Recent Developments toward Real-Time Chemical Monitoring of

technologies in the last two years for clinical monitoring of chemicals. We have focused on methods that either give ..... Applications of less or non...
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Chemical Monitoring in Clinical Settings: recent developments towards real-time chemical monitoring of patients Marsilea Adela Booth, Sally Anne Nicola Gowers, Chi Leng Leong, Michelle L Rogers, Isabelle Camille Samper, Aidan Paul Wickham, and Martyn G. Boutelle Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04224 • Publication Date (Web): 30 Oct 2017 Downloaded from http://pubs.acs.org on October 31, 2017

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Chemical Monitoring in Clinical Settings: recent developments towards real-time chemical monitoring of patients Marsilea Adela Booth,ϒ§ Sally A. N. Gowers,ϒ§ Chi Leng Leong,ϒ§ Michelle L. Rogers,ϒ§ Isabelle C. Samper,ϒ§ Aidan P. Wickham,ϒ§ and Martyn G. Boutelle§* §Department

of Bioengineering, Imperial College London, London, SW7 2AZ, United Kingdom *Correspondence: [email protected]

Introduction This review covers references found in the recent literature (from 2015) describing clinically relevant chemical monitoring that either gives continuous chemical information in real time or has the near prospect of doing so. From the time of Paracelsus in the 16th century, medicine has believed in the value of levels of endogenous chemicals to detect disease and external chemicals, such as drugs, to cure disease. One of his most famous statements “only the dose makes the poison”1 is an analytical observation that the concentration of a drug determines the difference between a medicine and a toxin. The ability to use chemical information in clinical practice has been limited by the analytical technology available. Chemical monitoring is now vital in clinical settings as chemical changes can provide important information that can aid clinical decision-making. Current chemical analysis is typically conducted by taking repeated blood samples that are sent away to the central lab for analysis, sometimes taking several hours to obtain results. For some analytes, detection is moving towards being performed in the critical care unit. The next important stage is to provide chemical information on the same timescales as, for example, electrocardiograms or brain intracranial pressure. That is moment–by-moment realtime signals. Such chemical signals will enable two things: firstly, they will naturally combine with other real-time patient data to allow pattern recognition to detect the type of illness, and in particular the moment of deterioration in a patient. Secondly, they will allow the clinical care team to respond to the chemical level change with therapy, and critically to monitor the effectiveness of the therapy. For example, in the world of traumatic brain injury patients there is now consensus opinion advice that, in response to low brain glucose measured by microdialysis, the flux of glucose into the brain should be increased by increasing local blood flow or blood glucose concentration.2 Therapy administration and patient response can then be monitored dynamically. This will become what is known as standard-of–care; that is standard clinical practice. Advances in standard-of-care are limited by technological developments and then clinical trials to validate these developments. As such, research into novel analytical technologies that can improve patient care is of great importance.

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Clinical chemical measurement is potentially a huge area of research. Given that the field of chemical sensors is vast and this review is focused on recent advances in analytical chemical sensors for a clinical setting, coverage of novel sensor designs and applications will inevitably be incomplete. Nevertheless, here we highlight recent advances and promising new technologies in the last two years for clinical monitoring of chemicals. We have focused on methods that give either continuous real-time information, or are at least capable of rapid repeat measurement. From an analytical point-of-view, continuous or high-frequency repeated measurements are preferable as they allow changes in chemical markers to be identified early. In practice, this is not yet possible for all samples, however, we have highlighted technologies which either attempt to meet this need or have the potential to. Given the above, normal point-of-care technologies are beyond the scope of this review and have been summarised well elsewhere.3 Additionally, bodily samples such as cartilage and bone are being characterised,4,5 however, are not currently being regularly monitored in vivo so have not been included here. In this review we have first highlighted how analytical measurements can be used for therapeutic drug monitoring and can provide novel potential tools for personalised drug treatment. We have then summarised recent advances in the monitoring of disease markers and the presence or dysregulation of endogenous markers within different samples of interest. Invasive sampling such as blood collection or tissue excision can provide samples for chemical testing. Alternatively, non-invasive methods can be used to measure chemicals in bodily fluids such as tears, saliva, sweat, exhaled breath, urine and stool samples, which can be used as surrogates for invasive measurements. We have considered both invasive and non-invasive sampling, and the areas covered are summarised in Figure 1A. With non-invasive monitoring, metabolic markers are often targets of interest. Meanwhile, the detection of cancer biomarkers and other pathological markers are often performed via more invasive sample collection methods in blood and tissue. The typical timeline for chemical sensor translation from the lab to the clinic can be long (Figure 1B). This review focuses mainly on technologies at the early stages of this development process (indicated by the blue box in Figure 1B), between lab-based research and in vivo testing, however, which has the potential to progress to standardof-care.

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Figure 1 A. Schematic indicating the different bodily samples discussed in this review, with select common targets of interest for each listed below. B. Typical timeline progression of a chemical sensor to be used in clinical monitoring. Blue shading represents the approximate stages of the technologies covered in this review.

Drug Monitoring Therapeutic drug monitoring (TDM) may be required to control pharmaceutical dosage or to provide a personalised treatment course that is safe and effective, for example in cases where drug toxicity may be an issue. As most therapeutic drugs are transported in the bloodstream, blood sampling has become a common means of monitoring drugs, however, there is a growing trend for non-invasive methods of TDM. Progress in the development of novel biosensors and nanobiosensors aimed at TDM has been summarised elsewhere.6 Although chemotherapy drugs can be very effective in targeting cancer cells, they are usually administered systemically and can lead to severe side effects as they also target healthy cells. Therefore, it is essential that the correct dosage be given to obtain efficacy while minimising toxic effects. Jonas et al. have developed implantable microdevices that can deliver precise doses of different drugs locally, allowing the drug to interact with the living tumour. The device assays the local efficacy of a particular drug in vivo as a predictor of systemic efficacy.7 Similarly, Klinghoffer et al. have developed a novel technology platform that allows local delivery and simultaneous assessment of multiple drugs within a tumour in vivo.8 A high-throughput immunoassay has been developed for therapeutic monitoring of the chemotherapy drug tegafur, a pro-drug of 5-fluorouracil in plasma.9 Becher et al. have carried out a proof-of-concept study in head-and-neck cancer patients showing that monitoring of the drug cetuximab using mass spectroscopy can rapidly predict drug efficacy and adjust dosage if required.10 Aubé et al. presented a surface plasmon resonance (SPR)-based assay to detect antibodies raised in the sera of leukemia patients exposed to chemotherapeutic treatment with E. coli L-asparaginase.11 Monitoring antibodies against chemotherapy drugs using a similar sensing platform could provide a means to monitor immune responses to a range of different treatments. Recent work by Phairatana et al. have proposed using retrodialysis to deliver the chemotherapy drug carboplatin locally to the tumour.12 In this setup a second microdialysis probe is placed in the healthy tissue close to the tumour and the dialysate outflow is measured for the presence of carboplatin using a novel microfluidic sensing system. This would enable diffusion of carboplatin into surrounding healthy tissue to be tracked in order to tightly control chemotherapy treatment (Figure 2).

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Figure 2 A. Schematic of novel online carboplatin delivery and detection system. B. On-chip carboplatin calibration using differential pulse voltammetry (DPV) to detect increasing concentrations of carboplatin by reduction in the DPV peak height. Reproduced from Phairatana, T.; Leong, C. L.; Gowers, S. A. N.; Patel, B. A.; Boutelle, M. G. Analyst 2016, 141, 6270-6277,12 with permission of The Royal Society of Chemistry. Images copyright MGB group.

Digital microfluidics has been combined with magnetic bead-based immunoassays and tissue liquid extraction and integrated on a miniaturised platform to quantify oestradiol in milligram-sized core needle biopsies of breast tissue samples from patients with breast cancer before and after a course of treatment.13 This approach may prove useful to evaluate patient response and to tailor treatment of breast cancer. TDM can be useful in controlling dosage of various medications. Recently, Kivlehan et al. have investigated using a membrane-coated voltammetric sensor for the anaesthetic drug propofol towards creating a feedback controlled closed-loop system.14 The system was tested in human whole blood spiked with propofol and was integrated into a catheter for in vivo monitoring. Similarly, the dosage of the anticoagulant heparin should be tightly regulated during surgery and post-operative care as high doses and prolonged use can lead to serious side effects. A sensitive assay was developed for colorimetric and luminescent detection of heparin using gold nanoparticles functionalised with ruthenium (II) complexes.15 The assay was tested with fetal bovine serum, however, has yet to be tested in human biological fluids. TDM is particularly useful for diseases that require long-term medication, for example for depression, as drugs can accumulate in the body and can cause psychological dependence. Zhou et al. have developed a method of hollow fiber liquid–liquid–liquid microextraction and online sweeping micellar electrokinetic chromatography for the

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sensitive detection of five common second-generation antidepressants simultaneously in urine or plasma samples.16 Kang et al. have developed a reusable electrochemical sensor based on a graphene-chitosan composite to detect the antipsychotic medication clozapine.17 Bluett et al. have used HPLC-selected reaction monitoring mass spectrometry to monitor methotrexate, routinely administered for rheumatoid arthritis, with improved specificity and sensitivity over traditional immunoassays.18 In a proofof-concept study, the drug was tested in patient urine samples and could be detected 105 hours after administration, demonstrating that this methodology could be a potential tool to monitor adherence to therapy. Similarly, research developing minimally invasive methods for monitoring levels of antibiotics is of great interest in order to maximise efficiency while combatting the emergence of antimicrobial resistance, brought about in large part by the inappropriate use of antimicrobial agents. In order to address this issue, analytical techniques must be highly sensitive and be capable of detecting multiple antibiotics simultaneously, as ICU patients are typically treated with a cocktail of antibiotics. Pinder et al. have shown that high-performance liquid chromatography can be used to simultaneously monitor levels of the antibiotics piperacillin, meropenem, ceftazidime and flucloxacillin in human plasma.19 Recently, a minimally invasive device has been reported, which is aimed at monitoring beta-lactam antibiotics (such as penicillin) in real time.20 The device uses a microspike array coated with pH-sensitive iridium oxide and beta-lactamase immobilised in a hydrogel and has shown promising results in artificial interstitial fluid. The group of Wang have built a wearable stretchable electrochemical biosensor for organophosphate (OP) nerve agents based on a disposable glove.21 Based on selectivity given by organophosphate hydrolase the worn device can detect OPs on fruit. The group have since developed this technology into a minimally-invasive microneedle biosensor for transdermal monitoring of the nerve agent organophosphate.22 Novel work in the Lunte group has employed microdialysis coupled with microchip electrophoresis and electrochemical detection to carry out real-time monitoring of nitrite production following infusion of nitroglycerin in a freely moving sheep.23 This technology has great potential for monitoring a wide variety of chemicals in numerous clinical applications.

Blood The most commonly collected biological fluid for clinical testing is blood. Both as a body fluid itself, as well as the fact that it is pumped through all major organ systems, blood contains a wealth of clinical information. Key markers monitored clinically include chemicals that relate to the status of critical hospitalised patients such as pH, O2, CO2, electrolytes, glucose and lactate. Continuous monitoring of these has been thoroughly covered in a recent review by Frost and Meyerhoff.24 Similarly, there are various diagnostic point-of-care blood tests, many of which are one-off measurements hence beyond the scope of this review, and are covered in a recent review by Nayak et al.3 Targets such as bacteria or circulating tumour cells have not been included in this review unless RNA or DNA have been extracted, on the basis that it is not a ‘chemical’ that is being monitored. Glucose monitoring is obviously a major component of chemical sensing within blood. Many of these technologies have been well established and are standard-of-care for diabetics. Despite ubiquity, monitoring glucose in blood is invasive. Hence, non-invasive glucose sensing is of great interest, such as measurements performed in sweat or transdermal measurements.

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Blood as a sample possesses many challenges due to its numerous components, as well as its dynamic properties. Chemical sensors in blood in vivo are required to perform under a constantly changing physiological environment, with both active and passive responses. A promising method to overcome this is demonstrated by Plaxco et al. whereby a “dual-frequency” measurement approach allows calibration-free detection of selected drugs with electrochemical aptamer-based biosensors in awake, ambulatory animals.25,26 A further challenge with blood can be to detect compounds found in low concentrations within a plethora of blood components. Technology to combat this includes the development of ultrasensitive techniques, such as the single molecule array (Simoa) assay proposed by Wu et al. for detecting cytokines.27 By isolating capture magnetic beads into individual wells, very low limits of detection were achieved as only low concentrations of fluorescent product were required to reach a detectable range. Aubé et al. also address the complex matrix of blood when developing an SPR-based sensor to detect L-asparaginase antibodies in patient sera.11 The preliminary sensor was challenged due to the variability and composition of the sera samples. After addition of a secondary detection step, the SPR biosensor was able to be successfully applied directly to sera samples. Even in the case of non-implanted sensors, the stability of the biorecognition element (enzyme, DNA, antibody etc.) may present a limitation. Methods to engineer compounds to improve stability, such as that by Guo et al.,28 look to address this. Gaddes et al. use calorimetry via a quartz crystal resonator to detect urea, however, they separate the transducing element of the sensor from the sample solution.29 The sample interacts in a layer-by-layer enzyme-coated reaction column producing heat, which can be measured by a transducer component that is physically separated and located below the column. In this way, the transducer does not come into contact with the sample, providing a robust sensing system. A list of detection platforms designed for blood monitoring is displayed in Table 1, with a few highlighted examples for cancer detection discussed in detail below. Monitoring blood samples for cancer biomarkers can provide a means for earlier detection of a diverse range of cancers such as breast, colorectal, lung, and prostate cancer. Despite the level of prostate specific antigen (PSA) in blood being commonly used as a test for prostate cancer, it can be non-specific as elevated PSA levels may be caused by other factors. Cheng et al. use the ratio of free (non-protein bound) PSA (fPSA) to total PSA (t-PSA) as a way to provide improved discrimination between prostate cancer and benign prostatic hyperplasia, and propose a surface-enhanced Raman scattering (SERS)-based immunoassay to detect this (Figure 3).30 The SERSbased assay was tested on 30 clinical samples and simultaneous detection of free and protein-bound PSA was demonstrated. Parathyroid hormone-related peptide has been implicated in tumour and cancer progression. A multiplexed system is proposed by Otieno et al. as an ultrasensitive immunoassay via magnetic beads and an ink-jet printed gold nanoparticle chamber coupled with amperometric detection.31 The immunoassay, offering a simple, rapid, method to simultaneously detect multiple peptide fragments, was tested in serum samples from cancer patients with solid tumours and compared to cancer-free individuals. Meanwhile, targeted delivery for cancer detection is addressed by layer-by-layer coatings by Dang et al.32 Fluorescence imaging of rare-earth-based down-conversion nanoparticles provided optimal biological and optical performance for use as a diagnostic probe for high-grade ovarian cancer. This work indicates nanoparticles can serve as imaging tools for medical diagnostics and have the potential

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for monitoring tumour metastasis, treatment response and real-time imaging-guided surgery.

Figure 3. Schematic showing the sequential SERS-based assay process to detect free PSA (f-PSA) and complexed PSA (c-PSA) simultaneously. (i) Mixing of sample with t-PSA antibody-conjugated magnetic beads. (ii) Addition of SERS nano-tags able to form sandwich immunocomplexes with the magnetic beads for subsequent separation (iii) allowing simultaneous detection of (iv) f-PSA and (v) c-PSA. Reproduced from Cheng, Z.; Choi, N.; Wang, R.; Lee, S.; Moon, K. C.; Yoon, S. Y.; Chen, L.; Choo, J. ACS Nano. 2017, 11, 4926-4933.30 Copyright 2017 American Chemical Society.

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Target chemical Circulating tumour cell RNA27,33-36,

Exosomes37 C-reactive protein38,39

Separation technology and device Immunomagnetic beads27 Microfluidic chip33 Stainless steel wires34 Polypyrrole nanochip35,36 Immunomagnetic beads Oriented capture antibody38 Paper-based device39 Microsensor chip Magnetic nanoparticles

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Sensing methodology Optical fluorescence27 Nucleic acid sequencing33,34 Picogreen assay for DNA35,36

Blood fluid Whole blood27,33,34 Plasma,35,36

Blood plasma Blood

Carcinoembryonic antigen40,42

Microsensor chip40 Microspheres42

Parathyroid hormone-related peptide31 Tumour-associated antigens43 Serum ferritin for iron status44 Prostate specific antigen (PSA)30,40,45

Magnetic beads Capture antibodies Lateral flow immunoassay Magnetic beads30 Microsensor chip40 Microcontact imprinted chips45 Lateral flow assay46,47 Microfluidic chip with magnetic beads Aptamer-based

Optical fluorescence Surface plasmon resonance (SPR)-based39 Enzyme-linked immunosorbent assay (ELISA)39 Microring resonators Quantum dot barcode technology with optical readout Microring resonators40 Optical trapping and two-photon excitation fluorescence42 Multiplexed peptide assay Nanoplasmonic biosensor Mobile device-coupled SERS nano-tags and Raman spectroscopy30 Microring resonators40 Surface plasmon resonance (SPR) biosensor45 Optical detection46,47 Chemiluminescent assay Electrochemical biosensor

Microfluidic chip

Chromogenic assay fluorescence detection

Serum Serum Serum, saliva and urine Plasma

Fluorescence based assay51 SPR-based assay52 Detection by potentiometry

Plasma51 Serum and Urine52 Serum

Thyroid-stimulating hormone54 Sorbitol55 Cytokines56 Creatinine57

Microfluidics with a DNAChip51 Antibody-coated Au nanoprisms52 Ionophore based ion-selective membrane Antibody fragments Sorbitol dehydrogenase Magnetic beads Picric acid capped silver nanoparticles

Field-effect transistor (FET) biosensor Fluorescence of fibre-optic biosensor Single-molecule array (Simoa) based assay Colorimetric detection via absorption

Testosterone58

Capture antibodies and microfluidic chip

Serum Blood plasma Serum Serum and cerebrospinal fluid Serum and urine

Alpha-fetoprotein40 Hepatitis B Virus genome41

Phospholipases A2 (PLA2) and sPLA2-IB46,47 interleukin-8 (IL-8)48 Typtophan49 Anticoagulants, unfractionated herapin and low molecular weight herapin50 Cardiac biomarkers, cTnI, NT-proBNP51 and CTnT51,52 Chloride ions53

Competitive chemiluminescence enzyme immunoassay

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Human serum Serum or plasma Blood finger prick Serum

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Table 1 – Summary of target chemicals and analytical techniques used for blood or serum monitoring.

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Emerging technologies for monitoring chemicals in blood include a move towards integrated devices to improve whole blood processing without sample preparation.27,59,60 This allows rapid analysis60 in whole blood instead of serum, lower volume blood samples 59 and high-throughput sample analysis.27 In terms of targets, extracellular vesicles, which are intercellular communicators, allow cell-specific and hence information-rich targets61 which could be used in clinical cancer diagnosis37 and monitoring. Genomic sequences and other nucleic acid sequences, in particular when looking at circulating tumour DNA, are also being analysed for their abilities as cancer biomarkers.62 Applications of less or non-invasive technology for blood sample analysis, such as Raman spectroscopy, represent an emerging area of interest. This can be used to monitor blood glucose, covered in a recent review by Pandey et al.63 Other examples of Raman include SERS to detect targets,30,64 or fibre-enhanced Raman spectroscopy as an ultrasensitive method to diagnose anaemia and haemolytic disorders.65 Additionally, using Raman to measure the pH of blood via erythrocyte non-invasively in vivo in mice has been proposed.65 Monitoring the pH of arterial blood allows determination of the acid-base status, and can be performed using a confocal laser tweezers Raman system. Furthermore, Raman spectroscopy can be used for the non-invasive assessment of red blood cells collected from blood donation, and monitor lactate production from these cells during storage periods.66

Tissue Directly measuring within tissue using implanted sensors can provide the highest time resolution, as the sample is not diluted through sampling and extraction methods. However, the main challenges of implanted sensors include the stability of the sensor/tissue interface, in vivo calibrations and obtaining ethical approval, especially if the sensors contain biorecognition elements.

Brain Direct measurements in the brain started over 40 years ago when the Adams group used implanted electrochemical techniques to study neurochemicals in vivo.67,68 Following on from this work, the Ungerstedt group introduced the use of microdialysis sampling in the brain.69 In both cases, it was many years (20-40 years) before clinical use of these techniques was considered. To date, there are three main clinical methods of monitoring the brain, which fall in the categories of either non-invasive or invasive methods and will now be discussed.

Non-invasive neurochemical monitoring Near-infrared spectroscopy (NIRS) is popular for tissue oxygenation measurement as it is non-invasive. It is difficult however to make NIRS quantitative as only the product of the scattering coefficients for the two wavelengths used is known. Time-domain NIRS has been used in neonatal brain to measure median values of individual scattering coefficients and hence the concentrations of oxy- and deoxyhaemaglobin (103 µM and 42.6 µM, respectively).70 Continuous NIRS is becoming increasingly used for monitoring pre-term infants, although a recent Cochrane systematic review concluded that there were insufficient data to determine whether or not this had healthcare benefits.71 NIRS has also been used as part of a wearable 3D brain imaging system designed for astronaut use. The system combines NIRS oxygen with electrophysiology (brain,

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muscle, heart), temperature and accelerometers. The system dramatically shows a 10fold increase in blood volume during a simulated American football tackle compared to a shaking of the head indicating ‘no’.72 Non-invasive through skull neurochemical monitoring although highly desirable, is technically very challenging. Surface-enhanced spatially-offset Raman spectroscopy (SENSORS) has been tested in experimental skull models.73 Using principle component analysis through skull serotonin detection limits of 100 µm are claimed. Although too high for neuroscience use at present, this is a novel approach. A Raman signature has also been used to study the isolated brain following a traumatic brain injury model.74 The primary finding at two days in the injured core territory is not surprisingly a large increase in haemoglobin, with smaller increases in cholesterol and decreases in lipids. Similar changes are seen in the pericontusional core, which is perhaps more interesting, as it represents potentially salvageable tissue, but this is not the main focus of the paper.

Invasive neurochemical monitoring It has long been a goal to use the invasive methodologies developed for high time resolution experimental neurochemical monitoring in the human brain. This work is now coming to fruition. The two main approaches are implantable electrochemical sensors and microdialysis and will now be considered. Implanted electrochemical sensors Following a proof-of-concept study in a single patient,75 a team from University of Washington, Wake Forest and Virginia Tech have used carbon fibre electrodes (CFE) with fast-scan cyclic voltammetry (FSCV) in deep brain stimulation (DBS) patients to show that sub-second changes in human brain dopamine are associated with relief and regret in an investment game (Figure 4A).75 A team from the Mayo clinic have compared the performance of a carbon fibre electrode with that of the structurally more robust boron-doped diamond in human volunteers undergoing DBS and have recorded mechanically-stimulated release of the neurochemical adenosine (Figure 4B).76 This group have also demonstrated in rats, pigs and primates avoidance of stimulation artefacts by synchronising FSCV measurement of brain dopamine using carbon fibre electrodes with automatic DBS, allowing control of the dopamine released.77 Rigid electrodes are not ideal for longer term human brain monitoring as the brain moves during respiration. To address such concerns boron-doped diamond electrode elements have been successfully combined into flexible, biocompatible parylene films and used to detect dopamine.78 Adaptation of electrochemical biosensors for human use is, as yet, at an earlier stage. However, microfabrication technologies for brain biosensors have recently been reviewed.79 The Wightman group has developed novel combinations of voltage waveforms that allows measurement of brain oxygen, dopamine and local field potential in rapid succession at a carbon fibre electrode.80 It was tested during induced spreading depolarisations, a phenomenon that occurs spontaneously in the injured human brain. Given that this is the same electrode type used in the two human brain studies described above for dopamine, it can be considered very close to translation. A novel multiprobe device designed for monitoring electrophysiology, glucose and oxygen in the injured brain has been tested in experimental models.81

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A

higher bets

medium bets

lower bets

B

In vivo (Human)

normalized dopamine concentration (σ)

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Figure 4 A. Normalised dopamine concentration in the human brain during high, medium and low bets showing subsecond levels of dopamine can be used as a chemical surrogate for reward prediction error (RPE) relating to satisfaction in a risk-taking exercise. Two-way ANOVA shows a significant difference between dopamine responses for positive and negative RPEs for both higher and lower bets but not for medium-size bets. B. Diamond electrode response showing an adenosine-like signature secondary to mechanical stimulation in the subthalamic nucleus of an awake human undergoing DBS. Part A reproduced with permission from Proceedings of the National Academy of Sciences USA Kishida, K. T.; Saez, I.; Lohrenz, T.; Witcher, M. R.; Laxton, A. W.; Tatter, S. B.; White, J. P.; Ellis, T. L.; Phillips, P. E. M.; Montague, P. R. Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 200-205.75 Part B reproduced from Bennet, K. E.; Tomshine, J. R.; Min, H.-K.; Manciu, F. S.; Marsh, M. P.; Paek, S. B.; Settell, M. L.; Nicolai, E. N.; Blaha, C. D.; Kouzani, A. Z.; Chang, S.-Y.; Lee, K. H. Frontiers in Human Neuroscience 2016, 10, 102.76

Microdialysis The in vivo sampling technique of microdialysis was first introduced for clinical use some 25 years ago. Most studies collect hourly samples for conventional analysis and so are beyond the scope of this review. A recent consensus statement2 on the use of microdialysis for monitoring patients with acute traumatic brain injury (TBI) highlighted brain dialysate glucose levels and the lactate/pyruvate ratio as being of greatest clinical value. The relationship between dialysate concentrations and tissue concentrations is complex and depends on flow rates; in an elegant study the group of Sahuquillo has used extrapolation to zero flow in the human brain to reappraise estimates of basal brain concentrations of common energy metabolites.82 While online analysis of a dialysis stream is common in experimental studies, it is much rarer clinically. The Boutelle group first described clinical online microdialysis (rapid sampling microdialysis, rsMD, flow injection analysis at 30 second intervals) during monitoring of brain aneurysm surgery.83 With improved sensitivity and noise rejection, rsMD was extended to intensive care monitoring of TBI patients84 demonstrating that secondary insults to the injured human brain such as spreading depolarisations drive down brain glucose levels. Meanwhile, 3D-printed enzyme reactors for online analysis of microdialysate glucose and lactate have been described for animal use.85 Microfluidic chips incorporating ion-selective electrodes for potassium and biosensors for glucose and lactate have now been used to give moment-by-moment recording in continuous online microdialysis (coMD).86 The improved temporal resolution allows the chemical signature (glucose, lactate and potassium) of a human brain spreading depolarisation to

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be resolved as shown in Figure 5 A - B. The damage caused by microdialysis implantation has been successfully mitigated by infusion of the anti-inflammatory drug dexamethasone, greatly improving the speed of response and the long-term stability of continuous online microdialysis.87 Smaller microfabricated microdialysis probes have been developed by the Kennedy group.88 The 45 x 180 µm cross section probe is perfused at 100 nl/min and resolves 14 neurochemicals when coupled to LC-MS analysis (Figure 5 C-D). A 32 x 32 pixel FET array detector for microdialysis samples has been described.89 Analysis is currently for multiple ionic species, but the principle can be extended to other analytes. A combined microdialysis probe/deep brain stimulator/electro-recording device has been tested during epilepsy surgery.90 Alternative access to brain cerebral spinal fluid (CSF) has been provided by a flow monitor for a ventricular shunt placed in the human brain to treat hydrocephalus. Thermal dilution is used to measure CSF flow (in the range 3–200 mL/h) and importantly can detect shunt blockage, a potential clinical emergency. It is possible to imagine that such a system could incorporate neurochemical sensors in future. SERS has been combined with a microfluidic chip for highly sensitive drug detection,91 such an approach would make a highly flexible detection system for endogenous compounds in human microdialysate.

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Figure 5 A. Placement of a microdialysis probe in the injured human brain relative to the electrocorticogram (ECoG) strip electrodes. The arrows correspond to the potential paths of two spreading depolarisation (SD) waves. B. Metabolic and chemical signature of an SD wave recorded in a TBI patient. The increase in potassium indicates the depolarisation of the cells surrounding the microdialysis probe. The fall in the local concentrations of glucose and the rise in the level of lactate indicates that the demand for energy is outstripping the supply, thus creating a mismatch that is potentially harmful to the local tissue area. C. Layout of microfabricated microdialysis probe. The probe has three ports including an inlet, an outlet, and a spare port for other potential uses. The inset SEM images show (i) the cross-section of channels depicting the semicircular shape and thin polysilicon top layer, (ii) the porous anodic aluminium oxide membrane over sampling area, and (iii) the channel pattern at the sampling probe tip. D. Mass chromatograms of a representative fraction of microdialysis sample (1.5 μL) collected from the striatum of an anesthetised rat through the microfabricated probe showing 14 detectable neurochemicals: acetylcholine, choline, taurine, glutamine, serine, glucose, glutamate, phenylalanine, 5-hydroxyindoleacetic acid, homovanillic acid, tyrosine, 3,4-dihydroxyphenylacetic acid, 3,4-dihydroxyphenylalanine and dopamine. Part A-B reproduced from Rogers, M. L.; Leong, C.; Gowers, S.; Samper, I.; Jewell, S. L.; Khan, A.; McCarthy, L.; Pahl, C.; Tolias, C. M.; Walsh, D. C.; Strong, A. J.; Boutelle, M. G. J. Cereb. Blood Flow Metab. 2017, 37, 1883 – 1895,86 open access on SAGE. Part C-D reproduced from Lee, W. H.; Ngernsutivorakul, T.; Mabrouk, O. S.; Wong, J.-M. T.; Dugan, C. E.; Pappas, S. S.; Yoon, H. J.; Kennedy, R. T. Anal. Chem. 2016, 88, 1230-1237.88 Copyright 2016 American Chemical Society.

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Other tissue Detecting cancer Cancer biomarkers Detecting cancer biomarkers in tissue is a challenging research area and can be done using many techniques including electrochemistry, immunosensing or fluorescence. Recently, porous silicon nanoneedle arrays and fluorescently-labelled peptides have been combined to detect the cytosolic activity of Cathepsin B, a biomarker for a wide range of solid tumours. The device was successfully used to map human oesophageal cancerous tissue samples without altering the target tissue and holds promise to be coupled to an endoscope for bedside measurements.92 Another biomarker cytokeratin 17, of interest for lung cancer diagnosis, has been detected in a human lung biopsy with a newly developed packaged plasmonic optical fibre immunosensor that uses SPR.93 It could potentially lead to minimally invasive in vivo clinical evaluation of cancerous tissue. Another study94 reported the development of a real-time electrochemical biosensing system integrating molecular imprinting technology to detect carcinoembryonic antigen, a common marker for many cancers. The potentiometric sensor was applied to pancreatic cyst fluid samples from patients and validated against conventional ELISA. Measurements were obtained within 5 min and required only 1% of the volume needed for the ELISA test, showing promise for its use at the bedside for rapid diagnostics or decision-making during medical procedures. Raman spectroscopy Raman spectroscopic techniques have been investigated for numerous clinical diagnostic applications and represent a promising clinical tool for monitoring of cancerous tissue. A recent study95 developed an endoscopy-coupled Raman spectroscopy system to monitor colon tissue in vivo in real time on patients undergoing a routine surveillance endoscopy. The resulting spectra allowed discrimination between healthy patients and patients with inflammatory bowel disease as well as indication of the level of disease activity when present. Another subcellular Raman analysis approach96 was to subtract the cytoplasm from nucleus spectra to identify a Raman signature for rectal and colon tumour tissue. This new tool was applied to human tissue samples and was found to have excellent diagnosis performances. A novel method combining SERS with nanoparticle technology was also recently developed to look at glucose and lactate metabolism in microdialysis-sampled tumours of living rats.97 Mass spectrometry For the majority of solid tissue tumours, surgical removal remains the gold standard of care. The tumour is identified and together with a margin of healthy tissue, it is excised, although there is no visible guide for the surgeon to follow. The main problem is that the biopsy can only be assessed postoperatively via histological analysis. This surgical technique is therefore inadequate and cancerous cells could potentially remain in the patient. Rapid evaporative ionisation mass spectrometry (REIMS) is a technique that can identify tissue states based upon chemical composition. Coupling REIMS to tissue cutting tools that produce an aerosol can be achieved with minimal disruption to the surgical environment and protocols. Tools such as diathermy knives produce a vapour that has a chemical composition characteristic of the tissue that was cut.98 Passing this

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aerosol to the mass spectrometer allows for rapid and highly selective information to be passed continuously to the surgeon in less than two seconds.99 Mass spectrometric profiles are highly specific to the tissue cut allowing the surgeon to distinguish cancerous and healthy cells in real time. The border of the tumour is better defined, limiting the removal and damage to the surrounding healthy tissue. This coupling of REIMS with electrosurgical techniques is known as the intelligent knife (iKnife). Recently, it has also been used for the direct analysis of mucosal bacterial communities in the clinical setting, paving the way for real-time applications within mucosalassociated diseases.100 A non-destructive version, similar to the iKnife, has also recently been reported.101 The MasSpec Pen can analyse the molecular composition of tissue by applying a small water droplet to the surface and then analysing the droplet using mass spectrometry. In this way, the tissue does not need to be cut to produce an aerosol and therefore limits tissue destruction. The MasSpec Pen has been used so far in mouse models to identify cancerous tumours as well as ex vivo human tissue samples, showing the potential for online clinical applications. Another technique was recently developed by Chen et al. to perform in situ and in vivo endoscopic mass spectrometry with a novel sampling probe.102 The device combines a sampling motorised cotton thread with an industrial endoscope and a commercial mass spectrometer. It has been used to profile the liver of a living mouse and the surface of a volunteer’s tongue. This technology could potentially be used for tumour detection or to assess tissue during surgery or endoscopic therapies.

Transplant organs A different important approach in tissue monitoring is to sample the tissue for molecules of interest in order to simplify analysis. This can be done clinically using microdialysis. Viability assessment of transplant organs is imperative to the transplant process and this can be done using microdialysis. In a recent study,103 rapid sampling microdialysis was employed to monitor porcine kidney metabolism continuously by measuring cortex and medulla lactate levels in real time. This technique was validated as a tool to assess organ viability during preservation. A novel non-invasive approach to assess transplant kidneys has been to produce a conformal microfluidic device that fits the shape of the organ to sample molecular species from the cortex (Figure 6).104 The fluid collected from porcine kidneys was analysed for pathological biomarkers including heat shock protein 70 and kidney injury molecule-1. In another study, real-time monitoring of local levels of calcium ions and nitric oxide has been achieved simultaneously using a newly fabricated amperometric/potentiometric dual microsensor in a rat kidney.105 This technology could potentially be used in transplant kidneys to provide an indication of organ health. Ex vivo lung perfusion is an emerging preservation technique that allows lung assessment prior to transplantation by monitoring biomolecules in the perfusate.106 Detection of endothelin-1, a biomarker of lung function, is a valuable tool for surgeons as levels can provide a prediction of patient outcome. They have been measured in lung perfusate with a newly-engineered rapid competitive electrochemical assay.107 Organs can also be monitored for chemical heterogeneities within the tissue to gain information on their function. Microneedle arrays have been employed to monitor pH

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distribution in real time in an ex vivo rat heart during cycles of global ischaemia and reperfusion.108 A

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Figure 6 A. Schematic of a conformal microfluidic device for sampling biomarkers from the surface of whole organs (scale bar = 500 µm). B. Photograph of a 3D printed conformal microfluidic device. C. Photograph of the device used on a kidney with trans-channel microneedles. D. 3D topographical characterisation of the 3D printed conformal microchannel. Reproduced from Singh, M.; Tong, Y.; Webster, K.; Cesewski, E.; Haring, A. P.; Laheri, S.; Carswell, B.; O'Brien, T. J.; Aardema, C. H.; Senger, R. S.; Robertson, J. L.; Johnson, B. N. Lab Chip 2017, 17, 2561-2571,104 with permission of The Royal Society of Chemistry.

Muscle Rapid sampling microdialysis (rsMD) has been used to monitor muscle ischaemia in real-time during reconstructive free-flap surgery.109 A clinical microdialysis probe was inserted in the flap and the dialysate sample was monitored for glucose and lactate; the ratio of the two metabolites gave a dynamic measure of ischaemia. Superoxide release from skeletal muscle cells has been measured using nanoporous gold with immobilised cytochrome-c with a measured LOD of 3.7 nM.110 Continuous monitoring of mitochondria redox state in cardiac muscle has been made using resonance Raman to look ratiometrically at two electron transport chain redox couples. This has been used to predict cardiac dysfunction and cardiac arrest during ischaemia and reperfusion.111 This is an exciting development which could have applications in other tissues.

Subcutaneous and transdermal

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Gowers et al. showed microdialysis is an effective method of monitoring glucose and lactate continuously in subcutaneous tissue,112 however, the procedure is invasive and uncomfortable for the patient. In recent years, the use of semi-penetrative or transdermal microneedles has become increasingly popular, with many research groups investigating new ways to fabricate them as well as possible applications such as electrophysiology113-118 and drug delivery.119,120 Transdermal electrodes are minimally invasive, penetrating just through the first layer of skin and ideally not causing pain or drawing blood. Several groups have demonstrated the use of transdermal electrodes for continuous chemical monitoring. For example Sharma et al. have demonstrated the use of transdermal electrodes for monitoring glucose, lactate and theophylline121 (Figure 7 A-B). Mishra et al. have developed carbon-paste packed hollow microneedles for detecting OP nerve agents using organophosphorus hydrolase (Figure 7C-D).22 Other research groups have investigated other means of measuring metabolite concentrations via the skin. Hertzberg et al. employ a photothermal depth profiling method using infrared spectroscopy to monitor glucose non-invasively.122 The use of NIRS to measure transcutaneous oxygen during shock has been reviewed123 highlighting the value of new dark field instruments in real-time measurement of sublingual circulation.124 NIRS using three wavelengths has been shown to improve the stability of tissue oxygen measurements, but did not have sufficient sensitivity in vivo to detect changes in cytochrome aa3 following tissue ischaemia.125 NIRS was used to monitor acute limb compartmental syndrome in pigs and was able to detect significant changes in tissue oxygenation when tibial intracompartmental perfusion pressure varied and after fasciotomies.126 Other advances include a screen-printed tattoo skin sensor for measuring water content in the stratum corneum using impedance spectroscopy.127 Bandodkar et al. have shown in previous literature the use of tattoo based non-invasive monitoring of glucose.128 Miyamoto et al. have developed an on-skin, gas-permeable patch using nanomeshes to wirelessly detect temperature and pressure.129 Whilst this patch currently monitors physical properties, it has the potential to measure chemical quantitites and remains an important advancement in continuous clinical monitoring.

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Figure 7 A. Polycarbonate microneedle array sputter coated with 50 nm platinum. B. Optical coherence tomographic image showing polycarbonate microneedles embedded 800 μm into skin. C. Schematic of carbon-packed microneedles and their electrochemical detection of organophosphate nerve agents. D. Photograph of microneedle array; scale bar 10 mm. Part A-B reprinted from Sensing and Bio-Sensing Research, Vol.13, Sharma, S.; Saeed, A.; Johnson, C.; Gadegaard, N.; Cass, A. E. G. Rapid, low cost prototyping of transdermal devices for personal healthcare monitoring, pp. 104-108.121 Copyright 2017, with permission from Elsevier. Part C-D reproduced from Mishra, R. K.; Vinu Mohan, A. M.; Soto, F.; Chrostowski, R.; Wang, J. Analyst 2017, 142, 918-924,22 with permission of The Royal Society of Chemistry.

Wounds In situ monitoring of wounds has the potential to change wound management. Advances in new materials and flexible electronics have facilitated the development of point-ofcare sensors and smart dressings that can provide useful real-time diagnostic or theranostic information clinically. Using new and composite materials, e.g. pH-sensitive fluorescent dye-containing vesicles in a hydrogel film, infection-responsive coatings or surfaces that fluoresce when pathogenic wound biofilms are present have been developed.130 When incorporated into wound dressings and catheters, these smart dressings and catheters provide a visual warning of bacterial infection and potential intervention (e.g. blockage of catheter).130-132 Smart dressings utilising screen-printed biosensors have shown capability of detecting uric acid133 and bacterial toxins,134 reflecting the potential for electrochemical sensing in wounds for low-cost, selective and real-time monitoring of healing and in vivo infection signalling. Wireless communication has also facilitated the development of smart wound dressings. By coupling RFID technology in hydrogel membrane, Occhiuzzi et al. have demonstrated

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smart plasters that are able to collect and remotely transmit information about the condition of human skin.135 Recent advances such as stretchable electrodes that can conform to unique tissue geometries,136,137 self-assembling soft electronics138 and skinworn biofuel cells for epidermal energy harvesting139 are paving the way for selfpowered real-time continuous monitoring of wound healing.

Sweat Concentrations of analytes in blood plasma and sweat are related as the sweat glands have an excellent blood supply, although for some analytes sweat levels reflect underlying muscle activity. Microfiltration at the level of the sweat gland affects the size of molecules sampled. An excellent recent review of sweat monitoring was published by Heikenfield.140 Sweat can be collected non-invasively from the surface of a patient’s skin for analysis and easily integrated into a wearable device for continuous monitoring. For example, Sempionatto et al. have integrated a biosensor and an ion-selective electrode (ISE) for detecting lactate and potassium into the nosepads of eyeglasses with a Bluetooth transmitter to monitor sweat wirelessly and continuously.141 Similarly, Glennon et al. have developed a wireless wearable “watch” type device for monitoring sweat sodium content, integrated with a fluidic system to harvest sweat and store it for post-trial investigations.142 Notably, the Javey group have developed a flexible, wearable, wireless device for multiplexed sweat analysis (Figure 8A) that incorporates a detachable, disposable sensor array. The array has been developed for detecting calcium and pH.143 Additionally, a second array has been developed for glucose, lactate, potassium, and sodium (Figure 8B)144 Both sensor arrays incorporate a temperature sensor to calibrate the sensor response.143,144 Sweat is an attractive medium to monitor as it is non-invasive. However, given that a typical sweat gland produces sweat at 1-5 nL/min, sweat may not always be available, or not available in sufficient quantities for sensors to perform continuous measurement. One solution is to use microfluidic harvesting devices, as employed by Glennon et al.142 Another solution is to use iontophoresis to induce sweating. Emaminejad et al. have integrated an iontophoresis interface and chemical sensors into a single wearable platform for continuous sweat monitoring.145 The interface is used to electrically induce various sweat secretion profiles in real time and has been used for diagnosis of cystic fibrosis and for glucose monitoring.145 Sonner et al. have also realised a device that induces sweating by using carbachol to chemically stimulate sweat without hindering the ISE and impedance sensors used for sweat analysis.146 A microfluidic, multiple sensing patch for water and ions has been developed.147 The patch is soft and flexible and uses colorimetric sensors for water, glucose, lactate, chloride ions and pH, which can be quantified using the camera on a smartphone (Figure 8C-D). Anastasova et al. have also developed a wearable patch that uses microfluidics to deliver a continuous flow of sweat to sensors for continuous measurement of metabolites, electrolytes, and temperature.148 Lee et al. have developed a novel closed-loop device for glucose monitoring in sweat combined with multistage drug delivery via transdermal microneedles to control the patient’s glucose levels.120

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Figure 8 A. Image of a wearable sweat monitoring device comprising a flexible sensor array and flexible PCB. A schematic of the flexible sensor array demonstrating glucose, lactate, sodium, potassium and temperature sensors for sweat analysis is shown in B. C. An illustration of the top, middle, and back sides of a microfluidic device showing the microfluidic system, the colourimetric sensors (water, lactate, chloride, glucose, and pH) and the image processing reference markers. D. Image of the microfluidic device mounted on the forearm. Part A-B reprinted by permission from Macmillan Publishers Ltd: NATURE, Gao, W.; Emaminejad, S.; Nyein, H. Y. Y.; Challa, S.; Chen, K.; Peck, A.; Fahad, H. M.; Ota, H.; Shiraki, H.; Kiriya, D.; Lien, D.H.; Brooks, G. A.; Davis, R. W.; Javey, A., Nature 2016, 529, 509-514.144 Copyright 2016. Part C-D from 147. Reprinted with permission from AAAS.

Tears An electroanalytical system to detect damage to the eye by the release of high levels of ascorbic acid into the aqueous humour has been developed and tested with measurement directly in the low-volume tear film.149 The authors report the detection method as impedance, but in practice is consistent with amperometric oxidation of ascorbate in a two-electrode electrochemical cell. Results from a preliminary clinical study show promising results for a therapeutic readout that is efficient and reliable. Contact lenses are a very popular research area for the continuous monitoring of glucose concentrations due to their non-invasive nature. Currently there are few devices for patients, such as diabetics, to self-monitor their glucose levels. However, there are many limitations that need to be overcome before this technology can be used clinically, such as correlation of tear glucose with blood glucose and the high cost of production for a complex disposable analyser. The main emerging technology for measurements in tear fluid is the development of contact lenses that combine microfluidics, to capture tear fluid, and electrochemical sensor technologies, to analyse substrate concentrations, such as glucose. A contact lens incorporating microfluidics

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and glucose analysis, without putting stress on the eye has successfully been tested in animal studies where glucose blood level correlation was also assessed.150 An interesting way of sensing glucose using contact lenses is by using crystalline colloidal arrays embedded in hydrogel. Glucose combines with diols and borate ions changing the hydrogel volume, causing a shift in the Bragg refraction and the diffraction wavelength. This is read as a colour change by a spectrophotometer.151 This is a similar concept to that utilised by glucose-sensing contact lenses in the existing literature152,153 where concerns regarding interference of unwanted signals have been addressed in a similar holographic device based on boronic acids154,155 and initial clinical testing has been performed.152,154 Recently, relatively inexpensive designs are being produced, such as the integration of a poly(toluidine blue)-glucose oxidase amperometric biosensor and a microfluidic thread-based electroanalytical device, with measurements by micro-flow injection analysis.156 Here, a close correlation with blood levels was presented, showing great potential for future tear glucose monitoring. One drawback of this technology is the use of a conventional potentiostat and computer to make anlaysis possible. A multifunctional contact lenses that can monitor glucose and intraocular pressure simultaneously and independently has been designed to incorporate wireless technology.157 The sensors monitor glucose and pressure through the resistance and capacitance of the incorporated electronic device and the contact lens is wireless, through the use of an external antenna that is connected via a magnetic field when the reader coil is presented close to the contact lens.157 Multiplexing contact lens sensor technology in such a way shows promise for the development of continuous noninvasive clinical monitoring.

Saliva Saliva is an attractive non-invasive sample to monitor due to it being readily available, constantly produced and easy to obtain. Advances have been made to improve the detection of molecules in saliva, such as an antifouling bio-interface plasmonic sensor for HepB,158 and a salivary cortisol sensor using fibre optics.159 Additionally, advances include a new spectrophotometric assay method of measuring glutathione (a marker for oxidative stress),160 and a non-enzymatic method of detecting glucose in saliva demonstrating low detection limit and good resolution in low glucose concentration human saliva.161 Patients can willingly give a sample of saliva on demand. However, analysis of saliva samples taken from patients and sent off to a laboratory does not meet the requirements of clinical continuous monitoring. In order to monitor saliva continuously, rather than taking single measurements of saliva samples, several groups in recent years have adopted a mouthguard approach where biosensors are embedded into a wearable mouthpiece to perform continuous measurement of saliva. Kim et al. developed a wearable, wireless biosensor for detecting salivary uric acid (SUA) in the form of a mouthguard.162 The mouthguard can monitor SUA levels continuously in real time, transmitting information to a recipient via a Bluetooth transceiver. The device has been tested in vitro using artificial saliva, over a 4-hour operational period with measurements taken every 10 minutes, and performs with high stability, selectivity and sensitivity. Arakawa et al. have developed a similar mouthguard device that can

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perform real-time continuous monitoring of glucose in saliva.163 Incorporated into the mouthguard are the biosensor, potentiostat, and wireless transmitter to transmit the glucose readings to a nearby computer. At the time of writing the mouthguard sensor had not been tested in vivo, however, it has been tested in vitro using a ‘phantom jaw’ and artificial saliva, performing stable, long term (over 5 hours) continuous real-time monitoring. Petropolous et al. have recently developed a screen-printed, disposable lactate sensor for saliva, showing good correlation with a blood lactate sensor.164 Although testing took place in a laboratory, the design can be easily adapted for use in a clinical setting.

Breath Breath can be acquired non-invasively making it an easily accessible medium for continuous monitoring. To monitor breath continuously, the target analytes ideally need to be measured without impairing the patient’s breathing. Monitoring different analytes in breath can be difficult, involving complicated sampling methods. Breath humidity can be very variable during monitoring, which may affect the sensitivity of a particular sensing method. A common method of detecting analytes in breath is with mass spectrometry. This includes detecting the presence of biomarkers for different diseases.165 Gaugg et al. have used mass spectrometry to detect the oxidation products of fatty acids related to several different diseases in breath.166 Brock et al. have used a combination of proton transfer reaction time of flight mass spectrometry (PTR-TOF-MS) to detect the concentration of volatile organic compounds (VOC) and electrical impedance tomography to create cross sectional images of lungs, revealing correlations between VOC concentrations in breath and changes in ventilation of the lungs.167 As well as mass spectrometry, another popular method for monitoring breath is to observe changes in fluorescence. Using this method, Chien et al. have developed a ‘biosniffer’ to perform continuous monitoring of isopropanol,168 which has been identified as a disease marker.169-171 Ciaffoni et al. have combined the use of laser absorption spectroscopy with flow rate monitoring to provide a continuous measurement of oxygen consumption, rather than just oxygen concentration in exhaled breath.172 Despite being powerful analysis tools, mass spectrometers can be bulky, expensive, and not readily available in a clinical setting. Güntner et al. have developed an inexpensive acetone sensor to measure body fat burn rate in real time, showing results in good comparison to PTR-TOF-MS.173 Brannelly et al. have developed a screen-printed electrode for sensing ammonia in low-volume bodily samples, such as breath, inexpensively.174 Other developments in breath sensing include silicon nanowire FET sensor for disease detection through breath samples,175 and new sensor systems for detecting VOCs and volatile biomarkers related to cancers and oxidative stress.176,177

Urine & Stool Chemicals in body excrements, urine and stool, can provide a non-invasive window to assess health and detect malignancy of the body. Unlike in other bodily fluids where chemicals are fast changing and the continuous tracking of them could reveal important information, single or point-of-care measurements of chemicals in urine and stool have proven sufficient for diagnostic and health monitoring purposes. As the kidneys filter

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molecules from the bloodstream, urine is therefore a suspension of a large variety of markers reflective of the body processes and metabolism of ingested products. Renal function markers can be assessed electrochemically, such as creatinine by a potentiometric ISE.178 Using metal-organic frameworks with multi-walled carbon nanotubes, biosensors for dopamine and uric acids are fabricated to measure renal metabolism messengers and products.179 A calorimetric biosensing system coupling quartz crystal resonator and a disposable enzymatic cartridge can determine urea and demonstrates potential for a biofouling-free sensing system for multiple metabolites in urine.29 Immunosensors can be used to measure disease markers in urine noninvasively, for example inflammatory cytokines180 and protein markers linked to bladder cancer.181 Paper-based devices are also popular in urinary biomarker detection as seen by a colorimetric measurement of copper levels linked to Wilson’s disease using fluorescent paper,182 and paper-based metalloimmunoassay for a kidney disease marker.183 Recently, a rapid ligation-based recombinase polymerase amplification assay has been shown to provide rapid detection of aberrant chromosomal rearrangement in prostate cancer in urine in 60 minutes, paving the way for a faster diagnosis and an attractive surrogate for needle biopsies.184 New composite materials when integrated in urinary catheters can provide a visual detection of biofilm-led catheter blockage,131 prompting early intervention that will be beneficial to patients who require long-term bladder catheterisation. With the rise of miniaturised SERS, drug quantification in urine is also realised,185 making the determination of an optimum therapeutic dosage possible in the clinic. Analysis of stool samples helps to understand the gut microbiome by untargeted mass spectrometry.186 Identification of bacterial infections linked to gastric disorders is possible by microfluidic nucleic acid-based analysis and by ultrasensitive immunosensing system with voltage-controlled signal amplification.187,188

Future perspectives Since our first review in this area,189 we present in this review a dramatic increase in the progression of analytical technologies along the clinical translation time axis in Figure 1B. Interestingly, this progress is much greater than would have been predicted in 2013 on the basis of our future technology directions horizon scanning. We believe that a translational pathway has been established making it much faster to go from first biological studies to first human use. Specific funding streams now exist to support this crucial ‘bench-to-bedside’ first translational step, and critically, centres of clinical excellence are now more open to the idea of the first use of new technology. This raises the question of where is the barrier now? The answer lies in the availability of funding to demonstrate clinically what, from an analytical perspective, is self-evident: that a higher frequency of measurement sampling is better as it allows the clinical care team to detect much more quickly a deviation from previously ‘normal’ baseline values. As patterns of clinically relevant chemical changes are detected, it will be possible, for critically ill patients or in the operating room, to detect patient deterioration in real time. Just as importantly, it will be possible for clinicians to follow, in real time, the efficacy of their treatments as they are given. This raises the exciting possibility of using real-time analytical tissue measurement to personalise the dose of a drug, or intensity of therapy to the individual being treated. This is not without challenges. As analytical

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chemists, we are thrilled at seeing the real-time raw data; the clinical registrar looking at a patient data screen at 2.30 am is less thrilled. What they want is clear unambiguous answers –“…it’s ok”, “…things are starting to change”, “…we have a problem”. The analytical chemists working in this translational space have to implement that data pipeline building and information translation as well. We wait with great anticipation to see what future advances happen in the next couple of years.

Author Information Corresponding author Prof Martyn G Boutelle, Dept of Bioengineering, Imperial College London, UK * e-mail [email protected] Author contributions ϒ MAB, SG, CLL, MLR, ICS and APW contributed equally to this work. All authors wrote the manuscript, read and revised the manuscript. Conflicts of interest The authors declare no competing financial interests.

Dedication The MGB group dedicate this manuscript to Baby Wortley (Rogers)

Biographies Marsilea Adela Booth is a postdoctoral research associate with a Rutherford Foundation Freemasons Fellowship in the Bioengineering Department at Imperial College London under the joint direction of Prof. Martyn Boutelle and Prof. Molly Stevens. She obtained her BSc, Masters and PhD at the University of Auckland, New Zealand. Her research is focussed on the development and characterisation of novel delivery systems for regenerative medicine, as well as monitoring tissue and in vitro cell culture for metabolic markers. Sally A. N. Gowers is a postdoctoral research associate at Imperial College London under the direction of Prof. Martyn G. Boutelle and Dr. Danny O’Hare. She obtained her MChem at Oxford University and her PhD in Bioengineering at Imperial College London. Her research focuses on the development of wearable microfluidic biosensing systems for in vivo tissue monitoring. Chi Leng Leong is a postdoctoral research associate at Imperial College London under the direction of Prof. Martyn G Boutelle. She obtained her M.Eng. in Biomedical engineering and Ph.D. in Bioengineering at Imperial College London. Her research interests are in developing microfluidic devices and sensors for in vivo tissue chemical monitoring. She is currently working under jointly direction of Dr Xize Niu at the University of Southampton to build a wearable droplet microfluidic sensor for dermal tissue microdialysis funded by the EPSRC. Michelle L. Rogers is a postdoctoral research associate in the Department of Bioengineering at Imperial College London under the joint direction of Prof. Martyn G Boutelle and Dr. Danny O'Hare. She obtained her PhD in Bioengineering at Imperial College London. Her

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research focusses on the development of electrochemical biosensors for clinical and medical monitoring. Currently, she is developing a clinical monitoring system for patients undergoing free flap reconstructive surgery and a system for monitoring patients who are at high risk of compartment syndrome. Further to this, she is working on the development of a minimally invasive microneedle device for the detection of beta-lactam antibiotic concentration in the interstitial fluid of patients. Isabelle C. Samper was awarded an EPSRC International studentship to carry out a PhD in the Department of Bioengineering at Imperial College London under the direction of Prof. Martyn Boutelle. She obtained her Engineering Diploma at the National Superior School of Physics, Electronics and Materials (Phelma) at the Grenoble Institute of Technology, France. Her research focuses on engineering new portable wireless biosensing devices for clinical monitoring of tissue health using microdialysis and microfluidics. Aidan P. Wickham is a PhD student in the Department of Bioengineering at Imperial College London, UK, under supervision of Prof. Martyn Boutelle. His PhD is part of the EPSRC Neurotechnology Centre for Doctoral Training. He obtained his MEng at the University of Bristol and his MRes at Imperial College London. His research is focused on the development of wearable technology and data analysis for tracking the progression of Amyotrophic Lateral Sclerosis. Martyn G. Boutelle is Professor of Biomedical Sensors Engineering in the Department of Bioengineering, Imperial College London, UK where he is deputy departmental chair. His research focuses on the development of sensing technologies for real-time patient monitoring and actively collaborating with clinicians to use these technologies in basic clinical research. He obtained a BSc in Chemistry and PhD in Electrochemistry from Imperial College London, before working as a postdoc in the University Laboratory of Physiology and New Chemistry Department, University of Oxford.

Acknowledgements We wish to thank the following funding bodies. The Freemasons Foundation of New Zealand through the Royal Society of New Zealand-Rutherford Foundation (MAB), EPSRC (SG, CLL, ICS, APW), Imperial BRC (MLR). MGB wishes to acknowledge funding from EPSRC and NIH (R21 (NS092245)).

References (1) Paracelsus. dritte defensio 1538. (2) Hutchinson, P. J.; Jalloh, I.; Helmy, A.; Carpenter, K. L. H.; Rostami, E.; Bellander, B.M.; Boutelle, M. G.; Chen, J. W.; Claassen, J.; Dahyot-Fizelier, C.; Enblad, P.; Gallagher, C. N.; Helbok, R.; Hillered, L.; Le Roux, P. D.; Magnoni, S.; Mangat, H. S.; Menon, D. K.; Nordström, C.-H.; O’Phelan, K. H., et al. Intensive Care Med. 2015, 41, 1517-1528. (3) Nayak, S.; Blumenfeld, N. R.; Laksanasopin, T.; Sia, S. K. Anal. Chem. 2017, 89, 102123. (4) Bergholt, M. S.; St-Pierre, J. P.; Offeddu, G. S.; Parmar, P. A.; Albro, M. B.; Puetzer, J. L.; Oyen, M. L.; Stevens, M. M. ACS Cent. Sci. 2016, 2, 885-895. (5) Greenblatt, M. B.; Tsai, J. N.; Wein, M. N. Clin. Chem. 2017, 63, 464-474.

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(6) McKeating, K. S.; Aubé, A.; Masson, J.-F. Analyst 2016, 141, 429-449. (7) Jonas, O.; Landry, H. M.; Fuller, J. E.; Santini, J. T.; Baselga, J.; Tepper, R. I.; Cima, M. J.; Langer, R. Sci. Transl. Med. 2015, 7, 284ra257. (8) Klinghoffer, R. A.; Bahrami, S. B.; Hatton, B. A.; Frazier, J. P.; Moreno-Gonzalez, A.; Strand, A. D.; Kerwin, W. S.; Casalini, J. R.; Thirstrup, D. J.; You, S.; Morris, S. M.; Watts, K. L.; Veiseh, M.; Grenley, M. O.; Tretyak, I.; Dey, J.; Carleton, M.; Beirne, E.; Pedro, K. D.; Ditzler, S. H., et al. Sci. Transl. Med. 2015, 7, 284ra258. (9) Broto, M.; McCabe, R.; Galve, R.; Marco, M. P. Analyst 2017, 142, 2404-2410. (10) Becher, F.; Ciccolini, J.; Imbs, D. C.; Marin, C.; Fournel, C.; Dupuis, C.; Fakhry, N.; Pourroy, B.; Ghettas, A.; Pruvost, A.; Junot, C.; Duffaud, F.; Lacarelle, B.; Salas, S. Sci. Rep. 2017, 7, 2714. (11) Aubé, A.; Charbonneau, D. M.; Pelletier, J. N.; Masson, J.-F. ACS Sens. 2016, 1, 13581365. (12) Phairatana, T.; Leong, C. L.; Gowers, S. A. N.; Patel, B. A.; Boutelle, M. G. Analyst 2016, 141, 6270-6277. (13) Abdulwahab, S.; Ng, A. H. C.; Dean Chamberlain, M.; Ahmado, H.; Behan, L.-A.; Gomaa, H.; Casper, R. F.; Wheeler, A. R. Lab Chip 2017, 17, 1594-1602. (14) Kivlehan, F.; Chaum, E.; Lindner, E. Analyst 2015, 140, 98-106. (15) Li, J.; Cheng, M.; Li, M.-J. Analyst 2017, 142, 3733-3739. (16) Zhou, X.; He, M.; Chen, B.; Hu, B. Analyst 2015, 140, 1662-1671. (17) Kang, M.; Kim, E.; Winkler, T. E.; Banis, G.; Liu, Y.; Kitchen, C. A.; Kelly, D. L.; Ghodssi, R.; Payne, G. F. Biosens. Bioelectron. 2017, 95, 55-59. (18) Bluett, J.; Riba-Garcia, I.; Hollywood, K.; Verstappen, S. M. M.; Barton, A.; Unwin, R. D. Analyst 2015, 140, 1981-1987. (19) Pinder, N.; Brenner, T.; Swoboda, S.; Weigand, M. A.; Hoppe-Tichy, T. J. Pharm. Biomed. Anal. 2017, 143, 86-93. (20) Rawson, T. M.; Sharma, S.; Georgiou, P.; Holmes, A.; Cass, A.; O'Hare, D. Electrochem. Commun. 2017, 82, 1-5. (21) Mishra, R. K.; Hubble, L. J.; Martín, A.; Kumar, R.; Barfidokht, A.; Kim, J.; Musameh, M. M.; Kyratzis, I. L.; Wang, J. ACS Sens. 2017, 2, 553-561. (22) Mishra, R. K.; Vinu Mohan, A. M.; Soto, F.; Chrostowski, R.; Wang, J. Analyst 2017, 142, 918-924. (23) Scott, D. E.; Willis, S. D.; Gabbert, S.; Johnson, D.; Naylor, E.; Janle, E. M.; Krichevsky, J. E.; Lunte, C. E.; Lunte, S. M. Analyst 2015, 140, 3820-3829. (24) Frost, M. C.; Meyerhoff, M. E. Annu. Rev. Anal. Chem. 2015, 8, 171-192. (25) Arroyo-Currás, N.; Somerson, J.; Vieira, P. A.; Ploense, K. L.; Kippin, T. E.; Plaxco, K. W. Proc. Natl. Acad. Sci. U.S.A. 2017, 114, 645-650. (26) Li, H.; Dauphin-Ducharme, P.; Ortega, G.; Plaxco, K. W. JACS 2017, 139, 1120711213. (27) Ko, J.; Bhagwat, N.; Yee, S.; Black, T.; Redlinger, C.; Romeo, J.; O'Hara, M.; Raj, A.; Carpenter, E.; Stanger, B.; Issadore, D. Lab Chip 2017, 17, 3086-3096. (28) Guo, Z.; Murphy, L.; Stein, V.; Johnston, W. A.; Alcala-Perez, S.; Alexandrov, K. JACS 2016, 138, 10108-10111. (29) Gaddes, D. E.; Demirel, M. C.; Reeves, W. B.; Tadigadapa, S. Analyst 2015, 140, 80338040. (30) Cheng, Z.; Choi, N.; Wang, R.; Lee, S.; Moon, K. C.; Yoon, S. Y.; Chen, L.; Choo, J. ACS Nano 2017, 11, 4926-4933. (31) Otieno, B. A.; Krause, C. E.; Jones, A. L.; Kremer, R. B.; Rusling, J. F. Anal. Chem. 2016, 88, 9269-9275.

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(32) Dang, X.; Gu, L.; Qi, J.; Correa, S.; Zhang, G.; Belcher, A. M.; Hammond, P. T. Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 5179-5184. (33) Park, M. H.; Reátegui, E.; Li, W.; Tessier, S. N.; Wong, K. H. K.; Jensen, A. E.; Thapar, V.; Ting, D.; Toner, M.; Stott, S. L.; Hammond, P. T. JACS 2017, 139, 2741-2749. (34) Scherag, F. D.; Niestroj-Pahl, R.; Krusekopf, S.; Lücke, K.; Brandstetter, T.; Rühe, J. Anal. Chem. 2017, 89, 1846-1854. (35) Lee, H.; Jeon, S.; Seo, J. S.; Goh, S. H.; Han, J. Y.; Cho, Y. Biomaterials 2016, 101, 251257. (36) Jeon, S. H.; Lee, H. J.; Bae, K.; Yoon, K. A.; Lee, E. S.; Cho, Y. Theranostics 2016, 6, 828836. (37) Zhao, Z.; Yang, Y.; Zeng, Y.; He, M. Lab Chip 2016, 16, 489-496. (38) Vashist, S. K.; Schneider, E. M.; Luong, J. H. T. Analyst 2015, 140, 4445-4452. (39) Verma, M. S.; Tsaloglou, M. N.; Sisley, T.; Christodouleas, D.; Chen, A.; Milette, J.; Whitesides, G. M. Biosens. Bioelectron. 2018, 99, 77-84. (40) Washburn, A. L.; Shia, W. W.; Lenkeit, K. A.; Lee, S.-H.; Bailey, R. C. Analyst 2016, 141, 5358-5365. (41) Kim, J.; Biondi, M. J.; Feld, J. J.; Chan, W. C. W. ACS Nano 2016, 10, 4742-4753. (42) Li, C. Y.; Cao, D.; Qi, C. B.; Chen, H. L.; Wan, Y. T.; Lin, Y.; Zhang, Z. L.; Pang, D. W.; Tang, H. W. Biosens. Bioelectron. 2017, 90, 146-152. (43) Soler, M.; Estevez, M. C.; Villar-Vazquez, R.; Casal, J. I.; Lechuga, L. M. Anal. Chim. Acta 2016, 930, 31-38. (44) Srinivasan, B.; O'Dell, D.; Finkelstein, J. L.; Lee, S.; Erickson, D.; Mehta, S. Biosens. Bioelectron. 2018, 99, 115-121. (45) Ertürk, G.; Özen, H.; Tümer, M. A.; Mattiasson, B.; Denizli, A. Sens. Actuators, B 2016, 224, 823-832. (46) Chapman, R.; Lin, Y.; Burnapp, M.; Bentham, A.; Hillier, D.; Zabron, A.; Khan, S.; Tyreman, M.; Stevens, M. M. ACS Nano 2015, 9, 2565-2573. (47) Liu, N. J.; Chapman, R.; Lin, Y.; Bentham, A.; Tyreman, M.; Philips, N.; Khan, S. A.; Stevens, M. M. Nanoscale 2016, 8, 11834-11839. (48) Zirath, H.; Schnetz, G.; Glatz, A.; Spittler, A.; Redl, H.; Peham, J. R. Anal. Chem. 2017, 89, 4817-4823. (49) Majidi, M. R.; Karami, P.; Johari-Ahar, M.; Omidi, Y. Anal. Methods 2016, 8, 79107919. (50) Harris, L. F.; Rainey, P.; Lindahl, T. L.; Killard, A. J. Anal. Methods 2016, 8, 65006505. (51) Sonawane, M. D.; Nimse, S. B.; Song, K.-S.; Kim, T. Anal. Methods 2017, 9, 37733776. (52) Liyanage, T.; Sangha, A.; Sardar, R. Analyst 2017, 142, 2442-2450. (53) Pankratova, N.; Cuartero, M.; Jowett, L. A.; Howe, E. N. W.; Gale, P. A.; Bakker, E.; Crespo, G. A. Biosens. Bioelectron. 2018, 99, 70-76. (54) Gutiérrez-Sanz, Ó.; Andoy, N. M.; Filipiak, M. S.; Haustein, N.; Tarasov, A. ACS Sens. 2017, 2, 1278-1286. (55) Gessei, T.; Arakawa, T.; Kudo, H.; Mitsubayashi, K. Analyst 2015, 140, 6335-6342. (56) Wu, D.; Milutinovic, M. D.; Walt, D. R. Analyst 2015, 140, 6277-6282. (57) Parmar, A. K.; Valand, N. N.; Solanki, K. B.; Menon, S. K. Analyst 2016, 141, 14881498. (58) Wang, H.; Li, J.; Zhang, X.; Hu, B.; Liu, Y.; Zhang, L.; Cha, R.; Sun, J.; Jiang, X. Analyst 2016, 141, 815-819.

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(59) Kuan, D.-H.; Wang, I. S.; Lin, J.-R.; Yang, C.-H.; Huang, C.-H.; Lin, Y.-H.; Lin, C.-T.; Huang, N.-T. Lab Chip 2016, 16, 3105-3113. (60) Gao, R.; Cheng, Z.; deMello, A. J.; Choo, J. Lab Chip 2016, 16, 1022-1029. (61) Fuhrmann, G.; Herrmann, I. K.; Stevens, M. M. Nano Today 2015, 10, 397-409. (62) Abbosh, C.; Birkbak, N. J.; Wilson, G. A.; Jamal-Hanjani, M.; Constantin, T.; Salari, R.; Le Quesne, J. et al. Nature 2017, 545, 446-451. (63) Pandey, R.; Paidi, S. K.; Valdez, T. A.; Zhang, C.; Spegazzini, N.; Dasari, R. R.; Barman, I. Acc. Chem. Res. 2017, 50, 264-272. (64) Li, X.; Zhang, Y.; Xue, B.; Kong, X.; Liu, X.; Tu, L.; Chang, Y.; Zhang, H. Biosens. Bioelectron. 2017, 92, 517-522. (65) Yan, D.; Domes, C.; Domes, R.; Frosch, T.; Popp, J.; Pletz, M. W.; Frosch, T. Analyst 2016, 141, 6104-6115. (66) Atkins, C. G.; Buckley, K.; Chen, D.; Schulze, H. G.; Devine, D. V.; Blades, M. W.; Turner, R. F. Analyst 2016, 141, 3319-3327. (67) Adams, R. N. Anal. Chem. 1976, 48, 1126A-1138A. (68) Adams, R. N. Trends Neurosci., 1, 160-163. (69) Ungerstedt, U.; Pycock, C. B. Schweiz. Akad. Med. 1974, 30, 44-45. (70) Spinelli, L.; Zucchelli, L.; D, C.; Caffini, M.; Mehler, J.; Fló, A.; Ferry, A.; Filippin, L.; Macagno, F.; Cattarossi, L.; Torricelli, A. Neurophotonics 2017, 4, 041414. (71) Hyttel-Sorensen, S.; Greisen, G.; Als-Nielsen, B.; Gluud, C. Cochrane DB Syst. Rev. 2017, 9. (72) Strangman, G. E.; Ivkovic, V.; Zhang, Q. J. Appl. Physiol. 2017, ASAP. (73) Moody, A. S.; Baghernejad, P. C.; Webb, K. R.; Sharma, B. Anal. Chem. 2017, 89, 5688-5692. (74) Surmacki, J. M.; Ansel-Bollepalli, L.; Pischiutta, F.; Zanier, E. R.; Ercole, A.; Bohndiek, S. E. Analyst 2017, 142, 132-139. (75) Kishida, K. T.; Saez, I.; Lohrenz, T.; Witcher, M. R.; Laxton, A. W.; Tatter, S. B.; White, J. P.; Ellis, T. L.; Phillips, P. E. M.; Montague, P. R. Proc. Natl. Acad. Sci. U.S.A. 2016, 113, 200-205. (76) Bennet, K. E.; Tomshine, J. R.; Min, H.-K.; Manciu, F. S.; Marsh, M. P.; Paek, S. B.; Settell, M. L.; Nicolai, E. N.; Blaha, C. D.; Kouzani, A. Z.; Chang, S.-Y.; Lee, K. H. Front. Hum. Neurosci. 2016, 10, 102. (77) Lee, K. H.; Lujan, J. L.; Trevathan, J. K.; Ross, E. K.; Bartoletta, J. J.; Park, H. O.; Paek, S. B.; Nicolai, E. N.; Lee, J. H.; Min, H.-K.; Kimble, C. J.; Blaha, C. D.; Bennet, K. E. Sci. Rep. 2017, 7, 46675. (78) Fan, B.; Zhu, Y.; Rechenberg, R.; Rusinek, C. A.; Becker, M. F.; Li, W. Lab Chip 2017, 17, 3159-3167. (79) Weltin, A.; Kieninger, J.; Urban, G. A. Anal. Bioanal. Chem. 2016, 408, 4503-4521. (80) Hobbs, C. N.; Johnson, J. A.; Verber, M. D.; Mark Wightman, R. Analyst 2017, 142, 2912-2920. (81) Li, C.; Limnuson, K.; Wu, Z.; Amin, A.; Narayan, A.; Golanov, E. V.; Ahn, C. H.; Hartings, J. A.; Narayan, R. K. Biosens. Bioelectron. 2016, 77, 62-68. (82) Sánchez-Guerrero, A.; Mur-Bonet, G.; Vidal-Jorge, M.; Gándara-Sabatini, D.; Chocrón, I.; Cordero, E.; Poca, M.-A.; Mullen, K.; Sahuquillo, J. J. Cerebr. Blood F. Met. 2016, 37, 2742-2755. (83) Bhatia, R.; Hashemi, P.; Razzaq, A.; Parkin, M. C.; Hopwood, S. E.; Boutelle, M. G.; Strong, A. J. Neurosurgery 2006, 58, 313-320. (84) Feuerstein, D.; Manning, A.; Hashemi, P.; Bhatia, R.; Fabricius, M.; Tolias, C.; Pahl, C.; Ervine, M.; Strong, A. J.; Boutelle, M. G. J. Cerebr. Blood F. Met. 2010, 30, 1343-1355.

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(85) Su, C.-K.; Yen, S.-C.; Li, T.-W.; Sun, Y.-C. Anal. Chem. 2016, 88, 6265-6273. (86) Rogers, M. L.; Leong, C.; Gowers, S.; Samper, I.; Jewell, S. L.; Khan, A.; McCarthy, L.; Pahl, C.; Tolias, C. M.; Walsh, D. C.; Strong, A. J.; Boutelle, M. G. J. Cerebr. Blood F. Met. 2017, 37, 1883 - 1895. (87) Varner, E. L.; Leong, C. L.; Jaquins-Gerstl, A.; Nesbitt, K. M.; Boutelle, M. G.; Michael, A. C. ACS Chem. Neurosci. 2017, 8, 1779-1788. (88) Lee, W. H.; Ngernsutivorakul, T.; Mabrouk, O. S.; Wong, J.-M. T.; Dugan, C. E.; Pappas, S. S.; Yoon, H. J.; Kennedy, R. T. Anal. Chem. 2016, 88, 1230-1237. (89) Moser, N.; Leong, C. L.; Hu, Y.; Boutelle, M.; Georgiou, P. 2016 IEEE ISCAS 2016, 2847-2850. (90) Buchanan, R.; Gerace, C.; Gjini, K. In Compendium of in vivo monitoring in real-time molecular neuroscience; Wilson, G. S.; Michael, A. C.; World Scientific: Singapore, 2017, pp 95-117. (91) Kline, N. D.; Tripathi, A.; Mirsafavi, R.; Pardoe, I.; Moskovits, M.; Meinhart, C.; Guicheteau, J. A.; Christesen, S. D.; Fountain, A. W. Anal. Chem. 2016, 88, 10513-10522. (92) Chiappini, C.; Campagnolo, P.; Almeida, C. S.; Abbassi-Ghadi, N.; Chow, L. W.; Hanna, G. B.; Stevens, M. M. Adv. Mater. 2015, 27, 5147-5152. (93) Ribaut, C.; Loyez, M.; Larrieu, J. C.; Chevineau, S.; Lambert, P.; Remmelink, M.; Wattiez, R.; Caucheteur, C. Biosens. Bioelectron. 2017, 92, 449-456. (94) Yu, Y.; Zhang, Q.; Buscaglia, J.; Chang, C.-C.; Liu, Y.; Yang, Z.; Guo, Y.; Wang, Y.; Levon, K.; Rafailovich, M. Analyst 2016, 141, 4424-4431. (95) Pence, I. J.; Beaulieu, D. B.; Horst, S. N.; Bi, X.; Herline, A. J.; Schwartz, D. A.; Mahadevan-Jansen, A. Biomed. Opt. Express 2017, 8, 524-535. (96) Liu, W.; Wang, H.; Du, J.; Jing, C. Biosens. Bioelectron. 2017, 97, 70-74. (97) Hu, Y.; Cheng, H.; Zhao, X.; Wu, J.; Muhammad, F.; Lin, S.; He, J.; Zhou, L.; Zhang, C.; Deng, Y.; Wang, P.; Zhou, Z.; Nie, S.; Wei, H. ACS Nano 2017, 11, 5558-5566. (98) Balog, J.; Sasi-Szabo, L.; Kinross, J.; Lewis, M. R.; Muirhead, L. J.; Veselkov, K.; Mirnezami, R.; Dezso, B.; Damjanovich, L.; Darzi, A.; Nicholson, J. K.; Takats, Z. Sci. Transl. Med. 2013, 5, 194ra193-194ra193. (99) Golf, O.; Strittmatter, N.; Karancsi, T.; Pringle, S. D.; Speller, A. V. M.; Mroz, A.; Kinross, J. M.; Abbassi-Ghadi, N.; Jones, E. A.; Takats, Z. Anal. Chem. 2015, 87, 2527-2534. (100) Pruski, P.; MacIntyre, D. A.; Lewis, H. V.; Inglese, P.; Correia, G. D. S.; Hansel, T. T.; Bennett, P. R.; Holmes, E.; Takats, Z. Anal. Chem. 2017, 89, 1540-1550. (101) Zhang, J.; Rector, J.; Lin, J. Q.; Young, J. H.; Sans, M.; Katta, N.; Giese, N.; Yu, W.; Nagi, C.; Suliburk, J.; Liu, J.; Bensussan, A.; DeHoog, R. J.; Garza, K. Y.; Ludolph, B.; Sorace, A. G.; Syed, A.; Zahedivash, A.; Milner, T. E.; Eberlin, L. S. Sci. Transl. Med. 2017, 9, eaan3968. (102) Chen, L. C.; Naito, T.; Tsutsui, S.; Yamada, Y.; Ninomiya, S.; Yoshimura, K.; Takeda, S.; Hiraoka, K. Analyst 2017, 142, 2735-2740. (103) Hamaoui, K.; Gowers, S.; Damji, S.; Rogers, M.; Leong, C. L.; Hanna, G.; Darzi, A.; Boutelle, M.; Papalois, V. J. Surg. Res. 2015, 200, 332-345. (104) Singh, M.; Tong, Y.; Webster, K.; Cesewski, E.; Haring, A. P.; Laheri, S.; Carswell, B.; O'Brien, T. J.; Aardema, C. H.; Senger, R. S.; Robertson, J. L.; Johnson, B. N. Lab Chip 2017, 17, 2561-2571. (105) Kim, Y. S.; Ha, Y.; Sim, J.; Suh, M.; Lee, Y. Analyst 2016, 141, 297-304. (106) Machuca, T. N.; Cypel, M. J. Thorac. Dis. 2014, 6, 1054-1062. (107) Sage, A. T.; Bai, X.; Cypel, M.; Liu, M.; Keshavjee, S.; Kelley, S. O. Analyst 2015, 140, 8092-8096. (108) Zuliani, C.; Ng, F. S.; Alenda, A.; Eftekhar, A.; Peters, N. S.; Toumazou, C. Analyst 2016, 141, 4659-4666.

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(109) Rogers, M. L.; Brennan, P. A.; Leong, C. L.; Gowers, S. A. N.; Aldridge, T.; Mellor, T. K.; Boutelle, M. G. Anal. Bioanal. Chem. 2013, 405, 3881-3888. (110) Sadeghian, R. B.; Ostrovidov, S.; Han, J.; Salehi, S.; Bahraminejad, B.; Bae, H.; Chen, M.; Khademhosseini, A. ACS Sens. 2016, 1, 921-928. (111) Perry, D. A.; Salvin, J. W.; Romfh, P.; Chen, P.; Krishnamurthy, K.; Thomson, L. M.; Polizzotti, B. D.; McGowan, F. X.; Vakhshoori, D.; Kheir, J. N. Sci. Transl. Med. 2017, 9. (112) Gowers, S. A. N.; Curto, V. F.; Seneci, C. A.; Wang, C.; Anastasova, S.; Vadgama, P.; Yang, G. Z.; Boutelle, M. G. Anal. Chem. 2015, 87, 7763-7770. (113) Byun, D.; Cho, S. J.; Kim, S. J. Micromech. Microeng. 2013, 23, 125010-125024. (114) Forvi, E.; Bedoni, M.; Carabalona, R.; Soncini, M.; Mazzoleni, P.; Rizzo, F.; O'Mahony, C.; Morasso, C.; Cassarà, D. G.; Gramatica, F. Sens. Actuators, A 2012, 180, 177186. (115) Guvanasen, G. S.; Guo, L.; Aguilar, R. J.; Cheek, A. L.; Shafor, C. S.; Rajaraman, S.; Nichols, T. R.; DeWeerth, S. P. IEEE T. Neur. Sys. Reh. 2017, 25, 1440-1452. (116) Hsu, L. S.; Tung, S. W.; Kuo, C. H.; Yang, Y. J. Sensors 2014, 14, 12370-12386. (117) Kim, M.; Kim, T.; Kim, D. S.; Chung, W. K. Sensors 2015, 15, 16265-16280. (118) Nishinaka, Y.; Jun, R.; Prihandana, G. S.; Miki, N. 2013 17th Int. Conf. Solid-State Sensors, Actuators and Microsystems, TRANSDUCERS and EUROSENSORS 2013 2013, 1326-1327. (119) Cha, K. J.; Kim, T.; Park, S. J.; Kim, D. S. J. Micromech. Microeng. 2014, 24, 115015115015. (120) Lee, H.; Song, C.; Hong, Y. S.; Kim, M. S.; Cho, H. R.; Kang, T.; Shin, K.; Choi, S. H.; Hyeon, T.; Kim, D.-H. Sci. Adv. 2017, 3, e1601314-e1601314. (121) Sharma, S.; Saeed, A.; Johnson, C.; Gadegaard, N.; Cass, A. E. G. Sens. Bio-Sens. Res. 2017, 13, 104-108. (122) Hertzberg, O.; Bauer, A.; Küderle, A.; Pleitez, M. A.; Mäntele, W. Analyst 2017, 142, 495-502. (123) Lima, A. Curr. Opin. Crit. Care 2016, 22, 274-278. (124) Tanaka, S.; Harrois, A.; Nicolaï, C.; Flores, M.; Hamada, S.; Vicaut, E.; Duranteau, J. Crit. Care 2015, 19, 388. (125) Olenczak, J. J. Reconstr. Microsurg. 2016, 32, 712-718. (126) Cathcart, C. C.; Shuler, M. S.; Freedman, B. A.; Reno, L. R.; Budsberg, S. C. J. Orthop. Trauma 2014, 28, 365-369. (127) De Guzman, K.; Morrin, A. Electroanal. 2017, 29, 188-196. (128) Bandodkar, A. J.; Jia, W.; Yardimci, C.; Wang, X.; Ramirez, J.; Wang, J. Anal. Chem. 2015, 87, 394-398. (129) Miyamoto, A.; Lee, S.; Cooray, N. F.; Lee, S.; Mori, M.; Matsuhisa, N.; Jin, H.; Yoda, L.; Yokota, T.; Itoh, A.; Sekino, M.; Kawasaki, H.; Ebihara, T.; Amagai, M.; Someya, T. Nat. Nanotechnol. 2017, 12, 907-913. (130) Thet, N. T.; Alves, D. R.; Bean, J. E.; Booth, S.; Nzakizwanayo, J.; Young, A. E. R.; Jones, B. V.; Jenkins, A. T. A. ACS Appl. Mater. Interfaces 2016, 8, 14909-14919. (131) Milo, S.; Thet, N. T.; Liu, D.; Nzakizwanayo, J.; Jones, B. V.; Jenkins, A. T. A. Biosens. Bioelectron. 2016, 81, 166-172. (132) Kassal, P.; Zubak, M.; Scheipl, G.; Mohr, G. J.; Steinberg, M. D.; Murković Steinberg, I. Sens. Actuators, B 2017, 246, 455-460. (133) Kassal, P.; Kim, J.; Kumar, R.; De Araujo, W. R.; Steinberg, I. M.; Steinberg, M. D.; Wang, J. Electrochem. Commun. 2015, 56, 6-10. (134) Thet, N. T.; Jenkins, A. T. A. Electrochem. Commun. 2015, 59, 104-108.

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(135) Occhiuzzi, C.; Ajovalasit, A.; Sabatino, M. A.; Dispenza, C.; Marrocco, G. 2015 IEEE Int. Conf. RFID 2015, 182-188. (136) Norton, J. J. S.; Lee, D. S.; Lee, J. W.; Lee, W.; Kwon, O.; Won, P.; Jung, S.-Y.; Cheng, H.; Jeong, J.-W.; Akce, A.; Umunna, S.; Na, I.; Kwon, Y. H.; Wang, X.-Q.; Liu, Z.; Paik, U.; Huang, Y.; Bretl, T.; Yeo, W.-H.; Rogers, J. A. Proc. Natl. Acad. Sci. U.S.A. 2015, 112, 39203925. (137) Lee, S. P.; Klinker, L. E.; Ptaszek, L.; Work, J.; Liu, C.; Quivara, F.; Webb, C.; Dagdeviren, C.; Wright, J. A.; Ruskin, J. N.; Slepian, M.; Huang, Y.; Mansour, M.; Rogers, J. A.; Ghaffari, R. Proc. IEEE 2015, 103, 682-689. (138) Jang, K.-I.; Li, K.; Chung, H. U.; Xu, S.; Jung, H. N.; Yang, Y.; Kwak, J. W.; Jung, H. H.; Song, J.; Yang, C.; Wang, A.; Liu, Z.; Lee, J. Y.; Kim, B. H.; Kim, J.-H.; Lee, J.; Yu, Y.; Kim, B. J.; Jang, H.; Yu, K. J., et al. Nat. Commun. 2017, 8, 15894. (139) Jeerapan, I.; Sempionatto, J. R.; Pavinatto, A.; You, J.-M.; Wang, J. J. Mater. Chem. A 2016, 4, 18342-18353. (140) Heikenfeld, J. Electroanal. 2016, 28, 1242-1249. (141) Sempionatto, J. R.; Nakagawa, T.; Pavinatto, A.; Mensah, S. T.; Imani, S.; Mercier, P.; Wang, J. Lab Chip 2017, 17, 1834-1842. (142) Glennon, T.; O'Quigley, C.; McCaul, M.; Matzeu, G.; Beirne, S.; Wallace, G. G.; Stroiescu, F.; O'Mahoney, N.; White, P.; Diamond, D. Electroanal. 2016, 28, 1283-1289. (143) Nyein, H. Y. Y.; Gao, W.; Shahpar, Z.; Emaminejad, S.; Challa, S.; Chen, K.; Fahad, H. M.; Tai, L. C.; Ota, H.; Davis, R. W.; Javey, A. ACS Nano 2016, 10, 7216-7224. (144) Gao, W.; Emaminejad, S.; Nyein, H. Y. Y.; Challa, S.; Chen, K.; Peck, A.; Fahad, H. M.; Ota, H.; Shiraki, H.; Kiriya, D.; Lien, D.-H.; Brooks, G. A.; Davis, R. W.; Javey, A. Nature 2016, 529, 509-514. (145) Emaminejad, S.; Gao, W.; Wu, E.; Davies, Z. A.; Yin Yin Nyein, H.; Challa, S.; Ryan, S. P.; Fahad, H. M.; Chen, K.; Shahpar, Z.; Talebi, S.; Milla, C.; Javey, A.; Davis, R. W. Proc. Natl. Acad. Sci. U.S.A. 2017, 114, 4625-4630. (146) Sonner, Z.; Wilder, E.; Gaillard, T.; Kasting, G.; Heikenfeld, J. Lab Chip 2017, 17, 2550-2560. (147) 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. Med. 2016, 8, 366ra165. (148) Anastasova, S.; Crewther, B.; Bembnowicz, P.; Curto, V.; Ip, H. M.; Rosa, B.; Yang, G. Z. Biosens. Bioelectron. 2017, 93, 139-145. (149) Khan, M. S.; Misra, S. K.; Schwartz-Duval, A. S.; Daza, E.; Ostadhossein, F.; Bowman, M.; Jain, A.; Taylor, G.; McDonagh, D.; Labriola, L. T.; Pan, D. ACS Appl. Mater. 2017, 9, 8609-8622. (150) La Belle, J. T.; Adams, A.; Lin, C.-E.; Engelschall, E.; Pratt, B.; Cook, C. B. Chem. Commun. 2016, 52, 9197-9204. (151) Ruan, J. L.; Chen, C.; Shen, J. H.; Zhao, X. L.; Qian, S. H.; Zhu, Z. G. Polymer 2017, 9, 112. (152) Domschke, A.; March, W. F.; Kabilan, S.; Lowe, C. Diabetes Technol. Ther. 2006, 8, 89-93. (153) March, W. F.; Mueller, A.; Herbrechtsmeier, P. Diabetes Technol. Ther. 2004, 6, 782-789. (154) Kabilan, S.; Marshall, A. J.; Sartain, F. K.; Lee, M. C.; Hussain, A.; Yang, X.; Blyth, J.; Karangu, N.; James, K.; Zeng, J.; Smith, D.; Domschke, A.; Lowe, C. R. Biosens. Bioelectron. 2005, 20, 1602-1610.

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(155) Ward Muscatello, M. M.; Stunja, L. E.; Asher, S. A. Anal. Chem. 2009, 81, 49784986. (156) Agustini, D.; Bergamini, M. F.; Marcolino-Junior, L. H. Biosens. Bioelectron. 2017, 98, 161-167. (157) Kim, J.; Kim, M.; Lee, M.-S.; Kim, K.; Ji, S.; Kim, Y.-T.; Park, J.; Na, K.; Bae, K.-H.; Kyun Kim, H.; Bien, F.; Young Lee, C.; Park, J.-U. Nat. Commun. 2017, 8, 14997-14997. (158) Riedel, T.; Hageneder, S.; Surman, F.; Pop-Georgievski, O.; Noehammer, C.; Hofner, M.; Brynda, E.; Rodriguez-Emmenegger, C.; Dostálek, J. Anal. Chem. 2017, 89, 29722977. (159) Usha, S. P.; Shrivastav, A. M.; Gupta, B. D. Biosens. Bioelectron. 2017, 87, 178-186. (160) Ngamchuea, K.; Batchelor-McAuley, C.; Compton, R. G. Anal. Chem. 2017, 89, 29012908. (161) Wang, J.; Xu, L.; Lu, Y.; Sheng, K.; Liu, W.; Chen, C.; Li, Y.; Dong, B.; Song, H. Anal. Chem. 2016, 88, 12346-12353. (162) Kim, J.; Imani, S.; de Araujo, W. R.; Warchall, J.; Valdés-Ramírez, G.; Paixão, T. R. L. C.; Mercier, P. P.; Wang, J. Biosens. Bioelectron. 2015, 74, 1061-1068. (163) Arakawa, T.; Kuroki, Y.; Nitta, H.; Chouhan, P.; Toma, K.; Sawada, S. i.; Takeuchi, S.; Sekita, T.; Akiyoshi, K.; Minakuchi, S.; Mitsubayashi, K. Biosens. Bioelectron. 2016, 84, 106-111. (164) Petropoulos, K.; Piermarini, S.; Bernardini, S.; Palleschi, G.; Moscone, D. Sens. Actuators, B 2016, 237, 8-15. (165) Kumar, S.; Huang, J.; Abbassi-Ghadi, N.; Mackenzie, H. A.; Veselkov, K. A.; Hoare, J. M.; Lovat, L. B.; Španěl, P.; Smith, D.; Hanna, G. B. Ann. Surg. 2015, 262, 981-990. (166) Gaugg, M. T.; Bruderer, T.; Nowak, N.; Eiffert, L.; Sinues, P. M.-L.; Kohler, M.; Zenobi, R. Anal. Chem. 2017, 89, 10329-10334. (167) Brock, B.; Kamysek, S.; Silz, J.; Trefz, P.; Schubert, J. K.; Miekisch, W. J. Breath Res. 2017, 11, 016005-016005. (168) Chien, P. J.; Suzuki, T.; Tsujii, M.; Ye, M.; Toma, K.; Arakawa, T.; Iwasaki, Y.; Mitsubayashi, K. Biosens. Bioelectron. 2017, 91, 341-346. (169) Cikach, F. S.; Tonelli, A. R.; Barnes, J.; Paschke, K.; Newman, J.; Grove, D.; Dababneh, L.; Wang, S.; Dweik, R. A. Chest 2014, 145, 551-558. (170) Hanouneh, I. A.; Zein, N. N.; Cikach, F.; Dababneh, L.; Grove, D.; Alkhouri, N.; Lopez, R.; Dweik, R. A. Clin. Gastroenterol. Hepatol. 2014, 12, 516-523. (171) Phillips, M.; Cataneo, R. N.; Ditkoff, B. A.; Fisher, P.; Greenberg, J.; Gunawardena, R.; Kwon, C. S.; Tietje, O.; Wong, C. Breast Cancer Res. Treat. 2006, 99, 19-21. (172) Ciaffoni, L.; Oneill, D. P.; Couper, J. H.; Ritchie, G. A. D.; Hancock, G.; Robbins, P. A. Sci. Adv. 2016, 2, e1600560. (173) Güntner, A. T.; Sievi, N. A.; Theodore, S. J.; Gulich, T.; Kohler, M.; Pratsinis, S. E. Anal. Chem. 2017, 89, 10578-10584. (174) Brannelly, N. T.; Killard, A. J. Electroanal. 2017, 29, 162-171. (175) Shehada, N.; Cancilla, J. C.; Torrecilla, J. S.; Pariente, E. S.; Brönstrup, G.; Christiansen, S.; Johnson, D. W.; Leja, M.; Davies, M. P. A.; Liran, O.; Peled, N.; Haick, H. ACS Nano 2016, 10, 7047-7057. (176) Kahn, N.; Lavie, O.; Paz, M.; Segev, Y.; Haick, H. Nano Lett. 2015, 15, 7023-7028. (177) Obermeier, J.; Trefz, P.; Wex, K.; Sabel, B.; Schubert, J. K.; Miekisch, W. J. Breath Res. 2015, 9, 016008. (178) Guinovart, T.; Hernández-Alonso, D.; Adriaenssens, L.; Blondeau, P.; Rius, F. X.; Ballester, P.; Andrade, F. J. Biosens. Bioelectron. 2017, 87, 587-592.

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(179) Wang, M.-Q.; Ye, C.; Bao, S.-J.; Zhang, Y.; Yu, Y.-N.; Xu, M.-w. Analyst 2016, 141, 1279-1285. (180) Sánchez-Tirado, E.; Martínez-García, G.; González-Cortés, A.; Yáñez-Sedeño, P.; Pingarrón, J. M. Biosens. Bioelectron. 2017, 88, 9-14. (181) Chuang, C. H.; Du, Y. C.; Wu, T. F.; Chen, C. H.; Lee, D. H.; Chen, S. M.; Huang, T. C.; Wu, H. P.; Shaikh, M. O. Biosens. Bioelectron. 2016, 84, 126-132. (182) Cai, Y.; You, J.; You, Z.; Dong, F.; Du, S.; Zhang, L. Biosens. Bioelectron. 2018, 99, 332-337. (183) DeGregory, P. R.; Tsai, Y.-J.; Scida, K.; Richards, I.; Crooks, R. M. Analyst 2016, 141, 1734-1744. (184) Koo, K. M.; Wee, E. J. H.; Trau, M. Biosens. Bioelectron. 2017, 89, 715-720. (185) Hidi, I. J.; Jahn, M.; Weber, K.; Bocklitz, T.; Pletz, M. W.; Cialla-May, D.; Popp, J. Anal. Chem. 2016, 88, 9173-9180. (186) Melnik, A. V.; Da Silva, R. R.; Hyde, E. R.; Aksenov, A. A.; Vargas, F.; Bouslimani, A.; Protsyuk, I.; Jarmusch, A. K.; Tripathi, A.; Alexandrov, T.; Knight, R.; Dorrestein, P. C. Anal. Chem. 2017, 89, 7549-7559. (187) Mosley, O.; Melling, L.; Tarn, M. D.; Kemp, C.; Esfahani, M. M. N.; Pamme, N.; Shaw, K. J. Lab Chip 2016, 16, 2108-2115. (188) Luo, J.; Wang, J.; Mathew, A. S.; Yau, S. T. Anal. Chem. 2016, 88, 2010-2014. (189) Rogers, M. L.; Boutelle, M. G. Annu. Rev. Anal. Chem. 2013, 6, 427-453.

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BRAIN Metabolism, Oxygen Neurotransmitters Inflammatory cytokines

SALIVA

TEARS

Metabolic markers Cortisol Uric acid



B Full clinical validation

Glucose

Sensor Research and Design

In vivo testing

TISSUE/ ORGANS

BREATH

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Volatile organic compounds Acetone Oxygen consumption

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SWEAT Metabolic markers pH

URINE & STOOL

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Cancer biomarkers Metabolic markers Pathological biomarkers

BLOOD Metabolic markers Cancer biomarkers Pathological biomarkers Drugs

Cancer biomarkers Uric acid Creatinine

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Standard of care

A d* b* Page 37 of 44Analytical Chemistry reaction coil

Carboplatin

**

Carboplatin in dialysate

Peak current reduction/ nA

1 MD probe 2 2MD probe 1 RE/CE 3 Tumour Inlet 4 WE a* 5 Carboplatin efflux c* 6 Outlet O utlet Healthy tissue 7 Analysis chip 8 Syringe pump Reservoir Valve Interconnect Analysis chip with sensor 9 B10 100 11 0 μM 10 μM 80 12 20 μM 30 μM 60 40 μM 13 14 40 15 20 16 0 0 30 10 40 20 17 [carboplatin] / μM 18 19 20 21 ACS Paragon Plus Environment 22 23 24

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normalized dopamine concentration (σ)

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medium bets lower bets Analytical Chemistry

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In vivo (Human)

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Analytical Chemistry

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B (ii)

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D

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A Page 41 of 44Analytical Chemistry 1 2 3 Conformal 4 Microfluidic 5 6 7 8 9 10 11 12 D B13 Conformal Microchannel 14 15 10 16mm 17 C 18 19 Cross-section 20 ACS Paragon Plus Environment 21 500 µm 10 22mm

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Analytical Chemistry

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Analytical Chemistry

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