SUMMARY 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.

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.stochastic electrotransport | molecular transport | tissue clearing | tissue labeling | CLARITY

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 C-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 C-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 β-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.

a b s t r a c tSince the discovery of low-dimensional carbon allotropes, there is increasing interest in using carbon nanomaterials for biomedical applications. Carbon nanomaterials have been utilized in the biomedical field for bioimaging, chemical sensing, targeting, delivery, therapeutics, catalysis, and energy harvesting. Each application requires tailored surface functionalization in order to take advantage of a desired property of the nanoparticles. Herein, we review the surface immobilization of bio-molecules, including proteins, peptides, and enzymes, and present the recent advances in synthesis and applications of these conjugates. The carbon scaffold and the biological moiety form a complex interface which presents a challenge for achieving efficient and robust binding while preserving biological activity. Moreover, some applications require the utilization of the protein-nanocarbon system in a complex environment that may hinder its performance or activity. We analyze different strategies to overcome these challenges when using carbon nanomaterials as protein carriers, explore various immobilization techniques along with characterization methods, and present recent demonstrations of employing these systems for biomedical applications. Finally, we consider the challenges and future directions of this field.

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.

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.

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 atOceanografc 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

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