Tissue engineering has emerged as a viable approach to treat disease or repair damage in tissues and organs. One of the key elements for the success of tissue engineering is the use of a scaffold serving as artificial extracellular matrix (ECM). The ECM hosts the cells and improves their survival, proliferation, and differentiation, enabling the formation of new tissue. Here, we propose the development of a class of protein/polysaccharide-based porous scaffolds for use as ECM substitutes in cardiac tissue engineering. Scaffolds based on blends of a protein component, collagen or gelatin, with a polysaccharide component, alginate, were produced by freeze-drying and subsequent ionic and chemical crosslinking. Their morphological, physicochemical, and mechanical properties were determined and compared with those of natural porcine myocardium. We demonstrated that our scaffolds possessed highly porous and interconnected structures, and the chemical homogeneity of the natural ECM was well reproduced in both types of scaffolds. Furthermore, the alginate/gelatin (AG) scaffolds better mimicked the native tissue in terms of interactions between components and protein secondary structure, and in terms of swelling behavior. The AG scaffolds also showed superior mechanical properties for the desired application and supported better adhesion, growth, and differentiation of myoblasts under static conditions. The AG scaffolds were subsequently used for culturing neonatal rat cardiomyocytes, where high viability of the resulting cardiac constructs was observed under dynamic flow culture in a microfluidic bioreactor. We therefore propose our protein/polysaccharide scaffolds as a viable ECM substitute for applications in cardiac tissue engineering. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 769-781, 2018.

To create life‐like movements, living muscle actuator technologies have borrowed inspiration from biomimetic concepts in developing bioinspired robots. Here, the development of a bioinspired soft robotics system, with integrated self‐actuating cardiac muscles on a hierarchically structured scaffold with flexible gold microelectrodes is reported. Inspired by the movement of living organisms, a batoid‐fish‐shaped substrate is designed and reported, which is composed of two micropatterned hydrogel layers. The first layer is a poly(ethylene glycol) hydrogel substrate, which provides a mechanically stable structure for the robot, followed by a layer of gelatin methacryloyl embedded with carbon nanotubes, which serves as a cell culture substrate, to create the actuation component for the soft body robot. In addition, flexible Au microelectrodes are embedded into the biomimetic scaffold, which not only enhance the mechanical integrity of the device, but also increase its electrical conductivity. After culturing and maturation of cardiomyocytes on the biomimetic scaffold, they show excellent myofiber organization and provide self‐actuating motions aligned with the direction of the contractile force of the cells. The Au microelectrodes placed below the cell layer further provide localized electrical stimulation and control of the beating behavior of the bioinspired soft robot.

BackgroundAdvances in tissue clearing and molecular labeling methods are enabling unprecedented optical access to large intact biological systems. These developments fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While light sheet microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light sheet illumination.ResultsTo address this fundamental limitation, we have developed light sheet theta microscopy (LSTM), which uniformly illuminates samples from the same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing the imaging resolution, depth, and speed. We present a detailed characterization of LSTM, and demonstrate its complementary advantages over LSM for rapid high-resolution quantitative imaging of large intact samples with high uniform quality.ConclusionsThe reported LSTM approach is a significant step for the rapid high-resolution quantitative mapping of the structure and function of very large biological systems, such as a clarified thick coronal slab of human brain and uniformly expanded tissues, and also for rapid volumetric calcium imaging of highly motile animals, such as Hydra, undergoing non-isomorphic body shape changes.Electronic supplementary materialThe online version of this article (10.1186/s12915-018-0521-8) contains supplementary material, which is available to authorized users.

We utilized forebrain organoids generated from induced pluripotent stem cells of patients with a syndromic form of Autism Spectrum Disorder (ASD) with a homozygous protein-truncating mutation in CNTNAP2, to study its effects on embryonic cortical development. Patients with this mutation present with clinical characteristics of brain overgrowth. Patient-derived forebrain organoids displayed an increase in volume and total cell number that is driven by increased neural progenitor proliferation. Single-cell RNA sequencing revealed PFC-excitatory neurons to be the key cell types expressing CNTNAP2. Gene ontology analysis of differentially expressed genes (DEgenes) corroborates aberrant cellular proliferation. Moreover, the DEgenes are enriched for ASD-associated genes. The cell-type-specific signature genes of the CNTNAP2-expressing neurons are associated with clinical phenotypes previously described in patients. The organoid overgrowth phenotypes were largely rescued after correction of the mutation using CRISPR-Cas9. This CNTNAP2-organoid model provides opportunity for further mechanistic inquiry and development of new therapeutic strategies for ASD.

Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson’s disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platform’s robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinson’s disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

Autism spectrum disorder (ASD) represents a major public health burden but translating promising treatment findings from preclinical non-human models of ASD to the clinic has remained challenging. The recent development of forebrain organoids generated from human induced pluripotent stem cells (hiPSCs) derived from subjects with brain P a g e 4 o f 4 6

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Drug discovery for Parkinson’s disease (PD) is impeded by the lack of screenable phenotypes in scalable cell models. Here we present a novel unbiased phenotypic profiling platform that combines automation, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 PD patients and carefully matched healthy controls, generating the largest publicly available Cell Painting dataset to date. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated unbiased deep embeddings from each image, and applied these to train machine learning models to detect morphological disease phenotypes. Interestingly, our models captured individual variation by identifying specific cell lines within the cohort with high fidelity, even across different batches and plate layouts, demonstrating platform robustness and sensitivity. Importantly, our models were able to confidently separate LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for PD modeling and drug screening applications.