An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

Angelidis, Ilias; Simon, Lukas M.; Fernandez, Isis E.; Strunz, Maximilian; Mayr, Christoph H.; Greiffo, Flavia R.; Tsitsiridis, George; Ansari, Meshal; Graf, Elisabeth; Strom, Tim-Matthias; Nagendran, Monica; Desai, Tushar J.; Eickelberg, Oliver; Mann, Matthias; Theis, Fabian J.; Schiller, Herbert B.

doi:10.1038/s41467-019-08831-9

Cited by 483 publications

(417 citation statements)

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“…Transcriptomic analyses of aging tissues have revealed some common changes, such as increased inflammatory pathways in several tissues (Lee et al 2002;Rodwell et al 2004;O'Brown et al 2015;Su et al 2015;Benayoun et al 2019). Proteomic analyses of aging tissues have also found changes in immunological and stress response pathways in multiple organs (Ori et al 2015;Angelidis et al 2019).…”

Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

et al. 2019

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Aging is a pleiotropic process affecting many aspects of mammalian physiology. Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear. Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types. We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals. 2

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Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

et al. 2019

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“…In addition to functional diversity found in normal development, transcriptional heterogeneity can arise from pathological or compensatory processes associated with aging [31][32][33] . Skeletal muscle undergoes dramatic age-related changes with respect to functional deterioration and regenerative decline [34][35][36][37] , and so we next asked if the aging process impacted myonuclear transcription.…”

Section: Mainmentioning

confidence: 99%

Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

Petrany

Swoboda

Sun

et al. 2020

Preprint

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While the majority of cells contain a single nucleus, cell types such as trophoblasts, osteoclasts, and skeletal myofibers require multinucleation. One advantage of multinucleation can be the assignment of distinct functions to different nuclei, but comprehensive interrogation of transcriptional heterogeneity within multinucleated tissues has been challenging due to the presence of a shared cytoplasm. Here, we utilized single-nucleus RNA-sequencing (snRNA-seq) to determine the extent of transcriptional diversity within multinucleated skeletal myofibers. Nuclei from mouse skeletal muscle were profiled across the lifespan, which revealed the emergence of distinct myonuclear populations in postnatal development and their reactivation in aging muscle. Our datasets also provided a platform for discovery of novel genes associated with rare specialized regions of the muscle cell, including markers of the myotendinous junction and functionally validated factors expressed at the neuromuscular junction. These findings reveal that myonuclei within syncytial muscle fibers possess distinct transcriptional profiles that regulate muscle biology.

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“…The recently developed SoupX (preprint: Young & Behjati, ) uses this approach to directly correct the count data. Pragmatic approaches that ignore strongly ambient genes in downstream analysis have also been used to tackle this problem (Angelidis et al , ).…”

Section: Introductionmentioning

confidence: 99%

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

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Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

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An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

Cited by 483 publications

References 68 publications

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

Single-nucleus RNA-seq identifies transcriptional heterogeneity in multinucleated skeletal myofibers

Current best practices in single‐cell RNA‐seq analysis: a tutorial

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