(T1030-05-31) Harnessing a Cutting-Edge Time-Lapse Flow Cytometry Platform for Enhanced Immunogenicity Assessment

Purpose: In recent years, immunogenicity has emerged as a critical factor that can impact the success, safety, and efficacy of innovative new modalities. What makes it particularly challenging, however, is that the risk of immunogenicity is shaped by a combination of patient-specific and drug-related factors. Thus, characterizing unwanted immune responses and effectively understanding and managing immunogenicity has become essential as early as the preclinical stages of development, during which various in-vitro assays have been implemented to evaluate potential immune reactions and predict the immunogenic risk of new therapeutic modalities [1]. For example, T-cell proliferation assays are used to evaluate sequence-related risks by measuring T-cell responses, including proliferation, activation, and cytokine secretion. While DC: T proliferation assays are used to evaluate molecules with immune modulatory MoAs, especially those that contain T cell binding domains [2-3]. Yet, despite their widespread use in preclinical studies, these assays have several limitations, including significant variability, difficulties in fully replicating the complexity of immune responses, able to only evaluate a limited set of cell markers, are time consuming, and are unable to accurately predict long-term or temporal immune responses [4-5]. To address this gap, we performed a side-by-side comparison using the Time-Lapse Flow Cytometry platform developed by Lase Innovation [6]. The goal of the study was to leverage this platform to more efficiently identify potential immunogenic responses to biotherapeutics, enhancing precision in understanding the immune mechanisms involved and enabling more accurate risk assessments compared to traditional methods that rely on bulk flow analysis.

Methods: In our preliminary proof-of-concept study, we assessed two widely used positive controls: Phytohemagglutinin (PHA) sourced from Sigma and a CD3/CD28 bispecific antibody (bsAb) obtained from InvivoGen. PBMCs and T cells were cultured in RPMI-1640 medium supplemented with 10% (v/v) FBS and 1% (v/v) penicillin/streptomycin, maintained at 37°C in a 5% CO₂ atmosphere. Dendritic cells (DCs) were maintained using LGM-3™ Lymphocyte Growth Medium-3 from Lonza, supplemented with 1% (v/v) penicillin/streptomycin, GM-CSF, and IL-4, as per the vendor’s recommendations. In the CD4+ T Cell Proliferation assay, frozen PBMCs from healthy donors were sourced from Lonza. Isotype controls or medium treatments were used as negative controls. T cell proliferation was assessed by flow cytometry (FACS) using CTFR (CellTrace™ Far Red Cell Proliferation) staining, which allowed for the tracking of cell division and the quantification of proliferative responses. In the DC:CD4+T Cell Proliferation Assay, monocyte-derived dendritic cells (MoDCs) were incubated with the test articles for 24-48 hours. After the test articles were removed from the culture medium, the matured MoDCs were then co-cultured with autologous CD4+ T cells for 7 days. T cell activation and proliferation were then evaluated as indicators of the immune response. Time-Lapse Flow Cytometry: The method was fully described previously by Kwok et al. in Nature Biomedical Engineering (2024). Briefly, frozen hPBMCs were thawed and labeled with optical barcodes, referred to as laser particles (LPs). The cells were then stimulated with the test article for 12 hours. Following stimulation, the cells were stained with a combination of surface markers, including both releasable and conventional antibodies. The stained cells were processed through a flow cytometer and recaptured for further analysis. Subsequently, the antibodies were released, and the cells were re-incubated with the test article for 38 hours Cycle 1-, 62 hours during Cycle 2-, and 90-hours during Cycle 3. After each cycle, the cells were re-stained and reprocessed through the cytometer to ensure comprehensive data collection and analysis. Flow cytometry data analysis: FCS files obtained from the flow cytometer were analyzed using the FlowJo software. Single-color compensation controls were acquired for each run. Compensation matrices were generated using data from approximately 10,000 events per sample.

Results: Kinetic changes in human T cell activation were assessed over a 4-day culture period (12, 38, 62, and 90 hours) following stimulation with PHA and a CD3/CD28 bispecific antibody (bsAb). Using Time-Lapse Flow Cytometry, expression dynamics of CD69, CD25, CD38, and PD-1 were tracked and correlated with bulk CD4⁺ T cell and DC:CD4⁺ T cell proliferation assay results. These markers exhibited distinct temporal expression patterns at the single-cell level, with kinetic analyses revealing differences between PHA- and bsAb-stimulated conditions compared to media control. While PHA and bsAb are well-established activators in conventional assays, these methods fail to capture the dynamic temporal changes in marker expression observed in this study, which limits their predictive power. Our results with Time-Lapse Flow Cytometry show potential in identifying immunogenic responses earlier and with greater sensitivity and specificity, compared to conventional assays.

Conclusion: The implementation of the Time-Lapse Flow Cytometry enabled detection of immune cell activation phenotypes at the single-cell level over time, capturing dynamic changes and transient activation states which were not previously captured through conventional methods. Thus, it could provide deeper insights into the immune activation process and enable more accurate early assessment of immunogenicity profiles during drug development.

References: 1. Sauna, Z. E., Jawa, V., Balu-Iyer, S., et al. (2023). Understanding preclinical and clinical immunogenicity risks in novel biotherapeutics development. Frontiers in Immunology, 14, Article 1151888.
2. Ducret, A., Ackaert, C., Bessa, J., et al. (2022). Assay format diversity in pre-clinical immunogenicity risk assessment: Toward a possible harmonization of antigenicity assays. mAbs, 14(1), Article 1993522.
3. Siegel, M., Steiner, G., Franssen, L. C., et al. (2022). Validation of a dendritic cell and CD4+ T cell restimulation assay contributing to the immunogenicity risk evaluation of biotherapeutics. Pharmaceutics, 14(12), Article 2672.
4. Zhou, Y., Penny, H. L., Kroenke, M. A., et al. (2022). Immunogenicity assessment of bispecific antibody-based immunotherapy in oncology. Journal for ImmunoTherapy of Cancer, 10, Article e004225.
5. Walsh, R. E., Nix, A., Ackaert, C., et al. (2024). Preclinical immunogenicity risk assessment of biotherapeutics using CD4 T cell assays. Frontiers in Immunology, 15, Article 1406040.
6. Kwok, S. J. J., Forward, S., Fahlberg, M. D., et al. (2024). High-dimensional multi-pass flow cytometry via spectrally encoded cellular barcoding. Nature Biomedical Engineering, 8, 310–324.

Acknowledgements: This study was sponsored and funded by Takeda Development Center Americas, Inc. J.R., X.Z. and M.Q. are employees of Takeda Development Center Americas, Inc., Cambridge, MA, USA; whereas YS, S.K., T. B. are employees of LASE Innovation, Inc., Waltham, Massachusetts, USA.