Ageing hallmarks exhibit organ-specific temporal signatures

Schaum, Nicholas; Lehallier, Benoit; Hahn, Oliver; Pálovics, Róbert; Hosseinzadeh, Shayan; Lee, Song Eun; Sit, Rene; Lee, Davis P.; Losada, Patricia Morán; Zardeneta, Macy E.; Fehlmann, Tobias; Webber, James T.; McGeever, Aaron; Calcuttawala, Kruti; Zhang, Fan; Mathur, Vidhu; Tan, Weilun; Zee, Alexander; Tan, Michelle; Pisco, Angela Oliveira; Karkanias, Jim; Neff, Norma; Keller, Andreas; Darmanis, Spyros; Quake, Stephen R.; Wyss‐Coray, Tony

doi:10.1038/s41586-020-2499-y

Cited by 356 publications

(320 citation statements)

References 37 publications

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“…Although correlation is not causation, we were interested in correlating Adnp gene transcript concentrations to muscle disease genes at the single-cell level [ 13 , 65 , 66 ]. We resorted to data mining of two libraries, the young (3-month-old) and the aged (18 and 24 months) male mice, and focused on 8 genes as illustrated in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

Single Cell ADNP Predictive of Human Muscle Disorders: Mouse Knockdown Results in Muscle Wasting

Kapitansky

Karmon

Sragovich

et al. 2020

Cells

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Activity-dependent neuroprotective protein (ADNP) mutations are linked with cognitive dysfunctions characterizing the autistic-like ADNP syndrome patients, who also suffer from delayed motor maturation. We thus hypothesized that ADNP is deregulated in versatile myopathies and that local ADNP muscle deficiency results in myopathy, treatable by the ADNP fragment NAP. Here, single-cell transcriptomics identified ADNP as a major constituent of the developing human muscle. ADNP transcript concentrations further predicted multiple human muscle diseases, with concentrations negatively correlated with the ADNP target interacting protein, microtubule end protein 1 (EB1). Reverting back to modeling at the single-cell level of the male mouse transcriptome, Adnp mRNA concentrations age-dependently correlated with motor disease as well as with sexual maturation gene transcripts, while Adnp expressing limb muscle cells significantly decreased with aging. Mouse Adnp heterozygous deficiency exhibited muscle microtubule reduction and myosin light chain (Myl2) deregulation coupled with motor dysfunction. CRISPR knockdown of adult gastrocnemius muscle Adnp in a Cas9 mouse resulted in treadmill (male) and gait (female) dysfunctions that were specifically ameliorated by treatment with the ADNP snippet, microtubule interacting, Myl2—regulating, NAP (CP201). Taken together, our studies provide new hope for personalized diagnosis/therapeutics in versatile myopathies.

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Section: Resultsmentioning

confidence: 99%

Single Cell ADNP Predictive of Human Muscle Disorders: Mouse Knockdown Results in Muscle Wasting

Kapitansky

Karmon

Sragovich

et al. 2020

Cells

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“…The aging phenotype is associated with notable transcriptional changes [27], and we used scRFE to learn the top ranked genes associated with each age. When considering the unique age-specific genes, we found that Ppp1c is the gene with the highest mean decrease in Gini score that is specific to the 24-month mice and not in the 3-or 18month lists (FACS dataset).…”

Section: Top Feature Importances Reveal Gene Ontology Patterns By Agementioning

confidence: 99%

Single-cell identity definition using random forests and recursive feature elimination

Park

Vorperian

Wang

et al. 2020

Preprint

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Single cell RNA sequencing (scRNA-seq) enables detailed examination of a cell's underlying regulatory networks and the molecular factors contributing to its identity. We developed scRFE (single-cell identity definition using random forests and recursive feature elimination, pronounced 'surf') with the goal of easily generating interpretable gene lists that can accurately distinguish observations (single-cells) by their features(genes) given a class of interest. scRFE is an algorithm implemented as a Python package that combines the classical random forest method with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. The package is compatible with Scanpy, enabling a seamless integration into any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset. We applied scRFE to the Tabula Muris Senis and reproduced commonly known aging patterns and transcription factor reprogramming protocols, highlighting the biological value of scRFE's learned features.

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“…We considered 76 tissue-cell types in the TMS FACS data, 26 tissue-cell types in the TMS droplet data, and 17 tissues in an accompanying bulk RNA-Seq mouse aging study 55 (referred to as the bulk data) with sufficient sample size. We performed differential gene expression (DGE) analysis for each tissue-cell type separately, treating all cells from the tissue-cell type as samples.…”

Section: Identification Of Aging-related Genesmentioning

confidence: 99%

“…We considered five datasets, namely the TMS FACS data, the TMS droplet data, the data in Schaum et al 55 (referred to as the bulk data), the data in Kimmel et al 23 , and the data in Kowalczyk et al 25 . For the TMS FACS data and the TMS droplet data, we filtered out genes expressed in fewer than 3 cells, filtered out cells expressing fewer than 250 genes, and discarded cells with a total number of counts fewer than 5,000 for the FACS data and a total number of unique molecular identifiers (UMIs) fewer than 2,500 for the droplet data.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…As shown in Supp. Table 1, we considered 76 tissue-cell types in 23 tissues with more than 100 cells in both young (3m) and old (18m, 24m) age groups for the TMS FACS data; 26 tissue-cell types in 11 tissues with more than 500 cells in both young (1m, 3m) and old (18m, 21m, 24m, 30m) age groups for the TMS droplet data; and all 17 tissues for the bulk data 55 . We required more cells for the TMS droplet data than the TMS FACS data because the droplet data has a much lower sequencing depth (6000 UMIs per cell, as compared to 0.85 million reads per cell for the FACS data).…”

Section: Differential Gene Expression Analysismentioning

confidence: 99%

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Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures Revision 1

Zhang

Darmanis

et al. 2019

Preprint

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Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset 35 to systematically characterize gene expression changes during aging across diverse cell types in mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. We integrated the aging-related genes to construct a cell-wise aging score that allowed us to investigate the aging status of different cell types from a transcriptomic perspective. Overall, our analysis provides one of the most comprehensive and systematic characterization of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

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Ageing hallmarks exhibit organ-specific temporal signatures

Cited by 356 publications

References 37 publications

Single Cell ADNP Predictive of Human Muscle Disorders: Mouse Knockdown Results in Muscle Wasting

Single Cell ADNP Predictive of Human Muscle Disorders: Mouse Knockdown Results in Muscle Wasting

Single-cell identity definition using random forests and recursive feature elimination

Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures Revision 1

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