Differentiation reveals the plasticity of age-related change in murine muscle progenitors

Kimmel, Jacob C.; Hendrickson, David G.; Kelley, David R.

doi:10.1101/2020.03.05.979112

Cited by 5 publications

(24 citation statements)

References 110 publications

(175 reference statements)

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“…CellRank is a method to quantitatively analyze RNA velocity-induced vector fields in high dimensions. While other approaches have started to address this, they either ignore the stochastic nature of cellular fate decisions and velocity uncertainty 76,77 , or they focus on questions other than trajectory reconstruction 78 . The original velocyto 18 model proposed an idea to find initial and terminal states that was based on simulating a Markov process forwards or backwards in time, however, their implementation relied on a 2D t-SNE embedding, was computationally expensive because of the sampling, ignored uncertainty in velocity vectors and did not allow the separation into individual initial and terminal states.…”

Section: Discussionmentioning

confidence: 99%

CellRank for directed single-cell fate mapping

Theis

Lange

Bergen

et al. 2020

Preprint

113

202

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Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

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Section: Discussionmentioning

confidence: 99%

CellRank for directed single-cell fate mapping

Theis

Lange

Bergen

et al. 2020

Preprint

113

202

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“…4B). It has previously been reported that aged myogenic cells do not differentiate into mature myocytes as effectively as young cells [51,23]. We found that transiently reprogrammed aged cells were more differentiated than aged control cells based on pseudotemporal distributions, expression of marker genes, and gene set analysis (Fig.…”

Section: Transient Multipotent Reprogramming Improves Myogenesis In Aged Cellsmentioning

confidence: 53%

“…We first wanted to determine if the transcriptional distance between young and aged control cells – the magnitude of aging – was decreased by transient reprogramming. We measured the magnitude of aging using the maximum mean discrepancy (MMD), a statistic from representation learning that measures the similarity of two populations and provides a test for statistical significance [22, 23]. Here, we computed the MMD across cell populations using autoencoder latent variables to capture changes across the entire transcriptome.…”

Section: Resultsmentioning

confidence: 99%

“…We further quantified RNA velocity maps using phase simulations, a technique from dynamical systems to measure properties of vector fields by simulating the motion of a point moving through the field [36, 23, 37]. Here, we simulated reprogrammed cells in each velocity map and evolved their positions based on the inferred velocities of neighboring cells (Methods).…”

Section: Resultsmentioning

confidence: 99%

“…We re-analyzed publically available single cell RNA-seq profiles of young (3 months old) and aged (20 months old) myogenic cells after in vitro differentiation to identify an aging gene signature (GEO : GSE145256) [23]. We extracted 329 genes that were changed more than 2-fold between young and aged myogenic cells with a false discovery rate q < 0.10 (Wilcoxon Rank Sum test, Benjamini-Hochberg FDR control).…”

Section: Scoring Aging Gene Expression In Myogenic Multipotent Reprogramming Experimentsmentioning

confidence: 99%

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Partial reprogramming restores youthful gene expression through transient suppression of cell identity

Roux

Zhang

Paw

et al. 2021

Preprint

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Transient induction of pluripotent reprogramming factors has been reported to reverse some features of aging in mammalian cells and tissues. However, the impact of transient reprogramming on somatic cell identity programs and the necessity of individual pluripotency factors remain unknown. Here, we mapped trajectories of transient reprogramming in young and aged cells from multiple murine cell types using single cell transcriptomics to address these questions. We found that transient reprogramming restored youthful gene expression in adipocytes and mesenchymal stem cells but also temporarily suppressed somatic cell identity programs. We further screened Yamanaka Factor subsets and found that many combinations had an impact on aging gene expression and suppressed somatic identity, but that these effects were not tightly entangled. We also found that a transient reprogramming approach inspired by amphibian regeneration restored youthful gene expression in aged myogenic cells. Our results suggest that transient pluripotent reprogramming poses a neoplastic risk, but that restoration of youthful gene expression can be achieved with alternative strategies.

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