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The use of T cells in cellular immunotherapy has increased significantly over the last decade, especially with the advent of chimeric antigen receptor (CAR) T cell technology1. While T cell-based therapies have been met with great clinical success, they are heterogeneous and cell characteristics and identities can vary significantly between donors2. The heterogeneity of T cell populations can have major impacts on therapeutic efficacy and complicate conclusions from clinical trials. For this reason, it is crucial to understand T cell heterogeneity during product manufacturing and formulation to determine the optimal formulations of T cell immunotherapies.
In a broad sense, T cells can be divided into two groups: CD4+ helper T cells, which secrete immunomodulatory cytokines, and CD8+ cytotoxic T cells, which directly lyse cells presenting the cognate antigen on major histocompatibility complex (MHC) I molecules. CD4+ helper T cells can differentiate into a myriad of specific subsets that produce a unique set of cytokines. Some suggest that a defined ratio of CD4+ to CD8+ T cells in the final cell product maximize in vivo efficacy and persistence but could complicate cell manufacturing3. Regulatory T cells (Tregs), a subset of CD4+ T cells, are immunosuppressive and diminish the activation of immune cells. Regulatory T cells have been implicated in mediating tumor tolerance and, if present during CAR T cell manufacturing, can inhibit the antitumor immune response of CAR T cells4,5,6. Other CD4+ subsets such as T helper 17 (Th17) T cells promote tumor clearance and can be leveraged to increase the efficacy of T cell therapies. Thus, increasing Th17 CD4+ T cells is of special interest in solid tumor settings where increased production of interleukin 17 (IL-17) promotes antitumor immune response5,7,8,9. Understanding the importance of Th17 cells in tumor responses has prompted more investigation into strategies to generate these cells in vitro7,9. Therefore, methods for detecting these T cell subsets are crucial for optimizing T cell therapies and should be robust, easy, scalable, and reproducible.
Flow cytometry is the most common method to determine the identity of an immune cell. However, flow cytometry requires live, intact cells that must be analyzed on the same day they are harvested. For intracellular cytokine staining (ICS), protein secretion inhibition is required to retain cytokines within the T cells. However, different inhibitor compounds have differential effects on the secretion of specific cytokines, forcing users to create specialized cocktails for the detection of the cytokine of interest10,11. Additionally, the fidelity of flow cytometry relies on antibody clones that bind specifically and potently to their target. The use of different antibody clones can cause varying results and lead to imprecise conclusions, making the method difficult to standardize12. Further, analysis of data collected via flow cytometry, and thus conclusions drawn from the data, can vary greatly between users based on how gates are set13,14. For these reasons, flow cytometry is not ideal for precise quality control during cell therapy development.
The assessment of genomic methylation at specific gene loci is an alternative method of determining cell identity. The rationale for using DNA methylation to identify specific cell types has been described in multiple articles and relies on specific methylation patterns present in a given cell population15,16,17. In target cells, certain sites contain unmethylated nucleotides, while in non-target cells these sites are methylated. This pattern can be detected through bisulfite conversion, a process that exclusively converts unmethylated cytosine nucleotides (C) to uracil (U). Primers and probes can be designed against the converted sites, then amplified and detected through qPCR16.
The assay described herein measures the frequency of immune cell subtypes within heterogeneous populations by quantifying the number of specific unmethylated loci compared to a housekeeping gene. Copies of the unmethylated regulatory elements for the CD8 B gene are measured in the CD8 assay, FoxP3 in Treg, and IL-17 in Th17 in this assay. These loci were identified by isolating the specific cell population of interest (e.g., CD8+ T cells) and performing bisulfite sequencing to identify loci that are unmethylated in only that cell type15. Primers and probes are developed against the uniquely unmethylated loci and samples are analyzed via qPCR. A standard curve of known copy numbers is created from dilutions of a high copy number standard plasmid, allowing for the conversion of a Ct value to a transcript copy number.
To evaluate assay performance, control samples are required, including a reference gDNA and a calibrator plasmid. The reference genomic material is a pooled blood sample from multiple donors with known immune cell frequencies and is used for a quality control (QC) assay performance check. The calibrator sample is a synthesized plasmid that contains the CD8, FoxP3, and GAPDH gene sequences in an equimolar ratio. It is used as a measure of the bisulfite conversion efficiency, because bisulfite conversions within different genomic regions will vary in efficiency. The ratio of the target to GAPDH within the calibrator is 1, but due to differences of the bisulfite conversion efficiency, the ratio can vary. This calibration factor is applied to the CD8 and Treg assays. FoxP3, the gene used in the Treg assay, is located on the X chromosome. Because this assay measures only unmethylated copies of FoxP3, and only one copy of FoxP3 is unmethylated in Tregs, this assay is sex agnostic and can be used with both male and female samples18. The Th17 assay does not utilize a calibrator sample or GAPDH as a housekeeping gene. Instead, another primer set targeting the methylated loci present in non-Th17 cells is included and the total number of cells is reflected by the sum of the methylated and unmethylated copies of IL-17. The data obtained from the qPCR is entered into a preset analysis template that performs quality control checks on the data and calculates the percentage of the target cell within the starting population. This provides automated and unbiased data analysis, thereby removing user subjectivity and improving standardization capabilities.
This assay uses gDNA, which can be isolated by a leukapheresis procedure using cultured or fixed cells, or frozen cell pellets. The low sample requirement, flexibility in sample starting material, high accuracy, and standardized analysis address the limitations associated with flow cytometry and are ideal for quality control purposes during cell therapy process development.