Live-cell imaging is a key technique in cell biology; however, analyzing and interpreting acquired live-cell imaging data can prove difficult due to its complexity and multidimensionality. By integrating machine learning and live-cell imaging, researchers are hoping to streamline the analysis workflow. 

In this eBook, in partnership with Sartorius (Göttingen, Germany), we highlight how AI and machine learning have been implemented to make live-cell image analysis more efficient for quantifying cell viability, investigating mesenchymal stem cell heterogeneity and assessing subcellular features.

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Contents

• Introduction
• Report: MSCProfiler: A single cell image processing workflow to investigate mesenchymal stem cell heterogeneity
• Application Note: AI-driven label-free quantification of cell viability using live-cell analysis
• Research Article: High-resolution visualization and assessment of basal and OXPHOS-induced mitophagy in H9c2 cardiomyoblasts
• Research Article: Image analysis workflows to reveal the spatial organization of cell nuclei and chromosomes
• Application Note: Cell-based phenotypic screening with Incucyte® Live-Cell Analysis Systems

This eBook was supported by Sartorius.