우리는 박사에게 감사의 말씀을 전합니다. 우리는 이전 연구와 리비 Fairless 및 Shiyuan 리우의 연결 복원에 대한 도움말의 일환으로 준비 조직을 사용할 수 있도록하기위한 에드 캘러웨이와 마티 Usrey. 그리고 화이트 홀 재단 :이 작품은 NIH (EY018683 NEI)에 의해 투자되었다.

<ol>
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저자는 공개 아무것도 없어.

신경 해부학 연구는 신경 과학 및 connectomics 및 구조 - 기능 관계에서 최근 관심의 기둥이 선택적 신경 인구의 상세한 형태 학적 특성에 대한 열정을 갱신했다 남아있다. 전통적으로, 신경 해부학 적 연구는 전문가 neuroanatomists에 의해 정의 된 신경 세포의 형태 학적으로 별개의 클래스로 신경 질적 분류에 의존하고있다. 신경 세포를 복원 및 형태 학적 데이터를 추출하는 기술의 진보에 의해,이 공?...

신경 해부학은 &quot;connectomics&quot;에서 신경 과학 (1) 최근 관심의 기초 중 하나가 신경 인구의 형태 학적 다양성과 특정 뉴런 (2) 사이의 연결을 이해하기위한 열정을 갱신했다입니다. 라벨링 방법 및 재구성 신경 세포는 크게 유전 및 바이러스 매개 회로 추적 등을 포함하여 혁신과 개선의 연결 인구 5의보다 포괄적 인 형태 설문 조사를 수 있도록, 3, 4에 접근한다. 개별 뉴런 라벨의 개선 이외에, 정량 데이터 분석 기술들은 그 데이터 형태 5, 6에 기초하여 별개의 개체군으로 뉴런의 독립적 인 편향 구분 가능 나왔다. 이러한 편견 기술은 더 traditiona의시 개선된다한 세기 넘게 분야에서 표준이되어왔다 L 질적 분류 방법. 이 연구의 목표는, 개요 단계별하는 것입니다, 선택적 인구 내에서 신경 세포의 바이러스 매개 라벨의 조합이하는 이러한 신경 세포의 포괄적 인 샘플 및 정량 데이터 분석의 대규모 복원은 독립적 인 클러스터링을 기반으로 통계 평가. 이러한 방법을 결합함으로써, 우리는 선택적 신경 인구 내에서 포괄적 인 샘플링과 형태 학적으로 독특한 신경 종류의 편견 분류를 용이하게하기 위해 수집 및 신경 해부학 데이터의 분석을 향한 새로운 접근 방식을 설명합니다. 이러한 방법의 예를 들어, 우리는 짧은 꼬리 원숭이의 시상 망상 핵 (TRN)의 단일 섹터 내에서 뉴런의 대규모 인구의 분석에 대해 설명합니다. 이 자료는 이전 연구 7에서 있습니다. 선택적 지느러미 latera에 돌출 TRN 뉴런을 라벨에 대한 방법EGFP 4, 8 인코딩 수정 광견병 바이러스의 수술 주입을 사용하여 시상 (dLGN)의 리터의 geniculate 핵이 설명되어 있습니다 (특정 자재 / 장비, 행 2의 표 참조). 이 변형 광견병 바이러스는 유전자 필수적인 코트 단백질을 코딩하는 바이러스 트랜스 - 시냅스 움직임을 제거 없다. 바이러스 주사 부위 엑손 단자 입사되면, 감염된 신경 5, 9, 10의 전체에 걸쳐 수상 arborization EGFP 발현을 구동하는 중요한 이점 전통적인 역행 트레이서처럼 작용한다. 따라서,이 G-삭제 광견병 바이러스는 선택적으로 감염 및 사출과 역행 전송 다음 모든 신경 인구 라벨을 사용할 수있다. 특정 신경 세포 집단의 포괄적 분석을 수행하기 위해, 샘플은 중요인구 내에서 뉴런의 광범위한 분포. 바이러스 - 매개 표지 기술은 완전한 세포를 생성하기 때문에, &quot;골지체 형상은&quot;바이러스 주사 부위 축삭 많은 뉴런 채우고, 그 뇌 구조의 전체 범위 내에서 신경 세포의 매우 큰 샘플을 재구성 할 수있다. 개질 광견병 바이러스 감염과 뉴런의 다수의 라벨에 있으므로 효과적이기 때문에 또한, 동물 당 수백 뉴런을 재구성 할 수있다. dLGN 투 TRN 뉴런의 광범위한 샘플을 생성하기 위해 TRN (11)의 영상 분야에 걸쳐 160 뉴런 샘플링 절차가 설명된다. 현미경, 카메라, 및 재구성 소프트웨어를 포함하는 신경 복원 시스템을 이용하여 각각의 신경 세포를 복원하는 과정을 설명한다. 또한 설명합니다 (TRN 내의이 경우) 뇌 구조 내의 개별 뉴런의 위치를 ​​결정하는 바이러스 주입 윗몸을 확인하는 방법이다체적 형상 복원을 사용합니다 (dLGN 내의 이러한 경우) 구조 내에서 전자 볼륨 및 위치. 각 신경 세포를 측정 형태 측정이 설명되어 있습니다에 형태 학적 데이터를 내보낼 독립적 인 클러스터를 수행하는 단계는 기반 분석. 클러스터링 방법에 제한이 있습니다 가능한 다른 클러스터링 알고리즘의 다양한도 있습니다. 따라서, 이러한 옵션과 더 일반적으로 사용되는 알고리즘의 몇 가지의 장점이 설명되어 있습니다. 클러스터 분석 클러스터의 고유성의 통계적 검증을 제공하지 않습니다. 따라서, 추가 단계가 최적의 클러스터뿐만 아니라 내부 및 클러스터에 걸쳐 형태 데이터 사이의 관계를 확인하기 위해 설명된다. 그 TRN 뉴런을 확인하기 위해 TRN 데이터 세트에 대한 클러스터를 평가하기위한 통계 방법이 설명되어 있습니다 (10) 독립적 인 형태 학적 지표에 따라 세 가지 고유 한 클러스터로 그룹화됩니다. 따라서, 선택적으로 표시하는 단계를 요약하여, 재구성, 특정 신경 세포 집단에서 형태 학적 자료를 분석, 우리는 인구 내에서 신경 세포 사이의 형태 학적 차이를 정량화하는 방법을 설명합니다. 짧은 꼬리 원숭이 원숭이 TRN의 시각적 부문 내에서 서로 다른 신경 세포 유형의 선행 연구 결과는 별도의 통계적인 평가 방법으로 확인된다. 함께, 우리는 이러한 기술은 신경 해부학 데이터 세트에 광범위하게 적용 할 수 뇌를 통해 신경 인구의 다양성의 정량적 분류를 확립 도움이되기를 바랍니다.

<table><tbody><tr><td>SAD&amp;Delta;G-EGFP</td><td>E.M. Callaway Laboratory, Salk Institute</td><td></td><td>Prepared by Dr. F. Osakada. G-deleted rabies virus available through the Salk Institute Viral Core</td></tr><tr><td>Recording electrode: platinum/iridium or tungsten</td><td>FHC</td><td>UEPSGGSE1N2M</td><td>Visit website (www.fh-co.com) for alternative order specifications</td></tr><tr><td>Nanoject II</td><td>Drummod Scientific</td><td>3-000-204, 110V</td><td>Alternatives: picospritzer, Hamilton syringe</td></tr><tr><td>Freezing microtome</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>DAB</td><td>Sigma Aldrich</td><td>D5905-50TAB</td><td>3,3&#039;-Diaminobenzidine tetrahydrochloride, tablet, 10 mg substrate per tablet. Caution: carcinogen - must be bleached before discarding</td></tr><tr><td>Cytochrome C</td><td>Sigma Aldrich</td><td>C2037-100MG</td><td></td></tr><tr><td>Catalase</td><td>Sigma-Aldich</td><td>C9322-5G</td><td></td></tr><tr><td>Rabbit anti-GFP</td><td>Life Technologies/Thermo Fisher</td><td>#A-11122&amp;nbsp;</td><td>Primary antibody</td></tr><tr><td>Biotinylated goat anti-rabbit</td><td>Vector Laboratories</td><td>#BA-1000</td><td>Secondary antibody</td></tr><tr><td>Neurolucida System&amp;nbsp;</td><td>MicroBrightField</td><td></td><td>Software for neuron tracing and analysis. http://www.mbfbioscience.com/neurolucida</td></tr><tr><td>Neurolucida Explorer</td><td>MicroBrightField</td><td></td><td>Data export software</td></tr><tr><td>Microfire Camera&amp;nbsp;</td><td>Optronics</td><td></td><td>2-Megapixel true color microscope camera. http://www.simicroscopes.com/pdfs/microfire.pdf</td></tr><tr><td>Nikon E800 Microscope</td><td>Nikon Instruments Inc.</td><td></td><td>Biological research microscope. http://www.microscopyu.com/museum/eclipseE800.html</td></tr><tr><td>Matlab</td><td>The MathWorks Inc.&amp;nbsp;</td><td></td><td>Matrix-based computational mathematics software. http://www.mathworks.com</td></tr><tr><td>Microsoft Office Excel</td><td>Microsoft</td><td></td><td>Spreadsheet program</td></tr></tbody></table>

large-scale reconstructions and independent, unbiased clustering based on morphological metrics to classify neurons in selective populations

이 프로토콜은 독립적 바이어스 클러스터링의 사용과 결합 된 신경 세포의 대규모 복원이 선택적 신경 세포 집단 중에서 관찰 학적 특성의 광범위한 조사를 만들 분석 요약. 이러한 기술들의 조합은 신경 해부학 정보의 수집 및 분석을위한 새로운 방법을 구성한다. 함께, 이러한 기술은 대규모을 활성화하고 선택적 신경 인구 때문에보다 포괄적 인 샘플링 및 인구 내에서 형태 학적으로 독특한 신경 클래스를 설명하는 편견 양적 방법을 설정합니다. 이 프로토콜은 선택적으로 신경 세포에 라벨을 수정 광견병 바이러스의 사용을 설명합니다. 관심의 대상 뇌 구조로 정위 주입 다음 역행 추적 등의 광견병 바이러스의 역할을 G는-삭제 및 신경 세포에서 EGFP의 전달과 표현을위한 수단으로 역할을합니다. 뉴런의 많은 수는이를 사용하여 감염기술과 개별 뉴런의 &quot;골지 같은&quot;전체 채우기를 생산, 자신의 수상 돌기에 걸쳐 GFP를 표현한다. 따라서, 바이러스 매개 역행 추적 방법은 완전한 세포 칠을 생성함으로써 기존의 염료 계 역행 추적 기술을 개선시킨다. 연구중인 뇌 영역의 모든 영역에 걸쳐 개별 잘 격리 뉴런은 신경 세포의 대표적인 샘플을 획득하기 위해 재 선정된다. 이 프로토콜은 세포 기관을 재구성하고 여러 조직 섹션에 걸쳐 표시 신경 세포의 수지상 arborization 패턴을 완료하는 절차를 설명합니다. 뇌 구조 내의 각 뉴런의 위치를 ​​포함하여 형태 학적 데이터, 추가 분석을 위해 추출된다. 표준 프로그래밍 함수는 독립 클러스터 분석 학적 지표에 기초하여 클러스터 평가를 수행하는 데에 이용 하였다. 클러스터 분석 PERFO의 이러한 분석의 유틸리티, 통계적 평가를 확인하려면원숭이 원숭이의 시상 (TRN)의 시상 망상 핵에 재건 (160) 뉴런에 rmed가되었다. 원래 클러스터 분석하고 여기에 수행 통계적 평가는 모두 TRN 뉴런 세 소집단, 독특한 형태 학적 특성을 각각으로 분리되어 있음을 나타냅니다.

우리는 선택적 모집단 내의 뉴런의 대규모 복원이 dLGN (5)에 변형 광견병 바이러스의 가능한 다음 주입 이전임을 보여 주었다. 최근, 동일 조직은 TRN 시각적 섹터 (160)의 뉴런을 재구성하기 위해 이용 하였다 (브래그 등, 리뷰].도 2A-B) 상술 한 상세한 방법 론적 단계에 따라. TRN 연구에서 TRN 뉴런의 세 가지 고유의 클러스터는 독립적 10 형태...

Watch this Scientific Journal Video about Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations at JoVE.com

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

이 프로토콜은 수정 된 광견병 바이러스가 형광 마커를 표현, 독립, 편견 클러스터가 서로 다른 신경 세포의 서브 클래스 간의 형태 학적 통계의 종합적인 특성을 사용하는 것이 분석과 역 행성 감염 다음 레이블이 선택적 신경 인구의 대규모 복원에 대해 설명합니다.

형태 학적 지표를 바탕으로 대규모 재건과 독립, 편견 클러스터링은 선택적 인구에서 뉴런을 분류하기

This protocol outlines large-scale reconstructions of neurons combined with the use of independent and unbiased clustering analyses to create a ...

large-scale-reconstructions-independent-unbiased-clustering-based-on

Universidad de Cartagena UDEC

Research

JoVE Journal

Neuroscience

6.9K 조회수.  Geisel School of Medicine at Dartmouth.  이 프로토콜은 수정 된 광견병 바이러스가 형광 마커를 표현, 독립, 편견 클러스터가 서로 다른 신경 세포의 서브 클래스 간의 형태 학적 통계의 종합적인 특성을 사용하는 것이 분석과 역 행성 감염 다음 레이블이 선택적 신경 인구의 대규모 복원에 대해 설명합니다. 

비디오: Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

여러 스케일에서 디지털의 연결 형태론의 자동 숄 분석

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

연구

신경과학

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to the site of inflammation and are activated to prevent a further spreading of the invasion. This is also reflected by changes in the behavior and the morphological appearance of the immune cells. In cancerous tissue, similar morphokinetic changes have been observed in the behavior of microglial cells: intra-tumoral microglia have less complex 3-dimensional shapes, having less-branched cellular processes, and move more rapidly than those in healthy tissue. The examination of such morphokinetic properties requires complex 3D microscopy techniques, which can be extremely challenging when executed longitudinally. Therefore, the recording of a static 3D shape of a cell is much simpler, because this does not require intravital measurements and can be performed on excised tissue as well. However, it is essential to possess analysis tools that allow the fast and precise description of the 3D shapes and allows the diagnostic classification of healthy and pathogenic tissue samples based solely on static, shape-related information. Here, we present a toolkit that analyzes the discrete Fourier components of the outline of a set of 2D projections of the 3D cell surfaces via Self-Organizing Maps. The application of artificial intelligence methods allows our framework to learn about various cell shapes as it is applied to more and more tissue samples, whilst the workflow remains simple.

Here, we provide a workflow that allows the identification of healthy and pathological cells based on their 3-dimensional shape. We describe the process of using 2D projection outlines based on the 3D surfaces to train a Self-Organizing Map that will provide objective clustering of the investigated cell populations.

The appearance and the movements of immune cells are driven by their environment. As a reaction to a pathogen invasion, the immune cells are recruited to ...

암 연구학

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with simultaneous intracellular biocytin filling allows a correlated analysis of their structural and functional properties. With this method it is possible to identify and characterize both pre- and postsynaptic neurons by their morphology and electrophysiological response pattern. Paired recordings allow studying the connectivity patterns between these neurons as well as the properties of both chemical and electrical synaptic transmission. Here, we give a step-by-step description of the procedures required to obtain reliable paired recordings together with an optimal recovery of the neuron morphology. We will describe how pairs of neurons connected via chemical synapses or gap junctions are identified in brain slice preparations. We will outline how neurons are reconstructed to obtain their 3D morphology of the dendritic and axonal domain and how synaptic contacts are identified and localized. We will also discuss the caveats and limitations of the paired recording technique, in particular those associated with dendritic and axonal truncations during the preparation of brain slices because these strongly affect connectivity estimates. However, because of the versatility of the paired recording approach it will remain a valuable tool in characterizing different aspects of synaptic transmission at identified neuronal microcircuits in the brain.

Patch-clamp recordings and simultaneous intracellular biocytin filling of synaptically coupled neurons in acute brain slices allow a correlated analysis of their structural and functional properties. The aim of this protocol is to describe the essential technical steps of electrophysiological recording from neuronal microcircuits and their subsequent morphological analysis.

급성 뇌 조각에서 신경 세포의 미세 회로의 전기 생리 및 형태 학적 특성은 페어 패치 클램프 녹음을 사용하여

The combination of patch clamp recordings from two (or more) synaptically coupled neurons (paired recordings) in acute brain slice preparations with ...

Analyzing Dendritic Morphology in Columns and Layers

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

열 및 레이어에 수지상 형태학 분석

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical properties, synaptic connections, and in vivo functions, but their basic functional and anatomical organization from cellular to network scale is poorly understood. Here we describe a method for the three-dimensional imaging of fluorescently-labeled neurons across large areas of the brain for the investigation of the cortical cellular organization. Specific types of neurons are labeled by the injection of fluorescent retrograde neuronal tracers or expression of fluorescent proteins in transgenic mice. Block brain samples, e.g., a hemisphere, are prepared after fixation, made transparent with tissue clearing methods, and subjected to fluorescent immunolabeling of the specific cell types. Large areas are scanned using confocal or two-photon microscopes equipped with large working distance objectives and motorized stages. This method can resolve the periodic organization of the cell type-specific microcolumn functional modules in the mouse neocortex. The procedure can be useful for the study of three-dimensional cellular architecture in the diverse brain areas and other complex tissues.

Here we describe a procedure for tissue clearing, fluorescent labeling, and large-scale imaging of mouse brain tissue which, thereby, enables visualization of the three-dimensional organization of cell types in the neocortex.

마우스 피 질 세포 조직의 대형 3-차원 영상

The mammalian neocortex is composed of many types of excitatory and inhibitory neurons, each with specific electrophysiological and biochemical ...

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

How cortical network activity processes information is of importance to a large number of basic and clinical scientific questions. The protocol described here identifies the basic building blocks of this circuitry. The in-depth studies of cortical regions will ultimately provide other scientists with the circuit components needed for an understanding of how the brain acquires, processes and stores information and what goes wrong in disease, while the electrophysiological and morphological data are widely used by computational neuroscientists in the construction of model networks that explore information processing. The protocol outlined here describes how biocytin-filled cells recorded in the CA2 region of the hippocampus are recovered and then reconstructed in&#160;3D. Additionally, the protocol describes the demonstration of calcium binding protein or peptide content in recorded interneurons.

The protocol outlined here describes the immunofluorescence analysis, biocytin recovery and high-quality reconstructions of hippocampal CA2 interneurons following the intracellular electrophysiological recordings in vitro, allowing neuronal characterization and ultimately fine neuronal anatomy to be studied.

Biocytin 복구 및 채워진된 Hippocampal CA2 수의 3D 복원

How cortical network activity processes information is of importance to a large number of basic and clinical scientific questions. The protocol described ...

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D. As these models are often extremely complex, a virtual reality environment is also important to be set up to overcome occlusion and to take full advantage of the 3D structure. Here, a step-by-step guide from image segmentation to reconstruction and analysis is described in detail.

The described pipeline is designed for the segmentation of electron microscopy datasets larger than gigabytes, to extract whole-cell morphologies. Once the cells are reconstructed in 3D, customized software designed around individual needs can be used to perform a qualitative and quantitative analysis directly in 3D, also using virtual reality to overcome view occlusion.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction ...

Ballistic Labeling of Pyramidal Neurons in Brain Slices and in Primary Cell Culture

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of pyramidal neurons and dendritic spines, a ballistic labeling technique can be utilized. In the present protocol, pyramidal neurons are labeled with DilC18(3) dye and analyzed using neuronal reconstruction software to assess neuronal morphology and dendritic spines. To investigate neuronal structure, dendritic branching analysis and Sholl analysis are performed, allowing researchers to draw inferences about dendritic branching complexity and neuronal arbor complexity, respectively. The evaluation of dendritic spines is conducted using an automatic assisted classification algorithm integral to the reconstruction software, which classifies spines into four categories (i.e., thin, mushroom, stubby, filopodia). Furthermore, an additional three parameters (i.e., length, head diameter, and volume) are also chosen to assess alterations in dendritic spine morphology. To validate the potential of wide application of the ballistic labeling technique, pyramidal neurons from in vitro cell culture were successfully labeled. Overall, the ballistic labeling method is unique and useful for visualizing neurons in different brain regions in rats, which in combination with sophisticated reconstruction software, allows researchers to elucidate the possible mechanisms underlying neurocognitive dysfunction.

We present a protocol to label and analyze pyramidal neurons, which is critical for evaluating potential morphological alterations in neurons and dendritic spines that may underlie neurochemical and behavioral abnormalities.

뇌 슬라이스와 기본 세포 배양에서 피라미드 뉴런의 탄도 라벨링

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of ...

Automatic Identification of Dendritic Branches and their Orientation

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

Presented is a computational tool that allows simple and direct automatic measurement of orientations of neuronal dendritic branches from 2D fluorescence images.

수지상 가지 및 방향자동 식별

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology ...

A Tissue Clearing Method for Neuronal Imaging from Mesoscopic to Microscopic Scales

A detailed protocol is provided here to visualize neuronal structures from mesoscopic to microscopic levels in brain tissues. Neuronal structures ranging from neural circuits to subcellular neuronal structures are visualized in mouse brain slices optically cleared with ScaleSF. This clearing method is a modified version of ScaleS and is a hydrophilic tissue clearing method for tissue slices that achieves potent clearing capability as well as a high-level of preservation of fluorescence signals and structural integrity. A customizable three dimensional (3D)-printed imaging chamber is designed for reliable mounting of cleared brain tissues. Mouse brains injected with an adeno-associated virus vector carrying enhanced green fluorescent protein gene were fixed with 4% paraformaldehyde and cut into slices of 1-mm thickness with a vibrating tissue slicer. The brain slices were cleared by following the clearing protocol, which include sequential incubations in three solutions, namely, ScaleS0 solution, phosphate buffer saline (&#8211;), and ScaleS4 solution, for a total of 10.5&#8211;14.5 h. The cleared brain slices were mounted on the imaging chamber and embedded in 1.5% agarose gel dissolved in ScaleS4D25(0) solution. The 3D image acquisition of the slices was carried out using a confocal laser scanning microscope equipped with a multi-immersion objective lens of a long working distance. Beginning with mesoscopic neuronal imaging, we succeeded in visualizing fine subcellular neuronal structures, such as dendritic spines and axonal boutons, in the optically cleared brain slices. This protocol would facilitate understanding of neuronal structures from circuit to subcellular component scales.

The protocol provides a detailed method of neuronal imaging in brain slice using a tissue clearing method, ScaleSF. The protocol includes brain tissue preparation, tissue clarification, handling of cleared slices and confocal laser scanning microscopy imaging of neuronal structures from mesoscopic to microscopic levels.

중시경에서 현미경 비늘에 이르기까지 신경 세포 이미징을위한 조직 제거 방법

A detailed protocol is provided here to visualize neuronal structures from mesoscopic to microscopic levels in brain tissues. Neuronal structures ranging ...