Skip to main page content
U.S. flag

An official website of the United States government

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013;8(3):e58859.
doi: 10.1371/journal.pone.0058859. Epub 2013 Mar 19.

Real-time motion analysis reveals cell directionality as an indicator of breast cancer progression

Michael C Weiger  1 , Vidya Vedham, Christina H Stuelten, Karen Shou, Mark Herrera, Misako Sato, Wolfgang Losert, Carole A Parent
Affiliations

Real-time motion analysis reveals cell directionality as an indicator of breast cancer progression

Michael C Weiger et al. PLoS One. 2013.

Abstract

Cancer cells alter their migratory properties during tumor progression to invade surrounding tissues and metastasize to distant sites. However, it remains unclear how migratory behaviors differ between tumor cells of different malignancy and whether these migratory behaviors can be utilized to assess the malignant potential of tumor cells. Here, we analyzed the migratory behaviors of cell lines representing different stages of breast cancer progression using conventional migration assays or time-lapse imaging and particle image velocimetry (PIV) to capture migration dynamics. We find that the number of migrating cells in transwell assays, and the distance and speed of migration in unconstrained 2D assays, show no correlation with malignant potential. However, the directionality of cell motion during 2D migration nicely distinguishes benign and tumorigenic cell lines, with tumorigenic cell lines harboring less directed, more random motion. Furthermore, the migratory behaviors of epithelial sheets observed under basal conditions and in response to stimulation with epidermal growth factor (EGF) or lysophosphatitic acid (LPA) are distinct for each cell line with regard to cell speed, directionality, and spatiotemporal motion patterns. Surprisingly, treatment with LPA promotes a more cohesive, directional sheet movement in lung colony forming MCF10CA1a cells compared to basal conditions or EGF stimulation, implying that the LPA signaling pathway may alter the invasive potential of MCF10CA1a cells. Together, our findings identify cell directionality as a promising indicator for assessing the tumorigenic potential of breast cancer cell lines and show that LPA induces more cohesive motility in a subset of metastatic breast cancer cells.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1:
Figure 1:. Cell lines of the MCF10A series show distinct migration properties.
(A) Migration potential of M1–M2 (benign, black circles) and M3–M4 (tumorigenic, red triangles) cell lines after 4 h was assessed with the transwell assay using collagen IV coated membranes and no biased stimulation (see also Fig. S1A). (B) Phase images of the M1–M4 cell lines after 0 and 12 h of unconstrained migration. The dash vertical line indicates the initial location of the sheet edge. Scale bar  = 100 µm. (C) Quantification of the net displacement (during the 3–12 h time frame) is presented as in panel A. (D) M1–M4 cells were first pretreated with 25 µg/mL Mitomycin C for 20 min and then allowed to migrate into open space under conditions identical to panel B (black bars). The net displacement (mean ± SD) is shown compared to control (w/o drug) conditions (white bars), n = 2. For panels A and C results are presented as mean ±95% CI of 6–7 independent experiments. Statistical significance: * p<0.05, ** p<0.01, *** p<0.001, ****p<0.0001 (Tukey-Kramer test, n = 6–7). All comparisons were made with M1 cells unless indicated by pairing-brackets.
Figure 2
Figure 2. Cell lines of the MCF10A series show distinct migration speed and directionality.
(A) PIV analysis enables the mapping of velocity fields associated with the underlying epithelial sheet motions captured by phase time-lapse imaging (scale bar  = 100 µm). Spatial profiles of directionality and speed are depicted with white vectors and a heat map, respectively (right panel). (B) Left: Aggregate speed distributions, determined over all times and space, were compiled from 5–6 independent experiments for each cell line. Right: Quantification of the mean of the average speed (mean ±95% CI) for each cell line; M1–M2 (benign, black circles) and M3–M4 (tumorigenic, red triangles). (C) Left: Rose plots depicting aggregate directionality distributions were compiled over all times and space for each cell line (n = 5–6). Right: Variability of the direction of motion was quantified by the coefficient of variation (CV) and reported as mean ±95% CI. Statistical significance: * p<0.05, ** p<0.01, *** p<0.001 (Tukey-Kramer test, n = 5–6). All comparisons were made with M1 cells unless indicated by pairing-brackets.
Figure 3
Figure 3. Cell lines of the MCF10A series show distinct responses to EGF and LPA.
(A–D) M1–M4 cells were stimulated with 5 ng/mL EGF (red) or 1 µM LPA (blue) and perturbations of average speed and of directionality (angle distributions and CV) compared to controls (black) were assessed (mean ± SD). Rose plots depict controls (unfilled, black bars) and 5 ng/mL EGF (filled, red bars) or 1 µM LPA (filled, blue bars). Statistical significance: * p<0.05, ** p<0.01, *** p<0.001 (Tukey-Kramer test, n = 3 for all conditions except M2 with EGF where n = 2). All comparisons were made with M1 cells unless indicated by pairing-brackets.
Figure 4
Figure 4. Cell lines of the MCF10A series display distinct spatiotemporal speed and directionality patterns during collective motion.
Spatiotemporal heat plots show the average (A) speed and (B) directionality as a function of both position and time from the edge to the center for the sheet. This yielded a spatial map of average speeds and directionalities starting at the sheet edge and moving away, toward the inner regions of the sheet. We defined cos (180°) = 1 (motion directed toward open space) and cos (0°) = −1 (motion directed away from open space). Since cell movement is minimal during the first 3 h, spatiotemporal plots were generated between 3 and 12 h. Cells were stimulated with buffer (control), EGF (5 ng/ml) or LPA (1 µM). Data show heat plots calculated from representative experiments (n = 3 for all conditions except M2 with EGF where n = 2).
Figure 5
Figure 5. The migratory phenotype of MDA-MB 231T is similar to that of M4 cells.
(A) Phase contrast images of MDA-MB-231T cells moving into open space after 12 h under control (Black), 5 ng/mL EGF (red), and 1 µM LPA (blue) treatments. Bar = 100 µm. (B) Right: The effects of EGF and LPA treatment on average speed (top) and directionality, CV (bottom) determined over all times and space, were compiled from 5–6 independent experiments and reported as mean ± SD. Left: Aggregate directionality profiles for control, EGF and LPA conditions. Statistical significance: *p<0.05 (Tukey-Kramer test, n = 3). (C) Representative spatiotemporal heat plots show speed responses in control, EGF, and LPA treated cells. See Fig. 4 for details.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Friedl P, Gilmour D (2009) Collective cell migration in morphogenesis, regeneration and cancer. Nat Rev Mol Cell Biol 10: 445–457. - PubMed
    1. Friedl P, Wolf K (2010) Plasticity of cell migration: a multiscale tuning model. J Cell Biol 188: 11–19. - PMC - PubMed
    1. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144: 646–674. - PubMed
    1. Howlader N NA, Krapcho M, Neyman N, Aminou R, Altekruse SF, et al. (2009) SEER Cancer Statistics Review, 1975–2009 (Vintage 2009 Populations). Bethesda, MD: National Cancer Institute.
    1. Gupta GP, Massague J (2006) Cancer metastasis: building a framework. Cell 127: 679–695. - PubMed

Publication types