{"title":"Studying temporal dynamics of single cells: expression, lineage and regulatory networks.","authors":"Xinhai Pan, Xiuwei Zhang","doi":"10.1007/s12551-023-01090-5","DOIUrl":null,"url":null,"abstract":"<p><p>Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s12551-023-01090-5.</p>","PeriodicalId":9094,"journal":{"name":"Biophysical reviews","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10937865/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biophysical reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s12551-023-01090-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.
Supplementary information: The online version contains supplementary material available at 10.1007/s12551-023-01090-5.
期刊介绍：
Biophysical Reviews aims to publish critical and timely reviews from key figures in the field of biophysics. The bulk of the reviews that are currently published are from invited authors, but the journal is also open for non-solicited reviews. Interested authors are encouraged to discuss the possibility of contributing a review with the Editor-in-Chief prior to submission. Through publishing reviews on biophysics, the editors of the journal hope to illustrate the great power and potential of physical techniques in the biological sciences, they aim to stimulate the discussion and promote further research and would like to educate and enthuse basic researcher scientists and students of biophysics. Biophysical Reviews covers the entire field of biophysics, generally defined as the science of describing and defining biological phenomenon using the concepts and the techniques of physics. This includes but is not limited by such areas as: - Bioinformatics - Biophysical methods and instrumentation - Medical biophysics - Biosystems - Cell biophysics and organization - Macromolecules: dynamics, structures and interactions - Single molecule biophysics - Membrane biophysics, channels and transportation
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
