TY - JOUR
T1 - Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells
AU - Pedersen, Natasja Wulff
AU - Chandran, P. Anoop
AU - Qian, Yu
AU - Rebhahn, Jonathan
AU - Petersen, Nadia Viborg
AU - Hoff, Mathilde Dalsgaard
AU - White, Scott
AU - Lee, Alexandra J.
AU - Stanton, Rick
AU - Halgreen, Charlotte
AU - Jakobsen, Kivin
AU - Mosmann, Tim
AU - Gouttefangeas, Cécile
AU - Chan, Cliburn
AU - Scheuermann, Richard H.
AU - Hadrup, Sine Reker
PY - 2017
Y1 - 2017
N2 - 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 (
AB - 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 (
KW - Antigen-specific T cells
KW - Automated gating
KW - Computational analysis
KW - Flow cytometry
KW - Major histocompatibility complex dextramers
KW - Major histocompatibility complex multimers
U2 - 10.3389/fimmu.2017.00858
DO - 10.3389/fimmu.2017.00858
M3 - Journal article
C2 - 28798746
SN - 1664-3224
VL - 8
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 858
ER -