Practical Tips for Planning a Successful Multiplex Immunoassay Experiment

When planning multiplex immunoassay experiments, a thorough study planning process is imperative for generating reproducible data and gaining translatable biological insights for wider applications.

Here are 5 key steps to consider before running a multiplex immunoassay:

1. Clearly define research question – to provide focus and direction across all stages of study execution, including the experimental design, sample collection and handling, data collection and processing, as well as the limitations of the study.

2. Select suitable controls – to ensure a valid comparative analysis and meaningful results, the experimental and control groups should be matched as closely as possible to reduce confounding factors, such as age, ethnicity, sex, and underlying conditions.

3. Power your study – to ensure a higher probability of correctly detecting a difference between the study groups, given that the effect is there.

Calculating power for a study is based on assumptions on the effect size – the magnitude of difference between the study groups. If there is no overlap between the data in the experimental group and the control group, there is a considerable difference, and the effect size and thus the study power are high, thereby requiring a smaller sample size. However, if the overlap is larger than the difference between the groups, the effect size is less significant, resulting in lower power, and a need for a larger sample size.

4. Apply consistency in protein sample collection and storage – to minimize pre-analytical factor influence on the data output and confidence with which conclusions are drawn.

A set of factors related to sample collection, processing, and storage should be considered when measuring proteins in biological samples and interpreting results. While efforts to minimize the effects introduced by pre-analytical variability should aim to be consistent. Documenting the sample collection, handling, and storage process is therefore imperative for obtaining meaningful results, even with samples that have not been handled optimally.

5. Randomize samples appropriately to study type – to minimize the possibility of missing true biological variation or misidentifying it as e.g. technical variations.

While the overall goal is to minimize the experimental variables across the study, different considerations will apply to different study types, such as single- or multi- plate studies, multi-batch studies or studies with longitudinal design.

Ideally, a power analysis should be performed before collecting samples and running a study. However, power calculations can also be performed retrospectively, or when adding additional samples to the study is not an option. In this case, the calculation can tell us if we will have enough power to justify the study. Alternatively, retrospective calculations may be useful when designing a scaled-up study from a smaller pilot.