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Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays.

ABSTRACT: Recent advances in optical technologies such as multi-photon microscopy and optogenetics have revolutionized our ability to record and manipulate neuronal activity. Combining optical techniques with electrical recordings is of critical importance to connect the large body of neuroscience knowledge obtained from animal models to human studies mainly relying on electrophysiological recordings of brain-scale activity. However, integration of optical modalities with electrical recordings is challenging due to generation of light-induced artifacts. Here we report a transparent graphene microelectrode technology that eliminates light-induced artifacts to enable crosstalk-free integration of 2-photon microscopy, optogenetic stimulation, and cortical recordings in the same in vivo experiment. We achieve fabrication of crack- and residue-free graphene electrode surfaces yielding high optical transmittance for 2-photon imaging down to ~?1?mm below the cortical surface. Transparent graphene microelectrode technology offers a practical pathway to investigate neuronal activity over multiple spatial scales extending from single neurons to large neuronal populations.

SUBMITTER: Thunemann M

PROVIDER: S-EPMC5964174 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays.

Thunemann Martin M Lu Yichen Y Liu Xin X Kılıç Kıvılcım K Desjardins Michèle M Vandenberghe Matthieu M Sadegh Sanaz S Saisan Payam A PA Cheng Qun Q Weldy Kimberly L KL Lyu Hongming H Djurovic Srdjan S Andreassen Ole A OA Dale Anders M AM Devor Anna A Kuzum Duygu D

Nature communications 20180523 1

Recent advances in optical technologies such as multi-photon microscopy and optogenetics have revolutionized our ability to record and manipulate neuronal activity. Combining optical techniques with electrical recordings is of critical importance to connect the large body of neuroscience knowledge obtained from animal models to human studies mainly relying on electrophysiological recordings of brain-scale activity. However, integration of optical modalities with electrical recordings is challeng ...[more]

PMID: 29789548

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Project description:AbstractStudies have provided evidence that human cerebral organoids (hCOs) recapitulate fundamental milestones of early brain development, but many important questions regarding their functionality and electrophysiological properties persist. High-density microelectrode arrays (HD-MEAs) represent an attractive analysis platform to perform functional studies of neuronal networks at the cellular and network scale. Here, we use HD-MEAs to derive large-scale electrophysiological recordings from sliced hCOs. We record the activity of hCO slices over several weeks and probe observed neuronal dynamics pharmacologically. Moreover, we present results on how the obtained recordings can be spike-sorted and subsequently studied across scales. For example, we show how to track single neurons across several days on the HD-MEA and how to infer axonal action potential velocities. We also infer putative functional connectivity from hCO recordings. The introduced methodology will contribute to a better understanding of developing neuronal networks in brain organoids and provide new means for their functional characterization.Impact statementHuman cerebral organoids (hCOs) represent an attractive in vitro model system to study key physiological mechanisms underlying early neuronal network formation in tissue with healthy or disease-related genetic backgrounds. Despite remarkable advances in the generation of brain organoids, knowledge on the functionality of their neuronal circuits is still scarce. Here, we used complementary metal-oxide-semiconductor (CMOS)-based high-density microelectrode arrays (HD-MEAs) to perform large-scale recordings from sliced hCOs over several weeks and quantified their activity across scales. Using single-cell and network metrics, we were able to probe aspects of hCO neurophysiology that are more difficult to obtain with other techniques, such as patch clamping (lower yield) and calcium imaging (lower temporal resolution). These metrics included, for example, extracellular action potential (AP) waveform features and axonal AP velocity at the cellular level, as well as functional connectivity at the network level. Analysis was enabled by the large sensing area and the high spatiotemporal resolution provided by HD-MEAs, which allowed recordings from hundreds of neurons and spike sorting of their activity. Our results demonstrate that HD-MEAs provide a multi-purpose platform for the functional characterization of hCOs, which will be key in improving our understanding of this model system and assessing its relevance for translational research.Graphical abstractSupplementary informationThe online version contains supplementary material available at 10.1557/s43577-022-00282-w.

Project description:IntroductionCell-cell communication plays a pivotal role in biological systems' coordination and function. Electrical properties have been linked to specification and differentiation of stem cells into targeted progeny, such as neurons and cardiomyocytes. Currently, there is a critical need in developing new ways to complement fluorescent indicators, such as Ca2+-sensitive dyes, for direct electrophysiological measurements of cells and tissue. Here, we report a unique transparent and biocompatible graphene-based electrical platform that enables electrical and optical investigation of human embryonic stem cell-derived cardiomyocytes' (hESC-CMs) intracellular processes and intercellular communication.MethodsGraphene, a honeycomb sp2 hybridized two-dimensional carbon lattice, was synthesized using low pressure chemical vapor deposition system, and was tested for biocompatibility. Au and graphene microelectrode arrays (MEAs) were fabricated using well-established microfabrication methods. Au and graphene MEAs were interfaced with hESC-CMs to perform both optical and electrical recordings.ResultsOptical imaging and Raman spectroscopy confirmed the presence of monolayer graphene. Viability assay showed biocompatibility of graphene. Electrochemical characterization proved graphene's functional activity. Nitric acid treatment further enhanced the electrochemical properties of graphene. Graphene electrodes' transparency enabled both optical and electrical recordings from hESC-CMs. Graphene MEA detected changes in beating frequency and field potential duration upon β-adrenergic receptor agonist treatment.ConclusionThe transparent graphene platform enables the investigation of both intracellular and intercellular communication processes and will create new avenues for bidirectional communication (sensing and stimulation) with electrically active tissues and will set the ground for investigations reported diseases such as Alzheimer, Parkinson's disease and arrhythmias.

Project description:Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.Supplementary informationThe online version contains supplementary material available at 10.1186/s40537-022-00677-1.

Dataset Information

Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays.

Publications

Deep 2-photon imaging and artifact-free optogenetics through transparent graphene microelectrode arrays.

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