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Focal molography (“molography” in short) is a sensitive implementation of a diffractometric biosensor and has emerged as a new platform technology to study biomolecular interactions label-free in complex fluids and living cells. In contrast to established refractometric biosensors, in particular surface plasmon resonance, molography is almost insensitive to environmental noise, i.e. temperature gradients and nonspecific binding. Molography achieves this by modulating the analyte binding at a high spatial frequency and reads it out in Fourier space via diffraction of light at the bound molecules, i.e. molography applies the spatial lock-in principle for discrimination of the binding signal from disturbing effects on the sensor surface. In previous implementations of focal molography, the sensor was illuminated by a waveguide mode. While this arrangements has an outstanding resolution, it suffers from several drawbacks such as wavefront instabilities of the guided mode, the relatively high refractive index contrast at the waveguide interfaces and the manufacturing cost of waveguide and grating couplers. In this paper, we propose a simpler and more robust configuration for focal molography. Instead of a waveguide mode, it is based on darkfield illumination by total internal reflection (TIR) of a free space mode. We derive the coherent binding pattern, describe the fabrication process, show that its intensity distribution is as expected, derive the quantitative readout formula and perform a background and noise analysis. Real-time binding curves of streptavidin in buffer and concentrated bovine serum albumin solution show that TIR molography exhibits excellent resolution and robustness., Sensors and Actuators B: Chemical, 349, ISSN:0925-4005

In vitro diagnostics relies on the quantification of minute amounts of a specific biomolecule, called biomarker, from a biological sample. The majority of clinically relevant biomarkers for conditions beyond infectious diseases are detected by means of binding assays, where target biomarkers bind to a solid phase and are detected by biochemical or physical means. Nonspecifically bound biomolecules, the main source of variation in such assays, need to be washed away in a laborious process, restricting the development of widespread point-of-care diagnostics. Here, we show that a diffractometric assay provides a new, label-free possibility to investigate complex samples, such as blood plasma. A coherently arranged sub-micron pattern, that is, a peptide mologram, is created to demonstrate the insensitivity of this diffractometric assay to the unwanted masking effect of nonspecific interactions. In addition, using an array of low-affinity binders, we also demonstrate the feasibility of molecular profiling of blood plasma in real time and show that individual patients can be differentiated based on the binding kinetics of circulating proteins., ACS Sensors, 6 (3), ISSN:2379-3694

Label-free optical biosensors are an invaluable tool for molecular interaction analysis. Over the past 30 years, refractometric biosensors and, in particular, surface plasmon resonance have matured to the de facto standard of this field despite a significant cross reactivity to environmental and experimental noise sources. In this paper, we demonstrate that sensors that apply the spatial affinity lock-in principle (part I) and perform readout by diffraction overcome the drawbacks of established refractometric biosensors. We show this with a direct comparison of the cover refractive index jump sensitivity as well as the surface mass resolution of an unstabilized diffractometric biosensor with a state-of-the-art Biacore 8k. A combined refractometric diffractometric biosensor demonstrates that a refractometric sensor requires a much higher measurement precision than the diffractometric to achieve the same resolution. In a conceptual and quantitative discussion, we elucidate the physical reasons behind and define the figure of merit of diffractometric biosensors. Because low-precision unstabilized diffractometric devices achieve the same resolution as bulky stabilized refractometric sensors, we believe that label-free optical sensors might soon move beyond the drug discovery lab as miniaturized, mass-produced environmental/medical sensors. In fact, combined with the right surface chemistry and recognition element, they might even bring the senses of smell/taste to our smart devices., Sensors, 21 (1), ISSN:1424-8220

In recent years, cell-based assays have been frequently used in molecular interaction analysis. Cell-based assays complement traditional biochemical and biophysical methods, as they allow for molecular interaction analysis, mode of action studies and even drug screening processes to be performed under physiologically relevant conditions. In most cellular assays, biomolecules are usually labeled to achieve specificity. In order to overcome some of the drawbacks associated with label-based assays, we have recently introduced cell-based molography as a biosensor for the analysis of specific molecular interactions involving native membrane receptors in living cells. Here, we expand this assay to cytosolic protein-protein interactions. First, we created a biomimetic membrane receptor by tethering one cytosolic interaction partner to the plasma membrane. The artificial construct is then coherently arranged into a two-dimensional pattern within the cytosol of living cells. Thanks to the molographic sensor, the specific interactions between the coherently arranged protein and its endogenous interaction partners become visible in real-time without the use of a fluorescent label. This method turns out to be an important extension of cell-based molography because it expands the range of interactions that can be analyzed by molography to those in the cytosol of living cells., 22 pages, 4 figures

Stretchable electronic devices hold the potential to revolutionize noninvasive healthcare diagnostics and treatments. Current clinical healthcare technologies are of limited use in ambulatory, long-term settings. A new class of epidermal electronics is designed to conform closely to and with the irregular shape of the human body is emerging, providing an improved functional interface even during motion, while being imperceptible to the user. This review discusses challenges associated with long-term interactions between electronic devices and the skin without interfering with its regulatory and protective function. We report on the current state of the art of monitoring devices for the detection of temperature, motion, biopotentials or biomarkers alongside therapeutic approaches for thermal treatment or drug delivery. In particular, focus is brought on the long-term application of such devices with the associated challenges in terms of materials, wearability, communication and energy supply. With a number of obstacles left to tackle, epidermal stretchable electronics represents a powerful tool in the rising field of personalized medicine.

Diffractometric biosensing is a promising technology to overcome critical limitations of refractometric biosensors, the dominant class of label-free optical transducers. These limitations manifest themselves by higher noise and drifts due to insufficient rejection of refractive index fluctuations caused by variation in temperature, solvent concentration, and most prominently, non-specific binding. Diffractometric biosensors overcome these limitations with inherent self-referencing on the submicron scale with no compromise on resolution. Despite this highly promising attribute, the field of diffractometric biosensors has only received limited recognition. A major reason is the lack of a general quantitative analysis. This hinders comparison to other techniques and amongst different diffractometric biosensors. For refractometric biosensors, on the other hand, such a comparison is possible by means of the refractive index unit (RIU). In this publication, we suggest the coherent surface mass density, $\Gamma_{\rm{coh}}$, as a quantity for label-free diffractometric biosensors with the same purpose as RIU in refractometric sensors. It is easy to translate $\Gamma_{\rm{coh}}$ to the total surface mass density $\Gamma_{\rm{tot}}$, which is an important parameter for many assays. We provide a generalized framework to determine $\Gamma_{\rm{coh}}$ for various diffractometric biosensing arrangements which enables quantitative comparison. Additionally, the formalism can be used to estimate background scattering in order to further optimize sensor configurations. Finally, a practical guide with important experimental considerations is given to enable readers of any background to apply the theory. Therefore, this paper provides a powerful tool for the development of diffractometric biosensors and will help the field to mature and unveil its full potential.

The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence-based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most artificial intelligence research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labelled by the researcher to provide a training target for the algorithms to converge to the desired result. This labelling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by the ability of cycle-consistent generative adversarial networks to perform style transfer, we outline a method whereby a computer-generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but also able to precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, images from a live/dead assay of C. elegans, and X-ray computed tomography of metallic nanowire meshes. Labelling training data to train machine learning models is very time intense. A new method shows that content transformation can be effectively learned from generated data, avoiding the need for any manual labelling in segmentation and classification tasks.

DNA functionalized gold nanoparticles (DNA-AuNPs) have shown great potential for biosensing as they combine the excellent optical properties of gold nanoparticles and the molecular recognition function of DNA. Since the DNA density determines the assay performance and the stability of the conjugate, a precise control of the surface density of DNA-AuNP is crucial for an optimized biosensor. Here we report a simple assay for quantifying multiple unlabeled DNAs on AuNPs. The assay relies on potassium cyanide (KCN) to first dissolve the AuNPs, which then releases surface bound DNA for quantification through a double-stranded DNA dye. Using this analytical quantification method, we investigated several strategies to control the surface density of DNA-AuNPs. Besides the precise control of DNA density, the stability of DNA-AuNPs after conjugation is also important in developing a biosensor with optimal performance. Without proper storing conditions, DNA-AuNPs are unstable and aggregate over time. To overcome this problem, we developed a long-term storage solution to ensure the stability and quality of DNA-AuNPs after conjugation which would benefit any DNA-AuNP-based biosensor.

The segmentation of images is a common task in a broad range of research fields. To tackle increasingly complex images, artificial intelligence (AI) based approaches have emerged to overcome the shortcomings of traditional feature detection methods. Owing to the fact that most AI research is made publicly accessible and programming the required algorithms is now possible in many popular languages, the use of such approaches is becoming widespread. However, these methods often require data labeled by the researcher to provide a training target for the algorithms to converge to the desired result. This labeling is a limiting factor in many cases and can become prohibitively time consuming. Inspired by Cycle-consistent Generative Adversarial Networks’ (cycleGAN) ability to perform style transfer, we outline a method whereby a computer generated set of images is used to segment the true images. We benchmark our unsupervised approach against a state-of-the-art supervised cell-counting network on the VGG Cells dataset and show that it is not only competitive but can also precisely locate individual cells. We demonstrate the power of this method by segmenting bright-field images of cell cultures, a live-dead assay of C.Elegans and X-ray-computed tomography of metallic nanowire meshes.

Focal molography is a label-free optical biosensing method that relies on a coherent pattern of binding sites for biomolecular interaction analysis. Reactive immersion lithography (RIL) is central to the patterning of molographic chips but has potential for improvements. Here, we show that applying the idea of image reversal to RIL enables the fabrication of coherent binding patterns of increased quality (i.e., higher analyte efficiency). Thereby the detection limit of focal molography in biological assays can be improved.

Some minor issues were discovered after publication of Opt Lett. 43, 5801 (2018) and are corrected here. They do not change the main message of the paper.

The induction of a strong cytotoxic T cell response is an important prerequisite for successful immunotherapy against many viral diseases and tumors. Nucleotide vaccines, including mRNA vaccines with their intracellular antigen synthesis, have been shown to be potent activators of a cytotoxic immune response. The intracellular delivery of mRNA vaccines to the cytosol of antigen presenting immune cells is still not sufficiently well understood. Here, we report on the development of a lipid nanoparticle formulation for the delivery of mRNA vaccines to induce a cytotoxic CD 8 T cell response. We show transfection of dendritic cells, macrophages, and neutrophils. The efficacy of the vaccine was tested in an aggressive B16F10 melanoma model. We found a strong CD 8 T cell activation after a single immunization. Treatment of B16F10 melanoma tumors with lipid nanoparticles containing mRNA coding for the tumor-associated antigens gp100 and TRP2 resulted in tumor shrinkage and extended the overall survival of the treated mice. The immune response can be further increased by the incorporation of the adjuvant LPS. In conclusion, the lipid nanoparticle formulation presented here is a promising vector for mRNA vaccine delivery, one that is capable of inducing a strong cytotoxic T cell response. Further optimization, including the incorporation of different adjuvants, will likely enhance the potency of the vaccine.

Implantable electronics address therapeutical needs of patients with electrical signaling dysfunctions such as heart problems, neurological disorders or hearing impairments. While standard electronics are rigid, planar and made of hard materials, their surrounding biological tissues are soft, wet and constantly in motion. These intrinsic differences in mechanical and chemical properties cause physiological responses that constitute a fundamental challenge to create functional long-term interfaces. Using soft and stretchable materials for electronic implants decreases the mechanical mismatch between implant and biological tissues. As a result, tissue damage during and after implantation is reduced, leading not only to an attenuated foreign body response, but also enabling completely novel applications. However, but for a few exceptions, soft materials are not sufficient to create long-term stable functional implants. In this work, we review recent progress in interfacing both the central (CNS) and peripheral nervous system (PNS) for long-term functional devices. The basics of soft and stretchable devices are introduced by highlighting the importance of minimizing physical as well as mechanical mismatch between tissue and implant in the CNS and emphasizing the relevance of an appropriate surface chemistry for implants in the PNS. Finally, we report on the latest materials and techniques that provide further electronic enhancements while reducing the foreign body reaction. Thus, this review should serve as a guide for creating long-term functional implants to enable future healthcare technologies and as a discussion on current ideas and progress within the field.