Analysis of Collective Migration Patterns Within Tumors.
Cancer
Collective cell migration
Quantitative biology
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2023
2023
Historique:
entrez:
18
1
2023
pubmed:
19
1
2023
medline:
21
1
2023
Statut:
ppublish
Résumé
Metastasis is a hallmark of cancer and the leading cause of mortality among cancer patients. Cancer, in its most deadly form, is thus not only a disease of uncontrolled cell growth but also a disease of uncontrolled cell migration. The study of tumor cell migration requires both experimental systems that are representative of the complex tumor environment as well as quantitative tools to analyze migration patterns. In this chapter, we focus on experimental and analytical methods to capture and analyze cell migration in live explants from mouse intestinal tumors. We first describe a protocol to extract and perform ex vivo live imaging on intestinal tumors in mice. We then provide a step-by-step image analysis workflow using freely available software and custom analysis scripts for extracting several parameters related to collective cell migration and cell and tissue organization.
Identifiants
pubmed: 36653715
doi: 10.1007/978-1-0716-2887-4_18
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
305-323Informations de copyright
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Friedl P, Gilmour D (2009) Collective cell migration in morphogenesis, regeneration and cancer. Nat Rev Mol Cell Biol 10:445–457
doi: 10.1038/nrm2720
Clark AG, Vignjevic DM (2015) Modes of cancer cell invasion and the role of the microenvironment. Curr Opin Cell Biol 36:13–22
doi: 10.1016/j.ceb.2015.06.004
SEER (2017) Cancer as a disease. US NIH-NCI, Bethesda. https://training.seer.cancer.gov/disease/categories/classification.html
Chanrion M, Kuperstein I, Barrière C, El Marjou F, Cohen D, Vignjevic D, Stimmer L, Paul-Gilloteaux P, Bièche I, Tavares SDR et al (2014) Concomitant Notch activation and p53 deletion trigger epithelial-to-mesenchymal transition and metastasis in mouse gut. Nat Commun 5:25–38
doi: 10.1038/ncomms6005
Staneva R, Marjou FE, Barbazan J, Krndija D, Richon S, Clark AG, Vignjevic DM (2019) Cancer cells in the tumor core exhibit spatially coordinated migration patterns. J Cell Sci 132:jcs220277
Rasband WS (1997) ImageJ. US NIH, Bethesda. https://imagej.nih.gov/ij/
Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682
doi: 10.1038/nmeth.2019
de Chaumont F, Dallongeville S, Chenouard N, Hervé N, Pop S, Provoost T, Meas-Yedid V, Pankajakshan P, Lecomte T, Le Montagner Y et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696
doi: 10.1038/nmeth.2075
Stirling DR, Swain-Bowden MJ, Lucas AM, Carpenter AE, Cimini BA, Goodman A (2021) CellProfiler 4: improvements in speed, utility and usability. BMC Bioinf 22(1):433
Schmidt U, Weigert M, Broaddus C, Myers G (2018) Cell detection with star-convex polygons BT. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G (eds) Medical image computing and computer assisted intervention – MICCAI 2018. Springer, Berlin, pp 265–273
doi: 10.1007/978-3-030-00934-2_30
Weigert M, Schmidt U, Haase R, Sugawara K, Myers G (2020) Star-convex polyhedra for 3D object detection and segmentation in microscopy. In: 2020 IEEE Winter conference on Applications of Computer Vision (WACV), pp 3655–3662
doi: 10.1109/WACV45572.2020.9093435
Tinevez J-Y, Perry N, Schindelin J, Hoopes GM, Reynolds GD, Laplantine E, Bednarek SY, Shorte SL, Eliceiri KW (2017) TrackMate: an open and extensible platform for single-particle tracking. Methods 115:80–90
doi: 10.1016/j.ymeth.2016.09.016
Ershov D, Phan M-S, Pylvänäinen JW, Rigaud SU, Le Blanc L, Charles-Orszag A, Conway JRW, Laine RF, Roy NH, Bonazzi D et al. (2021) Bringing TrackMate into the era of machine-learning and deep-learning. bioRxiv2021.09.03.458852
Crocker JC, Grier DG (1996) Methods of digital video microscopy for colloidal studies. J Colloid Interface Sci 179:298–310
doi: 10.1006/jcis.1996.0217
Sadati M, Taheri Qazvini N, Krishnan R, Park CY, Fredberg JJ (2013) Collective migration and cell jamming. Differentiation 86:121–125
doi: 10.1016/j.diff.2013.02.005
Garcia S, Hannezo E, Elgeti J, Joanny J-F, Silberzan P, Gov NS (2015) Physics of active jamming during collective cellular motion in a monolayer. Proc Natl Acad Sci 112:15314–15319
doi: 10.1073/pnas.1510973112
Park J-A, Kim JH, Bi D, Mitchel JA, Qazvini NT, Tantisira K, Park CY, McGill M, Kim S-H, Gweon B et al (2015) Unjamming and cell shape in the asthmatic airway epithelium. Nat Mater 14:1040–1048
doi: 10.1038/nmat4357
Palamidessi A, Malinverno C, Frittoli E, Corallino S, Barbieri E, Sigismund S, Beznoussenko GV, Martini E, Garre M, Ferrara I et al (2019) Unjamming overcomes kinetic and proliferation arrest in terminally differentiated cells and promotes collective motility of carcinoma. Nat Mater 18:1252–1263
doi: 10.1038/s41563-019-0425-1
Kepten E, Weron A, Sikora G, Burnecki K, Garini Y (2015) Guidelines for the fitting of anomalous diffusion mean square displacement graphs from single particle tracking experiments. PLoS One 10:e0117722
doi: 10.1371/journal.pone.0117722
Selmeczi D, Mosler S, Hagedorn PH, Larsen NB, Flyvbjerg H (2005) Cell motility as persistent random motion: theories from experiments. Biophys J 89:912–931
doi: 10.1529/biophysj.105.061150
Maiuri P, Rupprecht J-F, Wieser S, Ruprecht V, Bénichou O, Carpi N, Coppey M, De Beco S, Gov N, Heisenberg C-P et al (2015) Actin flows mediate a universal coupling between cell speed and cell persistence. Cell 161:374–386
doi: 10.1016/j.cell.2015.01.056
Choi SM, Kim WH, Côté D, Park C-W, Lee H (2011) Blood cell assisted in vivo particle image velocimetry using the confocal laser scanning microscope. Opt Express 19:4357–4368
doi: 10.1364/OE.19.004357
Vig DK, Hamby AE, Wolgemuth CW (2016) On the quantification of cellular velocity fields. Biophys J 110:1469–1475
doi: 10.1016/j.bpj.2016.02.032
Raffel M, Willert CE, Scarano F, Kähler CJ, Wereley ST, Kompenhans J (2018) Particle image velocimetry: a practical guide. Springer, Cham
doi: 10.1007/978-3-319-68852-7
Liberzon A, Käufer T, Bauer A, Vennemann P, Zimmer E (2021) OpenPIV-Python. https://doi.org/10.5281/zenodo.593157
Vennemann P (2008) Particle image velocimetry for microscale blood flow measurement (Thesis)
Thielicke W, Stamhuis EJ (2014) PIVlab – towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB. J Open Res Softw 2:e30
doi: 10.5334/jors.bl
Tseng Q, Duchemin-Pelletier E, Deshiere A, Balland M, Guillou H, Filhol O, Théry M (2012) Spatial organization of the extracellular matrix regulates cell–cell junction positioning. Proc Natl Acad Sci 201106377
Staple DB, Farhadifar R, Röper J-C, Aigouy B, Eaton S, Jülicher F (2010) Mechanics and remodelling of cell packings in epithelia. Eur Phys J E 33:117–127
doi: 10.1140/epje/i2010-10677-0
Bi D, Lopez JH, Schwarz JM, Manning ML (2015) A density-independent rigidity transition in biological tissues. Nat Phys 11:1074–1079
doi: 10.1038/nphys3471
Atia L, Bi D, Sharma Y, Mitchel JA, Gweon BA, Koehler S, DeCamp SJ, Lan B, Kim JH, Hirsch R et al (2018) Geometric constraints during epithelial jamming. Nat Phys 14:613–620
doi: 10.1038/s41567-018-0089-9
Versaevel M, Grevesse T, Gabriele S (2012) Spatial coordination between cell and nuclear shape within micropatterned endothelial cells. Nat Commun 3:671
doi: 10.1038/ncomms1668
Preibisch S, Saalfeld S, Tomancak P (2009) Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25:1463–1465
doi: 10.1093/bioinformatics/btp184
Parslow A, Cardona A, Bryson-Richardson RJ (2014) Sample drift correction following 4D confocal time-lapse imaging. J Vis Exp 12:51086
Aigouy B, Umetsu D, Eaton S (2016) Segmentation and quantitative analysis of epithelial tissues BT. In: Dahmann C (ed) Drosophila: methods and protocols. Springer, New York, pp 227–239
doi: 10.1007/978-1-4939-6371-3_13
Aigouy B, Cortes C, Liu S, Prud’Homme B (2020) EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning. Development 147(dev194589)
Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, Sebastian Seung H (2017) Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33:2424–2426
doi: 10.1093/bioinformatics/btx180
Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, Schiegg M, Ales J, Beier T, Rudy M et al (2019) ilastik: interactive machine learning for (bio)image analysis. Nat Methods 16:1226–1232
doi: 10.1038/s41592-019-0582-9
Stringer C, Wang T, Michaelos M, Pachitariu M (2021) Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18:100–106
doi: 10.1038/s41592-020-01018-x
Meijering E, Dzyubachyk O, Smal I (2012) Chapter nine – methods for cell and particle tracking. In: P. M. B. T.-M. in E. conn (ed) Imaging and spectroscopic analysis of living cells. Academic, Amsterdam, pp 183–200
doi: 10.1016/B978-0-12-391857-4.00009-4