In the context of large-scale human system immunology studies, controlling for technical and biological variability is crucial to ensure that experimental data support research conclusions. In this study, we report on a universal workflow to evaluate both technical and biological variation in multiparameter flow cytometry, applied to the development of a 10-color panel to identify all major cell populations and T cell subsets in cryopreserved PBMC. Replicate runs from a control donation and comparison of different gating strategies assessed the technical variability associated with each cell population and permitted the calculation of a quality control score. Applying our panel to a large collection of PBMC samples, we found that most cell populations showed low intraindividual variability over time. In contrast, certain subpopulations such as CD56 T cells and Temra CD4 T cells were associated with high interindividual variability. Age but not gender had a significant effect on the frequency of several populations, with a drastic decrease in naive T cells observed in older donors. Ethnicity also influenced a significant proportion of immune cell population frequencies, emphasizing the need to account for these covariates in immune profiling studies. We also exemplify the usefulness of our workflow by identifying a novel cell-subset signature of latent tuberculosis infection. Thus, our study provides a universal workflow to establish and evaluate any flow cytometry panel in systems immunology studies.
This work was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award numbers U19AI118626 and R24AI108564. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The flow cytometry data presented in this article have been submitted to the ImmPort database under accession number SDY820.
The online version of this article contains supplemental material.
- Received October 13, 2016.
- Accepted December 6, 2016.
- Copyright © 2017 by The American Association of Immunologists, Inc.