Image-based feedback and analysis system for digital microfluidics

Abstract

Digital microfluidics (DMF) is a technology that provides a means of manipulating nL-μL volumes of liquids on an array of electrodes. By applying an electric potential to an electrode, these discrete droplets can be controlled in parallel which can be transported, mixed, reacted, and analyzed. Typically, an automation system is interfaced with a DMF device that uses a standard set of basic instructions written by the user to execute droplet operations. Here, we present the first feedback method for DMF that relies on imaging techniques that will allow online detection of droplets without the need to reactivate all destination electrodes. Our system consists of integrating open-source electronics with a CMOS camera and a zoom lens for acquisition of the images that will be used to detect droplets on the device. We also created an algorithm that uses a Hough transform to detect a variety of droplet sizes and to detect singular droplet dispensing and movement failures on the device. As a first test, we applied this feedback system to test droplet movement for a variety of liquids used in cell-based assays and to optimize different feedback actuation schemes to improve droplet movement fidelity. We also applied our system to a colorimetric enzymatic assay to show that our system is capable of biological analysis. Overall, we believe that using our approach of integrating imaging and feedback for DMF can provide a platform for automating biological assays with analysis.

Position and feedback for digital microfluidic system based on light intensity information

“…Figure 8 shows the comparison result of feedback and nonfeedback driving method. It can be seen that the success rate is relatively high whether there is feedback or not when droplet driving times are small (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). But in the experiment without feedback, the success rate of moving droplet began to decrease significantly, especially in the case of driving 60 times, where it was only 65%.…”

“…Some researchers [16][17][18] proposed a droplet position method based on image and visual relay. This position method can be used to detect the distribution of several droplets on an EWOD device.…”

One cell, one drop, one click: hybrid microfluidic mammalian single-cell isolation

“…The automation system serves the purpose to provide electrode actuation and impedance feedback and the syringe pump system is to control the flow rates in the device. [43,59] Since these two hardware systems are operating on different software protocols, we developed our own Python based framework with a simple user interface for one-click droplet operations. Second, an aqueous flow containing fresh media with mammalian cells (MCF-7 breast cancer cell line) was introduced into the trapping device at a concentration of 10 5 -10 6 cells mL -1 (see Figure 2A for an image of six individually trapped cells).…”

BioScript: programming safe chemistry on laboratories-on-a-chip

“…The DMFB ISA can be extended by integrating sensors [Alistar and Gaudenz 2017;Barsoukov and Macdonald 2005;Bhattacharjee and Najjaran 2012;Cooreman et al 2005;Gao et al 2013;J. Schertzer et al 2012;Lederer et al 2012;Li et al 2014Li et al , 2015Murran and Najjaran 2012;Ren et al 2004;Sadeghi et al 2012;Shih et al 2013Shih et al , 2011Suni 2008], optical detectors [Luan et al 2008[Luan et al , 2012Srinivasan et al 2004;White Royal et al 2013], heaters [Luo et al 2015], or online video monitoring capabilities [Basu 2013;Hu et al 2013;Li et al 2017;Shin and Lee 2010;Vo et al 2017]. Sensors and actuators create a łcyber-physicalž feedback loop between the host PC controller and the DMFB.…”

“…The ability to perform sensing, computation, and actuation based on the results of the computation adds control flow to the instruction set of the DMFB. Prior work has applied feedback-control for precise droplet positioning [Alistar and Gaudenz 2017;Basu 2013;Bhattacharjee and Najjaran 2012;Hu et al 2013;Li et al 2015Li et al , 2017Murran and Najjaran 2012;Shih et al 2011;Vo et al 2017] and online error detection and recovery [Alistar and Pop 2015;Alistar et al 2016;Hsieh et al 2014;Ibrahim and Chakrabarty 2015a,b;Ibrahim et al 2017;Jaress et al 2015;Li et al 2017;Luo et al 2013a,b;Poddar et al 2016;Zhao et al 2010]; efforts to leverage these capabilities to provide control flow constructs at the language syntax level have been far more limited [Curtis and Brisk 2015;Curtis et al 2018;.…”