Why do you need to check the quality of your coupling?
Non-homogeneous coupling results in skewed measurements, high CVs and bad quality assays.
Coupling of antibodies to Luminex beads is an important determinant of the quality and reliability of the assay to be developed. Efficiency and homogeneity are the two main features that describe coupling quality. Efficiency describes whether coupling of the molecule to the beads has been achieved and is routinely assessed by use of secondary anti-species antibodies. Homogeneity describes whether the same coupling has been achieved across all beads. When median fluorescence intensities (MFIs) are used as output for the measurements, it is assumed that a homogeneous coupling has been achieved. However, no step is taken by the software to assess and correct results from situations of non-homogeneous coupling. This, in turn, can result in skewed measurements, irreprodicible data and overall bad quality assays as the example below shows.
How does the Coupling QC Tool work?
The Tool takes advantage of the single-bead measurements available in the .lxb files generated for each sample. It calculates and plots the distribution of fluorescence intensities of all beads corresponding to the same bead region. Only bead regions with more than 30 bead counts are considered. A single Gaussian distribution where most beads display similar intensities is an example of a homogeneous coupling.
The Tool then takes random samples with different sample sizes (i.e. different number of beads) from the original distribution and calculates the median fluorescence intensity in each case. In other words, it generates multiple datasets/technical replicates from the original measurement which are then used to calculate the CV for the different sample sizes. Non-homogeneous distributions will result in higher CVs.
When can you apply the coupling QC tool?
During coupling verification early in the assay development process.
To optimise coupling protocols.
To optimise assay parameters like antibodies, antibody concentrations etc.
To filter out outliers during analysis of large sample datasets.