Micromachined optical flow cell for sensitive measurement of droplets in tubing
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Here a micromachined flow cell with enhanced optical sensitivity is presented that allows high-throughput analysis of microdroplets. As a droplet flows through multiple concatenated measurement points, the rate of enzymatic reaction in the droplet can be fully characterized without stopping the flow. Since there is no cross-talk between the droplets, the flow cell is capable of continuously measuring biochemical assays in a droplet flow and thus is suitable to be used for continuous point-of-care diagnostics monitoring. This paper describes the design and operation of the device and its validation by application to the accurate and continuous quantification of glucose concentrations using an oxidase enzymatic assay. The flow cell forms an important component in the miniaturization of chemical and bio analyzers into portable or wearable devices.
KeywordsMicrofluidics Droplet microfluidics Absorbance Real-time monitoring Continuous measurement Enzymatic assays Optical detections Point-of-care diagnostics
Miniaturization of chemical assays in microfluidic devices has become practical with the development of microfabrication technologies, with the significant advantages such as reduced amounts of samples and reagents, cost-effectiveness, robustness and sensitive assays (Whitesides 2006). In general, these systems are developed to provide high-throughput, parallel, and automated analytical analyses.
Continuous microfluidics normally involves single phase flow in microchannels (Teh et al. 2008). Devices based on continuous microfluidics have been developed to scale down the amount of reagents from millilitres or microlitres to nanolitres and are used to study chemical reactions or synthesize materials in a short interval of time. However, there are a number of disadvantages associated with continuous microfluidics such as slow and weak mixing in microchannels, Taylor dispersion of the analytes and sample loss or contamination of biomolecules on the surface of the channel walls (Teh et al. 2008; Huebner et al. 2008).
In contrast, droplet-based microfluidics encapsulates biomolecules or analytes of interest into discrete droplets and perform analysis with these ‘digital’ units (Sista et al. 2008). In droplet microfluidics, nanolitre to femtolitre sized discrete droplets are generated in the microchannel by pinching off continuous aqueous stream with an immiscible carrier phase, which is usually oil. Mixing in droplets is faster as compared to continuous microflows because of the fluidic circulations inside the droplets (Tiselius 1937). Analytes of interest are compartmentalized into droplet plugs, and the carrier phase prevents the sample from contact with the surface wall and hence, eliminates sample loss on the channel surfaces. Carrier phase also prevents the leakage of the molecules and cross-contamination between the droplets. Thus, in droplet-based microfluidics, each droplet acts as a separate microreactor and multiple of them (as many as millions) can be transported and analysed in a single device (Ostergaard and Jensen 2009). Whereas in continuous microfluidics, it requires separate microchannels for each sample loading or complicated fluidic controls to clean the channels between the sample loadings due to the contamination problems.
Colorimetry, in which an analyte-specific reagent reacts with the target analyte to give a coloured product, offers a promising platform to quantify analytes via absorption spectrophotometry and is highly suitable for accurate quantitative analysis in microfluidics for multiple biochemical assays (Ellerbee et al. 2009; Sieben et al. 2010; Rushworth et al. 2015; Tietz et al. 2006). Colorimetric assays are typically slow (Nelson and Cox 2008; Sugiura and Hirano 1977), therefore, a rate kinetic method is used to quantify the metabolite concentrations. Colorimetric assays have already been applied in droplet microfluidics such as Srinivasan et al. (2004) developed a droplet-based system to measure the kinetics of glucose in droplets. The optical detection was performed on a plane perpendicular to microfluidic chip consisting of LED and photodiode with a path length of 475 μm. Droplets of glucose and reagents were pipetted onto the electrowetting plate, merged to produce a final volume of 2 μL, mixed for 15 s, and the absorbance measurements were taken for the 30s to generate Michaelis-Menten parameters. Similarly, Fradet et al. (2015) fused the droplets of sample and reagent (β-d-glucosidase and 4-nitrophenyl β-d-glucopyranoside), immobilised the resulting droplet and followed the colour development over time using an optical microscope. The authors studied different enzyme concentrations, generated Michaelis-Menten kinetics, and quantified the effect of the inhibitor on enzyme activity. In a recent study, Gielen et al. (2015) developed a sampler that was able to generate high-resolution concentration gradients of haloalkane dehalogenase DbjA and analysed 150 combinations of enzyme and substrate in less than 5 min. In another study Gielen et al. (2013) chose to monitor the similar enzymatic reaction by oscillating reaction-containing droplets back-and-forth through an absorption flow cell. The authors generated droplets of 4-nitrophenyl glucopyranoside hydrolysis by sweet almond β-glucosidase and passed through the detector consisting of LED and photodiode with a path length of 200 μm. Different concentrations of the substrate were loaded into the droplets, merged to initiate the reaction, and the absorbance was measured for each set of concentration by flowing the droplets back and forth in the detector to generate Michaelis-Menten parameters. The above-mentioned methods were able to follow the color development in detail and obtained accurate reaction rates, however, the throughput is still low. The chief reason for such a low throughput analysis is due to a single detection point to measure the color development in the droplets, therefore, either stop flow or oscillation flow is needed. This low throughput analysis has yet to match the potentials of high throughput analysis that droplet-based microfluidics can offer, for example, thousands of droplets can be produced per second in a single T-junction (Gu et al. 2011).
This paper shows how sensitivities can be improved by upgrading the flow cell from 3D printed cartridge (Hassan et al. 2016; Nightingale et al. 2017) to a micromilled cartridge with the limit of detection (LOD) of 0.1 mM as compared to 0.35 mM (3D-printed).
2 Materials and methods
2.1 Materials and reagents
Glucose oxidase, horseradish peroxidase, 4-aminoantipyrine, phenol and phosphate buffered saline (PBS) tablets were bought from Sigma-Aldrich (Dorset, U.K) and prepared in 0.1 M PBS at pH of 7.4. Ultrapure water (18.2 MΩ cm, MilliQ) was used for the preparation of all solutions. The reagents were mixed in equal volumes to achieve the final concentrations as follows: glucose oxidase (30 U mL−1), peroxidase (30 U mL−1), 4-aminoantipyrine (1.54 mM) and phenol (22 mM). Red food dye (East End Foods plc) and D-glucose were dissolved in the DI water and 0.1 M PBS respectively to prepare the standard solutions.
2.2 Droplet generation
T-junction microchip to generate droplets was fabricated by designing a mould in SolidWorks (Dassault Systemes), printing (Objet500 Connex3 3D printer, Stanford Marsh Ltd) and half curing PDMS bonding as outlined previously (Hassan et al. 2016). One inlet was used to pump FC-40 carrier fluid with 0.35% w/w (surfactant non-ionic tri-block copolymer surfactant) synthesized in-house (Utada et al. 2005). The sample and the reagents were injected in a volume ratio of 1:1 via Syringe pumps (PHD 2000/Harvard Apparatus). Polytetrafluoroethylene (PTFE) tubing (0.4 mm ID, 0.7 mm OD, Adtech Polymer Engineering Ltd.) was used to transport the droplets through the flow cell.
2.3 Flow cell fabrication
As indicated in the schematic of the cartridge in Fig. 1a, there are 4 opening holes in the channels that provide the passage for light. The holes (0.4 mm) and a channel (0.7 mm) to hold the tubing were micromilled in a PMMA piece (3 mm thick) using LPKF Protomat S100 micromilling machine as discussed previously (Hassan et al. 2015, 2017). The second part was also fabricated similarly without the tubing channel (Fig. 1a). Both parts were aligned via alignment pins and glued ((Super Glue, Loctite) together. The cartridge can slot into the detection cell (Fig. 1b). The detection cell was equipped with LEDs (ASMT-QGBE-NFH0E, Avago Technologies) and light to voltage convertors (TSL257, Texas Advance Optical Solutions). Figure 1c shows the schematic of the fully assembled 4-detector flow cell (the size of the device is 28 × 8 × 6 mm).
2.4 Data analysis
A microcontroller (Arduino Nano) was used to collect the signal from the photodiode which was displayed via LabView (National Instruments) connected to the computer. Beer-Lambert law was modified to measure the absorption for each droplet which normalises the intensity values based on the intensity values of FC-40 oil levels (Hassan et al. 2016).
3 Results and discussion
Figure 3 indicates (from top to bottom) that the change in colour intensity of the droplets generated at T-junction is preserved without contamination or smearing along the tubing, and without dependence on length or flow rate of the droplets. In this experiment about 20 s were required to switch from one concentration to the other completely which can be further reduced by shortening the tubing lengths from the inlet reservoirs and the droplet generation.
Having calibrated the flow cell with glucose reaction droplets, a micromilled (4 detectors) flow cell and 3D printed flow cell (Hassan et al. 2016) were used to determine glucose concentrations. Contrary to the 7-detector flow cell reported previously (Hassan et al. 2016), 4-detector flow cell was found to be enough to achieve required resolution (R2 > 0.99). This is because to provide a flow cell with low-power consumption for POC applications. Droplets of different concentrations of glucose (0.5, 2.5, 5, 15 mM) were generated and flowed through the multi-detector flow cell. The droplets passed through the consecutive detectors and the absorbances were measured as shown in Fig. 4b and c. It can be clearly seen from the graphs that the absorbances were higher for the micromilled detector (similar concentrations). This is because of the reduced effect of refractive index due to the better alignment of the holes.
This paper presents the development of optical flow cell for accurate quantification of enzymatic colorimetric assays in droplets. The flow cell was fabricated with precise micromilling, analogous to a previously reported 3D printed device (Hassan et al. 2016). The flow cell was capable of measuring absorbances with %RSD of 3.2% (detector-to-detector) as compared to 3D printed flow cell (6.5%). The flow cell is compact, low cost and can be used in portable analyzers.
We thank the funding from the Engineering and Physical Sciences Research Council UK (EP/M012425/1).
S. H., and X. N. conceived and designed the experiments; S.H. performed the experiments and analyzed the data; S. H., A. M. N. and X. N. wrote the paper.
Compliance with ethical standards
Conflicts of interest
The authors declare no conflict of interest.
- F. Gielen, T. Buryska, L. Van Vliet, M. Butz, J. Damborsky, Z. Prokop, F. Hollfelder, Interfacing microwells with nanoliter compartments: A sampler generating high-resolution concentration gradients for quantitative biochemical analyses in droplets. Anal. Chem. 87, 624–632 (2015)CrossRefGoogle Scholar
- D.L. Nelson, M.M. Cox, Lehninger principles of biochemistry 5th ed. Book, 1–1294 (2008)Google Scholar
- N.W. Tietz, C.A. Burtis, E.R. Ashwood, D.E. Bruns, Tietz textbook of clinical chemistry and molecular diagnostics, vol 2006 (Elsevier Health Sciences, 2006), p. 5Google Scholar
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