Publications by authors named "Jingyang Qian"
Article Synopsis
- A major challenge in spatially resolved transcriptomics (SRT) is the evaluation of computational methods, often hampered by biases in simulated data.
- The paper introduces scCube, a Python package designed to create independent and varied simulations of SRT data while preserving spatial expression patterns of genes.
- The effectiveness of scCube is validated against existing simulators and shown to be useful in benchmarking techniques for spot deconvolution, gene imputation, and resolution enhancement through three detailed applications.
Article Synopsis
- A new computational method called SpaTrio was developed to integrate single-cell multi-omics and spatial transcriptomics data for a better understanding of tissue information at a spatial scale.
- SpaTrio uses probabilistic alignment, demonstrating strong accuracy and reliability through simulations and biological dataset evaluations.
- The method helps uncover complex spatial relationships and gene expression regulations in cells, revealing valuable insights into the multimodal biology of tissues.
Gaining a better understanding of autoprotection against drug-induced liver injury (DILI) may provide new strategies for its prevention and therapy. However, little is known about the underlying mechanisms of this phenomenon. We used single-cell RNA sequencing to characterize the dynamics and functions of hepatic non-parenchymal cells (NPCs) in autoprotection against DILI, using acetaminophen (APAP) as a model drug.
View Article and Find Full Text PDFSpatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations.
View Article and Find Full Text PDFArticle Synopsis
- Tissues display complex variations in gene expression and their spatial arrangement, but traditional single-cell RNA sequencing (RNA-seq) methods often lose this crucial spatial data.
- The proposed method, single-cell spatial position associated co-embeddings (scSpace), reconstructs cells within a pseudo-space using spatial transcriptome references to identify spatially distinct cell subpopulations.
- Benchmarking with various datasets shows that scSpace effectively reveals spatial relationships in complex tissues like the brain and liver, and has potential applications in understanding diseases like melanoma and COVID-19.
Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.
View Article and Find Full Text PDFSpatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods.
View Article and Find Full Text PDFTracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema.
Comput Struct Biotechnol J
July 2022
COVID-19 has caused severe threats to lives and damage to property worldwide. The immunopathology of the disease is of particular concern. Currently, researchers have used gene co-expression networks (GCNs) to deepen the study of molecular mechanisms of immune responses to COVID-19.
View Article and Find Full Text PDFBackground: Microbiome big data from population-scale cohorts holds the key to unleash the power of microbiomes to overcome critical challenges in disease control, treatment and precision medicine. However, variations introduced during data generation and processing limit the comparisons among independent studies in respect of interpretability. Although multiple databases have been constructed as platforms for data reuse, they are of limited value since only raw sequencing files are considered.
View Article and Find Full Text PDF[Mechanism of Xuanfei Baidu Tang in treatment of COVID-19 based on network pharmacology].
Zhongguo Zhong Yao Za Zhi
May 2020
Article Synopsis
- * Researchers gathered data on the herbs from various databases, assessed their effects on lung functions, and identified 326 potential targets connected to COVID-19, with 109 specifically linked to pathways involved in viral infection and lung injuries.
- * The findings indicated that XFBD operates through a multi-herb approach, primarily targeting the lungs, and influences several biological pathways related to immunity, inflammation, and metabolism, suggesting it helps restore balance in the body during COVID-19.