A next step to interpret the findings generated by genome-wide association studies is to associate
molecular quantitative traits with disease-associated alleles. To this end, researchers are linking disease
risk alleles with gene expression quantitative trait loci (eQTL). However, gene expression at the
mRNA level is only an intermediate trait and flow cytometry analysis can provide more downstream
and biologically valuable protein level information in multiple cell subsets simultaneously using freshly
obtained samples. Because the throughput of flow cytometry is currently limited, experiments may
need to span over several weeks or months to obtain a sufficient sample size to demonstrate genetic
association. Therefore, normalisation methods are needed to control for technical variability and compare
flow cytometry data over an extended period of time. We show how the use of normalising
fluorospheres improves the repeatability of a cell surface CD25-APC mean fluorescence intensity phenotype
on
Genome-wide association (GWA) studies have revolutionised the mapping of common genetic variants, mostly single nucleotide polymorphisms (SNPs), with susceptibility to a wide range of common, multifactorial disorders [
Multicolour flow cytometry analysis can provide rich protein level data simultaneously on different subsets of cells; this is of particular importance for post-GWA investigations as genetic heterogeneity identified in disease-associated regions can differentially affect various cell subsets. However, the throughput of current flow cytometry approaches, including data analysis and sample collection, is limited to a small number of samples per day or week, especially when fresh blood is required. As the identification of subtle molecular effects directed by common genetic variants may require the analysis of a relatively large number of samples, flow cytometry experiments may need to span over several months. Owing to the complexity of flow cytometry technology, various technical artifacts, including variability in reagents or measuring instruments, can create time-related biases. Consequently, normalisation procedures are necessary to enable the comparison of samples analysed at different dates.
Similar issues have been identified in the context of gene expression microarray analysis. For these analyses researchers typically take advantage of the large number of independent measurements (one per gene or probe), implicitly using the rank of a gene of interest as a summary statistic. Such techniques are not available for flow cytometry data, and therefore specific approaches are required.
With the motivation of understanding the molecular effects of type 1 diabetes (T1D) risk variants located in the IL2 receptor
Using multicolour flow cytometry analysis, we previously identified CD25 cell surface expression on CD4
(a) Black crosses show nonnormalised MFIs in the CD4
To better control for technical day-to-day variability of the flow cytometry measures, MFIs were converted to molecules of equivalent fluorochrome (MEF) using six peak calibration beads (Dakocytomation, see Methods). For each experimental day, the MFIs of the six peak calibration beads were measured using flow cytometer settings identical to the ones used for the analysed samples. Using the MFI to MEF correspondence provided by the manufacturer we fitted a linear model
Typical flow cytometry procedures to control for day-to-day technical variability use a fluorochrome-conjugated isotype control antibody to quantify the background, nonspecific, fluorescence intensity. Subtraction procedures are then applied to compare the background level with the observed intensity in order to estimate the fraction of positive cells, as defined by cells with a fluorescence level exceeding background [
(a) Correlation between the fraction of CD25-positive cells in the CD4
These differences are consistent with the fact that the MFI and the fraction of CD25+positive cells provide different types of information. Therefore, these summary statistics require different normalisation approaches: one using normalisation beads, the other using an isotype control.
The calibration beads used in this study are broad spectrum beads, which means that the same set of beads can be used to normalise fluorochromes at different wavelengths (e.g., PE and APC using the same set of beads). Alternative normalisation tools use spectrum matched beads, that is, fluorescent beads whose light spectrum matches exactly the fluorochrome of interest, for example, APC. Such spectrum matched beads are required to standardise flow cytometry measurements across different laboratories or flow cytometers [
To better understand the impact of broad versus spectrum matched normalising beads, we analysed normalising beads from another dataset generated during the same time period using the same flow cytometer. For this additional dataset broad spectrum (Dakocytomation) and APC spectrum matched (BD Biosciences) were tested. For technical reasons, and also to better understand the effect of variability in photomultiplier tube voltage (controlling the light detection sensitivity), flow cytometer settings were not kept constant through time for these additional beads data. Indeed we observed that, as expected, the normalisation coefficient is strongly negatively correlated with the APC photomultiplier tube voltage (Figure
Variability across time of the normalisation coefficient for broad spectrum beads (black) and APC spectrum matched beads (red). The blue line shows the APC photomultiplier tube voltage setting used to measure the beads MFI.
Variability across time of the isotype control MFIs (red crosses, one point per sample) and the normalising beads MFIs (black line, one point per experimental day). MFIs are scaled such that the value is equal to one for the first day, and a logarithmic scale is used for the
(a) Repeatability for the CD4
We then investigated whether MFIs obtained by measuring the isotype control fluorescence are usable for MFI normalisation, in contrast with the traditional use for background subtraction. Isotype controls are primarily used to provide information on nonspecific binding via Fc receptors present on the cells of interest. In our analyses, we attempted to block such Fc binding using mouse IgG immunoglobulin (Sigma-Aldrich Company), thereby making the isotype control primarily a measurement of the autofluorescence [
In Figure
Overall, the isotype MFIs are highly variable and affected by donor-to-donor variability. In addition, the biological variability captured by the isotype control MFIs (average MFI less than 2) is not significant when analysing higher CD25 cell surface MFIs in the CD4
We then investigated alternative procedures allowing for the control of flow cytometry day-to-day technical variability in MFI measurements in the absence of calibration beads. First, we investigated whether we could use the 192 samples analysed to estimate the trend associated with technical variability, and use this estimate to correct for time-related biases. Because of CD25 genotype-phenotype correlations [
We then investigated a second procedure for MFI normalisation, a flow cytometry approach analogous to quantile normalisation for gene expression microarray data. In the context of microarray data, quantile normalisation takes advantage of a large number of independent data points (one point per gene or probe) to rank a gene of interest within the overall distribution of gene intensities. This procedure corrects at least partially for variability across independent microarray experiments. Flow cytometry analysis, on the other hand, does not provide large numbers of independent data points. However, some partially uncorrelated MFI measures are available when analysing independent cell subsets. Therefore, we recoded our MFI phenotype of interest (computed in CD4
We have identified CD25 cell surface expression on CD4
We also investigated two alternative normalisation methods, less effective than normalising beads in this example but useful in situations where fluorescent beads are absent. A potentially useful approach consists of using the MFIs obtained from a different population of cells within the same sample, thus providing an internal normalisation. However, this procedure will only be useful in a situation where a different population with repeatable MFI values exists.
In spite of these results, normalisation of fluorescence intensity data from flow cytometry remains challenging. Indeed, controlling the technical variability of such a complex experimental procedure over extended periods of time is difficult. The development of methods for higher throughput flow cytometry, enabling the analysis of dozens of samples on the same day, may address some of these issues by shortening the duration of the experiment. However, when the phenotype of interest requires the analysis of fresh blood, which is the case in this study, the limiting factor becomes the number of blood samples collected per day, which is unlikely to become much higher. We have shown recently that CD25 cell surface expression on memory cells is decreased and more variable if frozen peripheral blood mononuclear cells are analysed [
An elegant approach to circumvent normalisation issues is the use of a nested design comparing, on each experimental day, both categories of samples (e.g., individuals with different genotypes, or cases/controls). When using this design, only phenotypes of individuals analysed on the same day are compared with each other, thus avoiding biases associated with day-to-day technical variability. When the study is balanced (i.e., the same number of samples from each category is analysed on each day) the loss of statistical power to detect phenotype differences is minimal, while the design becomes much more robust to technical variability.
The anti-human monoclonal antibodies used for cell surface immunostaining were APC-conjugated anti-CD25 (BD Biosciences, clones M-A251 and 2A3), Alexa-Fluor 700-conjugated anti-CD4, Alexa-Fluor 488-conjugated anti-CD127, and Pacific Blue-conjugated anti-CD45RA (BioLegend). The isotype control antibodies used were APC-conjugated mouse IgG1 (BD Biosciences) and Alexa-Fluor 488-conjugated mouse IgG1 (BioLegend). To minimize potential variation due to antibody batch differences, all antibodies were obtained prior to the start of the experiment and all vials of antibody derived from the same clone and labelled with the same fluorochrome were pooled prior to usage. To better visualize lower-level CD25 expression, we increased CD25 detection sensitivity by simultaneously using two anti-CD25 monoclonal antibodies, (labelled with the same fluorochrome (clones 2A3 and M-A251), that recognize distinct epitopes on the CD25 molecule and therefore do not cross-compete. Prior to staining, whole blood samples were blocked with mouse IgG immunoglobulin (Sigma-Aldrich Company) at a concentration of 2
All immunostained samples were analyzed using a BD LSRII Flow Cytometer with BD FACSDiVa Software (BD Biosciences). Each day donor samples were evaluated, we also analysed six peak normalising fluorospheres (Blank Beads and Calibration Beads, Dakocytomation) for MFI normalisation purposes. For our second dataset, where voltage settings were allowed to vary, six peak normalising fluorospheres (Blank Beads and Calibration Beads, Dakocytomation) and BD Calibrite APC Beads (BD Biosciences) were tested on each experimental day.
The flow cytometry data were analyzed using FlowJo (Tree Star, Inc.). The remaining data processing/statistical analysis was performed using the R programing language. Gates were automatically extracted from the FlowJo output using an in-house XML parsing script based on the R XML 2.3.0 library. These gates were applied to the raw FCS files using the R flowCore 1.8.3 library. Repeatability
The authors thank the Wellcome Trust (WT), the Juvenile Diabetes Research Foundation (JDRF), and the National Institute for Health Research (NIHR) for funding the JDRF/WT Diabetes and Inflammation laboratory. The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (079895). This work was also supported by the National Institute of Health (USA) Grant P01 AI39671 and a grant from the JDRF UK Centre for Diabetes Genes, Autoimmunity and Prevention (D-GAP; 4-2007-1003). Linda S. Wicker is a WT Principal Research Fellow. Vincent Plagnol is a JDRF advanced postdoctoral fellow. They acknowledge the use of the NIHR Biomedical Research Centre Cambridge BioResource and thank Sarah Nutland, Jennifer Sambrook, and Willem Ouwehand for contributing to this study.