Nazarbayev University School of Engineering and Digital Sciences
Abstract
High-throughput microscopy is an approach that emerged a decade ago. It is an
efficient tool to solve numerous tasks in cell biology and drug discovery in combination
with automated microscopy. Automated microscopy is suited to perform direct
observation of living cells and allows to obtain consistent and straightforward results
from dynamic processes. The aim of various images processing tools is to study cell
migration. However, there is a deficiency in the tools to perform accurate and efficient
image processing of unlabeled cells, compared to fluorescently labeled ones. Labelfree microscopic techniques like Phase contrast and DIC result in low contrast images,
therefore the tools designed for fluorescent image segmentation are inefficient. To
overcome this limitation, many methods to automatically analyze images of wound
healing were developed within the last decade, but these tools require manual tuning
of parameters and lack of automation to process stacks of thousands of images.
This work focuses on building an efficient image processing pipeline for brightfield
image segmentation. The solution proposed herein is a filtering sequence to modify
the bright-field image intensity histogram so that it resembles that of fluorescent
images. In addition, a conditional operator was introduced, which is a logic loop to test
the quality of wound gap segmentation in unsupervised mode. This allowed achieving
>95% accuracy of segmentation in the un-supervised mode. The processing pipeline
for cell spreading is further enhanced by the transformation of image coordinates to
reduce the dimensionality of the image, and to simplify the image edge to a single
direction gradient.
The tools developed in this Ph.D. project were applied to evaluate the effect of
inhibiting microtubule dynamics on cell motility and spreading. It was confirmed that
wound healing closure occurs in a non-linear manner for the majority of cell lines
studied. A piecewise regression analysis was performed to specify the period when
the wound closure occurs at constant velocity. Further, it was found that the
observations made in the first 12 hours after scratching the cell layer were optimal for
obtaining precise measurements of the wound edge for both normal and cancer cells.
A reduction in cell motility in response to microtubule inhibitors' action occurred in a
dose-dependent manner at concentrations below the apparent cytotoxic doses. It was
demonstrated that the speed of wound closure observed in 24 hours did not depend
on a change in cell proliferation induced by the microtubule drugs. Therefore, the image processing pipeline developed in this Ph.D. research project
can significantly reduce time consumption for RAW data processing and at the same
time greatly increase the precision in analyzing motility-based assays