scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals Evidence that the cell cycle is a series of uncoupled, memoryless phases

2018 ◽  
Author(s):  
Hui Xiao Chao ◽  
Randy I. Fakhreddin ◽  
Hristo K. Shimerov ◽  
Rashmi J. Kumar ◽  
Gaorav P. Gupta ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Dna Synthesis ◽  
Cell Cycle Progression ◽  
Phase 1 ◽  
Cell Cycle Phase ◽  
Cell Cycle Checkpoints ◽  
Cycle Phase ◽  
Erlang Distribution ◽  
Cycle Progression ◽  
Gap Phase

The cell cycle is canonically described as a series of 4 phases: G1 (gap phase 1), S (DNA synthesis), G2 (gap phase 2), and M (mitosis). Various models have been proposed to describe the durations of each phase, including a two-state model with fixed S-G2-M duration and random G1 duration1,2; a “stretched” model in which phase durations are proportional3; and an inheritance model in which sister cells show correlated phase durations2,4. A fundamental challenge is to understand the quantitative laws that govern cell-cycle progression and to reconcile the evidence supporting these different models. Here, we used time-lapse fluorescence microscopy to quantify the durations of G1, S, G2, and M phases for thousands of individual cells from three human cell lines. We found no evidence of correlation between any pair of phase durations. Instead, each phase followed an Erlang distribution with a characteristic rate and number of steps. These observations suggest that each cell cycle phase is memoryless with respect to previous phase durations. We challenged this model by perturbing the durations of specific phases through oncogene activation, inhibition of DNA synthesis, reduced temperature, and DNA damage. Phase durations remained uncoupled in individual cells despite large changes in durations in cell populations. To explain this behavior, we propose a mathematical model in which the independence of cell-cycle phase durations arises from a large number of molecular factors that each exerts a minor influence on the rate of cell-cycle progression. The model predicts that it is possible to force correlations between phases by making large perturbations to a single factor that contributes to more than one phase duration, which we confirmed experimentally by inhibiting cyclin-dependent kinase 2 (CDK2). We further report that phases can show coupling under certain dysfunctional states such as in a transformed cell line with defective cell cycle checkpoints. This quantitative model of cell cycle progression explains the paradoxical observation that phase durations are both inherited and independent and suggests how cell cycle progression may be altered in disease states.

scholarly journals DNA damage checkpoint dynamics drive cell cycle phase transitions

2017 ◽  
Author(s):  
Hui Xiao Chao ◽  
Cere E. Poovey ◽  
Ashley A. Privette ◽  
Gavin D. Grant ◽  
Hui Yan Chao ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Dna Damage ◽  
Phase Transitions ◽  
Cell Cycle Progression ◽  
S Phase ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Dna Damage Checkpoint ◽  
Cycle Progression ◽  
Dna Damage Checkpoints

ABSTRACTDNA damage checkpoints are cellular mechanisms that protect the integrity of the genome during cell cycle progression. In response to genotoxic stress, these checkpoints halt cell cycle progression until the damage is repaired, allowing cells enough time to recover from damage before resuming normal proliferation. Here, we investigate the temporal dynamics of DNA damage checkpoints in individual proliferating cells by observing cell cycle phase transitions following acute DNA damage. We find that in gap phases (G1 and G2), DNA damage triggers an abrupt halt to cell cycle progression in which the duration of arrest correlates with the severity of damage. However, cells that have already progressed beyond a proposed “commitment point” within a given cell cycle phase readily transition to the next phase, revealing a relaxation of checkpoint stringency during later stages of certain cell cycle phases. In contrast to G1 and G2, cell cycle progression in S phase is significantly less sensitive to DNA damage. Instead of exhibiting a complete halt, we find that increasing DNA damage doses leads to decreased rates of S-phase progression followed by arrest in the subsequent G2. Moreover, these phase-specific differences in DNA damage checkpoint dynamics are associated with corresponding differences in the proportions of irreversibly arrested cells. Thus, the precise timing of DNA damage determines the sensitivity, rate of cell cycle progression, and functional outcomes for damaged cells. These findings should inform our understanding of cell fate decisions after treatment with common cancer therapeutics such as genotoxins or spindle poisons, which often target cells in a specific cell cycle phase.

scholarly journals The lncRNA EPB41L4A-AS1 regulates gene expression in the nucleus and exerts cell type-dependent effects on cell cycle progression

2021 ◽  
Author(s):  
Helle Samdal ◽  
Siv Anita Hegre ◽  
Konika Chawla ◽  
Nina-Beate Liabakk ◽  
Per Arne Aas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Cycle Progression ◽  
Phase Distribution ◽  
Cell Types ◽  
Metabolic Reprogramming ◽  
Cycle Phase ◽  
Cell Type ◽  
Chip Sequencing ◽  
Cell Cycle Phase Distribution

AbstractThe long non-coding RNA (lncRNA) EPB41L4A-AS1 is aberrantly expressed in various cancers and has been reported to be involved in metabolic reprogramming and as a repressor of the Warburg effect. Although the biological relevance of EPB41L4A-AS1 is evident, its functional role seems to vary depending on cell type and state of disease. By combining RNA sequencing and ChIP sequencing of cell cycle synchronized HaCaT cells we previously identified EPB41L4A-AS1 to be one of 59 lncRNAs with potential cell cycle functions. Here, we demonstrate that EPB41L4A-AS1 exists as bright foci and regulates gene expression in the nucleus in both cis and trans. Specifically, we find that EPB41L4A-AS1 positively regulates its sense overlapping gene EPB41L4A and influences expression of hundreds of other genes, including genes involved in cell proliferation. Finally, we show that EPB41L4A-AS1 affects cell cycle phase distribution, though these effects vary between cell types.

scholarly journals The G2-phase enriched lncRNA SNHG26 is necessary for proper cell cycle progression and proliferation

2021 ◽  
Author(s):  
Helle Samdal ◽  
Siv A. Hegre ◽  
Konika Chawla ◽  
Nina-Beate Liabakk ◽  
Per A. Aas ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Rna Polymerase Ii ◽  
Cell Cycle Progression ◽  
Phase Distribution ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Chip Sequencing ◽  
Cell Cycle Phase Distribution ◽  
Regulation Of Cell Cycle

AbstractLong noncoding RNAs (lncRNAs) are involved in the regulation of cell cycle, although only a few have been functionally characterized. By combining RNA sequencing and ChIP sequencing of cell cycle synchronized HaCaT cells we have previously identified lncRNAs highly enriched for cell cycle functions. Based on a cyclic expression profile and an overall high correlation to histone 3 lysine 4 trimethylation (H3K4me3) and RNA polymerase II (Pol II) signals, the lncRNA SNHG26 was identified as a top candidate. In the present study we report that downregulation of SNHG26 affects mitochondrial stress, proliferation, cell cycle phase distribution, and gene expression in cis- and in trans, and that this effect is reversed by upregulation of SNHG26. We also find that the effect on cell cycle phase distribution is cell type specific and stable over time. Results indicate an oncogenic role of SNHG26, possibly by affecting cell cycle progression through the regulation of downstream MYC-responsive genes.

scholarly journals A single-cell atlas of human glioblastoma reveals a single axis of phenotype in tumor-propagating cells

2018 ◽  
Author(s):  
Sören Müller ◽  
Elizabeth Di Lullo ◽  
Aparna Bhaduri ◽  
Beatriz Alvarado ◽  
Garima Yagnik ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Cycle Progression ◽  
Molecular Subtypes ◽  
Suppressor Cells ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Myeloid Derived Suppressor Cells ◽  
Cycle Progression ◽  
Anatomical Structures ◽  
Quantitative Immunohistochemistry

AbstractTumor-propagating glioblastoma (GBM) stem-like cells (GSCs) of the proneural and mesenchymal molecular subtypes have been described. However, it is unknown if these two GSC populations are sufficient to generate the spectrum of cellular heterogeneity observed in GBM. The lineage relationships and niche interactions of GSCs have not been fully elucidated. We perform single-cell RNA-sequencing (scRNA-seq) and matched exome sequencing of human GBMs (12 patients; >37,000 cells) to identify recurrent hierarchies of GSCs and their progeny. We map sequenced cells to tumor-anatomical structures and identify microenvironment interactions using reference atlases and quantitative immunohistochemistry. We find that all GSCs can be described by a single axis of variation, ranging from proneural to mesenchymal. Increasing mesenchymal GSC (mGSC) content, but not proneural GSC (pGSC) content, correlates with significantly inferior survival. All clonal expressed mutations are found in the GSC populations, with a greater representation of mutations found in mGSCs. While pGSCs upregulate markers of cell-cycle progression, mGSCs are largely quiescent and overexpress cytokines mediating the chemotaxis of myeloid-derived suppressor cells. We find mGSCs enriched in hypoxic regions while pGSCs are enriched in the tumor’s invasive edge. We show that varying proportions of mGSCs, pGSCs, their progeny and stromal/immune cells are sufficient to explain the genetic and phenotypic heterogeneity observed in GBM. This study sheds light on a long-standing debate regarding the lineage relationships between GSCs and other glioma cell types.

scholarly journals Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

2014 ◽  
Author(s):  
Lucas Dennis ◽  
Andrew McDavid ◽  
Patrick Danaher ◽  
Greg Finak ◽  
Michael Krouse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Single Cells ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Expression Variability ◽  
Cell Cycle Genes ◽  
Cell Expression ◽  
Cell Gene

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

scholarly journals PDE-constrained optimization for estimating population dynamics over cell cycle from static single cell measurements

2020 ◽  
Author(s):  
Karsten Kuritz ◽  
Alain R Bonny ◽  
João Pedro Fonseca ◽  
Frank Allgöwer
Keyword(s):  
Cell Cycle ◽  
Population Dynamics ◽  
Single Cell ◽  
Real Time ◽  
Cell Cycle Progression ◽  
K562 Cells ◽  
Estimation Of Parameters ◽  
Cycle Progression ◽  
Real Time Analysis ◽  
Cellular Environment

AbstractMotivationUnderstanding how cell cycle responds and adapts dynamically to a broad range of stresses and changes in the cellular environment is crucial for the treatment of various pathologies, including cancer. However, measuring changes in cell cycle progression is experimentally challenging, and model inference computationally expensive.ResultsHere, we introduce a computational framework that allows the inference of changes in cell cycle progression from static single-cell measurements. We modeled population dynamics with partial differential equations (PDE), and derive parameter gradients to estimate time- and cell cycle position-dependent progression changes efficiently. Additionally, we show that computing parameter sensitivities for the optimization problem by solving a system of PDEs is computationally feasible and allows efficient and exact estimation of parameters. We showcase our framework by estimating the changes in cell cycle progression in K562 cells treated with Nocodazole and identify an arrest in M-phase transition that matches the expected behavior of microtubule polymerization inhibition.ConclusionsOur results have two major implications: First, this framework can be scaled to high-throughput compound screens, providing a fast, stable, and efficient protocol to generate new insights into changes in cell cycle progression. Second, knowledge of the cell cycle stage- and time-dependent progression function allows transformation from pseudotime to real-time thereby enabling real-time analysis of molecular rates in response to treatments.AvailabilityMAPiT toolbox (Karsten Kuritz 2020) is available at github: https://github.com/karstenkuritz/MAPiT.

scholarly journals STAU2 protein level is controlled by caspases and the CHK1 pathway and regulates cell cycle progression in the non-transformed hTERT-RPE1 cells

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lionel Condé ◽  
Yulemi Gonzalez Quesada ◽  
Florence Bonnet-Magnaval ◽  
Rémy Beaujois ◽  
Luc DesGroseillers
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Dna Damage ◽  
Steady State ◽  
Posttranscriptional Regulation ◽  
Cell Cycle Progression ◽  
Rna Binding ◽  
Regulation Of Gene Expression ◽  
Cycle Progression

AbstractBackgroundStaufen2 (STAU2) is an RNA binding protein involved in the posttranscriptional regulation of gene expression. In neurons, STAU2 is required to maintain the balance between differentiation and proliferation of neural stem cells through asymmetric cell division. However, the importance of controlling STAU2 expression for cell cycle progression is not clear in non-neuronal dividing cells. We recently showed that STAU2 transcription is inhibited in response to DNA-damage due to E2F1 displacement from theSTAU2gene promoter. We now study the regulation of STAU2 steady-state levels in unstressed cells and its consequence for cell proliferation.ResultsCRISPR/Cas9-mediated and RNAi-dependent STAU2 depletion in the non-transformed hTERT-RPE1 cells both facilitate cell proliferation suggesting that STAU2 expression influences pathway(s) linked to cell cycle controls. Such effects are not observed in the CRISPR STAU2-KO cancer HCT116 cells nor in the STAU2-RNAi-depleted HeLa cells. Interestingly, a physiological decrease in the steady-state level of STAU2 is controlled by caspases. This effect of peptidases is counterbalanced by the activity of the CHK1 pathway suggesting that STAU2 partial degradation/stabilization fines tune cell cycle progression in unstressed cells. A large-scale proteomic analysis using STAU2/biotinylase fusion protein identifies known STAU2 interactors involved in RNA translation, localization, splicing, or decay confirming the role of STAU2 in the posttranscriptional regulation of gene expression. In addition, several proteins found in the nucleolus, including proteins of the ribosome biogenesis pathway and of the DNA damage response, are found in close proximity to STAU2. Strikingly, many of these proteins are linked to the kinase CHK1 pathway, reinforcing the link between STAU2 functions and the CHK1 pathway. Indeed, inhibition of the CHK1 pathway for 4 h dissociates STAU2 from proteins involved in translation and RNA metabolism.ConclusionsThese results indicate that STAU2 is involved in pathway(s) that control(s) cell proliferation, likely via mechanisms of posttranscriptional regulation, ribonucleoprotein complex assembly, genome integrity and/or checkpoint controls. The mechanism by which STAU2 regulates cell growth likely involves caspases and the kinase CHK1 pathway.

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