Biology subjects

Ibanez-Sole, O.

Publications and source records attributed to Ibanez-Sole, O..

3 recordsLinked to original sources

bioRxiv2022

Age- or lifestyle-induced accumulation of genotoxicityis associated with a generalized shutdown of long genetranscription

A causative role for DNA damage as a molecular driver of aging has long been advocated. Transcription-blocking lesions (TBLs) accumulate with age in a stochastic manner. Thus, gene expression data might reflect the gene length-dependent accumulation of TBLs. Here we present an analysis of gene expression as a function of gene length in several independent single-cell RNA sequencing datasets of mouse and human aging. We found a pervasive age-associated downregulation of long gene expression, which is seen across species, datasets, sexes, tissues and cell types. Furthermore, long gene downregulation was also observed in premature aging models such as UV-radiation and smoke exposure, and in gene expression data from progeroid diseases Cockayne syndrome and trichothiodystrophy. Finally, we analyzed the length of differentially expressed genes associated to age in both mice and humans. Downregulated genes were significantly longer than upregulated genes. These data highlight a previously undetected hallmark of cellular aging and provide strong support for age-associated accumulation of genotoxic damage inducing a generalized shutdown of RNA polymerase II-mediated long gene transcription. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/501099v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1897c08org.highwire.dtl.DTLVardef@12e202borg.highwire.dtl.DTLVardef@141f70borg.highwire.dtl.DTLVardef@1c1f9f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Lack of evidence for increased transcriptional noise in aged tissues

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.

bioinformatics↗

bioRxiv2021

Triku: a feature selection method based on nearest neighbors for single-cell data

Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Triku is a feature selection method that favours genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the nearest neighbor graph. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on mutual information and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms, and contain fewer ribosomal and mitochondrial genes. Triku is available at https://gitlab.com/alexmascension/triku.

bioinformatics↗