Toward Regeneration of the Heart: Bioengineering Strategies for Immunomodulation.

Myocardial Infarction (MI) is the most common cardiovascular disease. An average-sized MI causes the loss of up to 1 billion cardiomyocytes and the adult heart lacks the capacity to replace them. Although post-MI treatment has dramatically improved survival rates over the last few decades, more than 20% of patients affected by MI will subsequently develop heart failure (HF), an incurable condition where the contracting myocardium is transformed into an akinetic, fibrotic scar, unable to meet the body\u27s need for blood supply. Excessive inflammation and persistent immune auto-reactivity have been suggested to contribute to post-MI tissue damage and exacerbate HF development. Two newly emerging fields of biomedical research, immunomodulatory therapies and cardiac bioengineering, provide potential options to target the causative mechanisms underlying HF development. Combining these two fields to develop biomaterials for delivery of immunomodulatory bioactive molecules holds great promise for HF therapy. Specifically, minimally invasive delivery of injectable hydrogels, loaded with bioactive factors with angiogenic, proliferative, anti-apoptotic and immunomodulatory functions, is a promising route for influencing the cascade of immune events post-MI, preventing adverse left ventricular remodeling, and offering protection from early inflammation to fibrosis. Here we provide an updated overview on the main injectable hydrogel systems and bioactive factors that have been tested in animal models with promising results and discuss the challenges to be addressed for accelerating the development of these novel therapeutic strategies

Mimicking the extracellular matrix – a biomaterials approach to inhibit tissue fibrosis

Epithelial tissue is marked by the presence of a specialized, highly cross-linked, sheet-like extracellular matrix, the basement membrane. Tissue-invasive events, such as the epithelial-to-mesenchymal transition (EMT) - a key event in gastrulation, tissue fibrosis and cancer metastasis – are characterized by irreversible structural changes of the basement membrane through proteolytic processing by matrix metalloproteinases (MMPs). We have recently reported a previously unidentified laminin fragment that is released during EMT by MMP2 and that modulates key EMT-signalling pathways. Specifically, interaction of the laminin fragment with α3β1-integrin triggers the down-regulation of MMP2 expression, thereby constituting a cell-basement membrane-cell feedback mechanism. Inhibiting MMPs has been proposed as a strategy to prevent pathological cell migration and basement membrane breakdown in the course of EMT. Here, we explore this cell-matrix-cell feedback mechanism to target pathological EMT in the course of tissue fibrosis. We present an electrospun biomaterial that is functionalized with the recombinant laminin fragment and that can be directly interfaced with epithelial tissue to interfere with EMT pathways and inhibit MMP2 expression and activity in vitro and in vivo. We demonstrate how interaction of the functionalized synthetic membrane with peritoneal tissue inhibits mesothelial EMT in a mouse model of TGFβ-induced peritoneal fibrosis by decreasing active MMP2 levels, and propose a mechanism of how the laminin fragment acts downstream of α3β1-integrin in epithelial cells, after it is released from the basement membrane

Hypoxia-mimicking bioactive glass/collagen glycosaminoglycan composite scaffolds to enhance angiogenesis and bone repair.

One of the biggest challenges in regenerative medicine is promoting sufficient vascularisation of tissue-engineered constructs. One approach to overcome this challenge is to target the cellular hypoxia inducible factor (HIF-1α) pathway, which responds to low oxygen concentration (hypoxia) and results in the activation of numerous pro-angiogenic genes including vascular endothelial growth factor (VEGF). Cobalt ions are known to mimic hypoxia by artificially stabilising the HIF-1α transcription factor. Here, resorbable bioactive glass particles (38 μm and 100 μm) with cobalt ions incorporated into the glass network were used to create bioactive glass/collagen-glycosaminoglycan scaffolds optimised for bone tissue engineering. Inclusion of the bioactive glass improved the compressive modulus of the resulting composite scaffolds while maintaining high degrees of porosity (\u3e97%). Moreover, in vitro analysis demonstrated that the incorporation of cobalt bioactive glass with a mean particle size of 100 μm significantly enhanced the production and expression of VEGF in endothelial cells, and cobalt bioactive glass/collagen-glycosaminoglycan scaffold conditioned media also promoted enhanced tubule formation. Furthermore, our results prove the ability of these scaffolds to support osteoblast cell proliferation and osteogenesis in all bioactive glass/collagen-glycosaminoglycan scaffolds irrespective of the particle size. In summary, we have developed a hypoxia-mimicking tissue-engineered scaffold with pro-angiogenic and pro-osteogenic capabilities that may encourage bone tissue regeneration and overcome the problem of inadequate vascularisation of grafts commonly seen in the field of tissue engineering

Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays

With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences. Often, many similar biological sequences need to be made for a specific application meaning numerous, sometimes prohibitively expensive, lab experiments are necessary for their optimization. This paper presents a transfer learning design of experiments workflow to make this development feasible. By combining a transfer learning surrogate model with Bayesian optimization, we show how the total number of experiments can be reduced by sharing information between optimization tasks. We demonstrate the reduction in the number of experiments using data from the development of DNA competitors for use in an amplification-based diagnostic assay. We use cross-validation to compare the predictive accuracy of different transfer learning models, and then compare the performance of the models for both single objective and penalized optimization tasks

Printed smart devices for anti-counterfeiting allowing precise identification with household equipment

Counterfeiting has become a serious global problem, causing worldwide losses and disrupting the normal order of society. Physical unclonable functions are promising hardware-based cryptographic primitives, especially those generated by chemical processes showing a massive challenge-response pair space. However, current chemical-based physical unclonable function devices typically require complex fabrication processes or sophisticated characterization methods with only binary (bit) keys, limiting their practical applications and security properties. Here, we report a flexible laser printing method to synthesize unclonable electronics with high randomness, uniqueness, and repeatability. Hexadecimal resistive keys and binary optical keys can be obtained by the challenge with an ohmmeter and an optical microscope. These readout methods not only make the identification process available to general end users without professional expertise, but also guarantee device complexity and data capacity. An adopted open-source deep learning model guarantees precise identification with high reliability. The electrodes and connection wires are directly printed during laser writing, which allows electronics with different structures to be realized through free design. Meanwhile, the electronics exhibit excellent mechanical and thermal stability. The high physical unclonable function performance and the widely accessible readout methods, together with the flexibility and stability, make this synthesis strategy extremely attractive for practical applications

Quantitative volumetric Raman imaging of three dimensional cell cultures

The ability to simultaneously image multiple biomolecules in biologically relevant three-dimensional (3D) cell culture environments would contribute greatly to the understanding of complex cellular mechanisms and cell–material interactions. Here, we present a computational framework for label-free quantitative volumetric Raman imaging (qVRI). We apply qVRI to a selection of biological systems: human pluripotent stem cells with their cardiac derivatives, monocytes and monocyte-derived macrophages in conventional cell culture systems and mesenchymal stem cells inside biomimetic hydrogels that supplied a 3D cell culture environment. We demonstrate visualization and quantification of fine details in cell shape, cytoplasm, nucleus, lipid bodies and cytoskeletal structures in 3D with unprecedented biomolecular specificity for vibrational microspectroscopy

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a non-destructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell

Unsupervised hyperspectral data mining and bioimaging by information entropy and self-modeling curve resolution

Unsupervised estimation of the dimensionality of hyperspectral microspectroscopy datasets containing pure and mixed spectral features, and extraction of their representative endmember spectra, remains a challenge in biochemical data mining. We report a new versatile algorithm building on semi-nonnegativity constrained self-modeling curve resolution and information entropy, to estimate the quantity of separable biochemical species from hyperspectral microspectroscopy, and extraction of their representative spectra. The algorithm is benchmarked with established methods from satellite remote sensing, spectral unmixing, and clustering. To demonstrate the widespread applicability of the developed algorithm, we collected hyperspectral datasets using spontaneous Raman, Coherent Anti-stokes Raman Scattering and Fourier Transform IR, of seven reference compounds, an oil-in-water emulsion, and tissue-engineered extracellular matrices on poly-L-lactic acid and porcine jejunum-derived small intestine submucosa scaffolds seeded with bovine chondrocytes. We show the potential of the developed algorithm by consolidating hyperspectral molecular information with sample microstructure, pertinent to fields ranging from gastrophysics to regenerative medicine

Enzyme prodrug therapy achieves site-specific, personalized physiological responses to the locally produced nitric oxide

Nitric oxide (NO) is a highly potent but short-lived endogenous radical with a wide spectrum of physiological activities. In this work, we developed an enzymatic approach to the site-specific synthesis of NO mediated by biocatalytic surface coatings. Multilayered polyelectrolyte films were optimized as host compartments for the immobilized β-galactosidase (β-Gal) enzyme through a screen of eight polycations and eight polyanions. The lead composition was used to achieve localized production of NO through the addition of β-Gal–NONOate, a prodrug that releases NO following enzymatic bioconversion. The resulting coatings afforded physiologically relevant flux of NO matching that of the healthy human endothelium. The antiproliferative effect due to the synthesized NO in cell culture was site-specific: within a multiwell dish with freely shared media and nutrients, a 10-fold inhibition of cell growth was achieved on top of the biocatalytic coatings compared to the immediately adjacent enzyme-free microwells. The physiological effect of NO produced via the enzyme prodrug therapy was validated ex vivo in isolated arteries through the measurement of vasodilation. Biocatalytic coatings were deposited on wires produced using alloys used in clinical practice and successfully mediated a NONOate concentration-dependent vasodilation in the small arteries of rats. The results of this study present an exciting opportunity to manufacture implantable biomaterials with physiological responses controlled to the desired level for personalized treatment