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

Ciupe SM, Devlin BH, Markert ML, Kepler TB - BMC Immunol. (2013)

Bottom Line: Spectratyping generates data about T-cell receptor CDR3 length distribution for each BV gene but is technically complex.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, Virginia Tech, 460 McBryde Hall, Blacksburg, VA 24060, USA. stanca@vt.edu

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.

Show MeSH

Related in: MedlinePlus

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750526&req=5

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.

Mentions: We derived estimates and 95% confidence intervals for parameters α and C for each of eight individuals by fitting y(n), as given by (2), to the measured Df values in Table 1 for CD4 T-cell numbers n. For the fitting routine we used a descent method for univariate functions [18]. The parameter values and their confidence intervals are presented in Table 3. The regression curves and data are presented in Figure 3.


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

Ciupe SM, Devlin BH, Markert ML, Kepler TB - BMC Immunol. (2013)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750526&req=5

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.
Mentions: We derived estimates and 95% confidence intervals for parameters α and C for each of eight individuals by fitting y(n), as given by (2), to the measured Df values in Table 1 for CD4 T-cell numbers n. For the fitting routine we used a descent method for univariate functions [18]. The parameter values and their confidence intervals are presented in Table 3. The regression curves and data are presented in Figure 3.

Bottom Line: Spectratyping generates data about T-cell receptor CDR3 length distribution for each BV gene but is technically complex.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Mathematics, Virginia Tech, 460 McBryde Hall, Blacksburg, VA 24060, USA. stanca@vt.edu

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.

Show MeSH
Related in: MedlinePlus