Skip to main content
SpringerLink
Log in

Machine learning-based morphological quantification of replicative senescence in human fibroblasts

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Although aging has been investigated extensively at the organismal and cellular level, the morphological changes that individual cells undergo along their replicative lifespan have not been precisely quantified. Here, we present the results of a readily accessible machine learning-based pipeline that uses standard fluorescence microscope and open access software to quantify the minute morphological changes that human fibroblasts undergo during their replicative lifespan in culture. Applying this pipeline in a widely used fibroblast cell line (IMR-90), we find that advanced replicative age robustly increases (+28–79%) cell surface area, perimeter, number and total length of pseudopodia, and nuclear surface area, while decreasing cell circularity, with phenotypic changes largely occurring as replicative senescence is reached. These senescence-related morphological changes are recapitulated, albeit to a variable extent, in primary dermal fibroblasts derived from human donors of different ancestry, age, and sex groups. By performing integrative analysis of single-cell morphology, our pipeline further classifies senescent-like cells and quantifies how their numbers increase with replicative senescence in IMR-90 cells and in dermal fibroblasts across all tested donors. These findings provide quantitative insights into replicative senescence, while demonstrating applicability of a readily accessible computational pipeline for high-throughput cell phenotyping in aging research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Hayflick L, Moorhead PS. The serial cultivation of human diploid cell strains. Exp Cell Res. 1961;25:585–621.

    Article  CAS  PubMed  Google Scholar 

  2. Fumagalli M, Rossiello F, Mondello C. d'Adda di Fagagna F: Stable cellular senescence is associated with persistent DDR activation. PLoS One. 2014;9:e110969.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Campisi J. d'Adda di Fagagna F: Cellular senescence: when bad things happen to good cells. Nat Rev Mol Cell Biol. 2007;8:729–40.

    Article  CAS  PubMed  Google Scholar 

  4. Gorgoulis V, Adams PD, Alimonti A, Bennett DC, Bischof O, Bishop C, Campisi J, Collado M, Evangelou K, Ferbeyre G, et al. Cellular Senescence: Defining a Path Forward. Cell. 2019;179:813–27.

    Article  CAS  PubMed  Google Scholar 

  5. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Demaria M, Ohtani N, Youssef SA, Rodier F, Toussaint W, Mitchell JR, Laberge RM, Vijg J, Van Steeg H, Dollé ME, et al. An essential role for senescent cells in optimal wound healing through secretion of PDGF-AA. Dev Cell. 2014;31:722–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Krtolica A, Parrinello S, Lockett S, Desprez PY, Campisi J. Senescent fibroblasts promote epithelial cell growth and tumorigenesis: a link between cancer and aging. Proc Natl Acad Sci U S A. 2001;98:12072–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rodier F, Campisi J. Four faces of cellular senescence. J Cell Biol. 2011;192:547–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jun JI, Lau LF. The matricellular protein CCN1 induces fibroblast senescence and restricts fibrosis in cutaneous wound healing. Nat Cell Biol. 2010;12:676–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Phillip JM, Wu PH, Gilkes DM, Williams W, McGovern S, Daya J, Chen J, Aifuwa I, Lee JSH, Fan R, et al. Biophysical and biomolecular determination of cellular age in humans. Nat Biomed Eng. 2017;1(7):0093.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cristofalo VJ, Kritchevsky D. Cell size and nucleic acid content in the diploid human cell line WI-38 during aging. Med Exp Int J Exp Med. 1969;19:313–20.

    CAS  PubMed  Google Scholar 

  12. Greenberg SB, Grove GL, Cristofalo VJ. Cell size in aging monolayer cultures. In Vitro. 1977;13:297–300.

    Article  CAS  PubMed  Google Scholar 

  13. Sherwood SW, Rush D, Ellsworth JL, Schimke RT. Defining cellular senescence in IMR-90 cells: a flow cytometric analysis. Proc Natl Acad Sci U S A. 1988;85:9086–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nishio K, Inoue A, Qiao S, Kondo H, Mimura A. Senescence and cytoskeleton: overproduction of vimentin induces senescent-like morphology in human fibroblasts. Histochem Cell Biol. 2001;116:321–7.

    Article  CAS  PubMed  Google Scholar 

  15. Kim YM, Byun HO, Jee BA, Cho H, Seo YH, Kim YS, Park MH, Chung HY, Woo HG, Yoon G. Implications of time-series gene expression profiles of replicative senescence. Aging Cell. 2013;12:622–34.

    Article  CAS  PubMed  Google Scholar 

  16. Nelson DM, McBryan T, Jeyapalan JC, Sedivy JM, Adams PD. A comparison of oncogene-induced senescence and replicative senescence: implications for tumor suppression and aging. Age (Dordr). 2014;36:9637.

    Article  PubMed  Google Scholar 

  17. Ramilowski JA, Yip CW, Agrawal S, Chang JC, Ciani Y, Kulakovskiy IV, Mendez M, Ooi JLC, Ouyang JF, Parkinson N, et al. Functional annotation of human long noncoding RNAs via molecular phenotyping. Genome Res. 2020;30:1060–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zannas AS, Kosyk O, Leung CS. Prolonged Glucocorticoid Exposure Does Not Accelerate Telomere Shortening in Cultured Human Fibroblasts. Genes (Basel). 2020;11(12):1425.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Liu H, Wu M, Jia Y, Niu L, Huang G, Xu F. Control of fibroblast shape in sequentially formed 3D hybrid hydrogels regulates cellular responses to microenvironmental cues. NPG Asia Materials. 2020;12:1–12.

    Article  Google Scholar 

  20. Khatau SB, Bloom RJ, Bajpai S, Razafsky D, Zang S, Giri A, Wu PH, Marchand J, Celedon A, Hale CM, et al. The distinct roles of the nucleus and nucleus-cytoskeleton connections in three-dimensional cell migration. Sci Rep. 2012;2:488.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Barry DJ, Durkin CH, Abella JV, Way M. Open source software for quantification of cell migration, protrusions, and fluorescence intensities. J Cell Biol. 2015;209:163–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Welter EM, Kosyk O, Zannas AS. An open access, machine learning pipeline for high-throughput quantification of cell morphology. STAR Protoc. 2022;4:101947.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Leung CS, Kosyk O, Welter EM, Dietrich N, Archer TK, Zannas AS. Chronic stress-driven glucocorticoid receptor activation programs key cell phenotypes and functional epigenomic patterns in human fibroblasts. iScience. 2022;25(9):104960.

  24. Min KW, Zealy RW, Davila S, Fomin M, Cummings JC, Makowsky D, McDowell CH, Thigpen H, Hafner M, Kwon SH, et al. Profiling of m6A RNA modifications identified an age-associated regulation of AGO2 mRNA stability. Aging Cell. 2018;17:e12753.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Laberge RM, Zhou L, Sarantos MR, Rodier F, Freund A, de Keizer PL, Liu S, Demaria M, Cong YS, Kapahi P, et al. Glucocorticoids suppress selected components of the senescence-associated secretory phenotype. Aging Cell. 2012;11:569–78.

    Article  CAS  PubMed  Google Scholar 

  26. Itahana K, Campisi J, Dimri GP. Methods to detect biomarkers of cellular senescence: the senescence-associated beta-galactosidase assay. Methods Mol Biol. 2007;371:21–31.

    Article  CAS  PubMed  Google Scholar 

  27. Crabbe L, Verdun RE, Haggblom CI, Karlseder J. Defective telomere lagging strand synthesis in cells lacking WRN helicase activity. Science. 2004;306:1951–3.

    Article  CAS  PubMed  Google Scholar 

  28. Borel F, Lacroix FB, Margolis RL. Prolonged arrest of mammalian cells at the G1/S boundary results in permanent S phase stasis. J Cell Sci. 2002;115:2829–38.

    Article  CAS  PubMed  Google Scholar 

  29. Mackey LC, Annab LA, Yang J, Rao B, Kissling GE, Schurman SH, Dixon D, Archer TK. Epigenetic Enzymes, Age, and Ancestry Regulate the Efficiency of Human iPSC Reprogramming. Stem Cells. 2018;36:1697–708.

    Article  CAS  PubMed  Google Scholar 

  30. Bisogno LS, Yang J, Bennett BD, Ward JM, Mackey LC, Annab LA, Bushel PR, Singhal S, Schurman SH, Byun JS, et al. Ancestry-dependent gene expression correlates with reprogramming to pluripotency and multiple dynamic biological processes. Sci Adv. 2020;6(47):eabc3851.

  31. French SL, Vijey P, Karhohs KW, Wilkie AR, Horin LJ, Ray A, Posorske B, Carpenter AE, Machlus KR, Italiano JE Jr. High-content, label-free analysis of proplatelet production from megakaryocytes. J Thromb Haemost. 2020;18:2701–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, Schiegg M, Ales J, Beier T, Rudy M, et al. ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019;16:1226–32.

    Article  CAS  PubMed  Google Scholar 

  33. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist RA, Moffat J, et al. Cell Profiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 2006;7(10):R100.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of Image Analysis. Nat Methods. 2012;9(7):671–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Phillip JM, Han KS, Chen WC, Wirtz D, Wu PH. A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nat Protoc. 2021;16:754–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Razali NM, Wah YB. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. J Stat Modeling Anal. 2011;2:21–33.

    Google Scholar 

  38. Lim T-S, Loh W-Y. A comparison of tests of equality of variances. Comput Stat Data Anal. 1996;22:287–301.

    Article  Google Scholar 

  39. Gastwirth JL, Gel YR, Miao W. The impact of Levene’s test of equality of variances on statistical theory and practice. Stat Sci. 2009;24:343–60.

    Article  Google Scholar 

  40. Xia Y. Correlation and association analyses in microbiome study integrating multiomics in health and disease. Prog Mol Biol Transl Sci. 2020;171:309–491.

    Article  CAS  PubMed  Google Scholar 

  41. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47:583–621.

    Article  Google Scholar 

  42. Ostertagova E, Ostertag O, Kováč J. Methodology and application of the Kruskal-Wallis test. In: Applied Mechanics and Materials. Trans Tech Publ; 2014. p. 115–20.

    Google Scholar 

  43. Sidney S. Nonparametric statistics for the behavioral sciences. J Nerv Ment Dis. 1957;125:497.

    Article  Google Scholar 

  44. Giraudoux P. Spatial analysis and data mining for field ecologists [R Package Pgirmess Version 1.6. 9]. Comprehensive R Archive Network (CRAN); 2018.

    Google Scholar 

  45. Grömping U. The R Primer. J Stat Softw. 2013;52:1–5.

    Article  Google Scholar 

  46. Team RC: R: A language and environment for statistical computing. 2013.

    Google Scholar 

  47. Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A. Orange: data mining toolbox in Python. J Mach Learn Res. 2013;14:2349–53.

    Google Scholar 

  48. Rostam HM, Reynolds PM, Alexander MR, Gadegaard N, Ghaemmaghami AM. Image based Machine Learning for identification of macrophage subsets. Sci Rep. 2017;7:3521.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wiese DM, Ruttan CC, Wood CA, Ford BN, Braid LR. Accumulating Transcriptome Drift Precedes Cell Aging in Human Umbilical Cord-Derived Mesenchymal Stromal Cells Serially Cultured to Replicative Senescence. Stem Cells Transl Med. 2019;8:945–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang AS, Dreesen O. Biomarkers of Cellular Senescence and Skin Aging. Front Genet. 2018;9:247.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Ogrodnik M. Cellular aging beyond cellular senescence: Markers of senescence prior to cell cycle arrest in vitro and in vivo. Aging Cell. 2021;20:e13338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hwang ES, Yoon G, Kang HT. A comparative analysis of the cell biology of senescence and aging. Cell Mol Life Sci. 2009;66:2503–24.

    Article  CAS  PubMed  Google Scholar 

  53. Rebehn L, Khalaji S, KleinJan F, Kleemann A, Port F, Paul P, Huster C, Nolte U, Singh K, Kwapich L, et al. The weakness of senescent dermal fibroblasts. Proc Natl Acad Sci U S A. 2023;120:e2301880120.

    Article  CAS  PubMed  Google Scholar 

  54. Chan M, Yuan H, Soifer I, Maile TM, Wang RY, Ireland A, O’Brien JJ, Goudeau J, Chan LJ, Vijay T, et al. Novel insights from a multiomics dissection of the hayflick limit. Elife. 2022;11:e70283.

  55. Xiao Y, Zhang Y, Xiao F. Comparison of several commonly used detection indicators of cell senescence. Drug Chem Toxicol. 2020;43:213–8.

    Article  CAS  PubMed  Google Scholar 

  56. Lee BY, Han JA, Im JS, Morrone A, Johung K, Goodwin EC, Kleijer WJ, DiMaio D, Hwang ES. Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Aging Cell. 2006;5:187–95.

    Article  CAS  PubMed  Google Scholar 

  57. Rorteau J, Chevalier FP, Bonnet S, Barthélemy T, Lopez-Gaydon A, Martin LS, Bechetoille N, Lamartine J. Maintenance of chronological aging features in culture of normal human dermal fibroblasts from old donors. Cells. 2022;11(5):858.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mertens J, Paquola ACM, Ku M, Hatch E, Böhnke L, Ladjevardi S, McGrath S, Campbell B, Lee H, Herdy JR, et al. Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects. Cell Stem Cell. 2015;17:705–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mertens J, Herdy JR, Traxler L, Schafer ST, Schlachetzki JCM, Böhnke L, Reid DA, Lee H, Zangwill D, Fernandes DP, et al. Age-dependent instability of mature neuronal fate in induced neurons from Alzheimer's patients. Cell Stem Cell. 2021;28:1533–1548.e1536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Sommer C, Straehle C, Koethe U. Hamprecht FA: Ilastik: Interactive learning and segmentation toolkit. IEEE international symposium on biomedical imaging: From nano to macro. 2011;2011:230–3.

    Google Scholar 

  61. Goliwas KF, Richter JR, Pruitt HC, Araysi LM, Anderson NR, Samant RS, Lobo-Ruppert SM, Berry JL, Frost AR. Methods to Evaluate Cell Growth, Viability, and Response to Treatment in a Tissue Engineered Breast Cancer Model. Sci Rep. 2017;7:14167.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was in part supported by a Summer Undergraduate Research Fellowship (SURF) to EMW and by funding from the National Institute of Environmental Health Sciences (Z01-ES071006-21) to TKA. The following cell line was obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: I90-83.

Author information

Authors and Affiliations

  1. Department of Psychiatry, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA

    Emma M. Welter, Sofia Benavides, Oksana Kosyk & Anthony S. Zannas

  2. Chromatin and Gene Expression Section, Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Durham, NC, 27709, USA

    Trevor K. Archer

  3. Department of Genetics, University of North Carolina at Chapel Hill, 438 Taylor Hall, 109 Mason Farm Road, Chapel Hill, NC, 27599, USA

    Anthony S. Zannas

Authors
  1. Emma M. Welter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sofia Benavides
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Trevor K. Archer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oksana Kosyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anthony S. Zannas
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EMW, OK, and ASZ conceptualized the study. ATK contributed dermal fibroblasts. EMW, SB, and OK cultured and treated cells. EMW, SB, and ASZ performed analyses. ASZ acquired funding and supervised the work. EMW and ASZ drafted the manuscript. All authors critically edited and approved the final manuscript.

Corresponding author

Correspondence to Anthony S. Zannas.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplemental information

ESM 1

Supplemental Table 1. Primers used for q-PCR for senescence confirmation. (DOCX 13 kb)

ESM 2

Supplemental Figure 1. Brief schematic of all morphological quantification steps. (PDF 92 kb)

ESM 3

Supplemental Figure 2. Representative IMR-90 CellProfiler images for the quantification of cell surface area (A) and pseudopodia (B). Colors shown in (A) represent the surface area of different cells identified by CellProfiler, with individual cells distinguished by randomly selected colors. White branches shown in (B) represent cellular projections (pseudopodia) as captured by CellProfiler. (PDF 928 kb)

ESM 4

Supplemental Figure 3. Confirmation of senescence-related gene expression and staining. (A) Percent proliferation measured by total number of EdU+ divided by total number of cells. (B) Representative images used for proliferation quantifications with DAPI and EdU fluorescent staining (scalebar is equivalent to 400 μm). (C-E) Graphs depicting mRNA levels generated by qPCR for senescence-related genes normalized to early passage. **p < 0.01; ***p < 0.001; ****p < 0.0001. (PDF 3236 kb)

ESM 5

Supplemental Figure 4. Morphological analysis in primary human dermal fibroblast with results averaged across all donors (n = 6) for cell perimeter (A), cell circularity (B), and total pseudopodia length per cell (C). (PDF 669 kb)

ESM 6

Supplemental Figure 5. Single-cell predictions of senescence based on logistic regression classification models in dermal fibroblasts from male (A) and female (B) donors. (PDF 418 kb)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Welter, E.M., Benavides, S., Archer, T.K. et al. Machine learning-based morphological quantification of replicative senescence in human fibroblasts. GeroScience 46, 2425–2439 (2024). https://doi.org/10.1007/s11357-023-01007-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-023-01007-w

Keywords

Search

Navigation