Interferometric Biosensor for High Sensitive Label-Free Recording of HiPS Cardiomyocytes Contraction in Vitro.

Heart disease remains a leading cause of global mortality, underscoring the need for advanced technologies to study cardiovascular diseases and develop effective treatments. We introduce an innovative interferometric biosensor for high-sensitivity and label-free recording of human induced pluripotent stem cell (hiPSC) cardiomyocyte contraction in vitro. Using an optical cavity, our device captures interference patterns caused by the contraction-induced displacement of a thin flexible membrane. First, we demonstrate the capability to quantify spontaneous contractions and discriminate between contraction and relaxation phases. We calculate a contraction-induced vertical membrane displacement close to 40 nm, which implies a traction stress of 34 ± 4 mN/mm2. Finally, we investigate the effects of a drug compound on contractility amplitude, revealing a significant reduction in contractile forces. The label-free and high-throughput nature of our biosensor may enhance drug screening processes and drug development for cardiac treatments. Our interferometric biosensor offers a novel approach for noninvasive and real-time assessment of cardiomyocyte contraction.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Passive Recording of Bioelectrical Signals from Non-Excitable Cells by Fluorescent Mirroring.

Bioelectrical variations trigger different cell responses, including migration, mitosis, and mutation. At the tissue level, these actions result in phenomena such as wound healing, proliferation, and pathogenesis. Monitoring these mechanisms dynamically is highly desirable in diagnostics and drug testing. However, existing technologies are invasive: either they require physical access to the intracellular compartments, or they imply direct contact with the cellular medium. Here, we present a novel approach for the passive recording of electrical signals from non-excitable cells adhering to 3D microelectrodes, based on optical mirroring. Preliminary results yielded a fluorescence intensity output increase of the 5,8% in the presence of a HEK-293 cell on the electrode compared to bare microelectrodes. At present, this technology may be employed to evaluate cell-substrate adhesion and monitor cell proliferation. Further refinements could allow extrapolating quantitative data on surface charges and resting potential to investigate the electrical phenomena involved in cell migration and cancer progression.

Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks.

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the "noisy" connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.