ex-vivo brain slices preparation from mouse brain tissue for studying hippocampal-cortical and olfactory bulb circuits

HD-MEA에 대한 멀티모달 대규모 신경 기록 및 분석 

Neuronal ensembles, often termed cell assemblies, are pivotal in neural coding, facilitating intricate computations for processing multiscale neural information1,2,3. These ensembles underpin the formation of expansive neuronal networks and their nuanced microcircuits4. Such networks and their oscillatory patterns drive advanced brain functions, including perception and cognition. While extensive research has explored specific neuronal types and synaptic pathways, a deeper understanding of how neurons collaboratively form cell assemblies and influence spatiotemporal information processing across circuits and networks remains elusive5.
Acute, ex-vivo brain slices are pivotal electrophysiological tools for studying intact neural circuits, offering a controlled setting to probe oscillatory activity patterns of neural function, synaptic transmission, and connectivity, with implications in pharmacological testing and disease modeling6,7,8. This study protocol highlights two key brain circuits - the hippocampal-cortical (HC) involved in learning and memory processes9,10, and the olfactory bulb (OB) responsible for odor discrimination11,12,13. In these two regions, new functional neurons are continuously generated by adult neurogenesis throughout life in mammalian brains14. Both circuits demonstrate multidimensional dynamic neural activity patterns and inherent plasticity that participate in rewiring the existing neural network and facilitate alternative information processing strategies when required15,16.
Acute, ex-vivo brain slice models are indispensable for delving into brain functionality and understanding disease mechanisms at the microcircuit level. However, in-vitro cell cultures derived from human induced pluripotent stem cells (iPSCs) neuronal networks offer a promising avenue of translational research, seamlessly connecting findings from animal experiments to potential human clinical treatment17,18. These human-centric in-vitro assays serve as a reliable platform for assessing pharmacological toxicity, enabling precise drug screening, and furthering research into innovative cell-based therapeutic strategies19,20. Recognizing the pivotal role of the iPSC neuronal model, we have dedicated the third module of this protocol study to thoroughly investigate the functional characteristics of its derived networks and to fine-tune the associated cell culture protocols.
These electrogenic neural modules have been commonly studied using techniques like calcium (Ca2+ imaging), patch-clamp recordings, and low-density microelectrode arrays (LD-MEA). While Ca2+ imaging offers single-cell activity mapping, it is a cell-labeling-based method hindered by its low temporal resolution and challenges in long-term recordings. LD-MEAs lack spatial precision, while patch-clamp, being an invasive single-site technique and laborious, often yields a low success rate21,22,23. To address these challenges and effectively probe network-wide activity, large-scale simultaneous neural recordings have emerged as a pivotal approach for understanding the computational principles of neural dynamics underlying brain complexity and their implications in health and disease24,25.
In this JoVE protocol, we demonstrate a large-scale neural recording method based on the high-density MEA (HD-MEA) for capturing spatiotemporal neuronal activity across various brain modalities, including hippocampal and olfactory bulb circuits from ex-vivo mouse brain acute slices (Figures 1A-C) and in-vitro human iPSC-derived neuronal networks (Figures 1D-E), previously reported by our group and other colleagues26,27,28,29,30,31,32,33,34,35. The HD-MEA, built on complementary-metal-oxide-semiconductor (CMOS) technology, boasts on-chip circuitry and amplification, allowing sub-millisecond recordings across a 7mm2 array size36. This non-invasive approach captures multi-site, label-free extracellular firing patterns from thousands of neuronal ensembles simultaneously using 4096 microelectrodes at a high spatiotemporal resolution, revealing the intricate dynamics of local field potentials (LFPs) and multiunit spiking activity (MUA)26,29.
Given the vastness of the data generated by this methodology, a sophisticated analytical framework is essential, yet poses challenges37. We have developed computational tools that encompass automatic event detection, classification, graph theory, machine learning, and other advanced techniques (Figure 1F)26,29,38,39. Integrating the HD-MEA with these analytical tools, a holistic approach is devised to probe the intricate dynamics from individual cell assemblies to broader neural networks across diverse neural modalities. This combined approach deepens our grasp of the computational dynamics in normal brain functions and offers insights into anomalies present in pathological conditions28. Moreover, insights from this approach can propel advancements in brain-inspired modeling, neuromorphic computing, and neural learning algorithms. Ultimately, this method holds promise in uncovering the core mechanisms behind neural network disruptions, potentially identifying biomarkers, and guiding the creation of precise diagnostic tools and targeted treatments for neurological conditions.

저자 스포트라이트: HD-MEA 기술을 통한 대규모 신경 역학 발전

CMOS 통합 고밀도 미세전극 어레이에서 Multimodal Large-Scale Neuronal Ensemble Dynamics 기록 및 분석

Our research explores the frontiers of neural technology by integrating high-density CMOS-based microelectrode array for decoding neural communication and large networks. We aim to answer how neural information across scales is encoded in unique detail, enhancing our understanding of brain function and dysfunction in health and disease. Navigating the complex area of neuronal ensemble research, we confront challenges like achieving precise signal resolution amidst brain activity, and ensuring the biocompatibility of our CMOS-based microelectrode arrays.These hurdles are pivotal in accurately capturing and interpreting the rich tapestry of neural interaction using multimodal recordings. Our research addresses a critical gap in neuroscience, the lack of a comprehensive method to recode and analyze the dynamics of larger scale neuronal ensemble with high spatial and temporal resolution. This gap hinders our understanding of complex brain networks and function in health and disease.Our protocol enables multimodal, label-free, high-resolution recordings across the hippocampus, olfactory bulb, and human IPSC-derived neurons, providing a versatile tool for diverse experiments. This unique approach facilitates unparalleled insight into neuronal dynamics, bridging the research gap between various brain regions and model systems, significantly advancing our understanding of neural function and disorder. Future endeavors in our lab will deeply investigate neural computations and dynamics from genes to networks, aiming to bridge molecular and functional signatures in health and disease.Through advanced bioelectronics and neural technology, we'll focus on neuroplasticity, olfactory coding, developing AI, and memory-enhancing strategies for novel therapeutics and brain machine interfaces.

Watch this Scientific Journal Video about Ex-Vivo Brain Slices Preparation from Mouse Brain Tissue for Studying Hippocampal-Cortical and Olfactory Bulb Circuits at JoVE.com

해마-피질 및 후각 전구 회로를 연구하기 위한 생쥐 뇌 조직에서 생체 외 뇌 절편 준비

시작하려면 브레인 슬라이스 복구 및 유지 관리 작업 영역을 설정합니다. 브레인 슬라이스 준비 작업 공간을 준비하고 비브라톰을 설정합니다. 다음으로, 뇌 추출 및 준비 작업 공간을 설정합니다.그런 다음 추출된 쥐 뇌를 뇌 준비 작업 공간에서 차가운 탄산 절단 용액이 있는 90mm 플라스틱 배양 접시로 옮깁니다. 아가로스 곰팡이에 위치하기 위해 뇌의 방향을 지정합니다. 아가로스 몰드의 로스트랄 끝에 슈퍼 접착제를 바릅니다.주걱을 사용하여 수평 슬라이싱을 위해 등쪽이 아래로 향하게 하여 뇌를 몰드에 넣습니다. 해마와 후각 구근 조직을 자릅니다. 그리고 유리 파스퇴르 피펫을 사용하여 각 라운드 후에 조각을 수집합니다.섭씨 32도의 수조에서 45분 동안 인공 대뇌 척수액으로 채워진 회수 챔버에서 절편을 배양한 다음 실온에서 1시간 동안 배양합니다.

ex-vivo-brain-slices-preparation-from-mouse-brain-tissue-for-studying

Research

JoVE Journal

Neuroscience

Ex-Vivo Brain Slices Preparation from Mouse Brain Tissue for Studying Hippocampal-Cortical and Olfactory Bulb Circuits

609 조회수.  German Center for Neurodegenerative Diseases (DZNE). 시작하려면 브레인 슬라이스 복구 및 유지 관리 작업 영역을 설정합니다. 브레인 슬라이스 준비 작업 공간을 준비하고 비브라톰을 설정합니다. 다음으로, 뇌 추출 및 준비 작업 공간을 설정합니다.그런 다음 추출된 쥐 뇌를 뇌 준비 작업 공간에서 차가운 탄산 절단 용액이 있는 90mm 플라스틱 배양 접시로 옮깁니다. 아가로스 곰팡이에 위치하기 위해 뇌의 방향을 지정합?...

비디오: 해마-피질 및 후각 전구 회로를 연구하기 위한 생쥐 뇌 조직에서 생체 외 뇌 절편 준비

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from patterns of spatiotemporal neuronal activity. These dynamic patterns emerging from functional groups of interconnected neuronal ensembles facilitate precise computations for processing and coding multiscale neural information, thereby driving higher brain functions. To probe the computational principles of neural dynamics underlying this complexity and investigate the multiscale impact of biological processes in health and disease, large-scale simultaneous recordings have become instrumental. Here, a high-density microelectrode array (HD-MEA) is employed to study two modalities of neural dynamics - hippocampal and olfactory bulb circuits from ex-vivo mouse brain slices and neuronal networks from in-vitro cell cultures of human induced pluripotent stem cells (iPSCs). The HD-MEA platform, with 4096 microelectrodes, enables non-invasive, multi-site, label-free recordings of extracellular firing patterns from thousands of neuronal ensembles simultaneously at high spatiotemporal resolution. This approach allows the characterization of several electrophysiological network-wide features, including single/-multi-unit spiking activity patterns and local field potential oscillations. To scrutinize these multidimensional neural data, we have developed several computational tools incorporating machine learning algorithms, automatic event detection and classification, graph theory, and other advanced analyses. By supplementing these computational pipelines with this platform, we provide a methodology for studying the large, multiscale, and multimodal dynamics from cell assemblies to networks. This can potentially advance our understanding of complex brain functions and cognitive processes in health and disease. Commitment to open science and insights into large-scale computational neural dynamics could enhance brain-inspired modeling, neuromorphic computing, and neural learning algorithms. Furthermore, understanding the underlying mechanisms of impaired large-scale neural computations and their interconnected microcircuit dynamics could lead to the identification of specific biomarkers, paving the way for more accurate diagnostic tools and targeted therapies for neurological disorders.

Here, we employ HD-MEA to delve into computational dynamics of large-scale neuronal ensembles, particularly in hippocampal, olfactory bulb circuits, and human neuronal networks. Capturing spatiotemporal activity, combined with computational tools, provides insights into neuronal ensemble complexity. The method enhances understanding of brain functions, potentially identifying biomarkers and treatments for neurological disorders.

Large-scale neuronal networks and their complex distributed microcircuits are essential to generate perception, cognition, and behavior that emerge from ...