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Cell Migration pp 119-134 | Cite as

Using Systems Microscopy to Understand the Emergence of Cell Migration from Cell Organization

  • Staffan StrömbladEmail author
  • John G. Lock
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1749)

Abstract

Cell migration is a dynamic process that emerges from fine-tuned networks coordinated in three-dimensional space, spanning molecular, subcellular, and cellular scales, and over multiple temporal scales, from milliseconds to days. Understanding how cell migration arises from this complexity requires data collection and analyses that quantitatively integrate these spatial and temporal scales. To meet this need, we have combined quantitative live and fixed cell fluorescence microscopy, customized image analysis tools, multivariate statistical methods, and mathematical modeling. Collectively, this constitutes the systems microscopy strategy that we have applied to dissect how cells organize themselves to migrate. In this overview, we highlight key principles, concepts, and components of our systems microscopy methodology, and exemplify what we have learnt so far and where this approach may lead.

Key words

Cell migration Systems microscopy Quantitative Statistics Modeling Systems biology Cancer cell 

References

  1. 1.
    Horton ER, Byron A, Askari JA et al (2015) Definition of a consensus integrin adhesome and its dynamics during adhesion complex assembly and disassembly. Nat Cell Biol 17:1577–1587CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Schiller HB, Friedel CC, Boulegue C, Fässler R (2011) Quantitative proteomics of the integrin adhesome show a myosin II-dependent recruitment of LIM domain proteins. EMBO Rep 12:259–266CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Shafqat-Abbasi H, Kowalewski JM, Kiss A et al (2016) An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes. Elife 5:e11384CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Odenthal J, Takes R, Friedl P (2016) Plasticity of tumor cell invasion: governance by growth factors and cytokines. Carcinogenesis 37:1117–1128PubMedGoogle Scholar
  5. 5.
    Alexander S, Weigelin B, Winkler F, Friedl P (2013) Preclinical intravital microscopy of the tumour-stroma interface: invasion, metastasis, and therapy response. Curr Opin Cell Biol 25:659–671CrossRefPubMedGoogle Scholar
  6. 6.
    Lock JG, Wehrle-Haller B, Strömblad S (2008) Cell-matrix adhesion complexes: master control machinery of cell migration. Semin Cancer Biol 18:65–76CrossRefPubMedGoogle Scholar
  7. 7.
    Callan-Jones AC, Voituriez R (2016) Actin flows in cell migration: from locomotion and polarity to trajectories. Curr Opin Cell Biol 38:12–17CrossRefPubMedGoogle Scholar
  8. 8.
    Lock JG, Strömblad S (2010) Systems microscopy: an emerging strategy for the life sciences. Exp Cell Res 316:1438–1444CrossRefPubMedGoogle Scholar
  9. 9.
    Lock JG, Mamaghani MJ, Shafqat-Abbasi H et al (2014) Plasticity in the macromolecular-scale causal networks of cell migration. PLoS One 9:e90593CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Kowalewski JM, Shafqat-Abbasi H, Jafari-Mamaghani M et al (2015) Disentangling membrane dynamics and cell migration; differential influences of F-actin and cell-matrix adhesions. PLoS One 10:e0135204CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Hernández-Varas P, Berge U, Lock JG, Strömblad S (2015) A plastic relationship between vinculin-mediated tension and adhesion complex area defines adhesion size and lifetime. Nat Commun 25:7524CrossRefGoogle Scholar
  12. 12.
    Kiss A, Gong X, Kowalewski JM et al (2015) Non-monotonic cellular responses to heterogeneity in talin protein expression-level. Integr Biol 7:1171–1185CrossRefGoogle Scholar
  13. 13.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol 67:301–320CrossRefGoogle Scholar
  14. 14.
    Granger CWJ (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37:424–438CrossRefGoogle Scholar
  15. 15.
    Masuzzo P, Martens L, Cell Migration Workshop Participants (2014) An open data ecosystem for cell migration research. Trends Cell Biol 25:55–58CrossRefPubMedGoogle Scholar
  16. 16.
    Masuzzo P, Van Troys M, Ampe C, Martens L (2016) Taking aim at moving targets in computational cell migration. Trends Cell Biol 26:88–110CrossRefPubMedGoogle Scholar
  17. 17.
    Kraus OZ, Grys BT, Ba J et al (2017) Automated analysis of high-content microscopy data with deep learning. Mol Syst Biol 13:924CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Style RW, Boltyanskiy R, German GK et al (2014) Traction force microscopy in physics and biology. Soft Matter 10:4047–4055CrossRefPubMedGoogle Scholar
  19. 19.
    Aoki K, Kamioka Y, Matsuda M (2013) Fluorescence resonance energy transfer imaging of cell signaling from in vitro to in vivo: basis of biosensor construction, live imaging, and image processing. Develop Growth Differ 55:515–522CrossRefGoogle Scholar
  20. 20.
    Sample V, Mehta S, Zhang J (2014) Genetically encoded molecular probes to visualize and perturb signaling dynamics in living biological systems. J Cell Sci 127:1151–1160CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X (2015) RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.Department of Biosciences and NutritionKarolinska InstitutetHuddingeSweden
  2. 2.EMBL Australia Node in Single Molecule Science, School of Medical Sciences, and ARC Centre of Excellence in Advanced Molecular ImagingUniversity of New South WalesSydneyAustralia

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