Lack of evidence for increased transcriptional noise in aged tissues
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).
-
scRNAseq dataset of murine aging lungGene Expression Omnibus, GSE124872.
-
scRNAseq dataset of murine aging lung, spleen and kidneyGene Expression Omnibus, GSE132901.
-
Human Lung Cell AtlasSynapse, syn21041850.
-
scRNAseq datasets of adult mammalian lungsGene Expression Omnibus, GSE133747.
-
scRNAseq dataset of human aging pancreasGene Expression Omnibus, GSE81547.
-
scRNAseq dataset of human aging skinGene Expression Omnibus, GSE130973.
-
scRNAseq dataset of murine aging brainGene Expression Omnibus, GSE129788.
-
scRNAseq dataset of murine aging dermal fibroblastsGene Expression Omnibus, GSE111136.
Article and author information
Author details
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.
Reviewing Editor
- Marcus M Seldin, University of California, Irvine, United States
Publication history
- Received: May 18, 2022
- Accepted: December 23, 2022
- Accepted Manuscript published: December 28, 2022 (version 1)
Copyright
© 2022, Ibañez-Solé et al.
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.
Metrics
-
- 468
- Page views
-
- 82
- Downloads
-
- 0
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data is typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n=12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n=2) during an instrumental task from calcium fluorescence in orbitofrontal cortex (OFC). DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array (FPGA) hardware for real time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.
-
- Computational and Systems Biology
- Immunology and Inflammation
High-throughput sequencing of adaptive immune receptor repertoires is a valuable tool for receiving insights in adaptive immunity studies. Several powerful TCR/BCR repertoire reconstruction and analysis methods have been developed in the past decade. However, detecting and correcting the discrepancy between real and experimentally observed lymphocyte clone frequencies is still challenging. Here we discovered a hallmark anomaly in the ratio between read count and clone count-based frequencies of non-functional clonotypes in multiplex PCR-based immune repertoires. Calculating this anomaly, we formulated a quantitative measure of V- and J-genes frequency bias driven by multiplex PCR during library preparation called Over Amplification Rate (OAR). Based on the OAR concept, we developed an original software for multiplex PCR-specific bias evaluation and correction named iROAR: Immune Repertoire Over Amplification Removal (https://github.com/smiranast/iROAR). The iROAR algorithm was successfully tested on previously published TCR repertoires obtained using both 5' RACE (Rapid Amplification of cDNA Ends)-based and multiplex PCR-based approaches and compared with a biological spike-in-based method for PCR bias evaluation. The developed approach can increase the accuracy and consistency of repertoires reconstructed by different methods making them more applicable for comparative analysis.