GENOMIC DISSECTION OF CANCER AND STEM CELL SIGNATURES IN CELLULAR TRANSFORMATION

Ph.DDOCTOR OF PHILOSOPH

Bowhead: Bayesian modelling of cell velocity during concerted cell migration

<div><p>Cell migration is a central biological process that requires fine coordination of molecular events in time and space. A deregulation of the migratory phenotype is also associated with pathological conditions including cancer where cell motility has a causal role in tumor spreading and metastasis formation. Thus cell migration is of critical and strategic importance across the complex disease spectrum as well as for the basic understanding of cell phenotype. Experimental studies of the migration of cells in monolayers are often conducted with ‘wound healing’ assays. Analysis of these assays has traditionally relied on how the wound area changes over time. However this method does not take into account the shape of the wound. Given the many options for creating a wound healing assay and the fact that wound shape invariably changes as cells migrate this is a significant flaw. Here we present a novel software package for analyzing concerted cell velocity in wound healing assays. Our method encompasses a wound detection algorithm based on cell confluency thresholding and employs a Bayesian approach in order to estimate concerted cell velocity with an associated likelihood. We have applied this method to study the effect of siRNA knockdown on the migration of a breast cancer cell line and demonstrate that cell velocity can track wound healing independently of wound shape and provides a more robust quantification with significantly higher signal to noise ratios than conventional analyses of wound area. The software presented here will enable other researchers in any field of cell biology to quantitatively analyze and track live cell migratory processes and is therefore expected to have a significant impact on the study of cell migration, including cancer relevant processes. Installation instructions, documentation and source code can be found at <a href="http://bowhead.lindinglab.science" target="_blank">http://bowhead.lindinglab.science</a> licensed under GPLv3.</p></div

Effect of wound geometry on decreasing wound area.

<p>Cells moving at a constant velocity can cause significantly different changes in wound area depending on the initial perimeter as illustrated in the following examples: The wound shown in (A) decreases in area by 48%. The wound in (B) has the same shape as in (A) and a smaller perimeter, and decreases in area by 75%. Similarly, the wound in (C) has the same perimeter length as in (A) but a different shape and decreases in area by 58%. Finally the wound in (D) is the same shape as in (C) and a smaller perimeter, and decreases in area by 86%. The velocity of the moving cells is identical in all cases.</p

Comparison of mean velocity and area change for TScratch sample data.

<p>(A) Wound detection in non-fluorescent images. (B) Mean area change. (C) Mean velocity. Blue lines illustrate starve response median value and red lines illustrate 110% of this value. (D) Signal to noise comparison of the area change and mean velocity.</p

Comparison of mean velocity and area change for wound healing experiments with 3 different siRNA knockdowns.

<p>(A) Mean area change. (B) Mean velocity. Blue lines illustrate PLK1 mean response and red lines illustrate 3 times PLK1 response. (C) Signal to noise comparison of the area change and mean velocity.</p

Analysis of a single wound healing experiment of MDA-MB-231 cells under POU5F1 siRNA knockdown.

<p>(A) Detected wound at 1 hour (blue line) progressing to 15 hours (white line). The algorithm utilizes a combination of measured data (blue) and predicted data (red) in order determine the change in wound perimeter (B), wound area (C) and the derived cell velocity (D). Expected data which is found to be missing can be imputed by the model (green). All error bars signify one standard deviation of data and predictions colored accordingly.</p