During early embryonic development,cell fate commitment represents a critical transition or“tipping point”of embryonic differentiation,at which there is a drastic and qualitative shift of the cell populations.In thi...During early embryonic development,cell fate commitment represents a critical transition or“tipping point”of embryonic differentiation,at which there is a drastic and qualitative shift of the cell populations.In this study,we presented a computational approach,scGET,to explore the gene–gene associations based on single-cell RNA sequencing(scRNAseq)data for critical transition prediction.Specifically,by transforming the gene expression data to the local network entropy,the single-cell graph entropy(SGE)value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level.Being applied to five scRNA-seq datasets of embryonic differentiation,scGET accurately predicts all the impending cell fate transitions.After identifying the“dark genes”that are non-differentially expressed genes but sensitive to the SGE value,the underlying signaling mechanisms were revealed,suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation.The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.展开更多
The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The deve...The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.展开更多
基金supported by the National Key R&D Program of China(2017YFA0505500)National Natural Science Foundation of China(11771152,12026608,11901203,31930022,and 31771476)+7 种基金Guangdong Basic and Applied Basic Research Foundation(2019B151502062,and 2021A1515012317)Strategic Priority Research Program of the Chinese Academy of Sciences(XDB38040400)Shanghai Municipal Science and Technology Major Project(2017SHZDZX01)Japan Society for the Promotion of Science KAKENHI(15H05707)Japan Science and Technology Agency Moonshot R&D(JPMJMS2021)Japan Agency for Medical Research and Development(JP20dm0307009)UTokyo Center for Integrative Science of Human Behavior(CiSHuB)the International Research Center for Neurointelligence(WPI-IRCN)at The University of Tokyo Institutes for Advanced Study(UTIAS).
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.11771152,11901203,11971176,and 12026608)Guangdong Basic and Applied Basic Research Foundation,China(Grant Nos.2019B151502062 and 2021A1515012317)China Postdoctoral Science Foundation(Grant Nos.2019M662895 and 2020T130212).
文摘During early embryonic development,cell fate commitment represents a critical transition or“tipping point”of embryonic differentiation,at which there is a drastic and qualitative shift of the cell populations.In this study,we presented a computational approach,scGET,to explore the gene–gene associations based on single-cell RNA sequencing(scRNAseq)data for critical transition prediction.Specifically,by transforming the gene expression data to the local network entropy,the single-cell graph entropy(SGE)value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level.Being applied to five scRNA-seq datasets of embryonic differentiation,scGET accurately predicts all the impending cell fate transitions.After identifying the“dark genes”that are non-differentially expressed genes but sensitive to the SGE value,the underlying signaling mechanisms were revealed,suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation.The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.
基金supported by the National Natural Science Foundation of China(12026608,62172164,12131020,and 12271180)the Natural Science Foundation of Guangdong Province(2021A1515012317).
文摘The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.
