The tumor-associated extracellular matrix (ECM) provides critical biochemical micro-environment cues, as well as an essential structural scaffold, for solid tumors to survive and grow (see Pickup et al. 2014 for review). With a view to enabling more translational and turnkey 3D in vitro assays for cancer biology, we have developed and optimized techniques for seeding, growing and automatically quantifying the properties of multiple tumor spheroids in ECMs in 96-well format using real-time live-cell analysis. Matrigel (Corning) was dispensed across a range of volumes (20 - 50 μL) and concentrations (1 - 5 mg mL-1) into flat-bottomed 96-well TC micro-plates to form a solidified base layer. Tumor cells (A549, MCF-7, SKOV-3, MDA-MB-231) were seeded on top (1 - 2K cells per well), and in some experiments a full ECM sandwich was created by addition of a further volume of Matrigel (2 - 25%, 0.2 - 5 mg mL-1). Using a custom autofocusing method, phase contrast, bright-field and fluorescence images (10x) were captured every 6h for 7 days from within the cell incubator (IncuCyte S3 live-cell analysis system). Typically, 20 - 80 spheroids were analyzed in each well. All four cell types formed multiple cell aggregates within the first 3 days, ranging in diameter from 30 - 80 μM. A549, SKOV-3 and MCF-7 multi-spheroids grew as round aggregates while MDA-MB-231 spheroids displayed stellate branching characteristic of an invasive morphology. At Matrigel volumes less than 40 μL or concentrations less than 3 mg mL-1, cells penetrated to the base of the plate and grew as ‘flat 2D' cultures. Using a novel bright-field image analysis algorithm, the number, area and average size of the spheroids could be computed over time non-invasively and without the use of fluorescent labels. Once formed, A549, SKOV-3, MCF-7 and MDA-MB-231 multi-spheroids increased 3.0-, 1.6-, 3.8- and 3.3-fold in size over 4 days, respectively. Treatment of A549 multi-spheroids with the DNA enzyme topo-isomerase inhibitor camptothecin (1μM) inhibited growth with comparable spheroid size at day 0 and day 7 post treatment (average brightfield area 1.4 x104 μM2). Using fluorescent protein reporters for apoptosis (Annexin V) and cell viability (IncuCyte CytoTox Green) we could verify camptothecin-induced cell death (fluorescence values 149±16% of control (Annexin V) and 243±51% of control (CytoTox). A concomitant decrease of stably expressed RFP (to 3±1% of control) was observed. The combination of protocol developments, novel image acquisition/analysis algorithms and cell health reporters creates an integrated solution for measuring growth and vitality of multiple small spheroids in a relevant and 3D bio-matrix over time. This approach should be applicable to primary- and patient-derived organoid tumor samples as well as cancer cell lines. Pickup, MW, Muow, JK, Weaver, MW (2014), EMBO Rep. 15(12): 1243-1253 Citation Format: Kalpana Patel, Miniver Oliver, Nevine Holtz, Tim Jackson, Nicholas Dana, Gillian Lovell, Nicola J. Bevan, Tim J. Dale, Derek J. Trezise. Development and optimization of Matrigel-based multi-spheroid 3D tumor assays using real-time live-cell analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5030.
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.
To develop new therapeutics, researchers are exploring the role of the immune system in defending the body against tumors. Modelling induced malignant cell death in vitro is of paramount importance. Tumor and immune cell co-cultures were created in 96 well plates and using live-cell analysis, various parameters of tumor killing were quantified in real-time. Red nuclear labeled target cells and various densities of pre-activated PBMCs (α-CD3/IL2, 4 d) were seeded in combination with IncuCyte Annexin V green apoptosis detection reagent. Images were acquired every 2 h for 3 d using IncuCyte. Analysis of the fluorescence images provides measurement of target cell number and apoptosis. Enhancement of the phase contrast image analytics enabled single cell segmentation, permitting determination of effector cell parameters; cell number, shape and, using fluorescently labeled surface marker antibodies, protein expression levels. In addition, studies into spatial interactions of target and effector cells were conducted. To exemplify how these new analytical features can be used to investigate the biology of tumor cell killing, studies of a α-hCD3xCD19 bi-specific T-cell engager antibody induced cytotoxicity were performed. Further characterization of effects on cell cycle during target cell death and use of more advanced 3D models of immune cell killing were also assessed, demonstrating the flexibility of live-cell analysis as a powerful tool for analyzing immune cell killing. Advances in data analytics has enabled the multiplexing of target cell quantification alongside the interrogation of effector cell properties in live cells. The added insight gained from these approaches will hopefully lead to improved immuno-therapeutics.
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 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; Truxima® 110.3 ng/mL), while 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. Citation Format: Gillian Lovell, Daniel A. Porto, Jasmine Trigg, Nevine Holtz, Nicola Bevan, Timothy Dale, Daniel Appledorn. AI-driven image analysis enables simplified, label-free cytotoxicity screening. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5418.
The uptake and clearance of tumor cells can be promoted with monoclonal antibodies (mAbs) via antibody-dependent phagocytosis (ADCP) or through the blockade of “don’t-eat-me” signals, such as CD47. These mechanisms hold immunotherapeutic promise and are studied extensively in drug development. High-throughput assays were conducted using pHrodo®, a pH-sensitive fluorophore, that enables live-cell imaging and kinetic quantification of phagocytosis. pHrodo® labeled target cells were incubated with mAbs and added to effector cells in 96-well plates. Images were acquired using the Incucyte® Live-Cell Analysis System and fluorescence automatically quantified with integrated software. In line with known pro-phagocytic effects, anti-CD47 promoted phagocytosis of CCRF-CEM tumor cells by blocking CD47 “don’t-eat-me” signals. Clinical anti-CD20 mAbs Truxima® and Rituximab both promoted ADCP of Ramos target cells by primary macrophages. In addition, the ADCP response of different Rituximab isotypes were compared. Anti-CD20-IgG1 mAb and Fc mutated anti-CD20-IgG1fut (non-fucosylated) exhibited concentration-dependent responses (EC50 values of 25.1 ng/mL and 42.8 ng/mL, respectively), however anti-CD20-IgG1NQ (non-glycosylated) showed no response. ADCP was also examined in adherent target cells with varied HER2 profiles. Anti-HER2 (Trastuzumab) induced an ADCP response in HER2-positive AU565 cells but not in HER2-low MDA-MB-231 cells, consistent with established correlations between HER2 expression and ADCP response. These data exemplify that live-cell analysis is a powerful approach that enables functional quantification of ADCP and is amenable to antibody screening for therapeutic candidates.
Dysregulation of signal transduction pathways is associated with cancer initiation, progression, and recurrence. The PI3K/Akt signaling pathway has been extensively investigated as a therapeutic target due to its mechanistic association with several hallmarks of cancer. Studying dynamic changes in kinase activity can be difficult, and assays to measure these changes in live cells in a physiologically relevant environment are lacking. Standard approaches to monitoring Akt kinase activity are limited to end point assays which lack the ability to monitor the effects of treatments over time. Here we demonstrate the utility of the Incucyte® Kinase Akt Lentivirus Reagent, encoding a kinase translocation reporter based on a green fluorescent protein-tagged Akt substrate whose subcellular localization is phosphorylation-dependent, and a red fluorescent nuclear protein to denote the nuclear/cytoplasmic boundary. To demonstrate inhibition of Akt, A549 cells stably expressing the reporter were treated with compounds targeting the PI3K/Akt kinase pathway, including allosteric Akt inhibitors MK2206 and API-1, competitive Akt inhibitors AZD5363 and Ipatasertib, and upstream PI3K kinase inhibitors LY294002 and PI-103. Quantification of treatment responses using the Incucyte® Live Cell Analysis System showed concentration-dependent inhibition of Akt activity for all compounds with varying kinetic profiles over 24 hours. To study activation of Akt, HeLa cells were first cultured in the absence of serum to reduce Akt activity. After 4 hours of incubation in serum-free conditions, the cells were treated with either epidermal growth factor (EGF) or recombinant insulin-like growth factor (R3-IGF-1) to activate Akt. An increase in Akt activity was observed for both treatments. While R3-IGF-1-induced activation was sustained over the 12-hour time course, activation by EGF diminished over time. Cell lines with mutations in the tumor suppressor PTEN showed no response to serum starvation or activation with EGF or R3-IGF-1. In addition to monitoring Akt activity over time, the integrated red nuclear restricted protein of the reporter enables concurrent measurements of proliferation. The selective Akt inhibitor MK2206 decreased Akt activity in a similar concentration-dependent manner in both T-47D and MDA-MB-231 cell lines. In contrast, measurements of red object count from the same cells reveal differential effects of MK2206 on proliferation between the two cell lines, with concentration-dependent inhibition of T-47D cell growth but little effect on MDA-MB-231 cells. Overall, these data highlight the utility of the Incucyte® Kinase Akt Lentivirus Reagent to provide valuable kinetic measurements of Akt activity using live cells within a physiologically relevant environment. Citation Format: John N. Rauch, Susan K. Foltin, Libuse Oupicka, Matthew Dilsaver, Grigory S. Filonov, Gillian Lovell, Jasmine Trigg, Cicely L. Schramm. Dynamic live-cell visualization and quantification of Akt activity using a genetically-encoded, fluorescent kinase translocation reporter [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 156.
The ability of neutrophils to release extracellular traps (NETs) is one of several mechanisms by which the body defends against infection. When neutrophils encounter invading pathogens, the cells release a mixture of antimicrobial proteins and chromatin to trap and degrade microbes. NETs have been implicated in a number of disorders including atherosclerosis, systemic lupus erythematosus (SLE) and thrombosis. NETosis can be stimulated in vitro using a number of methods including chemical compounds, microbes and microcrystals. In order to better understand the signaling pathways involved, we developed a fully kinetic live-cell imaging assay for NETosis (96-well format, IncuCyte S3). Using a fluorescent cell impermeant DNA-binding reagent (IncuCyte CytoTox Green), NET release was visualised and quantified in real time. In both primary human neutrophils and differentiated ‘neutrophil-like’ dHL60 cells, PMA (100nM) induced rapid (2h onset, peak 4–6h), time-dependent increase in fluorescence and nuclear degradation. A concomitant increase in myeloperoxidase, elastase (immunofluorescence) and cell-free DNA (Picogreen) was observed, validating the NETosis signal. PMA-induced NETosis was ROS-dependent (CellRox Red) but did not cause externalisation of phosphatidylserine (PS, Annexin V). In contrast, ionomycin (5mM) induced NETosis more rapidly (onset time <15′), did not induce ROS but did externalise PS. Neutrophils and dHL60 cells were both able to phagocytose bacterial bioparticles (IncuCyte pHrodo-E-coli). Taken together, these data illustrate how NETs and neutrophil signaling pathways can be robustly, meaningfully and efficiently analysed using automated live-cell imaging and compatible detection reagents.
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