Recent Progress in Microfluidics-based Biosensing - Analytical

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Review

Recent Progress in Microfluidics-based Biosensing yanling Song, Bingqian Lin, Tian Tian, Xing Xu, Wei Wang, Qingyu Amos Ruan, Jingjing Guo, Zhi Zhu, and Chaoyong James Yang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b05007 • Publication Date (Web): 09 Nov 2018 Downloaded from http://pubs.acs.org on November 10, 2018

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Analytical Chemistry

Recent Progress in Microfluidics-based Biosensing Yanling Song1, Bingqian Lin2, Tian Tian2, Xing Xu2, Wei Wang1, Qingyu Ruan2, Jingjing Guo2, Zhi Zhu2, Chaoyong Yang1,2*

1: Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China. 2: MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.

RECEIVED DATE (to be automatically inserted after your manuscript is accepted if required according to the journal that you are submitting your paper to) * To whom correspondence should be addressed. Tel: (+86) 21-683-83993, E-mail: [email protected]

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1. Introduction Biosensors are analytical devices or systems used to detect a specific target by converting and amplifying a biomolecular recognition event to a dateable semi-quantitative or quantitative signal. Biosensors are powerful analytical tools for the detection of biological or chemical molecules.1 In general, a biosensor consists of biological recognition and signal output elements. There are some definitions insist that the recognition element be physically adjacent to the signal output element. We appreciate this definition, but here we provide a board definition of the biosensor or biosensing system that incorporates any method coupling these two key elements, and ultimately achieving “sample-in-answer-out”. The recognition sensing element can generally be classified in terms of its interaction with the analytes. Several classic modes of biosensing interaction include: antibody-antigen,2,3 nucleic acid probe and complementary target,4 aptamer5 or peptide and corresponding target,6,7 enzymesubstrate,8 ligand-receptor and host-guest interactions.9 The availability of a variety of interaction formats allows many biosensors to be designed, each for a specific target. On the other hand, to be suitable in a variety of application scenarios, it is important to have a choice of signal transduction and output methods. Common forms of signal transduction applied in biosensing includes electrochemical,10,11 optical,12 magnetic,13 surface plasmon resonance (SPR),14 and mass-sensitive15 transducers. In recent years, new types of signal outputs derived from existing transducers or new measurement principles have been developed to further increase the sensitivity, selectivity, portability, or sample throughput of microfluidic biosensors. Biosensors have been widely adopted in many fields, such as medical diagnosis, health monitoring, food safety surveillance, environmental assessment, etc,16 and they are attracting increasing attention due to their convenience, reliability, rapid response and low-cost. Analytical systems requiring sophisticated instruments and/or multi-step manual operations have limited application in some areas, but microfluidics-based biosensing has grown immensely during the past decade. Devices which integrate multiple functions or steps in one main unit have greatly increased their popularity. Typically these microfluidic systems with micrometer-scaled features can realize a series of processes, including sample transfer, mixing, separation and signal output, with smaller sample volume, lower materials consumption, less cost, faster turnaround time and increased automation than traditional biosensing systems.


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In this review, we will summarize advances of microfluidics-based biosensing in the last two years. We will first highlight several new signal transduction and output methods, which are of great potential or have been applied in microfluidics-based biosensing. Next, we will review recent progress and developing trends in microfluidics-based biosensing, including advances in microchannel-based, discrete microfluidic-based and paper-based microfluidic biosensing systems. We will further describe how these advances fit into a wider application scopes and highlight several common real case scenarios. Finally, we will discuss the future outlook and remaining challenges, especially the barriers in translation and commercialization.

Figure 1. Schematic of different parts of a microfluidics-based biosensor including biological

recognition element, microfluidic device, and signal readout.

2. New Signal Transduction and Output Strategies In a typical biosensor, the signal transducer transforms the target recognition event into a measurable signal, which is usually accompanied by signal amplification and processing. Classical optical, electrical, thermal, magnetic and mass transducers have been developed for many years and are also widely used in microfluidic systems. In this review, we will not go into the details of classical signal transduction methods but will highlight new principles or methods for signal conversion and output developed in the past two years, which have promising potential or been



applied in microfluidic biosensing. These new kinds of signal outputs are not just format changes, but aim to increase simplicity, sensitivity, selectivity, portability, and/or sample throughput.

2.1 Optical Readout Optical transducers represent a board class of common and popular signaling methods, all involving an optical change in the sensing layer, including absorption, transmittance, scattering, reflection, refraction, and emission, as a result of interaction between of the analyte and recognition element. Optical sensors are commonly based on colorimetry, UV absorption, fluorescence, chemiluminescence, surface-enhanced Raman scattering (SERS), or surface plasmon resonance (SPR), which can be detected by the naked eye or an optical sensing instrument, with the advantages of versatility, non-destruction, sensitivity, universality, and simultaneous detection of multiple targets. However, when incorporating into a microfluidic system, it is necessary to consider integration of the external light source and other optical components (for absorbance and fluorescence) or catalytic biosensors (for chemiluminescence).17,18 These requirements limit applications of optical readout in microfluidics-based biosensing. On the other hand, colorimetric transducers can be easily integrated into a microfluidic system, but their sensitivity is limited. Therefore, it is urgent to develop optical transducers of high resolution that can be easily integrated into microfluidic chips.19-22 Very recently, Johnsson’s group introduced semisynthetic sensor proteins for use in paper-based metabolic assays. These new proteins can convert the metabolite signal into a color change recognizable by the naked eye, unlike current commercial methods using chemiluminescence signal output.23 This approach relies on the selective oxidation of metabolites catalyzed by a specific enzyme to generate NADPH, which can be readout by a sensing complex. The sensing complex consists of an NADPH-dependent receptor, the luciferase NanoLuc (NLuc), and a fluorescently labeled ligand (Figure 2A). With the production of NADPH, the ligand and receptor interact, bringing the fluorophore close to the luciferase and resulting in bioluminescence resonance energy transfer (BRET). Because of the quantitative generation of NADPH with the oxidation of metabolite, the concentration of metabolites can be measured by the ratio of the emission intensities of NLuc and the fluorophore. All the reagents and buffer needed in the reaction can be pre-lyophilized on paper, allowing detection by a digital camera. The user only needs to add a drop of blood, thus


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simplifying the procedure and allowing integration with a microfluidic system. In this work, the authors used an oxidation reaction to produce NADPH for metabolite detection, in combination of synthetic chemistry and protein engineering to achieve point-of-care (POC) testing of metabolites in blood. The sensing approached can be used for the detection of a great variety of target molecules considering the fact that NADPH is a widely coenzyme in numerous oxidation reactions. This work represents an excellent example of how classic recognition reactions can be combined with new types of transducers for better detection and easier operation. In addition to the method using intensity value as a signal output method, barcoding methods allow for multiplexing, accurate and objective analysis.24 The “one code one target strategy” relies on barcode pattern rather than light wavelength to differentiate different targets, providing a highthroughput signal readout capability. Barcode technology presents graphic identifiers that arrange black stripes and blanks of different widths according to certain rules to encode express a set of information. Designing barcodes that can be simply integrated into microfluidic platforms is a breakthrough in the development of barcode-based output for microfluidics-based biosensing. Jiang’s group developed an efficient fabrication process for paper-based barcode chips (PBCs) for multiplexed immunoassays.25 The constant thickness of one sheet of paper comprises one barcode stripe, which can be stacked to form a series of barcodes representing the information of multiplex targets. They used three sheets to form the wide stripe, and one sheet for narrow stripe. The PBCs were pre-immobilized with capture probes, and skived into uniform pieces. Then, the PBCs were integrated into a lateral flow immunoassay (LFIA) system, allowing simultaneous detection of multiplex targets by a barcode scanner. In addition to Jiang’s modular design, there are also barcode technologies based on multiplex colors. Gu et al. developed an NIR-light-triggered dynamic barcode label, based on a striped color material self-assembled in capillaries (Figure 2B).26 These smart materials formed a heterogeneous striped pattern, precisely tailored by confined colloidal nanoparticle self-assembly parameters. Thus, the structural color hydrogel can form complex stripe pattern with multiple widths and colors as barcode label. Although this barcode technology was demonstrated, it remains challenging to be integrated into a wide variety of microfluidic biosensors.



Figure 2. New principles or methods of optical readout. (A) NADPH triggers ligand binding to receptor, thereby increasing BRET of fluorophore and NLuc. Reproduced from Yu, Q.; Xue, L.; Hiblot, J.; Griss, R.; Fabritz, S.; Roux, C.; Binz, P.-A.; Haas, D.; Okun, J. G.; Johnsson, K. Science 2018, 361, 1122-1126 (ref 23). Copyright 2018 The American Association for the Advancement of Science. (B) The structural color PNIPAM/rGO hydrogel stripes were used as barcode labels, which were dynamic under NIR scanning. b) monocolor stripes barcode, c) composite bicolor stripes barcode, and d) 2D stripes barcode. Reproduced from Zhao, Z.; Wang, H.; Shang, L. R.; Yu, Y. R.; Fu, F. F.; Zhao, Y. J.; Gu, Z. Z., Adv. Mater. 2017, 29 (46) (ref 26). Copyright 2017 John Wiley and Sons.

2.2 Magnetic Readout In addition to optical readout, a magnetic sensor is another appealing transducer, which is relatively resistant to external environmental interference. The changes or disturbances in magnetic fields can been detected by magnetic sensors. The most commonly used format is magnetoresistance, which involves the capability of a material to change its electrical resistance under an externallyapplied magnetic field. Wang’s group developed a series of microfluidic platforms with an array of magnetic sensors, employing the giant magnetoresistance (GMR) effect.27,28 These platforms can be applied not only in biomarker detection, but also binding affinity characterization (Figure 3A).29 With multi-channel design, these platforms are capable of multiplex analysis, enabling highthroughput readout, while keeping reactions under the same conditions. Unlike optical readout, which requires a light source and other optical components, magnetic readout can be integrated on the chip more easily.


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2.3 Temperature Almost all chemical reactions are accompanied by changes in heat, endotherms or exotherms. The change in heat is so small that it is lost in the noise of ambient temperatures and cannot be measured with a common thermometer. Thus, it is difficult to achieve signal output by direct detection of the temperature change of the recognition process. Therefore, a strategy is needed to link the concentration of targets with measurable change of temperature. Li’s group initially used a thermometer for biomolecule sensing (Figure 3B).30 They applied oxide nanoparticles (Fe3O4 NPs) tagged with detection antibody to form sandwich ELISA complexes. Then Fe3O4 NPs were dissolved in acidic conditions to release Fe3+, which reacted with potassium ferrocyanide to convert it to Prussian blue (PB) NPs. The formed PB NPs are highly sensitive near-infrared (NIR) laserdriven photothermal probes to convert the assay signal into heat through the photothermal effect, allowing quantitative readout of the immunoassay using a thermometer. To expand the range of targets, Lu’s group made use of target-responsive functional DNAs (e.g. structure-switching aptamer or RNA-cleaving DNAzyme) and photo-thermal nanoparticles encapsulated in liposomes, realizing quantitative detection of cocaine and UO2+.31 These systems are good examples of how the thermometer can be used affordably for signal output. In the future, for more accurate measurement of temperature change, the microfluidic device must also serve as thermal insulation to decrease noise from the external environment.

Figure 3. (A) Schematic of kinetic assay with the magneto-nanosensor platform. Serial dilutions of the complexes were flowed into four channels, where each channel contains six immobilized sensors, which act as “bait”. Reproduced from Lee, J.-R.; Bechstein, D. J. B.; Ooi, C. C.; Patel, A.; Gaster, R. S.; Ng, E.; Gonzalez, L. C.; Wang, S. X. Nat. Commun. 2016, 7 (ref 29). Copyright 2016 Springer Nature. (B) Schematic of the thermometer as the quantitative output relied on nanoparticle-mediated photothermal immunoassay. Reproduced from Fu, G.; Sanjay, S. T.; Dou, M.; Li, X. Nanoscale 2016, 8, 5422-5427



(ref 30). Copyright 2016 RSC Publicaion.

2.4 Distance Inspired by reading thermometers, distance-based readout has also been widely incorporated into various microfluidics systems. Distance-based signal outputs provide visualized and quantitative results without the need for external instruments.32-34 Measurable distance-based signals are generated by precipitation or aggregation, interactions between the cellulose substrate or predeposited chemical reagents to form products with limited mobility. The higher the target concentration, the greater the amount of reaction product produced, results in a greater output distance. For example, our group translated cocaine concertation into a bar chart reading by a cascaded invertase-GOx-HRP enzymatic amplification for brown poly(DAB) generation (Figure 4A).35 Different from previous analyses based on the same cascaded reaction performed step-bystep mixing, we mixed invertase, GOx, HRP, and substrate together for a one-step-initiated cascaded enzymatic amplification. The single step method not only simplifies the analysis process but also produces a larger distance signal than the step-by-step method. Very recently, Li et al. applied onchip electrophoretic titration (ET) of neutralization boundary (NB) and EDTA photo-catalysis for melamine detection. The EDTA, H2O2 and colorless leucomalachite green (LMG) were pre-added in the anode. Under UV light, EDTA photo-catalyzed H2O2 and LMG to form H2O and colored malachite green cation (MG+). In the electric field, MG+ migrated to a channel and was neutralized by base, producing a colorless product. When the target was present, H2O2 was consumed by the target, resulting in a decrease of MG+. Therefore, the higher the target concentration is, the lower the MB+ concentration and the shorter the output distance will be, resulting in a distance-based quantitative output.36 In addition to polymer production, the accumulation of microparticles can also form a visible bar for quantitative detection by the naked eye. Chen’s group quantitatively detected oligonucleotides with a dipstick-type bar readout using particle accumulation trapped by a microfluidic chip (Figure 4B).37 When targets were present, magnetic microparticles (MMPs), polystyrene microparticles (PMPs), and target oligonucleotides formed a complex, leaving free PMPs in inverse proportion to the target concentration. The MMP-target-PMP complexes were removed by capillary flow-driven microfluidic circuitry containing a magnetic separator, allowing free PMPs to be trapped at the narrowing nozzle downstream to form a visual bar. These distance-based biosensors are


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straightforward and simple. Nevertheless, they share the same challenge with colorimetric-based approaches where the results are susceptible to influence by real samples. Another strategy to develop a distance-based microfluidic biosensor is to measure the distance of ink bars derived by volumetric expansion from gas production reactions. Common gas production reactions are based on the decomposition of hydrogen peroxide to oxygen, catalyzed by catalases or nanoparticles. In a sealed microfluidic chip, the generated gas pushes the pre-filled dye to move along microchannels, thereby establishing a correlation between the travel distance and the target concentrations.38-40 In the last two years, there have been many modifications in this strategy, including use of nanoparticles, microchip design and applied fields. However, the relatively unsatisfactory reproducibility caused by batch effects of the microchip production, variation of catalyst activity, and human operation errors also need to be addressed.

2.5 Catalytic Small Molecule Production In addition to changes in physical parameters such as distance and temperature, translating molecular recognition signal into the generation of small molecules by target-triggered catalyzed reactions.41 For example, the enzyme horseradish peroxidase (HRP) capable of oxidizing various chromogenic substrates (e.g., TMB, DAB, ABTS) into colored products, has been widely used in ELISA or other biosensors. Similar to colored products oxidized by HRP, other small molecules produced by enzyme-catalyzed reaction can be read out by portable instruments. For example, glucose can be used as a signal molecule with a user-friendly glucose meter by integrating a targettriggered glucose-generation reaction.42,43 Zhang et al. demonstrated a lateral flow device using competitive assay for quantitative detection of analytes based on a personal glucose meter (Figure 4C).44 The analyte signal was transduced to glucose by an invertase-conjugated molecular recognition element. In addition, the glucose product can be further detected by chemical reaction. Tian et al. reported a method combining glucoamylase trapped in a stimuli-responsive DNA-based hydrogels and a paper-based microfluidic for POC detection.45 Upon target introduction, the released glucoamylase can catalyze the hydrolysis of amylose to produce glucose, which can be detected by further reactions to produce a visual color change. Apart from glucose, a gas generation reaction can also be applied for signal transduction. Lin et al. developed a lateral flow assay combined with a handheld pressure meter readout.46 Based on the production of O2 from the reaction



of H2O2 catalyzed by Pt nanoparticles, the pressure change can be read by a handheld pressure meter.47-50 Compared to microfluidic biosensors with distance output, the microfluidic biosensors which produce gases for readout have higher sensitivity and resolution. In order to increase the reading range, the range of distance variation can be increased, but it is limited by the volume of droplet or product.

Figure 4. New principles or methods based on distance readout (A & B) and small molecule production (C). (A) Schematic of the ID-Opad (Integrated Distance-based Origami paper analytical device). Reproduced from Tian, T.; An, Y.; Wu, Y.; Song, Y.; Zhu, Z.; Yang, C. ACS Appl. Mat. Interfaces 2017, 9, 30480-30487 (ref 35). Copyright 2017 American Chemical Society. (B) Schematic of particle accumulation in a microfluidic chip to form a visual bar readout. Reproduced from Zhao, Z. C.; Bao, Y. Y.; Chu, L. T.; Ho, J. K. L.; Chieng, C. C.; Chen, T. H. Lab Chip 2017, 17, 3240-3245 (ref 37). Copyright 2017 Royal Society of Chemistry. (C) Schematic of a lateral flow device for quantitative detection of analytes based on personal glucose meter. Reproduced from Zhang, J.; Shen, Z.; Xiang, Y.; Lu, Y. ACS Sens. 2016, 1, 1091-1096 (ref 44). Copyright 2016 American Chemical Society.

3. Microfluidics-based Biosensing Microfluidics-based biosensing can be classified in different categories on the basis of


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microfluidic system, including microchannel-based, discrete microfluidic-based and paper-based biosensing. There are many alternative classifications. Nevertheless, the aim of this review is not to give a comprehensive presentation, but rather to highlight the latest research progress in microfluidics-based biosensing. We will list several representative development examples of each category.

3.1 Microchannel-based Biosensing So far, microchannel-based microfluidics continues to represent the most widely used category of microfluidics. They are based on micro-scale continuous flow regimes and valves or pumps to manipulate a stream of fluid. Continuous-flow microchannel based biosensors have been developed for many years, realizing a variety of functions, such as sample collection, processing, analysis, and even integration of signal readout. There has been great interest in integrating and streamlining of the operation into one device have made microchannel-based biosensors popular in the users’ daily lives. In this section, we will describe representative examples of microfluidic biosensors implemented in microchannels with emphasized in wearable microfluidic biosensing and fully integrated microfluidic biosensing.

3.1.1 Wearable Microfluidic Biosensing Recent advances in microfluidics-based biosensing has emphasized the need for practicality.51 A convergence of advances in microfabrication, materials and electronic components is helping to develop the next generation of wearable microfluidic biosensors, which can adhere directly to the n nskin.52 Wearable biosensors developed in previous work mainly focused on the measurement of physical parameters, such as temperature, motion, strain, or potential, while wearable devices developed in the last two years can realize quantitative chemical or biochemical analysis, expanding wearable biosensing to health applications. The Rogers group reported an ultrathin, soft and flexible microfluidic biosensor that can continuously collect sweat exuding through surface skin pores.53,54 The device is composed of a three-subsystem assembly (Figure 5): (1) a skin-compatible adhesive layer for sweat harvesting, (2) a sealed microfluidic system containing polydimethylsiloxane (PDMS) microchannels and reservoirs filled with reagents for colorimetric analysis, and (3) a magnetic loop antenna along with near-field communication (NFC) electronics for transmitting



signals to a smartphone and image processing. The particular microchannel layer contains four independent circular chambers with chromogenic reagents for pH, lactate, chloride, and glucose concentration detection, and an orbicular serpentine channel surrounding the four reservoirs. The reservoirs and channel are connected by independent guiding channels to hole segments in the skincompatible adhesive layer. To avoid fluid obstruction caused by backpressure, all chambers and channels link to an outlet channel on the top-layer of the device. The patch with embedded biosensors can quantify the pH, and the concentrations of lactate, glucose, and creatinine by a chloride chromogenic reaction. To further expand the types of detection targets, fluorometric sensing was integrated in similar wearable microfluidic biosensors, allowing for in situ measurement of sodium and zinc in sweat.55 Although this field has developed rapidly in the past two years, the device is still unable to provide real time monitoring of changes in target concentration, which needs to be addressed in the future.

Figure 5. Schematic illustrations and images of a wearable microfluidic biosensor. (A) Schematic illustration of a wearable microfluidic biosensor and an enlarged image of the integrated NFC system (inset). (B) Illustration of three layers of the device. (C) Cross-sectional diagrams of the cuts. (D) Optical image of a fabricated device attached to the skin. Reproduced from Koh, A.; Kang, D.; Xue, Y.; Lee, S.; Pielak, R. M.; Kim, J.; Hwang, T.; Min, S.; Banks, A.; Bastien, P.; Manco, M. C.; Wang, L.; Ammann, K. R.; Jang, K.-I.; Won, P.; Han, S.; Ghaffari, R.; Paik, U.; Slepian, M. J.; Balooch, G., et al. Sci. Transl.


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Med. 2016, 8 (ref 53). Copyright 2016 The American Association for the Advancement of Science.

3.1.2 Fully Integrated Microfluidic Biosensing Thanks to reduced dimensions, microchannel-based biosensors can integrate complex laboratory procedures and achieve multiplex analysis in a small device. Because of their convenience and simplicity, fully integrated sample-in-answer-out microfluidics are now being rapidly developed. Optical transducers, particularly those that can be detected with the naked eye without additional light sources, can be easily integrated into the chip. This type of sensor has been developed continuously in the past few years. Recent development trend is to further process data with software on a mobile phone for more accurate results. Since a number of reviews have summarized this part, we will not discuss it in detail.56-58 Because of the miniaturization of the electrodes, electrochemical transducers are easy to be integrated on the microfluidic chip, and the improved electrode materials further enhanced their sensitivity. For example, John’s group modified manganese-reduced graphene oxide (Mn3O4-RGO) nanocomposite thin layer into electrode.59 The antibodies functionalized microelectrode was in lower-shape, offering large surface area for molecule loading. This nanocomposite working electrode, together with a reference and counter electrodes were integrated on the microfluidic chip for human cardiac troponin I detection (Figure 6A). With the improved reaction capacity at the sensor surface, the biosensor can reach a wide detection range from 0.008–20 ng/mL. Similarly, Dong et al. modified electrochemical working electrode with porous hierarchical graphene foam and electrospun carbon-doped titanium dioxide nanofibers (nTiO2), yielding high charge transfer resistance and great specific surface area.60 Immobilized with anti-ErbB2, this electrochemical biosensor realized highly sensitive detection of ErbB2 ranging from 0.1 fM to 0.1 M using pulse voltammetry. Compare to the intensity-based signal, distance-based readout can be designed to achieve spatial separation, realizing multiplex detection. Moreover, distance-based readout can also be detected by the naked eye, making it integratable into the microfluidic system. Li’s group developed a Multiplexed Bar-chart SpinChip (MB-SpinChip) for visual quantitative detection of multiple pathogens (Figure 6B).61 First, magnetic DNA-beads (aptamer-Pt nanoparticles hybridized with complementary-DNA-modified magnetic beads), H2O2 and dyes are pre-injected into the



corresponding recognition, amplification and indicator microwells. Then the spin unit is rotated to connect the sample, which allows a single sample injection to be distributed into four recognition microwells. After sample introduction, multiple hermetic reaction chambers are formed by manually rotating the sectorial spin unit. Meanwhile, the sample starts reacting with the preloaded aptasensor to form the pathogen-aptamer-PtNP complex. Under a magnetic field, the unbound aptamer is retained in the recognition microwell, while the complexes diffuse in solution. Finally, by holding the right edge of MB-SpinChip, the solutions in recognition microwells are shaken into amplification microwells for gas generation. Catalyzed by PtNPs, oxygen gas is quickly produced and drives the dyes traveling in different bar-chart channels. Different detection chambers contain different aptasensors targeting multiplex pathogens. Using this chip, three pathogens spiked in juice were specifically quantified with LOD of about 10 CFU/mL with visual quantitative readout. In addition, Qin’s group has been committed to the development of a Volumetric-bar-chart Chip (VChip) for POC testing. Recently, they reported an integrated Competitive V-Chip (CV-Chip), with different concentrations of controls at one end to realize multiplex detection of abused drugs.62

Figure 6. Schematic illustrations and images of fully integrated microfluidic biosensing. (A) Schematic illustration of the fabrication of microfluidic biochip embedded APTES-Mn3O4-RGO as a working electrode for detection of cardiac troponin I. Reproduced from Singh, N.; Ali, M. A.; Rai, P.; Sharma, A.; Malhotra, B. D.; John, R., ACS Appl. Mat. Interfaces 2017, 9 (39), 33576-33588 (ref 59). Copyright 2017 American Chemical Society. (B) Five patterned PMMA layers of the MBSpinChip, photograph and 3D schematic of an assembled MB-SpinChip. Reproduced from Wei, X.; Zhou, W.; Sanjay, S. T.; Zhang, J.; Jin, Q.; Xu, F.; Dominguez, D. C.; Li, X. Anal. Chem. 2018, 90,


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9888-9896 (ref 61). Copyright 2018 American Chemical Society. With the development of new materials, the optical or electrical properties of the materials have been improved, making them as the highly sensitive outputs integrated into microfluidic chips. However, the long-term stability and the ability to resist matrix interference of materials need to be further improved. On the other hand, the appearance of new signal output modes also expands the diversity of signal outputs of integrated chip, such as distance-based readout. Nevertheless, the new principle based signal output integrated on the chip is still sufficient, and more methods based on new principles are anticipated.

3.2 Discrete Microfluidics-based Biosensing Typical discrete microfluidics can be categorized into droplet and microarray modes, both of which can dispense sample into different microreactors for multi-functional chambers or massive parallel processing. Compared to the microchannel-based counterparts, discrete microfluidics-based biosensing system usually provides isolation of different functional areas or reaction chambers by oil phase, eliminating the need for complex design or fabrication, thus is more suitable for multistep or multi-parallel reactions.

3.2.1

Droplets as Discrete Functional Chambers

Microfluidics-based biosensors can use multiplex discrete regions for different steps or reactions, allow individual reactions taking place without interfering with each other. For example, a recent study by our group presented a handheld and low-cost POC platform, which integrates the entire ELISA process with distance readout.63 The poly (methyl methacrylate) (PMMA) chip is designed with several aqueous reservoirs containing ELISA reagents and wash buffer, physically separated by oil-filled reservoirs. Samples are loaded by pipetting, and immune complexes are formed and washed by tagging the capture antibody-modified magnetic beads through a series of reservoirs. Functional beads are then moved to the reservoir filled with H2O2 for gas generation catalyzed by PtNPs on beads. The evolved O2 pushes the pre-injected red dye into the channel to generate a detectable distance readout, which is related to the target concentration. This portable microfluidic chip allows integration of ELISA steps and visual distance output, and can be applied for biomarkers detection within 2 hours. In addition to forming multiple separated reactors, emulsion droplets offer interfaces that can be modified with functional modules for target recognition and signal transduction. For example,



nanopore sensing can be constructed in the interface between droplet and outside phase. It measures the ionic current traveling through the pore, which can identify the translocation or obstruction of molecules. Nanopore sensing can achieve label-free detection of analyte at the single-molecule level, making it an ideal signal output for biosensor. Takeuchi et al. developed a pesticide vapor sensor combining agarose-gel-based chip and aptamer based-nanopore sensing system.64 They formed a droplet interface bilayer (DIB) by droplet-contact method, and a sheet was inserted to separate the contact planes (Figure 7A). The electrodes were placed in each chamber and connected to the amplifier to measure the ionic current through a nanopore inserted in DIB. To absorb volatile organic compounds, a solidified aqueous phase formed by agarose is used as the interface between the droplets, rather than oil. This nanopore-based biosensor enabled highly specific and sensitive detection in a short period of time, which is useful for on-site applications. Besides static interfaces, dynamic complex emulsion droplets can also be applied as powerful liquid sensing particles. Swager’s group developed the interfacial bioconjugation manufacturing of liquid particles with intrinsic optical properties in large scale.65 The immiscible structured phases between internal hydrocarbon and fluorocarbon, form a dynamic interface for controlled interfacial reactivity. The target binding can introduce the internal morphological change, which can be visualized and detected by light transmission. They used these liquid particles for enzymes, antibodies, nucleic acids and carbohydrates biosensing.

3.2.2 Droplets as Massive Parallel Reactors Typical discrete microfluidics can provide millions of microreactors for massive parallel reaction and sensing as well. Since each reaction operates individually, this kind of biosensor can not only eliminate the interference between different reactions, but also ensure the uniformity of each reaction. Thus, this droplets-based separation allows a more sensitive and reliable measurement of analytes, which is critical for detecting low abundance targets. For example, Qiu et al. developed droplet microfluidics to study the heterogeneity of cytokine secretion at single-cell level.66 Immune cells were randomly encapsulated in droplets, with either single cell or no cell in each droplet, ensuring that the secretion by each cell is isolated in each droplet without interference between cells. And an aptamer based membrane-anchored sensor was developed, which can change from low to high fluorescence state when target protein is secreted, realizing real-time detection. In addition to study of single-cell heterogeneity, massive parallel


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droplets can also be applied in low abundance targets detection. Spoto’s group distributed 1 μL sample to 20 nL droplets containing aptamer modified gold nanoparticles, and identified lysozyme with a 44.6 femtomolar LOD using chemiluminescence.67 This droplet-based biosensor not only reduced the quantities of reagents, but also avoided non-specific adsorption of compounds in real samples on the biosensor solid surface. Although these discrete microchannel-based biosensors afford orders of magnitude higher sensitivity than conventional detection in bulk solution, there are several problems in the process of droplet formation, such as complex operation or sample waste. To this end, many advances in microreactor generation have been developed.68 For example, the Uchiyama group applied inkjet printing for producing microdroplets as the microreactors.69 Taking advantages of precise volume control and high throughput, inkjet printing allows monodisperse droplet to be generated and dispersed in an oil phase, without a complicated fabrication process. Compared to droplet formation methods, this online system avoids liquid transfer, thus decreasing cross-contamination and sample loss. Other techniques such as using a vibrating capillary,70 or based on an HPLC T-junction and capillary have also been reported.71 Currently, droplet generation method with easier operation and higher automation is a developing trend but remains as a pressing issue to be solved in the popularization of this technology.

3.2.3 Digital Microfluidics-based Biosensing Among the different microfluidic biosensors developed for simplification and user-friendliness, digital microfluidics (DMF) exhibits advanced automation and flexibility for performing multiplex and parallel operations. Hence, the DMF platforms are considered to have huge potential for industrialization of biosensing. DMF is a droplet-based microfluidic system based on the electrowetting-on-dielectric (EWOD) principle. Typically, the DMF platform is used in the sandwich configuration, consisting of a substrate patterned on an array of electrodes and a cover, usually as an ITO-modified glass slide with a hydrophobic layer, a transparent electrode, and droplets sandwiched by two plates. DMF systems use a direct current (DC) or alternating current (AC) voltage for droplet operation, to perform basic manipulations on an individual droplet (dispensing, transport, splitting, and merging), and to execute arbitrary complex operations. Recently, many studies have focused on enhancing and advancing these devices by combing this



platform with other techniques for biosensing. For example, our group integrated DMF with SERS for sensitive detection of disease biomarkers.72 Combining the advantages of the remarkable signal amplification and spatial resolution of the SERS-tagged-probe, and the automation capability of DMF, this SERS-based biosensing on a DMF chip realized sensitive, rapid, automated detection of disease biomarkers with low reagent consuming. Fluorescence biosensors can also be integrated with DMF systems, for example, using large-scale micro-goblet laser arrays or fluorescence-labeled molecular beacons acting as biosensors.73 Moreover, Duan et al. developed a multiplex and labelfree detection based on a DMF system with thin-film acoustic sensors (Figure 7B).74 A thin-film bulk acoustic wave sensor array (total thickness ∼5 μm) was stacked on a silicon substrate to form a solid-state microfluidic chip. The adsorption of biomolecules can cause a resonant frequency shift, that helps to monitor each step—an improvement over conventional ELISA, which does not output results until the final step. To expand the clinical use of DMF based biosensors, Wheeler’s group integrated tissue-liquid extraction and competitive ELISA for quantitative detection of estradiol directly from core needle biopsy (CNB) tissue samples.75 The system allowed each measurement to be performed in 40 min via on-chip tissue extraction and immunoassay, thus meeting the requirements for POC testing. Very recently, they further promoted the practical application of DMF-based biosensors by developing a new instrument (size: 25 cm × 20 cm × 28 cm, weigh: 4 kg), named the MeaslesRubella Box (MR Box) (Figure 7C a). MR Box contains a pogo pin linked to a DMF cartridge interface, an EMT, environmental sensors and a 12-V laptop power supply. This work included development of a portable control system and low-cost DMF cartridges (Figure 7C b) to realize parallel detection of measles and rubella immunoglobulin G from a small volume of capillary blood. To assess the practicality of this system, they conducted field testing in a refugee camp located in remote Kenya, the first report of DMF-based biosensor use outside of the laboratory.76 These results demonstrated a potential role for DMF-based biosensor in global serological surveillance, particularly in areas with limited access to centralized laboratories. Remarkable progress has been achieved towards developing multifunctional DMF-based biosensors. In the future, the fabrication of fine electrodes and dielectric features need to be further improved, to generate smaller droplets and transport them with higher accuracy. Also, the modification of surfaces for high durability and low cross contamination also needs to be considered.


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Figure 7. (A) Side view of nanopore-based biosensor for volatile organic compounds. Reproduced from Fujii, S.; Nobukawa, A.; Osaki, T.; Morimoto, Y.; Kamiya, K.; Misawa, N.; Takeuchi, S., Lab Chip 2017, 17 (14), 2421-2425 (ref 64). Copyright 2017 Royal Society of Chemistry. (B) Schematic of the chip with DMF based sensor and cross-sectional schematic of thin-film acoustic sensors. Reproduced from Zhang, M.; Huang, J.; Lu, Y.; Pang, W.; Zhang, H.; Duan, X. ACS Sens. 2018, 3, 1584-1591 (ref 74). Copyright 2018 American Chemical Society. (C) Photograph of the Measles-Rubella Box. Reproduced from Ng, A. H. C.; Fobel, R.; Fobel, C.; Lamanna, J.; Rackus, D. G.; Summers, A.; Dixon, C.; Dryden, M. D. M.; Lam, C.; Ho, M.; Mufti, N. S.; Lee, V.; Asri, M. A. M.; Sykes, E. A.; Chamberlain, M. D.; Joseph, R.; Ope, M.; Scobie, H. M.; Knipes, A.; Rota, P. A., et al. Sci. Transl. Med. 2018, 10 (ref 75). Copyright 2018 The American Association for the Advancement of Science.

3.3 Paper-based Biosensing Microfluidic paper-based devices (PADs), as an emerging class of microfluidic devices, have attracted great interest, because they make use of the inherent merits of paper, including low cost, portability and ideal biocompatibility, and they retain the characteristics of microfluidic devices such as miniaturization and integration.77-81 The porous structure of paper allows capillary-flow, enabling reagents storage, mixing and reaction, while eliminating the need for external instruments. Numerous reviews have summarized advances and achievements of PADs.33,82-86 Herein, we will discuss some novel technologies combined with PADs to provide new functionalities. PADs modified with nanomaterials have received extensive attention, because their outstanding physical and chemical characteristics provide conventional PADs with versatility and the potential for multifunctional performance. Graphene oxide (GO),87 carbon dots,88 fluorescent nanospheres (RNs),89 magnetic nanoparticles90 and quantum dots91,92 have been successfully



incorporated with PADs. For example, the different affinities of GO for fluorescent single-stranded DNA and double stranded DNA have served as an ideal platform for on-chip fluorescent analysis in PADs.93 In addition, while most nanomaterials modifications of PADs required chemical deposition of Au, an in situ nanoparticles growth and patterning approach has been developed for highly sensitive blood analysis.88 To achieve ultrasensitive analysis, new systems for signal transduction and amplification have also been constructed and combined with PADs. A case in point is the incorporation of the Cas enzyme system, the most revolutionary gene editing technology. Zhang et al. proposed an improved system named SHERLOCKv2 (specific high-sensitivity enzymatic reporter unlocking 2), which combined isothermal preamplification with CRISPR enzymology to achieve multiplex detection.94 The system made use of the specific cleavage ability of LwaCas13a, PsmCas13b, and CcaCas13b against independent dinucleotide reporters and the collateral activity of AsCas12a (Figure 8A). Targets were pre-amplified by RPA, followed by amplification by the Cas system, where reporters labeled with different fluorophores were cleaved by the corresponding Cas enzymes, achieving simultaneous detection of four kinds of DNA/RNA targets. To attain visual readout, LFTs were incorporated based on the destruction of FAM-biotin reporters. Such LFTs enabled detection of ZIKA or dengue virus RNA at concentrations as low as 2 attomolar. Moreover, the engineered system was also capable of detecting of an EGFR mutation from patient liquid biopsy samples. It is anticipated that more detection platforms based on Cas enzyme will be integrated with microfluidic systems for convenient and versatile biosensing. Aside from incorporation with new materials and amplification systems, PADs with new designs have also been widely reported. Integrated PADs enabling sample-in-answer-out analysis are advanced PADs that possess enhanced performances while maintain the simplicity of conventional PADs. For instance, the Whitesides’ group developed a PGM assisted threedimensional PAD (3D PADs) (Figure 8B).

95

After introducing a drop of blood to the sample

zone and incubation with reagents, the 3D PAD was folded like a greeting card, connecting the sample zone with the detection area and transferring the molecular signal into electronic signals detected by a glucose meter. The device was simple and user-friendly, achieving full sample-toanswer diagnosis. However, sometimes a simple folding of 3D PADs is not sufficient for complex analysis. To this end, manipulation of flow control for fluidic communication is quite vital.


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Prevailing methods for flow control include designing different shapes of channels,96 introduction of chemical reagents to change the viscosity of the liquid,97 and incorporation of controllable valves.98 Specifically, Chen et al. developed a versatile PAD with hollow rivets as movable valves.99 Compared with other strategies, the rivet-assisted valve was easy to fabricate, user-friendly and reusable, eliminating the need for any assistance by chemical reagents and was compatible with various applications. The fluid was controlled by connecting or disconnecting different layers of channels via the valve, enabling the liquid to stop or flow at the desired timepoints. The system was further applied for complicated ELISA reactions, where several steps, including antibody immobilization, sample incubation, washing and detection, were required. Similarly, the same group also developed another movable valve using plastic combining spines, the manipulation of which was similar to using a calendar.100 As a proof-of-concept, Fe (II) and NO2− in a local lake were measured based on a colorimetric method. Overall, with these simple, reusable and controllable valves, multiplex assays can be achieved by connection of different layers of channels, enabling controllable and efficient reagent mixing and reaction. In addition to serving as a filter or chromatograph stationary phase, paper has also been explored as a centrifuge. Inspired by whirligig toys, a hand-powered paper centrifuge was developed, which was capable of separating plasma from whole blood at an ultra-speed of 125000 rpm within 1.5 min (Figure 8C).101 The portable and ultralow-cost (20 cents) paper centrifuge serves as a powerful tool for POC diagnostics especially in resource limited regions. On the whole, the development of PADs provides ideal tools for portable and rapid biosensing, especially for POC testing. Conventional PADs restricted to simple analyses are no longer sufficient to meet the growing need for versatile biosensors. PADs combined with multifunctional nanomaterials or latest technology hold great potential for highly sensitive and accurate detection of a broad range of targets. In addition, integrated and miniaturized PADs enabling sample-inanswer-out are well-positioned for future personalized diagnosis. It is expected that incorporation of more novel technology and multi-discipline functionalities with PADs will lead to paper-based biosensors applicable in next-generation personalized diagnosis.



Figure 8. (A) Schematic of lateral-flow detection with SHERLOCK. Reproduced from Gootenberg, J. S.; Abudayyeh, O. O.; Kellner, M. J.; Joung, J.; Collins, J. J.; Zhang, F. Science 2018, 360, 439-444(ref 94). Copyright 2018 The American Association for the Advancement of Science (B) Schematic of a PGM assisted 3D PAD. Reproduced from Wang, C. C.; Hennek, J. W.; Ainla, A.; Kumar, A. A.; Lan, W. J.; Im, J.; Smith, B. S.; Zhao, M.; Whitesides, G. M. Anal. Chem. 2016, 88, 6326-6333 (ref 95). Copyright 2016 American Chemical Society. (C) Hand-powered paper centrifuge. Reproduced from Bhamla, M. S.; Benson, B.; Chai, C.; Katsikis, G.; Johri, A.; Prakash, M. Nat. Biomed. Eng. 2017, 1, 0009 (ref 101). Copyright 2017 Springer Nature.

4. Applications of Microfluidic Biosensing Currently, microfluidic biosensors are widely applied in many fields, including fundamental research and actual practice. In this section, we will not provide a comprehensive summary of application fields. Instead, we will point to the latest research progress and development trends, especially in the application of microfluidic biosensing for single cell analysis.

4.1 Nucleic Acids Microfluidic chips combined with a variety of output methods have extended the application of rapid nucleic acid detection.102 Teengam et al. reported a paper-based microfluidic platform combining the aggregation of pyrrolidinyl peptide nucleic acid (acpcPNA)-induced nanoparticles for colorimetric detection. In the presence of complementary DNA strands of PNA (peptide nucleic acid), the nanoparticles were dispersed due to the formation of a DNA-PNA duplex. Otherwise, the nanoparticles were aggregated by PNA without DNA. The proposed method exhibited an LOD of 1.53 nM for Middle East respiratory syndrome coronavirus (MERS-CoV) DNA, 1.27 nM for mycobacterium tuberculosis (MTB) DNA and 1.03 nM for human papillomavirus (HPV) DNA.103 McArdle et al. presented a centrifugal microfluidic device combined with electrocatalytic PtNPs for detection of microRNA-134 in plasma and cerebrospinal fluid with electrochemical output.104 Mayr et al. reported a device for the detection of DNA and RNA molecules without transcription


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and amplification using fluorescence readout. The target molecules were specifically stained and immobilized via a double hybridization of complementary Cy5-labeled probes and capture probes in a microarray. The ultra-sensitive fluorescence readout yielded a detection limit of 39 fM and 16 fM for DNA and RNA, respectively.105 Overall, microfluidic biosensors combined with different output strategies provide a variety of means for sensitive and accurate detection of nucleic acids, and will be the main methods of detection in future practice.

4.2 Proteins Immunoassays on microfluidic chips can generally shorten analysis time, reduce reagent consumption, and replicate experimental conditions precisely.106 Gao et al. reported a wash-free magnetic immunoassay for the detection of cancer markers using a SERS-based microdroplets biosensor. (Figure 9A).107 In the microdroplets, the free and magnetic-bar-bound SERS tags were segregated by splitting the microdroplets into smaller parts. The LOD of this biosensor was below 0.1 ng/mL, and it is expected to potentially be applied in early diagnosis of prostate cancer. Piraino et al. designed a multiplexed digital-analog microfluidic device for quadruplicate detection of 3-4 biomarkers in independent and isolated unit cells. The platform achieved single molecule detection with a detection limit of 10 fM for GFP in buffer and 12 fM in human serum. Additionally, the platform was applied in multiplexed detection of 1 pM anti-Ebola IgG and differentiation of three common Ebola strains.108 In addition to antibodies, nucleic acid aptamers are also widely used in bioanalysis due to their stability, easy modification, easy synthesis and high affinity. The application of aptamers on a chip usually combines different signal outputs. Jin et al. developed an impedimetric microfluidic analysis system using anti-Cry1Ab aptamer linked to magnetic beads. The LOD was 0.015 nM for concentration from 0 to 0.2 nM.109 Uddin et al. presented a microfluidic disc platform for the detecting of proteins. The aptamer-coated magnetic nano- or microbeads agglutinated in the presence of proteins, and detection was achieved using optomagnetic readout and optical imaging.110 Giuffrida et al. reported a digital microfluidic-based method using gold nanoparticleenhanced chemiluminescence with aptamer interaction for detection of human lysozyme.111 In general, the combination of biological recognition molecules and signal output modes is used in most microfluidic sensors to detect proteins. Integration of microchip and signal output mode of microfluidic sensors to realize integrated rapid identification and detection of biomarkers is an



important goal.

4.3 Small Molecules and Ions Due to their speed, accuracy and small size, microfluidic biosensors are also widely used for the detection of small molecules and ions. For example, the detection and supervision of illegal drugs is of great significance for the prevention of drug abuse. Krauss et al. developed a field-testing platform using a polyester-toner, rotation-driven microfluidic device along with a smartphone for presumptive identification of illicit drugs (Figure 9B). Use of a microfluidic device allowed simultaneous testing of multiple reaction chambers from a single input. The LOD’s for cocaine and methamphetamine were 0.25 and 0.75 mg/mL, respectively.112 Food safety monitoring has always been an important area for health concerns. Rapid and accurate monitoring of hazardous substances in food is of great significance. A droplet-based microfluidic immunosensor was reported by Choi et al. to achieve accurate and rapid detection of melamine, which has been illegally used as an adulterant in food products. In this assay, the competitive reaction was performed between native melamine and fluorescein isothiocyanateconjugated melamine against the anti-hapten antibody. The LOD of 300 ppb for quantification of melamine was obtained by detection with fluorescence polarization.113 Similarly, competitive immunoassay was used in paper-based microfluidic devices for detection of aflatoxin B1, a highly toxic foodborne substance. A point-of-need testing was demonstrated with an LOD of 1.31 ng/mL, showing potentials in small molecular testing in food safety monitoring.114


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Figure 9.

A) Schematic illustration of the SERS-based microdroplet sensor for wash-free magnetic

immunoassay. Reproduced from Gao, R.; Cheng, Z.; deMello, A. J.; Choo, J. Lab Chip 2016, 16, 10221029 (ref 107). Copyright 2016 Royal Society of Chemistry. B) Device design used to define detection threshold values for methamphetamine (two-step architecture) and cocaine (one-step architecture), with inset describing each feature of one domain. Reproduced from Krauss, S. T.; Remcho, T. P.; Lipes, S. M.; Aranda, R. t.; Maynard, H. P., 3rd; Shukla, N.; Li, J.; Tontarski, R. E., Jr.; Landers, J. P. Anal. Chem. 2016, 88, 8689-8697 (ref 112). Copyright 2016 American Chemical Society. C) Schematic illustration of the mode of action of the DNAzyme and envisioned paper device with the DNAzyme sensor. Reproduced from Ali, M. M.; Brown, C. L.; Jahanshahi-Anbuhi, S.; Kannan, B.; Li, Y.; Filipe, C. D. M.; Brennan, J. D. Sci. Rep. 2017, 7, 12335 (ref 121). Copyright 2017 Springer Nature.

4.4 Microorganisms Bacterial infection caused by contaminated food or water seriously threatens human health. Rapid and accurate detection of bacteria is of great value, and microfluidic biosensors provide a faster and more accurate means than traditional methods.115-117 For example, lateral flow assays are simple and rapid in detecting food- or water-related pathogens.118-120 Ali et al. developed a paper-based sensor for E. coli detection using preprinted composite ink, which included a fluorogenic DNAzyme probe, lysozyme and pullulan/trehalose sugars. (Figure 9C) In the presence of a target, the fluorogenic DNAzyme probe can recognize the bacteria and generate a fluorescence signal. The detection limit



for E.coli was 100 cells/mL within 5 minutes in a variety of sample matrices.121 Rapid and high-throughput detection of drug-resistant bacteria is urgently needed in laboratory and clinical diagnosis. Xu et al. developed a microfluidic chip for detection and antimicrobial susceptibility testing (AST) of multiple pathogens. The PDMS microchip was composed of cell culture chambers with embedded paper substrates and sample introduction channels. The paper substrate preloaded with chromogenic media and antimicrobial agents could achieve identification and AST assays of multiple pathogens based on dynamic changes in color in different chambers. The accuracy of the microchip showed a coincidence of 94.1% compared with a conventional approaches.40 Droplet microfluidics also been applied for detection and identification of bacteria. Scheler et al. reported the use of dodecylresorufin (C12R) as a metabolic marker of bacteria for bacteria in droplet microfluidic, which showed promise for high-throughput screening in microbiology.122

4.5 Cells There is an increasing interest in developing microfluidic biosensors for cell analysis to assess the cellular function and evaluate the biological responses toward different periods or conditions. 123,124

With the advantages of shorter analysis time, lower consumption of samples and reagents and

more reliable results, the integration of microfluidic with conventional analytical methods can potentially enhance the detection performance.125 There have been several great reviews summarizing the heterogeneous molecular and physical properties.126 Therefore, in this section we will mainly emphasize the design of the biosensing component, including biosensors for intact or secreted cell sections, and physical characterization. Development of an efficient biosensing platform capable of continuous monitoring of secreted biomarkers is important to evaluate their responses to stimuli from the outside environment or drugs. For example, Shin et al. designed a label-free microfluidic EC impedance spectroscopy (EIS) biosensor for cell-secreted liver biomarkers, including human albumin and glutathione-Stransferase-alpha (GSTα). This biosensor was integrated with a human liver-on-a-chip platform to measure the metabolic activity of the organoids for 7 days. The agreement of results with those of standard methods validated the accuracy of the sensing platform, which is potentially capable of long-term monitoring of human organoids in vitro.127 Riahi et al. reported a microfluidic bead-based


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electrochemical biosensor for in-line measurement of cell-secreted biomarkers. In this work, the biomarker-recognition molecules were immobilized on disposable magnetic microbeads. Microvalves were integrated in the microfluidic chip for programmable operations of the immunoassay. The quantification of biomarkers produced by hepatocytes was achieved during a continuous 5-day assessment.128 Multiplexed profiling of secreted biomarker at the single-cell level can provide a comprehensive assessment and full identification of the variations. Since the singlecell barcode chip (SCBC) was proposed, it has been applied in many studies. This platform combined a microchamber for single cell isolation and an antibody barcode array for secreted cytokine capture, and then used fluorescein-labeled antibody for signal readout. Recently, Xue et al. employed the SCBC to CAR-T polyfunctionality identification, enabling simultaneous analysis of 42 biomarkers and thousands of single cells in parallel (Figure 10A).129 Additionally, microfluidic biosensors can be used to study single-cell metabolites to obtain insights of intracellular molecular mechanisms. The Shi’s group applied a fluorescent glucose analog (2-NBDG) to evaluate the glucose uptake of metabolically active tumor cells, helping to identify rare metabolically active tumor cells in pleural effusion.130 The glucose analog enters a cell via glucose transporters, and is phosphorylated to produce the fluorescent 2-NBDG-6-phosphate, eventually converting the metabolic situation into an optical signal output. Li et al. also described a multicolor fluorescence detection-based microfluidic device (MFD-MD) for single-cell metabolomics study (Figure 10B).131 As representative small-molecule metabolites, hydrogen peroxide (H2O2), glutathione (GSH), and cysteine (Cys) were selected for single-cell and cell heterogeneity analysis. After cell lysis and separation of intracellular contents by microchip electrophoresis, the three fluorescence signals were observed by their corresponding fluorescent probes FS (H2O2 probe) and Cy-3-NO2 (GSH and Cys probe). This platform enabled simultaneous detection of three targets at the single-cell level, and expansion to more targets is a future goal. Using non-fluorescent labeling methods, such as metal element labeling, may be one of the potential development directions.132 In addition to identification and quantification of molecules, physiological characteristics can provide important information. Séverine Le Gac et al. combined a microfluidic biosensing platform with fluorescently-tagged phospholipid for measurement of lipid bilayer properties and ion channels (Figure 10C).133 The Tao group designed a microfluidic system containing micro-holes for single-



cell trapping, and described the binding-induced membrane deformation by optical imaging and thermodynamic modeling. Although these physiological signals provide valuable information, their connection and integration with molecular information is one of the problems that needs to be addressed in this field.

Figure 10. (A) Work flow for single-cell secreted protein profiling. (i) Schematic of single cell barcode chip. (ii) Automated imaging to detect single cells and fluorescence signals for protein secretion. (iii) Quantification of protein profile, statistics and advanced informatics. Reproduced from Xue, Q.; Bettini, E.; Paczkowski, P.; Ng, C.; Kaiser, A.; McConnell, T.; Kodrasi, O.; Quigley, M. F.; Heath, J.; Fan, R.; Mackay, S.; Dudley, M. E.; Kassim, S. H.; Zhou, J. J. Immuno.Ther. Cancer 2017, 5, 85 (ref 129). Copyright 2017 BioMed Central. (B) Schematic of the simultaneous detection of the metabolites H2O2, GSH, and Cys by MFD-MD at the single-cell level. Reproduced from Li, Q.; Chen, P.; Fan, Y.; Wang, X.; Xu, K.; Li, L.; Tang, B. J. A. c. Anal. Chem. 2016, 88, 8610-8616 (ref 131). Copyright 2016

American Chemical Society. (C) Schematic of the multi-parametric characterization of lipid membranes and ion channel recording. Reproduced from Greiving, V. C. S.; de Boer, H. L.; Bomer, J. G.; van den Berg, A.; Le Gac, S. Electrophoresis 2018, 39, 496-503 (ref 133). Copyright 2018 John Wiley and Sons.

5. Conclusion and Perspective The intersection of biosensors, microfluidics, and nanomaterials is expanding the frontiers of traditional disciplines in fundamental research and commercialization. This review has highlighted the new signal transduction and output methods and discussed recent progress and applications in microfluidic‑based biosensing. Most of the representative examples described here have been used in microfluidic systems, but it is believed that the latest technologies in chemistry and biology also can be applied in microfluidics-based biosensing. Moreover, portability, automation, low cost, high throughput, and mass production are the trends for future developments, which will promote the popularization and commercialization of microfluidics-based biosensing. Among microfluidics-


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based biosensors, wearable sensors are afield to pay particular attention in the future, especially in the development of sampling methods, properties of adhesive materials and flexible electronic devices. In our predictions, self-powered biosensors are the future trend of development, the power of which can be supplied by human body movement. It is predicted that the number of microfluidic sensors implanted in the human body will increase rapidly, where real-time information can be sensed and reported. Moreover, real-time transmission of information obtained from sensors to smart devices or data centers is also of particular interest, which can provide more accurate information. Further, although many examples exist for microfluidics-based biosensors detecting DNA/RNA and proteins, there are still many opportunities with new applications for ions, small molecules, organelles and cell detection. Overall, the field of microfluidics based biosensing is expected to continue to flourish with the development and breakthroughs in materials, chemistry, biology, electronics and manufacturing industry.

Biographies Dr. Yanling Song received her Bachelor degree in Chemical Biology from Xiamen University in 2010, and PhD in Chemical Biology from Xiamen University in 2016. From 2016 to 2018, she was an assistant professor in Fuzhou University, now Dr. Yanling Song joins in Professor Chaoyong Yang’s group at the institute of Molecular Medicine, Shanghai Jiao Tong University School of Medicine. Her current research focus on the development of microfluidic methods for molecular evolution, biosensing and liquid biopsy.

Ms. Bingqian Lin received her Bachelor degree from China University of Geosciences in 2013, and Master degree from Xiamen University in 2016. Now she is a PhD student in Professor Chaoyong Yang’s group at Xiamen University. Her research interests include development of microfluidic-based biosensors, molecular recognition, molecular engineering, and point-of-care testing.

Ms. Tian Tian received her Bachelor degree in Chemistry from Fuzhou University, China in 2014, and now is a PhD student in Professor Chaoyong Yang’s group at Xiamen University. Her research interests include development of paper-based biosensors and single cell analysis.

Ms Xing Xu received her Bachelor degree from East China Normal University, China in 2016 and now is a PhD student in Professor Chaoyong Yang’s group in Xiamen University. Her current research focuses on microfluidic-based single cell analysis.



Dr. Wei Wang received his Bachelor degree in Medicine from Shandong University (2008), and PhD in Biochemistry from the University of Missouri, Columbia (2014). From 2015 to 2017, he was a postdoc at Peking University. Dr. Wei Wang is now an assistant professor in Professor Chaoyong Yang’s group at the Institute of Molecular Medicine, Shanghai Jiao Tong University School of Medicine.

Mr. Qingyu Ruan received his Bachelor degree in Chemistry from Xiamen University, China, in 2015, now is a PhD candidate at the Collaborative Innovation Center of Chemistry for Energy Materials, College of Chemistry and Chemical Engineering, Xiamen University. His PhD research focuses on developing digital microfluidic systems for applied and fundamental research in singlecell analyses of integrative genome and transcriptome.

Ms. Jingjing Guo received her Bachelor degree in Chemistry from Nanjing University, China in 2015, and now is a PhD student in Professor Chaoyong Yang’s group at Xiamen University. Her research interests include molecular engineering and microfluidic-based biosensors.

Prof. Zhi Zhu received her Bachelor degree in Chemistry from Peking University in 2006 and her PhD in Analytical Chemistry from University of Florida in 2011. She joined Xiamen University, China as Assistant Professor in 2011 and Associate Professor since 2012. She won American Chemical Society DAC Graduate Fellowship in 2009, Chinese Government Award for Outstanding Students Abroad in 2010 and National Excellent Young Investigator Award in 2014. Her current research is particularly focused on molecular recognition, molecular engineering, and point-of-care testing.

Prof. Chaoyong Yang is a professor at Shanghai Jiao Tong University School of Medicine and Xiamen University. He received his PhD from University of Florida in 2006 and conducted his postdoctoral research at the University of California, Berkeley from 2006 to 2007. He won a Chinese Government Award for Outstanding Students Abroad (2005) and is the recipient of American Chemical Society DAC Graduate Fellowship in 2005, CAPA Distinguished Faculty Award in 2012, National Outstanding Young Investigator Award in 2013, Chinese Young Analyst Award in 2015 and Chinese Chemical Society-Royal Society of Chemistry Young Chemist Award in 2018. His current research is particularly focused on molecular engineering, molecular recognition, high throughput evolution, single cell analysis and microfluidics.

Acknowledgements We thank the National Natural Science Foundation of China (21735004, 2175024, 21775128,


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21435004, 21521004), and the Program for Changjiang Scholars and Innovative Research Team in University (IRT13036) for their financial support.

References (1) Jayanthi, V. S. P. K. S. A.; Das, A. B.; Saxena, U. Biosens. Bioelectron. 2017, 91, 15-23. (2) Wen, W.; Yan, X.; Zhu, C.; Du, D.; Lin, Y. Anal. Chem. 2017, 89, 138-156. (3) Lai, Y.; Wang, L.; Liu, Y.; Yang, G.; Tang, C.; Deng, Y.; Li, S. J. Biomed. Nanotechnol. 2018, 14, 44-65. (4) Du, Y.; Dong, S. Anal. Chem. 2017, 89, 189-215. (5) Fujii, S.; Nobukawa, A.; Osaki, T.; Morimoto, Y.; Kamiya, K.; Misawa, N.; Takeuchi, S. J. L. o. a. C. Lab Chip. 2017, 17, 2421-2425. (6) Kim, Y. S.; Raston, N. H. A.; Gu, M. B. Biosens. Bioelectron. 2016, 76, 2-19. (7) Tan, Y.; Tian, T.; Liu, W.; Zhu, Z.; Yang, C. J. Biotechnol. J. 2016, 11, 732-745. (8) Saidur, M. R.; Aziz, A. R. A.; Basirun, W. J. Biosens. Bioelectron. 2017, 90, 125-139. (9) Bazin, I.; Tria, S. A.; Hayat, A.; Marty, J.-L. Biosens. Bioelectron. 2017, 87, 285-298. (10) Zhou, Q.; Lin, Y.; Zhang, K.; Li, M.; Tang, D. Biosens. Bioelectron. 2018, 101, 146-152. (11) Li, X.; Zhai, T.; Gao, P.; Cheng, H.; Hou, R.; Lou, X.; Xia, F. J. N. C. Nat. Commun. 2018, 9, 40. (12) Zhang, H.; Zhang, H.; Aldalbahi, A.; Zuo, X.; Fan, C.; Mi, X. Biosens. Bioelectron. 2017, 89, 96106. (13) Farka, Z.; Jurik, T.; Kovar, D.; Trnkova, L.; Skladal, P. Chem. Rev. 2017, 117, 9973-10042. (14) Masson, J.-F. ACS Sens. 2017, 2, 16-30. (15) Hwang, H. J.; Ryu, M. Y.; Park, C. Y.; Ahn, J.; Park, H. G.; Choi, C.; Ha, S.-D.; Park, T. J.; Park, J. P. Biosens. Bioelectron. 2017, 87, 164-170. (16) Mehrotra, P. J Oral Biol Craniofac Res 2016, 6, 153-159. (17) Hu, B. F.; Li, J. J.; Mou, L.; Liu, Y.; Deng, J. Q.; Qian, W.; Sun, J. S.; Cha, R. T.; Jiang, X. Y. Lab Chip. 2017, 17, 2225-2234. (18) Varma, V. B.; Wu, R. G.; Wang, Z. P.; Ramanujan, R. V. Lab Chip. 2017, 17, 3514-3525. (19) Olanrewaju, A. O.; Ng, A.; DeCorwin-Martin, P.; Robillard, A.; Juncker, D. Anal. Chem. 2017, 89, 6846-6853. (20) Challa, P. K.; Peter, Q.; Wright, M. A.; Zhang, Y. W.; Saar, K. L.; Carozza, J. A.; Benesch, J. L. P.; Knowles, T. P. J. Anal. Chem. 2018, 90, 3849-3855. (21) Bandara, G. C.; Heist, C. A.; Remcho, V. T. Anal. Chem. 2018, 90, 2594-2600. (22) Delgado, S. M. T.; Kinahan, D. J.; Sandoval, F. S.; Julius, L. A. N.; Kilcawley, N. A.; Ducree, J.; Mager, D. Lab Chip. 2016, 16, 4002-4011. (23) Yu, Q.; Xue, L.; Hiblot, J.; Griss, R.; Fabritz, S.; Roux, C.; Binz, P.-A.; Haas, D.; Okun, J. G.; Johnsson, K. Science. 2018, 361, 1122-1126. (24) Du, K.; Park, M.; Griffiths, A.; Carrion, R.; Patterson, J.; Schmidt, H.; Mathies, R. Anal. Chem. 2017, 89, 12433-12440. (25) Yang, M.; Zhang, W.; Yang, J.; Hu, B.; Cao, F.; Zheng, W.; Chen, Y.; Jiang, X. Sci. Adv. 2017, 3, eaao4862. (26) Zhao, Z.; Wang, H.; Shang, L.; Yu, Y.; Fu, F.; Zhao, Y.; Gu, Z. Adv. Mater. 2017, 29, 1704569. (27) Lee, J.-R.; Sato, N.; Bechstein, D. J. B.; Osterfeld, S. J.; Wang, J.; Gani, A. W.; Hall, D. A.; Wang, S. X. Sci. Rep. 2016, 6. (28) Lee, J.-R.; Haddon, D. J.; Wand, H. E.; Price, J. V.; Diep, V. K.; Hall, D. A.; Petri, M.; Baechler,



E. C.; Balboni, I. M.; Utz, P. J.; Wang, S. X. Sci. Rep. 2016, 6. (29) Lee, J.-R.; Bechstein, D. J. B.; Ooi, C. C.; Patel, A.; Gaster, R. S.; Ng, E.; Gonzalez, L. C.; Wang, S. X. Nat. Commun. 2016, 7. (30) Fu, G.; Sanjay, S. T.; Dou, M.; Li, X. Nanoscale 2016, 8, 5422-5427. (31) Zhang, J.; Xing, H.; Lu, Y. Chem. Sci. 2018, 9, 3906-3910. (32) Rahbar, M.; Nesterenko, P. N.; Paull, B.; Macka, M. Anal. Chem. 2017, 89, 11918-11923. (33) Yang, Y. Y.; Noviana, E.; Nguyen, M. P.; Geiss, B. J.; Dandy, D. S.; Henry, C. S. Anal. Chem. 2017, 89, 71-91. (34) Yamada, K.; Citterio, D.; Henry, C. S. Lab Chip. 2018, 18, 1485-1493. (35) Tian, T.; An, Y.; Wu, Y.; Song, Y.; Zhu, Z.; Yang, C. ACS Appl. Mater. Interfaces 2017, 9, 30480-30487. (36) Li, W.-L.; Kong, F.-Z.; Zhang, Q.; Liu, W.-W.; Kong, H.; Liu, X.-P.; Khan, M.-I.; Wahid, A.; Saud, S.; Xiao, H.; Cao, C.-X.; Fan, L.-Y. Anal. Chem. 2018, 90, 6710-6717. (37) Zhao, Z. C.; Bao, Y. Y.; Chu, L. T.; Ho, J. K. L.; Chieng, C. C.; Chen, T. H. Lab Chip. 2017, 17, 3240-3245. (38) Tian, T.; Li, J.; Song, Y.; Zhou, L.; Zhu, Z.; Yang, C. J. Lab Chip. 2016, 16, 1139-1151. (39) Ma, Y.; Mao, Y.; Huang, D.; He, Z.; Yan, J.; Tian, T.; Shi, Y.; Song, Y.; Li, X.; Zhu, Z.; Zhou, L.; Yang, C. J. Lab Chip. 2016, 16, 3097-3104. (40) Song, Y.; Shi, Y.; Li, X.; Ma, Y.; Gao, M.; Liu, D.; Mao, Y.; Zhu, Z.; Lin, H.; Yang, C. J. Anal. Chem. 2016, 88, 8294-8301. (41) Wu, J. H.; Wang, C. H.; Ma, Y. D.; Lee, B. Lab Chip. 2018, 18, 1633-1640. (42) Xiang, Y.; Lu, Y. Nat. Chem. 2011, 3, 697. (43) Lin, B.; Liu, D.; Yan, J.; Qiao, Z.; Zhong, Y.; Yan, J.; Zhu, Z.; Ji, T.; Yang, C. J. ACS Appl. Mater. Interfaces 2016, 8, 6890-6897. (44) Zhang, J.; Shen, Z.; Xiang, Y.; Lu, Y. ACS Sens. 2016, 1, 1091-1096. (45) Tian, T.; Wei, X.; Jia, S.; Zhang, R.; Li, J.; Zhu, Z.; Zhang, H.; Ma, Y.; Lin, Z.; Yang, C. J. Biosens. Bioelectron. 2016, 77, 537-542. (46) Lin, B.; Guan, Z.; Song, Y.; Song, E.; Lu, Z.; Liu, D.; An, Y.; Zhu, Z.; Zhou, L.; Yang, C. Lab Chip. 2018, 18, 965-970. (47) Wang, Y.; Yang, L.; Li, B.; Yang, C. J.; Jin, Y. Anal. Chem. 2017, 89, 8311-8318. (48) Liu, D.; Tian, T.; Chen, X.; Lei, Z.; Song, Y.; Shi, Y.; Ji, T.; Zhu, Z.; Yang, L.; Yang, C. Analyst 2018, 143, 1294-1304. (49) Li, J.; Liu, F.; Zhu, Z.; Liu, D.; Chen, X.; Song, Y.; Zhou, L.; Yang, C. ACS Appl. Mater. Interfaces 2018, 10, 13390-13396. (50) Song, Y.; An, Y.; Liu, W.; Hou, W.; Li, X.; Lin, B.; Zhu, Z.; Ge, S.; Yang, H.-h.; Yang, C. Chem. Commun. 2017, 53, 11774-11777. (51) Zhang, D.; Liu, Q. Biosens. Bioelectron. 2016, 75, 273-284. (52) Bandodkar, A. J.; Jeerapan, I.; Wang, J. ACS Sens. 2016, 1, 464-482. (53) Koh, A.; Kang, D.; Xue, Y.; Lee, S.; Pielak, R. M.; Kim, J.; Hwang, T.; Min, S.; Banks, A.; Bastien, P. Sci. Transl. Med. 2016, 8, 366ra165. (54) Choi, J.; Kang, D.; Han, S.; Kim, S. B.; Rogers, J. A. Adv. Healthcare Mater. 2017, 6, 1601355. (55) Sekine, Y.; Kim, S. B.; Zhang, Y.; Bandodkar, A. J.; Xu, S.; Choi, J.; Irie, M.; Ray, T. R.; Kohli, P.; Kozai, N.; Sugita, T.; Wu, Y.; Lee, K.; Lee, K.-T.; Ghaffari, R.; Rogers, J. A. Lab Chip. 2018, 18, 2178-2186.


Page 32 of 36

Page 33 of 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(56) Chen, S.; Shamsi, M. H. Journal of Micromechanics Microengineering 2017, 27, 083001. (57) Xia, Y.; Si, J.; Li, Z. Biosens. Bioelectron. 2016, 77, 774-789. (58) Li, W.; Zhang, L.; Ge, X.; Xu, B.; Zhang, W.; Qu, L.; Choi, C.-H.; Xu, J.; Zhang, A.; Lee, H. Chem. Soc. Rev. 2018, 47, 5646-5683. (59) Singh, N.; Ali, M. A.; Rai, P.; Sharma, A.; Malhotra, B. D.; John, R. ACS Appl. Mater. Interfaces 2017, 9, 33576-33588. (60) Ali, M. A.; Mondal, K.; Jiao, Y.; Oren, S.; Xu, Z.; Sharma, A.; Dong, L. ACS Appl. Mater. Interfaces 2016, 8, 20570-20582. (61) Wei, X.; Zhou, W.; Sanjay, S. T.; Zhang, J.; Jin, Q.; Xu, F.; Dominguez, D. C.; Li, X. Anal. Chem. 2018, 90, 9888-9896. (62) Tang, R.; Yang, H.; Gong, Y.; You, M.; Liu, Z.; Choi, J. R.; Wen, T.; Qu, Z.; Mei, Q.; Xu, F. Lab Chip. 2017, 17, 1270-1279. (63) Liu, D.; Li, X.; Zhou, J.; Liu, S.; Tian, T.; Song, Y.; Zhu, Z.; Zhou, L.; Ji, T.; Yang, C. Biosens. Bioelectron. 2017, 96, 332-338. (64) Fujii, S.; Nobukawa, A.; Osaki, T.; Morimoto, Y.; Kamiya, K.; Misawa, N.; Takeuchi, S. Lab Chip. 2017, 17, 2421-2425. (65) Zhang, Q.; Scigliano, A.; Biver, T.; Pucci, A.; Swager, T. M. Bioorg. Med. Chem. 2018. (66) Qiu, L.; Wimmers, F.; Weiden, J.; Heus, H. A.; Tel, J.; Figdor, C. G. Chem. Commun. 2017, 53, 8066-8069. (67) Giuffrida, M. C.; Cigliana, G.; Spoto, G. Biosens. Bioelectron. 2018, 104, 8-14. (68) Li, X.; Zhang, D.; Zhang, H.; Guan, Z.; Song, Y.; Liu, R.; Zhu, Z.; Yang, C. Anal. Chem. 2018, 90, 2570-2577. (69) Zhang, W.; Li, N.; Koga, D.; Zhang, Y.; Zeng, H.; Nakajima, H.; Lin, J.-M.; Uchiyama, K. Anal. Chem. 2018, 90, 5329-5334. (70) Hu, Y.; Xu, P.; Luo, J.; He, H.; Du, W. Anal. Chem. 2017, 89, 745-750. (71) Chen, J.; Luo, Z.; Li, L.; He, J.; Li, L.; Zhu, J.; Wu, P.; He, L. Lab Chip. 2018, 18, 412-421. (72) Wang, Y.; Ruan, Q.; Lei, Z.-C.; Ling, S.-C.; Zhu, Z.; Zhou, L.; Yang, C. Anal. Chem. 2018, 90, 5224-5231. (73) Wondimu, S. F.; von der Ecken, S.; Ahrens, R.; Freude, W.; Guber, A. E.; Koos, C. Lab Chip. 2017, 17, 1740-1748. (74) Zhang, M.; Huang, J.; Lu, Y.; Pang, W.; Zhang, H.; Duan, X. ACS Sens. 2018, 3, 1584-1591. (75) Abdulwahab, S.; Ng, A. H. C.; Chamberlain, M. D.; Ahmado, H.; Behan, L.-A.; Gomaa, H.; Casper, R. F.; Wheeler, A. R. Lab Chip. 2017, 17, 1594-1602. (76) Ng, A. H.; Fobel, R.; Fobel, C.; Lamanna, J.; Rackus, D. G.; Summers, A.; Dixon, C.; Dryden, M. D.; Lam, C.; Ho, M. Sci. Transl. Med. 2018, 10, eaar6076. (77) Martinez, A. W.; Phillips, S. T.; Butte, M. J.; Whitesides, G. M. Angew. Chem., Int. Ed. 2007, 46, 1318-1320. (78) Zhang, H.; Qiu, X.; Zou, Y.; Ye, Y.; Qi, C.; Zou, L.; Yang, X.; Yang, K.; Zhu, Y.; Yang, Y. Sci. Transl. Med. 2017, 9, eaaf9209. (79) Zhang, D.; Gao, B.; Chen, Y.; Liu, H. Lab Chip. 2018, 18, 271-275. (80) Tang, R. H.; Yang, H.; Choi, J. R.; Gong, Y.; Feng, S. S.; Pingguan-Murphy, B.; Huang, Q. S.; Shi, J. L.; Mei, Q. B.; Xu, F. Crit. Rev. Biotechnol. 2017, 37, 411-428. (81) Safavieh, M.; Kanakasabapathy, M. K.; Tarlan, F.; Ahmed, M. U.; Zourob, M.; Asghar, W.; Shafiee, H. ACS Biomater. Sci. Eng. 2016, 2, 278-294.



(82) Yamada, K.; Shibata, H.; Suzuki, K.; Citterio, D. Lab Chip. 2017, 17, 1206-1249. (83) Gong, M. M.; Sinton, D. Chem. Rev. 2017, 117, 8447-8480. (84) Cunningham, J. C.; DeGregory, P. R.; Crooks, R. M. Annu. Rev. Anal. Chem. 2016, 9, 183-202. (85) Fernandes, S. C.; Walz, J. A.; Wilson, D. J.; Brooks, J. C.; Mace, C. R. Anal. Chem. 2017, 89, 5655-5665. (86) Tian, T.; Bi, Y. P.; Xu, X.; Zhu, Z.; Yang, C. Y. Anal. Methods 2018, 10, 3567-3581. (87) Hui, C. Y.; Liu, M.; Li, Y.; Brennan, J. D. Angew. Chem., Int. Ed. 2018, 130, 4639-4643. (88) Hu, S. W.; Qiao, S.; Xu, B. Y.; Peng, X.; Xu, J. J.; Chen, H. Y. Anal. Chem. 2017, 89, 2131-2137. (89) Qiu, Z. L.; Shu, J.; Tang, D. P. Anal. Chem. 2017, 89, 5152-5160. (90) Liu, X.; Zhang, C.; Liu, K.; Wang, H.; Lu, C.; Li, H.; Hua, K.; Zhu, J.; Hui, W.; Cui, Y.; Zhang, X. Anal. Chem. 2018, 90, 3430-3436. (91) Malhotra, K.; Noor, M. O.; Krull, U. J. Analyst 2018, 143, 3049-3058. (92) Kokkinos, C. T.; Giokas, D. L.; Economou, A. S.; Petrou, P. S.; Kakabakos, S. E. Anal. Chem. 2018, 90, 1092-1097. (93) Hui, C. Y.; Liu, M.; Li, Y.; Brennan, J. D. Angew. Chem., Int. Ed. 2018, 0. (94) Gootenberg, J. S.; Abudayyeh, O. O.; Kellner, M. J.; Joung, J.; Collins, J. J.; Zhang, F. Science. 2018, 360, 439-444. (95) Wang, C. C.; Hennek, J. W.; Ainla, A.; Kumar, A. A.; Lan, W. J.; Im, J.; Smith, B. S.; Zhao, M.; Whitesides, G. M. Anal. Chem. 2016, 88, 6326-6333. (96) Xu, G.; Nolder, D.; Reboud, J.; Oguike, M. C.; van Schalkwyk, D. A.; Sutherland, C. J.; Cooper, J. M. Angew. Chem., Int. Ed. 2016, 55, 15250-15253. (97) Wei, X.; Tian, T.; Jia, S.; Zhu, Z.; Ma, Y.; Sun, J.; Lin, Z.; Yang, C. J. Anal. Chem. 2015, 87, 4275-4282. (98) Jiang, Y.; Hao, Z.; He, Q.; Chen, H. RSC Adv. 2016, 6, 2888-2894. (99) Li, B.; Yu, L.; Qi, J.; Fu, L.; Zhang, P.; Chen, L. Anal. Chem. 2017, 89, 5707-5712. (100) Shangguan, J. W.; Liu, Y.; Wang, S.; Hou, Y. X.; Xu, B. Y.; Xu, J. J.; Chen, H. Y. ACS Sens. 2018, 3, 1416-1423. (101) Bhamla, M. S.; Benson, B.; Chai, C.; Katsikis, G.; Johri, A.; Prakash, M. Nat.Biomed. Eng. 2017, 1, 0009. (102) Zhang, L.; Ding, B.; Chen, Q.; Feng, Q.; Lin, L.; Sun, J. TrAC, Trends Anal. Chem. 2017, 94, 106-116. (103) Teengam, P.; Siangproh, W.; Tuantranont, A.; Vilaivan, T.; Chailapakul, O.; Henry, C. S. Anal. Chem. 2017, 89, 5428-5435. (104) McArdle, H.; Jimenez-Mateos, E. M.; Raoof, R.; Carthy, E.; Boyle, D.; ElNaggar, H.; Delanty, N.; Hamer, H.; Dogan, M.; Huchtemann, T.; Krtvelyessy, P.; Rosenow, F.; Forster, R. J.; Henshall, D. C.; Spain, E. Sci. Rep. 2017, 7, 1750. (105) Mayr, R.; Haider, M.; Thunauer, R.; Haselgrubler, T.; Schutz, G. J.; Sonnleitner, A.; Hesse, J. Biosens. Bioelectron. 2016, 78, 1-6. (106) Tang, C. K.; Vaze, A.; Rusling, J. F. Lab Chip. 2017, 17, 484-489. (107) Gao, R.; Cheng, Z.; deMello, A. J.; Choo, J. Lab Chip. 2016, 16, 1022-1029. (108) Piraino, F.; Volpetti, F.; Watson, C.; Maerkl, S. J. ACS Nano. 2016, 10, 1699-1710. (109) Jin, S.; Ye, Z.; Wang, Y.; Ying, Y. Sci. Rep. 2017, 7, 43175. (110) Uddin, R.; Burger, R.; Donolato, M.; Fock, J.; Creagh, M.; Hansen, M. F.; Boisen, A. Biosens. Bioelectron. 2016, 85, 351-357.


Page 34 of 36

Page 35 of 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(111) Giuffrida, M. C.; Cigliana, G.; Spoto, G. Biosens. Bioelectron. 2018, 104, 8-14. (112) Krauss, S. T.; Remcho, T. P.; Lipes, S. M.; Aranda, R. t.; Maynard, H. P., 3rd; Shukla, N.; Li, J.; Tontarski, R. E., Jr.; Landers, J. P. Anal. Chem. 2016, 88, 8689-8697. (113) Choi, J. W.; Min, K. M.; Hengoju, S.; Kim, G. J.; Chang, S. I.; deMello, A. J.; Choo, J.; Kim, H. Y. Biosens. Bioelectron. 2016, 80, 182-186. (114) Busa, L. S.; Mohammadi, S.; Maeki, M.; Ishida, A.; Tani, H.; Tokeshi, M. Analyst 2016, 141, 6598-6603. (115) Murdock, R. C.; Gallegos, K. M.; Hagen, J. A.; Kelley-Loughnane, N.; Weiss, A. A.; Papautsky, I. Lab Chip. 2017, 17, 332-340. (116) Oh, S. J.; Park, B. H.; Choi, G.; Seo, J. H.; Jung, J. H.; Choi, J. S.; Kim, D. H.; Seo, T. S. Lab Chip. 2016, 16, 1917-1926. (117) Chen, J. G.; Xu, Y. C.; Yan, H.; Zhu, Y. Z.; Wang, L.; Zhang, Y.; Lu, Y.; Xing, W. L. Lab Chip. 2018, 18, 2441-2452. (118) Raeisossadati, M. J.; Danesh, N. M.; Borna, F.; Gholamzad, M.; Ramezani, M.; Abnous, K.; Taghdisi, S. M. Biosens. Bioelectron. 2016, 86, 235-246. (119) Park, J.; Shin, J. H.; Park, J. K. Anal. Chem. 2016, 88, 3781-3788. (120) Zhao, Y.; Wang, H.; Zhang, P.; Sun, C.; Wang, X.; Wang, X.; Yang, R.; Wang, C.; Zhou, L. Sci. Rep. 2016, 6, 21342. (121) Ali, M. M.; Brown, C. L.; Jahanshahi-Anbuhi, S.; Kannan, B.; Li, Y.; Filipe, C. D. M.; Brennan, J. D. Sci. Rep. 2017, 7, 12335. (122) Scheler, O.; Kaminski, T. S.; Ruszczak, A.; Garstecki, P. ACS Appl. Mater. Interfaces 2016, 8, 11318-11325. (123) Zilionis, R.; Nainys, J.; Veres, A.; Savova, V.; Zemmour, D.; Klein, A. M.; Mazutis, L. Nat. Protoc. 2017, 12. (124) Song, Y.; Tian, T.; Shi, Y.; Liu, W.; Zou, Y.; Khajvand, T.; Wang, S.; Zhu, Z.; Yang, C. Chem. Sci. 2017, 8, 1736-1751. (125) Huang, W. F.; Chang, C. L.; Brault, N. D.; Gur, O.; Wang, Z.; Jalal, S. I.; Low, P. S.; Ratliff, T. L.; Pili, R.; Savran, C. A. Lab Chip. 2017, 17, 415-428. (126) Kaestli, A. J.; Junkin, M.; Tay, S. Lab Chip. 2017, 17, 4124-4133. (127) Shin, S. R.; Kilic, T.; Zhang, Y. S.; Avci, H.; Hu, N.; Kim, D.; Branco, C.; Aleman, J.; Massa, S.; Silvestri, A. J. A. S. Adv. Sci. 2017, 4, 1600522. (128) Riahi, R.; Shaegh, S. A.; Ghaderi, M.; Zhang, Y. S.; Shin, S. R.; Aleman, J.; Massa, S.; Kim, D.; Dokmeci, M. R.; Khademhosseini, A. Sci. Rep. 2016, 6, 24598. (129) Xue, Q.; Bettini, E.; Paczkowski, P.; Ng, C.; Kaiser, A.; McConnell, T.; Kodrasi, O.; Quigley, M. F.; Heath, J.; Fan, R. J. Immunother. Cancer. 2017, 5, 85. (130) Tang, Y.; Wang, Z.; Li, Z.; Kim, J.; Deng, Y.; Li, Y.; Heath, J. R.; Wei, W.; Lu, S.; Shi, Q. Proc. Natl. Acad. Sci. 2017, 114, 2544-2549. (131) Li, Q.; Chen, P.; Fan, Y.; Wang, X.; Xu, K.; Li, L.; Tang, B. J. A. c. Anal. Chem. 2016, 88, 86108616. (132) Han, G.; Spitzer, M. H.; Bendall, S. C.; Fantl, W. J.; Nolan, G. P. Nat. Protoc. 2018, 1. (133) Greiving, V. C. S.; de Boer, H. L.; Bomer, J. G.; van den Berg, A.; Le Gac, S. Electrophoresis 2018, 39, 496-503.



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