Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells
| Autor: | Cliburn Chan, Kivin Jakobsen, Charlotte Halgreen, Rick Stanton, Natasja Wulff Pedersen, Sine Reker Hadrup, Cécile Gouttefangeas, P. Anoop Chandran, Jonathan Rebhahn, Scott R. White, Richard H. Scheuermann, Mathilde Dalsgaard Hoff, Tim R. Mosmann, Alexandra J. Lee, Yu Qian, Nadia Viborg Petersen |
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| Rok vydání: | 2017 |
| Předmět: |
lcsh:Immunologic diseases. Allergy
0301 basic medicine T cell Immunology major histocompatibility complex dextramers Bioengineering Variation (game tree) Computational biology major histocompatibility complex multimers antigen-specific T cells Major histocompatibility complex Major histocompatibility complex multimers Flow cytometry automated gating 03 medical and health sciences Antigen medicine Immunology and Allergy Computational analysis Antigen-specific T cells Original Research Genetics biology medicine.diagnostic_test flow cytometry MHC multimer 030104 developmental biology medicine.anatomical_structure Medical Microbiology Automated gating Major histocompatibility complex dextramers computational analysis biology.protein lcsh:RC581-607 CD8 |
| Zdroj: | Frontiers in Immunology Pedersen, N W, Chandran, P A, Qian, Y, Rebhahn, J, Petersen, N V, Hoff, M D, White, S, Lee, A J, Stanton, R, Halgreen, C, Jakobsen, K, Mosmann, T, Gouttefangeas, C, Chan, C, Scheuermann, R H & Hadrup, S R 2017, ' Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells ', Frontiers in Immunology, vol. 8, 858 . https://doi.org/10.3389/fimmu.2017.00858 Frontiers in Immunology, Vol 8 (2017) |
| ISSN: | 1664-3224 |
| Popis: | 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 ( |
| Databáze: | OpenAIRE |
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