Multiplex immunoassays confer many advantages over the widely adopted singleplex immunoassays for basic research and biomarker discovery, but successful assays rely on careful and thorough development and validation. Often, assay development is limited by the availability of good antibody pairs, matrix interference, cross talk between assays, and lot-to-lot variability in assay reagents. The following tips highlight some critical considerations in developing multiplex immunoassays and outline steps for avoiding common development and validation pitfalls for users who develop their own assays.


Lot-to-lot and/or vendor-to-vendor variability in raw materials can impact assay performance.

Key aspects in developing multiplex immunoassays include selecting targets, screening high-quality antibody pairs, refining buffer formulations, constructing calibration curves, and optimizing reagents to reduce plex-level effects and cross-reactivity between assays. Variability in the raw materials used for these processes must be carefully tracked in order to reduce the subsequent impact on the finished panel. Robust multiplex assay development should employ strategies to control between-lot variation by sourcing raw materials from the same lot whenever possible.


Antibodies used in multiplex assay development require platform-specific validation.

The quality of a finished multiplex immunoassay depends critically on the pairing of high-quality capture and detection antibodies. Antibodies validated for singleplex immunoassays on one platform don’t always display the same quality in a multiplex setting. In addition, a positive western blot result does not always translate to equal performance in a suspension format. Thus, careful validation of antibody pairs must be carried out on the same platform that will be used for the final assay in order to ensure optimum performance.


There can be variability in sample data generated using different technologies.

One common requirement for the validation of multiplex assays is their alignment with an established technology such as an ELISA. While this can be helpful for identifying data trends, users must be careful in determining the basis for assigning a benchmark platform, as data generated from different methods sometimes do not show the expected alignment. The use of different affinity reagents, immobilization methods, buffering conditions (for example, blockers), and mode of signal detection can inherently contribute to discordance between platforms. One approach to maximize the alignment of results is to use external calibrators, such as WHO/NIBSC standards, whenever possible.


Between-lot variability in assay reagents can impact long-term or large-scale studies.

Between-lot variability in assay reagents can give rise to data variability for users conducting longitudinal studies with multiple lots. Selection of appropriate controls and normalization criteria are necessary in order to monitor and adjust for this potential variability. Appropriate controls can include manufacturer provided assay controls as well as control samples that were aliquoted in advance to avoid freeze-thaw cycling. Interestingly, between-lot variability is not unique to multiplex assays, as it is also observed when performing ELISAs and other immunoassay techniques. Hence, optimal study results can be achieved by selecting appropriate experimental controls as well as reducing the number of different assay lots that are used over the entire research study.


Identifying important biomarkers to analyze can be a formidable task due to the number of potential targets and at times the complexity of the biology.

The advent of high-throughput technologies has enabled a much faster rate of biomarker identification. However, the sheer volume of data can complicate the process of extracting meaningful, biologically relevant, and actionable information for some users. With over 13,000 publications on “biomarker discovery” listed in PubMed over the past 15 years, and over 2000 since 2015, it would be impossible to comprehensively review each individual effort. The problem is compounded by the lack of integration of data and expert knowledge–driven approaches to combine data reduction, classification, and visualization with knowledge of disease-related pathways. Given the massive data throughput of multiplex assays, the analytical scope is also expected to be complex and should be tailored specifically to the required end point in data output.


Align your expectations with assay performance characteristics.

In order to achieve optimal results from multiplex immunoassay experiments, users must understand the performance characteristics and capabilities of the assay. When deciding on multiplex immunoassays, it is important to verify that the performance characteristics of each analyte in the assay panel meet the needs of the project. Parameters to check include assay working range, limit of detection and coefficients of variance. Other factors that can impact final results include variation between lots and between platforms, calibration to a reference standards, and differences between assay vendors. Such issues should be considered and controlled for as much as possible. Ultimately, multiplex assay performance expectations should be driven by accuracy and precision requirements in alignment with the intended study.


Establish communication channels with assay vendors and suppliers.

Multiplex immunoassays are used extensively in both research and diagnostics, and their continued success relies on careful validation processes. Selection of the appropriate biomarkers and the associated reagents to develop multiplex assays should start with open communications between the end user and the assay vendor and between the assay vendor and suppliers of the raw materials. This ensures the assays are manufactured according to validation methods that apply to a wide range of academic and clinical research settings.


There is documented variability between xMAP-based assays from different vendors.

The findings from several multi-site comparison reports suggest that multiplex assays can vary in performance, specifically in terms of absolute concentration (Berthoud et al. 2011, Christiansson et al. 2014). It is critical to perform careful optimization and validation of any commercial multiplex assays prior to any large-scale studies. Standardization of assay reagents and harmonization of the validation method is the key to alignment between assay vendors. The choice of a multiplex assay panel should be aligned to its application, either for biomarker screening or validation.


Berthoud TK et al. (2011). Comparison of commercial kits to measure cytokine responses to Plasmodium falciparum by multiplex microsphere suspension array technology. Malar J 10, 115.

Christiansson L et al. (2014). The use of multiplex platforms for absolute and relative protein quantification of clinical material. EuPA Open Proteom 3, 37–47.

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