scholarly journals Dissecting Transition Cells from Single-cell Transcriptome Data through Multiscale Stochastic Dynamics

2021 ◽  
Author(s):  
Peijie Zhou ◽  
Shuxiong Wang ◽  
Tiejun Li ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Stochastic Dynamics ◽  
Multiple Scales ◽  
Coarse Grained ◽  
Rate Theory ◽  
Transcriptome Data ◽  
Stochastic Dynamical Systems ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractAdvances of single-cell technologies allow scrutinizing of heterogeneous cell states, however, analyzing transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique for the underlying stochastic dynamical systems that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transition, and distinguishes meta-stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using the coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. Mathematical analysis reveals consistency of the method with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms and benchmarking with seven existing tools, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

scholarly journals Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peijie Zhou ◽  
Shuxiong Wang ◽  
Tiejun Li ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Stochastic Dynamics ◽  
Coarse Grained ◽  
State Transitions ◽  
Rate Theory ◽  
Transcriptome Data ◽  
Cell State ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractAdvances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

scholarly journals Lifelong single-cell profiling of cranial neural crest diversification

2021 ◽  
Author(s):  
Peter Fabian ◽  
Kuo-Chang Tseng ◽  
Mathi Thiruppathy ◽  
Claire Arata ◽  
Hung-Jhen Chen ◽  
...  
Keyword(s):  
Neural Crest ◽  
Single Cell ◽  
Cell Fate ◽  
Intrinsic Property ◽  
Chromatin Accessibility ◽  
Specific Cell ◽  
List Type ◽  
Cranial Neural Crest ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

AbstractThe cranial neural crest generates a huge diversity of derivatives, including the bulk of connective and skeletal tissues of the vertebrate head. How neural crest cells acquire such extraordinary lineage potential remains unresolved. By integrating single-cell transcriptome and chromatin accessibility profiles of cranial neural crest-derived cells across the zebrafish lifetime, we observe region-specific establishment of enhancer accessibility for distinct fates. Neural crest-derived cells rapidly diversify into specialized progenitors, including multipotent skeletal progenitors, stromal cells with a regenerative signature, fibroblasts with a unique metabolic signature linked to skeletal integrity, and gill-specific progenitors generating cell types for respiration. By retrogradely mapping the emergence of lineage-specific chromatin accessibility, we identify a wealth of candidate lineage-priming factors, including a Gata3 regulatory circuit for respiratory cell fates. Rather than multilineage potential being an intrinsic property of cranial neural crest, our findings support progressive and region-specific chromatin remodeling underlying acquisition of diverse neural crest lineage potential.HighlightsSingle-cell transcriptome and chromatin atlas of cranial neural crestProgressive emergence of region-specific cell fate competencyChromatin accessibility mapping identifies candidate lineage regulatorsGata3 function linked to gill-specific respiratory programGraphical Abstract

scholarly journals De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

2016 ◽  
Vol 19 (2) ◽  
pp. 266-277 ◽  
Author(s):  
Dominic Grün ◽  
Mauro J. Muraro ◽  
Jean-Charles Boisset ◽  
Kay Wiebrands ◽  
Anna Lyubimova ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
De Novo ◽  
Transcriptome Data ◽  
Cell Identity ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

scholarly journals Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision

2019 ◽  
Author(s):  
Jiang Xie ◽  
Fuzhang yang ◽  
Jiamin Sun ◽  
Jiao Wang
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Neural Stem Cell ◽  
Integrative Analysis ◽  
Cell Fate Decisions ◽  
Multi Stage ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Neural stem cell (NSC) differentiation is one of many multi-stage lineage systems that require multiple cell fate decisions. Recent single-cell transcriptome datasets became available at individual differentiation, however, a systematic and integrative analysis of multiple datasets at multiple temporal points of NSC differentiation is lacking. Results Here we investigate five NSC differentiation paths by analyzing and comparing four different single-cell transcriptome datasets. By constructing gene regulatory networks for each cell type, we delineate their regulatory patterns via analyses of differential topology and network diffusion. Among the five NSC differentiation paths, we find 12 common differentially expressed genes, with one common three-gene regulatory pattern shared by all paths. The identified regulatory pattern, partly supported by previous experimental evidence, is found to be essential to all differentiation paths, however, plays a different role in each path when regulating other genes. Conclusions Together, our integrative analysis provides both common and specific regulatory mechanisms for each of the five NSC differentiation paths, and the approach can be applied to analyzing other complex multi-stage lineage systems.

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