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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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Single-cell Transcriptome Study as Big Data

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Genomics, Proteomics & bioinformatics 2016年第1期14卷 21-30页

作者： Pingjian Yu Wei Lin Genomics and bioinformatics lab Baylor Institute for Immunology Research

The rapid growth of single-cell RNA-seq studies （scRNA-seq） demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing （NGS）-based studies, we propose a workflow that accounts for the unique characteris- tics of scRNA-seq data and primary objectives of single-cell studies.

关键词： Single-cell RNA -seq Big data TranscriptionaSingle-cell heterogenc-ity SignaSingle-cell normaSingle-cellization

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Challenges in Understanding Genome-Wide DNA Methylation

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Journal of Computer Science & Technology 2010年第1期25卷 26-34页

作者：张伟奇 Andrew D. Smith Cold Spring Harbor laboratory Cold Spring Harbor bioinformatics Division TNLIST and MOE Key Lab of Bioinformatics Tsinghua University Department of Biological Sciences University of Southern California

DNA methylation is a chemical modification of the bases in genomes. This modification, most frequently found at CpG dinucleotides in eukaryotes, has been identified as having multiple critical functions in broad and diverse species of animals and plants, while mysteriously appears to be lacking from several other well-studied species. DNA methylation has well known and important roles in genome stability and defense, its pattern change highly correlates with gene regulation. Much evidence has linked abnormal DNA methylation to human diseases. Most prominently, aberrant DNA methylation is a common feature of cancer genomes. Elucidating the precise functions of DNA methylation therefore has great biomedical significance. Here we provide an update on large-scale experimental technologies for detecting DNA methylation on a genomic scale. We also discuss new prospect and challenges that computational biologist will face when analyzing DNA methylation data.

关键词： DNA methylation epigenome computational epigenomics

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Impact of gene mutation in the development of Parkinson’s disease

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Genes & Diseases 2019年第2期6卷 120-128页

作者： Suganya Selvaraj Shanmughavel Piramanayagam Computational Biology lab Department of BioinformaticsBharathiar UniversityCoimbatore641046India Professor Computational Biology LabDepartment of BioinformaticsBharathiar UniversityCoimbatore641046India

Parkinson’s disease(PD)is the second most common age related neurodegenerative disorder worldwide and presents as a progressive movement *** seven million to 10 million people have Parkinson’s *** is typically sporadic in *** of dopaminergic neurons from substantia nigra pars compacta(SNpc)and the neuronal intracellular Lewy body inclusions are the major cause of *** mutation and protein aggregation play a pivotal role in the degeneration of dopamine *** the actual cause of dopamine degeneration remains ***,several rare familial forms of PD are associated with genetic loci,and the recognition of causal mutations has provided insight into the disease ***,the molecular pathways and gene transformation that trigger neuronal susceptibility are inadequately *** discovery of a mutation in new genes has provided a basis for much of the ongoing molecular work in the PD field and testing of targeted *** gene mutation in a dominantly or recessively inherited gene results a great impact in the development of Parkinson’s *** this review,we summarize the molecular genetics of PD.

关键词： Gene mutation Mitochondrial dysfunction Parkinson’s disease Protein aggregation Susceptibility genes

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Comprehensive simulation of metagenomic sequencing data with non-uniform sampling distribution

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Frontiers of Electrical and Electronic Engineering in China 2018年第2期6卷 175-185页

作者： Shansong Liu Kui Hua Sijie Chen Xuegong Zhang MOE Key lab of bioinformatics Bioinformatics Division TNLIST and Department of Automation Tsinghua University Beijing 100084 China

Background： Metagenomic sequencing is a complex sampling procedure from unknown mixtures of many genomes. Having metagenome data with known genome compositions is essential for both benchmarking bioinformatics software and for investigating influences of various factors on the data. Compared to data from real microbiome samples or from defined microbial mock community, simulated data with proper computational models are better for the purpose as they provide more flexibility for controlling multiple factors. Methods： We developed a non-uniform metagenomic sequencing simulation system （nuMetaSim） that is capable of mimicking various factors in real metagenomic sequencing to reflect multiple properties of real data with customizable parameter settings. Results： We generated 9 comprehensive metagenomic datasets with different composition complexity from of 203 bacterial genomes and 2 archaeal genomes related with human intestine system. Conclusion： The data can serve as benchmarks for comparing performance of different methods at different situations, and the software package allows users to generate simulation data that can better reflect the specific properties in their scenarios.

关键词： simulation metagenomic sequencing data non-uniform sampling nuMetaSim

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Dropout training for SVMs with data augmentation

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Frontiers of Computer Science 2018年第4期12卷 694-713页

作者： Ning CHEN Jun ZHU Jianfei CHEN Ting CHEN MOE Key lab of bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology TNLIST Tsinghua University Beijing 100084 China State Key lab of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China

Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines （SVMs）, one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square （IRLS） algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re- weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we con- sider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representa- tions. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.

关键词： dropout SVMs logistic regression data aug- mentation iteratively reweighted least square

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Improved kernels for triangle packing in tournaments

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Science China(Information Sciences) 2023年第5期66卷 100-113页

作者： Hanchun YUAN Qilong FENG Jianxin WANG School of Computer Science and Engineering Central South University Hunan Provincial Key lab on bioinformatics Central South University

Triangle packing problem has been paid lots of attention to in the literature. In this paper, we study the kernelization of the triangle packing problem in tournaments. For the parameterized arc-disjoint triangle packing problem in tournaments, we find a maximal arc-disjoint triangle packing with the number of vertices bounded by 2.5k. Based on the relation between the maximal arc-disjoint triangle packing and the vertices outside of the packing, a kernel of size 3.5k for the problem is obtained, improving the previous best one 6k. For the parameterized vertex-disjoint triangle packing problem in sparse tournaments, several new properties between the triangles in maximal vertex-disjoint triangle packing and the arcs in the feedback arc set are presented, which result in a kernel of size 7k for the problem, improving the previous best one of *** also give a 7k vertex kernel for the parameterized feedback vertex set problem in sparse *** kernelization process presented in this paper for the parameterized arc-disjoint triangle packing can be applied to solve the parameterized arc-disjoint triangle packing problem on other restricted directed graph classes.

关键词： tournaments triangle packing kernelization fixed-parameter tractable graph algorithms

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Toward a unified information framework for cell atlas assembly

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National Science Review 2022年第3期9卷 7-10页

作者： Sijie Chen Yanting Luo Haoxiang Gao Fanhong Li Jiaqi Li Yixin Chen Renke You Hairong Lv Kui Hua Rui Jiang Xuegong Zhang bioinformatics Division of BNRIST and Department of Automation MOE Key Lab of BioinformaticsTsinghua University Fuzhou Institute of Data Technology School of Medicine Tsinghua University School of Life Sciences Center for Synthetic and Systems Biology Tsinghua University

Cells are the basic structural and functional unit of the human body. Virtually all cells of a human body possess the same genome, but they exhibit colorful heterogeneities in phenotypes and *** heterogeneities includ...

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DeepRetention:A Deep Learning Approach for Intron Retention Detection

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Big Data Mining and Analytics 2023年第2期6卷 115-126页

作者： Zhenpeng Wu Jiantao Zheng Jiashu Liu Cuixiang Lin Hong-Dong Li Hunan Provincial Key lab on bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease *** methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained *** metrics are not able to describe the pattern of sequencing depth of intronic reads,which is an intuitive and informative characteristic of retained *** hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR *** we present DeepRetention,a novel approach for IR detection by modeling the pattern of sequencing depth of *** to the lack of a gold standard dataset of IR,we first compare DeepRetention with two state-of-the-art methods,*** and IRFinder,on simulated RNA-seq datasets with retained *** results show that DeepRetention outperforms these two ***,DeepRetention performs well when it is applied to third-generation long-read RNA-seq data,while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing ***,we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease(AD)*** differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene *** parent genes of differential IRs are enriched in AD-related *** summary,DeepRetention detects IR from a new angle of view,providing a valuable tool for IR analysis.

关键词： Alternative Splicing(AS) Intron Retention(IR) intronic reads distribution pattern RNA-seq

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Tumor microenvironment pathways:Cross regulation in breast cancer metastasis

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Genes & Diseases 2022年第2期9卷 310-324页

作者： Rama Rao Malla P.Kiran Cancer Biology lab Department of Biochemistry and BioinformaticsInstitute of ScienceGITAM(Deemed to be University)Visakhapatnam 530045India

The tumor microenvironment(TME)is heterogeneous and contains a multiple cell population with surrounded immune cells,which plays a major role in regulating *** multifunctional pathways,Hedgehog(Hh),Wnt,Notch,and NF-kB,cross-regulates metas-tasis in breast *** review presents substantial evidence for cross-regulation of TME components and signaling pathways,which makes breast TME more heterogeneous and com-plex,promoting breast cancer progression and metastasis as a highly aggressive *** discoursed the importance of stromal and immune cells as well as their crosstalk in bridging the *** also discussed the role of Hh and Notch pathways in the intervention be-tween breast cancer cells and macrophages to support TME;Notch signaling in the bidirec-tional communication between cancer cells and components of TME;Wnt signal pathway in controlling the factors responsible for EMT and NF-kB pathway in the regulation of genes con-trolling the inflammatory *** also present the role of exosomes and their miRNAs in the cross-regulation of TME cells as well as pathways in the reprogramming of breast TME to support ***,we examined and discussed the targeted small molecule inhibitors and natural compounds targeting developmental pathways and proposed small molecule nat-ural compounds as potential therapeutics of TME based on the multitargeting *** conclu-sion,the understanding of the molecular basis of the cross-regulation of TME pathways and their inhibitors helps identify molecular targets for rational drug discovery to treat breast can-cers.

关键词： Breast cancer Cross-regulation Hedgehog NF-kB Notch Tumor microenvironment Wnt

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