Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (<0.1% of live, single lymphocytes), SWIFT outperformed the other tools. As used in this study, none of the algorithms offered a completely automated pipeline for identification of MHC multimer populations, as varying degrees of human interventions were needed to complete the analysis. In this study, we demonstrate the feasibility of using automated analysis pipelines for assessing and identifying even rare populations of antigen-responsive T cells and discuss the main properties, differences, and advantages of the different methods tested

CD8+ T cells from patients with narcolepsy and healthy controls recognize hypocretin neuron-specific antigens

Autoreactive T cells are suspected to destroy hypocretin-producing neurons in narcolepsy. Here the authors detect CD8 T cells recognizing narcolepsy-related proteins in healthy individuals and in patients with narcolepsy, and show that the frequency of self-reactive CD8 T cells differs between patients and controls sharing the same HLA-II risk allele

T-cell Responses in the Microenvironment of Primary Renal Cell Carcinoma-Implications for Adoptive Cell Therapy

Abstract In vitro expansion of large numbers of highly potent tumor-reactive T cells appears a prerequisite for effective adoptive cell therapy (ACT) with autologous tumor-infiltrating lymphocytes (TIL) as shown in metastatic melanoma (MM). We therefore sought to determine whether renal cell carcinomas (RCC) are infiltrated with tumor-reactive T cells that could be efficiently employed for adoptive transfer immunotherapy. TILs and autologous tumor cell lines (TCL) were successfully generated from 22 (92%) and 17 (77%) of 24 consecutive primary RCC specimens and compared with those generated from metastatic melanoma. Immune recognition of autologous TCLs or fresh tumor digests was observed in CD8+ TILs from 82% of patients (18/22). Cytotoxicity assays confirmed the tumoricidal capacity of RCC-TILs. The overall expansion capacity of RCC-TILs was similar to MM-TILs. However, the magnitude, polyfunctionality, and ability to expand in classical expansion protocols of CD8+ T-cell responses was lower compared with MM-TILs. The RCC-TILs that did react to the tumor were functional, and antigen presentation and processing of RCC tumors was similar to MM-TILs. Direct recognition of tumors with cytokine-induced overexpression of human leukocyte antigen class II was observed from CD4+ T cells (6/12; 50%). Thus, TILs from primary RCC specimens could be isolated, expanded, and could recognize tumors. However, immune responses of expanded CD8+ RCC-TILs were typically weaker than MM-TILs and displayed a mono-/oligofunctional pattern. The ability to select, enrich, and expand tumor-reactive polyfunctional T cells may be critical in developing effective ACT with TILs for RCC. In summary, TILs isolated from primary RCC specimens could recognize tumors. However, their immune responses were weaker than MM-TILs and displayed a mono-/oligofunctional pattern. The ability to select and expand polyfunctional T cells may improve cell therapy for RCC. Cancer Immunol Res; 6(2); 222–35. ©2018 AACR.</jats:p

Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (&lt;0.1% of live, single lymphocytes), SWIFT outperformed the other tools. As used in this study, none of the algorithms offered a completely automated pipeline for identification of MHC multimer populations, as varying degrees of human interventions were needed to complete the analysis. In this study, we demonstrate the feasibility of using automated analysis pipelines for assessing and identifying even rare populations of antigen-responsive T cells and discuss the main properties, differences, and advantages of the different methods tested