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

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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

Bagging with CTD–A Novel Signature for the Hierarchical Prediction of Secreted Protein Trafcking in Eukaryotes

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Genomics, Proteomics & bioinformatics 2013年第6期11卷 385-390页

作者： Geetha Govindan Achuthsankar S.Nair Department of computational biology and bioinformatics University of Kerala

Protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contaified in the protein. Many methods reported prediction of the subcellular location of proteins from sequence information. However, most of these prediction methods use a flat structure or parallel architecture to perform prediction. In this work, we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein-sorting pathway hierarchically. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared. When evaluated by independent data testing, ensemble based-bagging algorithms with sequence feature composition, transition and distribution （CTD） successfully classified two datasets with accuracies greater than 90%. We compared our results with similar published methods, and our method equally performed with the others at two levels in the secreted pathway. This study shows that the feature CTD extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways.

关键词： Sequence driven features Sequence mapped features Autocorrelation Ensemble classifiter Protein Sorting

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

Genome-Wide Analysis of Epigenetic Modifications

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Journal of Computer Science & Technology 2010年第1期25卷 35-41页

作者：梁守丹 Department of bioinformatics and computational biology The University of Texas M.D. Anderson Cancer Center Houston

In plants and animals, gene expression can be altered by changes that do not alter the sequence of nucleotides in DNA but rather modify the chemical structure of either the DNA or the histones that interact with the DNA. These so-called epigenetic modifications are not transient, but persist through cell divisions. Rapidly advancing technologies, such as next-generation DNA sequencing, have dramatically increased our ability to survey epigenetic markers throughout an entire genome. These techniques are revealing in great detail that the many forms and stages of cancer are characterized by a massive number of epigenetic changes. Interpreting such epigenetic marks in cell differentiation and in carcinogenesis is computationally challenging. We review several examples of epigenetic data analysis and discuss the need for computational methods that will enable us to learn from the data the relationships between different kinds of histone modifications and DNA methylation.

关键词： epigenetics non-coding DNA DNA methylations histone modifications

STOCHASTIC SYNCHRONIZATION OF CIRCADIAN RHYTHMS

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Journal of Systems Science & Complexity 2010年第5期23卷 978-988页

作者： RajKumar Brojen SINGH Vikram SINGH Ram RAMASWAMY centre for Interdisciplinary Research in Basic Sciences Jamia Milia Islamia New Delhi 110025 India Center for computational biology and bioinformatics School of Information Technology Jawaharlal Nehru University New Delhi 110067 India Center for computational biology and bioinformatics School of Information Technology and School of Physical Sciences Jawaharlal Nehru University New Delhi 110067 India

Models of circadian genetic oscillators involving interlinked feedback processes in molecular level genetic networks in Drosophila melanogaster and Neurospora crassa are studied, and mechanisms whereby synchronization can arise in an assembly of cells are examined. The individual subcellular circadian oscillatory processes are stochastic in nature due to the small numbers of molecules that are involved, and are subject to large fluctuations. The authors investigate and present the simulations of the stochastic dynamics of ensembles of clock-regulating proteins in different nuclei that communicate via ancillary small molecules, environmental parameters, additive cellular noise, or through diffusive processes. The results show that the emergence of collective oscillations is a macroscopic observable which has its origins in the microscopic coupling between distinct cellular oscillators.

关键词： Coupling stochastic dynamics synchrony.

Towards efficiency in rare disease research： what is distinctive and important？

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Science China(Life Sciences) 2017年第7期60卷 686-691页

作者： Jinmeng Jia Tieliu Shi The Center for bioinformatics and computational biology Shanghai Key Laboratory of Regulatory Biology the Institute of Biomedical Sciences and School of Life Sciences East China Normal University Shanghai 200241 China

Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million *** the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing(NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused.

关键词： rare disease rare disease diagnosis and treatment data standard data sharing orphan drugs

computational Challenges in Deciphering Genomic Structures of Bacteria

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Journal of Computer Science & Technology 2010年第1期25卷 53-70页

作者：徐鹰 computational Systems biology Laboratory Department of Biochemistry and Molecular Biology and Institute of Bioinformatics The University of Georgia BESC BioEnergy Science Center College of Computer Science and Technology Jilin University

This article addresses how the functionalities of the cellular machinery of a bacterium might have constrained the genomic arrangement of its genes during evolution and how we can study such problems using computational approaches, taking full advantage of the rapidly increasing pool of the sequenced bacterial genomes, potentially leading to a much improved understanding of why a bacterial genome is organized in the way it is. This article discusses a number of challenging computational problems in elucidating the genomic structures at multiple levels and the information that is encoded through these genomic structures, gearing towards the ultimate understanding of the governing rules of bacterial genome organization.

关键词： bioinformatics microbial genomics genome structure comparative genome analysis biological pathways

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

Network-based method to infer the contributions of proteins to the etiology of drug side effects

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Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 124-134页

作者： Li, Rui Chen, Ting Li, Shao MOE Key Laboratory of bioinformatics and bioinformatics Division TNLIST Department of Automation Tsinghua University Beijing China Program in computational biology and bioinformatics University of Southern California Los Angeles USA

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug- target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation--maximization algorithm and a diffusion kernel-based approach to estimate each protein＇s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii （Lei-Gong-Teng）. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

关键词： network pharmacology drug targets side effects triptolide