Data analysis in flow cytometry: the future just started

Abstract

In the last 10 years, a tremendous progress characterized flow cytometry in its different aspects. In particular, major advances have been conducted regarding the hardware/instrumentation and reagent development, thus allowing fine cell analysis up to 20 parameters. As a result, this technology generates very complex datasets that demand for the development of optimal tools of analysis. Recently, many independent research groups approached the problem by using both supervised and unsupervised methods. In this article, we will review the new developments concerning the use of bioinformatics for polychromatic flow cytometry and propose what should be done to unravel the enormous heterogeneity of the cells we interrogate each day.

Fig. 1. A simple 6 differentiation-antigen staining can identify dozens of subsets of CD8+ T cells

Magnetically-enriched CD8+ peripheral blood mononuclear cells from a healthy donor were stained with multiple fluorescently-conjugated monoclonal antibodies directed to CD45RO, CCR7, CD62L, CD27, CD127 and CD11a to determine T cell differentiation state. Cells were acquired with a modified FACSAria (BD, San Josè, CA) able to detect 20 parameters (Details on the machine configuration can be found at: http://www3.niaid.nih.gov/labs/aboutlabs/VRC/flowCytometryCoreLaboratory/). Singlets were selected on the basis of FSC-A and FSC-H. Monocytes, B cells and dead cells were excluded by gating on Dump- (CD14-/CD19-) and PI- cells. CD8+ T cells were further selected for CD3 and CD8 positivity. Naïve (Nv; 1), Central Memory (CM; 2), Effector Memory (EM; 3) and Terminal Effectors (TE; 4) were defined on the basis of CD45RO and CCR7 expression. Within these populations, four different subsets (a, b, c, d) can be further defined by the expression of CD62L and CD27. As an example, subsequent analysis of CD127 and CD11a expression in the CD62L-,CD27- EM population can identify four more subsets. The same type of sequential analysis can be applied to all populations in a hierarchical way, thus leading to the hypothetic identification of 64 subsets. Data were compensated and analyzed with FlowJo version 9 (Treestar, Ashland, OR, USA). PI: Propidium Iodide; Pac Blue: Pacific Blue; Qd: Quantum Dot; Alx: Alexa.

Figure 2. Use of SPICE for the analysis of differentiation and activation of peripheral CD4+ T cells

Another manner of representing data related to the expression of differentiation and activation markers is shown in this Figure. The expression of CCR7, CD45RA, CD27, and HLA-DR has been investigated in living CD3+,CD4+,CD8- T cells from healthy donors (whose mean CD4+ T cell count in peripheral blood was 45%, with 1,109 cells/μL) and patients with HIV infection who were out of treatment (mean CD4+ T cell count 21%, 480 cells/μL). Positive and negative expression of antigen [for CD27 relative expression was further distinguished beween dim (dim) and bright (br)] were combined by Boolean gating to generate all possible subsets. Each colour in the pie corresponds to a specific combination of antigens indicated in the bottom part of the figure. CD45RA and CCR7 were used to define Naïve (CD45RA+CCR7+), CM (CD45RA-CCR7+), EM (CD45RA-CCR7-) and TE (CD45RA+CCR7). Note the striking difference in the composition of CD4+ T cell population. The black arrows indicate the way of reading the colours (clockwise in the upper part, left to right in the lower part).