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Macrophages Are Polarized toward an Inflammatory Phenotype by their Aged Microenvironment in the Human Skin

      Aging of the skin is accompanied by cellular as well as tissue environmental changes, ultimately reducing the ability of the tissue to regenerate and adequately respond to external stressors. Macrophages are important gatekeepers of tissue homeostasis, and it has been reported that their number and phenotype change during aging in a site-specific manner. How aging affects human skin macrophages and what implications this has for the aging process in the tissue are still not fully understood. Using single-cell RNA-sequencing analysis, we show that there is at least a 50% increase of macrophages in human aged skin, which appear to have developed from monocytes and exhibit more proinflammatory M1-like characteristics. In contrast, the cell-intrinsic ability of aged monocytes to differentiate into M1 macrophages was reduced. Using coculture experiments with aged dermal fibroblasts, we show that it is the aged microenvironment that drives a more proinflammatory phenotype of macrophages in the skin. This proinflammatory M1-like phenotype in turn negatively influenced the expression of extracellular matrix proteins by fibroblasts, emphasizing the impact of the aged macrophages on the skin phenotype.

      Graphical abstract

      Abbreviations:

      DEG (differentially expressed gene), ECM (extracellular matrix), FC (fold change), MDM (monocyte-derived macrophage), pM1 (partially polarized macrophage M1), scRNA-Seq (single-cell RNA sequencing)
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      References

        • Aegerter H.
        • Kulikauskaite J.
        • Crotta S.
        • Patel H.
        • Kelly G.
        • Hessel E.M.
        • et al.
        Influenza-induced monocyte-derived alveolar macrophages confer prolonged antibacterial protection.
        Nat Immunol. 2020; 21: 145-157
        • Bettinger D.A.
        • Yager D.R.
        • Diegelmann R.F.
        • Cohen I.K.
        The effect of TGF-beta on keloid fibroblast proliferation and collagen synthesis.
        Plast Reconstr Surg. 1996; 98: 827-833
        • Chan L.C.
        • Rossetti M.
        • Miller L.S.
        • Filler S.G.
        • Johnson C.W.
        • Lee H.K.
        • et al.
        Protective immunity in recurrent Staphylococcus aureus infection reflects localized immune signatures and macrophage-conferred memory.
        Proc Natl Acad Sci USA. 2018; 115: E11111-E11119
        • Cho R.H.
        • Sieburg H.B.
        • Muller-Sieburg C.E.
        A new mechanism for the aging of hematopoietic stem cells: aging changes the clonal composition of the stem cell compartment but not individual stem cells.
        Blood. 2008; 111: 5553-5561
        • Costantini A.
        • Viola N.
        • Berretta A.
        • Galeazzi R.
        • Matacchione G.
        • Sabbatinelli J.
        • et al.
        Age-related M1/M2 phenotype changes in circulating monocytes from healthy/unhealthy individuals.
        Aging (Albany NY). 2018; 10: 1268-1280
        • Covarrubias A.J.
        • Kale A.
        • Perrone R.
        • Lopez-Dominguez J.A.
        • Pisco A.O.
        • Kasler H.G.
        • et al.
        Senescent cells promote tissue NAD+ decline during ageing via the activation of CD38+ macrophages.
        Nat Metab. 2020; 2 ([published correction appears in Nat Metab 2021;3:120‒1]): 1265-1283
        • Dimri G.P.
        • Lee X.
        • Basile G.
        • Acosta M.
        • Scott G.
        • Roskelley C.
        • et al.
        A biomarker that identifies senescent human cells in culture and in aging skin in vivo.
        Proc Natl Acad Sci USA. 1995; 92: 9363-9367
        • Erol A.
        PPARalpha activators may be good candidates as antiaging agents.
        Med Hypotheses. 2005; 65: 35-38
        • Erol A.
        The functions of PPARs in aging and longevity.
        PPAR Res. 2007; 2007: 39654
        • Feru J.
        • Delobbe E.
        • Ramont L.
        • Brassart B.
        • Terryn C.
        • Dupont-Deshorgue A.
        • et al.
        Aging decreases collagen IV expression in vivo in the dermo-epidermal junction and in vitro in dermal fibroblasts: possible involvement of TGF-β1.
        Eur J Dermatol. 2016; 26: 350-360
        • Finak G.
        • McDavid A.
        • Yajima M.
        • Deng J.
        • Gersuk V.
        • Shalek A.K.
        • et al.
        MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
        Genome Biol. 2015; 16: 278
        • Franceschi C.
        • Bonafè M.
        • Valensin S.
        • Olivieri F.
        • De Luca M.
        • Ottaviani E.
        • et al.
        Inflamm-aging. an evolutionary perspective on immunosenescence.
        Ann N Y Acad Sci. 2000; 908: 244-254
        • Franceschi C.
        • Garagnani P.
        • Parini P.
        • Giuliani C.
        • Santoro A.
        Inflammaging: a new immune-metabolic viewpoint for age-related diseases.
        Nat Rev Endocrinol. 2018; 14: 576-590
        • Franceschi C.
        • Garagnani P.
        • Vitale G.
        • Capri M.
        • Salvioli S.
        Inflammaging and 'garb-aging'.
        Trends Endocrinol Metab. 2017; 28: 199-212
        • Ginhoux F.
        • Guilliams M.
        Tissue-resident macrophage ontogeny and homeostasis.
        Immunity. 2016; 44: 439-449
        • Guilliams M.
        • Svedberg F.R.
        Does tissue imprinting restrict macrophage plasticity?.
        Nat Immunol. 2021; 22: 118-127
        • Hao Y.
        • Hao S.
        • Andersen-Nissen E.
        • Mauck 3rd, W.M.
        • Zheng S.
        • Butler A.
        • et al.
        Integrated analysis of multimodal single-cell data.
        Cell. 2021; 184: 3573-3587.e29
        • He H.
        • Suryawanshi H.
        • Morozov P.
        • Gay-Mimbrera J.
        • Del Duca E.
        • Kim H.J.
        • et al.
        Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis.
        J Allergy Clin Immunol. 2020; 145: 1615-1628
        • Hearps A.C.
        • Martin G.E.
        • Angelovich T.A.
        • Cheng W.J.
        • Maisa A.
        • Landay A.L.
        • et al.
        Aging is associated with chronic innate immune activation and dysregulation of monocyte phenotype and function.
        Aging Cell. 2012; 11: 867-875
        • Italiani P.
        • Boraschi D.
        From monocytes to M1/M2 macrophages: phenotypical vs. functional Differentiation.
        Front Immunol. 2014; 5: 514
        • Jackaman C.
        • Radley-Crabb H.G.
        • Soffe Z.
        • Shavlakadze T.
        • Grounds M.D.
        • Nelson D.J.
        Targeting macrophages rescues age-related immune deficiencies in C57BL/6J geriatric mice.
        Aging Cell. 2013; 12: 345-357
        • Korsunsky I.
        • Millard N.
        • Fan J.
        • Slowikowski K.
        • Zhang F.
        • Wei K.
        • et al.
        Fast, sensitive and accurate integration of single-cell data with Harmony.
        Nat Methods. 2019; 16: 1289-1296
        • Lavker R.M.
        • Zheng P.S.
        • Dong G.
        Aged skin: a study by light, transmission electron, and scanning electron microscopy.
        J Invest Dermatol. 1987; 88: 44s-51s
        • Linton P.J.
        • Dorshkind K.
        Age-related changes in lymphocyte development and function.
        Nat Immunol. 2004; 5: 133-139
        • Love M.I.
        • Huber W.
        • Anders S.
        Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
        Genome Biol. 2014; 15: 550
        • Lumeng C.N.
        • Liu J.
        • Geletka L.
        • Delaney C.
        • Delproposto J.
        • Desai A.
        • et al.
        Aging is associated with an increase in T cells and inflammatory macrophages in visceral adipose tissue.
        J Immunol. 2011; 187: 6208-6216
        • Lun A.T.
        • McCarthy D.J.
        • Marioni J.C.
        A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.
        F1000Res. 2016; 5: 2122
        • McCabe M.C.
        • Hill R.C.
        • Calderone K.
        • Cui Y.
        • Yan Y.
        • Quan T.
        • et al.
        Alterations in extracellular matrix composition during aging and photoaging of the skin.
        Matrix Biol Plus. 2020; 8: 100041
        • McGinnis C.S.
        • Murrow L.M.
        • Gartner Z.J.
        DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors.
        Cell Syst. 2019; 8: 329-337.e4
        • Melamed D.
        • Scott D.W.
        Aging and neoteny in the B lineage.
        Blood. 2012; 120: 4143-4149
        • Palmer D.B.
        The effect of age on thymic function.
        Front Immunol. 2013; 4: 316
        • Parkinson H.
        • Kapushesky M.
        • Shojatalab M.
        • Abeygunawardena N.
        • Coulson R.
        • Farne A.
        • et al.
        ArrayExpress–a public database of microarray experiments and gene expression profiles.
        Nucleic Acids Res. 2007; 35: D747-D750
        • Plunde O.
        • Larsson S.C.
        • Artiach G.
        • Thanassoulis G.
        • Carracedo M.
        • Franco-Cereceda A.
        • et al.
        FADS1 (fatty acid desaturase 1) genotype associates with aortic valve FADS mRNA expression, fatty acid content and calcification.
        Circ Genom Precis Med. 2020; 13 (e002710)
        • Poynter M.E.
        • Daynes R.A.
        Peroxisome proliferator-activated receptor alpha activation modulates cellular redox status, represses nuclear factor-kappaB signaling, and reduces inflammatory cytokine production in aging.
        J Biol Chem. 1998; 273: 32833-32841
        • Quintin J.
        • Saeed S.
        • Martens J.H.A.
        • Giamarellos-Bourboulis E.J.
        • Ifrim D.C.
        • Logie C.
        • et al.
        Candida albicans infection affords protection against reinfection via functional reprogramming of monocytes.
        Cell Host Microbe. 2012; 12: 223-232
        • Ressler S.
        • Bartkova J.
        • Niederegger H.
        • Bartek J.
        • Scharffetter-Kochanek K.
        • Jansen-Dürr P.
        • et al.
        p16INK4A is a robust in vivo biomarker of cellular aging in human skin.
        Aging Cell. 2006; 5: 379-389
        • Reynolds G.
        • Vegh P.
        • Fletcher J.
        • Poyner E.F.M.
        • Stephenson E.
        • Goh I.
        • et al.
        Developmental cell programs are co-opted in inflammatory skin disease.
        Science. 2021; 371 (eaba6500)
        • Rezzani R.
        • Nardo L.
        • Favero G.
        • Peroni M.
        • Rodella L.F.
        Thymus and aging: morphological, radiological, and functional overview.
        Age (Dordr). 2014; 36: 313-351
        • Risinger Jr., G.M.
        • Updike D.L.
        • Bullen E.C.
        • Tomasek J.J.
        • Howard E.W.
        TGF-beta suppresses the upregulation of MMP-2 by vascular smooth muscle cells in response to PDGF-BB.
        Am J Physiol Cell Physiol. 2010; 298: C191-C201
        • Rojahn T.B.
        • Vorstandlechner V.
        • Krausgruber T.
        • Bauer W.M.
        • Alkon N.
        • Bangert C.
        • et al.
        Single-cell transcriptomics combined with interstitial fluid proteomics defines cell type-specific immune regulation in atopic dermatitis.
        J Allergy Clin Immunol. 2020; 146: 1056-1069
        • Schulz D.
        • Severin Y.
        • Zanotelli V.R.T.
        • Bodenmiller B.
        In-depth characterization of monocyte-derived macrophages using a mass cytometry-based phagocytosis assay.
        Sci Rep. 2019; 9: 1925
        • Solé-Boldo L.
        • Raddatz G.
        • Schütz S.
        • Mallm J.P.
        • Rippe K.
        • Lonsdorf A.S.
        • et al.
        Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming.
        Commun Biol. 2020; 3: 188
        • Stuart T.
        • Butler A.
        • Hoffman P.
        • Hafemeister C.
        • Papalexi E.
        • Mauck 3rd, W.M.
        • et al.
        Comprehensive integration of single-cell data.
        Cell. 2019; 177 (e21): 1888-1902
        • Swift M.E.
        • Kleinman H.K.
        • DiPietro L.A.
        Impaired wound repair and delayed angiogenesis in aged mice.
        Lab Invest. 1999; 79: 1479-1487
        • Tabib T.
        • Morse C.
        • Wang T.
        • Chen W.
        • Lafyatis R.
        SFRP2/DPP4 and FMO1/LSP1 define major fibroblast populations in human skin.
        J Invest Dermatol. 2018; 138 ([published correction appears in J Invest Dermatol 2018;138:2086]): 802-810
        • Tamoutounour S.
        • Guilliams M.
        • Montanana Sanchis F.
        • Liu H.
        • Terhorst D.
        • Malosse C.
        • et al.
        Origins and functional specialization of macrophages and of conventional and monocyte-derived dendritic cells in mouse skin.
        Immunity. 2013; 39: 925-938
        • Ahlers J.M.D.
        • Falckenhayn C.
        • Holzscheck N.
        • Solé-Boldo L.
        • Schütz S.
        • Wenck H.
        • et al.
        Single-cell RNA profiling of human skin reveals age-related loss of dermal sheath cells and their contribution to a juvenile phenotype.
        Front Genet. 2022; 12: 797747
        • van Beek A.A.
        • Van den Bossche J.
        • Mastroberardino P.G.
        • de Winther M.P.J.
        • Leenen P.J.M.
        Metabolic alterations in aging macrophages: ingredients for inflammaging?.
        Trends Immunol. 2019; 40: 113-127
        • Vorstandlechner V.
        • Laggner M.
        • Kalinina P.
        • Haslik W.
        • Radtke C.
        • Shaw L.
        • et al.
        Deciphering the functional heterogeneity of skin fibroblasts using single-cell RNA sequencing.
        FASEB J. 2020; 34: 3677-3692
        • Wang J.
        • Geiger H.
        • Rudolph K.L.
        Immunoaging induced by hematopoietic stem cell aging.
        Curr Opin Immunol. 2011; 23: 532-536
        • Wu T.
        • Hu E.
        • Xu S.
        • Chen M.
        • Guo P.
        • Dai Z.
        • et al.
        clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.
        Innovation (Camb). 2021; 2: 100141
        • Xue D.
        • Tabib T.
        • Morse C.
        • Lafyatis R.
        Transcriptome landscape of myeloid cells in human skin reveals diversity, rare populations and putative DC progenitors.
        J Dermatol Sci. 2020; 97: 41-49
        • Young M.D.
        • Behjati S.
        SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data.
        GigaScience. 2020; 9 (giaa151)
        • Zhang Y.
        • Parmigiani G.
        • Johnson W.E.
        ComBat-seq: batch effect adjustment for RNA-seq count data.
        NAR Genom Bioinform. 2020; 2 (lqaa078)

      Supplementary References

        • Ahlers J.M.D.
        • Falckenhayn C.
        • Holzscheck N.
        • Solé-Boldo L.
        • Schütz S.
        • Wenck H.
        • et al.
        Single-cell RNA profiling of human skin reveals age-related loss of dermal sheath cells and their contribution to a juvenile phenotype.
        Front Genet. 2022; 12: 797747
        • Carlson Marc GO.db
        A set of annotation maps describing the entire gene ontology. R package version 3.8.2.
        Bioconductor Package Maintainer, 2019
        • Finak G.
        • McDavid A.
        • Yajima M.
        • Deng J.
        • Gersuk V.
        • Shalek A.K.
        • et al.
        MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.
        Genome Biol. 2015; 16: 278
        • Hao Y.
        • Hao S.
        • Andersen-Nissen E.
        • Mauck 3rd, W.M.
        • Zheng S.
        • Butler A.
        • et al.
        Integrated analysis of multimodal single-cell data.
        Cell. 2021; 184: 3573-3587.e29
        • He H.
        • Suryawanshi H.
        • Morozov P.
        • Gay-Mimbrera J.
        • Del Duca E.
        • Kim H.J.
        • et al.
        Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis.
        J Allergy Clin Immunol. 2020; 145: 1615-1628
        • Korsunsky I.
        • Millard N.
        • Fan J.
        • Slowikowski K.
        • Zhang F.
        • Wei K.
        • et al.
        Fast, sensitive and accurate integration of single-cell data with Harmony.
        Nat Methods. 2019; 16: 1289-1296
        • Love M.I.
        • Huber W.
        • Anders S.
        Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
        Genome Biol. 2014; 15: 550
        • Lun A.T.
        • McCarthy D.J.
        • Marioni J.C.
        A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.
        F1000Res. 2016; 5: 2122
        • McGinnis C.S.
        • Murrow L.M.
        • Gartner Z.J.
        DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors.
        Cell Syst. 2019; 8: 329-337.e4
        • Parkinson H.
        • Kapushesky M.
        • Shojatalab M.
        • Abeygunawardena N.
        • Coulson R.
        • Farne A.
        • et al.
        ArrayExpress–a public database of microarray experiments and gene expression profiles.
        Nucleic Acids Res. 2007; 35: D747-D750
      1. R Core Team R: A language and environment for statistical computing.
        R Foundation for Statistical Computing, Vienna, Austria2020
        • Reynolds G.
        • Vegh P.
        • Fletcher J.
        • Poyner E.F.M.
        • Stephenson E.
        • Goh I.
        • et al.
        Developmental cell programs are co-opted in inflammatory skin disease.
        Science. 2021; (eaba6500): 371
        • Stuart T.
        • Butler A.
        • Hoffman P.
        • Hafemeister C.
        • Papalexi E.
        • Mauck 3rd, W.M.
        • et al.
        Comprehensive integration of single-cell data.
        Cell. 2019; 177: 1888-1902.e21
        • Wu T.
        • Hu E.
        • Xu S.
        • Chen M.
        • Guo P.
        • Dai Z.
        • et al.
        clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.
        Innovation (Camb). 2021; 2: 100141
        • Xue D.
        • Tabib T.
        • Morse C.
        • Lafyatis R.
        Transcriptome landscape of myeloid cells in human skin reveals diversity, rare populations and putative DC progenitors.
        J Dermatol Sci. 2020; 97: 41-49
        • Young M.D.
        • Behjati S.
        SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data.
        GigaScience. 2020; 9 (giaa151)
        • Yu G.
        • He Q.Y.
        ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization.
        Mol Biosyst. 2016; 12: 477-479
        • Zhang Y.
        • Parmigiani G.
        • Johnson W.E.
        ComBat-seq: batch effect adjustment for RNA-seq count data.
        NAR Genom Bioinform. 2020; 2 (lqaa078)

      Linked Article

      • This Old Neighborhood Made M1 this Way
        Journal of Investigative DermatologyVol. 142Issue 12
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          When pttissues age, the composition and the properties of the residing cell populations change, resulting in low-grade inflammation, proteolysis, impaired nutrient sensing, and an increased number of senescent cells (López-Otín et al., 2013). Although the age-related alterations mentioned earlier seem to occur in most if not all tissues, there are also some age-related changes, which are tissue specific. For example, an accumulation of UV-induced DNA modifications or a decrease in hyaluronic acid synthesis seems to be specific to aging of skin cells and is especially pronounced in dermal fibroblast (Tigges et al., 2014).
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