Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

• Published in: BMC biology Vol. 20; no. 1; pp. 174 - 18
• Main Authors: Padovani, Francesco, Mairhörmann, Benedikt, Falter-Braun, Pascal, Lengefeld, Jette, Schmoller, Kurt M.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 05.08.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Analysis; Annotations; Automation; Bioimage analysis; Biomedical and Life Sciences; Cell culture; Cell Cycle; Cell cycle analysis; Cell division; Cell size; Cell tracking; Cell Tracking - methods; Data analysis; Deep learning; Deep-learning cell segmentation; Equipment and supplies; Graphical user interface; Hematopoietic stem cells; Histones; Image analysis; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Life Sciences; Live-cell imaging; Machine learning; Metadata; Methods; Microscopy; Neural networks; Saccharomyces cerevisiae; Software; Source code; Stem cells; Tagging; TOR protein; Tracking errors; User interface; Workflow; Yeast; Germany; Fiji; Live-cell imaging; Cell cycle analysis; Cell tracking; Deep-learning cell segmentation; Bioimage analysis
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-022-01372-6

Background High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae , histone Htb1 concentrations decrease with replicative age. Conclusions Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC