient samples, identify novel biomarkers and discover human-relevant disease mechanisms.

cell biology↗

bioRxiv2024

NADPH composite index analysis quantifies the relationship between compartmentalized NADPH dynamics and growth rates in cancer cells

NADPH, a highly compartmentalized electron donor in mammalian cells, plays essential roles in cell metabolism. However, little is known about how cytosolic and mitochondrial NADPH dynamics relate to cancer cell growth rates in response to varying nutrient conditions. To address this issue, we present NADPH composite index analysis, which quantifies the relationship between compartmentalized NADPH dynamics and growth rates using genetically encoded NADPH sensors, automated image analysis pipeline, and correlation analysis. Through this analysis, we demonstrated that compartmentalized NADPH dynamics patterns were cancer cell-type dependent. Specifically, cytosolic and mitochondrial NADPH dynamics of MDA-MB-231 decreased in response to serine deprivation, while those of HCT-116 increased in response to serine or glutamine deprivation. Furthermore, by introducing a fractional contribution parameter, we correlated cytosolic and mitochondrial NADPH dynamics to growth rates. Using this parameter, we identified cancer cell lines whose growth rates were selectively inhibited by targeting cytosolic or mitochondrial NADPH metabolism. Mechanistically, we identified citrate transporter as a key mitochondrial transporter that maintains compartmentalized NADPH dynamics and growth rates. Altogether, our results present a significant advance in quantifying the relationship between compartmentalized NADPH dynamics and cancer cell growth rates, highlighting a potential of targeting compartmentalized NADPH metabolism for selective cancer cell growth inhibitions.

systems biology↗

bioRxiv2024

An interpretable integration model improving disease-free survival prediction for gastric cancer based on CT images and clinical parameters

Preoperative prediction of disease-free survival of gastric cancer is significantly important in clinical practice. Existing studies showed the potentials of CT images in identifying predicting the disease-free survival of gastric cancer. However, no studies to date have combined deep features with radiomics features and clinical features. In this study, we proposed a model which embedded radiomics features and clinical features into deep learning model for improving the prediction performance. Our models showed a 3%-5% C-index improvement and 10% AUC improvement in predicting DFS and disease event. Interpretation analysis including T-SNE visualization and Grad-CAM visualization revealed that the model extract biologically meaning features, which are potentially useful in predicting disease trajectory and reveal tumor heterogeneity. The embedding of radiomics features and clinical features into deep learning model could guide the deep learning to learn biologically meaningful information and further improve the performance on the DFS prediction of gastric cancer. The proposed model would be extendable to related problems, at least in few-shot medical image learning. Key PointsO_LIAn integration model combining deep features, radiomics features and clinical parameters improved disease-free-survival prediction of gastric cancer by 3%-5% C-index. C_LIO_LIEmbedding radiomics and clinical features into deep learning model through concatenation and loss design improved feature extraction ability of deep network. C_LIO_LIThe model revealed disease progression trajectory and tumor heterogeneity. C_LI

cancer biology↗

bioRxiv2024

PhyloForge: Unifying micro and macro evolution with comprehensive genomic signals

With the explosive growth of biological data, the dimensions of phylogenetic research have expanded to encompass various aspects, including the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge-a robust tool designed to seamlessly integrate the demands of both micro- and macro-evolution, comprehensively utilizing diverse phylogenomic signals, such as genes, SNPs, structural variations, as well as mitochondrial and chloroplast genomes. PhyloForges groundbreaking innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customizable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants, and fungi, attests to its broad applicability in the field of phylogenetics. In summary, the developmental background and innovative features of PhyloForge position it with significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life.

molecular biology↗

bioRxiv2024

Brain-wide neuronal circuit connectome of human glioblastoma

Glioblastoma (GBM), a universally fatal brain cancer, infiltrates the brain and can be synaptically innervated by neurons, which drives tumor progression1-6. Synaptic inputs onto GBM cells identified so far are largely short-range and glutamatergic7-9. The extent of integration of GBM cells into brain-wide neuronal circuitry is not well understood. Here we applied a rabies virus-mediated retrograde monosynaptic tracing approach10-12 to systematically investigate circuit integration of human GBM organoids transplanted into adult mice. We found that GBM cells from multiple patients rapidly integrated into brain-wide neuronal circuits and exhibited diverse local and long-range connectivity. Beyond glutamatergic inputs, we identified a variety of neuromodulatory inputs across the brain, including cholinergic inputs from the basal forebrain. Acute acetylcholine stimulation induced sustained calcium oscillations and long-lasting transcriptional reprogramming of GBM cells into a more invasive state via the metabotropic CHRM3 receptor. CHRM3 downregulation suppressed GBM cell invasion, proliferation, and survival in vitro and in vivo. Together, these results reveal the capacity of human GBM cells to rapidly and robustly integrate into anatomically and molecularly diverse neuronal circuitry in the adult brain and support a model wherein rapid synapse formation onto GBM cells and transient activation of upstream neurons may lead to a long-lasting increase in fitness to promote tumor infiltration and progression.

cancer biology↗

bioRxiv2024

Birth weight and in-feed antibiotics alter the assembly and succession of the swine gut microbiota

Understanding the principles of gut microbiota assembly and succession during host development is critical for effective gut microbiome manipulation to optimize host health and growth. The objective of this study was to conduct a high-frequency sampling of the swine gut microbiota from controlled groups of pigs to increase understanding of the dynamics of microbial community development. Here, a total of 924 fecal samples from 44 piglets (22 low-birth-weight, LBW; 22 normal-birth-weight, NBW) over 21 time points (1-41 days of age) collected every two days. Community composition, assembly, and succession was determined using 16S rRNA gene amplicon sequencing. Alpha diversity continuously increased during the suckling stage, yet no significant increase was observed during the days post-weaning. Post-weaning in-feed antibiotics consistently decreased microbial diversity and changed the community structure in both LBW and normal birth weight (NBW) piglets. Delayed post-weaning gut microbial community maturation was observed in LBW piglets compared with NBW. Heterogeneity of the gut microbial community between piglets linearly decreased over time, as revealed by the within-time Bray-Curtis dissimilarities. Individuality analysis on the relatively stable stage revealed that the gut microbiota composition of some individuals over time, and the abundance of most genera between individuals, were highly variable. Dirichlet multinomial mixtures analysis supported an age-dependent microbiota developmental pattern and identified the age-discriminatory taxa. The importance of stochastic processes in microbial community assembly increased over time within primary and secondary successions, despite the fact that the most dominant factors influencing community assembly were homogeneous selection and dispersal limitation, which are deterministic. IMPORTANCEOur understanding of the assembly and succession of the swine gut microbial community is limited, and scientific advancement in this interdisciplinary topic is hampered by individuality and transient dynamics. The solution to the above foundational questions is not only ecologically relevant but also useful for practical swine production. Our study addresses ecological processes shaping the swine gut microbiota between piglets with contrasting birth weights and receiving post-weaning antibiotics. Persistent gut microbiota immaturity in LBW piglets suggests that efforts to accelerate microbial community succession might improve LBW piglet growth performance and disease resistance. Intra-individual variance both in community structure and genus abundance during the post-weaning period indicates the importance of repeated measurements for reliable observations. Additionally, neutral (stochastic) processes increased as a factor of community assembly within each stage of pig growth, indicating that early intervention and multiple follow ups may be critical in manipulating the gut microbiota development.

microbiology↗