Circulating lymphocyte subsets in normal adults are variable and can be clustered into subgroups

Abstract

Background: Flow cytometry is used to monitor lymphocyte subsets in both the clinical and research settings. An understanding of the degree of inter-and intrasubject variability of these populations is critical for data interpretation.Methods: Peripheral blood lymphocytes of 18 healthy adults were analyzed on two separate occasions using a multicolor flow cytometric panel with B, T, and NK cell markers. Variability was calculated using the coefficient of variation and compared between and within individuals using agglomerative clustering.Results: Each subject appears to have B and T cell subset profiles that are stable over the two time points, but differ from the profiles of other subjects. Thus, the range of measurements for a particular B or T cell subset is larger between subjects and narrower for an individual. In addition, the level of variability correlates inversely with the size of the lymphocyte subset. When lymphocyte profiles are analyzed by agglomerative clustering, replicate samples from the same individual tend to cluster. When single samples from different individuals are analyzed, individuals appear to cluster into different subgroups.Conclusions: Variability of lymphocyte subsets is usually greater between individuals than within a single individual and each person appears to have a characteristic profile of lymphocyte subsets. These results underscore the importance of obtaining a baseline value for each subject when investigating the impact of a treatment on lymphocyte subsets over time. These results also highlight the potential utility of cluster analysis as a tool for immune subset profiling and biomarker discovery.

Systems Immunology Reveals Markers of Susceptibility to West Nile Virus Infection

“…The methods employed here, including validated multiparameter immunophenotyping and genome-wide transcriptional profiling, provide data for discriminating between susceptible and resistant WNV subjects. The relevance of the genes identified here is heightened, as the critical differences were identified in a healthy convalescent cohort based on severity at the time of illness and were evident from the stable characteristics of individual immune cell frequencies and gene expression (5,6). We identified a predictive gene signature of susceptibility (67% accuracy) with a high level of significance to distinguish anti-WNV response patterns.…”

“…Definition of the biological signatures underlying immune responsiveness requires quantification of elements of innate and adaptive immune responses, a cohort exhibiting a range of clinical outcomes, and a sufficient sample size to accommodate natural variations in responsiveness. We have undertaken a comprehensive profile of stable characteristics of individual immune cell frequencies and gene expression (5,6) to define markers that identify key mechanisms of resistance and susceptibility to virus infection.…”

DNA Methylation in Whole Blood: Uses and Challenges

“…The result is an estimate of the underlying distribution of cell proportions (circle, triangle, and hexagon) for each sample within S1 many DNA methylation associations attributed to intrinsic epigenetic processes are likely due to subtle effects on cell type composition involving cells that have yet to be characterized. Current human leukocyte libraries account for only approximately seven subtypes, but numerous specific subtypes may exist when one counts activation states of many common types [57,58]. Memory versus naïve T or B cells, subsets of activated NK cells as well as different forms of dendritic and myeloid cells have yet to be studied.…”

Circulating B cells in type 1 diabetics exhibit fewer maturation-associated phenotypes

“…The control group of subjects were previously also used as healthy controls in two separate unrelated studies; one previous study evaluated B cell subsets and antibody repertoire in patients with systemic lupus erythematous[22], while the other previous study examined the variability of circulating lymphocytes in adults over time. [23] The diagnosis for T1D was established by clinical characteristics including initial presentation, laboratory values such as reduced C-peptide and insulin-dependence. The demographic and clinical characteristics of the patients and controls are provided in Table 1.…”

“…Typically samples were drawn in the morning and processed and stained on the same day for flow cytometry (a detailed procedure for staining and flow cytometry is provided in [23]). Samples were analyzed by multicolor immunophenotyping using the antibody panels listed in Table S1.…”