Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Pyatnitskiy, Mikhail A.; Arzumanian, Viktoriia A.; Radko, Sergey P.; Ptitsyn, K. G.; Вахрушев, И. В.; Poverennaya, Ekaterina V.; Ponomarenko, Elena A.

doi:10.3390/biology10111131

Cited by 23 publications

(12 citation statements)

References 44 publications

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“…It is worth noting that long-read sequencing technology, including sequencing chemistry and basecalling, has improved since the long reads in this analysis were generated. We also ran a similar analysis on a direct RNA ONT dataset generated with newer chemistry (SQK-RNA002) and basecalled with Guppy v5.0.7 [ 13 ]. Just as with the older data, the hybrid-read assembly achieves better accuracy than either the long or the short-read assembly.…”

Section: Resultsmentioning

confidence: 99%

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

et al. 2022

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Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus, and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie.

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Section: Resultsmentioning

confidence: 99%

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

et al. 2022

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“…A transcript is the next stage after the genome in the transfer of biological information, at which new characteristics appear, such as changes in the number of transcripts encoded by one gene and their expression. In total, about 14,000 genes are expressed in HepG2 cells [ 101 ]. At the transcriptome level for the HepG2 cell line, it has been shown that 50 genes are upregulated in comparison with normal hepatocytes [ 102 ].…”

Section: Comparison Of Hepg2 Normal Hepatocyte Hb and Hccmentioning

confidence: 99%

The Curious Case of the HepG2 Cell Line: 40 Years of Expertise

Arzumanian

Kiseleva

Poverennaya

2021

IJMS

Self Cite

172

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Liver cancer is the third leading cause of cancer death worldwide. Representing such a dramatic impact on our lives, liver cancer is a significant public health concern. Sustainable and reliable methods for preventing and treating liver cancer require fundamental research on its molecular mechanisms. Cell lines are treated as in vitro equivalents of tumor tissues, making them a must-have for basic research on the nature of cancer. According to recent discoveries, certified cell lines retain most genetic properties of the original tumor and mimic its microenvironment. On the other hand, modern technologies allowing the deepest level of detail in omics landscapes have shown significant differences even between samples of the same cell line due to cross- and mycoplasma infection. This and other observations suggest that, in some cases, cell cultures are not suitable as cancer models, with limited predictive value for the effectiveness of new treatments. HepG2 is a popular hepatic cell line. It is used in a wide range of studies, from the oncogenesis to the cytotoxicity of substances on the liver. In this regard, we set out to collect up-to-date information on the HepG2 cell line to assess whether the level of heterogeneity of the cell line allows in vitro biomedical studies as a model with guaranteed production and quality.

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“…As a result of two transcriptomic profilings at the Institute of Biomedical Chemistry (IBMC), expression data were obtained for the HepG2 cell line in different years (Supplementary File S1, Table S1). The first batch of IBMC data (IBMC.1) is described in Pyatnitskiy et al [6], and the raw data is available from the NCBI SRA repository under study accession number PRJNA765908. The second batch of IBMC data (IBMC.2) was obtained in 2021 (see Sections 4.1-4.3 above), and the raw data is available from the NCBI SRA repository under study accession number PRJNA956723.…”

Section: Resultsmentioning

confidence: 99%

Comparative Transcriptomic Analysis of Three Common Liver Cell Lines

Arzumanian

Pyatnitskiy

Poverennaya

2023

IJMS

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Background: Comparative transcriptomic analysis is a powerful approach for investigating the molecular mechanisms underlying various physiological and pathological processes, including liver disease. The liver is a vital organ with diverse functions, including metabolism and detoxification. In vitro models of liver cells, such as HepG2, Huh7, and Hep3B, have been widely used to study liver biology and pathology. However, there is limited information on the heterogeneity of these cell lines at the transcriptomic level. Objective: This study aimed to conduct a comparative transcriptomic analysis of three common liver cell lines (HepG2, Huh7, and Hep3B) using publicly available RNA-sequencing data. In addition, we compared these cell lines to primary hepatocytes, cells isolated directly from liver tissue and considered the gold standard for studying liver function and disease. Methods: Our study included sequencing data with the following criteria: total number of reads over 20,000,000, average read length of over 60 base pairs, Illumina sequencing, and non-treated cells. The data for the three cell lines were compiled: HepG2 (97 samples), Huh7 (39 samples), and Hep3B (16 samples). We performed differential gene expression analysis using the DESeq2 package, principal component analysis, hierarchical clustering on principal components, and correlation analysis to explore the heterogeneity within each cell line. Results: We identified numerous genes and pathways differentially expressed between HepG2, Huh7, and Hep3B, such as oxidative phosphorylation, cholesterol metabolism, and DNA damage. We report that the expression levels of important genes differ significantly between primary hepatocytes and liver cell lines. Conclusion: Our study provides new insights into the transcriptional heterogeneity of commonly used liver cell lines and highlights the importance of considering specific cell line. Consequently, transferring results without considering the heterogeneity of cell lines is impractical and may lead to inaccurate or distorted conclusions.

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Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Cited by 23 publications

References 44 publications

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

Improved transcriptome assembly using a hybrid of long and short reads with StringTie

The Curious Case of the HepG2 Cell Line: 40 Years of Expertise

Comparative Transcriptomic Analysis of Three Common Liver Cell Lines

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