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High variability of genomic instability and gene expression profiling in different HeLa clones.

ABSTRACT: The HeLa cell line is one of the most popular cell lines in biomedical research, despite its well-known chromosomal instability. We compared the genomic and transcriptomic profiles of 4 different HeLa batches and showed that the gain and loss of genomic material varies widely between batches, drastically affecting basal gene expression. Moreover, different pathways were activated in response to a hypoxic stimulus. Our study emphasizes the large genomic and transcriptomic variability among different batches, to the point that the same experiment performed with different batches can lead to distinct conclusions and irreproducible results. The HeLa cell line is thought to be a unique cell line but it is clear that substantial differences between the primary tumour and the human genome exist and that an indeterminate number of HeLa cell lines may exist, each with a unique genomic profile.

SUBMITTER: Frattini A

PROVIDER: S-EPMC4613361 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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High variability of genomic instability and gene expression profiling in different HeLa clones.

Frattini Annalisa A Fabbri Marco M Valli Roberto R De Paoli Elena E Montalbano Giuseppe G Gribaldo Laura L Pasquali Francesco F Maserati Emanuela E

Scientific reports 20151020

The HeLa cell line is one of the most popular cell lines in biomedical research, despite its well-known chromosomal instability. We compared the genomic and transcriptomic profiles of 4 different HeLa batches and showed that the gain and loss of genomic material varies widely between batches, drastically affecting basal gene expression. Moreover, different pathways were activated in response to a hypoxic stimulus. Our study emphasizes the large genomic and transcriptomic variability among differ ...[more]

PMID: 26483214

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Project description:BackgroundThe advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs).ResultsWe have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases.ConclusionsIn this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.

Dataset Information

High variability of genomic instability and gene expression profiling in different HeLa clones.

Publications

High variability of genomic instability and gene expression profiling in different HeLa clones.

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