Studying relative RNA localization from nucleus to the cytosol

The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In eukaryotic cells, understanding the distribution of RNA transcripts between the nucleus and cytosol is essential for gaining valuable insights...

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Published inNAR genomics and bioinformatics Vol. 7; no. 2; p. lqaf032
Main Authors Ntasis, Vasilis F, Guigó, Roderic
Format Journal Article
LanguageEnglish
Published England 01.06.2025
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ISSN2631-9268
2631-9268
DOI10.1093/nargab/lqaf032

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Abstract The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In eukaryotic cells, understanding the distribution of RNA transcripts between the nucleus and cytosol is essential for gaining valuable insights into the process of protein production. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, comparing transcript levels between compartments is challenging since measurements are relative to the unknown total RNA volume. Here, we show theoretically that if, in addition to nuclear and cytosolic RNAseq, whole-cell RNAseq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single-cell data available. Finally, we use our method to investigate the subcellular localization of transcripts using bulk RNAseq data from the ENCODE project.
AbstractList The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In eukaryotic cells, understanding the distribution of RNA transcripts between the nucleus and cytosol is essential for gaining valuable insights into the process of protein production. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, comparing transcript levels between compartments is challenging since measurements are relative to the unknown total RNA volume. Here, we show theoretically that if, in addition to nuclear and cytosolic RNAseq, whole-cell RNAseq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single-cell data available. Finally, we use our method to investigate the subcellular localization of transcripts using bulk RNAseq data from the ENCODE project.
The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In eukaryotic cells, understanding the distribution of RNA transcripts between the nucleus and cytosol is essential for gaining valuable insights into the process of protein production. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, comparing transcript levels between compartments is challenging since measurements are relative to the unknown total RNA volume. Here, we show theoretically that if, in addition to nuclear and cytosolic RNAseq, whole-cell RNAseq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single-cell data available. Finally, we use our method to investigate the subcellular localization of transcripts using bulk RNAseq data from the ENCODE project.The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In eukaryotic cells, understanding the distribution of RNA transcripts between the nucleus and cytosol is essential for gaining valuable insights into the process of protein production. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, comparing transcript levels between compartments is challenging since measurements are relative to the unknown total RNA volume. Here, we show theoretically that if, in addition to nuclear and cytosolic RNAseq, whole-cell RNAseq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single-cell data available. Finally, we use our method to investigate the subcellular localization of transcripts using bulk RNAseq data from the ENCODE project.
Author Guigó, Roderic
Ntasis, Vasilis F
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Snippet The precise coordination of important biological processes, such as differentiation and development, relies heavily on the regulation of gene expression. In...
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SubjectTerms Cell Nucleus - genetics
Cell Nucleus - metabolism
Cytosol - metabolism
Humans
RNA - genetics
RNA - metabolism
RNA, Messenger - genetics
RNA, Messenger - metabolism
Sequence Analysis, RNA - methods
Title Studying relative RNA localization from nucleus to the cytosol
URI https://www.ncbi.nlm.nih.gov/pubmed/40585305
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