Heterogeneity in old fibroblasts is linked to variability in reprogramming and wound healing

Abstract

Author contributions L.X. and A.M contributed equally to this study. S.M., E.M. and A.B. planned the study, with input from M.P.S. and J.W. S.M. and E.M. performed the reprogramming experiments, and analysed and interpreted data. S.M., E.M. and L.X. wrote the manuscript with the help of A.B. S.M. generated, processed and analysed bulk and single-cell RNA-seq datasets, analysed the metabolomics data, and performed most THY1-related and conditioned medium experiments. E.M. generated and propagated transgene-free iPS cell lines. All other studies were done by both E.M and S.M., unless otherwise noted. A.M. and S.M. performed wound healing experiments under the supervision of M.T.L. L.X. helped with reprogramming, FACS, and immunofluorescence experiments. F.J. generated the in vitro single-cell RNA-seq data under the supervision of M.P.S. R.S. generated the ChIP-seq libraries under the supervision of J.W. K.H. helped with statistics and PAGODA analysis. X.L. performed metabolomics experiments and helped with metabolomics data analysis and validation under the supervision of M.P.S. K.D. helped with reprogramming and western blotting experiments. L.P. helped with reprogramming and RT-qPCR experiments. C.E.A. and Y.S. performed the induced neuron reprogramming experiment under the supervision of M.W. B.A.B. helped with analysis of the epigenomic data. A.L.S.C. identified and collected the human samples. All authors discussed the results and commented on the manuscript. Data availabilityAll raw sequencing reads for population RNA-seq, ChIP-seq and single-cell RNA-seq data can be found under BioProject PRJNA316110. The command and configuration files, in addition to a list of all versioned dependencies present in the running environment, are available on the Github repository for this paper (https://github.com/brunetlab/Mahmoudi_et_al_2018) (except for the code for the processing of metabolomics data, which is available upon request).

TGF-β-driven downregulation of the Wnt/β-Catenin transcription factor TCF7L2/TCF4 in PDGFRα+fibroblasts

“…These PDGFRα + MSCs are found in most tissues, including bone marrow, heart, kidney, muscle, nerves, liver, lung, and skin in which they play crucial roles (Carr et al, 2019; Lemos and Duffield, 2018; Lynch and Watt, 2018; Rognoni et al, 2018). Despite their required normal activity during muscle regeneration (Joe et al, 2010; Heredia et al, 2013; Mathew et al, 2011; Fiore et al, 2016; Wosczyna et al, 2019), we and others have reported dysregulated behavior of these precursor cells in models of acute and chronic muscle damage, muscular dystrophy (MD), neurodegenerative diseases, and aging (Acuña et al, 2014; Contreras et al, 2016; Contreras et al, 2019c; González et al, 2017; Kopinke et al, 2017; Madaro et al, 2018; Mahmoudi et al, 2019; Lemos et al, 2015; Lukjanenko et al, 2019; Uezumi et al, 2014a). A common outcome of the dysregulation of these cells is fibrosing disorders, which include non-malignant fibroproliferative diseases with high morbidity and mortality (Lemos and Duffield, 2018; Wynn and Ramalingam, 2012).…”

“…The study of the heterogeneity of tissue-resident mesenchymal stromal populations emerges as an attractive field to underpin regeneration versus degenerative fibrosis (Mahmoudi et al, 2019; Lemos and Duffield, 2018; Lynch and Watt, 2018). Central to this idea is that different MSC populations and their lineage may have intrinsic properties that favor either permanent scar formation or regeneration via scar regression (Driskell et al, 2013; Malecova et al, 2018; Plikus et al, 2017; Rinkevich et al, 2015; Rognoni et al, 2018; Soliman et al, 2019 preprint; Furtado et al, 2016).…”

Reprogramming progressive cells display low CAG promoter activity

“…Variability in inflammatory cytokine production (Mahmoudi et al, 2019) and cellular competition (Shakiba et al, 2019) have been recently reported to mediate different reprogramming behaviors. Furthermore, we recently described a non-cell-autonomous mode of regulation of reprogramming by the transcription co-activator YAP via one of the secreted matricellular protein CYR61 (Hartman A.S. Scalf S.M., 2018).…”

Fractional re-distribution among cell motility states during ageing

“…Combining the spatial and activity patterns of single cells, we defined age-dependent motility states (Figure 4A-B). This approach highlights the immense amount of information that can be extracted at the single-cell level 5,19,28 , and demonstrates that dermal fibroblasts derived from healthy donors comprise a mixture of motile and non-motile cells, regardless of age (young, post-adolescent/adults or older adults). This is an important finding since it provides a novel outlook of ageing at the cellular level, and a potential mechanism of how populations of cells encode and manifest age-dependent phenotypes.…”

“…In humans, normal ageing is associated with diverse physiological changes that influence the magnitude and rates of progressive decline among individuals. These include the decreased abundance and activity of circulating cytotoxic immune cells, slower gait speed and declines in cardiorespiratory fitness [4][5][6][7][8] . There is also a high degree of variability between individuals, which suggests that there is not a uniform aging phenotype.…”