Quantification of Cardiomyocyte Beating Frequency Using Fourier Transform Analysis

Contraction pressure analysis using optical imaging in normal and MYBPC3-mutated hiPSC-derived cardiomyocytes grown on matrices with tunable stiffness

Current in vivo disease models and analysis methods for cardiac drug development have been insufficient in providing accurate and reliable predictions of drug efficacy and safety. Here, we propose a custom optical flow-based analysis method to quantitatively measure recordings of contracting cardiomyocytes on polydimethylsiloxane (PDMS), compatible with medium-throughput systems. Movement of the PDMS was examined by covalently bound fluorescent beads on the PDMS surface, differences caused by increased substrate stiffness were compared, and cells were stimulated with β-agonist. We further validated the system using cardiomyocytes treated with endothelin-1 and compared their contractions against control and cells incubated with receptor antagonist bosentan. After validation we examined two MYBPC3-mutant patient-derived cell lines. Recordings showed that higher substrate stiffness resulted in higher contractile pressure, while beating frequency remained similar to control. β-agonist stimulation resulted in both higher beating frequency as well as higher pressure values during contraction and relaxation. Cells treated with endothelin-1 showed an increased beating frequency, but a lower contraction pressure. Cells treated with both endothelin-1 and bosentan remained at control level of beating frequency and pressure. Lastly, both MYBPC3-mutant lines showed a higher beating frequency and lower contraction pressure. Our validated method is capable of automatically quantifying contraction of hiPSC-derived cardiomyocytes on a PDMS substrate of known shear modulus, returning an absolute value. Our method could have major benefits in a medium-throughput setting.

Classification of the Acoustics of Loose Gravel

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment; therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

Fibronectin-based nanomechanical biosensors to map 3D surface strains in live cells and tissue

Mechanical forces are integral to cellular migration, differentiation and tissue morphogenesis; however, it has proved challenging to directly measure strain at high spatial resolution with minimal perturbation in living sytems. Here, we fabricate, calibrate, and test a fibronectin (FN)-based nanomechanical biosensor (NMBS) that can be applied to the surface of cells and tissues to measure the magnitude, direction, and strain dynamics from subcellular to tissue length-scales. The NMBS is a fluorescently-labeled, ultra-thin FN lattice-mesh with spatial resolution tailored by adjusting the width and spacing of the lattice from 2-100 µm. Time-lapse 3D confocal imaging of the NMBS demonstrates 2D and 3D surface strain tracking during mechanical deformation of known materials and is validated with finite element modeling. Analysis of the NMBS applied to single cells, cell monolayers, and Drosophila ovarioles highlights the NMBS's ability to dynamically track microscopic tensile and compressive strains across diverse biological systems where forces guide structure and function.

Multi-directional cellular alignment in 3D guided by electrohydrodynamically-printed microlattices

Recapitulating aligned cellular architectures of native tissues in vitro is important to engineer artificial tissue analogs with desired biological functions. Here a novel strategy is presented to direct three-dimensional (3D) cellular alignment by embedding cell/collagen hydrogel into the predefined electrohydrodynamically-printed microlattices. The cell/collagen hydrogel, originally filled within the printed microlattices uniformly, was found to gradually develop into densely-populated and highly-aligned bands along the longitudinal direction of the printed microlattices. The cellular alignment was highly dependent on the height, spacing and orientation of the microlattices. The presented method was applicable to multiple cell types including primary cardiomyocytes and the gaps formed between the aligned bands and the lateral walls of the microlattice facilitated the subsequent seeding and rapid alignment of other cell types which enables to engineer anisotropic multicellular tissue constructs. The engineered cardiac patches expressed mature cardiomyocyte-specific phenotypes and exhibited synchronous contractive activities. Multilayer cellular alignment with varied orientation in 3D collagen hydrogel was successfully achieved by using electrohydrodynamically-printed microlattices with layer-specific orientations. This exploration offers a promising way to engineer complex 3D tissue constructs with predefined cellular alignments. STATEMENT OF SIGNIFICANCE: Fabrication of biomimetic highly-aligned complex cellular architectures has a great significance to recapitulate the unique mechanical and physiological functions of the engineered tissues (e.g., heart tissue, neuron, muscle). Here, we introduced a novel strategy to direct 3D cellular alignment by embedding cell/collagen hydrogel into the predefined electrohydrodynamically-printed microlattices without any external stimuli. The microscopical study of the dynamic alignment process of cells and collagen fibers contributed to exploring the mechanism of autonomous formation of highly-aligned cellular bands. Multilayer cellular alignment with varied orientation in 3D collagen hydrogel was successfully achieved by using the microlattices with layer-specific orientations, which showed a promising way to engineer complex 3D tissue constructs with predefined cellular alignments.