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Real-Time Analysis of Oxygen Gradient in Oocyte Respiration Using a High-Density Microelectrode Array.

ABSTRACT: Physiological events related to oxygen concentration gradients provide valuable information to determine the state of metabolizing biological cells. The existing oxygen sensing methods (i.e., optical photoluminescence, magnetic resonance, and scanning electrochemical) are well-established and optimized for existing in vitro analyses. However, such methods also present various limitations in resolution, real-time sensing performance, complexity, and costs. An electrochemical imaging system with an integrated microelectrode array (MEA) would offer attractive means of measuring oxygen consumption rate (OCR) based on the cell's two-dimensional (2D) oxygen concentration gradient. This paper presents an application of an electrochemical sensor platform with a custom-designed complementary-metal-oxide-semiconductor (CMOS)-based microchip and its Pt-coated surface MEA. The high-density MEA provides 16,064 individual electrochemical pixels that cover a 3.6 mm × 3.6 mm area. Utilizing the three-electrode configuration, the system is capable of imaging low oxygen concentration (18.3 µM, 0.58 mg/L, or 13.8 mmHg) at 27.5 µm spatial resolution and up to 4 Hz temporal resolution. In vitro oxygen imaging experiments were performed to analyze bovine cumulus-oocytes-complexes cells OCR and oxygen flux density. The integration of a microfluidic system allows proper bio-sample handling and delivery to the MEA surface for imaging. Finally, the imaging results are processed and presented as 2D heatmaps, representing the dissolved oxygen concentration in the immediate proximity of the MEA. This paper provides the results of real-time 2D imaging of OCR of live cells/tissues to gain spatial and temporal dynamics of target cell metabolism.

SUBMITTER: Tedjo W

PROVIDER: S-EPMC8393405 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

Dataset Information

Real-Time Analysis of Oxygen Gradient in Oocyte Respiration Using a High-Density Microelectrode Array.

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