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Statistical Design And Imaging Of Position-Encoded 3D Microarrays
All Theses and Dissertations (ETDs)
  • Pinaki Sarder, Washington University in St. Louis
Author's School
School of Engineering & Applied Science
Author's Department/Program
Electrical and Systems Engineering
English (en)
Date of Award
Degree Type
Degree Name
Doctor of Philosophy (PhD)
Chair and Committee
Arye Nehorai
We propose a three-dimensional microarray device with microspheres having controllable positions for error-free target identification. Here targets: such as mRNAs, proteins, antibodies, and cells) are captured by the microspheres on one side, and are tagged by nanospheres embedded with quantum-dots: QDs) on the other. We use the lights emitted by these QDs to quantify the target concentrations. The imaging is performed using a fluorescence microscope and a sensor. We conduct a statistical design analysis to select the optimal distance between the microspheres as well as the optimal temperature. Our design simplifies the imaging and ensures a desired statistical performance for a given sensor cost. Specifically, we compute the posterior Cramér-Rao bound on the errors in estimating the unknown target concentrations. We use this performance bound to compute the optimal design variables. We discuss both uniform and sparse concentration levels of targets. The uniform distributions correspond to cases where the target concentration is high or the time period of the sensing is sufficiently long. The sparse distributions correspond to low target concentrations or short sensing durations. We illustrate our design concept using numerical examples. We replace the photon-conversion factor of the image sensor and its background noise variance with their maximum likelihood: ML) estimates. We estimate these parameters using images of multiple target-free microspheres embedded with QDs and placed randomly on a substrate. We obtain the photon-conversion factor using a method-of-moments estimation, where we replace the QD light-intensity levels and locations of the imaged microspheres with their ML estimates. The proposed microarray has high sensitivity, efficient packing, and guaranteed imaging performance. It simplifies the imaging analysis significantly by identifying targets based on the known positions of the microspheres. Potential applications include molecular recognition, specificity of targeting molecules, protein-protein dimerization, high throughput screening assays for enzyme inhibitors, drug discovery, and gene sequencing.

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Citation Information
Pinaki Sarder. "Statistical Design And Imaging Of Position-Encoded 3D Microarrays" (2010)
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