One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data.Although substantial studies have been conducted in recent years,more effecti...One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data.Although substantial studies have been conducted in recent years,more effective methods are still strongly needed to infer the developmental processes accurately.This work devises a new method,named DTFLOW,for determining the pseudotemporal trajectories with multiple branches.DTFLOW consists of two major steps:a new method called Bhattacharyya kernel feature decomposition(BKFD)to reduce the data dimensions,and a novel approach named Reverse Searching on k-nearest neighbor graph(RSKG)to identify the multi-branching processes of cellular differentiation.In BKFD,we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm,and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix.The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets.We compare the efficiency of DTFLOW with the published state-of-the-art methods.Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories.The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.展开更多
βcells are defined by the ability to produce and secret insulin.Recent studies have evaluated that human pancreaticβcells are heterogeneous and demonstrated the transcript alterations ofβcell subpopulation in diabe...βcells are defined by the ability to produce and secret insulin.Recent studies have evaluated that human pancreaticβcells are heterogeneous and demonstrated the transcript alterations ofβcell subpopulation in diabetes.Single-cell RNA sequence(scRNA-seq)analysis helps us to refine the cell types signatures and understand the role of theβcells during metabolic challenges and diseases.Here,we construct the pseudotime trajectory ofβcells from publicly available scRNA-seq data in health and type 2 diabetes(T2D)based on highly dispersed and highly expressed genes using Monocle2.We identified three major states including 1)Normal branch,2)Obesity-like branch and 3)T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory.βcell function-maintain-related genes,insulin expression-related genes,and T2D-related genes enriched in three branches,respectively.Continuous pseudotime spectrum might suggest thatβcells transition among different states.The application of pseudotime analysis is conducted to clarify the different cell states,providing novel insights into the pathology ofβcells in T2D.展开更多
Skeletal muscle regeneration is a complex process where various cell types and cytokines are involved.Single-cell RNA-sequencing (scRNA-seq) provides the opportunity to deconvolute heterogeneous tissue into individual...Skeletal muscle regeneration is a complex process where various cell types and cytokines are involved.Single-cell RNA-sequencing (scRNA-seq) provides the opportunity to deconvolute heterogeneous tissue into individual cells based on their transcriptomic profiles.Recent scRNA-seq studies on mouse muscle regeneration have provided insights to understand the transcriptional dynamics that underpin muscle regeneration.However,a database to investigate gene expression profiling during skeletal muscle regeneration at the single-cell level is lacking.Here,we collected over 105 000 cells at 7 key regenerative time-points and non-injured muscles and developed a database,the Singlecell Skeletal Muscle Regeneration Database (SCSMRD).SCSMRD allows users to search the dynamic expression profiles of genes of interest across different cell types during the skeletal muscle regeneration process.It also provides a network to show the activity of regulons in different cell types at different time points.Pesudotime analysis showed the state changes trajectory of muscle stem cells (MuSCs) during skeletal muscle regeneration.This database is freely available at https://scsmrd.fengs-lab.com.展开更多
Disease-associated microglia(DAM)are observed in neurodegenerative diseases,demyelinating disorders,and aging.However,the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophi...Disease-associated microglia(DAM)are observed in neurodegenerative diseases,demyelinating disorders,and aging.However,the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis(ALS)remain unclear.Using a mouse model of ALS that expresses a human SOD1 gene mutation,we found that the microglia subtype DAM begins to appear following motor neuron degeneration,primarily in the brain stem and spinal cord.Using reverse transcription quantitative polymerase chain reaction,RNAscope in situ hybridization,and flow cytometry,we found that DAM increased in number as the disease progressed,reaching their peak in the late disease stage.DAM responded to disease progression in both SOD1G93A mice and sporadic ALS and C9orf72-mutated patients.Motor neuron loss in SOD1G93A mice exhibited 2 accelerated phases:P90 to P110(early stage)and P130 to P150(late stage).Some markers were synchronized with the accelerated phase of motor neuron loss,suggesting that these proteins may be particularly responsive to disease progression.Through pseudotime trajectory analysis,we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia.Interestingly,we used the colony-stimulating factor 1 receptor(CSF1R)inhibitor PLX5622 to deplete microglia in SOD1G93A mice and observed that DAM survival is independent of CSF1R.An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes.These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS.Inducing the DAM phenotype and enhancing its function during the early phase of disease progression,or the time window between P130 and P150 where motor neuron loss slows,could serve as a neuroprotective strategy for ALS.展开更多
The recent advancement of single-cell RNA sequencing(scRNA-seq)technologies facilitates the study of cell lineages in developmental processes and cancer.In this study,we developed a computational method,called redPATH...The recent advancement of single-cell RNA sequencing(scRNA-seq)technologies facilitates the study of cell lineages in developmental processes and cancer.In this study,we developed a computational method,called redPATH,to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm.Besides,we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights.We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets,as well as other single-cell transcriptome data.In particular,we identified a stem cell-like subpopulation in malignant glioma cells.These cells express known proliferative markers,such as GFAP,ATP1A2,IGFBPL1,and ALDOC,and remain silenced for quiescent markers such as ID3.Furthermore,we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth.In conclusion,redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time.redPATH is available at https://github.com/tinglabs/redPATH.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11571368,11931019,11775314,and 11871238)the Fundamental Research Funds for the Central Universities,China(Grant No.2662019QD031).
文摘One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data.Although substantial studies have been conducted in recent years,more effective methods are still strongly needed to infer the developmental processes accurately.This work devises a new method,named DTFLOW,for determining the pseudotemporal trajectories with multiple branches.DTFLOW consists of two major steps:a new method called Bhattacharyya kernel feature decomposition(BKFD)to reduce the data dimensions,and a novel approach named Reverse Searching on k-nearest neighbor graph(RSKG)to identify the multi-branching processes of cellular differentiation.In BKFD,we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm,and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix.The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets.We compare the efficiency of DTFLOW with the published state-of-the-art methods.Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories.The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.
基金supported by the National Key R&D Program of China(2019YFA0801900,2018YFA0800300)the National Natural Science Foundation of China(31971074)+3 种基金the Science and Technology Innovation Action Plan of Shanghai Science and Technology Committee(18140901300)the Open Research Fund of the National key laboratory of genetic engineering(SKLGE1803)the Shanghai Municipal Science and Technology Major Project(2017SHZDZX01)Shanghai Frontiers Science Research Base of Exercise and Metabolic Health.
文摘βcells are defined by the ability to produce and secret insulin.Recent studies have evaluated that human pancreaticβcells are heterogeneous and demonstrated the transcript alterations ofβcell subpopulation in diabetes.Single-cell RNA sequence(scRNA-seq)analysis helps us to refine the cell types signatures and understand the role of theβcells during metabolic challenges and diseases.Here,we construct the pseudotime trajectory ofβcells from publicly available scRNA-seq data in health and type 2 diabetes(T2D)based on highly dispersed and highly expressed genes using Monocle2.We identified three major states including 1)Normal branch,2)Obesity-like branch and 3)T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory.βcell function-maintain-related genes,insulin expression-related genes,and T2D-related genes enriched in three branches,respectively.Continuous pseudotime spectrum might suggest thatβcells transition among different states.The application of pseudotime analysis is conducted to clarify the different cell states,providing novel insights into the pathology ofβcells in T2D.
基金supported by the National Natural Science Foundation of China(31972539 and 32102513)the Science,Technology,and Innovation Commission of Shenzhen Municipality,China(JCYJ20180306173644635)+2 种基金the Fundamental Research Funds for the Central Universities,China(G2020KY05109)the Natural Science Basic Research Program of Shaanxi Province,China(2022JQ-644)the Basic Research Programs of Taicang,China(TC2021JC14)。
文摘Skeletal muscle regeneration is a complex process where various cell types and cytokines are involved.Single-cell RNA-sequencing (scRNA-seq) provides the opportunity to deconvolute heterogeneous tissue into individual cells based on their transcriptomic profiles.Recent scRNA-seq studies on mouse muscle regeneration have provided insights to understand the transcriptional dynamics that underpin muscle regeneration.However,a database to investigate gene expression profiling during skeletal muscle regeneration at the single-cell level is lacking.Here,we collected over 105 000 cells at 7 key regenerative time-points and non-injured muscles and developed a database,the Singlecell Skeletal Muscle Regeneration Database (SCSMRD).SCSMRD allows users to search the dynamic expression profiles of genes of interest across different cell types during the skeletal muscle regeneration process.It also provides a network to show the activity of regulons in different cell types at different time points.Pesudotime analysis showed the state changes trajectory of muscle stem cells (MuSCs) during skeletal muscle regeneration.This database is freely available at https://scsmrd.fengs-lab.com.
基金supported by grants from the National Natural Science Foundation to Zhi-Ying Wu(81671245)the Integrative Traditional Chinese and Western Medicine Innovation Team for Neurodegenerative Diseases of Zhejiang Province,and the Research Foundation for Distinguished Scholars of Zhejiang University to Zhi-Ying Wu(188020-193810101/089).
文摘Disease-associated microglia(DAM)are observed in neurodegenerative diseases,demyelinating disorders,and aging.However,the spatiotemporal dynamics and evolutionary trajectory of DAM during the progression of amyotrophic lateral sclerosis(ALS)remain unclear.Using a mouse model of ALS that expresses a human SOD1 gene mutation,we found that the microglia subtype DAM begins to appear following motor neuron degeneration,primarily in the brain stem and spinal cord.Using reverse transcription quantitative polymerase chain reaction,RNAscope in situ hybridization,and flow cytometry,we found that DAM increased in number as the disease progressed,reaching their peak in the late disease stage.DAM responded to disease progression in both SOD1G93A mice and sporadic ALS and C9orf72-mutated patients.Motor neuron loss in SOD1G93A mice exhibited 2 accelerated phases:P90 to P110(early stage)and P130 to P150(late stage).Some markers were synchronized with the accelerated phase of motor neuron loss,suggesting that these proteins may be particularly responsive to disease progression.Through pseudotime trajectory analysis,we tracked the dynamic transition of homeostatic microglia into DAM and cluster 6 microglia.Interestingly,we used the colony-stimulating factor 1 receptor(CSF1R)inhibitor PLX5622 to deplete microglia in SOD1G93A mice and observed that DAM survival is independent of CSF1R.An in vitro phagocytosis assay directly confirmed that DAM could phagocytose more beads than other microglia subtypes.These findings reveal that the induction of the DAM phenotype is a shared cross-species and cross-subtype characteristic in ALS.Inducing the DAM phenotype and enhancing its function during the early phase of disease progression,or the time window between P130 and P150 where motor neuron loss slows,could serve as a neuroprotective strategy for ALS.
基金the National Natural Science Foundation of China(Grant Nos.61872218,61721003,61673241,and 61906105)the National Key R&D Program of China(Grant No.2019YFB1404804)+1 种基金the Beijing National Research Center for Information Science and Technology(BNRist),Chinathe Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program,China.
文摘The recent advancement of single-cell RNA sequencing(scRNA-seq)technologies facilitates the study of cell lineages in developmental processes and cancer.In this study,we developed a computational method,called redPATH,to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm.Besides,we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights.We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets,as well as other single-cell transcriptome data.In particular,we identified a stem cell-like subpopulation in malignant glioma cells.These cells express known proliferative markers,such as GFAP,ATP1A2,IGFBPL1,and ALDOC,and remain silenced for quiescent markers such as ID3.Furthermore,we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth.In conclusion,redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time.redPATH is available at https://github.com/tinglabs/redPATH.
