Abstract 2156: Quantifying cell subsets and heterogeneity in living cultures using real time live-cell analysis

• Published in: Cancer research (Chicago, Ill.) Vol. 79; no. 13_Supplement; p. 2156
• Main Authors: Bevan, Nicola, Jackson, Tim, Szybut, Clare, Kelsey, Lauren, Campwala, Hinnah, Dale, Tim, Dana, Nicholas, Holtz, Nevine, Endsley, Eric, Appledorn, Dan, Schramm, Cicely, Skerlos, Laura, Lister, Richard, Trezise, Derek J.
• Format: Journal Article
• Language: English
• Published: 01-07-2019
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-2156

Considerable heterogeneity exists in the properties and activity of individual cells, even in the simplest cell system. This arises from fundamental differences in the basic cell types present, genetic or epigenetic variations, the stage of cell cycle or differentiation, and the impact of each cell’s unique and dynamic local microenvironment. Such heterogeneity is mirrored by the diversity of pharmacological response at the cellular level, where even seemingly identical cells may respond differently and at different times to drug treatments and perturbagens. Accordingly, analysis at the cell-by-cell level promises valuable and additional biological insight beyond which whole population measures may deliver. Here, we describe new, enabling and industrial-scale, live-cell analysis solutions for quantifying the phenotypic biology of cell subsets in heterogeneous cultures. Time-lapse images of cultured cancer and immune cells in 96-well microplates were automatically collected using an IncuCyte S3 live analyzer (Essen Bioscience). Using new segmentation algorithms, the boundaries of individual cells (typically 300-1000 per image) were identified in each image of the sequence. Parameters and features were extracted from single cells, such as cell area, eccentricity and fluorescence (e.g. with cell labels, cytotoxicity and apoptosis probes). Populations of cells could be identified and classified over time using custom ‘flow cytometry’-like visualisation and classification tools. Using this new approach, we demonstrate cell-by-cell analysis for a range of different primary and immortalised, adherent and non-adherent, living cells for up to 7 days in culture (e.g. Jurkat, A549, human PBMCs). This was coupled with a novel live-cell, fluorescent antibody-based labeling strategy (IncuCyte FabFluor-488) to probe for specific subsets within the cultures. Example data generated in PBMCs during T cell activation (anti-CD3/IL-2, 10 ng/mL) demonstrates the change in cell shape from small, spherical cells (average area 81 ± 0.5 µm2, eccentricity 0.57 ± 0.002) to larger, flatter cells (117 ± 4 µm2, 0.69 ± 0.004) over 120 h. With the addition of FabFluor-488-CD71, it was possible to show an associated, temporal increase in CD71 expression within the activated T cell subset (75 ± 1% of large cells were CD71 positive compared to 12 ± 1% of smaller cells at 48 h, increasing to >90% in the larger cells by 120 h). Other example data sets for subset analysis of proliferation, cell death and cell cycle measurements as well as immuno-phenotyping will be shared to illustrate the value of this approach.
Citation Format: Nicola Bevan, Tim Jackson, Clare Szybut, Lauren Kelsey, Hinnah Campwala, Tim Dale, Nicholas Dana, Nevine Holtz, Eric Endsley, Dan Appledorn, Cicely Schramm, Laura Skerlos, Richard Lister, Derek J. Trezise. Quantifying cell subsets and heterogeneity in living cultures using real time live-cell analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2156.