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Single-cell omics: experimental workflow, data analyses and applications

Science China Life Sciences 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical laboratory the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City) Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang University Key laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research Institute of Intelligent Computing Zhejiang Lab Shanghai Research Institute for Intelligent Autonomous Systems Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of Medicine National Key laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Beijing Institutes of Life Science Chinese Academy of Sciences Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology (CQB)Peking University Zhejiang Key laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women's HospitalZhejiang University School of Medicine Key laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal University

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome,proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial sc RNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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The emerging role of mesenchymal stem cell-derived extracellular vesicles to ameliorate hippocampal NLRP3 inflammation induced by binge-like ethanol treatment in adolescence

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Neural Regeneration Research 2025年第4期20卷 1153-1163页

作者： Susana Mellado María JoséMorillo-Bargues Carla Perpiñá-Clérigues Francisco García-García Victoria Moreno-Manzano Consuelo Guerri María Pascual Department of Physiology School of Medicine and DentistryUniversity of ValenciaValenciaSpain bioinformatics and Biostatistics Unit Príncipe Felipe Research CenterValenciaSpain Neuronal and Tissue Regeneration laboratory Príncipe Felipe Research CenterValenciaSpain

Our previous studies have reported that activation of the NLRP3(NOD-,LRR-and pyrin domain-containing protein 3)-inflammasome complex in ethanol-treated astrocytes and chronic alcohol-fed mice could be associated with neuroinflammation and brain *** stem cell-derived extracellular vesicles(MSC-EVs)have been shown to restore the neuroinflammatory response,along with myelin and synaptic structural alterations in the prefrontal cortex,and alleviate cognitive and memory dysfunctions induced by binge-like ethanol treatment in adolescent *** the therapeutic role of the molecules contained in mesenchymal stem cell-derived extracellular vesicles,the present study analyzed whether the administration of mesenchymal stem cell-derived extracellular vesicles isolated from adipose tissue,which inhibited the activation of the NLRP3 inflammasome,was capable of reducing hippocampal neuroinflammation in adolescent mice treated with binge *** demonstrated that the administration of mesenchymal stem cell-derived extracellular vesicles ameliorated the activation of the hippocampal NLRP3 inflammasome complex and other NLRs inflammasomes(e.g.,pyrin domain-containing 1,caspase recruitment domain-containing 4,and absent in melanoma 2,as well as the alterations in inflammatory genes(interleukin-1β,interleukin-18,inducible nitric oxide synthase,nuclear factor-kappa B,monocyte chemoattractant protein-1,and C–X3–C motif chemokine ligand 1)and miRNAs(miR-21a-5p,miR-146a-5p,and miR-141-5p)induced by binge-like ethanol treatment in adolescent *** analysis further revealed the involvement of miR-21a-5p and miR-146a-5p with inflammatory target genes and NOD-like receptor signaling *** together,these findings provide novel evidence of the therapeutic potential of MSC-derived EVs to ameliorate the hippocampal neuroinflammatory response associated with NLRP3 inflammasome activation induced by binge drinking in adolescence.

关键词： adolescence binge-like ethanol treatment extracellular vesicles hippocampus mesenchymal stem cells neuroinflammation NOD-,LRR-and pyrin domain-containing protein 3(NLRP3)

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GALT Protein Database, a bioinformatics Resource for the Management and Analysis of Structural Features of a Galactosemia-related Protein and Its Mutants

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Genomics, Proteomics & bioinformatics 2009年第1期7卷 71-76页

作者： Antonio d’Acierno Angelo Facchiano Anna Marabotti laboratory of bioinformatics and Computational Biology Institute of Food Science Italian National Research Council (CNR)

We describe the GALT-Prot database and its related web-based application that have been developed to collect information about the structural and functional effects of mutations on the human enzyme galactose-l-phosphate uridyltransferase （GALT） involved in the genetic disease named galactosemia type I. Besides a list of missense mutations at gene and protein sequence levels, GALT-Prot reports the analysis results of mutant GALT structures. In addition to the structural information about the wild-type enzyme, the database also includes structures of over 100 single point mutants simulated by means of a computational procedure, and the analysis to each mutant was made with several bioinformatics programs in order to investigate the effect of the mutations. The web-based interface allows querying of the database, and several links are also provided in order to guarantee a high integration with other resources already present on the web. Moreover, the architecture of the database and the web application is flexible and can be easily adapted to store data related to other proteins with point mutations. GALT-Prot is freely available at http：//***/GALT/.

关键词： database mutation homology modeling galactosemia GALT enzyme

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GSA and BIGD:Filling the Gap of bioinformatics Resource and Service in China

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Genomics, Proteomics & bioinformatics 2017年第1期15卷 11-13页

作者： Jingchu Luo College of Life Sciences State Key Laboratory of Protein and Plant Gene Research and Center for BioinformaticsPeking UniversityBeijing 100871China

In the 2017 first issue of this Journal - Genomes, Proteomes and bioinformatics - a special database article entitled ＂GSA： Gen- ome Sequence Archive＂ is published. This article provides a brief introduction to the platform developed by the authors from the BIG Data Center （BIGD） of Beijing Institute of Genomics （BIG）, Chinese Academy of Sciences （CAS）. The aim of the GSA project is to collect, integrate, and archive raw sequence data submitted by domestic and international users. It is one of the major activities being carried on by a team of around 50 young bioinformaticians at BIGD. In addition to the GSA system, they are also working on several bioinformatics service-orientated projects as described in one of their recent publications .

关键词： GSA and BIGD Filling the Gap of bioinformatics Resource and Service in China

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Exploring Traditional Chinese Medicine by A Novel Therapeutic Concept of Network Target

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Chinese Journal of Integrative Medicine 2016年第9期22卷 647-652页

作者：李梢 Ministry of Education Key laboratory of bioinformatics and bioinformatics Division Tsinghua National Laboratory for Information Science and Technology/Department of Automation Tsinghua University

Traditional Chinese medicine（TCM） holds a holistic theory, and specializes in balancing disordered human body using numerous natural products, particularly Chinese herbal formulae. TCM has certain treatment advantages for patients suffering from various complex diseases. However, due to the complex nature of TCM, it remains difficult to unveil such holistic medicine by the current reductionism research strategies, which treat both herbal ingredients and targets in isolation. Recently, an emerging network pharmacology approach has been introduced to tackle this bottleneck problem. A TCM-derived novel therapeutic concept, ＂network target＂, which is different from the Western medicine＇s ＂onetarget＂ concept, has been proposed from China. The network target strategy is able to illustrate the complex interactions among the biological systems, drugs, and complex diseases from a network perspective, and thus provides an innovative approach to access ancient remedies in a precision manner and at a systematic level, which also highlights TCM＇s potential in current medical systems.

关键词： traditional Chinese medicine network target network pharmacology herbal formula complex disease

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Feature Rescaling of Support Vector Machines

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Tsinghua Science and Technology 2011年第4期16卷 414-421页

作者：武征鹏张学工 MOE Key laboratory of bioinformatics and bioinformatics Division Department of Automation Tsinghua University

Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.

关键词： support vector machines (SVMs) feature rescaling multiple kernel learning (MKL) kernel-targetalignment (KTA)

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Translational bioinformatics: Past, Present, and Future

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Genomics, Proteomics & bioinformatics 2016年第1期14卷 31-41页

作者： Jessica D.Tenenbaum Department of Biostatistics and bioinformatics Duke University School of Medicine

Though a relatively young discipline, translational bioinformatics （TBI） has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contex- tualization of the term TBI, describes the discipline＇s brief history and past accomplishments, as well as current loci, and concludes with predictions of future directions in the field.

关键词： Translational bioinformatics Biomarkers Genomics Precision medicine Personalized medicine

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Highly Regional Genes:graph-based gene selection for single-cell RNA-seq data

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Journal of Genetics and Genomics 2022年第9期49卷 891-899页

作者： Yanhong Wu Qifan Hu Shicheng Wang Changyi Liu Yiran Shan Wenbo Guo Rui Jiang Xiaowo Wang Jin Gu MOE Key laboratory of bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing 100084China

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)*** with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity *** mathematically find the optimal expression patterns that can maximize the proposed scoring *** comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation ***,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

关键词： Single-cell RNA-sequencing Feature selection Spatially resolved transcriptomic data Regional patterns Graphical models

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VASC: Dimension Reduction and Visualization of Single-cell RNA-seq Data by Deep Variational Autoencoder

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Genomics, Proteomics & bioinformatics 2018年第5期16卷 320-331页

作者： Dongfang Wang Jin Gu MOE Key laboratory of bioinformatics BNRIST Bioinformatics Division & Center for Synthetic and Systems BiologyDepartment of Automation Tsinghua University

Single-cell RNA sequencing(scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell subpopulations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data(VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.

关键词： Single cell RNA sequencing Deep variational autoencoder Dimension reduction Visualization Dropout

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Complicated evolutionary patterns of microRNAs in vertebrates

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Science China(Life Sciences) 2008年第6期51卷 552-559页

作者： WANG XiaoWo, ZHANG XueGong & LI YanDa MOE Key laboratory of bioinformatics and bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China MOE Key laboratory of bioinformatics and bioinformatics Division TNLIST / Department of Automation Tsinghua University Beijing China

MicroRNAs (miRNAs) are a class of ～22 nt long endogenous non-coding RNAs that play important regulatory roles in diverse organisms. Up to now, little is known about the evolutionary properties of these crucial regulators. Most miRNAs were thought to be phylogenetically conserved, but recently, a number of poorly-conserved miRNAs have been reported and miRNA innovation is shown to be an ongoing process. In this work, through the characterization of an miRNA super family, we studied the evolutionary patterns of miRNAs in vertebrates. Recently generated miRNAs seem to evolve rapidly during a certain period following their emergence. Multiple lineage-specific expansions were observed. Homolgous premiRNAs may produce mature products from the opposite stem arms following tandem duplications, which may have important contribution to miRNA innovation. Our observations of miRNAs' complicated evolutionary patterns support the notion that these key regulatory molecules may play very active roles in evolution.

关键词： microRNA evolution miRNA:miRNA* interconversion lineage specific duplication

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