Simple, Scalable Proteomic Imaging for High-Dimensional Profiling of Intact Systems.

Combined measurement of diverse molecular and anatomical traits that span multiple levels remains a major challenge in biology. Here, we introduce a simple method that enables proteomic imaging for scalable, integrated, high-dimensional phenotyping of both animal tissues and human clinical samples. This method, termed SWITCH, uniformly secures tissue architecture, native biomolecules, and antigenicity across an entire system by synchronizing the tissue preservation reaction. The heat- and chemical-resistant nature of the resulting framework permits multiple rounds (>20) of relabeling. We have performed 22 rounds of labeling of a single tissue with precise co-registration of multiple datasets. Furthermore, SWITCH synchronizes labeling reactions to improve probe penetration depth and uniformity of staining. With SWITCH, we performed combinatorial protein expression profiling of the human cortex and also interrogated the geometric structure of the fiber pathways in mouse brains. Such integrated high-dimensional information may accelerate our understanding of biological systems at multiple levels.

In-Vivo fluorescent nanosensor implants based on hydrogel-encapsulation: investigating the inflammation and the foreign-body response.

Nanotechnology-enabled sensors or nanosensors are emerging as promising new tools for various in-vivo life science applications such as biosensing, components of delivery systems, and probes for spatial bioimaging. However, as with a wide range of synthetic biomaterials, tissue responses have been observed depending on cell types and various nanocomponent properties. The tissue response is critical for determining the acute and long term health of the organism and the functional lifetime of the material in-vivo. While nanomaterial properties can contribute significantly to the tissue response, it may be possible to circumvent adverse reactions by formulation of the encapsulation vehicle. In this study, five formulations of poly (ethylene glycol) diacrylate (PEGDA) hydrogel-encapsulated fluorescent nanosensors were implanted into SKH-1E mice, and the inflammatory responses were tracked in order to determine the favorable design rules for hydrogel encapsulation and minimization of such responses. Hydrogels with higher crosslinking density were found to allow faster resolution of acute inflammation. Five different immunocompromised mice lines were utilized for comparison across different inflammatory cell populations and responses. Degradation products of the gels were also characterized. Finally, the importance of the tissue response in determining functional lifetime was demonstrated by measuring the time-dependent nanosensor deactivation following implantation into animal models.

Transcutaneous Measurement of Essential Vitamins Using Near-Infrared Fluorescent Single-Walled Carbon Nanotube Sensors.

Vitamins such as riboflavin and ascorbic acid are frequently utilized in a range of biomedical applications as drug delivery targets, fluidic tracers, and pharmaceutical excipients. Sensing these biochemicals in the human body has the potential to significantly advance medical research and clinical applications. In this work, a nanosensor platform consisting of single-walled carbon nanotubes (SWCNTs) with nanoparticle corona phases engineered to allow for the selective molecular recognition of ascorbic acid and riboflavin, is developed. The study provides a methodological framework for the implementation of colloidal SWCNT nanosensors in an intraperitoneal SKH1-E murine model by addressing complications arising from tissue absorption and scattering, mechanical perturbations, as well as sensor diffusion and interactions with the biological environment. Nanosensors are encapsulated in a polyethylene glycol diacrylate hydrogel and a diffusion model is utilized to validate analyte transport and sensor responses to local concentrations at the boundary. Results are found to be reproducible and stable after exposure to 10% mouse serum even after three days of in vivo implantation. A geometrical encoding scheme is used to reference sensor pairs, correcting for in vivo optical and mechanical artifacts, resulting in an order of magnitude improvement of p-value from 0.084 to 0.003 during analyte sensing.

Stochastic electrotransport selectively enhances the transport of highly electromobile molecules.

Nondestructive chemical processing of porous samples such as fixed biological tissues typically relies on molecular diffusion. Diffusion into a porous structure is a slow process that significantly delays completion of chemical processing. Here, we present a novel electrokinetic method termed stochastic electrotransport for rapid nondestructive processing of porous samples. This method uses a rotational electric field to selectively disperse highly electromobile molecules throughout a porous sample without displacing the low-electromobility molecules that constitute the sample. Using computational models, we show that stochastic electrotransport can rapidly disperse electromobile molecules in a porous medium. We apply this method to completely clear mouse organs within 1-3 days and to stain them with nuclear dyes, proteins, and antibodies within 1 day. Our results demonstrate the potential of stochastic electrotransport to process large and dense tissue samples that were previously infeasible in time when relying on diffusion.

In Silico Investigation of the Clinical Translatability of Competitive Clearance Glucose-Responsive Insulins.

The glucose-responsive insulin (GRI) MK-2640 from Merck was a pioneer in its class to enter the clinical stage, having demonstrated promising responsiveness in in vitro and preclinical studies via a novel competitive clearance mechanism (CCM). The smaller pharmacokinetic response in humans motivates the development of new predictive, computational tools that can improve the design of therapeutics such as GRIs. Herein, we develop and use a new computational model, IM3PACT, based on the intersection of human and animal model glucoregulatory systems, to investigate the clinical translatability of CCM GRIs based on existing preclinical and clinical data of MK-2640 and regular human insulin (RHI). Simulated multi-glycemic clamps not only validated the earlier hypothesis of insufficient glucose-responsive clearance capacity in humans but also uncovered an equally important mismatch between the in vivo competitiveness profile and the physiological glycemic range, which was not observed in animals. Removing the inter-species gap increases the glucose-dependent GRI clearance from 13.0% to beyond 20% for humans and up to 33.3% when both factors were corrected. The intrinsic clearance rate, potency, and distribution volume did not apparently compromise the translation. The analysis also confirms a responsive pharmacokinetics local to the liver. By scanning a large design space for CCM GRIs, we found that the mannose receptor physiology in humans remains limiting even for the most optimally designed candidate. Overall, we show that this computational approach is able to extract quantitative and mechanistic information of value from a posteriori analysis of preclinical and clinical data to assist future therapeutic discovery and development.

Molecular Recognition and In Vivo Detection of Temozolomide and 5-Aminoimidazole-4-carboxamide for Glioblastoma Using Near-Infrared Fluorescent Carbon Nanotube Sensors.

There is a pressing need for sensors and assays to monitor chemotherapeutic activity within the human body in real time to optimize drug dosimetry parameters such as timing, quantity, and frequency in an effort to maximize efficacy while minimizing deleterious cytotoxicity. Herein, we develop near-infrared fluorescent nanosensors based on single walled carbon nanotubes for the chemotherapeutic Temozolomide (TMZ) and its metabolite 5-aminoimidazole-4-carboxamide using Corona Phase Molecular Recognition as a synthetic molecular recognition technique. The resulting nanoparticle sensors are able to monitor drug activity in real-time even under in vivo conditions. Sensors can be engineered to be biocompatible by encapsulation in poly(ethylene glycol) diacrylate hydrogels. Selective detection of TMZ was demonstrated using U-87 MG human glioblastoma cells and SKH-1E mice with detection limits below 30 µM. As sensor implants, we show that such systems can provide spatiotemporal therapeutic information in vivo, as a valuable tool for pharmacokinetic evaluation. Sensor implants are also evaluated using intact porcine brain tissue implanted 2.1 cm below the cranium and monitored using a recently developed Wavelength-Induced Frequency Filtering technique. Additionally, we show that by taking the measurement of spatial and temporal analyte concentrations within each hydrogel implant, the direction of therapeutic flux can be resolved. In all, these types of sensors enable the real time detection of chemotherapeutic concentration, flux, directional transport, and metabolic activity, providing crucial information regarding therapeutic effectiveness.

A wavelength-induced frequency filtering method for fluorescent nanosensors in vivo.

Fluorescent nanosensors hold the potential to revolutionize life sciences and medicine. However, their adaptation and translation into the in vivo environment is fundamentally hampered by unfavourable tissue scattering and intrinsic autofluorescence. Here we develop wavelength-induced frequency filtering (WIFF) whereby the fluorescence excitation wavelength is modulated across the absorption peak of a nanosensor, allowing the emission signal to be separated from the autofluorescence background, increasing the desired signal relative to noise, and internally referencing it to protect against artefacts. Using highly scattering phantom tissues, an SKH1-E mouse model and other complex tissue types, we show that WIFF improves the nanosensor signal-to-noise ratio across the visible and near-infrared spectra up to 52-fold. This improvement enables the ability to track fluorescent carbon nanotube sensor responses to riboflavin, ascorbic acid, hydrogen peroxide and a chemotherapeutic drug metabolite for depths up to 5.5 ± 0.1 cm when excited at 730 nm and emitting between 1,100 and 1,300 nm, even allowing the monitoring of riboflavin diffusion in thick tissue. As an application, nanosensors aided by WIFF detect the chemotherapeutic activity of temozolomide transcranially at 2.4 ± 0.1 cm through the porcine brain without the use of fibre optic or cranial window insertion. The ability of nanosensors to monitor previously inaccessible in vivo environments will be important for life-sciences research, therapeutics and medical diagnostics.

Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin.

Despite considerable progress, development of glucose-responsive insulins (GRIs) still largely depends on empirical knowledge and tedious experimentation-especially on rodents. To assist the rational design and clinical translation of the therapeutic, we present a Pharmacokinetic Algorithm Mapping GRI Efficacies in Rodents and Humans (PAMERAH) built upon our previous human model. PAMERAH constitutes a framework for predicting the therapeutic efficacy of a GRI candidate from its user-specified mechanism of action, kinetics, and dosage, which we show is accurate when checked against data from experiments and literature. Results from simulated glucose clamps also agree quantitatively with recent GRI publications. We demonstrate that the model can be used to explore the vast number of permutations constituting the GRI parameter space and thereby identify the optimal design ranges that yield desired performance. A design guide aside, PAMERAH more importantly can facilitate GRI's clinical translation by connecting each candidate's efficacies in rats, mice, and humans. The resultant mapping helps to find GRIs that appear promising in rodents but underperform in humans (i.e., false positives). Conversely, it also allows for the discovery of optimal human GRI dynamics not captured by experiments on a rodent population (false negatives). We condense such information onto a "translatability grid" as a straightforward, visual guide for GRI development.

Implantable Nanosensors for Human Steroid Hormone Sensing In Vivo Using a Self-Templating Corona Phase Molecular Recognition.

Dynamic measurements of steroid hormones in vivo are critical, but steroid sensing is currently limited by the availability of specific molecular recognition elements due to the chemical similarity of these hormones. In this work, a new, self-templating synthetic approach is applied using corona phase molecular recognition (CoPhMoRe) targeting the steroid family of molecules to produce near infrared fluorescent, implantable sensors. A key limitation of CoPhMoRe has been its reliance on library generation for sensor screening. This problem is addressed with a self-templating strategy of polymer design, using the examples of progesterone and cortisol sensing based on a styrene and acrylic acid copolymer library augmented with an acrylated steroid. The pendant steroid attached to the corona backbone is shown to self-template the phase, providing a unique CoPhMoRE design strategy with high efficacy. The resulting sensors exhibit excellent stability and reversibility upon repeated analyte cycling. It is shown that molecular recognition using such constructs is viable even in vivo after sensor implantation into a murine model by employing a poly (ethylene glycol) diacrylate (PEGDA) hydrogel and porous cellulose interface to limit nonspecific absorption. The results demonstrate that CoPhMoRe templating is sufficiently robust to enable a new class of continuous, in vivo biosensors.

Implanted Nanosensors in Marine Organisms for Physiological Biologging: Design, Feasibility, and Species Variability.

In recent decades, biologists have sought to tag animals with various sensors to study aspects of their behavior otherwise inaccessible from controlled laboratory experiments. Despite this, chemical information, both environmental and physiological, remains challenging to collect despite its tremendous potential to elucidate a wide range of animal behaviors. In this work, we explore the design, feasibility, and data collection constraints of implantable, near-infrared fluorescent nanosensors based on DNA-wrapped single-wall carbon nanotubes (SWNT) embedded within a biocompatible poly(ethylene glycol) diacrylate (PEGDA) hydrogel. These sensors are enabled by Corona Phase Molecular Recognition (CoPhMoRe) to provide selective chemical detection for marine organism biologging. Riboflavin, a key nutrient in oxidative phosphorylation, is utilized as a model analyte in in vitro and ex vivo tissue measurements. Nine species of bony fish, sharks, eels, and turtles were utilized on site at Oceanogràfic in Valencia, Spain to investigate sensor design parameters, including implantation depth, sensor imaging and detection limits, fluence, and stability, as well as acute and long-term biocompatibility. Hydrogels were implanted subcutaneously and imaged using a customized, field-portable Raspberry Pi camera system. Hydrogels could be detected up to depths of 7 mm in the skin and muscle tissue of deceased teleost fish ( Sparus aurata and Stenotomus chrysops) and a deceased catshark ( Galeus melastomus). The effects of tissue heterogeneity on hydrogel delivery and fluorescence visibility were explored, with darker tissues masking hydrogel fluorescence. Hydrogels were implanted into a living eastern river cooter ( Pseudemys concinna), a European eel ( Anguilla anguilla), and a second species of catshark ( Scyliorhinus stellaris). The animals displayed no observable changes in movement and feeding patterns. Imaging by high-resolution ultrasound indicated no changes in tissue structure in the eel and catshark. In the turtle, some tissue reaction was detected upon dissection and histopathology. Analysis of movement patterns in sarasa comet goldfish ( Carassius auratus) indicated that the hydrogel implants did not affect swimming patterns. Taken together, these results indicate that this implantable form factor is a promising technique for biologging using aquatic vertebrates with further development. Future work will tune the sensor detection range to the physiological range of riboflavin, develop strategies to normalize sensor signal to account for the optical heterogeneity of animal tissues, and design a flexible, wearable device incorporating optoelectronic components that will enable sensor measurements in moving animals. This work advances the application of nanosensors to organisms beyond the commonly used rodent and zebrafish models and is an important step toward the physiological biologging of aquatic organisms.

Insulin Detection Using a Corona Phase Molecular Recognition Site on Single-Walled Carbon Nanotubes.

Corona phase molecular recognition (CoPhMoRe) is a technique whereby an external, adsorbed phase around a colloidal nanoparticle is selected such that its molecular conformation or interaction recognizes a specific target analyte. In this work, we employ a high-throughput screening of a library of poly(ethylene glycol) (PEG)-conjugated lipids adsorbed onto near-infrared fluorescent single-walled carbon nanotubes to discover a corona phase selective for insulin. We find that a C16-PEG(2000 Da)-ceramide causes a 62% fluorescent intensity decrease of the (10,2) chirality nanotube in the presence of 20 µg/mL insulin. The insulin protein has no prior affinity toward the C16-PEG(2000 Da)-ceramide molecules in free solution, verified by isothermal titration calorimetry, and the interaction occurs only upon their adsorption onto the single-walled carbon nanotube scaffolds. Testing a panel of proteins originating from human blood as well as short 7 amino acid fragments of the insulin peptide rules out nonselective recognition mechanisms such as molecular weight, isoelectric point, and hydrophobicity-based detection. Interestingly, longer fragments of isolated α- and ß-peptide chains of insulin are detected by the construct, albeit with lower affinity compared to that of the intact insulin protein, suggesting that the construct recognizes insulin in its native form and conformation. Finally, the insulin recognition and the quantification of its solution concentration were demonstrated both in buffer and in blood serum, showing that the CoPhMoRe construct works in this complex environment despite the presence of potential nonspecific adsorption. Our results further motivate the search for nonbiological synthetic recognition sites and open up a new path for continuous insulin monitoring in vivo with the hope of improving glycemic control in closed-loop artificial pancreas systems.

Rational Design of Glucose-Responsive Insulin Using Pharmacokinetic Modeling.

A glucose responsive insulin (GRI) is a therapeutic that modulates its potency, concentration, or dosing of insulin in relation to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. Current GRI design lacks a theoretical basis on which to base fundamental design parameters such as glucose reactivity, dissociation constant or potency, and in vivo efficacy. In this work, an approach to mathematically model the relevant parameter space for effective GRIs is induced, and design rules for linking GRI performance to therapeutic benefit are developed. Well-developed pharmacokinetic models of human glucose and insulin metabolism coupled to a kinetic model representation of a freely circulating GRI are used to determine the desired kinetic parameters and dosing for optimal glycemic control. The model examines a subcutaneous dose of GRI with kinetic parameters in an optimal range that results in successful glycemic control within prescribed constraints over a 24 h period. Additionally, it is demonstrated that the modeling approach can find GRI parameters that enable stable glucose levels that persist through a skipped meal. The results provide a framework for exploring the parameter space of GRIs, potentially without extensive, iterative in vivo animal testing.

Glucose-responsive insulin by molecular and physical design.

The concept of a glucose-responsive insulin (GRI) has been a recent objective of diabetes technology. The idea behind the GRI is to create a therapeutic that modulates its potency, concentration or dosing relative to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. From the perspective of the medicinal chemist, the GRI is also important as a generalized model of a potentially new generation of therapeutics that adjust potency in response to a critical therapeutic marker. The aim of this Perspective is to highlight emerging concepts, including mathematical modelling and the molecular engineering of insulin itself and its potency, towards a viable GRI. We briefly outline some of the most important recent progress toward this goal and also provide a forward-looking viewpoint, which asks if there are new approaches that could spur innovation in this area as well as to encourage synthetic chemists and chemical engineers to address the challenges and promises offered by this therapeutic approach.

Erratum: Glucose-responsive insulin by molecular and physical design.

This corrects the article DOI: 10.1038/nchem.2857.

A Pharmacokinetic Model of a Tissue Implantable Cortisol Sensor.

Cortisol is an important glucocorticoid hormone whose biochemistry influences numerous physiological and pathological processes. Moreover, it is a biomarker of interest for a number of conditions, including posttraumatic stress disorder, Cushing's syndrome, Addison's disease, and others. An implantable biosensor capable of real time monitoring of cortisol concentrations in adipose tissue may revolutionize the diagnosis and treatment of these disorders, as well as provide an invaluable research tool. Toward this end, a mathematical model, informed by the physiological literature, is developed to predict dynamic cortisol concentrations in adipose, muscle, and brain tissues, where a significant number of important processes with cortisol occur. The pharmacokinetic model is applied to both a prototypical, healthy male patient and a previously studied Cushing's disease patient. The model can also be used to inform the design of an implantable sensor by optimizing the sensor dissociation constant, apparent delay time, and magnitude of the sensor output versus system dynamics. Measurements from such a sensor would help to determine systemic cortisol levels, providing much needed insight for proper medical treatment for various cortisol-related conditions.