eBook: Multiplex Technology Guide - 27

analysis generally stratifies results into a range
of distinct groups using control samples against
which analyte results are compared to. For example, 'high positive' / 'positive' / 'low' positive/negative results can provide useful information for clinical decision-making-knowing what a patient's
viral load is allows clinicians to recommend a more
targeted course of action.

lower plateau of the curve. The dilution scheme is
selected to provide a lab-friendly process while appropriately covering the relevant detection range
of the assay between the anchor points. A background, or a 'blank' background sample with assay
buffer, is run along with the standard curve as well,
and often MFIs acquired in this well are subtracted
from all other wells to normalize the data against
any background fluorescence.

The process for establishing semi-quantitative analysis parameters is similar to that used for qualitative
analysis, but with more detail. MFI ranges, which are
used to categorize results, are often guided by the
values obtained from pre-categorized control samples. Once ranges are established for each category,
a set of blind samples is run to confirm the appropriate categorization results.

It is critical to consider the range of expected concentrations when designing a quantitative assay, and to
optimize parameters to deliver reliable results in that
range. A strong standard curve in a quantitative assay has anchor points reasonably close to the inflection point of the linear portion of the curve, but solidly in the plateau region, with middle points equally
spaced across the linear region. Ideally, the range of
quantification is limited to that linear portion.

Quantitative analysis
Quantitative data can provide the deepest insights,
but it requires the most complex type of analysis
and is generally the most challenging kind of assay
to optimize. Quantitative analysis uses a dilution series of standard materials of known concentration to
establish a response curve, charting the analyte MFI
against the known concentration of a given analyte.
The analyte MFI of the samples is then calculated
based on where those values fall in relation to the
concentration along the fitted curve.

Finally, reliable, reproducible standard material
is extremely important in the development of a
high-quality quantitative assay, as are samples of
known concentration. Once the assay is optimized
to deliver reproducible results for known standard
material, the appropriate value assignment is confirmed through the use of samples of known concentration or spiked samples.

Most quantitative immunoassays built using
xMAP® microspheres employ a sigmoidal curve fit,
such as a five-parameter logistic model or a cubic
spline curve fit. The curves in these assays generally have five to seven distinct concentration points
and span from an anchor point in the upper plateau of the curve to a second anchor point in the

Regardless of the analytical method chosen, users
can implement a number of strategies to ensure
data quality prior to reporting results. External controls run in an assay serve to certify the quality of the
data, building confidence in the results of unknown
sample. Generally, having at least one positive and
one negative control in a qualitative assay can

Conclusions

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