Waste-aware dilution and mixing of biochemical samples with digital microfluidic biochips
14 Mar 2011-pp 1-6
TL;DR: An improved dilution/mixing algorithm (IDMA) is designed that optimizes the usage of intermediate droplets generated during the dilution process, which in turn reduces the demand of sample/reagent and production of waste.
Abstract: A key challenge in design automation of digital microfluidic biochips is to carry out on-chip dilution/mixing of biochemical samples/reagents for achieving a desired concentration factor (CF). In a bioassay, reducing the waste is crucial because the waste droplet handling is cumbersome and the number of waste reservoirs on-chip needs to be minimized to use limited volume of sample and expensive reagents and hence to reduce the cost of a biochip. The existing dilution algorithms attempt to reduce the number of mix/split steps required in the process but focus little on minimization of sample requirement or waste droplets. In this work, we characterize the underlying combinatorial properties of waste generation and identify the inherent limitations of two earlier mixing algorithms (BS algorithm by Thies et al., Natural Computing 2008; DMRW algorithm by Roy et al., IEEE TCAD 2010) in addressing this issue. Based on these properties, we design an improved dilution/mixing algorithm (IDMA) that optimizes the usage of intermediate droplets generated during the dilution process, which in turn, reduces the demand of sample/reagent and production of waste. The algorithm terminates in O(n) steps for producing a target CF with a precision of 1/2n. Based on simulation results for all CF values ranging from 1/1024 to 1023/1024 using a sample (100% concentration) and a buffer solution (0% concentration), we present an integrated scheme of choosing the best waste-aware dilution algorithm among BS, DMRW, and IDMA for any given value of CF. Finally, an architectural layout of a DMF biochip that supports the proposed scheme is designed.
Citations
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05 Nov 2012
TL;DR: REMIA is proposed, the first reactant minimization approach during sample preparation on digital microfluidic biochips (DMFBs) and can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.
Abstract: Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.
82 citations
TL;DR: This paper proposes the first reagent-saving mixing algorithm for biochemical samples of multiple target concentrations, which not only minimizes the consumption of reagents, but it also reduces the number of waste droplets and the sample preparation time by preparing the target concentrations concurrently.
Abstract: Recent advances in digital microfluidics have led to the promise of miniaturized laboratories, with the associated advantages of high sensitivity and less human-induced errors. Front-end operations such as sample preparation play a pivotal role in biochemical laboratories, and in applications in biomedical engineering and life science. For fast and high-throughput biochemical applications, preparing samples of multiple target concentrations sequentially is inefficient and time-consuming. Therefore, it is critical to concurrently prepare samples of multiple target concentrations. In addition, since reagents used in biochemical reactions are expensive, reagent-saving has become an important consideration in sample preparation. Prior work in this area does not address the problem of reagent-saving and concurrent sample preparation for multiple target concentrations. In this paper, we propose the first reagent-saving mixing algorithm for biochemical samples of multiple target concentrations. The proposed algorithm not only minimizes the consumption of reagents, but it also reduces the number of waste droplets and the sample preparation time by preparing the target concentrations concurrently. The proposed algorithm is evaluated on both real biochemical experiments and synthetic test cases to demonstrate its effectiveness and efficiency. Compared to prior work, the proposed algorithm can achieve up to 41% reduction in the number of reagent droplets and waste droplets, and up to 50% reduction in sample preparation time.
76 citations
Cites methods from "Waste-aware dilution and mixing of ..."
...The proposed reagent-saving mixing algorithm decomposes the given inputs to minimize the number of required reagent droplets....
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TL;DR: A multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips is proposed.
Abstract: Sample preparation is one of essential processes in biochemical reactions. Raw reactants are diluted in this process to achieve given target concentrations. A bioassay may require several different target concentrations of a reactant. Both the dilution operation count and the reactant usage can be minimized if multiple target concentrations are considered simultaneously during sample preparation. Hence, in this paper, we propose a multitarget sample preparation algorithm that extensively exploits the ideas of waste recycling and intermediate droplet sharing to reduce both reactant usage and waste amount for digital microfluidic biochips. Experimental results show that our waste recycling algorithm can reduce the waste and operation count by 48% and 37%, respectively, as compared to an existing state-of-the-art multitarget sample preparation method if the number of target concentrations is ten. The reduction can be up to 97% and 73% when the number of target concentrations goes even higher.
54 citations
Cites methods from "Waste-aware dilution and mixing of ..."
...Other related previous works include the bit-scanning (BS) method [25], the algorithm for dilution and mixing with reduced wastage (DMRW) [26], the improved dilution/mixing algorithm (IDMA) [27], IDSA [28], the ratioed mixing algorithm (RMA) [29], the De Bruijn graph-based multitarget preparation scheme (DBG) [30], the reactant minimization algorithm (REMIA) [32], and the graph-based optimal reactant minimization algorithm [33]....
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TL;DR: A compiler converts an assay, specified using the BioCoder language, into a sequence of electrode activations that execute out the assay on the DMFB; and a printed circuit board layout tool, which includes algorithms to reduce the number of control pins and PCB layers required to drive the chip from an external source.
51 citations
Cites background or methods from "Waste-aware dilution and mixing of ..."
...[66] Single target CV Reduce reagent usage and waste Algorithm (IDMA) Reactant minimization Huang et al....
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...Six of the algorithms that we have implemented generate a dilution tree that produces one droplet of a desired CV, using different optimization strategies [11,19,29,65,66,72]; four more algorithms generate multi-output DAGs (sometimes, but not always, a forest of trees) that produce multiple droplets of desired CVs [4,28,30,54]....
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20 Feb 2014
TL;DR: For the first time, an optimal sample preparation algorithm based on a minimum-cost maximum-flow model is presented that can obtain both the optimal cost of sample and buffer usage and the waste amount even for multiple-target concentrations.
Abstract: Sample preparation, which is a front-end process to produce droplets of the desired target concentrations from input reagents, plays a pivotal role in every assay, laboratory, and application in biomedical engineering and life science. The consumption of sample/buffer/waste is usually used to evaluate the effectiveness of a sample preparation process. In this paper, for the first time, we present an optimal sample preparation algorithm based on a minimum-cost maximum-flow model. By using the proposed model, we can obtain both the optimal cost of sample and buffer usage and the waste amount even for multiple-target concentrations. Experiments demonstrate that we can consistently achieve much better results not only in the consumption of sample and buffer but also the waste amount when compared with all the state-of-the-art of the previous approaches.
47 citations
Cites background from "Waste-aware dilution and mixing of ..."
...However, the works in [4, 5, 6] cannot consider more than one different target concentration value at the same time, and thus they...
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..., only the number of sample droplets ([2], [7]) or only the number of waste droplets ([5], [6])....
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References
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TL;DR: In this article, a microactuator for rapid manipulation of discrete microdroplets is presented, which is accomplished by direct electrical control of the surface tension through two sets of opposing planar electrodes fabricated on glass.
Abstract: A microactuator for rapid manipulation of discrete microdroplets is presented. Microactuation is accomplished by direct electrical control of the surface tension through two sets of opposing planar electrodes fabricated on glass. A prototype device consisting of a linear array of seven electrodes at 1.5 mm pitch was fabricated and tested. Droplets (0.7–1.0 μl) of 100 mM KCl solution were successfully transferred between adjacent electrodes at voltages of 40–80 V. Repeatable transport of droplets at electrode switching rates of up to 20 Hz and average velocities of 30 mm/s have been demonstrated. This speed represents a nearly 100-fold increase over previously demonstrated electrical methods for the transport of droplets on solid surfaces.
1,471 citations
"Waste-aware dilution and mixing of ..." refers background in this paper
...) discrete droplets of nanoliter or picoliter volume of the sample/reagent fluids on a two-dimensional electrode array based on a special phenomenon called electrowetting-on-dielectric (EWOD) [8], [4]....
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...In many digital microfluidic (DMF) biochips, electrical actuation is used to manipulate (transporting, merging, splitting, mixing, dispensing, etc.) discrete droplets of nanoliter or picoliter volume of the sample/reagent fluids on a two-dimensional electrode array based on a special phenomenon called electrowetting-on-dielectric (EWOD) [8], [4]....
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TL;DR: To understand the opportunities and limitations of EWD microfluidics, this paper looks at the development of lab-on-chip applications in a hierarchical approach.
Abstract: The suitability of electrowetting-on-dielectric (EWD) microfluidics for true lab-on-a-chip applications is discussed. The wide diversity in biomedical applications can be parsed into manageable components and assembled into architecture that requires the advantages of being programmable, reconfigurable, and reusable. This capability opens the possibility of handling all of the protocols that a given laboratory application or a class of applications would require. And, it provides a path toward realizing the true lab-on-a-chip. However, this capability can only be realized with a complete set of elemental fluidic components that support all of the required fluidic operations. Architectural choices are described along with the realization of various biomedical fluidic functions implemented in on-chip electrowetting operations. The current status of this EWD toolkit is discussed. However, the question remains: which applications can be performed on a digital microfluidic platform? And, are there other advantages offered by electrowetting technology, such as the programming of different fluidic functions on a common platform (reconfigurability)? To understand the opportunities and limitations of EWD microfluidics, this paper looks at the development of lab-on-chip applications in a hierarchical approach. Diverse applications in biotechnology, for example, will serve as the basis for the requirements for electrowetting devices. These applications drive a set of biomedical fluidic functions required to perform an application, such as cell lysing, molecular separation, or analysis. In turn, each fluidic function encompasses a set of elemental operations, such as transport, mixing, or dispensing. These elemental operations are performed on an elemental set of components, such as electrode arrays, separation columns, or reservoirs. Examples of the incorporation of these principles in complex biomedical applications are described.
1,094 citations
Additional excerpts
..., point-of-care clinical diagnostics, high-throughput sequencing, toxicity monitoring and proteomics [1], [2], [3], [4], [5]....
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TL;DR: This paper studies the effects of varying droplet aspect ratios on linear-array droplet mixers, and proposes mixing strategies applicable for both high and low aspect ratio systems, and presents a split-and-merge mixer that takes advantage of the ability to perform droplet splitting at these ratios.
Abstract: The mixing of analytes and reagents for a biological or chemical lab-on-a-chip is an important, yet difficult, microfluidic operation. As volumes approach the sub-nanoliter regime, the mixing of liquids is hindered by laminar flow conditions. An electrowetting-based linear-array droplet mixer has previously been reported. However, fixed geometric parameters and the presence of flow reversibility have prevented even faster droplet mixing times. In this paper, we study the effects of varying droplet aspect ratios (height ∶ diameter) on linear-array droplet mixers, and propose mixing strategies applicable for both high and low aspect ratio systems. An optimal aspect ratio for four electrode linear-array mixing was found to be 0.4, with a mixing time of 4.6 seconds. Mixing times were further reduced at this ratio to less than three seconds using a two-dimensional array mixer, which eliminates the effects of flow reversibility. For lower aspect ratio (≤0.2) systems, we present a split-and-merge mixer that takes advantage of the ability to perform droplet splitting at these ratios, resulting in a mixing time of less than two seconds.
491 citations
"Waste-aware dilution and mixing of ..." refers methods in this paper
...This array mixer uses merge-and-split operation for mixing two droplets [13]....
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TL;DR: In this paper, the authors review the state-of-the-art in digital microfluidics, with a discussion of device formats, actuation physics, and biological and non-biological applications.
Abstract: The digital revolution has come to microfluidics. In digital microfluidics (DMF), discrete droplets are manipulated by applying electrical fields to an array of electrodes. In contrast to microchannels, in DMF each sample and reagent is individually addressable, which facilitates exquisite control over chemical reactions. Here, we review the state-of-the-art in DMF, with a discussion of device formats, actuation physics, and biological and non)biological applications. Along the way, we identify the key players in the field, and speculate on the advances and challenges that lie ahead. As with other fronts in the digital revolution, there have been and will be unexpected developments as DMF matures, but we posit that the future is bright for this promising technology.
324 citations
"Waste-aware dilution and mixing of ..." refers background in this paper
..., point-of-care clinical diagnostics, high-throughput sequencing, toxicity monitoring and proteomics [1], [2], [3], [4], [5]....
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...) discrete droplets of nanoliter or picoliter volume of the sample/reagent fluids on a two-dimensional electrode array based on a special phenomenon called electrowetting-on-dielectric (EWOD) [8], [4]....
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TL;DR: Developing two critical new functions in handling protein solutions and standard proteomic reagents with electrowetting-on-dielectric (EWOD) actuation leading to an integrated chip for multiplexed sample preparation for MALDI-MS, suggesting that the tedious process of sample preparation can be automated on-chip for MalDI- MS applications as well as other high-throughput proteomics applications.
Abstract: To realize multiplexed sample preparation on a digital microfluidic chip for high-throughput Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS), several fluidic functions need to be integrated. These include the generation of multiple droplets from a reservoir and parallel in-line sample purification. In this paper, we develop two critical new functions in handling protein solutions and standard proteomic reagents with electrowetting-on-dielectric (EWOD) actuation, leading to an integrated chip for multiplexed sample preparation for MALDI-MS. The first is a voltage sequence designed to generate a series of droplets from each of the three reservoirs—proteomic sample, rinsing fluid, and MALDI reagents. It is the first time that proteomic reagents have been dispensed using EWOD in an air (as opposed to oil) environment. The second is a box-in-box electrode pattern developed to allow droplet passing over dried sample spots, making the process of in-line sample purification robust for parallel processing. As a result, parallel processing of multiple sample droplets is demonstrated on the integrated EWOD-MALDI-MS chip, an important step towards high-throughput MALDI-MS. The MS results, collected directly from the integrated devices, are of good quality, suggesting that the tedious process of sample preparation can be automated on-chip for MALDI-MS applications as well as other high-throughput proteomics applications.
288 citations
Additional excerpts
..., point-of-care clinical diagnostics, high-throughput sequencing, toxicity monitoring and proteomics [1], [2], [3], [4], [5]....
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