Effect of capillary fluid flow on single cancer cell cycle dynamics, motility, volume and morphology

November 01, 2022

Hubert M. Taïeb (1), Guillaume Herment (1), Tom Robinson (2) and Amaia Cipitria (1) (3) (4)
Lab on a Chip. Issue 1 (30 November 2022). DOI: https://doi.org/10.1039/D2LC00322H


From primary tumours and disseminating to secondary organs, cancer cells experience a wide variety of fluid flow profiles when passing through blood vessels or the lymphatic system before extravasation. Sinusoidal capillaries are a common site for extravasation. Therefore, we aim to investigate how metastatic cancer cells react to a biophysical cue such as capillary fluid flow by quantifying its effect on metastatic cell cycle progression, motility, cell and nuclear volume, and morphology. We use MDA-MB-231 breast cancer cells genetically modified with fluorescent ubiquitination-based cell cycle indicator 2 (FUCCI2) as a model system. Single cells are trapped using a microfluidic device and exposed to different laminar flows. Quantitative time-lapse imaging in both 2D epifluorescence and 3D confocal microscopy is performed using in-house software FUCCItrack. In addition, 3D time-lapse with cell and nuclear segmentation is performed with a deep learning approach to streamline the image processing of big datasets. We show that at a single cell level, faster fluid flow leads to a shorter S/G2/M phase and an overall shorter cell cycle, as well as increase in cell motility irrespective of the flow direction. 3D time-lapse confocal imaging of MDA-FUCCI2 single cells reveals the evolution of cell and nuclear volume and morphology as a function of a specific cell cycle phase. Both cell and nuclear volume increase linearly over time. Cell morphology elongates more strongly during the S/G2/M phase, whereas the nuclear shape remains constant. Under the highest flow conditions, only during the S/G2/M phase can we observe a more elongated nucleus, while the cell sphericity remains similar to the control. Collectively, this data, together with the deep learning approach, provides new insights into the potential impact of fluid flow at a single cell level.

How Our Software Was Used

A deep learning approach was chosen to automatically segment 3D time-lapse multi-channel images.

Author Affiliation

(1) Department of Biomaterials, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
(2) Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, 14476 Potsdam, Germany
(3) Group of Bioengineering in Regeneration and Cancer, Biodonostia Health Research Institute, 20014 San Sebastian, Spain
(4) IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain