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Published on: January 9, 2019
Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility
Natasja Wulff Pedersen1, P Anoop Chandran2, Yu Qian3
1Division of Immunology and Vaccinology, Veterinary Institute, Technical University of Denmark, Copenhagen, Denmark.
Insights
Automated analysis of T cells using computational tools like FLOCK, SWIFT, and ReFlow can reduce variation in flow cytometry data. SWIFT showed promise for detecting rare T cell populations, though human intervention was still needed.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- Manual analysis of flow cytometry data introduces variability in T cell assessment.
- Automated analysis of major histocompatibility complex (MHC) multimer-binding T cells offers a solution to reduce subjectivity and technical variation.
Purpose of the Study:
- To assess if currently available computational solutions can analyze MHC multimer-binding CD8+ T cells.
- To determine if automated analysis reduces technical variation across different laboratories.
Main Methods:
- Utilized a heterogeneous dataset from an MHC multimer proficiency panel.
- Analyzed flow cytometry data from 28 laboratories using three methods: FLOCK, SWIFT, and ReFlow.
- Screened for antigen-responsive T cell populations with frequencies ranging from 0.01% to 1.5%.
Main Results:
- All three programs (FLOCK, SWIFT, ReFlow) identified high to intermediate frequency MHC multimer-binding T cell populations with results comparable to manual gating.
- SWIFT demonstrated superior performance in identifying less frequent populations (<0.1% of live, single lymphocytes).
- None of the tested algorithms provided a fully automated pipeline, requiring some degree of human intervention.
Conclusions:
- Automated analysis pipelines are feasible for assessing antigen-responsive T cells, including rare populations.
- Different computational methods have distinct properties, advantages, and differences in their application.
- Further development is needed for fully automated T cell population identification in flow cytometry data.
Abstract:
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.
