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A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.

ABSTRACT: MicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequencing data from three independent studies and find substantial differences in isomiR composition that suggest that cell autonomous mechanisms may drive isomiR processing. We also find evidence of altered regulatory functions of different classes of isomiRs, when compared to their respective wild-type miRNA, which supports a biological role for many of the isomiRs that are expressed.

SUBMITTER: Smith CM

PROVIDER: S-EPMC8857176 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.

Smith Christopher Michael CM Hutvagner Gyorgy G

Scientific reports 20220218 1

MicroRNAs (miRNAs) are non-coding small RNAs which play a critical role in the regulation of gene expression in cells. It is known that miRNAs are often expressed as multiple isoforms, called isomiRs, which may have alternative regulatory functions. Despite the recent development of several single cell small RNA sequencing protocols, these methods have not been leveraged to investigate isomiR expression and regulation to better understand their role on a single cell level. Here we integrate sequ ...[more]

PMID: 35181712

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Project description:BackgroundThe analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.ResultsIn this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.ConclusionsIn general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

Dataset Information

A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.

Publications

A comparative analysis of single cell small RNA sequencing data reveals heterogeneous isomiR expression and regulation.

Similar Datasets

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