Guest Editors
Silvia Muñoz-Descalzo
University Institute of Biomedical and Healthcare Research (iUIBS), University of Las Palmas de Gran Canaria
Silvia Muñoz-Descalzo is a quantitative developmental biologist interested in understanding how cells integrate information and use it for cell fate decisions. A central theme of her research is quantitative analysis using a combination of cell biology, live imaging, and modeling. After a degree in biology at the University of Valencia, Spain, she pursued a PhD in Genetics.Given her interest in quantitative biology, she moved to the Department of Genetics at the University of Cambridge, UK, for a postdoctoral position in Alfonso Martinez Arias's group. For her second postdoctoral position, she moved to Kat Hadjantonakis' group at the Sloan-Kettering Institute in New York, USA, to expand her understanding of cell fate decision-making in the mouse embryo. She got her first independent position at the University of Bath. In 2017, she moved to the University of Las Palmas de Gran Canaria, Spain.
Sabine C. Fischer
Center of Computational and Theoretical Biology (CCTB), Faculty of Biology, University of Würzburg
Sabine Fischer is a computational biologist, studying spatial interactions in multicellular systems using image-based mechanistic and AI models. After a Diploma in Mathematics from the University of Würzburg, Germany, she pursued a PhD in mathematical biology at the University of Nottingham, UK.To foster stronger collaborations with biologists, Dr. Fischer joined the Department of Genetics at the University of Cambridge, UK, for a postdoctoral position in Alfonso Martinez Arias' group. For her second postdoctoral position, she moved to the group of Ernst Stelzer at the University of Frankfurt, Germany, to source large amounts of quantitative imaging data as input for her models. Since 2018, Sabine Fischer has been a professor at the Center for Computational and Theoretical Biology (CCTB) at the University of Würzburg.
Collection Overview
Quantitative image analysis is essential in cell and developmental biology. It enables precise, reproducible measurements of cellular and tissue dynamics to perform rigorous hypothesis testing and draw reliable conclusions. This Methods Collection aims to provide a comprehensive workflow, from image acquisition to segmentation and quantitative evaluation, ensuring that all steps are optimally aligned for accurate data extraction.
Proper image acquisition parameters, such as resolution, exposure time, and signal-to-noise ratio, are critical for downstream analysis. The collection details strategies to optimize these settings for robust segmentation, including classical thresholding, machine learning, and deep learning-based methods. The strengths and limitations of each approach are discussed, guiding tool selection based on sample complexity and imaging modality.
Beyond segmentation, the collection highlights techniques for tracking cells, measuring morphological features, and extracting statistical insights. It introduces widely used software platforms, including Fiji/ImageJ, CellProfiler, ilastik, and deep-learning frameworks, and demonstrates how to integrate these tools into reproducible analysis pipelines.
This collection offers practical insights and case studies to help researchers enhance the accuracy and reproducibility of image-based quantitative studies. Focusing on methodological rigor and data standardization, it provides a roadmap for extracting meaningful biological insights from imaging data, ultimately advancing cell and developmental biology research.
Articles
