Lack of evidence for increased transcriptional noise in aged tissues

Version of Record: February 16, 2023
Accepted Manuscript: December 28, 2022

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1. Part of Collection
Special Issue: Systems Genetics

Edited by David James et al.

Abstract
Data availability
Article and author information
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Abstract

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.

Data availability

Code availabilityThe Decibel and Scallop repositories can be found at https://gitlab.com/olgaibanez/decibel and https: //gitlab.com/olgaibanez/scallop, respectively. The reproducible Jupyter notebooks with the analyses carried out in this study can be found in figshare (https://doi.org/10.6084/m9.figshare.20402817.v1).

The following previously published data sets were used

1. Angelidis I et al
(2019) scRNAseq dataset of murine aging lung
Gene Expression Omnibus, GSE124872.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124872
1. Kimmel JC et al
(2019) scRNAseq dataset of murine aging lung, spleen and kidney
Gene Expression Omnibus, GSE132901.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132901
1. Pisco A et al
(2020) Tabula Muris Senis
Figshare, 12654728.

https://figshare.com/articles/dataset/Tabula_Muris_Senis_Data_Objects/12654728
1. Travaglini KJ et al
(2020) Human Lung Cell Atlas
Synapse, syn21041850.

https://www.synapse.org/#!Synapse:syn21041850/wiki/600865
1. Raredon MS et al
(2019) scRNAseq datasets of adult mammalian lungs
Gene Expression Omnibus, GSE133747.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133747
1. Enge M et al
(2017) scRNAseq dataset of human aging pancreas
Gene Expression Omnibus, GSE81547.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81547
1. Solé-Boldó et al
(2020) scRNAseq dataset of human aging skin
Gene Expression Omnibus, GSE130973.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130973
1. Ximerakis et al
(2019) scRNAseq dataset of murine aging brain
Gene Expression Omnibus, GSE129788.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129788
1. Salzer et al
(2018) scRNAseq dataset of murine aging dermal fibroblasts
Gene Expression Omnibus, GSE111136.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111136
1. Martinez-Jimenez et al
(2017) scRNAseq dataset of murine aging T cells
Zenodo, 3522970.

https://zenodo.org/record/3522970#.Y2j8t77MJkg

Article and author information

Author details

Olga Ibañez-Solé

Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastian, Spain

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0552-9793
Alex M Ascensión

Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastian, Spain

Competing interests
The authors declare that no competing interests exist.
Marcos J Araúzo-Bravo

Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, San Sebastian, Spain

For correspondence
mararabra@yahoo.co.uk

Competing interests
The authors declare that no competing interests exist.
Ander Izeta

Tissue Engineering Laboratory, Biodonostia Health Research Institute, San Sebastian, Spain

For correspondence
ander.izeta@biodonostia.org

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-1879-7401

Funding

la Caixa" Foundation " (LCF/BQ/IN18/11660065)

Olga Ibañez-Solé

Instituto de Salud Carlos III (AC17/00012)

Olga Ibañez-Solé
Alex M Ascensión
Ander Izeta

Instituto de Salud Carlos III (PI19/01621)

Olga Ibañez-Solé
Alex M Ascensión
Ander Izeta

Ministerio de Ciencia e Innovación (PID2020-119715GB-I00)

Olga Ibañez-Solé
Alex M Ascensión
Ander Izeta

European Regional Development Fund (MCIN/AEI/10.13039/501100011033)

Olga Ibañez-Solé
Alex M Ascensión
Ander Izeta

H2020 Marie Skłodowska-Curie Actions (713673)

Olga Ibañez-Solé

Eusko Jaurlaritza (PRE_2020_2_0081)

Alex M Ascensión

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.