Regenerating aging cells -
Recent research has raised the exciting hypothesis that neurodegenerative disorders may stem from neurodevelopmental defects.
Highlighting this convergence between development, aging, and possibly regeneration, several talks offered a deep cellular understanding of temporal control of fate choice and neuronal wiring during brain development. Robert Johnston Johns Hopkins University, USA presented how the human retinal organoid model used in his lab recapitulates various aspects of retinal development, specifically cone specification in the foveola, the high-acuity region of the retina.
The cone composition balance seems to be highly plastic, which opens promising therapeutic avenues using retinal organoids as a powerful organ-in-a-dish test model Eldred et al. Giselle Cheung from Simon Hippenmeyer's lab ISTA, Austria investigates how cell-type diversity is generated in the superior colliculus of the mouse brain, using the very elegant MADM mosaic analysis with double markers clonal lineage-tracing model.
showed that superior colliculus progenitors are multipotent, with no visible spatial or temporal pattern governing cell fate. Pauline Spéder's lab Pasteur Institute, France is interested in the functional components of the neural stem cell niche in the Drosophila brain.
Cortex glia are an important part of this niche. It is a complex cell network of syncytial, spatially segregated units, each housing individual stem cell clones.
These glial cells can fuse, a property likely modulating signal transmission and crosstalk between stem cell subpopulations over space and time Rujano et al. Interestingly, the junctions between cortex glia and stem cells, and between the stem cells themselves, are also important for building the stereotyped architecture of the niche.
They also direct the axonal path of newborn neurons and further impact adult behavior Banach-Latapy et al. This talk highlighted a provocative concept of a modular niche, able to physically and functionally segregate distinct stem cell subpopulations, which is worth exploring in the mammalian brain.
Finally, in addition to cell fate determination, neuronal wiring choice is crucial for circuit formation during development. Maheva Andriatsilavo from Bassem Hassan's lab Paris Brain Institute, France showed how individuality emerges within Drosophila dorsal cluster neurons targeting either the proximal or distal optic lobes.
Wiring involves two successive stochastic processes Andriatsilavo et al. The first step involves activation of Notch signaling though lateral inhibition, which restricts growth.
In a second step, microtubule growth and stabilization selects a further subpopulation of Notch-OFF neurons that will finally reach the distal target. These temporally restricted, individualized wiring patterns contribute to behavioral differences between individuals.
Regeneration following injury requires cells from surrounding tissue to proliferate and replenish tissues with the correct cell identities in the right pattern. Regeneration seems to make use of many transcriptional regulators and signaling pathways that also specify tissues during development.
However, it is unclear how these are deployed to regenerate missing structures, particularly given that the starting point for regeneration is unpredictable. Michalis Averof IGFL, Lyon, France has developed a system to study leg regeneration in Parhyale hawaiensis.
He reported that, despite a large overlap in gene usage during leg development and regeneration, the temporal patterns of gene expression and the underlying mechanisms are different in both processes Sinigaglia et al. It will be interesting to explore this further in Ciona , in which cardiac development has been studied at single-cell resolution and which Lionel Christiaen reported can completely regenerate their heart, involving cells from non-cardiac lineages.
Meanwhile, Roger Revilla-i-Domingo University of Vienna, Austria has established the sponge Suberites domuncula as a model in which to study regeneration, with a complete genome assembly and single-cell transcriptomes Revilla-I-Domingo et al. This not only expands the systems that can be studied for their regenerative capacities, but also provides one with key relevance for evolutionary studies.
He showed the successful implementation of transgenesis, promising interesting new insights in the near future. Neoblasts are planarian pluripotent stem cells that faithfully replenish the cell-type diversity of missing tissue following injury.
Single-cell sequencing revealed that most neoblasts already express distinct fate-specific transcription factors that drive them preferentially to specific cell identities.
However, some neoblasts are also able to adapt their fate choice to contextual cues from the wounded tissue. This provides a framework for understanding how intrinsic and extrinsic information is integrated to drive cell-fate choices during regeneration Reddien, Hernán López-Schier New York University Abu Dhabi, UAE considered how regenerated cell types acquire the correct tissue pattern.
Studying the regeneration of the neuromast, a mechanosensory organ in zebrafish, his lab uncovered how the complex pattern of hair cells within this organ is produced by the combination of local lateral inhibition mediated by Notch and deterministic symmetry breaking mediated by Wnt signaling from surrounding tissue Kozak et al.
This regenerative potential is sustained by slow-cycling nephron progenitor cells retained in the juvenile kidney, which are characterized by low translation levels throughout the catshark's life.
The molecular mechanisms underlying the maintenance of these cells will be key to infer possible nephrogenesis reactivation routes in mammals. Faithful to this spirit, Irene Miguel-Aliaga Imperial College London, UK sparked thought-provoking discussions following her keynote lecture on the first day of the conference.
Her lab investigates sex differences in organ development and maintenance and has demonstrated key differences between male and female gut. Moreover, she discussed the important concept of organ functional crosstalk that occurs through neurons, hormones and metabolites. In Drosophila , they have uncovered intricate connections between the intestine and the reproductive system and have shown that male enterocytes are metabolically specified by their proximity to the testis.
Even adult organs with low cell-replacement dynamics, such as the brain, retain stem cells in specialized niches that are sensitive to sex and changing physiological states.
Fiona Doetsch Biozentrum, University of Basel, Switzerland discussed how the choroid plexus in mice, an epithelial structure floating in the lateral brain ventricles that contributes to the production of cerebrospinal fluid, integrates physiological fluctuations and secretes molecular signals affecting adult neural stem cells in the adjacent ventricular niche.
Her lab showed that the choroid plexus can change its secretome over short- or long-time windows, from daily circadian cycles to aging. Its secretome also differs between males and females Silva-Vargas et al. Zayna Chaker, from the Doetsch lab, presented work on pregnancy that supports an emerging model of regionally distinct adult neural stem cell subpopulations that give rise to specific neuronal or glial cell types in response to physiological or pathological stimuli.
She showed how pregnancy recruits restricted pools of adult neural stem cells to generate temporally controlled waves of neurogenesis in the mother's olfactory bulb Chaker et al.
These pregnancy-associated neurons are functional during the first week of perinatal care and disappear around weaning. Although transient, this on-demand neurogenesis is behaviorally relevant, as it regulates sensitivity to pup odor and own pup recognition.
The environment is an important regulator of development and regeneration. Abderrahman Khila's lab IGFL, Lyon, France studies a case of phenotypic plasticity observed in the males of the water strider Microvelia longipes. Male legs are disproportionally long and are used as weapons to dominate egg-laying sites and access to females.
Variation in this exaggerated growth is determined by the interaction between genetic variation and environmental factors, such as nutrition Toubiana and Khila, ; Toubiana et al. Finally, Guo Huang UCSF, USA guided the audience through an elegant evolutionary perspective of heart regeneration across 41 different vertebrate species.
Regenerative potential in mammals is known to decrease after a brief perinatal time window, but the molecular mechanisms underpinning this are still not fully understood. The Huang lab's phylogenetic analysis reveals an intriguing relationship between thermogenesis and heart regeneration capability: animals with higher body temperature also have higher metabolic rates and a lower proportion of diploid cardiomyocytes, which negatively impacts regeneration capacity.
The Huang lab also identified adrenergic and thyroid hormone signaling, both involved in thermogenesis, as two major regulators in establishing this trade-off between body temperature and heart regenerative capacity in evolution and development Hirose et al.
Two main theories have been put forward to understand why organisms age. The mutation accumulation theory argues that aging is a consequence of accumulation of near neutral variants that have a negative impact only after reproductive maturity and are thus not efficiently removed from the gene pool.
A non-mutually exclusive theory is antagonistic pleiotropy, which posits that positive selection acts on variants that increase reproductive fitness but have detrimental effects later in life. Dario Valenzano Leibniz Institute on Aging, Jena, Germany has pioneered the use of turquoise killifish to study aging.
Turquoise killifish exist as diverse populations in fragmented habitats and can vary significantly in their lifespan, providing an opportunity to explore signatures of selection.
The Valenzano lab's work suggests that mutation accumulation, which is compatible with the nearly-neutral theory of molecular evolution, is the most likely mechanism driving the lifespan differences between killifish populations.
This is largely due to their small population size and genetic isolation rendering purifying selection ineffective. Interestingly, the effective human population size is also relatively small and has gone through several bottlenecks.
It is tempting to speculate that different human populations may age differently as a result of different rates of mutation accumulation and purification Cui et al.
Ants provide another remarkable opportunity to address the epigenetic component of aging. Individuals in an ant colony are essentially identical genetically but display a large diversity of morphology and physiology.
Claude Desplan New York University, USA is using the jumping ant, Harpegnathos saltator , in which the queen lives about ten times longer than the workers. If the queen is removed from the colony, a worker takes its place. This worker changes its behavior, produces eggs and displays a lifespan expansion similar to that of the queen.
Intriguingly, the increase in lifespan of these pseudo-queens is accompanied by production of insulin to support egg production; this would be expected to shorten lifespan, not extend it. This has led to the discovery of a mechanism by which the effects of insulin signaling on reproduction and lifespan are uncoupled Yan et al.
Other talks focused on cellular functions that decay during aging. Michael Rera CNRS, INSB, France explored how loss of intestinal integrity correlates with the probability of death across individuals of different species, over time, giving new insights on the aging process Tricoire and Rera, Allison Bardin Institut Curie, Paris, France and Benjamin Boumard from her lab focused on genomic integrity during aging and showed that adult tissues become genetically mosaic as a result of mechanisms ranging from mitotic recombination to chromosome loss Riddiford et al.
Germ cells have specific maintenance and repair mechanisms to protect their genome integrity. Björn Schumacher University of Cologne, Germany showcased the power of C.
elegans as a system to uncover unexpected mechanisms affecting genome stability in the germline. He showed how somatic stress results in signaling via p38 to the female germline to induce apoptosis, and that impaired signaling results in aneuploidy in the next generation.
He also presented striking differences in how maternal and paternal DNA breaks are repaired that could help explain complex inheritance patterns in humans Soltanmohammadi et al. In many species, oocytes must survive for up to a few decades.
Oocyte quality, however, declines with maternal age, and several mechanisms have been proposed to explain this deterioration. Elvan Böke CRG, Barcelona, Spain discussed how frog and human primordial non-growing oocytes minimize damage from reactive oxygen species by drastically reducing protein levels of Complex I of their electron transport chain a major reactive oxygen species-generating complex Rodríguez-Nuevo et al.
Interestingly, loss of Complex I seems to be a recurrent adaptation during evolution Maclean et al. The Böke lab also found that assemblies of lysosomes, autophagosomes and proteasomes form in healthy mouse oocytes, sequestering aggregated proteins. These assemblies do not have degradative activity in immature oocytes, but become degradative at the final stages of oocyte growth, possibly providing a mechanism to pass an aggregate-free cytoplasm to the zygote.
Interestingly, analogous strategies to maintain proteostasis in the eggs exist in other animals, including Drosophila and C. elegans Bohnert and Kenyon, ; Fredriksson et al. Cellular senescence is a form of stress-related cell cycle arrest in which cells permanently cease division but still functionally contribute to tissue development, regeneration and aging.
Accumulation of senescent cells in a tissue leads to loss of regenerative capacity and to aging. However, transient senescence can be beneficial for development and regeneration.
Senescent cell function is thought to be mediated by secretion of various senescence-associated proteins. Bill Keyes IGMBC, Strasbourg, France showed that senescent cells can break off large membrane-bound fragments of themselves.
Two speakers discussed senescence during aging. Pura Muñoz-Cánoves Altos Labs, San Diego, USA and Pompeu Fabra University, Barcelona, Spain showed that senescent cells emerge in skeletal muscle after an injury.
These senescent cells secrete inflammatory and fibrotic factors that are detrimental to muscle regeneration.
Reducing the secretion of these inflammatory factors improves regeneration capacity in young and old mice, suggesting that the senescent cells induce cell cycle arrest of muscle stem cells via paracrine signaling Moiseeva et al. Camille Lafage, from Sandrine Humbert's group Université Grenoble Alpes, France presented how altered neural stem cell physiology in a mouse model for Huntington's disease leads to reduced adult neurogenesis in the subventricular zone.
They hypothesized that stem cells carrying the huntingtin mutation enter a senescent state. Han Li Pasteur Institute, Paris, France illustrated a positive role of senescent cells, which are important for muscle cell reprogramming.
Her lab showed that muscle can be reprogrammed to awaken stem cells, but this happens only upon injury. This context-specific plasticity is facilitated by proteins secreted by senescent cells Chiche et al. We are grateful to Claude Desplan and Allison Bardin for putting together an exciting program spanning a diverse palette of model systems and countries.
Both the talks and posters were selected to maximize interactions among all participants and foster those between young trainees and established scientists specifically. Looking forward to the next edition!
We thank Claude Desplan and Alison Bardin for a stimulating program and the participants for sharing their work, and for their feedback on the manuscript. We apologize to the poster presenters whose work we could not include owing to space limitations.
is supported by the Research Fund for Excellent Junior Researchers, University of Basel Universität Basel. The Cochella Lab is supported by Career Award from the National Science Foundation NSF. Development invites you to submit your latest research to our upcoming special issue: Uncovering Developmental Diversity.
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Skip Nav Destination Close navigation menu Article navigation. Volume , Issue Normalized ATAC-seq signal profiles of proximal promoters were visualized for key genes using the Integrative Genomic Viewer v.
We used z -score statistics to define the activity status of transcription factors from the EnrichR analysis results by matching the differential expression of target genes with activatory and inhibitory interactions from the Bioconductor package DoRothEA v.
To define the activity status of transcription factors from IPA upstream regulators analysis results, IPA-calculated z -score and analysis bias was taken into account. Activity predictions were further corrected by differential expression of transcription factors using DESeq2. The expression z -score statistical value was calculated to functionally classify transcription factors as activators or repressors on the basis of the proportion of upregulated and downregulated target genes.
We further calculated the chromatin accessibility z -score to estimate the prevalence of HOMER motif enrichment in open versus closed regions that together with the predicted transcription factor function enabled us to validate the RNA-seq activity predictions using ATAC-seq data.
For each transcription factor, we merged the target genes from EnrichR and IPA results, split them into upregulated and downregulated and processed them to functional enrichment analysis of canonical pathways KEGG, Reactome and GO:BP using R package gprofiler2 v. Electronic GO annotations were excluded.
Moreover, we scored as 1 point if the transcription factor was associated with senescence in literature. For each transcription factor upregulated, target genes from the EnrichR and IPA results were merged and intersected with the list of SASP genes.
For SASP genes, we extracted GO:MF terms, clustered them into 12 categories adhesion molecule, chemokine, complement component, cytokine, enzyme, enzyme regulator, extracellular matrix constituent, growth factor, hormone, ligand, proteinase and receptor and estimated the enrichment of GO:MF clusters with a hypergeometric test using the R function phyper.
Correction for multiple comparisons was performed using the Benjamini—Hochberg procedure. Moreover, we used ATAC-seq data analysis to score 1 point in cases in which the transcription factor motif was present in the promoter region of at least one SASP gene within the cluster.
They were associated with five GO:MF categories extracellular matrix constituent, cytokine, chemokine, complement component and growth factor , for which we selected the most common target genes.
For the analysis of lipid metabolism, we constructed a gene set using data from multiple sources: KEGG pathway maps fatty acid degradation, cholesterol metabolism, regulation of lipolysis in adipocytes , WikiPathways fatty acid oxidation, fatty acid beta oxidation, mitochondrial LC-fatty acid beta-oxidation, fatty acid omega oxidation, fatty acid biosynthesis, triacylglyceride synthesis, sphingolipid metabolism general overview , sphingolipid metabolism integrated pathway , cholesterol metabolism includes both Bloch and Kandutsch—Russell pathways and cholesterol biosynthesis and literature research , , For reconstructing cell—cell communication networks, we modified the single-cell-based method, FunRes, to account for bulk gene expression profiles In brief, transcription factors with an expression value of more than 1 TPM were considered to be expressed.
Receptors regulating these transcription factors were detected using a Markov chain model of signal transduction to detect high-probability intermediate signalling molecules Ligand—receptor interactions between two cell populations were reconstructed if 1 the receptor is expressed and regulates any transcription factor, 2 the ligand is expressed and 3 the receptor—ligand interaction is contained in the cell—cell interaction scaffold.
Finally, a score is assigned to every interaction by multiplying the average receptor and ligand expression in their respective cell populations. Significance was assessed by permuting cell population labels times and recomputing the interaction scores in the permuted datasets.
greater than the mean of the permuted interaction scores. Only significant interactions were retained in the final network. We used the Bioconductor package SPIA v.
For each interaction, differentially expressed target transcription factors in non-senescent SCs were split into upregulated and downregulated in comparison to senescent SCs.
As a reference set of genes, we took a list of target transcription factors from all of the interactions studied. For each pathway, we calculated the ratio of ligand—receptor interactions that activate or inhibit the pathway to the total number of interactions analysed.
For results representation, we selected eight activated and eight inhibited pathways with the highest ratio of interactions. The sample size of each experimental group is described in the corresponding figure caption, and all of the experiments were conducted with at least three biological replicates unless otherwise indicated.
GraphPad Prism was used for all statistical analyses except for sequencing-data analysis. represented as error bars. Results from each group were averaged and used to calculate descriptive statistics. Mann—Whitney U -tests independent samples, two-tailed were used for comparisons between groups unless otherwise indicated.
Experiments were not randomized. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. The bulk RNA-seq, scRNA-seq and ATAC-seq data supporting the findings of this study have been deposited at the GEO under accession number GSE Source data are provided with this paper.
Fuchs, E. Tissue stem cells: architects of their niches. Cell Stem Cell 27 , — Article CAS Google Scholar. Sousa-Victor, P.
Control of satellite cell function in muscle regeneration and its disruption in ageing. Cell Biol. Roy, A. et al. A blueprint for characterizing senescence.
Cell , — Franceschi, C. Chronic inflammation inflammaging and its potential contribution to age-associated diseases. A 69 , S4—S9 Article Google Scholar. Geriatric muscle stem cells switch reversible quiescence into senescence.
Nature , — Article ADS CAS Google Scholar. Garcia-Prat, L. Autophagy maintains stemness by preventing senescence. Nature , 37—42 Ho, T. Autophagy maintains the metabolism and function of young and old stem cells. Brunet, A. Ageing and rejuvenation of tissue stem cells and their niches.
Hong, X. Mitochondrial dynamics maintain muscle stem cell regenerative competence throughout adult life by regulating metabolism and mitophagy. Cell Stem Cell 29 , — Basisty, N. A proteomic atlas of senescence-associated secretomes for aging biomarker development.
PLoS Biol. Baker, D. Naturally occurring p16 Ink4a -positive cells shorten healthy lifespan. Bussian, T. Clearance of senescent glial cells prevents tau-dependent pathology and cognitive decline. Chang, J.
Clearance of senescent cells by ABT rejuvenates aged hematopoietic stem cells in mice. Childs, B. Senescent intimal foam cells are deleterious at all stages of atherosclerosis.
Science , — Xu, M. Senolytics improve physical function and increase lifespan in old age. Ng, P. Untangling senescent and damage-associated microglia in the aging and diseased brain.
FEBS J. Zhu, Y. Aging Cell 14 , — Demaria, M. An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA. Cell 31 , — Krizhanovsky, V. Senescence of activated stellate cells limits liver fibrosis.
Munoz-Espin, D. Programmed cell senescence during mammalian embryonic development. Storer, M. Senescence is a developmental mechanism that contributes to embryonic growth and patterning.
Chiche, A. Injury-induced senescence enables in vivo reprogramming in skeletal muscle. Cell Stem Cell 20 , — Mosteiro, L. Tissue damage and senescence provide critical signals for cellular reprogramming in vivo. Science , aaf Ocampo, A. In vivo amelioration of age-associated hallmarks by partial reprogramming.
Zhang, J. A novel near-infrared fluorescent probe for sensitive detection of β-galactosidase in living cells. Acta , 97— Debacq-Chainiaux, F. Protocols to detect senescence-associated beta-galactosidase SA-βgal activity, a biomarker of senescent cells in culture and in vivo.
Serrano, A. Fibrosis development in early-onset muscular dystrophies: mechanisms and translational implications. Cell Dev. Prieto, L. Insights from in vivo studies of cellular senescence. Cells 9 , Moiseeva, V.
Context-dependent roles of cellular senescence in normal, aged, and disease states. Wree, A. NLRP3 inflammasome activation is required for fibrosis development in NAFLD.
Chan, M. Novel insights from a multiomics dissection of the Hayflick limit. eLife 11 , e Tilstra, J. NF-κB inhibition delays DNA damage-induced senescence and aging in mice. Kuilman, T. Oncogene-induced senescence relayed by an interleukin-dependent inflammatory network.
Kandhaya-Pillai, R. TNFalpha-senescence initiates a STAT-dependent positive feedback loop, leading to a sustained interferon signature, DNA damage, and cytokine secretion. Aging 9 , — Meng, X. TGF-β: the master regulator of fibrosis.
Benayoun, B. Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. Genome Res. Stegeman, R. Transcriptional signatures of aging. Shavlakadze, T. Age-related gene expression signature in rats demonstrate early, late, and linear transcriptional changes from multiple tissues.
Cell Rep. Jung, S. FunRes: resolving tissue-specific functional cell states based on a cell-cell communication network model. Tarca, A. A novel signaling pathway impact analysis. Bioinformatics 25 , 75—82 Nazir, S. Interaction between high-density lipoproteins and inflammation: function matters more than concentration!
Drug Deliv. Chong, M. CD36 initiates the secretory phenotype during the establishment of cellular senescence. EMBO Rep. Saitou, M. An evolutionary transcriptomics approach links CD36 to membrane remodeling in replicative senescence. Omics 14 , — Yousefzadeh, M.
An aged immune system drives senescence and ageing of solid organs. Hernandez-Segura, A. Unmasking transcriptional heterogeneity in senescent cells. Kang, C. The DNA damage response induces inflammation and senescence by inhibiting autophagy of GATA4.
Science , aaa Vizioli, M. Mitochondria-to-nucleus retrograde signaling drives formation of cytoplasmic chromatin and inflammation in senescence.
Genes Dev. West, A. Mitochondrial DNA stress primes the antiviral innate immune response. Article ADS Google Scholar. De Cecco, M. L1 drives IFN in senescent cells and promotes age-associated inflammation. Nature , 73—78 Saito, Y.
Exercise enhances skeletal muscle regeneration by promoting senescence in fibro-adipogenic progenitors.
Suelves, M. uPA deficiency exacerbates muscular dystrophy in MDX mice. Grounds, M. Strength at the extracellular matrix-muscle interface.
Sports 15 , — Segales, J. Sestrin prevents atrophy of disused and aging muscles by integrating anabolic and catabolic signals. Sacco, A.
Le, G. Freeze injury of the tibialis anterior muscle. Methods Mol. Fumagalli, M. Telomeric DNA damage is irreparable and causes persistent DNA-damage-response activation.
FoxO maintains a genuine muscle stem-cell quiescent state until geriatric age. Ewels, P. The nf-core framework for community-curated bioinformatics pipelines.
Andrews, S. Krueger, F. Cunningham, F. Ensembl Nucleic Acids Res. Kim, D. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Liao, Y. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.
Bioinformatics 30 , — Robinson, M. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , — Love, M. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
Genome Biol. Raudvere, U. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists update. Kanehisa, M.
KEGG: Kyoto Encyclopedia of Genes and Genomes. Jassal, B. The reactome pathway knowledgebase. CAS Google Scholar.
Martens, M. WikiPathways: connecting communities. Subramanian, A. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Natl Acad.
USA , — Liberzon, A. Molecular signatures database MSigDB 3. Bioinformatics 27 , — Merico, D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5 , e Kucera, M. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations.
FResearch 5 , Shannon, P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Ashburner, M. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. UniProt, C. UniProt: a worldwide hub of protein knowledge.
Cortazar, A. VerSeDa: vertebrate secretome database. Database , baw Thul, P. A subcellular map of the human proteome. Science , eaal Freund, A. Inflammatory networks during cellular senescence: causes and consequences.
Trends Mol. Perl, K. mHG: minimum-hypergeometric test. R package version 1. The GTEx Consortium. The Genotype-Tissue Expression GTEx project. Kaminow, B. Frankish, A. GENCODE reference annotation for the human and mouse genomes.
Hao, Y. Integrated analysis of multimodal single-cell data. McGinnis, C. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. Corces, M. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues.
Methods 14 , — Picelli, S. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Chen, S. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34 , i—i Langmead, B. Fast gapped-read alignment with Bowtie 2.
Methods 9 , — Danecek, P. Twelve years of SAMtools and BCFtools. Gigascience 10 , giab Ramirez, F. deepTools: a flexible platform for exploring deep-sequencing data. Zhang, Y. Model-based analysis of ChIP-Seq MACS. Genome biology 9 , R Quinlan, A. BEDTools: a flexible suite of utilities for comparing genomic features.
Heinz, S. Simple combinations of lineage-determining transcription factors prime cis -regulatory elements required for macrophage and B cell identities. Cell 38 , — Robinson, J. Integrative genomics viewer.
Kuleshov, M. Enrichr: a comprehensive gene set enrichment analysis web server update. Kramer, A. Causal analysis approaches in ingenuity pathway analysis.
Garcia-Alonso, L. Benchmark and integration of resources for the estimation of human transcription factor activities. Han, H. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Kolberg, L. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler.
FResearch 9 , ELIXIR Flor, A. A signature of enhanced lipid metabolism, lipid peroxidation and aldehyde stress in therapy-induced senescence. Cell Death Discov. Stahl, A. A current review of fatty acid transport proteins SLC Pflugers Arch. Zani, I.
Scavenger receptor structure and function in health and disease. Cells 4 , — Ravichandran, S. SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.
Bioinformatics 36 , — Google Scholar. Download references. We thank M. Jardí, A. Navarro, J. Ballestero, K. Slobodnyuk, M. González, J. López and M. Raya for their technical contributions; A. Harada and K.
Tanaka for assistance in ATAC-seq; all of the members of the P. laboratory for discussions; J. Campisi for pMR mice; J. Fernández-Blanco PRBB Animal Facility ; O. Rebollo IBMB Molecular Imaging Platform ; V.
Raker for manuscript editing; and the members of the Myoage network A. Maier for human material. We acknowledge funding from MINECO-Spain RTI, to P. and E. and A. were supported by FPI and Maria-de-Maeztu predoctoral fellowships, respectively, and V.
by a Marie Skłodowska-Curie individual fellowship. Parts of the figures were drawn using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3. Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.
Victoria Moiseeva, Andrés Cisneros, Valentina Sica, Oleg Deryagin, Eva Andrés, Jessica Segalés, Laura Ortet, Vera Lukesova, Antonio L. Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China. Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China. CIC bioGUNE-BRTA Basque Research and Technology Alliance , Bizkaia Technology Park, Derio, Spain.
University of Science and Technology of China, Hefei, China. Genomic Unit, Centro Nacional de Investigaciones Cardiovasculares and CIBERCV, Madrid, Spain. Institute for Research in Biomedicine and BIST, Barcelona, Spain.
Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg. Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.
Division of Transcriptomics. Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan. Antonio L. Cardiovascular Regeneration Program, CNIC Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain. You can also search for this author in PubMed Google Scholar.
Conceptualization: P. and V. Formal analysis: V. Investigation: V. Writing—original draft: P. Writing—review and editing: P. Funding acquisition: P. Resources: Y. Visualization: V.
and P. Supervision: P. Correspondence to Eusebio Perdiguero or Pura Muñoz-Cánoves. Nature thanks Tamara Tchkonia and the other, anonymous, reviewer s for their contribution to the peer review of this work. a Quantification of Renilla luciferase activity in regenerating muscle from young and old mice at the indicated d.
c RT-qPCR of mRFP, Cdkn2a , and p19 ARF in young and old muscle tissue from pMR mice at the indicated d. and Cdkn2a for young 3 d. d Freshly isolated SCs were obtained from skeletal muscle tissue and cultured in the presence of etoposide 1 μM or DMSO for 4 days.
Unstained samples were used to determine the threshold for C12FDG and SPiDER populations. Histogram representation of SPiDER intensity, representative images of SA-β-gal and quantification are shown.
e Histogram representation of SPiDER-β-gal staining and gating strategy employed for isolation of SPiDER Low , SPiDER Medium , and SPiDER High populations from injured skeletal muscle at 3 d.
regenerating tissue of young mice. Scoring was calculated with cell size, SA-β-gal, lamin B1, and proliferation rate Fig. Cells were divided into two major populations with anti-CD45 antibodies to overcome differences in auto-fluorescence of hematopoietic and non-hematopoietic populations.
Each cell type was labelled with indicated antibodies and nuclei with 4,6-diaminidophenylindole DAPI. Results are displayed as means±s. Young and old pMR mice were subjected to CTX injury, treated with vehicle or GCV during the course of regeneration and analysed at 7 d.
c Force measurements in EDL muscles of vehicle- or GCV-treated old pMR mice at 10 d. e Force measurements in EDL muscles of vehicle- or GCV-treated young pMR mice at 10 d. g Muscles of young WT mice were injured with CTX and treated with vehicle or senolytics during regeneration and analysed at 7 d.
Treatments were administered from 3 to 7 d. Scale bars 50 μm. Results are displayed as mean ± s. and 5 d. c Young pMR mice were subjected to micropunctures in TA muscles and treated for 7 days with GCV, starting from the day of injury, sacrificed at 7 d.
and TA muscles were analysed. i Representative images of SA-β-gal and MYH3 staining in cryosections of TA muscles from young WT and mdx mice. a Gating strategy used to simultaneously isolate SCs, FAPs, and MCs from WT mice.
Representative histogram plots from cytofluorimetric analysis are employed to assess SPiDER levels in the cell populations. FMO controls and non-injured samples were used to determine the threshold for SPiDER within each cell population.
b Representative pictures of Pax7, TCF4 and CD11b expression in sorted SCs, FAPs and MCs respectively.
c Heatmap of gene expression levels of the indicated genes in basal, NSen and Sen SCs, FAPs and MCs. d Single-cell expression levels for select gene markers. h BrdU incorporation was quantified in cells obtained at 7 d.
control and pos. control cells. a Scheme showing 36 different conditions 3 cell types x 12 conditions assessed by RNA-seq. b Principal component analysis PCA of the full transcriptome of senescent Sen , non-senescent NSen , and Basal SCs, FAPs, and MCs isolated from resting Basal and regenerating muscles of young and old mice at 3 and 7 d.
c PCA of Sen, NSen, and Basal SCs, FAPs, and MCs from basal and regenerating muscles at 3 d. of young and old mice. d Scheme indicating the number of differentially expressed genes in Sen vs NSen SCs, FAPs, and MCs from young and old mice at 3 and 7 d.
e Venn-diagram showing the overlap between differentially expressed genes in SCs, FAPs, and MCs from young mice at 3 d. f Heatmap of genes that were differentially expressed DE uniquely by one population of interest and the corresponding canonical pathways enrichment CP analysis g:Profiler web server.
The heatmap shows log 2 FC for Sen versus vs their NSen counterparts isolated from regenerating muscles of young mice at 3 d. a top Heatmap of unique and common differentially expressed genes in young Y or old O Sen FAPs and CP enrichment g:Profiler web server of exclusively differentially expressed genes in G conditions.
bottom Venn-diagram showing the overlap between differentially expressed genes of Sen FAPs from young and old mice at 3 d. b As in a for MCs. c As in a for SCs. d Clusters of gene sets GSEA differentially enriched at 3 d. in old Sen populations, but not in young Sen populations.
Grey edges indicate gene overlap. a Clusters of gene sets GSEA differentially enriched from Sen vs Basal SCs, FAPs, and MCs from young and old mice at 3 d.
a Venn-diagram showing the overlap between differentially expressed genes in SCs, FAPs, and MCs from old mice at 3 d.
b Heatmap of commonly regulated genes in Sen SCs, FAPs, and MCs from young and old mice at 3 and 7 d. d Common clusters of gene sets GSEA from Sen vs NSen and Sen vs basal SCs, FAPs and MCs from young and old mice at 3 and 7 d.
a Differential ATAC-seq peaks for Sen vs NSen SCs, FAPs, and MCs from old mice at 3 and 7 d. b Normalized ATAC-seq signal profiles in the indicated gene regions from Sen vs NSen SCs, FAPs, and MCs from young and old mice at 7 d.
a Scheme indicating the number of upregulated SASP genes in SCs, FAPs, and MCs from young and old mice at 3 and 7 d. b Clusters of gene sets enriched in SASP-related genes from Sen SCs, FAPs, and MCs from old mice at 3 d.
SASP genes were identified using different published databases see methods. c Heatmap of upregulated SASP genes in Sen SCs, FAPs and MCs from young and old mice at 3 and 7 d. from young or old mice cultured for 24 hours in serum-deprived media, then the conditioned media were collected and the levels of the indicated protein were assessed.
Graphs represent the top 10 proteins whose levels were increased in the comparison. Common secreted proteins are indicated. a Mice were subjected to CTX injury and treated with either vehicle, bortezomib or SIS3 during the course of regeneration and analysed at 5 d.
Graphs represent the top 10 proteins whose levels were decreased in the comparison. e Cytoscape network showing ligand-receptor L-R interactions between Sen populations and NSen SCs from old mice at 3 d. predicted by a modified version of FunRes.
f Major activated and inhibited KEGG pathways predicted by SPIA in NSen SCs downstream the predicted interactions showed in e. Ratio of interactions represents the proportion of L-R that induce the pathway of interest. g pMR mice were injured with CTX and daily treated with vehicle or GCV from the day of injury to 4 d.
Representative images of EdU and Pax7 staining, arrows indicate double-positive cells related to Fig. h SPiDER — SCs were isolated at 3 d. After 3 days of culture, SC proliferation was assessed by BrdU incorporation. Representative images of BrdU staining are shown related to Fig.
i EDL from either WT or pMR donor mice were transplanted into WT or pMR recipient mice or vice versa. Recipient mice were treated every day with GCV, and muscle regeneration was analysed at 7 d. Representative images of MYH3 staining are shown related to Fig.
a Subnetwork of significant Cd36 upstream and downstream signalling interactions pulled out from FunRes global signalling interaction network for Sen SCs population at 3 d. Green nodes are related to NF-κB cascade, orange to MAPK signalling and violet to interferon regulatory factors IRFs.
b C2C12 cells were treated with etoposide to induce cellular senescence and harvested at the indicated time points. c TA muscles of young mice were subjected to CTX injury and mice were treated with control IgA or anti-CD36 antibody from 3 to 7 d. once per day. f Representative pictures of MYH3 and Sirius Red staining of regenerating TA muscles from IgA or anti-CD36 antibody-treated old mice at 7 d.
related to Fig. h EDL muscles of old mice were injured with CTX and mice were treated with IgA or anti-CD36 antibodies from 3 to 10 d. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions.
Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration. Download citation. Received : 23 December Accepted : 07 November Published : 21 December Issue Date : 05 January Anyone you share the following link with will be able to read this content:.
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Thank you for agihg nature. You are Probiotics for urinary tract health a browser version with limited support for CSS. To Gluten-free diet and gut health the best experience, we Ccells you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. An Author Correction to this article was published on 06 February Tissue regeneration requires coordination between resident stem cells and local niche cells 12. Luisa RegeneratinggZayna Chaker; Development, Regenerating aging cells and aging: a bizarre love triangle. Development 1 October ; 19 : dev The conference took place in Rgeenerating, France, where participants Regeneating recent conceptual advances Regeneating the general Regenerating aging cells that developmental processes do not end with embryogenesis. The meeting covered various aspects of how development relates to fitness, regeneration and aging across a refreshing diversity of evolutionarily distant organisms. The meeting had a friendly and open-minded atmosphere, triggering stimulating scientific discussions. Here, we provide an overview of the conference's main themes, including how developmental events shape regeneration and aging trajectories, how aging affects regeneration and the development of offspring, and the molecular mechanisms that are at play in these three inter-related processes.
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