Optimal Experimental Design for Efficient Optical Manipulation of Magnetic Beads Seeded in a Microwell Array

Abstract

Problem: In the field of genomics, proteomics and disease diagnostics, it is of great interest to capture individual targets (e.g., cells, bacteria, exosomes) on magnetic beads and to seed them in a microwell array. Optical manipulation of the magnetic beads in the array then allows selecting the targets of interest and moving them to another location for post analysis. However, this technology is hampered by the low fraction of beads that show Brownian motion within their microwell and thus can be optically trapped and manipulated. This is due to the fact that magnetic beads seeded in a microwell array are subject to attraction forces that cause them to adhere to the microwell surfaces. The challenge is to identify buffer conditions that prevent this from happening.

Approach: We investigated five factors that were believed to influence the fraction of magnetic beads that show Brownian motion within their microwell. To this end, we used an experimental approach to maximize the fraction of beads showing Brownian motion. First, an initial D-optimal five-factor completely randomized design was created, including specific control treatments, for the estimation of a response surface model including a two-level and a six-level categorical factor (bead type and type of surfactant). Subsequently, two I-optimal follow-up experiments were conducted to examine the effectiveness of higher levels for one of the quantitative factors under investigation.

Results: The initial D-optimal five-factor design indicated one insignificant quantitative factor. The remaining four factors all had a significant main effect. In addition, there were three significant interaction effects and one significant quadratic effect. The analysis of the data from the initial experimental design also revealed a strongly positive effect for two of the six surfactants. In the follow-up experiment, these two surfactants turned out to produce even larger predicted responses at higher concentrations.

• Brownian Motion
• Control Treatment
• D-Optimal Design
• I-Optimal Design
• Microwell Array
• Optimal Experimental Design
• Six-Level Categorical Factor

Additional information

Notes on contributors

D. Decrop

Dr. Eng. Decrop is a postdoctoral researcher at the Faculty of Bioscience Engineering of the University of Leuven. Her email address is [email protected].

J. Lammertyn

Dr. Eng. Lammertyn is Full Professor at the Faculty of Bioscience Engineering of the University of Leuven. His email address is [email protected].

P. Goos

Dr. Goos is Full Professor at the Faculty of Bioscience Engineering and Leuven Statistics Research Centre of the University of Leuven and at the Faculty of Applied Economics and StatUa Center for Statistics of the University of Antwerp. He is a Senior member of ASQ. His email address is [email protected].