Abstract
Many studies have found alterations in the positioning and morphology of intracellular organelles under different experimental conditions. Although the precise quantification of these changes is challenging, it is strongly facilitated in single cells that are seeded on micropatterned substrates. Indeed, the controlled microenvironment of the cell leads to a reproducible distribution of organelles, simplifying image analysis and minimizing the number of cells required for robust phenotypes. Here, we outline how alterations in the intracellular organization of lysosomes and mitochondria, as a result of different growth conditions, can be efficiently quantified in cells seeded on adhesive micropatterns.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gomes LC, Di Benedetto G, Scorrano L (2011) During autophagy mitochondria elongate, are spared from degradation and sustain cell viability. Nat Cell Biol 13(5):589–598
Molina AJA, Shirihai OS (2009) Monitoring mitochondrial dynamics with photoactivatable [corrected] green fluorescent protein. Methods Enzymol 457:289–304
Liberali P, Snijder B, Pelkmans L (2014) A hierarchical map of regulatory genetic interactions in membrane trafficking. Cell 157(6):1473–1487
Carter SB (1967) Haptotactic islands: a method of confining single cells to study individual cell reactions and clone formation. Exp Cell Res 48(1):189–193
Folch A, Toner M (2000) Microengineering of cellular interactions. Annu Rev Biomed Eng; 2:227‑56.
Théry M (2010) Micropatterning as a tool to decipher cell morphogenesis and functions. J Cell Sci 123(24):4201–4213
Whitesides GM, Ostuni E, Takayama S, Jiang X, Ingber DE (2001) Soft lithography in biology and biochemistry. Annu Rev Biomed Eng 3:335–373
Schauer K, Duong T, Bleakley K, Bardin S, Bornens M, Goud B (2010) Probabilistic density maps to study global endomembrane organization. Nat Methods 7(7):560–566
Théry M, Racine V, Piel M, Pépin A, Dimitrov A, Chen Y et al (2006) Anisotropy of cell adhesive microenvironment governs cell internal organization and orientation of polarity. Proc Natl Acad Sci 103(52):19771–19776
Duong T, Goud B, Schauer K (2012) Closed-form density-based framework for automatic detection of cellular morphology changes. Proc Natl Acad Sci 109(22):8382–8387
Vonaesch P, Cardini S, Sellin ME, Goud B, Hardt W-D, Schauer K (2013) Quantitative insights into actin rearrangements and bacterial target site selection from Salmonella Typhimurium infection of micropatterned cells. Cell Microbiol 15(11):1851–1865
Grossier J-P, Xouri G, Goud B, Schauer K (2014) Cell adhesion defines the topology of endocytosis and signaling. EMBO J 33(1):35–45
Schauer K, Grossier J-P, Duong T, Chapuis V, Degot S, Lescure A et al (2014) A novel organelle map framework for high-content cell morphology analysis in high throughput. J Biomol Screen 19(2):317–324
Rasband W ImageJ. U. S. National Institutes of Health, Bethesda, MD, pp 1997–2016 https://imagej.nih.gov/ij/
R Development Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna ISBN 3-900051-07-0, URL http://www.R-project.org
van Dongen SFM, Maiuri P, Marie E, Tribet C, Piel M (2013) Triggering cell adhesion, migration or shape change with a dynamic surface coating. Adv Mater 25(12):1687–1691
Kolodziej CM, Kim SH, Broyer RM, Saxer SS, Decker CG, Maynard HD (2012) Combination of integrin-binding peptide and growth factor promotes cell adhesion on electron-beam-fabricated patterns. J Am Chem Soc 134(1):247–255
Duong T (2007) ks: Kernel density estimation and Kernel discriminant analysis for multivariate data in R. J Stat Softw 21:1–16
Schauer K, Duong T, Gomes-Santos CS, Goud B (2014) Chapter 20 - Studying Intracellular trafficking pathways with probabilistic density maps. In: Perez F, Stephens DJ (eds) Methods in cell biology, vol 118. Academic Press, San Diego, CA, pp 325–343
Grossier J-P, Goud B, Schauer K (2014) Probabilistic density maps to study the spatial organization of endocytosis. Methods Mol Biol Clifton NJ 1174:117–138
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T et al (2012) Fiji - an open source platform for biological image analysis. Nat Methods 9(7):676
Acknowledgments
The authors greatly acknowledge the Cell and Tissue Imaging Facility (PICT-IBiSA @Burg and @Pasteur) and Nikon Imaging Center, Institut Curie (Paris), member of the French National Research Infrastructure France-BioImaging (ANR10-INBS-04). We thank Tarn Duong for advices on statistical analysis and kernel density estimation and Jean Philippe Grossier for providing scripts. This project was supported by grants from INFECT-ERA (ANR-14-IFEC-0002-04), the Centre National de la Recherche Scientifique and Institut Curie. The authors declare no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Latgé, B., Schauer, K. (2019). Determining the Intracellular Organization of Organelles. In: Fendt, SM., Lunt, S. (eds) Metabolic Signaling. Methods in Molecular Biology, vol 1862. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8769-6_19
Download citation
DOI: https://doi.org/10.1007/978-1-4939-8769-6_19
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-8768-9
Online ISBN: 978-1-4939-8769-6
eBook Packages: Springer Protocols