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Updated: May 17, 2026

Multiplex Cytokine Profiling of Stimulated Mouse Splenocytes Using a Cytometric Bead-based Immunoassay Platform

Multiplex Cytokine Profiling of Stimulated Mouse Splenocytes Using a Cytometric Bead-based Immunoassay Platform

Published on: November 9, 2017

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling.

David C Clarke¹, Melody K Morris, Douglas A Lauffenburger

¹Department of Biological Engineering and Center for Cellular Decision Processes, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Molecular & Cellular Proteomics : MCP

|October 17, 2012

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Mixed-effects modeling enhances quantitative analysis of multiplexed bead-based immunoassays. This method improves precision and sensitivity for cellular signaling studies, revealing batch effects and key pathway activations.

Area of Science:

Biotechnology
Systems Biology
Proteomics

Background:

Multiplexed bead-based flow cytometric immunoassays are valuable for studying cellular communication.
Widespread adoption is hindered by challenges in quantitative data analysis.
Robust statistical methods are needed for accurate interpretation of immunoassay data.

Purpose of the Study:

To apply mixed-effects modeling for normalization and statistical analysis of bead-based immunoassay data.
To enhance the precision and sensitivity of detecting ligand effects on cellular signaling pathways.
To accurately characterize cell signaling networks using quantitative proteomics data.

Main Methods:

Application of mixed-effects modeling to analyze bead-based immunoassay data.

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Normalization of data by subtracting technical effects estimated by the model.

Analysis of phospho-protein levels in HepG2 cells treated with various ligands.

Utilized constrained fuzzy logic for signaling network characterization.

Main Results:

Mixed-effects modeling effectively estimated technical and biological sources of variance.
Normalization using this method increased precision and sensitivity in detecting ligand-induced signaling changes.
Identified synergistic effects of TGF-α and TNF-α on p38 MAPK and JNK activation.
Revealed significant batch effects while finding no effects from loading order or plate position.

Conclusions:

Mixed-effects modeling provides deeper insights from multiplexed immunoassay data compared to traditional methods.
This statistical approach is crucial for accurate characterization of cell signaling networks.
The methodology offers a valuable tool for enhancing the utility of bead-based immunoassays in biological research.