• HIV;
  • lymphocyte subsets;
  • repeatability;
  • coefficient of variation


  1. Top of page
  2. Abstract


Quality assessment in flow cytometry cannot obey the same rules as those applicable to the measurement of chemical analytes. However, regular follow-up of known patients may provide a robust in-house control of cell subsets evaluation.


Sequential blood samples assessed for 32 HIV patients over several years and showing good stability were retrospectively assessed to establish coefficient of variations of the percentages of CD3+, CD4+, CD8+ cells, and CD4+ absolute counts (ACs).


Mean relative standard variations for the whole cohort were of 0.04, 0.14, 0.08, and 0.18 for CD3%, CD4%, CD8%, and CD4 ACs, respectively.


In-house follow-up of regularly checked compliant patients is a good alternative to traditional and costly repeatability and reproducibility studies for the validation of routine flow cytometry. © 2013 International Clinical Cytometry Society

Classical means of biological method validation rely on the repeatability and reproducibility of measurements, determined by assessing the same sample several times, either in a single or on iterative preparations. This is especially true for biochemical constants where single molecules are assayed in a chemical reaction ([1]). More variation can be expected for methods relying on non-covalent reactions such as the identification of antigens by antibodies. Enzyme-linked immunosorbent assays are therefore highly dependent on the affinity and avidity of the antibodies used, even though the antigen tested usually is also a single chemical molecule. In the world of cell assessments, things are even more problematic since measurements can be hampered by the complexity of a living cell, responding to an array of microenvironmental conditions. In spite of these potential limitations, flow cytometry is liable to yield highly reproducible results. This can be demonstrated in a costly way by traditional repeatability studies testing the same sample a number of different times. We herein argue that another potential mean to establish the stability of a flow cytometry experiment is to use data from patients benefiting from a regular follow-up, such as HIV-positive patients. The aim of anti-retroviral therapy is to restore acceptable levels of CD4+ peripheral blood cells and maintain them over time. Lymphocyte subsets assessments are therefore used to regularly verify compliance to treatment ([2, 3]). This indirectly provides an in-house control of the stability of the flow cytometry method used to measure these cell subsets.

Peripheral blood samples collected on EDTA are forwarded daily from the department of Infectious Diseases to the Immunology Laboratory of Nancy (France) University Hospital. For lymphocytes subsets assessment, a single-platform one-tube lysis-no-wash method is performed ([4]). Briefly, 50 µL of well-homogenized blood are mixed with 5 µL of a prepared combination of CD45-FITC/CD4-RD1/CD8-ECD/CD3-PC5 (Cytostat TetraCHROME®, Beckman Coulter, Miami, FL). After thorough vortexing, the samples are incubated for 15 min at 4°C in the dark. Red blood cells are then lysed using a TQ-Prep® workstation (Beckman Coulter) and the series of lysing and fixative reagents Immunoprep® (Beckman Coulter).

The same technician, using the same pipette as for distributing the blood aliquots, then adds 50 µL of a calibrated beads suspension (Flow Count®, Beckman Coulter) to each tube and vortexes thoroughly. The samples are then rapidly processed for flow cytometry analysis, with extra vortexing just before acquisition on an FC500 flow cytometer (Beckman Coulter).

The gating strategy first isolates live cells on a side scatter (SSC) forward scatter (FSC) plot. Lymphocytes are then gated through their bright CD45 labeling and small SSC. The percentages of total CD3+ cells, then of CD3+/CD4+ and CD3+/CD8+ cells are evaluated among the lymphocytes population. A specific gate allows counting the calibrated beads at the same time as the lymphocytes. The instrument provides automatically calculated absolute numbers of all cell types. Daily quality control is performed using Flow Check beads (Beckman Coulter) and the laboratory satisfactorily participates in national quality controls using stabilized cells. Results are forwarded to the clinicians as lymphocytes percentages and CD4 absolute count (AC).

Here, we present a series of 32 HIV patients tested between 1999 and 2012 who presented with long periods of stability, either at persistent low CD4 levels, in spite of therapy, or with good response to HAART and satisfactory median CD4 AC above 200/µL over time (n = 23). The number of tests performed per patient varied between 7 and 63 (Table 1, Figs. 1 and 2). The stability of subsets measurement was assessed for each patient by calculating the median and relative standard variation (RSD or coefficient of variation) using the Medcalc software (Mariakerke, Belgium).

Table 1. T Cell Subsets's Detailed Data of Each Individual Patient Included in the Study
UPN# Samples% CD3 median (range)% CD3 RSD% CD4 median (range)% CD4 RSD% CD8 median (range)% CD8 RSDAC CD4 median (range)AC CD4 RSD
  1. UPN = unique patient number; # samples = number of samples tested over follow-up time; RSD = relative standard deviation; AC = absolute count.

1785 (76–90)0.0626 (19–31)0.1755 (43–65)0.13212 (185–264)0.13
21584 (82–91)0.035 (2–7)0.3276 (71–83)0.0585 (58–111)0.22
32082 (74–87)0.0423 (20–26)0.0654 (43–60)0.08727 (582–1040)0.15
41279 (77–83)0.0246 (41–49)0.0631 (29–33)0.041637 (1276–2067)0.16
5988 (85–91)0.0326 (22–30)0.0955 (48–60)0.08724 (669–855)0.08
6862 (61–64)0.0228 (24–36)0.1431 (27–36)0.09479 (381–578)0.14
73680 (73–84)0.0424 (15–32)0.2047 (43–57)0.06544 (365–843)0.23
8873 (70–81)0.0523 (20–28)0.1145 (42–49)0.05390 (270–483)0.16
91176 (71–80)0.0429 (24–34)0.1041 (37–45)0.05394 (283–480)0.15
10884 (80–87)0.0312 (9–15)0.1464 (58–67)0.04286 (210–331)0.16
111085 (83–86)0.0116 (14–20)0.1167 (64–72)0.03289 (254–332)0.08
121558 (53–79)0.116 (5–8)0.1846 (38–61)0.1394 (38–125)0.26
13776 (69–82)0.0626 (25–31)0.1041 (34–44)0.10185 (152–230)0.18
141074 (70–77)0.0312 (11–18)0.1755 (50–63)0.07229 (199–346)0.19
15678 (77–80)0.0119 (16–22)0.1156 (52–57)0.04369 (333–391)0.06
16968 (65–75)0.0532 (30–36)0.0732 (27–33)0.07627 (516–671)0.07
171380 (77–82)0.0227 (23–33)0.1345 (42–52)0.07381 (289–456)0.13
182879 (71–90)0.0715 (7–19)0.2355 (33–93)0.19138 (81–242)0.26
191164 (60–66)0.0342 (38–44)0.0522 (20–23)0.05308 (263–390)0.12
202073 (69–78)0.0419 (15–22)0.1149 (43–51)0.05359 (183–485)0.21
212565 (53–85)0.1611 (5–14)0.2650 (38–66)0.1752 (26–121)0.40
22989 (84–91)0.0314 (11–16)0.1067 (66–74)0.04403 (324–471)0.14
236378 (63–86)0.0718 (11–28)0.1851 (34–67)0.14164 (90–465)0.36
242769 (60–81)0.088 (6–15)0.2842 (37–56)0.11134 (69–255)0.35
252982 (78–89)0.0311 (8–14)0.1260 (54–32)0.71290 (188–400)0.23
263260 (51–68)0.0822 (14–34)0.2130 (24–39)0.14257 (152–348)0.20
271078 (75–81)0.0217 (11–22)0.2254 (43–62)0.10215 (177–347)0.25
28985 (79–88)0.0312 (9–13)0.1169 (63–73)0.05138 (120–172)0.13
29765 (64–67)0.0230 (29–36)0.0829 (25–30)0.07757 (716–831)0.06
30888 (81–92)0.0414 (12–16)0.0969 (64–72)0.05240 (195–262)0.10
311388 (82–91)0.0313 (10–18)0.1766 (60–74)0.05196 (152–223)0.11
321570 (60–72)0.0536 (28–39)0.0933 (29–35)0.05230 (177–293)0.15

Figure 1. Follow-up example over eight years for a given patient (#3). The percentages of CD3, CD4, and CD8 T cells among lymphocytes are shown on the left axis and the ACs of CD4 T cells are on the right axis.

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Figure 2. Box-whisker plots of individual patients' variability of CD4 ACs.

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As a whole, CD3 percentages ranged between 51 and 92%, CD4 between 5 and 41%, and CD8 between 23 and 93%. Mean RSD for the whole cohort was of 0.04, 0.14, 0.08, and 0.18 for CD3%, CD4% CD8%, and CD4 AC, respectively. Table 1 provides this information, together with the median, minimal, and maximal values for each of the 32 patients. As expected, low and excellent coefficients of variation (CVs; 4% and 8% respectively) were observed for the highest percentage values of CD3+ and CD8+ cells. However, those observed for CD4 percentages and absolute numbers both were still lower than 20%, which is considered robust for cell enumeration ([5, 6]). When considering only patients with median CD4 AC above 200/µL, the respective mean CVs were even better at 3%, 12%, 7%, and 15% for CD3%, CD4%, CD8%, and CD4 AC, strengthening the stability of the flow cytometry assay. Indeed, the 200/µL threshold is critical in the monitoring of known patients, notably for investigation of compliance to treatment.

This short study demonstrates that in-house follow-up of regularly checked compliant patients provides a robust internal control for routine flow cytometry. In a setting with daily series of patients, the individual follow-up of each of them further consolidates inter-individual values and strengthens the significance of a sudden variation seen in one particular patient. Although this does not fully replace the run of stabilized control samples, such a process can further confirm the use of good medical practices in a specific flow environment, notably different from any chemistry laboratory.


  1. Top of page
  2. Abstract

The authors wish to acknowledge all the laboratory technical staff as well the para-medical personnel involved in the procurement of the data shown here. The authors declare no conflict of interest.


  1. Top of page
  2. Abstract
  • 1
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  • 2
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