scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

Dharmaratne, Malindrie; Kulkarni, Ameya; Fard, Atefeh Taherian; Mar, Jessica C.

doi:10.1101/2022.02.13.480299

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…Details on additional results are provided in the supplement. All supporting data and materials are available in the GigaScience GigaDB database [ 66 ].…”

Section: Data Availabilitymentioning

confidence: 99%

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

et al. 2022

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Background Single-cell RNA sequencing (scRNA-seq) methods have been advantageous for quantifying cell-to-cell variation by profiling the transcriptomes of individual cells. For scRNA-seq data, variability in gene expression reflects the degree of variation in gene expression from one cell to another. Analyses that focus on cell–cell variability therefore are useful for going beyond changes based on average expression and, instead, identifying genes with homogeneous expression versus those that vary widely from cell to cell. Results We present a novel statistical framework, scShapes, for identifying differential distributions in single-cell RNA-sequencing data using generalized linear models. Most approaches for differential gene expression detect shifts in the mean value. However, as single-cell data are driven by overdispersion and dropouts, moving beyond means and using distributions that can handle excess zeros is critical. scShapes quantifies gene-specific cell-to-cell variability by testing for differences in the expression distribution while flexibly adjusting for covariates if required. We demonstrate that scShapes identifies subtle variations that are independent of altered mean expression and detects biologically relevant genes that were not discovered through standard approaches. Conclusions This analysis also draws attention to genes that switch distribution shapes from a unimodal distribution to a zero-inflated distribution and raises open questions about the plausible biological mechanisms that may give rise to this, such as transcriptional bursting. Overall, the results from scShapes help to expand our understanding of the role that gene expression plays in the transcriptional regulation of a specific perturbation or cellular phenotype. Our framework scShapes is incorporated into a Bioconductor R package (https://www.bioconductor.org/packages/release/bioc/html/scShapes.html).

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“…Details on additional results are provided in the supplement. All supporting data and materials are available in the GigaScience GigaDB database [ 66 ].…”

Section: Data Availabilitymentioning

confidence: 99%

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

et al. 2022

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Deconstructing heterogeneity of replicative senescence in human mesenchymal stem cells at single cell resolution

et al. 2023

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Following prolonged cell division, mesenchymal stem cells enter replicative senescence, a state of permanent cell cycle arrest that constrains the use of this cell type in regenerative medicine applications and that in vivo substantially contributes to organismal ageing. Multiple cellular processes such as telomere dysfunction, DNA damage and oncogene activation are implicated in promoting replicative senescence, but whether mesenchymal stem cells enter different pre-senescent and senescent states has remained unclear. To address this knowledge gap, we subjected serially passaged human ESC-derived mesenchymal stem cells (esMSCs) to single cell profiling and single cell RNA-sequencing during their progressive entry into replicative senescence. We found that esMSC transitioned through newly identified pre-senescent cell states before entering into three different senescent cell states. By deconstructing this heterogeneity and temporally ordering these pre-senescent and senescent esMSC subpopulations into developmental trajectories, we identified markers and predicted drivers of these cell states. Regulatory networks that capture connections between genes at each timepoint demonstrated a loss of connectivity, and specific genes altered their gene expression distributions as cells entered senescence. Collectively, this data reconciles previous observations that identified different senescence programs within an individual cell type and should enable the design of novel senotherapeutic regimes that can overcome in vitro MSC expansion constraints or that can perhaps slow organismal ageing.

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Deconstructing replicative senescence heterogeneity of human mesenchymal stem cells at single cell resolution reveals therapeutically targetable senescent cell sub-populations

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Leeson

Aguado

et al. 2022

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Cellular senescence is characterised by a state of permanent cell cycle arrest. It is accompanied by often variable release of the so-called senescence-associated secretory phenotype (SASP) factors, and occurs in response to a variety of triggers such as persistent DNA damage, telomere dysfunction, or oncogene activation. While cellular senescence is a recognised driver of organismal ageing, the extent of heterogeneity within and between different senescent cell populations remains largely unclear. Elucidating the drivers and extent of variability in cellular senescence states is important for discovering novel targeted seno-therapeutics and for overcoming cell expansion constraints in the cell therapy industry. Here we combine cell biological and single cell RNA-sequencing approaches to investigate heterogeneity of replicative senescence in human ESC-derived mesenchymal stem cells (esMSCs) as MSCs are the cell type of choice for the majority of current stem cell therapies and senescence of MSC is a recognized driver of organismal ageing. Our data identify three senescent subpopulations in the senescing esMSC population that differ in SASP, oncogene expression, and escape from senescence. Uncovering and defining this heterogeneity of senescence states in cultured human esMSCs allowed us to identify potential drug targets that may delay the emergence of senescent MSCs in vitro and perhaps in vivo in the future.

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scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

Cited by 3 publications

References 59 publications

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

scShapes: a statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

Deconstructing heterogeneity of replicative senescence in human mesenchymal stem cells at single cell resolution

Deconstructing replicative senescence heterogeneity of human mesenchymal stem cells at single cell resolution reveals therapeutically targetable senescent cell sub-populations

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