Quantitative Characterization of Cell Behaviors through Cell Cycle Progression via Automated Cell Tracking

Abstract

Cell behaviors are reflections of intracellular tension dynamics and play important roles in many cellular processes. In this study, temporal variations in cell geometry and cell motion through cell cycle progression were quantitatively characterized via automated cell tracking for MCF-10A non-transformed breast cells, MCF-7 non-invasive breast cancer cells, and MDA-MB-231 highly metastatic breast cancer cells. A new cell segmentation method, which combines the threshold method and our modified edge based active contour method, was applied to optimize cell boundary detection for all cells in the field-of-view. An automated cell-tracking program was implemented to conduct live cell tracking over 40 hours for the three cell lines. The cell boundary and location information was measured and aligned with cell cycle progression with constructed cell lineage trees. Cell behaviors were studied in terms of cell geometry and cell motion. For cell geometry, cell area and cell axis ratio were investigated. For cell motion, instantaneous migration speed, cell motion type, as well as cell motion range were analyzed. We applied a cell-based approach that allows us to examine and compare temporal variations of cell behavior along with cell cycle progression at a single cell level. Cell body geometry along with distribution of peripheral protrusion structures appears to be associated with cell motion features. Migration speed together with motion type and motion ranges are required to distinguish the three cell-lines examined. We found that cells dividing or overlapping vertically are unique features of cell malignancy for both MCF-7 and MDA-MB-231 cells, whereas abrupt changes in cell body geometry and cell motion during mitosis are unique to highly metastatic MDA-MB-231 cells. Taken together, our live cell tracking system serves as an invaluable tool to identify cell behaviors that are unique to malignant and/or highly metastatic breast cancer cells.

Rab11A regulates the constitutive secretory pathway duringToxoplasma gondiiinvasion of host cells and parasite replication

“…We tracked this GFP-positive DG motion (Fig 2E and 2F; Suppl. Movies SM8 and SM9) and fitted the recorded xy positions over time using mathematical models of “directed” or “diffusive” motion (see M&M) [32]. We confirmed that the DG trajectory 2 is consistent with a “directed” motion (fitted curve, Fig 2F), characteristic of a vesicle moving along cytoskeleton tracks, in contrast to the trajectories 1 and 3, consistent with “confined diffusive” motions [32].…”

“…Movies SM8 and SM9) and fitted the recorded xy positions over time using mathematical models of “directed” or “diffusive” motion (see M&M) [32]. We confirmed that the DG trajectory 2 is consistent with a “directed” motion (fitted curve, Fig 2F), characteristic of a vesicle moving along cytoskeleton tracks, in contrast to the trajectories 1 and 3, consistent with “confined diffusive” motions [32]. This result, together with the inhibition of Rab11A-positive vesicle (Fig 1D) and DG [31] movements upon CD treatment, strongly suggests that Rab11A promotes DG transport by mediating DG tethering along actin filaments, at least at the parasite cortex.…”

Invasiveness of Cells Leads to Changes in Their Interaction Behavior with the Glycocalyx

“…MDA‐MB‐231 cells showed the fastest movement followed by MCF‐7 cells and the slowest cells were MCF‐10A cells on average (Figure S6, Supporting Information). The speed of MDA‐MB‐231 cells was one order of magnitude higher than the published average speed of mobile MDA‐MB‐231 cells in 3D Matrigel, and the speed of all three cell lines was in a similar range compared to the instantaneous migration speed in 2D on plastic observed by Wang et al Thus, the movement in the 2.5D glycocalyx resembles more that in 2D rather than that in 3D. In addition, we calculated the mean migration velocities according to the different interaction modes and found that cells penetrating the glycocalyx were the slowest (25 µm h −1 ), followed by adhered cells (38 µm h −1 ), and cells close to the glycocalyx were the fastest (46 µm h −1 ).…”

Quantitative assessment of cell contractility using polarized light microscopy

“…A slow acquisition speed presents an issue since cells may have the time to change their morphology over the course of the multi‐images acquisition, thus shifting the spatial position of the birefringent components between each frame. Notably, the rate of membrane protrusion and the speed at which a cell can migrate can be as high as 10 μm min −1 . Any slight frame‐to‐frame shift can introduce artifacts from the computational step that provides false retardance signals.…”