scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Jin, Suoqin; MacLean, Adam L.; Peng, Tao; Nie, Qing

doi:10.1093/bioinformatics/bty058

Cited by 92 publications

(127 citation statements)

References 41 publications

(73 reference statements)

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“…Single cell Energies (scEnergy) were calculated, projected onto UMAP space, and used to infer cell state transition probabilities and lineage relationships. We found that lower energies, while typically associated with committed/differentiated cell states (Jin et al, 2018;Teschendorff and Enver, 2017), are also associated with a quiescent cell state because the known quiescent Bu-HFSC (CD34 + ) population showed the lowest scEnergy of all the skin epithelial cell types ( Figure 3D, 4E, and S4H; see Methods section). Using increasingly robust parameters, which incorporate increasing numbers of UW epidermal cells from each cell state to infer lineage progression, scEpath predicted a near-linear path that originates from the Col17a1 Hi basal cell state that displayed the lowest energy of all interfollicular epidermal cells ( Figure 6E, S12C, and S12G).…”

Section: Pseudotemporal Trajectory and Rna Velocity Analyses Reveal Bmentioning

confidence: 89%

“…Next, we used single cell Energy path (scEpath) to infer cell lineage relationships (see Methods section) (Jin et al, 2018). Single cell Energies (scEnergy) were calculated, projected onto UMAP space, and used to infer cell state transition probabilities and lineage relationships.…”

Section: Pseudotemporal Trajectory and Rna Velocity Analyses Reveal Bmentioning

confidence: 99%

“…We performed pseudotemporal ordering of all interfollicular epidermal cells, including proliferative and non-proliferative basal cells and spinous cells, using two methods: Monocle 2 (Qiu et al, 2017) and scEpath (Jin et al, 2018). For Monocle 2, batch effect information was passed into the residualModelFormulaStr option in the "reduceDimension" function.…”

Section: Pseudotime and Trajectory Analysismentioning

confidence: 99%

“…For Monocle 2, batch effect information was passed into the residualModelFormulaStr option in the "reduceDimension" function. The scEpath method can quantify the energy landscape using scEnergy, which quantitatively measures the developmental potency of single cells (Jin et al, 2018) and was used in our analysis to predict the initial state in pseudotime. Pseudotemporal ordering was performed on batch corrected data, and batch correction was performed for both UW and WO samples using Combat.…”

Section: Pseudotime and Trajectory Analysismentioning

confidence: 99%

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Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Haensel¹,

Jin²,

Cinco³

et al. 2019

Preprint

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Our knowledge of transcriptional heterogeneities in epithelial stem/progenitor cell compartments is limited. Epidermal basal cells sustain cutaneous tissue maintenance and drive wound healing. Previous studies have probed basal cell heterogeneity in stem/progenitor potential, but a non-biased dissection of basal cell dynamics during differentiation is lacking. Using single-cell RNA-sequencing coupled with RNAScope and fluorescence lifetime imaging, we identify three non-proliferative and one proliferative basal cell transcriptional states in homeostatic skin that differ in metabolic preference and become spatially partitioned during wound re-epithelialization. Pseudotemporal trajectory and RNA velocity analyses produce a quasi-linear differentiation hierarchy where basal cells progress from Col17a1 high /Trp63 high state to early response state, proliferate at the juncture of these two states, or become growth arrested before differentiating into spinous cells. Wound healing induces plasticity manifested by dynamic basalspinous interconversions at multiple basal states. Our study provides a systematic view of epidermal cellular dynamics supporting a revised "hierarchical-lineage" model of homeostasis.

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Section: Pseudotemporal Trajectory and Rna Velocity Analyses Reveal Bmentioning

confidence: 89%

Section: Pseudotemporal Trajectory and Rna Velocity Analyses Reveal Bmentioning

confidence: 99%

Section: Pseudotime and Trajectory Analysismentioning

confidence: 99%

Section: Pseudotime and Trajectory Analysismentioning

confidence: 99%

See 2 more Smart Citations

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Haensel¹,

Jin²,

Cinco³

et al. 2019

Preprint

Self Cite

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“…We performed scEpath (Jin, MacLean et al, 2018) In G, data represent the mean value ± SD. NS, not significant; *P<0.05…”

Section: Identification Of Pseudotime-dependent Gene Dynamicsmentioning

confidence: 99%

Rapidly cycling stem cells regenerate the intestine independent ofLgr5^highcells

Sheng

Lin

et al. 2019

Preprint

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The +4 cells in intestinal crypts are DNA damage-resistant and contribute to regeneration. However, their exact identity and the mechanism underlying +4 cell-mediated regeneration remain unclear. Using lineage tracing, we show that cells marked by an Msi1 reporter (Msi1 + ) are enriched at the +4 position in intestinal crypts and exhibit DNA damage resistance. Single-cell RNA sequencing reveals that the Msi1 + cells are heterogeneous with the majority being intestinal stem cells (ISCs). The DNA damage-resistant subpopulation of Msi1 + cells is characterized by low-to-negative Lgr5 expression and is more rapidly cycling than Lgr5 high radio-sensitive crypt base columnar stem cells (CBCs); they enable fast repopulation of the intestinal epithelium independent of CBCs that are largely depleted after irradiation. Furthermore, relative to CBCs, Msi1 + cells preferentially produce Paneth cells during homeostasis and upon radiation repair. Together, we demonstrate that the DNA damage-resistant Msi1 + cells are rapidly cycling ISCs that maintain and regenerate the intestinal epithelium.

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Quantifying Landscape‐Flux via Single‐Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle

Zhu,

Wang

2024

Advanced Science

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Recent developments in single‐cell sequencing technology enable the acquisition of entire transcriptome data. Understanding the underlying mechanism and identifying the driving force of transcriptional regulation governing cell function directly from these data remains challenging. This study reconstructs a continuous vector field of the cell cycle based on discrete single‐cell RNA velocity to quantify the single‐cell global nonequilibrium dynamic landscape‐flux. It reveals that large fluctuations disrupt the global landscape and genetic perturbations alter landscape‐flux, thus identifying key genes in maintaining cell cycle dynamics and predicting associated functional effects. Additionally, it quantifies the fundamental energy cost of the cell cycle initiation and unveils that sustaining the cell cycle requires curl flux and dissipation to maintain the oscillatory phase coherence. This study enables the inference of the cell cycle gene regulatory networks directly from the single‐cell transcriptomic data, including the feedback mechanisms and interaction intensity. This provides a golden opportunity to experimentally verify the landscape‐flux theory and also obtain its associated quantifications. It also offers a unique framework for combining the landscape‐flux theory and single‐cell high‐through sequencing experiments for understanding the underlying mechanisms of the cell cycle and can be extended to other nonequilibrium biological processes, such as differentiation development and disease pathogenesis.

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scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 92 publications

References 41 publications

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Rapidly cycling stem cells regenerate the intestine independent ofLgr5^highcells

Quantifying Landscape‐Flux via Single‐Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle

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scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 92 publications

References 41 publications

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Defining epidermal basal cell states during skin homeostasis and wound healing using single-cell transcriptomics

Rapidly cycling stem cells regenerate the intestine independent ofLgr5highcells

Quantifying Landscape‐Flux via Single‐Cell Transcriptomics Uncovers the Underlying Mechanism of Cell Cycle

Contact Info

Product

Resources

About

Rapidly cycling stem cells regenerate the intestine independent ofLgr5^highcells