Bioimage Analysis for Life Scientists: Tools for Live Cell Imaging

Research output: Types of ThesisDoctoral ThesisCollection of Articles

Abstract

Live imaging is essential in visualizing biological processes such as normal tissue development, wound healing, and cancer — processes too small for the bare human eye to observe. Optical microscopy has enabled the magnification of these processes, and the integration of sensitive digital cameras has enabled the acquisition of images for subsequent observation and analysis. For these reasons, microscopy has become an indispensable tool in studying cells. However, extracting meaningful information from live imaging poses several challenges. Living cells are fragile and should be imaged in controlled environments and using low doses of light, often leading to the generation of noisy images. Low signal-to-noise ratios often hinder accurate object detection and tracking, while sample drifting complicates video analysis. Although several tools to improve the analysis of live cell imaging exist, many of them remain unreachable for life scientists as their usage requires programming skills or they lack proper documentation and user-friendly interfaces. These hinder their usability and reproducibility. To address these issues, we have developed user-friendly live cell image analysis tools for biologists. First, Fast4DReg, a Fiji plugin developed to swiftly correct drift in 4D images, enhances the quality of live imaging. Second, DL4MicEverywhere allows life scientists to implement deep learning on various computational platforms to improve and segment live cell imaging data. Third, TrackMate v7 is a sophisticated tracking software integrating cutting-edge segmentation algorithms into tracking pipelines, facilitating robust and precise cell tracking. To ensure the usability of these tools, we have written extensive documentation and step-by-step guides complemented with openly available test datasets. We incorporate these tools to enable quantitative analysis of the interaction between pancreatic cancer cells and endothelium during metastasis and in a study of cancer cell drug resistance. In summary, our user-friendly image analysis tools offer efficient and accessible solutions for processing and analyzing live cell imaging data, thus benefiting researchers across various fields and contributing to our understanding of cell behavior and disease processes.
Original languageEnglish
Supervisors/Advisors
  • Jacquemet, Guillaume, Supervisor
  • Toivola, Diana, Advisor
  • Kankaanpää, Pasi, Advisor
Place of PublicationÅbo
Publisher
Print ISBNs 978-952-12-4372-1, 978-952-12-4371-4
Electronic ISBNs 978-952-12-4372-1
Publication statusPublished - 26 Apr 2024
MoE publication typeG5 Doctoral dissertation (article)

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