Cell Tracking Profiler: a user-driven analysis framework for evaluating 4D live cell imaging data

Abstract

Analysis of cell shape and movement from 3D time-lapsed datasets is currently very challenging. We therefore designed Cell Tracking Profiler for analysing cell behaviour from complex datasets and demonstrate its effectiveness by analysing stem cell behaviour during muscle regeneration in zebrafish. AbstractAccurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm we have established a ground truth for muSC cell behaviour. This revealed that variability in segmentation and tracking algorithms from commonly used programs are errorprone, leading us to develop a fast semi-automated image analysis pipeline that allows user defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) operates through the freely available Icy platform, and allows usermanaged cell tracking from 3D time-lapsed datasets to provide measures of cell shape and movement. Using dimensionality reduction methods, multiple correlation and regression analyses we identify myosin II-dependent parameters of muSC behaviour during regeneration. CTP and the associated statistical tools we have developed thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets.

Neurotransmitter signaling regulates distinct phases of multimodal human interneuron migration

“…The binary probability maps and unsegmented/raw time stacks were then merged to form multi-channel time stacks, which were then used for the cell body tracking. The image analysis software Imaris (Bitplane, Oxford Instruments) was used for cell body tracking as its high efficiency in tracking cell migration has been well reported (Mitchell et al, 2020). Using the "Spots" function, segmented cell bodies in the binary probability maps were recognized as spots and their movement over time was captured with the "Autoregressive Motion" algorithm, creating tracks for the migration of each cell body.…”

Multi‐Photon Microscopy

“…Quantitative phase imaging (Cacace, Bianco, & Ferraro, 2020; Kasprowicz, Suman, & O'Toole, 2017) offers higher contrast and the ability to segment images for extracting quantitative information. Cell growth, cell movement (Hilsenbeck et al, 2016; Aragaki, Ogoh, Kondo, & Aoki, 2022; Mitchell et al, 2020), and the behavior of cell populations (Schott et al., 2018) can be studied. Label‐free imaging is advantageous because it avoids the use of fluorescent dyes that can perturb physiological function in cells, particularly if detergents are required to permeabilize membranes for successful labeling.…”