American Association for Cancer Research

April 14 - 19, 2023
Orange County Convention Center
Orlando, Florida 

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Booth #412

Visit our booth at AACR to learn about our innovative platforms and products.

  • The iQue® Advanced Flow Cytometry Platform - utilizes a fixed wide dynamic range allowing for the simultaneous collection of both the phenotypes and functional analysis of secreted cytokines, eliminating the discrepancy of different time points or the need to split samples for subsequent analysis.
  • The Octet® platform - is a comprehensive solution for screening and characterizing molecular interactions such as protein-protein or protein-drug, enabling an enviable assortment of applications throughout biologics development from early selection to validation to manufacturing.
  • The CellCelector platform - is a fully automated cell imaging and picking system developed for the screening, selection and isolation of single cells, clusters, organoids and spheroids, as well as single cell clones and adherent colonies.
  • Incucyte® Live-Cell Analysis Systems - are designed to efficiently capture cellular changes where they happen - in the incubator. Capture high-resolution fluorescence and bright field images and record data in real time over hours, days or weeks. From proliferation assays to immune killing of tumor spheroids, these flexible systems enable users to observe and quantify complex biological changes in real time.

Arrange a time to speak with one of our specialists at the booth, or if you’re not able to attend the show, we’re happy to arrange another time. Submit your details in the form below, and we’ll be in touch shortly.    

Live Poster Presentation

AI-driven image analysis enables simplified, label-free cytotoxicity screening

The increasing use of precious, patient-derived cells has driven the need for non-perturbing and label-free cell measurements, particularly in the oncology field. To address this we developed the Incucyte® AI Cell Health Analysis Software Module, which uses two pre-trained deep neural networks to perform automated, unbiased analysis of Phase contrast images to segment individual cells and perform label-free Live/Dead cell classification. The neural networks which perform cell instance segmentation and infer cell viability were trained on a wide diversity of cell types with varied morphologies, ensuring that the analysis is applicable across a broad variety of adherent and suspension tumor cell types.

Here, we demonstrate the application of this analysis across diverse and commonly used biological models of breast cancer, glioblastoma, and B-cell lymphoma. In each case, cells were treated with chemotherapeutic compounds or biosimilar antibodies and Phase contrast images were acquired at regular intervals over 3 - 4 days using the Incucyte® Live-Cell Analysis System. Using the Incucyte® AI Cell Health Analysis cells were accurately segmented and the percentage of dead cells were quantified over time without the requirement for a fluorescent reporter or other exogenous label, and with limited user input.

Four breast cancer cell lines were treated with a panel of chemotherapeutics designed to target specific expression patterns. AI Cell Health analysis showed that Estrogen receptor (ER) inhibitor Tamoxifen selectively induced >60% cell death only in ER positive cell lines BT474 and MCF7; dual epidermal growth factor receptor (EGFR/ HER2) inhibitor Lapatinib induced cell death in AU565, BT474 and MCF7 which express these surface markers. In contrast, Lapatinib and Tamoxifen induced morphological change - but minimal cell death - in triple negative MDA-MB-231 cells.

Three glioblastoma cell lines A172, U87 and T98G were treated with a larger panel of chemotherapeutic compounds and for four of the active compounds, efficacy was also determined. Cisplatin, doxorubicin, vinblastine and taxol induced concentration-dependent cell death in A172 and T98G cells; U87 cells displayed resistance to each of these compounds with a maximal 46.5 % cell death induced by doxorubicin.

Ramos B-cell lymphoma cells were exposed to increasing concentrations of monoclonal antibody Rituximab and the biosimilar Truxima®. The antibodies induced specific cell death via the surface marker CD20 in a time- and concentration-dependent manner with similar efficacy (IC50 Rituximab, 94.7 ng/mL; IC50 Truxima®, 110.3 ng/mL), while isotype antibody control IgG1 remained non-perturbing to cells.

These results demonstrate that the Incucyte® AI Cell Health Analysis is applicable to a broad range of cancer types cultured in 2D monolayer. This unbiased method enables accurate, label-free quantification of cytotoxic effects induced by clinically relevant therapeutics.


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