Adaptive frequency-domain filtering for neural signal preprocessing.

Electrical interference from various sources is a common issue for experimental extracellular electrophysiology recordings collected using multi-electrode array neural recording systems. This interference deteriorates the signal-to-noise ratio (SNR) of the raw electrophysiology signals and hampers the accuracy of data post-processing using techniques such as spike-sorting. Traditional signal processing methods to digitally remove electrical interference during post-processing include bandpass filtering to limit the signal to the relevant spectral range of the biological data, e.g., the spikes band (300 Hz - 7 kHz), targeted notch filtering to remove power line interference from standard alternating current mains electricity and common reference removal to minimize noise common to all electrodes. These methods require a priori knowledge of the frequency of the interfering signal source to address the unique electromagnetic interference environment of each experimental setup. We discuss an adaptive method for automatically removing narrow-band electrical interference through a spectral peak detection and removal (SPDR) step that can be applied during post-processing of the recorded data, based on the intuition that tall, narrowband signals localized in the signal spectrum correspond to interference, rather than the activity of neurons. A spectral peak prominence (SPP) threshold is used to detect these peaks in the frequency domain, which will then be removed via notch filtering. We applied this method to simulated waveforms and also experimental electrophysiology data collected from cerebral organoids to demonstrate its effectiveness for removing unwanted interference without significantly distorting the neural signals. We discuss that proper selection of the SPP threshold is required to avoid over-filtering, which can result in distortion of the electrophysiology data. We also compare the firing-rate activity in the filtered electrophysiology with fluorescence calcium imaging, a secondary cellular activity marker, to quantify signal distortion and provide bounds on SNR-based optimization of the SPP threshold. The adaptive filtering technique demonstrated in this paper is a powerful method that can automatically detect and remove interband interference in recorded neural signals, potentially enabling data collection in more naturalistic settings where external interference signals are difficult to eliminate.

Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype.

The role that mechanical forces play in shaping the structure and function of the heart is critical to understanding heart formation and the etiology of disease but is challenging to study in patients. Engineered heart tissues (EHTs) incorporating human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have the potential to provide insight into these adaptive and maladaptive changes. However, most EHT systems cannot model both preload (stretch during chamber filling) and afterload (pressure the heart must work against to eject blood). Here, we have developed a new dynamic EHT (dyn-EHT) model that enables us to tune preload and have unconstrained contractile shortening of >10%. To do this, three-dimensional (3D) EHTs were integrated with an elastic polydimethylsiloxane strip providing mechanical preload and afterload in addition to enabling contractile force measurements based on strip bending. Our results demonstrated that dynamic loading improves the function of wild-type EHTs on the basis of the magnitude of the applied force, leading to improved alignment, conduction velocity, and contractility. For disease modeling, we used hiPSC-derived cardiomyocytes from a patient with arrhythmogenic cardiomyopathy due to mutations in the desmoplakin gene. We demonstrated that manifestation of this desmosome-linked disease state required dyn-EHT conditioning and that it could not be induced using 2D or standard 3D EHT approaches. Thus, a dynamic loading strategy is necessary to provoke the disease phenotype of diastolic lengthening, reduction of desmosome counts, and reduced contractility, which are related to primary end points of clinical disease, such as chamber thinning and reduced cardiac output.

Bioelectrical interfaces with cortical spheroids in three-dimensions.

Objective.Three-dimensional (3D) neuronal spheroid culture serves as a powerful model system for the investigation of neurological disorders and drug discovery. The success of such a model system requires techniques that enable high-resolution functional readout across the entire spheroid. Conventional microelectrode arrays and implantable neural probes cannot monitor the electrophysiology (ephys) activity across the entire native 3D geometry of the cellular construct.Approach.Here, we demonstrate a 3D self-rolled biosensor array (3D-SR-BA) integrated with a 3D cortical spheroid culture for simultaneousin vitroephys recording, functional Ca2+imaging, while monitoring the effect of drugs. We have also developed a signal processing pipeline to detect neural firings with high spatiotemporal resolution from the ephys recordings based on established spike sorting methods.Main results.The 3D-SR-BAs cortical spheroid interface provides a stable, high sensitivity recording of neural action potentials (<50µV peak-to-peak amplitude). The 3D-SR-BA is demonstrated as a potential drug screening platform through the investigation of the neural response to the excitatory neurotransmitter glutamate. Upon addition of glutamate, the neural firing rates increased notably corresponding well with the functional Ca2+imaging.Significance.Our entire system, including the 3D-SR-BA integrated with neuronal spheroid culture, enables simultaneous ephys recording and functional Ca2+imaging with high spatiotemporal resolution in conjunction with chemical stimulation. We demonstrate a powerful toolset for future studies of tissue development, disease progression, and drug testing and screening, especially when combined with native spheroid cultures directly extracted from humans.

Organ-on-e-chip: Three-dimensional self-rolled biosensor array for electrical interrogations of human electrogenic spheroids.

Cell-cell communication plays a pivotal role in coordination and function of biological systems. Three-dimensional (3D) spheroids provide venues to explore cellular communication for tissue development and drug discovery, as their 3D architecture mimics native in vivo microenvironments. Cellular electrophysiology is a prevalent signaling paradigm for studying electroactive cells. Currently, electrophysiological studies do not provide direct, multisite, simultaneous investigation of tissues in 3D. In this study, 3D self-rolled biosensor arrays (3D-SR-BAs) of either active field-effect transistors or passive microelectrodes were implemented to interface human cardiac spheroids in 3D. The arrays provided continuous and stable multiplexed recordings of field potentials with high sensitivity and spatiotemporal resolution, supported with simultaneous calcium imaging. Our approach enables electrophysiological investigation and monitoring of the complex signal transduction in 3D cellular assemblies toward an organ-on-an-electronic-chip (organ-on-e-chip) platform for tissue maturation investigations and development of drugs for disease treatment, such as arrhythmias.

Bioelectronics with nanocarbons.

Characterizing the electrical activity of cardiomyocytes and neurons is crucial in understanding the complex processes in the heart and brain tissues, both in healthy and diseased states. Micro- and nanotechnologies have significantly improved the electrophysiological investigation of cellular networks. Carbon-based nanomaterials or nanocarbons, such as carbon nanotubes (CNTs), nanodiamonds (NDs) and graphene are promising building blocks for bioelectronics platforms owing to their outstanding chemical and physical properties. In this review, we discuss the various bioelectronics applications of nanocarbons and their derivatives. Furthermore, we touch upon the challenges that remain in the field and describe the emergence of carbon-based hybrid-nanomaterials that will potentially address those limitations, thus improving the capabilities to investigate the electrophysiology of excitable cells, both as a network and at the single cell level.