Abstract 1305: Classification of cell morphology using machine learning and label-free live-cell imaging

Abstract

Abstract Cell morphology is incredibly diverse and provides valuable insight into cellular dynamics, including cell health and differentiation. For ease of analysis, morphology studies often focus on quantifying one or two metrics, e.g. circularity or area. However, this may lead to incorrect conclusions as information about cell size, shape, brightness and texture all capture different nuances of morphology. By using multivariate data analysis (MVDA), multiple properties can be combined into a single metric that simultaneously describes the many different aspects of cell morphology. Supervised machine learning tools further enable identifying subpopulations of cells by their morphology alone. Here we describe a workflow for label-free classification of heterogeneous cells using phase contrast images. Classification of live and dead cells across range of cancer cell types was evaluated. Cells were seeded into 96-well plates and maintained in a physiologically relevant environment to ensure morphology was unperturbed. After 24h, cells were treated with compounds exerting cytotoxic effects via a range of mechanisms. All plates contained camptothecin (CMP, 10 µM) as a control for cell death and were in the presence of a fluorescent cell health reagent (Incucyte® Annexin V) to verify cell death. Images were acquired using an Incucyte® Live-Cell Analysis System (10x objective, every 2h for 3 days) and were segmented using the integrated Incucyte® Cell-by-Cell Analysis Software Module. For validation, cells were also classified based on fluorescence (Annexin V positive) to yield a dead cell percentage. An MVDA regression model was trained for each cell type using only the label-free morphology metrics extracted from the segmented phase contrast images of live (untreated cells, range of confluence values) and dead (10 µM CMP, 72h only) cells. This model was subsequently applied to all acquired images to classify every cell as live or dead. Time- and concentration-dependent increases in the fraction of dead cells closely matched that of the fluorescence classification for all tested conditions. For example, A549 cells treated with CMP produced EC50 values of 0.53 µM (label-free) and 0.66 µM (fluorescence). The analysis proved robust across multiple cell types and compounds, even in cases where morphological change occurred unrelated to cell death. In conclusion, our data demonstrates the utility of an MVDA approach for measuring cell morphology change using label-free live/dead classification as validation. Similar classifications may be applied to alternative biological paradigms which undergo morphological change, such as cell differentiation. Additionally, as the use of morphology metrics for classification requires accurate delineation of cells, improved cell segmentation tools utilizing convolutional neural network models may further enable application of these methods to more challenging cell types. Citation Format: Gillian F. Lovell, Daniel A. Porto, Timothy R. Jackson, Jasmine Trigg, Nicola Bevan, Christoffer Edlund, Rickard Sjöegren, Nevine Holtz, Daniel M. Appledorn, Timothy Dale. Classification of cell morphology using machine learning and label-free live-cell imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1305.