Biology subjects

zhou, X.

Publications and source records attributed to zhou, X..

12 recordsLinked to original sources

bioRxiv2026

An FEV-Associated Metastasis-Initiating-Like State Linked to Tumor Cell Plasticity and Immune Niche Remodeling in Prostate Cancer

Prostate cancer progression is accompanied by dynamic remodeling of malignant cell states, whose metastatic potential and regulatory programs remain poorly defined. Here, we integrated single-cell, spatial, bulk transcriptomic, epigenomic, and experimental data to characterize malignant cell state transitions and identify candidate regulators associated with metastatic progression. A distinct intermediate state exhibits increased copy-number alterations, stemness, proliferation, and MYC activity, together with the highest transcriptional similarity to metastatic tumor cells, and is therefore designated as a metastasis-initiating-like (MIC-like) state. Transcriptional and regulon dynamics along the malignant cell state trajectory highlight FEV as a prominent candidate associated with the MIC-like state, with concordant expression and regulon activity patterns. Functionally, FEV is associated with a hybrid epithelial-mesenchymal phenotype, and its perturbation disrupts this phenotype, accompanied by reduced epithelial and mesenchymal features as well as impaired prostate cancer cell migration and invasion. Beyond this tumor-intrinsic feature, FEV is also associated with reduced cytotoxicity and interferon responses in the tumor microenvironment through its regulation of MIF-family genes. In addition to its potential roles in tumor cell plasticity and immune remodeling, FEV shows a reciprocal association with androgen signaling and distinct expression patterns across treatment response groups, highlighting its potential relevance in therapeutic contexts.

bioinformatics↗

bioRxiv2026

Anatomy-Guided 3D Graph Networks for Couinaud Segmentation in Tumor Affected Livers

Image-based liver Couinaud segmentation is designed to automatically provide the locations of suspicious objects in liver CT/MR images. Once achieved, the physicians will be guided to the target slice and area where the suspicious node is located. However, conventional algorithms trained primarily on healthy liver images often fail to generalize to Hepatocellular Carcinoma (HCC) cases due to pathological structural distortions. In this work, we propose a robust two-stage framework that integrates a 3D Unet with a 3D Anatomical Structure-Guided Graph Convolutional Network (3D GCN). This two-stage strategy effectively isolates the liver volume to eliminate structural noise from neighboring organs, such as the spleen, allowing the framework to focus exclusively on the complex 3D anatomical relationships among the eight segments. To ensure the topological consistency required for global spatial reasoning, we implement a standardized preprocessing pipeline that normalizes liver-only volumes to exactly 50 frames along the z-axis. By combining a lightweight 3D UNet backbone with the 3D GCN for refined boundary reasoning, our model demonstrates superior generalization performance on unseen clinical datasets, achieving a mean Dice score of 0.828 in blind testing. By releasing our code and pretrained weights, we aim to provide the first publicly available deep learning resource for robust Couinaud segmentation.

bioinformatics↗

bioRxiv2026

Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE

Rapid advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the investigation of gene expression changes at the single-cell level, particularly for elucidating the heterogeneity among cells and complex biological processes. This technique reveals subtle molecular differences within individual cells, thereby offering a unique viewpoint for the investigation of cell cycle progression, cellular differentiation, and disease pathogenesis. However, accurately identifying and analyzing cell cycle dynamics in scRNA-seq data remains challenging due to the complexity of the data and the subtle differences between cell states. To address this challenge, we developed the integrated Sinusoidal and Piecewise AutoEncoder (SPAE), an autoencoder-based piecewise linear model, for characterizing the cell cycle dynamics and cell states in scRNA-seq data. Compared with existing methods, SPAE demonstrates substantially improved accuracy and robustness in cell cycle characterization. Additionally, SPAE can accurately predict cancer cell cycle transitions and effectively facilitate the removal of cell cycle effects from gene expression data. SPAE is available for non-commercial use at https://github.com/YaJahn/SPAE.

bioinformatics↗

bioRxiv2026

Identification and Characterization of Metastasis-initiating cells

Metastasis, the primary cause of cancer-related mortality, is a dynamic and complex process driven by a subset of cells known as metastasis-initiating cells (MICs). Accurate identification of MICs is therefore critical for metastasis diagnosis and therapeutic decision-making. However, current approaches rely either on mouse tracing experiments, which are difficult to translate to human systems, or on indirect strategies such as stemness, trajectory, pathway, and biomarker analyses that often yield inconsistent results. To address these limitations, we propose scMIC, a computational framework designed to explicitly and reliably identify MICs from single-cell data (available at https://github.com/swu13/scMIC). scMIC integrates an embedding-based representation, unbalanced optimal transport, and a top-k selection strategy to robustly capture metastasis-initiating potential. The framework was validated and applied across multiple cancer types, species, and multi-omics datasets. Our results demonstrate the reliability of scMIC for MIC identification, its potential clinical utility in metastasis prognosis, and its effectiveness in discovering metastasis-related gene programs and molecular biomarkers. Elucidating the mechanisms of metastasis initiation not only advances our understanding of metastatic progression but also enables the development of therapeutic strategies that target the more aggressive MIC population rather than non-MICs, thereby avoiding unintended increases in metastatic risk. Collectively, scMIC provides a powerful tool for cancer metastasis research and drug discovery.

bioinformatics↗

bioRxiv2024

deepBlastoid: A Deep Learning-Based High-Throughput Classifier for Human Blastoids Using Brightfield Images with Confidence Assessment

Recent advances in human blastoids have opened new avenues for modeling early human development and implantation. Human blastoids can be generated in large numbers, making them suitable for high-throughput screening, which often involves analyzing vast numbers of images. However, automated methods for evaluating and characterizing blastoid morphology are still underdeveloped. We developed a deep-learning model capable of recognizing and classifying blastoid brightfield images into five distinct quality categories. The model processes 53.2 images per second with an average accuracy of 87%, without signs of overfitting or batch eHects. By integrating a Confidence Rate (CR) metric, the accuracy was further improved to 97%, with low-CR images flagged for human review. In a comparison with human experts, the model matched their accuracy while significantly outperforming them in throughput. We demonstrate the value of the model in two real-world applications: (1) systematic assessment of the eHect of lysophosphatidic acid (LPA) concentration on blastoid formation, and (2) evaluating the impact of dimethyl sulfoxide (DMSO) on blastoids for drug screening. In the applications involving over 10,000 images, the model identified significant eHects of LPA and DMSO, which may have been overlooked in manual assessments. The deepBlastoid model is publicly available and researchers can train their own model according to their imaging conditions and blastoid culture protocol. deepBlastoid thus oHers a precise, automated approach for blastoid classification, with significant potential for advancing mechanism research, drug screening, and clinical in vitro fertilization (IVF) applications.

developmental biology↗

bioRxiv2024

A Cell Cycle-aware Network for Data Integration and Label Transferring of Single-cell RNA-seq and ATAC-seq

In recent years, the integration of single-cell multi-omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non-single omics perspective, but it still suffers many challenges, such as omics-variance, sparsity, cell heterogeneity and confounding factors. As we know, cell cycle is regarded as a confounder when analyzing other factors in single-cell RNA-seq data, but its not clear how it will work on the integrated single-cell multi-omics data. Here, we developed a Cell Cycle-Aware Network (CCAN) to remove cell cycle effects from the integrated single-cell multi-omics data while keeping the cell type-specific variations. This is the first computational model to study the cell-cycle effects in the integration of single-cell multi-omics data. Validations on several benchmark datasets show the out-standing performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA-seq datasets from different protocols, integrating paired and unpaired scRNA-seq and scATAC-seq data, accurately transferring cell type labels from scRNA-seq to scATAC-seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data.

bioinformatics↗

bioRxiv2023

The roles of RNA editing in cancer immunity through interacting with interferon

The interferon-activated tumor innate immunity can be primed by specific double-stranded RNA (dsRNA) sensors upon stimulation. A-to-I RNA editing in the dsRNA regions can have a potential function to regulate interferon-related cancer immunity. A systematical analysis of both the editing enzyme and specific enriched editing region in patients, tissues, and cell lines is performed to reveal the underlying mechanisms. We then validate the preferred editing of dsRNA regions, identify the hyper-editing in severe tumors, and discover the negative effect of editing on cancer immunity. Specifically, RNA editing acts as an inhibitor of PKR- and MDA5-related interferon pathways through the regulations of miRNAs and RNA-binding proteins and the deactivation of dsRNA sensors. With the alteration of interferons, subsequently, RNA editing represses the infiltration of CD8 and CD4 T cells and reduces the sensitivities of cancer drugs, such as cisplatin. These analyses on A-to-I RNA editing can improve the knowledge of tumorigenesis, immunology, and cancer-targeted immunotherapy. HighlightsO_LIThe preferred dsRNA region for RNA editing is validated. C_LIO_LIUpregulation of RNA editing in severe tumors is discovered. C_LIO_LIRNA editing inhibits PKR- and MDA5-related cancer immunity. C_LIO_LIRNA editing represses the infiltration of CD8 and CD4 T cells. C_LIO_LIRNA editing reduces the sensitivities of cancer drugs. C_LI

bioinformatics↗

bioRxiv2022

Alternative Polyadenylation alters UBASH3B-ZNF652 competition to involve in Triple-Negative Breast Cancer

Alternative polyadenylation (APA) is an RNA-processing mechanism which may affect gene expression. Its involvements in cancers including the triple-negative breast cancer (TNBC) have been demonstrated in previous studies. Given the lack of biomarkers for TNBC, in this study, we were committed to finding novel biomarkers related to TNBC prognosis from the perspective of APA-mediated microRNA regulation. For this goal, raw bulk RNA sequencing data was collected for the identification of nine survival-related APA events by multivariate Cox regression analysis and forward-selected Likelihood Ratio Test. They showed good prognosis ability on TNBC risk. Of them, the APA event in UBASH3B was proposed as a novel potential biomarker. This event disturbed microRNA regulation on its host gene and other competing tumor genes to possibly involve in the pathogenesis of TNBC.

bioinformatics↗

bioRxiv2022

Genetic control of RNA editing in Neurodegenerative disease

A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variant. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1,596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) and Parkinsons Progression Markers Initiative (PPMI). The large-scale and genome-wide identification of 95,637 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56, and DHX9, since their regulations on multiple RNA editing events probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of four proteins, expressions of 148 genes, and splicing of 417 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.

bioinformatics↗

bioRxiv2022

The Integrative Studies on the Functional A-to-I RNA Editing Events in Human Cancers

A-to-I RNA editing, constituting nearly 90% of all RNA editing events in human, has been reported to contribute to the tumorigenesis in diverse cancers. However, the comprehensive map for functional A-to-I RNA editing events in cancers is still insufficient. To fill this gap, we systematically and intensively analyzed multiple tumorigenic mechanisms of A-to-I RNA editing events in samples across 33 cancer types from The Cancer Genome Atlas. For individual candidate among ~ 1.5M quantified RNA editing events, we performed diverse types of down-stream functional annotations. Finally, we identified 24,236 potentially functional A-to-I RNA editing events, including the cases in APOL1, IGFBP3, GluA2, BLCAP, and miR-589-3p. These events showed significant results and might play crucial roles in the scenarios of tumorigenesis, due to their tumor-related editing frequencies or probable effects on altered expression profiles, protein functions, splicing patterns, and miRNA regulations of tumor genes. Our functional A-to-I RNA editing events (https://ccsm.uth.edu/CAeditome/) will help better understanding of cancer pathology from A-to-I RNA editing aspect.

bioinformatics↗

bioRxiv2020

Single-cell RNA-seq Imputation using Generative Adversarial Networks

Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100,000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

bioinformatics↗

bioRxiv2018

ADS-J1 Disaggregates Semen-derived Amyloid Fibrils

Semen-derived amyloid fibrils, composing SEVI (semen-derived enhancer of viral infection) fibrils and SEM1 fibrils, could remarkably enhance HIV-1 sexual transmission and thus, are potential targets for the development of an effective microbicide. Previously, we found that ADS-J1, apart from being an HIV-1 entry inhibitor, could also potently inhibit seminal amyloid fibrillization and block fibril-mediated enhancement of viral infection. However, the remodeling effects of ADS-J1 on mature seminal fibrils were unexplored. Herein, we investigated the capacity of ADS-J1 to disassemble seminal fibrils and the potential mode of action by applying several biophysical and biochemical measurements, combined with molecular dynamic (MD) simulations. We found that ADS-J1 effectively remodeled SEVI, SEM186-107 fibrils and endogenous seminal fibrils. Unlike epi-gallocatechin gallate (EGCG), a universal amyloid fibril breaker, ADS-J1 disaggregated SEVI fibrils into monomeric peptides, which was independent of oxidation reaction. MD simulations revealed that ADS-J1 displayed strong binding potency to the full-length PAP248-286 via electrostatic interactions, hydrophobic interactions and hydrogen bonds. ADS-J1 might initially bind to the fibrillar surface and then occupy the amyloid core, which eventually lead to fibril disassembly. Furthermore, the binding of ADS-J1 with PAP248-286 might induce conformational changes of PAP248-286. Disassembled PAP248-286 might not be favor to re-aggregate into fibrils. ADS-J1 also exerts abilities to remodel a panel of amyloid fibrils, including A{beta}1-42, hIAPP1-37 and EP2 fibrils. ADS-J1 displays promising potential to be a combination microbicide and an effective lead-product to treat amyloidogenic diseases.

pharmacology and toxicology↗