Biology subjects

Xi, R.

Publications and source records attributed to Xi, R..

At least 19 recordsLinked to original sources

bioRxiv2025

Spatiotemporal WNT and BMP gradients orchestrate regional enteroendocrine cell diversity along the Drosophila midgut

Enteroendocrine cells (EEs) in metazoan are diversified into multiple subtypes that occupy specific regions along the digestive tract to fulfill their functions. How to establish this regional pattern of EE subtypes is unclear. Here, we investigated the complex distribution patterns of three major EE subtypes along the length of the pupal and adult Drosophila midgut, and found that regional EE patterning is regulated by WNT and BMP signaling in a spatiotemporally dependent manner. Furthermore, there are both Notch-dependent and -independent cell division modes of EE progenitors that contribute to the generation of regional EE diversity. Our findings suggest that intercalated WNT and BMP morphogen gradients emanating from compartment boundaries play a critical role, not only in establishing regional ISC identity and the resulting EE diversity during development, but also in maintaining regional EE diversity in adulthood--a paradigm that may be conserved in mammals.

developmental biology↗

bioRxiv2025

CNA detection from spatial transcriptomics using SpaCNA

Spatial transcriptomics (ST) enables genome-wide profiling of gene expression while preserving spatial context, yet accurate detection of copy number alterations (CNAs) in tumor ST data remain challenging. Here we present SpaCNA, a spatial-aware computational framework that integrates multi-modal spatial information to improve CNA detection. SpaCNA constructs adjacency graphs using spatial proximity and H&E stained image similarity, refines the raw gene expressions, and implements a Hidden Markov Random Field model for robust CNA state inference. Through extensive benchmarking on simulated data and multi-cancer cohorts, SpaCNA demonstrates superior accuracy and outperforms existing methods in CNA detection and tumor region identification. In real-world applications to breast cancer and colorectal cancer, SpaCNA reveals spatially distinct subclones with context-dependent interactions within the microenvironment. Additionally, applied to a 3D ST dataset of head and neck squamous cell carcinoma, SpaCNA uncovers clone-specific CNAs associated with therapeutic resistance biomarkers across multiple slices. By facilitating precise spatial mapping of CNAs and tumor architecture, SpaCNA significantly enhances our understanding of intratumoral heterogeneity and spatial evolutionary patterns.

bioinformatics↗

bioRxiv2025

Mac/Lac-tosylceramide regulates intestinal homeostasis and secretory cell fate commitment by facilitating Notch signaling

Cell-to-cell communication via Delta-Notch signaling is widely used in various tissues and organs to regulate development and patterning; however, the mechanisms regulating Notch signaling for precise cell fate decisions remain poorly understood. Similar to mammals, the intestinal stem cells (ISCs) in the adult Drosophila midgut generate both absorptive and secretory cell progeny, guided by differential levels of Notch activation. Here we performed a forward genetic screen in Drosophila and identified glucosylceramide synthase (GlcT), a rate-limiting enzyme for glycosphingolipid (GSL) production, whose mutation causes the development of secretory cell tumors. Genetic analysis of the GSL synthesis pathway, combined with metabolite rescue experiments, revealed that the tumor formation is linked to a deficiency in Mactosylceramide/Lactosylceramide. This deficiency impaired the endocytic recycling of the Delta, subsequently reducing Notch signaling activation. Conditional knockout of UGCG, the mammalian ortholog of GlcT, in mouse small intestine caused an excessive differentiation of goblet cells, phenotypes similar to these caused by Notch inhibition. Our study suggests an evolutionarily conserved role for a specific GSL metabolite in modulating Notch signaling during stem cell fate decisions and provides a molecular connection between ceramide metabolism and Notch signaling in regulating tissue homeostasis and tumor formation.

developmental biology↗

bioRxiv2024

De novo non-compartmentalized microorganism for directed evolution of nanobodies

The creation of de novo microorganisms is a fascinating task, and microorganisms offer a wide range of applications for humanity. Herein, we designed a de novo microorganism, termed Syn-phage, as an artificial unencapsulated mimic of naturally occurring phage capable of evolving nanobodies with improved properties. Syn-phage composes unified genomic and phenotypic materials with phenotypic proteins encoded from its genetic information. Since Syn-phage can undergo amplification in E. coli. host, we established a protocol for the evolution of nanobodies with improved avidity or affinity, allowing super-resolution live-cell immunoimaging of endogenous proteins, a challenge posed in immunolabeling with antibodies. Utilizing a Syn-phage evolved nanobody, we visualized the microtubule nucleation factor hTPX2, recorded the dynamics of it during mitotic progression, and imaged it at a resolution beyond the diffraction limit of light via an AI-powered imaging pipeline. Hence, this study demonstrated the possibility to design de novo microorganisms for capitative applications.

biochemistry↗

bioRxiv2024

Interpretable high-resolution dimension reduction of spatial transcriptomics data by DeepFuseNMF

Spatial transcriptomics (ST) technologies have revolutionized tissue architecture studies by capturing gene expression with spatial context. However, high-dimensional ST data often have limited spatial resolution and exhibit considerable noise and sparsity, posing significant challenges in deciphering subtle spatial structures and underlying biological activities. Here, we introduce SpaHDmap, an interpretable dimension reduction framework that enhances spatial resolution by integrating ST gene expression with high-resolution histology images. SpaHDmap incorporates non-negative matrix factorization into a multimodal fusion encoder-decoder architecture, enabling the identification of interpretable, high-resolution embeddings. Furthermore, SpaHDmap can simultaneously analyze multiple samples and is compatible with various types of histology images. Extensive evaluations on synthetic and real ST datasets from various technologies and tissue types demonstrate that SpaHDmap can effectively produce highly interpretable, high-resolution embeddings, and detects refined spatial structures. SpaHDmap represents a powerful approach for integrating ST data and histology images, offering deeper insights into complex tissue structures and functions.

bioinformatics↗

bioRxiv2024

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets. In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of synthesized and real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

bioinformatics↗

bioRxiv2024

High-resolution detection of copy number alterations in single cells with HiScanner

Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from 65 cells across 11 neurotypical human brains, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. We also generated low-coverage scWGS data from 179 cells sampled from the same meningioma patient at two time points. For this serial dataset, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells.

genomics↗

bioRxiv2024

Gustatory-neuron-supplied R-spondin-2 is required for taste bud replenishment

Taste buds undergo continuous cell turnover throughout life, and taste cell replenishment relies strictly on innervation, a phenomenon first described almost 150 years ago. Recently, we provided evidence that R-spondin 2 (Rspo2) may be the long-sought gustatory neuron-supplied factor that regulates taste stem cell activity, via its interaction with taste stem/progenitor cell-expressed receptor Rnf43/Znrf3. Yet, whether gustatory-neuron-supplied Rspo2 is strictly required for taste tissue maintenance has not been resolved. Here, we set out to determine the necessity of gustatory-neuron-supplied Rspo2 in taste tissue homeostasis using genetic approaches. We used a mouse line that harbors the neomycin-resistance gene (NeoR) in one of the intron regions of the Rspo2 gene, which results in reduced expression of Rspo2. The number of taste buds is significantly reduced in these mice, compared to wild-type mice, in both anterior and posterior tongue. This phenotypic change was completely reversed by removing NeoR from the Rspo2 gene, thus making it normal. We also combined adeno-associated virus (AAV)-based delivery of Cre recombinase with a mouse line amenable to Cre-based ablation of the Rspo2 exons encoding the receptor-binding domains. Such deletion of Rspo2 in the nodose-petrosal-jugular ganglion complex led to nearly complete loss of taste buds in the circumvallate papilla. Thus, we demonstrate that Rspo2 is the long-sought gustatory-neuron-supplied factor that acts on taste stem cells to maintain taste tissue homeostasis. SignificanceWe have known for 150 years that innervation is required to induce and maintain cell replacement in taste buds. Until recently, the identity of the inducing factor produced by neurons was unknown. We have shown that R-spondin alone is sufficient to substitute for neuronal input to induce taste bud regeneration. Using a genetic loss-of-function approach, we now demonstrate that gustatory-neuron-expressed Rspo2 is required to maintain taste tissue homeostasis. Altogether, our work reveals that Rspo2 is the long-sought neuron-supplied factor that regulates the activity of taste stem/progenitor cells.

neuroscience↗

bioRxiv2023

Detecting copy number variations from single-cell chromatin sequencing data by AtaCNV

Singe-cell assay of transposase accessible chromatin sequencing (scATAC-seq) can unbiasedly profile genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often rely on copy number variations (CNVs). However, CNV detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNV, a computational algorithm that accurately detects high resolution CNVs from scATAC-seq data. We benchmark AtaCNV using simulation and real data and find AtaCNVs superior performance. Analyses of 10 scATAC-seq datasets shows that AtaCNV could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial and ovarian cancer samples, AtaCNV identifies subclones at distinct cellular states, suggesting important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale CNVs, demonstrating the importance of high-resolution CNV detection. These data show that AtaCNV can aid the integrative analysis for understanding the complex heterogeneity in cancer.

bioinformatics↗

bioRxiv2023

Directly selecting differentially expressed genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell-types and their associated marker genes are often identified by clustering and differential expression gene (DEG) analysis. scRNA-seq data contain many genes not relevant to cell-types and gene selection procedures are needed for more accurate clustering. An ideal gene selection procedure should select all DEGs between cell-types for best cell-type identification. However, because cell-types are unknown, gene selection and DEG analysis are performed separately using different methods. Genes are selected using surrogate criteria not directly related with clustering, which often miss important genes or select unimportant genes. Clustering accuracy could be seriously influenced because of the inferior gene selection. DEGs are often detected by comparing different clusters, leading to many false DEGs due to the selection bias problem. In this paper, we present Festem, a unified method for gene selection and DEG analysis in scRNA-seq studies. Festem investigates genes clustering information based on the observation that marginal distributions of DEGs are mixtures of their different cell-type-conditional distributions, and can directly select the clustering-informative DEGs and avoid the selection bias problem. Extensive simulation and real data analyses show that Festem achieves high precision and recall for DEG detection, and enables more accurate clustering and cell-type identification. Applications to several scRNA-seq datasets demonstrate that Festem can identify cell-types that are often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify CD8+ T cell-types and find that their marker genes are novel prognostic biomarkers.

bioinformatics↗

bioRxiv2022

Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis

Accurately predicting the effects of mutations in cancer has the potential to improve existing treatments and identify novel therapeutic targets. In this paper, we evidence for the first time that the large-scale pre-trained protein language models (PPLMs) are zero-shot predictors for two clinically relevant tasks: identifying diseasecausing mutations and predicting patient survival rate. Then we benchmark a series of state-of-the-art (SOTA) PPLMs on 2279 protein variants across 20 cancer-related genes. Our empirical results show that the PPLMs outperform the SOTA baseline, EVE [1], trained on multiple sequence alignment (MSA) data. We also demonstrate that the evolutionary index score, generated from the PPLMs softmax layer, is good indicator for both mutation pathogenicity and patient survival rate. Our paper has taken a key step toward the clinical utility of large-scale PPLMs.

bioinformatics↗

bioRxiv2022

Chromatin Remodeling in Patient-Derived Colorectal Cancer Models

Patient-Derived Organoids (PDO) and Xenografts (PDX) are the current gold standards for patient derived models of cancer (PDMC). Nevertheless, how patient tumor cells evolve in these models and the impact on drug response remains unclear. Herein, we compared the transcriptomic and chromatin accessibility landscapes of six matched sets of colorectal cancer (CRC) PDO, PDX, PDO-derived PDX (PDOX), and original patient tumors (PT) and discovered two major remodeling axes. The first axis delineates PDX and PDO from PT, and the second axis distinguishes PDX and PDO. PDOX were more similar to PDX than they were to PDO, indicating that the growth environment is a driving force for chromatin adaptation. Using bivariate genomic footprinting analysis, we identified transcription factors (TF) that differentially bind to open chromatins between matched PDO and PDOX. Among them, KLF14 and EGR2 footprints were enriched in all six PDOX relative to matched PDO, and silencing of KLF14 or EGR2 promoted tumor growth. Furthermore, EPHA4, a shared downstream target gene of KLF14 and EGR2, altered tumor sensitivity to MEK inhibitor treatment. Altogether, patient-derived CRC cells undergo both common and distinct chromatin remodeling in PDO and PDX/PDOX, driven largely by their respective microenvironments, which results in differences in growth and drug sensitivity and needs to be taken into consideration when interpreting their ability to predict clinical outcome.

cancer biology↗

bioRxiv2020

Single cell gene fusion detection by scFusion

Gene fusions are widespread in tumor cells and can play important roles in tumor initiation and progression. Using full length single cell RNA sequencing (scRNA-seq), gene fusions can now be detected at single cell level. However, scRNA-seq data has a high noise level and contains various technical artefacts that can lead to spurous fusion discoveries. Here we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell data, scFusion detected the invariant TCR gene recombinations in Mucosal-associated invariant T cells that many methods developed for bulk-data failed to detect. In a multiple myeloma data, scFusion detected the known recurrent fusion IgH-WHSC1, which was associated with overexpression of the WHSC1 oncogene.

bioinformatics↗

bioRxiv2020

Chromatin remodeling in peripheral blood cells reflects COVID-19 symptom severity

SARS-CoV-2 infection triggers highly variable host responses and causes varying degrees of illness in humans. We sought to harness the peripheral blood mononuclear cell (PBMC) response over the course of illness to provide insight into COVID-19 physiology. We analyzed PBMCs from subjects with variable symptom severity at different stages of clinical illness before and after IgG seroconversion to SARS-CoV-2. Prior to seroconversion, PBMC transcriptomes did not distinguish symptom severity. In contrast, changes in chromatin accessibility were associated with symptom severity. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif occupancy for individual PBMC cell types. The most extensive remodeling occurred in CD14+ monocytes where sub-populations with distinct chromatin accessibility profiles were associated with disease severity. Our findings indicate that pre-seroconversion chromatin remodeling in certain innate immune populations is associated with divergence in symptom severity, and the identified transcription factors, regulatory elements, and downstream pathways provide potential prognostic markers for COVID-19 subjects. One sentence summaryChromatin accessibility in immune cells from COVID-19 subjects is remodeled prior to seroconversion to reflect disease severity.

genomics↗

bioRxiv2020

Mucosal Associated Invariant T (MAIT) Cell Responses Differ by Sex in COVID-19

Sexual dimorphisms in immune responses contribute to coronavirus disease 2019 (COVID-19) outcomes, yet the mechanisms governing this disparity remain incompletely understood. We carried out sex-balanced sampling of peripheral blood mononuclear cells from confirmed COVID-19 inpatients and outpatients, uninfected close contacts, and healthy controls for 36-color flow cytometry and single cell RNA-sequencing. Our results revealed a pronounced reduction of circulating mucosal associated invariant T (MAIT) cells in infected females. Integration of published COVID-19 airway tissue datasets implicate that this reduction represented a major wave of MAIT cell extravasation during early infection in females. Moreover, female MAIT cells possessed an immunologically active gene signature, whereas male counterparts were pro-apoptotic. Collectively, our findings uncover a female-specific protective MAIT profile, potentially shedding light on reduced COVID-19 susceptibility in females.

immunology↗

bioRxiv2019

CNV-BAC: Copy Number Variation Detection in Bacterial Circular Genome

MotivationWhole genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. ResultsWe show that the replication bias is widespread using ~200 bacterial WGS data. We develop CNV-BAC that can properly normalize the replication bias as well as other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. Availability and implementationCNV-BAC is available at https://github.com/LinjieWu/CNV-BAC. Contactruibinxi@math.pku.edu.cn

bioinformatics↗

bioRxiv2019

Self-assembled primary tumor clusters for precision cancer therapy

Several patient-derived tumor models emerged recently as robust preclinical models. However, their potential to guide clinical therapy remained unclear. We report a novel model for personalized drug testing called patient-derived tumor-like cell clusters (PTC) that enabled us to accomplish personalized drug tests within 10-20 days. Mechanistically, PTCs result from the migration and aggregation of CD8+/CD44+ primary epithelial cells in a mixture of fibroblasts and macrophages, and form spheroid cultures. Phenotypic and genotypic profiling of PTCs showed a high degree of similarity with the original patient tumors, indicating that PTC structurally and functionally recapitulated original tumors. Over 200 PTC models have been established from at least six cancer types and diverse sampling approaches. Given its seamless integration with current clinical sampling approaches, the PTC platform will revolutionize chemotherapy programs for cancer patients.

cancer biology↗

bioRxiv2018

scRMD: Imputation for single cell RNA-seq data via robust matrix decomposition

MotivationSingle cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant noise increase, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values thus becomes an essential step in scRNA-seq data analysis.\n\nResultsIn this paper, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis.\n\nContactruibinxi@math.pku.edu.cn

bioinformatics↗