Advancing Drug Discovery With 3D Spheroid Models

Exploring the Versatility of Using 3D Spheroid Models in Research Simplifying ProgressIntroduction In the rapidly advancing world of biomedical science, the demand for more accurate, physiologically relevant models has never been greater. Traditional two-dimensional (2D) cell cultures, while valuable, often fall short in replicating the complex microenvironments found within living organisms. This has led to the emergence of three-dimensional (3D) spheroid models as a transformative tool in modern research. These multicellular structures more closely mimic the architecture, cellular interactions, and functionality of real tissues, making them an essential asset in areas such as drug discovery, cancer research, and regenerative medicine. One of the most powerful advantages of using 3D spheroid models lies in their compatibility with advanced live-cell analysis techniques. These technologies allow researchers to monitor dynamic biological processes in real time, providing critical insights into cell behavior, viability, proliferation, and response to treatment within a three-dimensional context. This realtime monitoring enhances the predictive power of in vitro studies, helping to bridge the gap between laboratory findings and clinical applications. Equally important is the integration of stem cell isolation methods into 3D model systems. Stem cells possess the unique ability to differentiate into various cell types, making them invaluable for tissue engineering and disease modeling. When incorporated into 3D spheroid models, isolated stem cells can form more physiologically relevant tissues, offering unprecedented opportunities for studying developmental biology, disease progression, and therapeutic interventions. This eBook, Exploring the Versatility of Using 3D Spheroid Models in Research, delves into how integrating live-cell analysis and stem cell isolation into spheroid research workflows enhances model reproducibility, data quality, and experimental flexibility. Whether you’re optimizing drug screening assays, modeling disease, or developing personalized therapies, these technologies represent a powerful duo for elevating the impact and reliability of your 3D cell culture research and for bringing us closer to more predictive, ethical, and effective research models. 2Table of Contents 1. Introduction 2. Table of Contents 3. Incucyte® CX3 Live-Cell Analysis System – Clarity from Complexity 4. Application Note: Enhanced Drug Discovery: Utilizing a 3D Spheroid Model and Live-Cell Analysis for Compound Profiling A. Methods B. Results 5. NexaGel®: Go Beyond 2D. Discover the Power of 3D Cell Culture. 6. Technical Note: Enhancing 3D Cancer Spheroid Models With an Innovative Synthetic Hydrogel A. Methods B. Results 7. CellCelector: Automated Single-Cell Analysis 8. Technical Note: CellCelector Nanowell Arrays 9. The iQue® 5 10. Application Note: Evaluating Antibody Drug Conjugates (ADCs) In Vitro Using 3D Tumor Spheroid Models 3Incucyte® CX3 Live-Cell Analysis System Clarity from Complexity Real-time analysis of 3D cell models over time is now accessible to every lab. The Incucyte® CX3 System transforms cell culture workflows with continuous, non-invasive imaging and kinetic analysis of live 3D models at unmatched throughput. Learn more at www.sartorius.com/incucyte-cx3 4Enhanced Drug Discovery: Utilizing a 3D Spheroid Model and Live-Cell Analysis for Compound Profiling Alpana Prajapat — Scientist - Sartorius Jasmine Trigg — Scientist - Sartorius Nicola Bevan — Manager - Sartorius Gillian Lovell — Manager - Sartorius Abstract In this study, we explore the use of a 3D single spheroid model combined with live-cell analysis as a robust assay for drug screening. By profiling a library of 880 FDA-approved drugs, we assessed changes in spheroid size and viability. Our results demonstrated the advantages of live-cell analysis, capturing detailed biological information and maintaining cell health in a physiologically relevant environment. Comparative analysis between 2D monolayer and 3D spheroid models revealed important insights into compound responses, highlighting the potential for improved in vitro to in vivo translation. This methodology supports multifaceted investigations, enhancing drug discovery efforts and contributing to the development of more effective treatments. 5Introduction Assessing the activity profile of a compound is crucial in any drug discovery strategy, especially when profiling large numbers of compounds (ranging from 100 to 100,000) in early-stage programs. Compound screening evaluates various parameters, including changes in cellular morphology and biochemical characteristics after treatment, as well as the effects of drugs in reversing these changes.¹ Phenotypic screening is commonly used for this profiling, allowing the quantification of cell function as an alternative to traditional target-based screening.² Traditional methods often focus on assessing direct target interactions through binding affinity, usually lacking functional potency measurements. A significant challenge in drug discovery is developing complex, biologically relevant, and predictive cell-based assays for compound screening. The use of threedimensional (3D) assay models is becoming more common, as these models better mimic the in vivo tissue environment. Incorporating more translational 3D cellular assays bridges the gap between 2D cell cultures and whole-animal models, enhancing research and screening efforts.³ Current methods for assessing the growth and shrinkage of 3D tumor spheroids have several limitations.⁴ These include time-consuming, costly, or labor-intensive workflows; the need to label cells (e.g., with a fluorescent probe), which can perturb biological processes and may not be suitable for primary tissue; disruption of environmental control when cells are removed from the incubator for imaging; and indirect readouts (e.g., ATP) that may miss important morphological insights or inaccurately report cell growth. Live-cell analysis offers significant advantages for profiling compounds using complex cell models. This technology captures more biological information than a single endpoint readout, providing insights into growth and morphological changes throughout the entire assay period. The cells are maintained in a physiologically relevant environment and remain undisturbed during the experiment. Image capture is complemented by comprehensive analysis algorithms, providing a simplified method for extracting maximum biological insights to inform decisions on key compounds. In this application note, we describe the use of live-cell analysis for kinetic, brightfield and fluorescent measurements of spheroid growth and viability in response to a library of 880 FDA-approved drugs. The data showcases the initial single concentration screening phase, assessing the quantification of these two parameters and how they provide information on compound activity. Identified compounds of interest are then further investigated for full responses and compared to data from a monoculture 2D assay. 67 Materials and Methods Cell Culture A549 adenocarcinoma cells were stably transfected with Incucyte® Nuclight Red Lentivirus to express a nuclear restricted fluorescent protein (A549-NR cells) and maintained in F-12K media supplemented with 10% FBS, 1% Pen/Strep, and 0.5 µg/mL puromycin. Assays were performed in the absence of puromycin. Materials used for cell culture and assay are described below (Table 1). Materials Supplier Cat. No. Final Concentration Ham’s F-12K (Kaighn’s) Medium Gibco 21127022 1X Characterized Fetal Bovine Serum (FBS) Cytiva HyClone™ SH30071.03 10% Pen/strep Gibco 15140-122 1% Puromycin Gibco A1113803 0.5 µg/mL PBS Gibco 14190094 1X 0.25% Trypsin Gibco 25200056 1X PrimeSurface® 3D Culture Spheroid Ultra-Low Attachement (ULA) 96-well U-bottom Plate S-bio MS9096UZPrimeSurface® 3D Culture Spheroid: Ultra-Low Attachement (ULA) 384well U-bottom Plate S-bio MS9384UZCamptothecin Sigma C9911 10 µM Cycloheximide TOCRIS Bioscience 4B/104803 10 µM DMSO Thermo Fisher D/4121/PB08 10 µM Incucyte® Nuclight Red Lentivirus (EF1α, Puro) Sartorius 4476 MOI 2 FDA-Approved Drugs Screening Library Cayman Chemical 23538 10 µM Table 1: Materials required for cell culture and assay.Compound Screen Assay A549-NR cells were harvested, counted, and seeded at 5,000 cells per well in 100 µL into 96-well ULA round bottomed plates. Plates were centrifuged (125 x g, 10 minutes at room temperature) and spheroid formation was monitored to desired size (200 – 500 µm in diameter) with Brightfield and HD phase-contrast image acquisition (4X magnification) every 6 hours using Incucyte® Live-Cell Analysis System (Incucyte® System). After 3 days, once spheroids had formed, compounds were added from the screening library (100 µL/well at 2X final assay concentration). DMSO (0.1%) was used as vehicle control and Camptothecin (CMP) and Cycloheximide (CHX) as positive controls for cytotoxic or cytostatic effects, respectively. In total 22, 96-well plates were treated (2 replicates per compound plate). Compound effects on spheroid growth, shrinkage, and viability were quantified using the Incucyte® System and analyzed using the Incucyte® Spheroid Analysis Software Module. Temporal changes were evaluated in spheroid area (Largest Brightfield Object Live-Cell Imaging Image Analysis Area (µm²)) and nuclear fluorescence within the brightfield masked boundary (Largest Brightfield Object Red Integrated Intensity (RCU x µm²)). Integrated intensity (spheroid area multiplied by the sum of pixel intensities) is used to reflect changes in both spheroid size and fluorescence to provide a quantification of viability. The overall screening process from imaging, analysis to insight is shown in Figure 1. For in depth assessment of selected compounds, full concentration response curves were tested using 2,500 cells per well in 50 µL into 384-well ULA round bottom plates. Compounds were picked from master library plates, diluted in assay media and serial diluted 1:3 to give a final assay concentration between 0.01 and 10 µM (50 µL/well added at 2X final assay concentration), with the inclusion of vehicle wells to quantify no-response wells. A similar process of spheroid formation followed by compound addition was followed. Insight 20 % of Compounds in Library 15 10 5 0 Day 7 10 Label Free Fluorescence Shrinkage Cytotoxic Disruptive Figure 1: Phenotypic Screening Workflow. Workflow depicts the use of live-cell analysis to collect images that are qualified to provide insight into compound mechanism of action. 8Results Data Analysis Workflow To evaluate the activity of the compound library, cell plates were analyzed over a 10-day period following a 3-day spheroid formation phase. This allowed for the collection of comprehensive time-course data for all test entities. The Incucyte® software’s microplate view facilitated rapid visualization of the full 96-well time course. Control responses were displayed in columns 1 (vehicle, DMSO 0.1%) and 12 (CMP or CHX, 10 µM), indicating a suitable and robust assay window (Figure 2A). The remaining 80 wells showed various compound responses within the plate. For both the brightfield area, which indicates spheroid growth or shrinkage, and red integrated intensity, which indicates cell viability, a clear assay window that increased over time was observed (Figure 2B, viability data shown). The response to CMP plateaued approximately 4 days post-treatment for both readouts, but an endpoint of 7 days (168 hours) was chosen to compare compound results. A Vehicle Red Integrated Intensity (RCU x µm²) Controls on each plate were used to calculate a Z’ value, a recognized measure of assay quality, where values greater than 0.4 indicate robust results suitable for single-shot screening assays. This highlighted strong separation between positive and negative control values. Across the 22 test plates, the Z’ value was equal to or above 0.4 for both readouts, (Figure 2C, viability). For this screen, duplicate plates were tested, allowing compound responses to be averaged across plates, therefore, a Z’ value of less than 0.4 would have been less concerning. Using a later time point (Day 10) did not improve the value, which remained very similar. B Compounds CMP CHX Red Integrated Intensity (RCU × µm²) C Z’ (Red Integrated Intensity) 3 ×10⁷ 2 ×10⁷ 1 ×10⁷ 0 1.0 0.8 0.6 0.4 0.2 0.0 Day 7 Vehicle CMP 0 48 96 Time (h) 144 192 1 2 3 4 5 6 7 8 9 1011 Plate Figure 2: Data Visualization and Analysis Process. (A) Microplate view of representative plate showing viability data over 7 days post spheroid formation. (B) Control data from representative plate to show assay window. (C) Spread of Z’ values across all 22 test plates for viability parameter at day 7 endpoint. 9Comparative Analysis Across Plates For the comparative analysis across plates, the average of the vehicle wells on each plate was used to define the no-effect response or maximal spheroid size (100%), while the average of the CMP wells defined the positive cytotoxic effect or maximal spheroid shrinkage (0%). All responses were then normalized to this range, and duplicate plates were averaged to facilitate easy comparison of all compounds. The library consists of compounds targeting a vast range of mechanisms of action. To gain an overview of responses, the normalized values were plotted across all plates for both spheroid size and viability (Figure 3A and B). When considering spheroid size, as defined by brightfield area, the graph shows that 60.5% of compound responses are within ± 20% of the vehicle, indicating limited effect. Conversely, 15.6% show a reduction in size greater than 50%, while 15.2% display an increase greater than 120%. Spheroid shrinkage may suggest either a cytotoxic or cytostatic response, while an increase in spheroid size could be linked to disruption of the compact spheroid. The viability data, as defined by red fluorescence, shows a similarly high number of compounds within the ± 20% range (69.6%). The data indicate that 7.3% of compounds A B Av Normalized Brightfield Area Day 7 (%) Av Norm. Red Integrated Intensity Day 7 (%) 200 150 100 50 0 150 100 50 0 Compounds Vehicle CMP Compounds Vehicle CMP display a cytotoxic profile with a reduction greater than 50%, while 11.3% show an increase greater than 120%. Increased fluorescence may result from shrinkage of the spheroid with no loss of fluorescence, leading to an increase in the integrated intensity value. When the two readouts are compared directly, regions of interest can be identified (Figure 3C): پ There is a cluster around 100% for both readouts, representing the no-effect compounds. This cluster shows a spread in the data for both readouts of ± 20%. پ Region A represents compounds that elicit a response of less than 50% for both readouts (5.3%), which would be considered cytotoxic in nature, causing reduced viability and spheroid shrinkage. پ Region B represents compounds that affect spheroid size but have limited effect on viability. Cycloheximide (CHX), a known cytostatic compound added as a control, sits in this region (red marker). This region could include both cytostatic compounds and compounds that cannot penetrate the spheroid structure to elicit a response. پ Region C represents compounds that display an increase in spheroid size (> 150%) with limited effect on viability, indicating the possibility of spheroid disruption. C 200 150 100 Av Norm. Red Integrated Intensity (%) 50 0 Compound CHX A B C 0 50 100 150 Av Norm. Brightfield Area (%) 200 Figure 3: Overview of Full Screen Results. Scatter plots display endpoint analyses showing hits based on Average Brightfield Area (A) and Red Integrated Intensity (B) readouts across duplicate plates, at 168 h (7 days) post-spheroid formation. Black line indicates the mean vehicle control values and magenta line denotes mean of vehicle ± 3 standard deviations. CMP, cytotoxic control is denoted in teal. Six compounds were > 200% for brightfield, two > 150% for red data. (C) Correlation between averaged readouts colored by plate number. Green lines denote 50%, red lines denote line of unity ± 20%, larger red marker shows the response to CHX. 10Further Insight For all plates, the entire time course of effect was collected, complete with images of responses, both of which can be used to provide more biological insights. Figure 4A shows images taken 7 days post-spheroid formation for various treatment groups, while Figures 4B and 4C show the complete time course for both measurements. The vehicle image shows a compact, red single spheroid with some ruffles around the edges, which has been cleanly masked using the integrated spheroid software. In contrast, the CMP-treated spheroid is smaller and has a reduction in visible fluorescence, defining the assay window. Clofarabine, which targets deoxycytidine kinase and DNA polymerase, mimics the CMP cytotoxic response (< 50% for both parameters, Figure 3C Region A), as shown in both the image and the time course graphs (purple line). A Vehicle CMP Cytotoxic Region A (Clofarabine) Cytostatic Region B (Flumazenil) Disruptive Region C (Vinblastine) (Vinorelbine) Flumazenil, targeting GABAA receptors, displays a more cytostatic profile with a similar reduction in spheroid size as clofarabine but no effect on viability (pink line, Figure 3C Region B). Both vinorelbine and vinblastine, which target microtubule processing, display a disruptive phenotype (Figure 3C Region C), causing the spheroid to lose its compact phenotype. This manifests as an increase in spheroid size (maximum 48 hours post-treatment) and a partial drop in fluorescence, which may be linked to the increased area of the mask and how the integrated intensity value is calculated (see methods for details). B Av Brightfield Area (µm²) C Av Red Integrated Intensity (RCU × µm²) 8 ×10⁵ 6 ×10⁵ 4 ×10⁵ 2 ×10⁵ Vehicle Vinblastine Vinorelbine CMP Clofarabine Flumazenil 0 2.5 ×10⁷ 2.0 ×10⁷ 1.5 ×10⁷ 1.0 ×10⁷ 5.0 ×10⁶ 48 Vehicle Vinblastine Vinorelbine 96 Time (h) CMP Clofarabine Flumazenil 144 0 0 48 96 Time (h) 144 Figure 4: Further Compound Insight. (A) Images of spheroid with brightfield mask (yellow outline) taken at 168 hours (day 7) for representative vehicle, CMP, and compound treated wells. Compounds selected from region A, B, and C of previous correlation graph (Figure 3C). Disruptive compound images shown at 72 hours (day 3) at peak of response. Time course graphs show the full response for each of these compounds for Brightfield Area (B) and Red Integrated Intensity (C). Data shown as mean of 2 wells for compounds and at least 8 wells for controls ± SEM. 1112 To further investigate responses, selected compounds were tested over a wide concentration range (0.01 – 10 µM) in a 384-well plate for maximal data collection. As previously described, A549-NR cells were allowed to form spheroids over 3 days before compound dilutions were added to the plate. The microplate view displays the full-time course data for all test compounds for the viability readout (Figure 5A), providing a quick visualization of active compounds compared to vehicle-treated wells. The accompanying table and graphs (Figure 5B and C) show the measured responses. CMP (teal box) displays a rapid, clear concentration-related effect, yielding an IC₅₀ value of 0.63 µM. Topotecan and Daunorubicin, both targeting topoisomerase isoforms, display similar cytotoxic effects. Notably, gemcitabine (no. 7), a DNA intercalator, resulted in a potent effect (0.06 µM). Overall, fourteen other DNA interference compounds were also highlighted as cytotoxic hits in the single-shot screen (< 50% for either readout). Other active compounds, such as PKC 412 (no. 1) and Amphotericin B (no. 5), acting through a range of targets, displayed potent effects. Deflazacort (no. 8), targeting glucocorticoid receptors, was shown to partially affect viability and was not initially identified as a hit for this parameter. Interestingly, twentythree glucocorticoid-interacting compounds were highlighted as active for the spheroid size parameter. Paclitaxel (no. 2), targeting tubulin, was highly potent at all concentrations tested. Figure 5: In-Depth Compound Profiling. (A) 384 well-plate view, showing Red Integrated Intensity over 7 days across range of compounds and concentrations. Teal box highlights CMP response. (B) Table lists responses for active compounds with target information. (C) Concentration response data for compounds taken at 7 days. Black dotted line presents average vehicle response. Data shown as mean of 4 wells for compounds ± SEM. Compound Target Log IC₅₀ Camptothecin Topoisomerase I-6.2 Topotecan Topoisomerase I-6.2 Daunorubicin Topoisomerase II-7.1 Gemcitabine DNA intercalating agent-7.2 Paclitaxel Tubulin < -8 PKC 412 Kinases-Multiple-6.2 Amphotercin B Ergosterol Biosynthesis-5.6 Deflazacort Glucocorticoid receptor-7.3 B C Red Integrated Intensity (RCU × µm²) 1.5 ×10⁷ 1.0 ×10⁷ 5.0 ×10⁶ 0-8-7-6-5 Log [Compound] (M) Camptothecin Topotecan Daunorubicin Gemcitabine Vehicle Red Integrated Intensity (RCU x µm²) A 6 7 8 9 10 11 1 2 3 4 5 Vehicle Red Integrated Intensity (RCU × µm²) 1.5 ×10⁷ 1.0 ×10⁷ 5.0 ×10⁶ 0-8-7-6-5 Log [Compound] (M) Camptothecin Topotecan Daunorubicin Gemcitabine VehicleComparison of Monolayer (2D) and Spheroid (3D) Response The use of 3D models has increased greatly in recent years with the promise of improved in vitro to in vivo translation. Using data generated in a previous experiment,⁵ we wanted to compare compound responses measured in a 2D monolayer assay to those from the 3D spheroid screen. For the monolayer assay, the compound library was screened using A549 cells expressing a nuclear-restricted NIR construct (Incucyte® Nuclight NIR Lentivirus) and tracked using livecell analysis over a 3-day period. The NIR fluorescence was quantified as a measure of viability and normalized to vehicle and CMP control wells, as described for the 3D data set previously. Figure 6A displays the direct comparison of the monolayer (2D) data to the spheroid (3D) data. The data shows a slight overall shift in sensitivity to compounds, with the 2D monolayer data displaying an increase or rightward shift compared to unity (1 to 1 correlation, red dotted line). This could be due to several reasons, including greater cell contact with compounds in the monolayer compared to the complexity of the spheroid cell model. In the spheroid model, the compound also needs to penetrate the structure to affect the cells within the spheroid. When comparing assays with different temporal outputs, live-cell analysis can add value to help identify the most appropriate endpoint to use for comparison. A 3D (Day 7) Av Norm. Red Integrated Intensity (%) 150 B 100 50 0 A 0 50 2D (Day 3) Norm. NIR Area (%) 100 150 Region A highlights those compounds producing a less than 50% effect in both assays (17.1%). Full time course data was investigated for two examples from this region (Figure 6B and C). Sunitinib, a kinase inhibitor, and Actinomycin D, a DNA transcription blocker, both display a strong cytotoxic effect similar in profile to CMP. Region B highlights compounds that display a cytotoxic effect in the monolayer assay but are inactive in the spheroid assay. The three examples in this region display very similar profiles with partial effects in the monolayer assay (~ 20%) and no effect in the spheroid assay for either measured parameter, with activity similar to the vehicle. These examples have various targets: Cisplatin interferes with DNA crosslinking, Lapatinib is a dual EGFR and ErbB2 inhibitor, and Telmisartan is a selective Angiotensin II receptor antagonist. This lack of correlation in activity could be due to many reasons as mentioned previously and highlights the importance of testing in different assays using multiple readouts. Live-cell analysis can support this type of multifaceted investigation with support for multiple assay models and analysis. Only five compounds show activity less than 50% in the 3D model and no activity in the monolayer. These compounds are active through a variety of targets and would need further follow-up investigations. B 2D (Day 3) NIR Total Integrated Intensity (NIRCU × µm²) C 3D (Day 7) Red Integrated Intensity (RCU × µm²) 2.5 ×10⁶ 2.0 ×10⁶ 1.5 ×10⁶ 1.0 ×10⁶ 5.0 ×10⁵ 0 3.0 ×10⁷ 2.0 ×10⁷ 1.0 ×10⁷ 0 Figure 6: Monolayer (2D) Verses Spheroid (3D) Effects. (A) Correlation between monolayer (2D) NIR fluorescence (Day 3) and spheroid 3D red fluorescence (Day 7) colored by plate number. Green lines denote 50%, red lines denote line of unity ± 20%. Bar graphs show the response for selected compounds for monolayer NIR at 72 hours (B) and spheroid red, integrated intensity 168 hours, with bar color indicating region A (teal) or B (magenta) (C). Data shown as mean of 2 wells for compounds and at least 8 wells for controls ± SEM. Vehicle CMP Sunitnib Actinomycin D Vehicle Lapatinib Telmisartan 13 Cisplatin CMP Sunitnib Actinomycin D Telmisartan Lapatinib CisplatinSummary and Outlook In this study, we demonstrated the effectiveness of using a 3D single spheroid model combined with live-cell analysis as a robust assay for compound screening. By assessing the activity profile of a compound library, we were able to evaluate various parameters, including spheroid size and viability. The use of phenotypic screening allowed for the quantification of growth and compound cytotoxicity, providing a comprehensive alternative to traditional targetbased screening methods. Our results highlighted the advantages of live-cell analysis, which captures more biological information than single endpoint readouts, offering insights into changes throughout the entire assay period. The cell models were maintained in a physiologically relevant environment, ensuring healthy cells and accurate data collection. The combination of brightfield and fluorescent measurements provided a detailed understanding of compound activity, with clear assay windows and robust Z’ values indicating high assay quality. Comparative analysis across plates and between 2D monolayer and 3D spheroid models revealed important insights into compound responses. The 3D spheroid model showed promise for improved in vitro to in vivo translation, highlighting the importance of testing in different assays using multiple readouts. This approach allowed us to identify cytotoxic, cytostatic, and disruptive compounds, providing a comprehensive understanding of their mechanisms of action. Overall, the integration of 3D cellular assays with live-cell analysis offers a powerful tool for drug discovery, enabling the identification and characterization of key compounds with potential therapeutic benefits. This methodology supports a multifaceted investigation, enhancing research and screening efforts, and ultimately contributing to the development of more effective and targeted treatments. References 1. Owens, J. Phenotypic Screening – Advances in Technologies and Techniques (2020, May 1). Retrieved from Technology Networks Drug Discovery: https://www. technologynetworks.com/drug-discovery/articles/ phenotypic-screening-advances-in-technologies-andtechniques-325091. 2. Sakharkar, M. K., Rajamanickam, K., Babu, C. S., Madan, J., Chandra, R., & Yang, J. Preclinical: Drug Target Identification and Validation in Human, (2019,). Encyclopedia of Bioinformatics and Computational Biology, Academic Press,. doi:https://doi.org/10.1016/ B978-0-12-809633-8.20665-1. 3. Costa, EC., et al.. 3D tumor spheroids: an overview on the tools and techniques used for their analysis (2016). Biotechnology advances, 34(8), 1427–1441. https://doi.org/10.1016/j.biotechadv.2016.11.002. 4. Mehta, G., et al.. Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy (2012). Journal of controlled release: official journal of the Controlled Release Society, 164(2), 192–204. https://doi.org/10.1016/j.jconrel.2012.04.045. 5. Lovell, G., Trigg, J., Rauch, J., Bevan, N. Cell-based phenotypic screening with Incucyte® Live-Cell Analysis Systems (2023). Sartorius. https://www.sartorius.com/en/products/live-cellimaging-analysis/live-cell-analysis-resources/ optimization-cell-based-phenotypic-screening. 14Go Beyond 2D. Discover the Power of 3D Cell Culture. Tired of oversimplified 2D models? NexaGel® 3D Cell Culture Matrices provide a ready-to-use, hydrogel-based scaffold that mimics native tissue environments — so your cells behave more like they do in vivo. Get more relevant insights, better reproducibility, and faster workflows — all with a matrix designed for consistency and scalability. Hydrogel-based. Ready-to-use. Reliable every time. Learn more at: www.sartorius.com 15Enhancing 3D Cancer Spheroid Models With an Innovative Synthetic Hydrogel Authors: Natasha Lewis, Daryl Cole, Kalpana Barnes Sartorius UK, Royston, Hertfordshire, UK Abstract Cancer spheroids serve as a more accurate model of tumor biology compared to traditional two-dimensional cell cultures. This application note examines the use of a synthetic hydrogel in cancer spheroid assays, offering a viable alternative to animal-derived matrices. Our findings indicate that while MCF7 and A549 cell lines successfully form spheroids within NexaGel®, they exhibit distinct growth dynamics and morphologies compared to those cultured in animal-derived extracellular matrices (ECM). These differences necessitate careful optimization of experimental parameters, including cell seeding densities, to achieve optimal results. Overall, NexaGel®* presents a promising and reproducible platform for cancer studies, enabling the development of more precise tumor models and potentially improving therapeutic strategies. Beyond cancer research, the versatility of synthetic matrices extends to applications in toxicology, regenerative medicine, and large-scale cell studies, offering new avenues for exploration and innovation in biomedical research. *NexaGel® is a registered trademark of Sartorius Bioanalytical Instruments, Inc. For details on the registrations please refer to our website sartorius.com/en/patents-and-trademarks. 16Introduction Cancer spheroids are three-dimensional aggregates of cells that more accurately represent the true in vivo properties of a tumour compared to traditional two-dimensional (2D) cultures. In 2D cultures, cells are provided with abundant oxygen and nutrients, which does not reflect the actual conditions within a tumor. In contrast, the center of a tumor is often hypoxic due to the lack of vasculature, leading to the formation of a necrotic core. This hypoxic environment is crucial for studying tumor biology as it influences cancer cell metabolism, survival, and resistance to therapies.¹ Spheroid structures provide a physiological geometry that enhances inter-cellular communication, establishes gradients of nutrients, oxygen, and pH, and can model the dynamics of drug penetration.² These features make cancer spheroids a vital tool for drug discovery, allowing researchers to evaluate the efficacy and toxicity of new therapeutic agents in a setting that closely mimics human tumors. Additionally, spheroids are instrumental in studying tumor biology, including cell-cell and cell-matrix interactions, tumor growth dynamics, and the mechanisms of metastasis. They are also used to evaluate new treatment modalities such as immunotherapies and combination therapies, providing insights into how these treatments can be optimized for better clinical outcomes. The extracellular matrix (ECM) is a fundamental component of tumour biology that requires consideration in such studies. The ECM not only provides structural support but also affects nutrient and drug penetration, influences cell migration and metastasis, and impacts interactions between cancer cells and other cell types, including fibroblasts and immune cells. These interactions are critical for understanding the tumor microenvironment and its role in cancer progression and treatment resistance.³ Many spheroid workflows leverage the properties of ECM in inducing spheroid formation and increasing applicability to in vivo environments. However, these ECMs are often naturally derived materials of animal origin. While animalderived ECMs have been instrumental in cancer research, they presents significant issues such as an undefined composition and batch-to-batch variability, which can affect experimental reproducibility and limit its use in clinical applications. To address these limitations, there is a clear need for reproducible, animal-origin free ECMs that can support 3D cell cultures and provide a more consistent and tunable environment for cancer spheroid formation. Synthetic hydrogels have emerged as a promising alternative, offering several advantages over traditional ECMs.⁴ These hydrogels can be precisely engineered to mimic the structural properties of individual tumor types, including molecular composition, stiffness, and degradability. This level of customization allows researchers to create more accurate models of the tumor microenvironment, facilitating the study of specific cancer types and their unique characteristics. Moreover, synthetic hydrogels can be designed to incorporate bioactive molecules, such as growth factors and peptides, to further enhance their functionality and relevance to in vivo conditions. By providing a more reproducible and specific environment, synthetic hydrogels hold the promise of advancing cancer research and improving therapeutic outcomes. They offer a pathway to more reliable studies, ultimately contributing to the development of more effective cancer treatments and personalized medicine approaches. This application note explores the use of NexaGel®, a synthetic hydrogel, in cancer spheroid assays. Methods Cell Culture A549 adenocarcinoma cells or MCF7 breast cancer cells were stably transfected with Incucyte® Nuclight Red Lentivirus to express a nuclear restricted fluorescent protein (A549-NR cells, MCF7-NR cells) and maintained in F-12K medium supplemented with 10% FBS, 1% Pen/Strep, and 0.5 µg/mL puromycin. Materials used for cell culture and assays are described in Table 1. 1718 Table 1: Reagents and materials used in this study. Materials Supplier Cat. No. Final Concentration Ham’s F-12K (Kaighn’s) Medium Gibco 21127022 1X Characterized Fetal Bovine Serum (FBS) Cytiva HyCloneTM SH30071.03 10 % Pen/strep Gibco 15140-122 1 % Puromycin Gibco A1113803 0.5 µg/mL PBS Gibco 14190094 1X 0.25 % Trypsin Gibco 25200056 1X Poly-L-ornithine Sigma-Aldrich A-004-M NexaGel® Sartorius NGH01 Incucyte® Nuclight Red Lentivirus (EF1α, Puro) Sartorius 4476 MOI 2 96 well plate Corning 3595 Embedded Multi-Spheroid Assay To induce embedded spheroid formation, cells were harvested using trypsin, counted, and cell suspensions were prepared to give a final cell concentration of 625-10,000 cells per well. NexaGel® was brought to room temperature and added to these cell suspensions in a 1:2 mixing ratio (one part medium to two parts hydrogel). This mixture was gently triturated 5-10 times to mix thoroughly and to avoid the addition of bubbles. The cell-hydrogel suspension was immediately added to a 96-well tissue culture plate (50 µL per well), and the plate was tilted gently to ensure the whole well was covered with the hydrogel. Bubbles were removed using 70% industrial methylated spirits (IMS) vapor. The plate was incubated at room temperature for 10-15 minutes to allow a soft hydrogel to form. 100 µL of cell culture medium was added on top of each gel. Spheroid formation was monitored with Brightfield and HD phase-contrast image acquisition (4X magnification) every 3 hours using an Incucyte® System. Multi-Spheroid Assay A 96-well tissue culture plate was pre-coated with poly-Lornithine (PLO) in order to improve hydrogel adherence to the vessel and reduce 2D cell growth on the base of the well. 75 µL 0.1% PLO solution was added to each well and the plate was incubated for 1 hour at room temperature. Prior to adding NexaGel®, the solution was aspirated, and the wells were washed once with PBS before proceeding to the next stage. Wells were coated with a thick layer of NexaGel® to facilitate formation of spheroids on top of the gel. NexaGel® was brought to room temperature, then cell culture medium was added in a 1:2 mixing ratio (one part medium to two parts hydrogel). This mixture was gently triturated 5-10 times to mix thoroughly and to avoid the addition of bubbles. The diluted hydrogel mixture was immediately added to the PLOcoated plate (50 µL per well) and the plate was tilted gently to ensure the whole well was covered. Bubbles were removed using 70% IMS vapor. The hydrogels were incubated at room temperature for 10-15 minutes to allow a soft hydrogel to form. Cells were harvested using trypsin, counted, and seeded at 625-10,000 cells per well in 100 µL medium into each well containing NexaGel®. Cell growth and spheroid formation was monitored with Brightfield, HD phase-contrast, and red fluorescence image acquisition (10X magnification) every 3 hours using an Incucyte® System.Results Spheroid Formation in Cancer Cell Lines Using NexaGel® As shown in Figure 1A, MCF7 cells embedded in NexaGel® form spheroid structures in a similar way to those cultured in animal-derived ECM. By 72 hours post seeding, spherical structures formed which then increased in size as the culture period continued. In comparison to spheroids embedded in animal-derived ECM, the spheroids that form in NexaGel® tended to be smaller and have a slower rate of growth, as quantified by the Incucyte® Spheroid Analysis Module (Figure 1B and C). A NexaGel® Matrigel® 24 h 24 h 72 h 72 h This variation in spheroid morphology and proliferation indicates that protocol adjustments are required when transitioning from another matrix to NexaGel®. For instance, increasing the initial seeding density may be required. Despite these differences, the cells maintain expected growth dynamics, characterized by an exponential growth phase followed by a plateau when nutrients are exhausted, with clear seeding-density dependent variations in proliferation and final spheroid area. 120 h 120 h B Total brightfield area (×104) C 400 300 200 100 0 0 24 48 72 96 120 144 Time elapsed (h) Total brightfield area (×104) 5,000 2,500 1,250 625 400 300 200 100 0 0 24 48 72 96 120 144 Time elapsed (h) 10,000 5,000 2,500 1,250 625 Figure 1: Growth of embedded MCF7 multi-spheroids in either NexaGel® or animal-derived ECM. A: Images of spheroid formation over time, from an initial seeding density of 5000 cells/well. B: Quantification of spheroid growth in NexaGel®. C: Quantification of spheroid growth in animal-derived ECM. 19Similarly to the MCF7 cell line spheroids, A549 cells also form spheroids when embedded in NexaGel® (Figure 2). Brightfield images (Figure 2A) reveal that these spheroids are smaller than those formed in animal-derived ECM, with a more pronounced size difference than observed in MCF7 cells. This slower growth is also reflected in quantification of total spheroid area, with the total area of the highest seeding density of spheroids in NexaGel® (10K cells/well) reaching approximately half the total area of 5K cells/well seeded in animal-derived ECM (Figure 2B and C). Despite this difference in magnitude a clear seeding density-dependent growth pattern persists. A NexaGel® Animal-derived ECM 24 h 24 h 72 h 72 h Slower growth may have advantages for assays that may suit longer timeframes or provide a clearer distinction between normal and abnormal growth patterns in assays examining cell proliferation. Overall, the data from these two cell lines underscore the importance of parameter optimization when using an alternative matrix like NexaGel® for embedded spheroid assays. Adjustments in seeding density and other parameters are necessary to achieve desired outcomes, underscoring the need for tailored approaches in experimental design. 120 h 120 h B Total brightfield area (×104) C 300 200 100 0 0 24 48 72 96 120 144 Time elapsed (h) Total brightfield area (×104) 10,000 5,000 2,500 1,250 625 300 200 100 0 0 24 48 72 96 120 144 Time elapsed (h) 5,000 2,500 1,250 625 Figure 2: Growth of embedded A549 multi-spheroids in either NexaGel® or animal-derived ECM. A: Images of spheroid formation over time from an initial seeding density of 5000 cells/well.. B: Quantification of spheroid growth in NexaGel®. C: Quantification of spheroid growth in animal-derived ECM. 20Assessing Spheroid Growth Dynamics Utilizing a multi-spheroid configuration in which cells are cultured on top of a hydrogel matrix enables the incorporation of fluorescent metrics into Incucyte® assays. NexaGel® is compatible with fluorescence imaging, and both brightfield and fluorescence metrics can be used to assess spheroid growth. Both MCF7 cells and A549 cells successfully formed spheroid structures when seeded onto NexaGel® at 5000 cells/well (Figure 3). The cells formed larger and more tightly compact spheroids more rapidly compared to their animalderived ECM. counterparts (Fig. 3A). A NexaGel® 24 h 72 h At higher density (10K cells/well), rather than aggregating together, MCF7 cells instead formed a monolayer that extended over the surface of the hydrogel (data not shown). In contrast, A549 spheroids formed on NexaGel® (Figure 3B) were smaller and expanded more slowly than those on animal-derived ECM. As observed in the embedded assay, optimizing cell seeding density is crucial depending on the desired experimental outcome. For instance, a cell monolayer on top of a hydrogel matrix may be advantageous for researchers investigating barrier function or surface interactions and can be achieved by adjusting the setup conditions. Animal-derived ECM B NexaGel® Animal-derived ECM 24 h 24 h 24 h 72 h 72 h 72 h 120 h 120 h 120 h 120 h Figure 3: Growth of spheroids cultured on top of either NexaGel® or animal-derived ECM. A: Brightfield and fluorescence images of MCF7-NR spheroid formation over time when cultured on either NexaGel® or animal-derived ECM B: Brightfield and fluorescence images of A549-NR spheroid formation over time when cultured on either NexaGel® or animal-derived ECM. 21Live-cell analysis allows for the extraction of both total brightfield area and total integrated fluorescence intensity from the images. For MCF7 cells, spheroids generally followed the expected seeding density-dependent pattern of size and growth (Figure 4A). In contrast, at a seeding density of 10,000 cells/well, the total area remained relatively constant for most of the time course, only increasing towards the end. This reflects the differential morphology observed at higher densities, i.e. monolayer vs. spheroid growth modalities. A Total brightfield area (×104) B 40 30 20 10 0 Total brightfield area (×104) 40 10,000 30 5,000 2,500 20 1,250 625 10 0 Similarly, integrated fluorescence intensity followed the densitydependent growth dynamics with the exception of 10K cells/ well (Figure 4B), which remained at a low level for the entire duration of the culture period. This reflects the fact that the cells are spread out in a monolayer, resulting in less concentrated fluorescence compared to dense spheroid structures. For A549 cells, both total brightfield area (Figure 4C) and integrated fluorescence intensity (Figure 4D) increased with both initial seeding density and with time, reaching a plateau for the highest density cells at around 96 hours. 0 24 48 72 96 120 144 Time elapsed (h) C Total integrated intensity (×104) 20 15 10 5 0 0 24 48 72 96 120 144 Time elapsed (h) 0 D 100 10,000 80 5,000 2,500 60 1,250 40 625 20 24 48 72 96 120 144 Time elapsed (h) Total integrated intensity (×104) 0 0 24 48 72 96 120 144 Time elapsed (h) 10,000 5,000 2,500 1,250 625 10,000 5,000 2,500 1,250 625 Figure 4: Quantification of spheroid growth metrics. A&C: Spheroid growth dynamics as analyzed using total brightfield area for MCF7-NR and A549-NR cells seeded onto NexaGel®, respectively. B&D: Quantification of cell viability, as indicated by integrated fluorescence intensity of the nuclear-restricted marker for MCF7-NR and A549-NR cells seeded onto NexaGel®, respectively. 22Summary and Outlook This technical note demonstrates the successful establishment of a cancer cell model using a synthetic hydrogel matrix, employing standardized protocols that can be adjusted and optimized according to cell type, assay, and the desired outcomes of the end user. This alternative to an animal-derived matrix empowers researchers to take control of their cell culture processes and advance towards more clinically translational models. In addition to the clear ethical advantages of using a synthetic matrix, its compatibility with live-cell analysis provides a robust workflow for utilizing 3D models in drug screening, personalized medicine, and regenerative medicine applications. The ability to tailor these models to specific research needs not only enhances the precision and relevance of experimental outcomes but also paves the way for innovative therapeutic strategies. As the field continues to evolve, the integration of synthetic matrices in cancer research holds promise for accelerating the development of effective treatments and improving patient outcomes. Synthetic matrices, especially those that can be adjusted in terms of composition and stiffness, also have wide applications beyond cancer research. For example, modeling specific organ functionality for toxicology, supporting complex cellular architecture, and increasing cell yield in large-scale studies, manufacturing and cell therapies. This versatility underscores the potential of synthetic matrices to revolutionize various fields of biomedical research and industry, offering new avenues for exploration and innovation. References 1. S. Y. Lee, et al., “Regulation of Tumor Progression by Programmed Necrosis,” Oxidative Medicine and Cellular Longevity, no. 1, 2018. 2. S. K. S. &. K. K. Han, “Challenges of applying multicellular tumor spheroids in preclinical phase.,” Cancer Cell Int, vol. 21, no. 152, 2021. 3. J. J. F. Sleeboom, et al., “The extracellular matrix as hallmark of cancer and metastasis: From biomechanics to therapeutic targets.,” Sci. Transl. Med., vol. 16, no. 728, 2024. 3. E. A. Aisenbrey and W. L. Murphy, “Synthetic alternatives to Matrigel,” Nat Rev Mater., vol. 5, no. 7, p. 539–551, 2020. 23Single Cell Analysis Advancing single-cell analysis for deepening our understanding of complex biological systems and disease mechanisms. Fully automated and uniquely versatile cell imaging and retrieval system to simplify single cell analysis. Discover an automated solution that reduces steps, streamlines workflows, and accelerates processing - while capturing cells without damage and enables incredibly high viability. Learn more at: www.sartorius.com 24CellCelector Nanowell Arrays Abstract The CellCelector Flex is a flexible and powerful system providing automated solutions to numerous technically challenging experimental procedures that are conventionally undertaken manually. Single cell screening and isolation, and single cell cloning workflows can be performed on the system using a wide range of CellCelector nanowell arrays for both clinical research and bioprocessing applications. These nanowell solutions, used in concert with the CellCelector Flex system, provide high-throughput and cost-effective alternatives to limiting dilution, FACS and other single cell screening and cloning methods. Sartorius nanowell arrays are available in various formats from 2 well chamber slides to 6 and 24 well plates. They feature from 100,000 to several million nanowells per plate/slide at nanowell volumes from several nanoliters down to a few picoliters. This technical note outlines the uses of the nanowell arrays and highlights specific protocols that can incorporate this technology to streamline workflows and reduce resource requirements. 25Single Cell Cloning CellCelector 4 nL Nanowell Plates (S200) CellCelector nanowell plates feature thousands of 200 µm square shape nanowells (S200) allowing cloning of hundreds or thousands of clones in parallel inside the nanowells (Figure 1). The volume of each nanowell is 4 nL which is sufficiently large for a clone of 50 - 100 cells (depending on cell size). These plates are available in standard 6 and 24 well formats which allow running multiple experiments sideby-side. An Ultra-Low Attachment (ULA) surface prevents cells from adhering and allows 3D spheroid formation with a 100 % efficient automated single cell or clone transfer from the nanowells to regular cell culture plates for expansion or analysis. Figure 2 highlights the benefits of using the nanowell plate system for single cell cloning experiments, where cells can be stressed by being isolated from their neighbors. This leads to reduced survival and growth due to the lack of cell crosstalk via cellular signalling. The nanowell plate allows each single cell to be physically isolated, but not alone, meaning the cells can still support one another through secretion of growth factors and other signalling molecules. Increased cell survival and growth is the result with happier, more robust clones for further analysis and expansion. Seeding Day 4 Figure 1: CellCelector Nanowell Plates. Micrographs illustrating S200 nanowell plate structure with up to 4,000 individual nanowells per well of a 24-well plate format array. iPSCs were seeded as single cells and incubated for 4 days until iPSC spheroids formed. The CellCelector identified nanowells containing one cell at seeding to ensure monoclonality of iPSC spheroids. Cell density ↓ Cell crosstalk x Survival | Growth ↓ Cell density ↑ Cell crosstalk √ Survival | Growth ↑ Cells Secreted molecules Surrounding medium Figure 2: CellCelector Nanowells Facilitate Cell Signalling and Growth Factor Sharing in Isolated Cells. Schematic detailing the benefits of the CellCelector nanowells over standard well formats. 26CellCelector S200 ULA nanowell plates are ideal for highthroughput single cell cloning and screening (CHO, HEK, PER.C6 etc.), single cell cloning after gene editing (e.g. stem cells like iPSC, etc.), isolation of sets of defined cell numbers (e.g. 1, 5, 20, 50, 100) for reference samples, and spheroid formation, imaging, and screening. A B Plasma B cell Antigen bead Bead-based assay Figure 3 exemplifies the utility of the CellCelector nanowell arrays for ensuring monoclonality when developing cell lines during high-throughput single cell cloning. CHO cells were grown from single cells in a nanowell plate, and their growth was monitored. On day 3, the bead-based secretion assay was performed within the plate to determine the highest secreting clones for expansion. After performing the assay, only the clones with the highest level of secretion that were derived from a single cell were selected for further culture. The data the CellCelector provides in combination with the nanowell arrays, empowers the researcher to reduce the time and the cost of performing single cell cloning assays for cell line development. Day 0: Detection of monoclonality Day 3: Detection of outgrowth Day 3: Detection of highly secreting clone Day 3: Transfer of high performing clone to 96- or 384 well plate Figure 3: Example Clone Product Analysis Implementing a CellCelector Nanowell-Based Secretion Assay. (A) Schematic of the Bead-based Secretion Assay. (B) Micrograph images from the CellCelector showing the selection process for a high producing clone. 27Single Cell Screening and Rare Single Cell Isolation CellCelector Sub-Nanoliter Nanowell Arrays (H100, U40, U25) Arrays of sub-nanoliter nanowells provide a next generation system for screening and isolation of single cells. Sartorius have developed a range of nanowell arrays with nanowell sizes ranging from 100 µm (0.9 nL) to 25 µm (21 pL volume). 100 µm nanowells feature a hexagonal shape providing high packing efficiency of nanowells and easy picking. They can also be used for single cell cloning up to approximately 16 cells per clone. Smaller 40 µm nanowells allow for co-localization of different cell types in the same wells for functional studies or receptor/reporter cell assays. Finally, 25 µm and smaller nanowells provide the isolation and analysis of rare single cells like circulating tumor cells (CTCs)/fetal cells or the screening of antibody/protein producing cells (e.g. B-Cells) through secretion assays. Effectively separating single cells in small wells with a defined spacing allows extremely high seeding densities of tens of thousands of cells without compromising on the ability to screen, analyze or to pick them individually. All small nanowells have an optimized and patent pending UFO design providing 100 % single cell recovery efficiency. A combined platform (Figure 4) allows for spatial selection and capture of individual cells of interest using the CellCelector platform and deeper interrogation of the genome of each cell using the ResolveDNA™ NGS workflow. While using the CellCelector to select those individual cells provides an accurate and reliable solution to individual cell isolation with the added benefit of keeping an automated record of the process (Figure 5). CellCelector sub-nanoliter nanowell arrays such as the 2 well slide U40 shown in Figure 6, are ideal for the screening of B-cells for antibody discovery, hybridoma cloning, cell-to-cell interaction functional assays, and rare cell isolation such as circulating tumour cells or fetal cells. The array configuration (Figure 6) allows for a single cell to be captured in each nanowell for isolated analysis and characterization prior to selection for downstream processing. Figure 4: CellCelector and ResolveDNA™ Workflow. CellCelector isolated single cells undergo primary template-directed amplification, followed by library preparation, sequencing, and analysis with BaseJumper™ software. Figure 5: Verify Single-Cell Capture. The automated workflow provides a live image during picking including cell tracking, plate to destination plate data as well as images before and after picking. Figure 6: Highly Accurate Isolation of Dissociated Cells. Using the CellCelector, isolation of rare cells or cells at low-density concentration can be accomplished using a 2 well Slide U40 nanowell array. These arrays, containing over 300K capture wells, provide the ability to first qualify a cell (blue circles) or reject a debris object (red circles) prior to isolation and capture. The process is very useful to isolate cells from precious clinical samples where often the total cell number is below 10,000 cells. 28Rare Single Cell Isolation SIEVEWELL Nanowell Arrays The SIEVEWELL chip consists of a thin membrane with nanowells of 20 µm in size and 25 µm in height. There are 370,000 nanowells on the chip while the outer dimensions of the chip correspond to the standard microscope slide format. Each nanowell is hexagonal in shape, making them ideal for automated cell detection and cell counting. The membrane is characterized by a very high transparency as well as very low autofluorescence and is therefore ideally suited for microscopic and optical measurements. Due to its unique sieve structure SIEVEWELL arrays are a simple solution for on-chip staining of rare single cells, like circulating tumor cells and fetal cells without any cell loss. Each nanowell contains 2 micropores at the bottom connected to a small liquid gap underneath. This captures the cells inside the nanowells and generates a one-directional flow from the top of the liquid chamber, through the pores into the liquid gap underneath (Figure 7). Due to this unique design, it is possible not only to separate single cells and capture them inside the nanowells but also to process them directly inside these wells, through fixation, permeabilization, blocking, incubation, and washing without disturbing or losing any cells. This completely cell loss-free on-chip processing makes SIEVEWELL technology extremely efficient for rare single cell applications, such as isolation of circulating tumor cells, fetal cells, and others. Fluorescently labeled cells in green and red were captured in a SIEVEWELL nanowell array (Figure 8), prior to being picked using the CellCelector Flex. A mixed population of cells were seeded into the array and captured in individual wells. Following cell seeding and capture, it was possible to individually pick separately labeled cells within the array and process them for continued expansion. Cells picked by the CellCelector Flex were automatically seeded into separate nanowells for expansion. Figure 9 shows the specificity of the system in picking cells without cross contamination. It is clear from the image that there was no crossover of fluorescence between wells. The chip design in combination with the CellCelector technology allowed for complete automation of the identification of single cells as well as 100% pure isolation of the desired target cells. Figure 7: SIEVEWELL Nanowell Arrays. The CellCelector SIEVEWELL nanowell array uses one-directional flow technology to capture individual cells and sequester each inside a single nanowell. The fluid volume below the nanowells is connected to two side ports through a micro-gap situated below the chip membrane. Cells will follow the liquid flow and become trapped in the nanowells. When a cell enters a nanowell it will block the micropores reducing the liquid flow through that nanowell. Other cells are therefore automatically redirected towards other, empty nanowells leading to a self-sorting nanowell array. Figure 8: Single Cell Fluorescence. Individual cells within a SIEVEWELL Nanowell Assay stained with green and red fluorescent markers. Figure 9: Robust Cross-Contamination Control. GFP and RFP cells were alternatively picked and grown to identify any individual cell cross-over between wells. 2930 Summary CellCelector nanowell arrays provide solutions to several technically challenging research areas, from CLD workflows, rare cell isolation, and single cell screening. The examples outlined in this document highlight the capabilities of the system and its nanowell technology for a range of challenging to perform workflows. Single cell cloning workflows are streamlined, reducing resources and time required to generate clones from single cells. Rare single cell isolation processes are simplified using nanowell arrays, allowing the identification and selection of rare cells such as circulating tumor cells. The CellCelector Flex, in combination with nanowell arrays, increases the efficiency of challenging workflows and experimental processes to reduce resource requirements, cost, and the amount of precious samples required. Array Listing Nanowell Array Volume (per nanowell) Nanowell Width x Depth Nanowell Count (per well) Usage 24 well S200 4 nL 200 µm x 100 µm 4000 CLD, single cell cloning, single cell screening 6 well S200 4 nL 200 µm x 100 µm 22,500 CLD, single cell cloning, single cell screening 24 well H100 0.9 nL 100 µm x 100 µm 14,000 Single cell screening, rare single cell isolation 6 well H100 0.9 nL 100 µm x 100 µm 60,000 Single cell screening, rare single cell isolation 24 well U40 50 pL 40 µm x 40 µm 36,000 Single cell screening, rare single cell isolation 6 well U40 50 pL 40 µm x 40 µm 160,000 Single cell screening, rare single cell isolation 2 well Slide U40 50 pL 40 µm x 40 µm 50,000 Single cell screening, rare single cell isolation Slide U25 21 pL 25 µm x 25 µm 200,000 Single cell screening, rare single cell isolation SIEVEWELL Slide 370K 8.6 pL 20 µm x 25 µm 370,000 (per slide) Rare single cell isolation SIEVEWELL Slide 90K 108 pL 50 µm x 50 µm 90,000 (per slide) Rare single cell isolationThe iQue® 5 Truly Fast. Simply Effortless. ccelerate your cell therapy research with high-throughput screening and comprehensive insights. The NEW iQue®️ 5 HTS by Cytometry Platform, delivers unmatched speed and flexibility in assay design. Learn more at: ww.sartorius.com/iQue-products 31Evaluating Antibody Drug Conjugates (ADCs) In Vitro Using 3D Tumor Spheroid Models Authors: Kirsty McBain, Kalpana Barnes & Nicola Bevan Sartorius UK Ltd., Units 2 & 3 The Quadrant, Newark Close, Royston, Hertfordshire, SG8 5HL, UK Abstract Antibody-drug conjugates (ADCs) combine highly targeted delivery of chemotherapeutic drug molecules with an immunotherapeutic intervention for cancer cell killing. A rapid increase in ADC development in recent years has created a need for robust and reliable techniques for assessing novel candidate drugs. The use of 3D advanced cell models can improve the translational ability of these in vitro techniques. Here, we present a combined Incucyte® Live- Cell Analysis and iQue® High-Throughput Screening (HTS) by Cytometry workflow for assessing anti-HER2 ADC activity in single and multi-spheroid models. Incucyte® Live-Cell Analysis data demonstrated greater induction of cytotoxicity by anti-HER2 ADCs (Kadcyla® and Enhertu®) compared to the monoclonal antibody (mAb) backbone on which they were based (Trastuzumab). iQue® HTS analysis revealed the unique Enhertu® bystander activity, by allowing the cellular composition of a spheroid co-culture to be measured. These techniques facilitate in-depth analysis of ADC activity and allow mechanistic differences between ADCs to be unpicked. 32Introduction ADCs are a unity between highly specific mAbs and cytotoxic chemotherapeutic drugs. This combination creates a powerful therapeutic for the treatment of several types of cancer: both solid tumors and hematological malignancies. Over the last few years, research and development of ADCs has accelerated, as evidenced by a 35% increase in clinical trials and a 90% increase in phase 1 clinical trials investigating ADCs in 2022 compared to in 2021.1 The two main targets for ADC development at this time, HER2 and TROP2, are overexpressed on breast cancers and comprise 20% of all ADC studies.1 ADCs have three main components: the targeting mAb, the cytotoxic payload, and a stable linker between them. Each element adds an extra layer of tumor targeting specificity to the ADC, on top of the highly specific delivery of the payload to the tumor cell following mAb binding. For example, the linker is highly stable prior to internalization of the antibody into the target cell, which ensures the payload is not released elsewhere in the body. ADC linkers can be categorized into cleavable or noncleavable types and are either severed by enzymatic activity of proteases or are degraded in the acidic lysosome.2 Another factor contributing to ADC specificity is the mechanism of action (MoA) of the cytotoxic payload. Since these are typically chemotherapeutic drugs, their MoAs act preferentially towards rapidly proliferating cancer cells, for example by inhibiting microtubule polymerization or inducing DNA damage.3-5 Recent years have seen a shift in the choice of in vitro models utilized for oncology research. The 2D cell monolayer has long been used due to its simplicity, costeffectiveness, and scalability, however it can lack some critical features of the 3D tumor microenvironment (TME). For this reason, many researchers now use 3D models, such as spheroids or organoids, to assess drug effects in vitro. Cell Culture and Maintenance Antibodies Three anti-HER2-hIgG1 antibodies were characterized: a Trastuzumab biosimilar (Absolute Antibody); Kadcyla® (Trastuzumab emtansine; a therapeutic-grade ADC based on Trastuzumab and the chemotherapy drug emtansine (also known as DM1), Midwinter Solutions) and Enhertu® (Trastuzumab deruxtecan, a therapeutic-grade ADC based on Trastuzumab and the chemotherapy drug deruxtecan (or DXd), Midwinter Solutions). An Anti-β-Gal-hIgG1 mAb from InvivoGen was used as an isotype control. These models provide a much closer reflection of the TME, with more complex cell-cell interactions and the option to include extracellular matrix (ECM) proteins. The tumor cells also grow in a more layered structure, with the outer layer undergoing fast proliferation and interaction with the TME, followed by a middle quiescent layer and an inner necrotic core.6,7 This facilitates a more representative modelling of drug behavior in vivo, for example by allowing the comparison of tumor penetration of candidate drugs. Traditional methods for measuring drug response in 3D models are often limited because they: 1. Require multiple workflows for quantification of different parameters, often using multiple instruments 2. Involve lengthy, time-consuming protocols, which require multiple rounds of optimization, fixation, and repetitive washes 3. Necessitate correlation of data from several different assays for each treatment condition, increasing the risk of data variability 4. Provide bulk measurement of cytotoxicity without a deeper investigation into spheroid cell type composition. In this application note, we present a combined Incucyte® Live-Cell Analysis and iQue® HTS by Cytometry approach for quantifying the function of two Trastuzumab (antiHER2) based ADCs: Trastuzumab emtansine (Kadcyla®) and Trastuzumab deruxtecan (Enhertu®), using both single and multi-spheroid 3D models. The Incucyte® Live-Cell Analysis System captures temporal information on mAb internalization and target cell death by quantifying spheroid size and fluorescence intensity. The iQue® 3 Cytometry Instrument measures target cell counts and viability after spheroid dissociation, enabling a closer look at different populations within a co-culture. Cell Lines BT474 cells (from a breast cancer cell line) or SKOV-3 cells (from an ovarian cancer cell line) were used as high HER2 expressing cell types. MDA-MB-231 cells, from a breast cancer cell line, express low levels of HER2 and were used as a negative control throughout. Antibody Internalization Target cells were seeded in an Ultra-Low Attachment (ULA) 96-well microplate (Corning® 7007) for 72 hours to promote 33spheroid formation. Antibodies, labeled with Incucyte® Human Fabfluor-pH Orange Antibody Labeling Dye (Sartorius 4812), were then added to spheroids. Phase and fluorescence images (10X) were captured every 15 minutes using the Incucyte® Live-Cell Analysis System. Internalization was quantified as an increase in Total Orange Area (µm2 /image). Single Spheroid ADC Cytotoxicity Target cells (transduced with Incucyte® Cytolight Green Lentivirus for stable expression of nuclear restricted GFP) were seeded in a ULA plate for 72 hours to promote spheroid formation. Phase and fluorescence images (10X) were captured using the Incucyte® Live-Cell Analysis System on a 3-hour repeating scan schedule for 9 days. Cell death was quantified as a reduction in spheroid area. Multi-spheroid ADC Cytotoxicity A flat bottom 96-well plate (Corning® 3595) was coated with a layer of Matrigel® (5 mg/mL) as per a validated Incucyte® protocol (Incucyte® Multi-Spheroid Assay for the Quantification of Multi-Spheroid Growth and Health on a Layer of Matrigel®).8 Incucyte® Nuclight Green Lentivirus labeled target cells (2 K/well) were seeded on top and incubated for 72 hours to promote multi-spheroid formation. Antibodies were added (2 µg/mL) and cells were monitored using the Incucyte® Live-Cell Analysis System via a repeating scan schedule (every 3 hours) for 10 days. Images were quantified for a Brightfield Green Integrated Intensity (GCU x µm2/image) over time as an indicator of cell death. Single Spheroid Antibody-Dependent Cellular Cytotoxicity (ADCC) Target cells labeled with Incucyte® Cytolight or Nuclight Green Lentivirus were seeded in a ULA plate (4 K/well) for 72 hours to promote spheroid formation. As MDA-MB-231 cells require Matrigel® to form tight spheroids, Matrigel® was added to culture media at a final concentration of 2%. Matrigel® was added to both target cell types to ensure differences in diffusion didn’t impact ADCC. Test antibodies were added at a range of concentrations. Natural killer (NK) cells were added (16 K/well) alongside IL-12 (10 ng/mL) to improve their longevity. Phase and fluorescence images (4X objective) were captured using the Incucyte® Live-Cell Analysis System on a 3-hour repeating scan schedule for 10 days. Cell death was quantified as a reduction in spheroid green mean intensity. Single Spheroid Bystander Activity Single spheroids were formed by mixing high HER2 expressing BT474 cells (labeled with Incucyte® Cytolight Green Lentivirus) and unlabeled, low HER2 expressing MDA-MB-231 cells at a 2:3 ratio. Antibodies were added after 72 hours and images were captured on a repeat scanning schedule (every 3 hours) using the Incucyte® Live-Cell Analysis System. On day 8, the spheroids were dissociated (using a previously validated protocol, T Cell Killing in Single Spheroids) and cells were labeled using the iQue® 3 Cell Membrane Integrity (R/Red) Dye.9 This enabled live cell counts of each cell type to be quantified using the iQue® HTS by Cytometry Platform. Incucyte® Multi-Spheroid Assay for the Quantification of Multi-Spheroid Growth and Health on a Layer of Matrigel® Results Kadcyla® The described experiments have utilized both single and multi-spheroid tumor models to profile the function of three antibodies: Trastuzumab, Kadcyla®, and Enhertu®. Trastuzumab is an anti-HER2 mAb therapeutic, whilst are ADCs which contain a and Enhertu® Trastuzumab backbone. Due to this commonality in structure, we expect the antibodies to share many functional capabilities, however, they do also include unique structural features which may distinguish their anti-tumor function (Figure 1). One difference is in the cytotoxic payload included on each ADC, with Kadcyla® including chemotherapy drug emtansine (DM1) whilst Enhertu® is linked to the payload deruxtecan (Dxd). Each ADC also differs in the number and positions of the payload conjugation sites, with DM1 binding to lysine residues in the Trastuzumab backbone in Kadcyla® at an average of 3.5 conjugations per antibody, whilst binding of Dxd in Enhertu® is via thioether bonds with cysteine residues, with 8 conjugations per molecule. 10,11 The cytotoxic payload in the ADCs also has different linker chemistries, with Kadcyla® containing a non-cleavable amine-to-sulfhydryl crosslinker (SMCC) and Enhertu® containing a valine-citrulline cleavable linker.12 3 34Trastuzumab Trastuzumab (monoclonal antibody) Variable region Fc region Kadcyla® Trastuzumab DM1 (cytotoxic agent) Non-cleavable linker Figure 1. Structures of anti-HER2 antibodies and ADCs Enhertu® Trastuzumab Results Dxd (cytotoxic agent) Cleavable linker Trastuzumab is an anti-HER2-IgG1 mAb. Kadcyla® is a modified version of Trastuzumab which includes non-cleavable linkers to the chemotherapy drug emtansine (DM1). Enhertu® is also an adapted version of Trastuzumab, with cleavable linkers to attach the cytotoxic payload, deruxtecan (Dxd). Antibody Internalization Efficient antibody internalization is critical for the delivery of the ADC cytotoxic payload into target cells. Initial experiments aimed to quantify the relative level of internalization of each of the antibodies into single spheroids formed from high HER2 expressing cells (BT474s). The antibodies were pre-labeled with Incucyte® Human Fabfluor-pH Orange Antibody Labeling Dye, which fluoresces upon internalization into the acidic lysosomal and endosomal pathways. Incucyte® images were captured over time and an increase in internalization was quantified as an increase in spheroid orange mean intensity. Phase and fluorescence images (Figure 2A) clearly show that fluorescence, and therefore internalization, in the presence of all three anti-HER2 antibodies was much higher than the IgG control. The time course graph (Figure 2B) provides a closer examination of the differences in internalization between the anti-HER2 antibodies and indicates considerably higher internalization of the two ADCs (Kadcyla® and Enhertu®) compared to the backbone antibody (Trastuzumab), with endpoint Orange Calibrated Units (OCU) intensity values of 31.8, 27.0 and 17.7, respectively. These data are in line with results generated previously for internalization of these antibodies in a 2D monolayer assay format (data available in Application Note: Cross-Platform Analysis of the Binding and Function of Anti-HER2 Antibody-Drug Conjugates (ADCs)).13 Cross-Platform Analysis of the Binding and Function of Anti-HER2 Antibody-Drug Conjugates (ADCs) A. Phase and Fluorescence Fluorescence Only No Antibody IgG Control Trastuzumab Kadcyla® Enhertu® B. Spheroid Orange Mean Intensity (OCU) 40 30 20 10 0 Figure 2. Internalization of ADCs into BT474 spheroids is greater than internalization of Trastuzumab. 0 12 24 Time (h) 36 Kadcyla® Enhertu® Trastuzumab IgG Control No Antibody 48 BT474 cells (4 K/well) were seeded into ultra-low attachment (ULA) plates for 72 hours to promote spheroid formation. Antibodies were labeled with Incucyte® Human Fabfluor-pH Orange Antibody Labeling Dye then added to spheroids (n=3). Phase and fluorescence images (10X objective) were captured every 15 minutes using the Incucyte® Live-Cell Analysis System. (A) Representative Incucyte® images of single spheroids taken 48 hours after antibody addition. (B) Internalization was quantified as an increase in Spheroid Orange Mean Intensity (OCU) and plotted over time. 35Single Spheroid ADC Cytotoxicity Following internalization of ADCs into target antigenexpressing cells, the chemotherapeutic drug payload is released, either through proteolytic degradation of the linker or by its metabolism in the acidic lysosome. This means the drug molecule can exert a cytotoxic effect, specifically towards the target cell. This reduces off-target cytotoxicity compared to administration of the chemotherapeutic drug alone. The mechanism of this cytotoxic effect can vary depending on the drug used. For example, the DM1 in Kadcyla® acts to inhibit microtubule assembly, whilst the Dxd payload in Enhertu® disrupts DNA replication through inhibition of topoisomerase I.14,15 To explore this cytotoxicity in vitro, a range of concentrations of antibody were tested with a monoculture of BT474 cells in a 3D single spheroid format. Reduction in spheroid size over time was quantified using the Incucyte® Live-Cell Analysis System and was used as a measure of cell death. Both ADCs induced a concentration dependent reduction in spheroid area over time, indicating induction of cytotoxicity (Figures 3C and 3D). The effect of Kadcyla® varied much more across the concentration range tested, with average spheroid size at endpoint of 0.92 x 105 µm2 at the highest concentration and 1.91 x 105 µm2 at the lowest concentration. Contrastingly, the sensitivity to varying concentrations of Enhertu® was much lower with the A. IgG Control B. 2.5 2.5 Spheroid Area x105(µm2) 2.0 1.5 1.0 0 48 96 144 192 240 Spheroid Area x105(µm2) 2.0 1.5 1.0 spheroid area ranging only from 1.15 x 105 µm2 ¬(25 µg/mL) to 1.68 x 105 µm2 (0.39 µg/mL). Only the highest concentration of Trastuzumab (25 µg/mL) had an impact on spheroid size (Figure 3B). Clearly this is not down to the action of chemotherapeutic drugs and instead may be the result of Trastuzumab’s other mechanisms of action, including inhibition of cell signaling, for example in the PI3K-AKT pathway.17 Maintenance of this activity in the Kadcyla® ADC may explain the much larger reduction in spheroid area at the highest Kadcyla® concentration compared to the second highest concentration tested (13 µg/mL). Across the concentration range tested, the IgG control had no effect on spheroid size (Figure 3A). Comparing the data in Figures 2 and 3, there is a clear difference in the time taken for internalization compared to the cytotoxic response. Internalization begins rapidly within the first 24 hours and starts to plateau between 24-48 hours. In contrast, with the cytotoxic effect, there is minimal difference between the conditions until after the 48 hours when the concentration dependent response starts to materialize. This time course could reflect the MoA of the ADCs in that they need to be internalized into the target cells first before the cytotoxic payload can be cleaved and start to induce cytotoxicity. Trastuzumab 0 Time (h) C. Kadcyla® D. 2.5 2.5 Spheroid Area x105(µm2) 2.0 1.5 1.0 Spheroid Area x105(µm2) 0 48 96 144 Time (h) 192 240 2.0 1.5 1.0 0 48 48 96 144 Time (h) Enhertu® 96 144 Time (h) 192 192 240 240 0.39 µg/mL 0.78 1.6 3.1 6.3 13 25 Figure 3. ADCs induced a concentration dependent increase in cytotoxicity of BT474 cells in a 3D single spheroid model. BT474 cells (1 K/well) were seeded in ULA plates for 72 hours to promote spheroid formation. Antibodies were added to spheroids at a range of concentrations (n=3). Incucyte® images were captured on a 3-hour repeating scan schedule for 9 days. Time-course data for change in Largest Object Area (µm2) for spheroids incubated with (A) IgG control, (B) Trastuzumab, (C) Kadcyla® and (D) Enhertu®. 36 5Multi-spheroid ADC Cytotoxicity Multi-spheroid tumour models can also be utilized to test the activity of ADCs in conditions more representative of the in vivo tumor environment. Adding further 3D complexity with increased cellular and ECM interactions, this model may bring us closer to a translational model for oncology drug research. This involved coating plates with a layer of Matrigel® before seeding the target cells (high HER2 expressing SKOV-3 or low HER2 expressing MDAMB-231 cells) on top. After 72 hours of multi-spheroid formation, antibodies were added at a single concentration (2 µg/mL). Images were captured using the Incucyte® Live-Cell Analysis System with the multispheroid scan type and analysis. The images in Figure 4A show the difference between SKOV-3 spheroids in the presence of each antibody on day 9. Compared to the IgG control, there was a visible A. IgG Control Trastuzumab reduction in the number and size of spheroids in the presence of each of the antibody treatments. A mask was applied which allowed quantification of death as a loss of fluorescence intensity within the spheroid brightfield object. Figure 4B shows that the ADCs induced a high level of death in the SKOV-3 spheroids, with a 63.9 and 73.7 % reduction in spheroid intensity at assay endpoint compared to the IgG control for Kadcyla® and Enhertu®, respectively. Trastuzumab also induced considerable cell death, but at a slightly reduced level compared to the ADCs (49.7% reduction from IgG control). Overall, the cell death at this concentration of antibody is considerably higher than was seen in the single spheroid model, which is likely due to differences in cell type and spheroid size. Figure 4C shows that this is an antigen positive cell type specific effect as there was no impact on HER2-low MDAMB-231 spheroids. Kadcyla® Enhertu® B. Brightfield Green Integrated Intensity x 106(GCU x µm2/Image) SKOV-3 2.5 2.0 1.5 1.0 0.5 0.0 0 48 96 144 Time (h) C. IgG Control Trastuzumab Kadcyla® Enhertu® 192 240 Brightfield Green Integrated Intensity x 106(GCU x µm2/Image) Figure 4. ADCs induce death of HER2 expressing SKOV-3 cells in a multi-spheroid model. MDA-MB-231 8 6 4 2 0 0 48 96 144 Time (h) 192 240 Incucyte® Nuclight Green Lentivirus labeled SKOV-3 and MDA-MB-231 cells (2 K/well) were seeded on a layer of Matrigel® (5 mg/mL) to promote multi-spheroid formation. Antibodies were added (2 µg/mL) and cells were monitored using the Incucyte® Live-Cell Analysis System via a repeating scan schedule (every 3 hours) for 10 days. (A) Representative images of SKOV-3 multi-spheroids from day 9. Cell death in (B) SKOV-3 and (C) MDA-MB-231 spheroids was quantified as a loss of fluorescence intensity of spheroids over time. 3738 7 Figure 5. Induction of NK-mediated ADCC activity is much greater with high HER2 expressing targets in the presence of Kadcyla®. Target cells (4 K/well) were MDA-MB-231 (HER2-low) or BT474 (HER2-high) labeled with Incucyte® Nuclight or Cytolight Green Lentivirus, respectively. Matrigel® was added (2%) to aid tight spheroid formation. After 72 hours, cells were mixed with test antibodies and natural killer (NK) cells (20 K/well). Phase and fluorescence images (4X) were captured using the Incucyte® Live-Cell Analysis System on a 3-hour repeating scan schedule for 10 days for spheroid green mean intensity (OCU). (A) Temporal microplate graph of intensity of BT474 spheroids. Outliers are removed in grey. (B) and (C) Bar charts for intensity of spheroids formed from BT474 and MDA-MB-231 targets. Spheroid Green Mean Intensity (OCU) Spheroid Green Mean Intensity (OCU) 150 15 50 5 100 10 0 0 0.08 0.08 0.31 0.31 1.3 1.3 5 5 B. C. Time (h) Time (h) BT474 MDA-MB-231 Single Spheroid ADCC A key MoA of Trastuzumab in the killing of HER2 expressing cancers is ADCC.17 It is expected that ADCs based on Trastuzumab would retain this killing function on top of the payload-induced cytotoxicity. To investigate this, NK cells, which are the main cell type involved in ADCC, were added to the single spheroid model. Incucyte® images were captured on a repeating schedule and cell death was quantified as a reduction in spheroid intensity over time. This metric was chosen as it allowed us to distinguish differences between both antibodies and concentrations in terms of induction of spheroid death. The temporal microplate graph (Figure 5A) shows the green intensity of spheroids formed from high HER2 expressing, green-labeled BT474 spheroids over time. The data highlights a much larger decrease in spheroid green intensity over time in the presence of the anti-HER2 antibodies, compared with the IgG control. This, combined with the bar chart in Figure 5B, shows that Kadcyla® induced the most cell death, followed by Trastuzumab, then Enhertu®. This contrasts with what was observed in the absence of NK cells (Figure 3), when the Enhertu® induced greater cytotoxicity than Trastuzumab. This may suggest that NK-mediated ADCC has a greater overall cytotoxic effect than ADC-induced cytotoxicity in this model, and that this effect is slightly attenuated in the Enhertu® antibody, perhaps due to the addition of the payload molecules to the Fc region. The antibodies had minimal effect on the intensity of the HER2-low MDAMB-231 spheroids (Figure 5C). A. No Antibody IgG Control 189.78 A 0 189.78 B 0 189.78 C 0 189.78 D 0 189.78 E 0 189.78 F 0 189.78 G 0 189.78 H 0 10d 10d 10d 10d 10d 10d 10d 10d 10d 10d 10d 10d 10d 10d 1 2 3 4 5 6 7 8 9 10 11 12 Trastuzumab Kadcyla® Enhertu® Antibody Green Mean Intensity IgG Control Trastuzumab Kadcyla® Enhertu® Single Spheroid Bystander Activity In 2022, Enhertu® was the first HER2-directed therapy to be approved for treatment of patients with low-HER2 expressing breast cancer.18 The reason it has been given this additional indication (alongside treatment for high HER2expressing breast cancers) is its potential to exert a unique ‘bystander’ effect. This activity is thought to be facilitated by the high membrane permeability of the Dxd payload, meaning that once the ADC has killed a HER2 expressing cell, the payload can then be released and diffuse easily into neighboring cells, regardless of their HER2 expression and result in their death.19 To examine this in vitro using a 3D single spheroid model, spheroids were first formed from a mixture of of high HER2 expressing BT474 cells (labeled with Incucyte® Cytolight Green Lentivirus) and low HER2 expressing, unlabeled MDA-MB-231 cells. The cells were mixed at a 2:3 BT474 to MDA-MB-231 ratio. Incucyte® images were captured every 3 hours for 8 days and a reduction in spheroid area used to quantify cell death (Figure 6A). This showed that spheroid area was reduced only in the presence of Enhertu® and remained comparable in the presence of the other three antibodies. However, the Incucyte® Live-Cell Analysis data alone lacked information on the growth of the individual cell types in the co-culture. To determine this, at assay endpoint, the spheroids were dissociated to create a single cell suspension. Cells were then labeled using iQue® 3 Cell Membrane Integrity (R/Red) Dye and analyzed for the green (BT474) and unlabeled (MDA-MB-231) live cell counts using the iQue® HTS by Cytometry Platform. The data in Figure 6B show that, as in each of the previous A. Spheroid Area [x106] (µm2) B. 1.0 0.8 0.6 0.4 0.2 0.0 Trastuzumab Kadcyla® IgG Control Enhertu® Live BT474 Counts 1500 1000 500 0 assays, all three of the anti-HER2 antibodies caused a reduction in the number of high HER2-expressing BT474 cells. The percentage cell death compared to IgG control was again greater for the two ADCs, at 96.4% and 97.2%, respectively, compared to Trastuzumab, which induced 46.4% cell death. Figure 6B shows that, unlike in the monoculture assays, there has been considerable death of the HER2-low cell type in the presence of Enhertu® when the cells are cocultured with the high HER2 expressing cells. This provides evidence that the bystander activity of Enhertu® can be observed using this in vitro model. These data also show that there has been an increase in the number of MDA-MB-231 cells compared to the IgG control in the wells treated with Kadcyla®. This suggests that as Kadcyla® induces cytotoxicity of the BT474 cells, the MDA-MB-231 cells have more space and/or nutrients to grow. This could explain why the Incucyte® Live-Cell Analysis quantification of spheroid area remains constant unless the antibody has induced death of both cell types in the co-culture (as seen with Enhertu®). for iQue® This demonstrates that, although we can measure the bulk reduction in spheroid area using the Incucyte® Live-Cell Analysis System (as in Figure 6A), we get little information on the cellular composition of the spheroid. Using the Incucyte® Live-Cell Analysis System to gather temporal information and then dissociating spheroids HTS Cytometry analysis allows quantification of the different cell types comprising the spheroid. This in turn can provide information on differential MoAs of test antibodies. C. 4000 3000 2000 1000 Live MDA-MB-231 Counts 0 48 96 Time (h) 144 192 Kadcyla® Trastuzumab IgG Control Figure 6. Bystander killing of low HER2 expressing cells is only seen with the Enhertu® ADC. Enhertu® 0 Trastuzumab IgG Control Kadcyla® Enhertu® Single spheroids were formed from a 2:3 ratio of high HER2 expressing BT474 cells (labeled with Incucyte® Cytolight Green Lentivirus) to low HER2 expressing, unlabeled MDA-MB-231 cells. Antibodies were added after 72 hours. Images were captured on a repeat scanning schedule (every 3 hours) using the Incucyte® Live-Cell Analysis System. On day 8, spheroids were dissociated and cells labeled using the iQue® 3 Cell Membrane Integrity (R/Red) Dye. Live cell counts were quantified using the iQue® 3 Platform. (A) Cell death was quantified from Incucyte® images as a reduction in spheroid area over time. Bar graphs show viable cell counts from the iQue® 3 Platform of (B) BT474 cells and (C) MDA-MB-231 cells. 39 8Conclusions These data describe the use of the Incucyte® Live-Cell Analysis System and iQue® HTS by Cytometry Platform to profile the in vitro function of ADCs. Incucyte® assays quantified antibody internalization and cytotoxicity over time, whilst dissociated spheroids could be assessed using the iQue® 3 Cytometry Instrument to reveal differences in spheroid composition. The advantages of this workflow include: 1. 3D single and multi-spheroid models provide a closer reflection of the TME, for example due to more complex cell-cell and cell-ECM interactions. This can mean a more translational model for drug development. 2. The Incucyte® Live-Cell Analysis System provides visual and temporal analysis of spheroids over time, with easy quantification of metrics such as spheroid size and fluorescence intensity allowing antibody internalization and cytotoxicity to be measured over time. 3. The iQue® HTS by Cytometry facilitates high-throughput analysis of dissociated cells, with the ability to distinguish individual cell types within a mixture. This can reveal mechanistic information on a drug’s activity. References 4. Mix and read reagents combined with validated protocols make experimentation simple and streamlined. 5. This workflow and the described advantages allow a comprehensive assessment of the function of ADCs in 3D models and can enhance drug discovery and biological research applications. This workflow and the described advantages allow a comprehensive assessment of the function of ADCs in 3D models and can enhance drug discovery and biological research applications. 1. The Clinical Landscape Of ADCs In 2023 Diverse Technologies Narrow Target. (2023) Available at: https://www. clinicalleader.com/doc/the-clinical-landscape-of-adcs-in-diverse-technologies-narrow-target-0001. 2. Hafeez, U. et al. (2020)“Antibody–drug conjugates for cancer therapy,” Molecules, 25(20), p. 4764. 3. Ponziani, S. et al. (2020) “Antibody-drug conjugates: The New Frontier of chemotherapy,” International Journal of Molecular Sciences, 21(15), p. 5510. 4. Dumontet, C. and Jordan, M.A. (2010) “Microtubule-binding agents: A dynamic field of cancer therapeutics,” Nature Reviews Drug Discovery, 9(10), p. 790–803. 5. Reuvers, T.G., Kanaar, R. and Nonnekens, J. (2020) “DNA damage-inducing anticancer therapies: From global to precision damage,” Cancers, 12(8), p. 2098. 6. Habanjar, O. et al. (2021) ‘3D cell culture systems: Tumor application, advantages, and disadvantages’, International Journal of Molecular Sciences, 22(22), p. 12200. 7. Zanoni, M. et al. (2016) ‘3D tumor spheroid models for in vitro therapeutic screening: A systematic approach to enhance the biological relevance of data obtained’, Scientific Reports, 6(1). doi:10.1038/srep19103. 8. Sartorius. (2020) Incucyte® Multi-Spheroid Assay for the Quantification of Multi-Spheroid Growth and Health on a Layer of Matrigel® 9. Sartorius. T Cell Killing in Single Spheroids Protocol 10. Chen, L. et al. (2016) “In-depth structural characterization of Kadcyla® (ADO-trastuzumab emtansine) and its biosimilar candidate,” mAbs, 8(7), p. 1210–1223. 11. Agency Committee for Medicinal Products for Human Use (CHMP), E.M. (2020) “CHMP assessment report: Enhertu,”. 12. Kenney, D.J. (2022) Taking aim with antibody-drug conjugates, Taking Aim with Antibody-Drug Conjugates. Available at: https://www.antibody.com/news/taking-aim-with-antibody-drug-conjugates. 13. Sartorius. (2023) Cross-Platform Analysis of the Binding and Function of Anti-HER2 Antibody- Drug Conjugates (ADCs) 14. Peddi, P.F. and Hurvitz, S.A. (2013) ‘Trastuzumab emtansine: The first targeted chemotherapy for treatment of breast cancer’, Future Oncology, 9(3), pp. 319–326. (References continue on next page) 40References (continued from previous page) 15. Iwata, T.N. et al. (2023) Data from a HER2-targeting antibody–drug conjugate, trastuzumab Deruxtecan (DS-8201A), enhances antitumor immunity in a mouse model [Preprint]. 16. Ewesuedo, R.B. and Ratain, M.J. (1997) ‘Topoisomerase I inhibitors’, The Oncologist, 2(6), pp. 359–364. 17. Valabrega, G., Montemurro, F. and Aglietta, M. (2007) ‘Trastuzumab: Mechanism of action, resistance and future perspectives in her2-overexpressing breast cancer’, Annals of Oncology, 18(6), pp. 977–984. 18. Center for Drug Evaluation and Research (no date) FDA approves fam-trastuzumab deruxtecan-nxki for HER2-low breast cancer, U.S. Food and Drug Administration. FDA. Available at: https://www.fda.gov/drugs/resources-information-approveddrugs/fda-approves-fam-trastuzumab-deruxtecan-nxki-her2-low-breast-cancer. 19. Ogitani, Y. et al. (2016) “Bystander killing effect of ds-8201A, a novel anti-human epidermal growth factor receptor 2 antibody–drug conjugate, in tumors with human epidermal growth factor receptor 2 heterogeneity,” Cancer Science, 107(7), p. 1039–1046. North America Phone +1 734 769 1600 Email: lps.opm.na@sartorius.com Europe Phone +44 1763 227400 Email: orderhandling.lps.ne@sartorius.com Japan Phone +81 3 6478 5202 Email: sartoriusap@sartorius.com China Phone +86 21 6878 2300 Find out more: www.sartorius.com/incucyte Find out more: www.sartorius.com/ique For questions, email: AskAScientist@sartorius.com Rest of Asia Pacific and other  countries around the world: Phone +65 6872 3966 Specifications subject to change without notice. ©2024 All rights reserved. All names of Sartorius products are registered trademarks and the property of Sartorius AG and/or one of its affiliated companies. AB-Drug Conjugates-Using-Spheroids-App-Note-2402-en-L-Sartorius Status: 02 | 2024 41 10Conclusion As the landscape of biomedical research continues to evolve, the integration of cutting-edge technologies into foundational models is redefining what’s possible in the lab. Three-dimensional spheroid models stand at the forefront of this evolution— offering a more realistic, functional alternative to traditional 2D cultures. When combined with advanced live-cell analysis and robust stem cell isolation techniques, these models become even more powerful, enabling researchers to generate deeper insights with greater precision and relevance. This synergy not only improves experimental outcomes but also supports the development of more ethical, efficient, and translatable research practices. From uncovering mechanisms of disease to accelerating the discovery of new therapies, 3D spheroid systems enhanced with live-cell monitoring and stem cell applications are shaping a future where in vitro research more faithfully mirrors the complexity of the human body. As you apply the knowledge and strategies outlined in this eBook, you’ll be better equipped to harness the full potential of 3D spheroid models—pushing the boundaries of what can be achieved in translational science and paving the way for the next generation of biomedical breakthroughs. Further Learning Webinar: Automating the Generation of Monoclonal Spheroids from A Single Cell Blog: Let’s Talk About the Sticky Situation that is Transferring Organoids and Spheroids Compendium: Incucyte® Live-cell Analysis Applications: 3D Cell Culture 42Supporting Products and Solutions Incucyte® Live-Cell Analysis System Incucyte® Spheroid Analysis Software Module Real-time, automated imaging and analysis of live spheroids within standard incubator conditions. Researchers can noninvasively track growth, morphology, and viability over time — eliminating the need for disruptive endpoint assays and offering a clearer view of dynamic cellular behavior in response to treatments or environmental changes. Learn More NexaGel® 3D Cell Culture Matrices A ready-to-use, versatile synthetic matrix system that closely replicates the human microenvironment, making it ideal for research involving a wide range of cell types. NexaGel® hydrogels feature optimized multi-functional ligands and concentration formulations, suitable for various applications, including 3D cell models, stem cell spheroids, and organoids. Learn More High Performance Cell Culture Media, Reagents, and Supplements for Life Science Research Sartorius offers a full portfolio of high-quality cell culture media, buffers, transfection reagents, growth factors, and cytokines for research applications so you can craft the perfect cell culture recipe for success in your lab. Explore solutions below. Enables kinetic acquisition and objective assessment of spheroids within a physiologically relevant environment. The integration of optimized protocols and Incucyte® software enables you to standardize your entire spheroid workflow, from generation, manipulation, and analysis, using imagedbased, label-free or fluorescence measurements. Characterize and assess spheroid growth or invasion and morphology during cultures and probe the effects of treatments through unbiased assessment of size, count and morphology. Learn More CellCelector Automated Cell Selection and Retrieval Platform A powerful platform for high-precision, automated isolation of individual stem cells or spheroids. Its micromanipulation capabilities allow researchers to select and relocate specific cell populations or spheroids based on user-defined criteria, enabling highly customizable and targeted experimentation. This is particularly valuable when working with heterogeneous stem cell populations or when generating uniform, clonal spheroid models for downstream analysis. Learn More Learn More 43Germany USA Sartorius Lab Instruments GmbH & Co. KG Otto-Brenner-Straße 20 37079 Göttingen Phone +49 551 308 0 For further information, visit sartorius.com Sartorius Corporation 3874 Research Park Drive Ann Arbor, MI 48108 Phone +1 734 769 1600 S