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

Nature Communications

Volumn 10, Issue 1, 2019, Pages

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

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^d MAX PLANCK INSTITUTE OF BIOCHEMISTRY   (Germany)

^e TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COLLAGEN TYPE 14; COLLAGEN TYPE 16; COLLAGEN TYPE 4; PROTEOME; TRANSCRIPTOME; CHOLESTEROL; COLLAGEN;

CAUSE OF DEATH; CELL COMPONENT; COLLAGEN; DISEASE INCIDENCE; DISEASE TREATMENT; GENETIC ANALYSIS; PROTEOMICS;

ADULT; AGING; AIRWAY EPITHELIUM CELL; ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; CELL ACTIVITY; CHOLESTEROL SYNTHESIS; COMPLEX FORMATION; CONTROLLED STUDY; EPIGENETICS; EXTRACELLULAR MATRIX; FIBROBLAST; GENE CONTROL; LUNG ALVEOLUS CELL; LUNG PARENCHYMA; MASS SPECTROMETRY; MOUSE; NONHUMAN; PROTEOMICS; SINGLE CELL ANALYSIS; TRANSCRIPTOMICS; ANIMAL; BIOSYNTHESIS; C57BL MOUSE; CYTOLOGY; EPITHELIUM CELL; GENE EXPRESSION PROFILING; GENETICS; LUNG; METABOLISM; PATHOLOGY;

MUS;

AGING; ANIMALS; CHOLESTEROL; COLLAGEN; EPITHELIAL CELLS; EXTRACELLULAR MATRIX; GENE EXPRESSION PROFILING; LUNG; MICE; MICE, INBRED C57BL; PROTEOME; PROTEOMICS; SINGLE-CELL ANALYSIS;

EID: 85062396680     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-019-08831-9     Document Type: Article

References (67)

1. Hogan, B. L. et al. Repair and regeneration of the respiratory system: complexity, plasticity, and mechanisms of lung stem cell function. Cell Stem Cell 15, 123–138 (2014).
2. Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).
3. Meiners, S., Eickelberg, O. & Konigshoff, M. Hallmarks of the ageing lung. Eur. Respir. J. 45, 807–827 (2015).
4. Lowery, E. M., Brubaker, A. L., Kuhlmann, E. & Kovacs, E. J. The aging lung. Clin. Interv. Aging 8, 1489–1496 (2013).
5. Bailey, K. L. et al. Aging causes a slowing in ciliary beat frequency, mediated by PKCepsilon. Am. J. Physiol. Lung Cell. Mol. Physiol. 306, L584–L589 (2014).
6. Panda, A. et al. Human innate immunosenescence: causes and consequences for immunity in old age. Trends Immunol. 30, 325–333 (2009).
7. Jane-Wit, D. & Chun, H. J. Mechanisms of dysfunction in senescent pulmonary endothelium. J. Gerontol. A Biol. Sci. Med. Sci. 67, 236–241 (2012).
8. Brandenberger, C. & Muhlfeld, C. Mechanisms of lung aging. Cell Tissue Res. 367, 469–480 (2017).
9. Sicard, D. et al. Aging and anatomical variations in lung tissue stiffness. Am. J. Physiol. Lung Cell. Mol. Physiol. 314, L946–L955 (2018).
10. Franks, T. J. et al. Resident cellular components of the human lung: current knowledge and goals for research on cell phenotyping and function. Proc. Am. Thorac. Soc. 5, 763–766 (2008).
11. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
12. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).
13. Han, X. et al. Mapping the mouse cell atlas by Microwell-Seq. Cell 173, 1307 (2018).
14. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).
15. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
16. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 e634 (2017).
17. Lefrancais, E. et al. The lung is a site of platelet biogenesis and a reservoir for haematopoietic progenitor. Nature 544, 105–109 (2017).
18. Heng, T. S. & Painter, M. W; Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
19. Aran, D., Hu, Z. & Butte, A. J. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 18, 220 (2017).
20. Mereu E. et al. matchSCore: matching single-cell phenotypes across tools and experiments. Preprint at https://doi.org/10.1101/314831 (2018).
21. Martinez-Jimenez, C. P. et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355, 1433–1436 (2017).
22. Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330 e314 (2017).
23. Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).
24. Cox, J. & Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinforma. 13(Suppl. 16), S12 (2012).
25. Burgstaller, G. et al. The instructive extracellular matrix of the lung: basic composition and alterations in chronic lung disease. Eur. Respir. J. 50, pii: 1601805 (2017).
26. Budinger, G. R. S. et al. The intersection of aging biology and the pathobiology of lung diseases: a joint NHLBI/NIA Workshop. J. Gerontol. A Biol. Sci. Med. Sci. 72, 1492–1500 (2017).
27. Ricard-Blum, S. The collagen family. Cold Spring Harb. Perspect. Biol. 3, a004978 (2011).
28. Ehnis, T., Dieterich, W., Bauer, M., Kresse, H. & Schuppan, D. Localization of a binding site for the proteoglycan decorin on collagen XIV (undulin). J. Biol. Chem. 272, 20414–20419 (1997).
29. Kolb, M., Margetts, P. J., Sime, P. J. & Gauldie, J. Proteoglycans decorin and biglycan differentially modulate TGF-beta-mediated fibrotic responses in the lung. Am. J. Physiol. Lung Cell. Mol. Physiol. 280, L1327–L1334 (2001).
30. Yamaguchi, Y., Mann, D. M. & Ruoslahti, E. Negative regulation of transforming growth factor-beta by the proteoglycan decorin. Nature 346, 281–284 (1990).
31. Wierer, M. et al. Compartment-resolved proteomic analysis of mouse aorta during atherosclerotic plaque formation reveals osteoclast-specific protein expression. Mol. Cell. Proteom. 17, 321–334 (2018).
32. Schiller, H. B. et al. Deep proteome profiling reveals common prevalence of MZB1-positive plasma B cells in human lung and skin fibrosis. Am. J. Respir. Crit. Care Med. 196, 1298–1310 (2017).
33. Schiller, H. B. et al. Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair. Mol. Syst. Biol. 11, 819 (2015).
34. Kiyozumi, D., Sugimoto, N. & Sekiguchi, K. Breakdown of the reciprocal stabilization of QBRICK/Frem1, Fras1, and Frem2 at the basement membrane provokes Fraser syndrome-like defects. Proc. Natl. Acad. Sci. USA 103, 11981–11986 (2006).
35. Petrou, P., Pavlakis, E., Dalezios, Y., Galanopoulos, V. K. & Chalepakis, G. Basement membrane distortions impair lung lobation and capillary organization in the mouse model for fraser syndrome. J. Biol. Chem. 280, 10350–10356 (2005).
36. Cormier, S. A. et al. T(H)2-mediated pulmonary inflammation leads to the differential expression of ribonuclease genes by alveolar macrophages. Am. J. Respir. Cell Mol. Biol. 27, 678–687 (2002).
37. Ruffell, D. et al. A CREB-C/EBPbeta cascade induces M2 macrophage-specific gene expression and promotes muscle injury repair. Proc. Natl. Acad. Sci. USA 106, 17475–17480 (2009).
38. Gorgoni, B., Maritano, D., Marthyn, P., Righi, M. & Poli, V. C/EBP beta gene inactivation causes both impaired and enhanced gene expression and inverse regulation of IL-12 p40 and p35 mRNAs in macrophages. J. Immunol. 168, 4055–4062 (2002).
39. Zhou, F. Molecular mechanisms of IFN-gamma to up-regulate MHC class I antigen processing and presentation. Int. Rev. Immunol. 28, 239–260 (2009).
40. Koeberle, A., Loser, K. & Thurmer, M. Stearoyl-CoA desaturase-1 and adaptive stress signaling. Biochim. Biophys. Acta 1861, 1719–1726 (2016).
41. Whitsett, J. A., Wert, S. E. & Weaver, T. E. Diseases of pulmonary surfactant homeostasis. Annu. Rev. Pathol. 10, 371–393 (2015).
42. Plantier, L. et al. Activation of sterol-response element-binding proteins (SREBP) in alveolar type II cells enhances lipogenesis causing pulmonary lipotoxicity. J. Biol. Chem. 287, 10099–10114 (2012).
43. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
44. Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).
45. Moor, A. E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).
46. Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 531 (2018).
47. Mondal, M., Liao, R., Xiao, L., Eno, T. & Guo, J. Highly multiplexed single-cell in situ protein analysis with cleavable fluorescent antibodies. Angew. Chem. Int. Ed. Engl. 56, 2636–2639 (2017).
48. Hynes, R. O. Stretching the boundaries of extracellular matrix research. Nat. Rev. Mol. Cell Biol. 15, 761–763 (2014).
49. Decaris, M. L. et al. Proteomic analysis of altered extracellular matrix turnover in bleomycin-induced pulmonary fibrosis. Mol. Cell. Proteom. 13, 1741–1752 (2014).
50. Moliva, J. I. et al. Molecular composition of the alveolar lining fluid in the aging lung. Age (Dordr.) 36, 9633 (2014).
51. Walski, M. et al. Pulmonary surfactant: ultrastructural features and putative mechanisms of aging. J. Physiol. Pharmacol. 60(Suppl. 5), 121–125 (2009).
52. Alder, J. K. et al. Telomere dysfunction causes alveolar stem cell failure. Proc. Natl. Acad. Sci. USA 112, 5099–5104 (2015).
53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, (15–21 (2013).
54. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
55. Kowalczyk, M. S. et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 25, 1860–1872 (2015).
56. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
57. Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 11, 319–324 (2014).
58. Thakur, S. S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell. Proteomics 10, M110 003699 (2011).
59. Scheltema, R. A. & Mann, M. SprayQc: a real-time LC-MS/MS quality monitoring system to maximize uptime using off the shelf components. J. Proteome Res. 11, 3458–3466 (2012).
60. Michalski, A. et al. Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111 011015 (2011).
61. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
62. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
63. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
64. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
65. Misharin, A. V., Morales-Nebreda, L., Mutlu, G. M., Budinger, G. R. & Perlman, H. Flow cytometric analysis of macrophages and dendritic cell subsets in the mouse lung. Am. J. Respir. Cell Mol. Biol. 49, 503–510 (2013).
66. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
67. Nagendran, M., Riordan, D. P., Harbury, P. B. & Desai, T. J. Automated cell-type classification in intact tissues by single-cell molecular profiling. Elife 7, pii: e30510 (2018).