(T-030) Integration of Temporal Single-Cell RNA Sequencing Data into Quantitative Systems Pharmacology Model of T Cell Differentiation

Daisy Lin, n/a – Senior Research Associate, Genmab; Brooke Robertson, n/a – Postdoctoral Fellow, Genmab; Vanessa Spires, n/a – Expert Research Associate, Genmab; Jeremy Gale, n/a – Senior Research Associate, Genmab; Omar Jabado, n/a – Director, Genmab; Manish Gupta, n/a – Senior Vice President, Genmab; Mark Fereshteh, n/a – Vice President, Genmab; Jordan Blum, n/a – Director, Genmab; Craig Thalhauser, n/a – Senior Director, Genmab

Objectives: We propose a novel method in which a Quantitative Systems Pharmacology (QSP) model was calibrated to time-course single-cell RNA sequencing (scRNA-seq) gene expression data. This approach was designed to mathematically model complex biological mechanisms underlying T cell activation and differentiation at the transcript level over multiple time points to capture both immediate and delayed transcriptional responses in healthy donors. This methodology provides a framework for one to explore the interplay between various signaling pathways and transcription factors that drive T cell behavior. This exploration offers insights into the regulation and modulation of immune response.

Methods: An optimized in vitro T cell exhaustion assay was conducted using isolated and purified CD3+ T cells from three apparently healthy donors. T cells were exhausted by repeated stimulation with IL-2 and anti-CD3/CD28 beads and collected at multiple time points for scRNA-seq analysis using the 10x Genomics flex platform. The raw data was analyzed using the Seurat toolkit in R [1]. Separately, T cells from the same donors were analyzed for cell surface expression and viability at baseline, determined by flow cytometry. A QSP model was then calibrated to the scRNA-seq data using Simbiology.

Results: Analysis revealed a time-dependent modulation of gene expression, with significant upregulation of activation, immune regulatory, and dysfunction markers post-stimulation. Similarly, clustering analysis identified several distinct T cell subpopulations, which were validated with flow cytometry. These distinct populations exhibited unique transcriptional trajectories, which correlate with known T cell differentiation states [2]. The QSP model, calibrated to these differentiation states and characterized by scRNA-seq, exhibits a novel technique.

Conclusions: Our study reveals that temporal patterns of gene expression contribute to the complexity of T cell activation and immune response, highlighting periods of transcriptional change that are crucial for immune function. These patterns are highly dynamic and benefit from QSP model analysis. Understanding T cell immunodynamics in response to periodic stimulation is crucial for understanding therapeutic response. By calibrating QSP models to scRNA-seq data, we will uncover early predictors of immune response and dysfunction offering valuable insights into the mechanisms of antigen presentation and therapeutic intervention.

Citations: Citations:
[1] Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018 Jun;36(5):411-420. doi: 10.1038/nbt.4096. Epub 2018 Apr 2. PMID: 29608179; PMCID: PMC6700744.
[2] Giles JR, Ngiow SF, Manne S, Baxter AE, Khan O, Wang P, Staupe R, Abdel-Hakeem MS, Huang H, Mathew D, Painter MM, Wu JE, Huang YJ, Goel RR, Yan PK, Karakousis GC, Xu X, Mitchell TC, Huang AC, Wherry EJ. Shared and distinct biological circuits in effector, memory and exhausted CD8+ T cells revealed by temporal single-cell transcriptomics and epigenetics. Nat Immunol. 2022 Nov;23(11):1600-1613. doi: 10.1038/s41590-022-01338-4. Epub 2022 Oct 21. PMID: 36271148; PMCID: PMC10408358.