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A novel statistical analysis and interpretation of flow cytometry data.

Banks HT, Kapraun DF, Thompson WC, Peligero C, Argilaguet J, Meyerhans A - J Biol Dyn (2013)

Bottom Line: A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed.Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme.This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors.

View Article: PubMed Central - PubMed

Affiliation: Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, USA. htbanks@ncsu.edu

ABSTRACT
A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.

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Comparison of estimated CD4 T cell division dynamics between Donor 1 (left) and Donor 2 (right). Top: Probability density function φ0(t) for time to first division for initially undivided CD4 T cells, scaled by the initial progressor fraction F0 of activated cells. Percentages indicate the fraction of undivided cells which will have entered their first division by the next measurement time (vertical dashed lines). On average, cells for Donor 1 complete their first division more rapidly than those for Donor 2; cells from Donor 1 are also more likely to have divided in response to stimulus before the end of the experiment. CD4T cells from both donors are estimated to respond more slowly and in greater frequency than CD8 T cells (compare Figure 9). However the total CD 8 T cell response for Donor 1 is greater than the CD4T cell response while the total CD4 and CD8 responses are comparable for Donor 2. Middle: Probability density functions for time to subsequent division or time to die (inverted) for CD4 T cells having completed at least one division. Cells from Donor 2 divide slightly more rapidly and more synchronously than those from Donor 1. Bottom: Division destiny, indicating the average number of divisions undergone by cells initially in the population (at t = t0). The fraction of cells with division destiny equal to zero estimates the relative abundance of cells which will not become activated to divide.
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Figure 8: Comparison of estimated CD4 T cell division dynamics between Donor 1 (left) and Donor 2 (right). Top: Probability density function φ0(t) for time to first division for initially undivided CD4 T cells, scaled by the initial progressor fraction F0 of activated cells. Percentages indicate the fraction of undivided cells which will have entered their first division by the next measurement time (vertical dashed lines). On average, cells for Donor 1 complete their first division more rapidly than those for Donor 2; cells from Donor 1 are also more likely to have divided in response to stimulus before the end of the experiment. CD4T cells from both donors are estimated to respond more slowly and in greater frequency than CD8 T cells (compare Figure 9). However the total CD 8 T cell response for Donor 1 is greater than the CD4T cell response while the total CD4 and CD8 responses are comparable for Donor 2. Middle: Probability density functions for time to subsequent division or time to die (inverted) for CD4 T cells having completed at least one division. Cells from Donor 2 divide slightly more rapidly and more synchronously than those from Donor 1. Bottom: Division destiny, indicating the average number of divisions undergone by cells initially in the population (at t = t0). The fraction of cells with division destiny equal to zero estimates the relative abundance of cells which will not become activated to divide.

Mentions: From the calibrated mathematical model, one can compute the probability density functions φi(t) and ψi(t) from which the times to divide and die are assumed to be drawn in the cyton model of cell division. One can also summarize the division destiny of each population of cells. These are summarized graphically in Figure 8 for CD4 T cells and Figure 9 for CD8 T cells.


A novel statistical analysis and interpretation of flow cytometry data.

Banks HT, Kapraun DF, Thompson WC, Peligero C, Argilaguet J, Meyerhans A - J Biol Dyn (2013)

Comparison of estimated CD4 T cell division dynamics between Donor 1 (left) and Donor 2 (right). Top: Probability density function φ0(t) for time to first division for initially undivided CD4 T cells, scaled by the initial progressor fraction F0 of activated cells. Percentages indicate the fraction of undivided cells which will have entered their first division by the next measurement time (vertical dashed lines). On average, cells for Donor 1 complete their first division more rapidly than those for Donor 2; cells from Donor 1 are also more likely to have divided in response to stimulus before the end of the experiment. CD4T cells from both donors are estimated to respond more slowly and in greater frequency than CD8 T cells (compare Figure 9). However the total CD 8 T cell response for Donor 1 is greater than the CD4T cell response while the total CD4 and CD8 responses are comparable for Donor 2. Middle: Probability density functions for time to subsequent division or time to die (inverted) for CD4 T cells having completed at least one division. Cells from Donor 2 divide slightly more rapidly and more synchronously than those from Donor 1. Bottom: Division destiny, indicating the average number of divisions undergone by cells initially in the population (at t = t0). The fraction of cells with division destiny equal to zero estimates the relative abundance of cells which will not become activated to divide.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753657&req=5

Figure 8: Comparison of estimated CD4 T cell division dynamics between Donor 1 (left) and Donor 2 (right). Top: Probability density function φ0(t) for time to first division for initially undivided CD4 T cells, scaled by the initial progressor fraction F0 of activated cells. Percentages indicate the fraction of undivided cells which will have entered their first division by the next measurement time (vertical dashed lines). On average, cells for Donor 1 complete their first division more rapidly than those for Donor 2; cells from Donor 1 are also more likely to have divided in response to stimulus before the end of the experiment. CD4T cells from both donors are estimated to respond more slowly and in greater frequency than CD8 T cells (compare Figure 9). However the total CD 8 T cell response for Donor 1 is greater than the CD4T cell response while the total CD4 and CD8 responses are comparable for Donor 2. Middle: Probability density functions for time to subsequent division or time to die (inverted) for CD4 T cells having completed at least one division. Cells from Donor 2 divide slightly more rapidly and more synchronously than those from Donor 1. Bottom: Division destiny, indicating the average number of divisions undergone by cells initially in the population (at t = t0). The fraction of cells with division destiny equal to zero estimates the relative abundance of cells which will not become activated to divide.
Mentions: From the calibrated mathematical model, one can compute the probability density functions φi(t) and ψi(t) from which the times to divide and die are assumed to be drawn in the cyton model of cell division. One can also summarize the division destiny of each population of cells. These are summarized graphically in Figure 8 for CD4 T cells and Figure 9 for CD8 T cells.

Bottom Line: A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed.Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme.This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors.

View Article: PubMed Central - PubMed

Affiliation: Center for Research in Scientific Computation and Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212, USA. htbanks@ncsu.edu

ABSTRACT
A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.

Show MeSH