Quantification of total T-cell receptor diversity by flow cytometry and spectratyping

Abstract

Background: T-cell receptor diversity correlates with immune competency and is of particular interest in patients undergoing immune reconstitution. Spectratyping generates data about T-cell receptor CDR3 length distribution for each BV gene but is technically complex. Flow cytometry can also be used to generate data about T-cell receptor BV gene usage, but its utility has not been compared to or tested in combination with spectratyping.

Results: Using flow cytometry and spectratype data, we have defined a divergence metric that quantifies the deviation from normal of T-cell receptor repertoire. We have shown that the sample size is a sensitive parameter in the predicted flow divergence values, but not in the spectratype divergence values. We have derived two ways to correct for the measurement bias using mathematical and statistical approaches and have predicted a lower bound in the number of lymphocytes needed when using the divergence as a substitute for diversity.

Conclusions: Using both flow cytometry and spectratyping of T-cells, we have defined the divergence measure as an indirect measure of T-cell receptor diversity. We have shown the dependence of the divergence measure on the sample size before it can be used to make predictions regarding the diversity of the T-cell receptor repertoire.

Figures

Figure 1

Measured and corrected divergence measures as function of inverted sample number. (a) Measured flow divergence, Df, (red solid diamonds) and corrected flow divergence, Df,corr, (blue circles) as functions of the inverted sample number 1/n; (b) Measured spectratype divergence, Ds, (red empty diamonds) and corrected spectratype divergence, Ds,corr, (blue circles) as a function of the inverted sample number 1/n0 in one DiGeorge patient.

Figure 2

Flow divergence, Df, as a function of sample size n (∙), presented on a log-log scale.

Figure 3

Flow divergence Df as a function of the inverted sample number 1/n in eight subjects. The solid line represents the fit of the three parameter linear model (2) to the data (∙). Results are presented on a log-log scale. The same model was fitted to a data set that excluded point (0.0017,0.366) for control 3 (dashed line). The best parameter estimates and their 90% confidence intervals are presented in Table 3.

Figure 4

Flow divergence Df as a function of the inverted sample number 1/n for the same slope C. The solid and dashed lines shows the fit of a three parameter linear model (3) to the data (∙). The results are presented on a log-log scale. The best parameter estimates and their 90% confidence intervals are presented in Table 4.

Figure 5

CD4 T-cell spectratype data. Spectratype histograms show the number of CD4 T-cells bearing receptors versus CDR3 length for each TCR BV families tested.