Abstract

Cell morphology is incredibly diverse and reveals valuable insight into cellular dynamics. Live-cell analysis has traditionally provided biological insight using simple kinetic readouts based on phase or fluorescence, such as measuring drug treatment responses using cell health reagents. However, advances in machine learning algorithms have facilitated the development of more complex quantitative methods that enable unbiased, automated identification and analysis of cell morphology using multivariate data analysis (MVDA).
The Incucyte® Advanced Label-Free Classification Software Module provides a turnkey solution for simplified label-free identification of populations based on morphologies. Here we present its application to biological paradigms that undergo morphological changes, such as cell health or differentiation, in physiologically relevant conditions.
Validation studies using okadaic acid in SH-SY5Y cells highlight how individual cells can be classified as live or dead and provide a label-free readout of cell health in a neurodegenerative model. Additionally, we describe how differentiation can be quantified based on distinct morphologies, such as during primary monocyte to macrophage differentiation, and exemplify its application to neural cells, such as microglia.

Overall, live-cell imaging and intuitive label-free analysis is a powerful approach that enables non-perturbing morphological quantification of sensitive neural cell types, with the potential to deliver greater insight into neurodegenerative disorders.