Multi-Target Sample Preparation Using MEDA Biochips
Multi-Target Sample Preparation Using MEDA Biochips
复制标题
使用 MEDA 生物芯片制备多目标样品
DOI：
10.1109/tcad.2019.2942002
复制
发表时间：
2019
影响因子：
2.9
通讯作者：
Lee, Chen-Yi
中科院分区：
文献类型：
--
作者：
Liang, Tung-Che;Chan, Yun-Sheng;Ho, Tsung-Yi;Chakrabarty, Krishnendu;Lee, Chen-Yi
样品制备是许多生化规程中的关键步骤，它将各种样品和/或试剂混合到含有目标浓度的溶液中。数字微流控生物芯片(DMFBs)已经被用作样品制备的平台，因为它们提供了需要更少的反应物消耗和减少人为错误的自动化过程。然而，现有的大多数方法只考虑两个反应物的样品制备，不能用于许多涉及多个反应物的生化应用。此外，在传统DMFBs上提出了可用于多反应物样品制备的现有方法，其中仅有1：1混合模型可用。在(1：1)混合模型中，一次只能混合两个相同体积的液滴，这导致较长的完成时间和有价值的反应物的浪费。为了克服这一限制，引入了微电极点阵(MEDA)体系结构；它提供了在单一操作中混合不同体积的多个液滴的灵活性。在这篇文章中，我们提出了一种通用的多反应物样品制备算法，该算法利用了MEDA生物芯片上的新型流体操作。我们还提出了一种改进的算法，当需要多个目标浓度时，增加了操作共享机会，从而进一步减少了反应物的使用。模拟实验表明，该方法在节约反应物成本、减少操作次数、减少浪费等方面优于已有方法。
Sample preparation, as a key procedure in many biochemical protocols, mixes various samples, and/or reagents into solutions that contain the target concentrations. Digital microfluidic biochips (DMFBs) have been adopted as a platform for sample preparation because they provide automatic procedures that require less reactant consumption and reduce human-induced errors. However, the most existing methods only consider two-reactant sample preparation, and they cannot be used for many biochemical applications that involve multiple reactants. In addition, the existing methods that can be used for multiple-reactant sample preparation were proposed on traditional DMFBs where only the (1:1) mixing model is available. In the (1:1) mixing model, only two droplets of the same volume can be mixed at a time, which results in higher completion time and the wastage of valuable reactants. To overcome this limitation, the micro-electrode-dot-array (MEDA) architecture has been introduced; it provides the flexibility of mixing multiple droplets of different volumes in a single operation. In this article, we present a generic multiple-reactant sample preparation algorithm that exploits the novel fluidic operations on MEDA biochips. We also propose an enhanced algorithm that increases the operation-sharing opportunities when multiple target concentrations are needed, and therefore the usage of reactants can be further reduced. The simulated experiments show that the proposed method outperforms existing methods in terms of saving reactant cost, minimizing the number of operations, and reducing the amount of waste.