Scaling up automated cell tracking

Abstract

Glioblastoma (GBM) cells are a highly motile, meaning they can infiltrate healthy tissue far from the original tumour site, contributing to the low survival rates post diagnosis [1]. At the CRUK Scotland Institute, we are investigating treatments to impede GBM motility to improve patient outcomes post diagnosis [2]. Manually tracking cell motility is a labour-intensive task, requiring researchers to evaluate their datasets by hand to extract key information about cell speed [3]. As such, a subset of cells are usually chosen for analysis (usually 10-20 cells per image region), which can lead to researcher bias influencing the results of the analysis [4]. We present a workflow here for the imaging and analysis of cells that have been treated with a nuclear dye prior to imaging. With the addition of the nuclear dye, and imaging in the fluorescence channel, we take advantage of the improved signal to noise ratio for cell nuclei segmentation using Cellpose 3 [5] and track the subsequent cell masks using the automated cell tracking package Btracks [6]. This process has been automated using python and is packaged together in a Jupyter Notebook to create a workflow that has been custom built with non-coders in mind.Poster presented as part of the Crick BioImage Analysis Symposium 2025.Permission has been given by authors to upload to Crick Figshare. Copyright remains with the original authors.</p