Orientation of surface immobilized capture proteins, such as antibodies, plays a critical role in the performance of immunoassays. The sensitivity of immunodiagnostic procedures is dependent on presentation of the antibody, with optimum performance requiring the antigen binding sites be directed toward the solution phase. This review describes the most recent methods for oriented antibody immobilization and the characterization techniques employed for investigation of the antibody state. The introduction describes the importance of oriented antibodies for maximizing biosensor capabilities. Methods for improving antibody binding are discussed, including surface modification and design (with sections on surface treatments, three-dimensional substrates, self-assembled monolayers, and molecular imprinting), covalent attachment (including targeting amine, carboxyl, thiol and carbohydrates, as well as “click” chemistries), and (bio)affinity techniques (with sections on material binding peptides, biotin-streptavidin interaction, DNA directed immobilization, Protein A and G, Fc binding peptides, aptamers, and metal affinity). Characterization techniques for investigating antibody orientation are discussed, including x-ray photoelectron spectroscopy, spectroscopic ellipsometry, dual polarization interferometry, neutron reflectometry, atomic force microscopy, and time-of-flight secondary-ion mass spectrometry. Future perspectives and recommendations are offered in conclusion.

Here, we have developed and evaluated a microfluidic-based human blood–brain-barrier (μBBB) platform that models and predicts brain tissue uptake of small molecule drugs and nanoparticles (NPs) targeting the central nervous system. By using a photocrosslinkable copolymer that was prepared from monomers containing benzophenone and N-hydroxysuccinimide ester functional groups, we were able to evenly coat and functionalize μBBB chip channels in situ, providing a covalently attached homogenous layer of extracellular matrix proteins. This novel approach allowed the coculture of human endothelial cells, pericytes, and astrocytes and resulted in the formation of a mimic of cerebral endothelium expressing tight junction markers and efflux proteins, resembling the native BBB. The permeability coefficients of a number of compounds, including caffeine, nitrofurantoin, dextran, sucrose, glucose, and alanine, were measured on our μBBB platform and were found to agree with reported values. In addition, we successfully visualized the receptor-mediated uptake and transcytosis of transferrin-functionalized NPs. The BBB-penetrating NPs were able to target glioma cells cultured in 3D in the brain compartment of our μBBB. In conclusion, our μBBB was able to accurately predict the BBB permeability of both small molecule pharmaceuticals and nanovectors and allowed time-resolved visualization of transcytosis. Our versatile chip design accommodates different brain disease models and is expected to be exploited in further BBB studies, aiming at replacing animal experiments.

A new and facile approach to selectively functionalize the internal and external surfaces of porous silicon (pSi) for drug delivery applications is reported. To provide a surface that is suitable for sustained drug release of the hydrophobic cancer chemotherapy drug camptothecin (CPT), the internal surfaces of pSi films were first modified with 1-dodecene. To further modify the external surface of the pSi samples, an interlayer was applied by silanization with (3-aminopropyl)triethoxysilane (APTES) following air plasma treatment. In addition, copolymers of N-(2-hydroxypropyl) acrylamide (HPAm) and N-benzophenone acrylamide (BPAm) were grafted onto the external pSi surfaces by spin-coating and UV crosslinking. Each modification step was verified using attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, water contact angle (WCA) measurements, X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). In order to confirm that the air plasma treatment and silanization step only occurred on the top surface of pSi samples, confocal microscopy was employed after fluorescein isothiocyanate (FITC) conjugation. Drug release studies carried out over 17 h in PBS demonstrated that the modified pSi reservoirs released CPT continuously, while showing excellent stability. Furthermore, protein adsorption and cell attachment studies demonstrated the ability of the graft polymer layer to reduce both significantly. In combination with the biocompatible pSi substrate material, the facile modification strategy described in this study provides access to new multifunctional drug delivery systems (DDS) for applications in cancer therapy.

As we rapidly approach a post-antibiotic era in which multi-drug resistant bacteria are ever-pervasive, antimicrobial peptides (AMPs) represent a promising class of compounds to help address this global issue. AMPs are best-known for their membrane-disruptive mode of action leading to bacteria cell lysis and death. However, many AMPs are also known to be non-lytic and have intracellular modes of action. Proline-rich AMPs (PrAMPs) are one such class, that are generally membrane permeable and inhibit protein synthesis leading to a bactericidal outcome. PrAMPs are highly effective against Gram-negative bacteria and yet show very low toxicity against eukaryotic cells. Here, we review both the PrAMP family and the past and current definitions for this class of peptides. Computational analysis of known AMPs within the DRAMP database (http://dramp.cpu-bioinfor.org/) and assessment of their PrAMP-like properties have led us to develop a revised definition of the PrAMP class. As a result, we subsequently identified a number of unknown and unclassified peptides containing motifs of striking similarity to known PrAMP-based DnaK inhibitors and propose a series of new sequences for experimental evaluation and subsequent addition to the PrAMP family.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF-SIMS data.Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF-SIMS peak lists using up-binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time-of-flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low-density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF-SIMS data.Artificial neural networks, in the form of self-organising maps, are introduced and provide a non-linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions.This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.

Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate sample set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.

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