[论文解读] Dilution with Digital Microfluidic Biochips: How Unbalanced Splits Corrupt Target-Concentration

Sudip Poddar, Robert Wille+2 more|arXiv (Cornell University)|Jan 2, 2019

Electrowetting and Microfluidic Technologies参考文献 19被引用 5

一句话总结

本文分析了数字微流控生物芯片（DMFBs）中不平衡分流对样本制备过程中目标浓度因子（CF）的干扰，表明负向分流误差（较小的子液滴）导致更大的CF误差。研究发现，多个分流误差会导致复杂且非直观的误差传播，使得在无全面仿真情况下难以预测最大误差，从而为设计无传感器、容错的样本制备算法提供依据。

ABSTRACT

Sample preparation is an indispensable component of almost all biochemical protocols, and it involves, among others, making dilutions and mixtures of fluids in certain ratios. Recent microfluidic technologies offer suitable platforms for automating dilutions on-chip, and typically on a digital microfluidic biochip (DMFB), a sequence of (1:1) mix-split operations is performed on fluid droplets to achieve the target concentration factor (CF) of a sample. An (1:1) mixing model ideally comprises mixing of two unit-volume droplets followed by a (balanced) splitting into two unit-volume daughter-droplets. However, a major source of error in fluidic operations is due to unbalanced splitting, where two unequal-volume droplets are produced following a split. Such volumetric split-errors occurring in different mix-split steps of the reaction path often cause a significant drift in the target-CF of the sample, the precision of which cannot be compromised in life-critical assays. In order to circumvent this problem, several error-recovery or error-tolerant techniques have been proposed recently for DMFBs. Unfortunately, the impact of such fluidic errors on a target-CF and the dynamics of their behavior have not yet been rigorously analyzed. In this work, we investigate the effect of multiple volumetric split-errors on various target-CFs during sample preparation. We also perform a detailed analysis of the worst-case scenario, i.e., the condition when the error in a target-CF is maximized. This analysis may lead to the development of new techniques for error-tolerant sample preparation with DMFBs without using any sensing operation.

研究动机与目标

• 研究体积分流误差对数字微流控生物芯片（DMFBs）中目标浓度因子（CF）的影响。
• 分析不平衡分流（产生体积不等的液滴）在多步稀释方案中对CF精度的影响。
• 识别CF误差达到最大的条件，尤其是在存在多个分流误差的情况下。
• 为开发无需回滚或传感的无传感器、容错型DMFB样本制备技术提供洞见。

提出的方法

• 使用考虑分流误差幅度（ε）和液滴选择（较大或较小体积）的递归方程，对CF误差传播进行理论建模。
• 对单个和多个分流误差下的混合-分流序列进行仿真，通过变化误差向量（例如 [−, +, +, −, +, −]）评估误差影响。
• 利用三维图和理论与仿真CF误差值的对比分析验证结果。
• 分析不同初始CF值与误差向量组合下的误差动态，评估最坏情况。
• 将误差表达式推广至n步混合-分流操作，以模拟累积误差效应。

实验结果

研究问题

• RQ1 单个不平衡分流误差如何影响DMFB稀释方案中的目标浓度因子（CF）？
• RQ2 在不平衡分流后选择较小或较大子液滴对CF误差有何影响？
• RQ3 多个连续分流误差如何相互作用以影响最终CF？能否预测最坏情况的误差向量？
• RQ4 给定一定数量的分流误差时，CF误差的最大可能值是多少？在何种条件下发生？

主要发现

• 负向分流误差（ε < 0），即选择较小液滴时，始终导致比正向误差更大的CF误差。
• CF误差的大小随分流误差绝对值（|ε|）的增加而增大，尤其在多个误差累积时更为显著。
• 对于三个连续的分流误差，CF误差对误差向量的依赖关系呈非单调性，所有配置中最大误差无明显规律可循。
• 观测到的最大CF误差（按128归一化）为1.977，出现在误差向量 [−, +, +, −, +, −] 的第57个位置的仿真中。
• 误差传播的复杂性使得在无全面仿真情况下，难以推导出用于识别最坏情况误差向量的确定性算法。