Neuroscience Research

Chemical synapses in the human brain are small.

Due to properties like size and molecular density, synapses and other structures of interest to neuroscience researchers have been difficult to study by conventional methods of microscopic investigation. Yet extracting information about the spatial organization, stoichiometry, interactions, and dynamics of these structures on a molecular level is essential; advancing our understanding of the human brain depends on developing more and better methods to do so.

理想的神经科学显微镜方法不仅应比常规手段实现更高的分辨率，而且还应区分特定的结构，并为多种样本类型提供完整而可靠的信息。

In providing exactly these capabilities, super-resolution microscopy with single-molecule localization (SMLM) has overcome many of the limitations of conventional neuroscience microscopy. This has already enabled dramatic improvement of our understanding of neuronal and brain function and will continue to create new opportunities for investigation and discovery in the field going forward.

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Neuroscience researchers face a unique challenge when selecting an imaging methodology: historically, no single method has been able to provide complete information about neuronal structures and processes at the molecular level, so selecting one has required compromising between capabilities or conducting complementary investigations via multiple methods.

例如，通过能够特定标记的方法，例如衍射限制的两光子显微镜，几乎看不到或无法发现许多感兴趣的结构。然而，电子显微镜提供了出色的分辨率，并且是唯一能够可视化感兴趣的结构的方法，该结构小于50 nm（例如突触裂缝和突触囊泡）缺乏荧光显微镜的能力，可轻松蛋白质标记特定结构。^1,2This restricts researchers' opportunities to perform complex investigations, including protein identification or neuronal tracing.

How is Super-Resolution SMLM Different?

Contrasting conventional methods of neuroscience microscopy, super-resolution microscopy both enables target-specific fluorescence labeling and achieves resolutions well below the diffraction limit of light microscopy. As a result, it can provide unique and often otherwise inaccessible insights into structures and processes.

在神经科学研究中使用的各种超分辨率成像技术中，包括结构化刷新显微镜（SIM），刺激的发射排放（STED）和扩展显微镜 - SMLM是唯一完全依赖于对分子分析的唯一方法。SMLM also provides the highest resolution.³结果，SMLM的横向分辨率为20 nm和特定的荧光标记，是推进神经科学的精美工具.

What is SMLM Used for in Neuroscience Research?

由于其在标记特异性和解决方案的标签上具有优势，因此SMLM有可能高度适用于神经科学研究人员和SMLM技术的许多感兴趣问题，并且已经在广泛的神经科学应用中使用。

To date, the results of these studies have demonstrated the applicability of the method to both structural and quantitative analysis as well as 3D imaging of both tissue and cultured samples.³In these applications, SMLM has been able to provide insight into the spatial organization, stoichiometry, and temporal dynamics of a broad range of structures and processes and provides significant advantages over other methods for investigating molecular interactions (molecular size, density, distance apart, overlap, etc.).

Does SMLM Have Limitations for Neuroscience Research?

在SMLM文献中，在组织样品上证明的SMLM研究比在培养细胞上证明的研究频率较低。对于需要在组织中进行3D成像的应用，这种不平衡尤其明显，并且可以归因于研究人员的几种挫败感。这些包括：

1. Sample preparation is more complex and time-consuming for tissue samples than for cultured cells
2. 组织样品表现出更多（荧光分子）的背景信号更多（更具破坏性）³（并且需要更高级的分析工具来处理）

结果，SMLM技术的可实现的成像深度可能会为使用组织切片和厚样品而构成挑战。

^{*某些仪器可实现出色的成像深度，尤其是在组织样品中的3D成像
（看 ”Bruker的Vutara VXL在神经科学应用中的优点" for more information).}

SMLM适用于神经元结构和过程的研究，包括（其他）：

• 神经发展
• Neural circuit assembly
• 突触形成
• 突触传输
• Synaptic proteins

What You Need to Know About SMLM for Neuroscience Applications

Depending on the equipment, sample, and fluorophore is used, SMLM methods have the potential to achieve high lateral localization precisions and ~20 nm spatial resolution. Yet the actual resolution and localization precision achieved when imaging both cultured samples and tissue slices depend on a variety of additional factors. These include, among others, labeling density and accuracy, signal size and strength, and microscope drift. Optimizing sample praparation methods for SMLM by, for example, increasing labeling antibody concentration, can resolve many of these issues and clear the way for successful analysis.

为什么选择工具与技术一样重要

SMLM成像系统本身的组件和功能可以限制各种SMLM技术中的任何一种分辨率和本地化精度 -特别是在组织切片中执行3D成像时. Moreover, SMLM-capable microscopes can require time-consuming setup that distracts from critical research processes. They also often pair with analytical tools that lag behind their imaging capabilities.

Ideal SMLM-Capable Neuroscience Microscope Features

鉴于仪器配置有可能任意限制SMLM调查的可实现结果，因此选择具有与您的特定研究目标保持一致的已知功能的设备很重要。经常归功于获得成功的最大机会的功能，并使研究人员能够充分利用SMLM对神经科学应用的好处，包括：

1. Dense and specific fluorescence labeling
2. Multiplexing capabilities
3. Enhanced imaging depth
4. Advanced analytical support

Bruker的Vutara VXL SMLM显微镜神经科学研究的优势：

布鲁克'sVutara VXL提供一流的易用性和成像深度，以支持广泛的不同实验需求。关键功能包括：

• 自动化工作流：
  SRX software使获取必要的数据集变得容易。工作流程使用户可以专注于生物学问题，而不是修补复杂的显微镜设置。
• Superior 3D Imaging & Sample Flexibility:
  专有双平面检测提供了三维信息，并在组织切片中进行成像。
• Unlimited Multiplexing Capabilities:
  The integrated微流体单元allows sequential labeling of an unlimited number of fluorescent probes.
• Expert Applications Support:
  Vutara VXL users can receive personalized guidance for sample prep optimization specific to their research objectives.

All SMLM methods rely on the selective on-off switching of fluorescent molecules. Classical STORM uses dye pairs (e.g., Cy3-Cy5), while dSTORM⁶（直接风暴）仅需要一个荧光团（例如Alexa647，CY5）和一个“开关缓冲液”。然而，在风暴和DSTORM中，具有不同波长的照明都开关这些荧光染料。

风暴的优势⁷掌上是这些染料分子在大型光子预算中高效，在棕榈发射的光激活蛋白质中，发射有限数量的光子不可逆地光漂白。结果，风暴可以实现更高的分辨率。

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• 用纳米分辨率映射大脑突触蛋白的三维分子分布⁴
• Revealing organizational principles for actin and spectrin in axons and dendrites⁵

与风暴类似，Palm使用可拍摄的荧光蛋白（例如PAGFP，MEOS2），这比用照片处理染料标记要容易。这些蛋白质用一个激光波长打开，然后成像，然后用另一种激光进行漂白。

棕榈和风暴之间的主要区别 - 掌握了棕榈的优势 - 它允许在内源水平上标记蛋白质。此外，由于其他SMLM技术提供了每个分子的许多定位事件，因此每个分子提供单个定位事件，通常更适用于与化学计量/真实分子计数有关的实验必威官网体育下载目标。

^{该技术的示例应用}

• 探索突触后脚手架蛋白如何以25 nm分辨率聚集在活性上⁸

The other SMLM techniques require sequentially staining target biological structures and repeatedly imaging them to produce a composite image. In PAINT methods, fluorophores are no longer permanently bound to targets, but rather float in buffer solution. Instead of a laser, transitioned binding and immobilization of labels (e.g., Nile red) out of the buffer to the investigated structure provide the on-off mechanism. This allows imaging of an unlimited number of structures of interest in the same sample and makes PAINT methods easier and more efficient to implement than other SMLM methods.

虽然光漂白减少了用于标记其他SMLM技术标记结构的发色团的光子预算，但¹⁰方法不受光漂白的影响。结果，涂料方法比其他技术可以实现更高的空间分辨率。

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• 突触蛋白的精确共定位研究⁹