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Statistical design and imaging of position-encoded three-dimensional microarrays.

机译:位置编码三维微阵列的统计设计和成像。

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摘要

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 Cramer-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.
机译:我们提出了一种具有微球的三维微阵列设备,该微球具有可控制的位置,用于无错误的目标识别。在这里,靶标(例如mRNA,蛋白质,抗体和细胞)在一侧被微球捕获,并在另一侧被嵌入量子点(QD)的纳米球标记。我们使用这些量子点发出的光来量化目标浓度。使用荧光显微镜和传感器进行成像。;我们进行统计设计分析,以选择微球之间的最佳距离以及最佳温度。我们的设计简化了成像,并确保了在给定传感器成本下所需的统计性能。具体来说,我们在估计未知目标浓度时计算误差的后Cramer-Rao界。我们使用这种性能约束来计算最佳设计变量。我们讨论目标的均匀和稀疏浓度水平。均匀分布对应于目标浓度高或感测时间段足够长的情况。稀疏分布对应于低目标浓度或较短的感测持续时间。我们使用数值示例来说明我们的设计概念。我们将图像传感器的光子转换因子及其背景噪声方差替换为其最大似然(ML)估计值。我们使用嵌入QD并随机放置在基质上的多个无目标微球的图像估算这些参数。我们使用矩量法估算来获得光子转换因子,其中用其ML估算值替换了QD的光强度水平和成像微球的位置;所提出的微阵列具有高灵敏度,有效包装和有保证的成像性能。通过根据微球的已知位置识别靶标,显着简化了成像分析。潜在的应用包括分子识别,靶向分子的特异性,蛋白质-蛋白质二聚化,酶抑制剂的高通量筛选测定,药物发现和基因测序。

著录项

  • 作者

    Sarder, Pinaki.;

  • 作者单位

    Washington University in St. Louis.;

  • 授予单位 Washington University in St. Louis.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 96 p.
  • 总页数 96
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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