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Quantifying continuous cell-cycle phase using single-cell gene expression data

ABSTRACT: Quantifying continuous cell-cycle phase using single-cell gene expression data

PROVIDER: PRJNA496506 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR8059120.fastq.gz	Fastqsanger.gz
	SRR8059130.fastq.gz	Fastqsanger.gz
	SRR8059140.fastq.gz	Fastqsanger.gz
	SRR8059150.fastq.gz	Fastqsanger.gz
	SRR8059160.fastq.gz	Fastqsanger.gz

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Project description:The cell cycle is known to regulate cell proliferation and cell fate decisions, the underlying mechanism of which is well-studied and conserved across species and cell types. To date, the cell cycle is receiving a renewed importance due to the rapid advancement of single-cell genomics technology. Especially in the analysis of single-cell gene expression, the cell cycle plays a key role in understanding the expression variation across cell states and cell types. The current study was designed to develop effective tools for assessing and predicting the cell cycle in single-cell gene expression data analysis. We collected both Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) – for determining the cell cycle – and single-cell RNA-seq (scRNA-seq) data from each individual using STRT-seq on the Fluidigm C1 platform. The cells were collected from iPSCs derived from 6 genotyped Yoruba cell lines. The experimental design controlled for C1 processing batch effects, as well as individual and gender effects. Using these data, we developed a supervised approach for predicting the cyclical ordering of single cells in the cell cycle using single-cell gene expression data. We used the FUCCI fluorescent intensities to determine a cyclical ordering of the individual cells, and to assign cell time labels for individual cells representing each cell's position in one complete cell cycle. We estimated the cyclical trend of expression levels for each gene based on the FUCCI-derived cell times, and identified candidate sets of cyclical genes for model training. We trained our model in in 5-fold cross-validation and evaluate the trained model in held-out validation samples and external datasets. We compared the prediction results with existing approaches for estimating the cell cycle using single-cell gene expression data: including unsupervised approaches to construct the cyclical ordering of single cells and approaches to categorically assign the cell cycle to individual cells. These results provide a benchmark for assessing the cell cycle in scRNA-seq data analysis and insights into the effects of the cell cycle on gene expression variation in stem cells.

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Quantifying continuous cell-cycle phase using single-cell gene expression data

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