Conference Presentation: Quantifying Morphological Change Using Label-Free Live-Cell Analysis
Presented at the 2021 Drug Discovery Conference | Hosted by ELRIG (European Laboratory Research & Innovation Group)
Overview
Cell morphology is incredibly diverse and reveals valuable insight into cellular dynamics. Traditionally, live-cell analysis has 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 cell populations based on morphologies. In this presentation, we discuss 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.
Speaker Bio
Jasmine Trigg | Scientist, Sartorius
Jasmine is currently a Scientist at Sartorius and part of the Cell Imaging and Applications group within the Bio Analytics team. She is involved in the research and development of novel applications for Incucyte® Live-Cell Analysis Systems.
She has worked across multiple research areas developing cellular assays to extend the suite of live-cell analysis applications. Jasmine has a background in neuroscience and genetic manipulation, with early works focused on using a combined structural and molecular biology approach to assess disease-associated proteins implicated in Alzheimer’s disease.
