Literature DB >> 28501636

Determining whether observed eukaryotic cell migration indicates chemotactic responsiveness or random chemokinetic motion.

A C Szatmary1, R Nossal2.   

Abstract

Chemotaxis, the motion of cells directed by a gradient of chemoattractant molecules, guides cells in immune response, development, wound healing, and cancer. Unfortunately, this process is difficult to distinguish from chemokinesis, i.e., stimulated random cell motion. Chemotaxis is frequently inferred by determining how many cells cross a boundary in a chemotaxis assay, for example how many cells crawl into a chemoattractant-infused filter, or how many cells enter a defined region in an under-agarose assay or agarose spot assay. To mitigate possible ambiguity in whether motion observed in these assays is directed by the chemoattractant gradient or by chemokinesis, we developed a mathematical model to determine when such methods indeed indicate directed motion of cells. In contrast to previous analyses of chemotaxis assays, we report not just the gradients that arise in the assays but also resulting cell motion. We applied the model to data obtained from rigorous measurements and show, as examples, that MDA-MB-231 breast-cancer cells are at least 20 times less sensitive to gradients of EGF or CXCL12 than neutrophils are to formyl peptides; we then used this information to determine the extent to which gradient sensing increases the rate of boundary crossing relative to a random-motility control. Results show, for example, that in the filter assay, 2-4 times as many neutrophils pass through the filter when exposed to a gradient as when the gradient is absent. However, in the other combinations of cells and assays we considered, only 10-20% more cells are counted as having migrated in a directed, rather than random, motility condition. We also discuss the design of appropriate controls for these assays, which is difficult for the under-agarose and agarose spot assays. Moreover, although straightforward to perform with the filter assay, reliable controls are often not done. Consequently, we infer that chemotaxis is frequently over-reported, especially for cells like MDA-MB-231 cells, which move slowly and are relatively insensitive to gradients. Such results provide insights into the use of chemotaxis assays, particularly if one wants to acquire and analyze quantitative data. Published by Elsevier Ltd.

Entities:  

Keywords:  Chemotaxis; Migration; Model

Mesh:

Substances:

Year:  2017        PMID: 28501636      PMCID: PMC5546118          DOI: 10.1016/j.jtbi.2017.05.014

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  31 in total

1.  A stochastic model for chemotaxis based on the ordered extension of pseudopods.

Authors:  Peter J M Van Haastert
Journal:  Biophys J       Date:  2010-11-17       Impact factor: 4.033

Review 2.  Recent developments in microfluidics-based chemotaxis studies.

Authors:  Jiandong Wu; Xun Wu; Francis Lin
Journal:  Lab Chip       Date:  2013-05-28       Impact factor: 6.799

3.  Measurement of the chemotaxis coefficient for human neutrophils in the under-agarose migration assay.

Authors:  R T Tranquillo; S H Zigmond; D A Lauffenburger
Journal:  Cell Motil Cytoskeleton       Date:  1988

Review 4.  Microfluidics for mammalian cell chemotaxis.

Authors:  Beum Jun Kim; Mingming Wu
Journal:  Ann Biomed Eng       Date:  2011-12-22       Impact factor: 3.934

5.  A 48-well micro chemotaxis assembly for rapid and accurate measurement of leukocyte migration.

Authors:  W Falk; R H Goodwin; E J Leonard
Journal:  J Immunol Methods       Date:  1980       Impact factor: 2.303

6.  Chemotactic factor concentration gradients in chemotaxis assay systems.

Authors:  D A Lauffenburger; S H Zigmond
Journal:  J Immunol Methods       Date:  1981       Impact factor: 2.303

7.  Improving the design of the agarose spot assay for eukaryotic cell chemotaxis.

Authors:  Alex C Szatmary; Christina H Stuelten; Ralph Nossal
Journal:  RSC Adv       Date:  2014-11-05       Impact factor: 3.361

8.  A model for a correlated random walk based on the ordered extension of pseudopodia.

Authors:  Peter J M Van Haastert
Journal:  PLoS Comput Biol       Date:  2010-08-12       Impact factor: 4.475

9.  Quantitative analysis of cell motility and chemotaxis in Dictyostelium discoideum by using an image processing system and a novel chemotaxis chamber providing stationary chemical gradients.

Authors:  P R Fisher; R Merkl; G Gerisch
Journal:  J Cell Biol       Date:  1989-03       Impact factor: 10.539

10.  Cooperative roles of SDF-1α and EGF gradients on tumor cell migration revealed by a robust 3D microfluidic model.

Authors:  Beum Jun Kim; Pimkhuan Hannanta-anan; Michelle Chau; Yoon Soo Kim; Melody A Swartz; Mingming Wu
Journal:  PLoS One       Date:  2013-07-15       Impact factor: 3.240

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  4 in total

1.  Chemotaxis Model for Breast Cancer Cells Based on Signal/Noise Ratio.

Authors:  Seongjin Lim; Hyeono Nam; Jessie S Jeon
Journal:  Biophys J       Date:  2018-10-04       Impact factor: 4.033

2.  Rotational Diffusion of Soft Vesicles Filled by Chiral Active Particles.

Authors:  Jiamin Chen; Yunfeng Hua; Yangwei Jiang; Xiaolin Zhou; Linxi Zhang
Journal:  Sci Rep       Date:  2017-11-03       Impact factor: 4.379

3.  Modeling neutrophil migration in dynamic chemoattractant gradients: assessing the role of exosomes during signal relay.

Authors:  Alex C Szatmary; Ralph Nossal; Carole A Parent; Ritankar Majumdar
Journal:  Mol Biol Cell       Date:  2017-09-27       Impact factor: 4.138

4.  The TIPE Molecular Pilot That Directs Lymphocyte Migration in Health and Inflammation.

Authors:  Honghong Sun; Mei Lin; Ali Zamani; Jason R Goldsmith; Amanda E Boggs; Mingyue Li; Chin-Nien Lee; Xu Chen; Xinyuan Li; Ting Li; Brigid L Dorrity; Ning Li; Yunwei Lou; Songlin Shi; Wei Wang; Youhai H Chen
Journal:  Sci Rep       Date:  2020-04-20       Impact factor: 4.379

  4 in total

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