In the area of instrument setup, much standardization can be achieved with the software packages available with newer digital cytometers. However, this instrumentation and software is not yet widely used in clinical research organizations or other clinical trial–associated laboratories. In addition, these systems do not continuously track performance but instead assume that the cytometer does not change over the course of a day. If sufficient warm-up time has been given (up to 2 hours for some types of lasers), this assumption might hold true. However, analyzing a control bead population before and after each experiment is also advisable for the detection of any performance changes that might occur over the course of a run. Finally, instrument setup software does not necessarily address standardization across different cytometers, particularly if those cytometers vary in their configuration. For example, cytometers equipped with green lasers (usually 532-nm emission) have better sensitivity for phycoerythrin itself and tandem phycoerythrin dyes21
than do those that use blue (usually 488-nm) lasers. In such cases, there will invariably be performance differences that cannot be overcome.
Analysis of multicolor flow cytometry necessarily involves compensation for optical spillover between detectors22
. Fortunately, automated algorithms are now available with most acquisition and analysis software that calculate compensation from a set of single-color controls. Additionally, the use of software-based compensation on newer digital instruments allows adjustment of compensation, if necessary, even after sample acquisition. However, variability and potential inaccuracy can still be introduced into the process via the following parameters: the type of single-color controls chosen (such as beads or cells), the antibody used to stain each control, the handling of those controls relative to the handling of experimental samples and the choice of a negative population associated with each compensated parameter. Normally compensation should not have to be adjusted after it has been computed by the software, but depending on the variables outlined above, some corrections may occasionally be necessary and this then becomes a subjective process and a source of variability.
Of course, the degree of optical spillover in a particular experiment is dependent on the choice of fluorochromes and antibodies used20
. Suboptimal panel design will negatively affect the quality of data because of the use of fluorochromes that are too dim for particular markers and/or that have excessive optical spillover. In general, efforts should be made to standardize reagent panels for particular purposes so data are comparable and development time is minimized. However, because research questions constantly change, there is always pressure to redesign existing panels, and the addition of even one more reagent often requires extensive rearrangement of fluorochrome-antibody combinations so acceptable performance is maintained. This is especially true as the number of fluorochromes in the experiment increases.
In addition to variability in compensation controls, there can be variability in the choice of gating controls used to determine positive-negative boundaries in the data23
. Fluorescence-minus-one controls22
include all the experimental staining reagents except one and can be useful for setting gates when staining is dim or smeared. However, these controls do not take into account background staining of the reagent that has been left out. This can be estimated by substitution of a non-staining antibody of the same isotype as the experimental reagent (isotype-matched control antibody), but the amount of background may still not be accurately assessed because of differences in concentration, the fluorochrome/protein ratio and inherent nonspecific binding. Also, it is still necessary to use isotype-matched control antibodies in the context of the other staining reagents to account for optical spillover between reagents.
Another useful type of control, the so-called ‘process control’, can be added to verify the performance of certain steps in the assay. For example, prestained lyophilized cells can be used to verify instrument setup and gating independently of sample handling and staining. Alternately or additionally, serial aliquots of a single cryopreserved sample may be thawed for each assay and stained to simultaneously verify the performance of that day’s staining, instrument setup and gating.
In the area of data analysis, there have been advances in gating tools and batch-analysis options. However, the analysis software now available does not allow efficient archiving and retrieval of large amounts of data or analysis across multiple experiments. The tools available are still highly focused on experiment-specific analysis and are generally insufficient to achieve the ultimate goal of reliable, single-step transformation of raw data into quantified results for large numbers of files.
Perhaps the largest single contributor to variability in flow cytometry is differences in gating. In one example of this, as part of a multisite standardization study1
, prestained lyophilized cells were distributed to 15 experienced laboratories and researchers were asked to acquire the samples and then analyze the data, and to also send the raw data files to a single laboratory for central analysis. The data from individual laboratory analyses showed a mean coefficient of variation of 20.5% across four samples, whereas the data from central analysis showed a mean coefficient of variation of 4%. This means that instrument setup and statistical counting errors accounted for only a very minor proportion of the variability, whereas individualized gating methods accounted for the vast majority of the inter-laboratory variation.
In the study described above, the inclusion of ‘dim’ populations for key markers such as CD4 and CD8 accounted for most of the gating variability noted. When populations are tightly clustered and easily discriminated from each other, such variability will of course be less. This means that a certain amount of gating variability can be avoided by optimal design of reagent panels. However, the remaining variability needs to be handled through the use of either a shared gating template or central analysis by a single operator. The shared template can still suffer from problems, as some adjustment of gates may be required between donors and between experiments, so there will still be a degree of operator bias. This can be minimized in some cases by the use of dynamic gates (available in some analysis software) that adjust to shifting data4
. However, such gates need to be rigorously tested and their settings must be optimized to ensure the desired results, and they might not be feasible for use in some situations.
Most flow cytometry data are reported as the percentage of cells positive for a particular marker or set of markers, with the denominator of the percentage being a chief subset of interest, such as CD4+ or CD8+ T cells, B cells and so on. Because the numbers of these subsets can vary, particularly in certain conditions such as infection with human immunodeficiency virus, it is sometimes desirable to convert percentages to absolute counts per microliter of blood (or per milliliter, for rarer subsets). This is straightforward if an absolute counting test for the subset of interest is done concomitantly with the blood draw for which immunophenotyping is done. However, such conversion is not routinely done in the vast majority of clinical immunomonitoring studies.
In cases in which a cell population displays a continuous distribution of staining intensity, rather than discrete positive and negative populations, it can be more appropriate to report the median fluorescence intensity of the entire cell population. However, differences in staining and instrument setup from experiment to experiment warrant the use of some type of standard to ensure the reproducibility of this approach. This could involve simply calculating the ratio of the median fluorescence for the experimental sample to that of a sample stained with isotype-matched control antibody. Alternately, so-called ‘quantitation beads’, which contain a known number of fluorochrome molecules per bead, can be used as a reference for converting raw fluorescence units to fluorochrome molecules per cell24
. If a 1:1 conjugate of antibody/fluorochrome is used for staining, these numbers are identical to the antibodies bound per cell.
Beyond simply reporting the results of a flow cytometry experiment, there are efforts under way to encourage more complete and consistent reporting of the methodology used to achieve that result. For example, the MIFlowCyt (minimal information about a flow cytometry experiment) standard25
has been approved by the International Society for Advancement of Cytometry for the reporting of any flow cytometry results. Basically, the standard specifies information that should be supplied with any experiment under the following headings: Experiment Overview, Flow Sample/Specimen Details, Instrument Details and Analysis Details.
For particular classes of experiments, other standards are being developed. For example, the MIATA (minimal information about T cell assays) approach26
aims to set standards about the reporting of tetramer and intracellular cytokine staining and other related T cell assays. Obviously, consistent adherence to such standards would increase the transparency of published data, making the data easier to interpret and reproduce.