Biology subjects

Bo, X.

Publications and source records attributed to Bo, X..

16 recordsLinked to original sources

bioRxiv2026

UcTCRp: a TCRβ-based framework for quantitative MAIT- and iNKT-associated repertoire-state profiling

MAIT and iNKT cells are conventionally identified using invariant or semi-invariant TCR chains, antigen-loaded tetramers, or transcriptomic phenotypes. These requirements limit their detection in public and clinical immune-repertoire datasets that contain only TCR{beta} sequences. Here we present UcTCRp, a TCR{beta}-only framework for profiling MAIT- and iNKT-associated repertoire states in bulk immune repertoires. UcTCRp integrates V-gene context and CDR3{beta} sequence features using a transformer-based representation pretrained on more than one million TCR{beta} sequences and supervised with curated cross-species MAIT, iNKT and conventional T cell references. The framework defines conserved model-informative TCR{beta} features, uses V-matched negative sampling to reduce germline-segment shortcuts, and generalizes across independent human and mouse datasets. In paired scRNA-seq/scTCR-seq datasets, UcTCRp recovered transcriptome-defined MAIT and iNKT cells and identified additional MAIT-like candidates supported by receptor evidence but missed by expression-only annotation. Bulk calibration against paired single-cell references and synthetic spike-in experiments established operating characteristics for repertoire-level abundance estimation. These results establish unpaired TCR{beta} repertoires as an actionable substrate for reconstructing unconventional T cell-associated immune states, enabling archived repertoire resources to be repurposed for systems-level studies of tissue immunity, disease and therapeutic response.

bioinformatics↗

bioRxiv2025

A Syngeneic Immunocompetent Mouse Model of Gallbladder Cancer Reveals Tumorigenesis and Therapeutic Response

PurposeGallbladder cancer (GBC) is a rare but highly lethal malignancy with one of the poorest prognoses among cancers of the digestive system cancers. However, current experimental models of GBC face critical limitations, particularly their dependence on immunodeficient hosts, which precludes the investigation of the tumor microenvironment. We aimed to establish a novel syngeneic immunocompetent mouse model that recapitulates human GBC and enables the investigation of tumor-immune interactions. Experimental DesignWe engineered a murine cell line (mGBC1-ZH) from normal mouse gallbladder organoids expressing Kras and Trp53 (encoding mouse p53) mutations. Tumorigenic potential of mGBC1-ZH was evaluated by subcutaneous and orthotopic implantation. RNA-Seq and WES was used to demonstrate its characterization and similarity with human GBC. Immunohistochemistry, CCK8, and transwell assays wereused to investigate the role of CXCL5 in GBC. Therapeutic responses to standard first-line chemotherapeutic agents was evaluated in the syngeneic GBC model. ResultsThis model supports both subcutaneous and orthotopic tumor growth in immunocompetent C57BL/6J hosts while preserving hallmark features of human GBC, including biliary epithelial differentiation (CK7+/CK19+), aggressive histopathology, and an immunosuppressive "cold" tumor microenvironment. Genomic characterization revealed recurrent chromosomal instability and copy number alterations mirroring human GBC. Comprehensive transcriptomic profiling revealed profound gene expression changes during the model development, and resembled transcriptional features between mGBC1-ZH and human GBC cell lines and samples. CXCL5 was found to be upregulated in human GBC, and promote proliferation, metastasis, and neutrophils infiltration of GBC in the syngeneic mouse model. Functional validation of the syngeneic GBC model demonstrated therapeutic responsiveness to frontline chemotherapeutics gemcitabine and cisplatin with significant in vivo tumor regression. ConclusionsIn summary, we establised a novel syngeneic GBC mouse model, overcoming the limitations of traditional models by enabling studies in immunocompetent hosts. This model provides valuable insights into the molecular evolution from normal gallbladder cells to transformed cancer cells and establishes a robust platform for both mechanistic studies and therapeutic development.

cancer biology↗

bioRxiv2025

A comprehensive benchmark and guide for sequence-function interpretable deep learning models in genomics

The development of sequence-based deep learning methods has greatly increased our understanding of how sequence determines function. In parallel, numerous interpretable algorithms have been developed to address complex tasks, such as elucidating sequence regulatory syntax and analyzing non-coding variants from trained models. However, few studies have systematically compared and evaluated the performance and interpretability of these algorithms. Here, we introduce a comprehensive benchmark framework for evaluating sequence-to-function models. We systematically evaluated multiple models and DNA language foundation models using 369 ATAC-seq datasets, employing diverse training strategies and evaluation metrics to uncover their critical strengths and limitations. Our benchmark study highlights that different model architectures and interpretability methods are better suited to specific scenarios. Negative samples derived from naturally inactive regions outperform synthetic sequences, whereas single-cell tasks require specialized models. Additionally, we demonstrate that interpretable sequence-function models can complement traditional sequence alignment methods in studying cross-species enhancer regulatory logic. We also provide a pipeline to help researchers select the optimal sequence-function prediction and interpretability algorithms.

bioinformatics↗

bioRxiv2024

Continual integration of single-cell multimodal data with MIRACLE

Single-cell sequencing technologies have revolutionized our understanding of cellular heterogeneity and facilitated the construction of multi-omics cell atlases via data integration. However, updating these atlases with new data conventionally requires reintegration of all data and is computationally intensive, hindering timely updates and dynamic adjustments in biological and medical research. To address this challenge, we present Multimodal Integration with Continual Learning (MIRACLE), a novel online learning framework for the adaptive and efficient integration of single-cell multimodal data. MIRACLE employs dynamic architectures and data rehearsal strategies to support continual learning, allowing diverse data to be integrated while minimizing information loss over time. Our evaluations demonstrate that MIRACLE achieves accurate online integration with reduced computational requirements, effectively updating and expanding atlases with new cross-tissue and cross-modal data, and precisely identifying novel cell types and transferring labels across datasets. MIRACLE provides an efficient and flexible tool for single-cell community to integrate, share and explore biological knowledge from single-cell multimodal data.

bioinformatics↗

bioRxiv2023

EnrichMiner: a biologist-oriented web server for mining biological insights from functional enrichment analysis

Functional enrichment analysis has been widely used to help researchers obtain biological insights from -omics data. However, the results are often redundant and difficult to digest. The key is developing tools to help users explore the relationships between the enriched terms, remove the redundant terms, and finally select representative terms. However, existing tools hardly make a good integration between enrichment analysis and representative terms selection in a biological-friendly manner. Here, we developed a biologist-oriented web server named EnrichMiner to provide a one-stop solution. It is a complete analysis pipeline from a gene list or a ranked gene table to published-style figures. More importantly, it provides user-friendly interfaces and rich interactive operations to help users explore the term relationships and remove redundancy. EnrichMiner has been integrated into the ExpressVis platform, and is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis/EnrichMiner and does not require login.

bioinformatics↗

bioRxiv2022

MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal data

AO_SCPLOWBSTRACTC_SCPLOWRapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies, i.e. mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integration involves major challenges, particularly regarding modality alignment and batch effect removal. Here we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation, and batch correction of mosaic data by employing self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to other methods and reliability by evaluating its performance in full trimodal integration and various mosaic tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells (PBMCs), and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis, and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS.

bioinformatics↗

bioRxiv2021

Dynamic interplay between structural variations and 3D chromosome organization in pancreatic cancer

Structural variations (SVs) are the greatest source of variation in the genome and can lead to oncogenesis. However, the identification and interpretation of SVs in human pancreatic cancer remain largely undefined due to technological limitations. Here, we investigate the spectrum of SVs and three-dimensional (3D) chromatin architecture in human pancreatic ductal epithelial cell carcinogenesis by using state-of-the-art long- read single-molecule real-time (SMRT) and high-throughput chromosome conformation capture (Hi-C) sequencing techniques. We find that the 3D genome organization is remodeled and correlated with gene expressional change. The bulk remodeling effect of cross-boundary SVs in the 3D genome partly depends on intercellular genomic heterogeneity. Meanwhile, contact domains tend to minimize these disrupting effects of SVs within local adjacent genomic regions to maintain overall stability of 3D genome organization. Moreover, our data also demonstrates complex genomic rearrangements involving two key driver genes CDKN2A and SMAD4, and elucidates their influence on cancer-related gene expression from both linear view and 3D perspective. Overall, this study provides a valuable resource and highlights the impact, complexity and dynamicity of the interplay between SVs and 3D genome organization, which further expands our understanding of pathogenesis of SVs in human pancreatic cancer.

genetics↗

bioRxiv2021

Structural variant selection for high-altitude adaptation using single-molecule long-read sequencing

Structural variants (SVs) can be important drivers of human adaptation with strong effects, but previous studies have focused primarily on common variants with weak effects. Here, we used large-scale single-molecule long-read sequencing of 320 Tibetan and Han samples, to show that SVs are key drivers of selection under high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict driver enhancers, target genes, upstream regulators, and biological functions, which we validate using enhancer reporter and DNA pull-down assays. We reveal diverse Tibetan-specific SVs affecting the cis- and trans-regulatory circuitry of diverse biological functions, including hypoxia response, energy metabolism, lung function, etc. Our study greatly expands the global SV landscape, reveals the central role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles that SVs can play in human biology.

genomics↗

bioRxiv2020

A framework for predicting potential host ranges of pathogenic viruses based on receptor ortholog analysis

Viral zoonoses are a serious threat to public health and global security, as reflected by the current scenario of the growing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. However, as pathogenic viruses are highly diverse, identification of their host ranges remains a major challenge. Here, we present a combined computational and experimental framework, called REceptor ortholog-based POtential virus hoST prediction (REPOST), for the prediction of potential virus hosts. REPOST first selects orthologs from a diverse species by identity and phylogenetic analyses. Secondly, these orthologs is classified preliminarily as permissive or non-permissive type by infection experiments. Then, key residues are identified by comparing permissive and non-permissive orthologs. Finally, potential virus hosts are predicted by a key residue-specific weighted module. We performed REPOST on SARS-CoV-2 by studying angiotensin-converting enzyme 2 orthologs from 287 vertebrate animals. REPOST efficiently narrowed the range of potential virus host species (with 95.74% accuracy).

microbiology↗

bioRxiv2020

Alleviation of neuropathic pain by over-expressing a soluble colony-stimulating factor 1 receptor

In this study, we aim to alleviate neuropathic pain by suppressing microgliosis and macrophage accumulation, which is achieved by over-expressing a non-functional soluble colony-stimulating factor 1 receptor using adeno-associated virus 9 vectors (AAV9/sCSF1R). AAV9/sCSF1R and AAV9/GFP were intrathecally administered into mouse lumbar spine. Two weeks later, these mice underwent partial sciatic nerve ligation to induce neuropathic pain. GFP and sCSF1R were highly expressed in dorsal root ganglia (DRG) and spinal cords in AAV9-injected mice. Nerve ligation alone or pre-treated with AAV9/GFP led to significant microgliosis in the lumbar spinal cords and macrophage accumulation in DRG and sciatic nerves. In nerve-ligated mice pre- treated with AAV9/sCSF1R the microglia densities in the dorsal and ventral horns and macrophage densities in DRG and sciatic nerves were significantly lower compared to nerve-ligated mice pre-treated with AAV9/GFP. Behavioural tests showed that nerve-ligated mice pre- treated with AAV9/sCSF1R had a significantly higher paw withdrawal threshold, indicating the alleviation of neuropathic pain. The results implicate that viral vector-mediated expression of sCSF1R may represent a novel strategy in long-term alleviation of neuropathic pain.

neuroscience↗

bioRxiv2020

HIT-scISOseq: High-throughput and High-accuracy Single-cell Full-length Isoform Sequencing for Corneal Epithelium

Single-cell isoform sequencing can reveal transcriptomic dynamics in individual cells invisible to bulk- and single-cell RNA analysis based on short-read sequencing. However, current long-read single-cell sequencing technologies have been limited by low throughput and high error rate. Here we introduce HIT-scISOseq for high-throughput single-cell isoform sequencing. This method was made possible by full-length cDNA capture using biotinylated PCR primers, and by our novel library preparation procedure that combines head-to-tail concatemeric full-length cDNAs into a long SMRTbell insert for high-accuracy PacBio sequencing. HIT-scISOseq yields > 10 million high-accuracy full-length isoforms in a single PacBio Sequel II 8M SMRT Cell, providing > 8 times more data output than the standard single-cell isoform PacBio sequencing protocol. We exemplified HIT-scISOseq by first studying transcriptome profiles of 4,000 normal and 8,000 injured corneal epitheliums from cynomolgus monkeys. We constructed dynamic transcriptome landscapes of known and rare cell types, revealed novel isoforms, and identified injury-related splicing and switching events that are previously not accessible with low throughput isoform sequencing. HIT-scISOseq represents a high-throughput, cost-effective, and technically simple method to accelerate the burgeoning field of long-read single-cell transcriptomics.

genomics↗

bioRxiv2020

A yeast BiFC-seq method for genome-wide interactome mapping

Genome-wide physical protein-protein interaction (PPI) mapping remains a major challenge for current technologies. Here, we report a high-efficiency yeast bimolecular fluorescence complementation method coupled with next-generation DNA sequencing (BiFC-seq) for interactome mapping. We applied this technology to systematically investigate an intraviral network of Ebola virus (EBOV). Two-thirds (9/13) of known interactions of EBOV were recaptured and five novel interactions were discovered. Next, we used BiFC-seq method to map the interactome of the tumor protein p53. We identified 97 interactors of p53 with more than three quarters are novel. Furthermore, in more complex background, we screened potential interactors by pooling two BiFC-libraries together, and revealing a network of 229 interactions among 205 proteins. These results show that BiFC-seq is a highly sensitive, rapid and economical method in genome-wide interactome mapping.

genomics↗

bioRxiv2019

DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection with higher accuracy. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.\n\nAuthor summaryWe developed a novel method, DeepHiC, for enhancing Hi-C data resolution from low-coverage sequencing data using generative adversarial network. DeepHiC is capable of reproducing highresolution (10-kb) Hi-C data with high quality even using 1/100 downsampled reads. Our method outperforms the previous methods in Hi-C data resolution enhancement, boosting accuracy in chromatin loops identification and TADs detection. Application of DeepHiC on mouse embryonic development data shows that enhancements afforded by DeepHiC facilitates the chromatin loops identification of these data achieving higher accuracy. We also developed a user-friendly web server (http://sysomics.com/deephic) that allows researchers to enhance their own low-resolution Hi-C data (40kb-1 Mb) with just few clicks.

bioinformatics↗

bioRxiv2019

Genome-wide identification and analysis of A-to-I RNA editing events in the malignantly transformed cell lines from BEP2D induced by α-particles radiation

Adenosine (A) to inosine (I) RNA editing is the most prevalent RNA editing mechanism in humans and play critical roles in tumorigenesis. However, the effects of radiation on RNA editing and the mechanisms of radiation-induced cancer were poorly understood. Here, we analyzed human bronchial epithelial BEP2D cells and radiation-induced malignantly transformed cells with next generation sequencing. By performing an integrated analysis of A-to-I RNA editing, we found that genome-encoded single-nucleotide polymorphisms (SNPs) might induce the downregulation of ADAR2 enzymes, and further caused the abnormal occurrence of RNA editing in malignantly transformed cells. These editing events were significantly enriched in differentially expressed genes between normal cells and cancer cells. In addition, oncogenes CTNNB1 and FN1 were highly edited and significantly overexpressed in cancer cells, thus may be responsible for the lung cancer progression. Our work provides a systematic analysis of RNA editing from lung tumor specimens with high-throughput RNA sequencing and DNA sequencing. Moreover, these results demonstrate further evidence for RNA editing as an important tumorigenesis mechanism.

genetics↗

bioRxiv2018

Time-resolved succession of epigenetic regulation during early mammalian development

During early mammalian embryonic development, different epigenetic mechanisms undergo dramatic changes; yet how these interconnected epigenetic layers function together to coordinate expression of the genetic code in a spatiotemporal manner remains unknown. Here, we describe a time-resolved study of the hierarchy of epigenetic marks and events, which we used to model transcriptional programs that cannot be understood by investigating steady state. We found that, following fertilization, the re-establishment of accessible chromatin together with transcription factors inherited from oocytes initiates transcription at the 2-cell stage, and then the introduction of active histone modification, H3K4me3, facilitates gene up-regulation at the 4-cell stage, then stabilization of higher-order chromatin structures at the 8-cell stage further enhances transcriptional activity. During the first lineage specification at ICM, transcription activity multifaceted regulation of epigenetic marks. Finally, we quantitatively model the stage succession of different epigenetic marks on transcriptional programs during early embryonic development.

bioinformatics↗

bioRxiv2018

New insights on human essential genes based on integrated multi-omics analysis

Essential genes are those whose functions govern critical processes that sustain life in the organism. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis for comprehensively and systematically elucidating the genetic and regulatory characteristics of human essential genes. First, essential genes act as \"hubs\" in protein-protein interactions networks, in chromatin structure, and in epigenetic modifications, thus are essential for cell growth. Second, essential genes represent the conserved biological processes across species although gene essentiality changes itself. Third, essential genes are import for cell development due to its discriminate transcription activity in both embryo development and oncogenesis. In addition, we develop an interactive web server, the Human Essential Genes Interactive Analysis Platform (HEGIAP) (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to give a global, multidimensional interpretation of gene essentiality. Our study provides a new view for understanding human essential genes.

genomics↗