As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches.

In the second edition of this series, we described the use of cell tracking dyes in combination with tetramer reagents and traditional phenotyping protocols to monitor levels of proliferation and cytokine production in antigen-specific CD8(+) T cells. In particular, we illustrated how tracking dye fluorescence profiles could be used to ascertain the precursor frequencies of different subsets in the T-cell pool that are able to bind tetramer, synthesize cytokines, undergo antigen-driven proliferation, and/or carry out various combinations of these functional responses.Analysis of antigen-specific proliferative responses represents just one of many functions that can be monitored using cell tracking dyes and flow cytometry. In this third edition, we address issues to be considered when combining two different tracking dyes with other phenotypic and viability probes for the assessment of cytotoxic effector activity and regulatory T-cell functions. We summarize key characteristics of and differences between general protein- and membrane-labeling dyes, discuss determination of optimal staining concentrations, and provide detailed labeling protocols for both dye types. Examples of the advantages of two-color cell tracking are provided in the form of protocols for (a) independent enumeration of viable effector and target cells in a direct cytotoxicity assay and (b) simultaneous monitoring of proliferative responses in effector and regulatory T cells.

Investigating the response of cells to specific agonists may involve the use of cell tracking dyes to assess the extent of stimulated proliferation, frequently reported as the proliferation index (PI). Calculation of PI uses a model for cell division that expects the cell number to double as cells proliferate through each successive generation. It is often useful to compare the PI of a stimulated control population with that of a population in the presence of some agent, whether chemical, pharmacologic, or cellular. For such comparison studies, the nature of the metric being used must be taken into account to accurately assess the extent of inhibition. Specifically, the metric used in ModFit LT (Verity Software House, Topsham, ME) and in FCS Express (De Novo Software, Los Angeles, CA) uses a metric with a lower limit of unity, whereas the metric used in FlowJo (Treestar, Ashland, OR) has a lower limit of zero. For studies involving cell proliferation comparisons using tracking dye dilution, a new equation is proposed as the appropriate calculation to use when determining the percent of relative response based on proliferation index values for a metric whose lower limit is unity.