H2020 MSCA Logo - Droplet-detection-and-measurement-in-microfluidic-channels-Elveflow - Startup-Technology - Innovation - NBIC Valley Review done thanks to the support of the AutoDropProd project H2020-MSCA-IF-2017 – Marie-Curie Action:

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Grant agreement number: 796514

Written by Remigijus Vasiliauskas, Postdoc :  remigijus.vasiliauskas@elvesys.com

Remigijus Vasiliauskas - Droplet-detection-and-measurement-in-microfluidic-channels-Elveflow - Microfluidic - Startup - Innovation

Droplet detection and measurement in microfluidic channels

Introduction to microfluidic droplet detection methods

The Droplet sorting- droplet measurement-microfluidic-channel-Elveflow-Innovation-history of microfluidics started in 1950 with the first ink jet printer, where small amounts of fluids were handled in precise manner to get fine text printed. From that day onwards, microfluidics has evolved a lot and now it is used to handle fluids in a very precise manner in volumes from 10-18 up to microliters [1] allowing reduction of costs by reducing volumes of rare or expensive materials used in experiments.

Next to that, the development of droplet-based microfluidics has significantly influenced chemical synthesis, synthetic biology and even created new fields, such as high throughput single cell analysis. Droplet based microfluidics involves small droplet creation out of a fluid stream and provides the added benefits of removing the effects of Taylor dispersion and the subsequently increased ease of droplet transport [2]. Microfluidic droplets can include, isolate and allow easy manipulation of reagents, particles, cells, or multicellular organisms [3].

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The biggest advantage of droplet-based microfluidics over other droplet-based systems is the uniformity or monodispersity of the droplets. To measure uniformity of the droplets, and compare them from experiment to experiment, the coefficient of variation (C.V.) is used. In case of microfluidics this number is usually below 5% and sometimes even below 2%, which is very good, compared to other droplet production techniques, such as batch mixers or sonicators, where a C.V. of 15% or more is acceptable. Even though droplets prepared using microfluidics are more uniform, in some cases even higher droplet uniformity is needed, i.e., for drug encapsulation or mixing of costly drugs. In such cases special, active droplet monitoring and feedback systems are required, which can detect, measure and regulate droplet size to avoid changes in size. The most difficult part which requires the most work is the droplet detection and size measurement. Thus, in this review we will focus on the three main methods for droplet detection and size measurement: optical imaging, laser or similar light-source initiated detection and electrical detection. if you want to learn more about our products for optical detection, click here.

1. Optical imaging of droplets in microfluidic devices

In the case of droplet detection using optical imaging, the droplets are photographed in the microfluidic channels and the images are analyzed in real time or offline. The main advantage of using the real time processing is the gained ability to regulate or stabilize droplet size during the experiment. However, real time analysis is very limited in terms of how many images per second it can process as it needs a lot of processing power. Nevertheless, with the very fast increase of computer power and improved algorithms of image processing, the speed could increase in the near future. Recently, it was shown that real time image processing could work at up to 1000 Hz rates when using a line camera [4]. However, the real-time analysis of full images and a droplet control using a feedback loop can work at up to 250 Hz when analyzing every droplet [4]. Even though the speed of processing is not so high, usually the speed of the pressure pump is much slower. Thus, to achieve 250 Hz controlled droplet production with the feedback system, fast response pressure pumps need to be used, as they have sufficient response time [5] compared to syringe pumps. At the same time, Crawford et al [4], have shown that droplet size uniformity can be increased compared to other pumping methods by using a feedback loop (Fig. 1).

Droplet Uniformity - Droplet-detection-and-measurement-in-microfluidic-channels-Elveflow - Microfluidics - Innovation - Startup - Technology
Fig. 1. Droplet uniformity over a long period of time using the feedback composed of optical imaging and pressure pump system to regulate the droplet size in comparison with the systems without feedback loop. From Crawford et al. Scientific Reports, 2017

The most interesting application of this method, next to droplet size measurement, is droplet sorting depending on their content. It has been shown that using this method droplets containing single cells [3] or microbes [6] can be sorted at up to 10 Hz rate with an amazing accuracy of 90% and without using any labeling of the cells (Fig. 2). In addition, it was shown that the system can separate empty droplets from occupied ones and also distinguish between different cell types [3] that are contained inside the droplets. At the same time this method allows to study formation of the droplets and the forces acting on them, at the same time allowing very precise control of droplet formation [7].

Cell sorting -  Droplet-detection-and-measurement-in-microfluidic-channels-Elveflow - Microfluidics - Innovation - Startup - Technology

Fig. 2. Droplets sorted using optical imaging system, by their content. One can observe cells inside the droplets, by which they were sorted. From Girault et al., Scientific Reports, 2017

Another very interesting way of using the same optical imaging principle was discussed in a patent application [8] with the main application of droplet velocity measurement in a microfluidic channel. Here the droplet is exposed to three different color flashes in a very short time frame and the image taken is still in a single frame. Thus, in one frame instead of one real image of droplet, one would get three images of the same droplet just illuminated with different light sources at different time frames. The detector then distinguishes the different colors, detects the droplet and calculates the droplet speed. This method could potentially work at higher speeds, however there is no such device yet on the market.

Even though there are a lot of applications of such system running in real time, in most cases it is being deployed in the offline regime for post-processing of images or videos containing droplets in a manual or automated regime. Software for automated droplet detection, measurement of their speed, size and size variation (C.V.) was developed [9] and is available as free at http://a-d-m.weebly.com/. Even though the software has its limitations, it is a good start when automating your droplet measurement process and can save a lot of time if used correctly. More information on droplet-based single-cell-encapsulation can be found here.

2. Laser (or LED) initiated detection of microfluidic droplets

Optical droplet detection in such systems is based on a directed light source entering the micro-channels and detecting droplets. The droplets can be detected by exciting fluorescent material in droplets, detecting back or forward scattered or even reflected light.

The main advantage of a laser induced detection system is a very high detection rate which allows detection of very high droplet production rates. The disadvantage of the system is the lack of a base line and requirement of calibration for every batch of experiments.

Depending on the detection system, the detector is usually placed in different positions. For the detection of fluorescent light, the detectors are usually placed around 90 degrees from the path of incident light at the same time avoiding back or forward scattered light. On the other hand, when working with big droplets (much bigger than the laser spot size), the detector is usually placed after the channel (fig. 3) to collect back scattered light. This is necessary for the droplets to block the light indicating their presence, and while the signal is being processed, the droplet size can be acquired [10]. In case of reflected or back scattered light detection, the sensor is placed in the path of incident light.

In most cases, such a system is used to detect the presence of droplets and to measure the frequency of droplet production [11], but it is not very difficult to upgrade the system to measure and automatically calculate the droplet or particle size [12]–[15]. In addition, the system allows to study internal structures of the cell (or droplet) as it is done in flow cytometry machines, however the interpretation of results is still a challenge [16].Laser droplet detection system - Droplet-detection-and-measurement-in-microfluidic-channels-Elveflow - Microfluidics - Innovation - Startup - Technology

Fig. 3. Example of laser (LED) droplet detection system. From Trivedi et al., Lab Chip, 2010. 

The system can be upgraded to detect several fluorescence wave lengths, which allows detection and sorting of different cells or enzymes with different fluorescence responses [16], [17]. For example, R. Cole et al. [17] have shown, that it is possible to integrate several optical cables emitting different wave lengths, which can excite different fluorescent materials, the fluorescent light is detected and droplets can be sorted according to the response.

The most common commercial devices using the laser detection principle are flow cytometers. Here, laser detection is used to detect droplet or cells, count them, measure size (forward scattering), sort cells and analyze insides of the cell (droplet) by using fluorescence signal [16].

3. Electrical techniques for microfluidic droplet detection

In case of electrical techniques, multiple sensor arrays are directly integrated into the microfluidic chip. Most often the capacitive (fig. 4) [18], electrochemical [19], impedance [20] and microwave [21] based electrical detection methods (sensors) are used.When a droplet or microparticle passes over the contacts it gives an electric signal, which is then recorded and analyzed. The advantage of this system is the possibility of sensing the content of individual droplets without any chemical or physical intrusion. On the other hand, the main disadvantage is the difficulty of integrating electric contacts in the chip which increases the price of the chip and prevents rapid prototyping. Compared with the techniques discussed earlier where the detector is outside of the chip and does not influence the chip itself, it is less universally applicable. Nevertheless, it was shown that the droplet presence, speed and size can be measured avoiding any cross contamination [18] using this technique. Thus, the technique is showing high potential and the need for further work in the field.

Capacitive detector - Droplets - Microfluidics - Elveflow - Startup - Innovation

Fig. 4. Capacitive detector integrated into a microfluidic chip. From Elbuken et al., 2011.

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References

[1]      G. M. Whitesides, “The origins and the future of microfluidics,” vol. 442, no. July, 2006.

[2]      S. Mashaghi, A. Abbaspourrad, D. A. Weitz, and A. M. van Oijen, “Droplet microfluidics: A tool for biology, chemistry and nanotechnology,” TrAC – Trends Anal. Chem., vol. 82, pp. 118–125, 2016.

[3]      M. Girault et al., “An on-chip imaging droplet-sorting system: A real-time shape recognition method to screen target cells in droplets with single cell resolution,” Sci. Rep., vol. 7, no. September 2016, pp. 1–10, 2017.

[4]      D. F. Crawford, C. A. Smith, and G. Whyte, “Image-based closed-loop feedback for highly mono-dispersed microdroplet production,” Sci. Rep., vol. 7, no. 1, pp. 1–9, 2017.

[5]      Elveflow, “OB1 MK3 – MICROFLUIDIC FLOW CONTROL SYSTEM,” www.elveflow.com, 2017. [Online]. Available: https://www.elveflow.com/microfluidic-flow-control-products/flow-control-system/pressure-controller/. [Accessed: 11-Jun-2018].

[6]      L. Dong, D. W. Chen, S. J. Liu, and W. Du, “Automated Chemotactic Sorting and Single-cell Cultivation of Microbes using Droplet Microfluidics,” Sci. Rep., vol. 6, no. January, pp. 1–8, 2016.

[7]      T. M. Moyle, L. M. Walker, and S. L. Anna, “Controlling thread formation during tipstreaming through an active feedback control loop,” Lab Chip, vol. 13, no. 23, p. 4534, 2013.

[8]      D. Tracy, A. Weber, and P. Stokes, “Low cost optical high speed discrete measurements system,” WO 2014/194042 A2, 2014.

[9]      Z. Z. Chong et al., “Automated droplet measurement (ADM): an enhanced video processing software for rapid droplet measurements,” Microfluid. Nanofluidics, vol. 20, no. 4, pp. 1–14, 2016.

[10]    L. L. Tavlarides and B. Jae-Heum, “Laser Capillary Spectrophotometric acquisition of bivariate drop size and concentartion data for liquid-liquid dispersion,” 5,074,658, 1991.

[11]    J. Li, J. Lindley-Start, A. Porch, and D. Barrow, “Continuous and scalable polymer capsule processing for inertial fusion energy target shell fabrication using droplet microfluidics,” Sci. Rep., vol. 7, no. 1, pp. 1–10, 2017.

[12]    V. Trivedi, A. Doshi, G. K. Kurup, E. Ereifej, P. J. Vandevord, and A. S. Basu, “A modular approach for the generation, storage, mixing, and detection of droplet libraries for high throughput screening,” Lab Chip, vol. 10, no. 18, p. 2433, 2010.

[13]    N. T. Nguyen, S. Lassemono, and F. A. Chollet, “Optical detection for droplet size control in microfluidic droplet-based analysis systems,” Sensors Actuators, B Chem., vol. 117, no. 2, pp. 431–436, 2006.

[14]    X. Wu, C. H. Chon, Y.-N. Wang, Y. Kang, and D. Li, “Simultaneous particle counting and detecting on a chip,” Lab Chip, vol. 8, no. 11, pp. 1943–1949, 2008.

[15]    S. W. Kettlitz, S. Valouch, and U. Lemmer, “Statistics of particle detection from single-channel fluorescence signals for flow cytometric applications,” IEEE Trans. Instrum. Meas., vol. 62, no. 7, pp. 1960–1971, 2013.

[16]    H. M. Shapiro, Practical Flow Cytometry. 2003.

[17]    R. H. Cole, Z. J. Gartner, and A. R. Abate, “Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers,” J. Vis. Exp., no. 111, pp. 1–7, 2016.

[18]    C. Elbuken, T. Glawdel, D. Chan, and C. L. Ren, “Detection of microdroplet size and speed using capacitive sensors,” Sensors Actuators, A Phys., vol. 171, no. 2, pp. 55–62, 2011.

[19]    T. Abadie, C. Sella, and L. Thouin, “Electrochemical detection of droplet content in microfluidic devices: Evidence of internal recirculating convection within droplets,” Electrochem. commun., vol. 80, pp. 55–59, Jul. 2017.

[20]    N. E. Yakdi, D. Bricault, F. Huet, and K. Ngo, “Detection and Sizing of Single Droplets Flowing in a Microfluidic Device by Impedance Measurement,” Procedia Eng., vol. 168, no. 0, pp. 1466–1470, 2016.

[21]    G. Yesiloz, M. S. Boybay, and C. L. Ren, “Label-free high-throughput detection and content sensing of individual droplets in microfluidic systems,” Lab Chip, vol. 15, no. 20, pp. 4008–4019, 2015.

 

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