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通过共聚焦显微镜重建单细胞先天荧光特征
通过共聚焦显微镜重建单细胞先天荧光特征
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JoVE Journal Biology
Reconstruction of Single-Cell Innate Fluorescence Signatures by Confocal Microscopy

通过共聚焦显微镜重建单细胞先天荧光特征

Full Text
3,150 Views
07:29 min
May 27, 2020

DOI: 10.3791/61120-v

Tomohiro Hirayama*1, Kyosuke Takabe*2, Tatsunori Kiyokawa1, Nobuhiko Nomura2,3, Yutaka Yawata*2,3

1Graduate School of Life and Environmental Sciences,University of Tsukuba, 2Faculty of Life and Environmental Sciences,University of Tsukuba, 3Microbiology Research Center for Sustainability,University of Tsukuba

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Please note that some of the translations on this page are AI generated. Click here for the English version.

Overview

This study presents a protocol for optically extracting and cataloging innate cellular fluorescence signatures from individual live cells. It emphasizes a non-invasive method suitable for analyzing various biological systems at single-cell resolution, including bacterial, fungal, yeast, plant, and animal cells.

Key Study Components

Research Area

  • Cellular fluorescence analysis
  • Single-cell resolution techniques
  • Phenotypic characterization of microorganisms

Background

  • Importance of non-invasive tagging methods
  • Challenges in analyzing cellular heterogeneity
  • Potential applications in microbiology

Methods Used

  • Confocal reflection microscopy
  • Multichannel confocal microspectroscopy
  • Image analysis for cell segmentation and dimensional reduction

Main Results

  • Successful extraction of innate cellular fluorescence signatures
  • Identification of phenotype variability among microbial populations
  • Demonstration of the impact of accurate cell segmentation on analysis

Conclusions

  • This study highlights a technique for high-resolution fluorescence signature analysis.
  • It opens avenues for understanding microbial phenotypic diversity in healthcare and ecological studies.

Frequently Asked Questions

What is the purpose of the presented protocol?
The protocol is designed to extract and analyze innate fluorescence signatures from live cells, enabling non-invasive identification.
What types of cells can this technique be applied to?
The technique is applicable to bacteria, fungi, yeasts, plants, and animal cells.
How does this method differ from traditional tagging methods?
This method does not require invasive tagging, allowing for more natural cellular states during analysis.
What technologies are necessary to implement this protocol?
The protocol requires a confocal microscope equipped for reflection microscopy and multichannel spectral imaging.
How does accurate cell segmentation affect results?
Accurate segmentation reduces variability in fluorescence signatures, leading to more reliable data interpretation.
What insights does this study contribute to microbial research?
It contributes to understanding phenotypic heterogeneity and physiological status within microbial populations.
Can machine learning be applied in this context?
Yes, machine learning models can be trained using the dataset for classification and prediction tasks based on fluorescence signatures.

在这里,提出了一种方案,用于光学提取和编目来自分布在三维空间中的每个个体活细胞的先天细胞荧光特征(即细胞自发荧光)。该方法适用于以单细胞分辨率研究各种生物系统的先天荧光特征,包括来自细菌,真菌,酵母,植物和动物的细胞。

该技术提供了一种独特的研究,可以在单细胞水平上证明细胞的身份或生理特性,而无需侵入性标记。该技术的主要优点是,它有助于为您的分析提供单像元级别的空间分辨率,并且它允许区分 和 后台过程。因此,该技术可能有助于病原微生物的鉴定和表型分析。

该技术还可用于表型异质性的研究,或监测感兴趣的微生物种群的生理状态。演示该程序的是我实验室的助理教授 Kyosuke Takabe。共聚焦反射显微镜和多通道共聚焦显微光谱装置。

将具有去扫描光谱通道的共聚焦显微镜连接到光电倍增管。为显微镜配备高数值孔径物镜,并具有足够的放大倍率。并为显微镜配备半反射镜,以适应共聚焦反射显微镜,共聚焦反射显微镜依靠入射光的细胞散射来可视化细胞形态。

对于多通道共聚焦显微光谱,为显微镜配备二向色镜,并使用激光功率计调整每个激发波长的照明强度。然后将显微镜下的输出设置为在激发波长范围内保持恒定。要通过显微镜对细菌进行成像,请在显微镜软件中将针孔大小设置为 1.0 面积单位,并为每个激发波长设置像素停留时间。

设置扫描分辨率。对于小细胞(如细菌),请使用 1024 x 1024 的扫描区域。设置 Z 扫描范围,以便覆盖感兴趣的区域。

使用 8 到 10 纳米的光谱窗口,设置 D 扫描检测器以捕获可见光波长范围。然后按从最长到最短激发波长的顺序采集多通道共聚焦显微光谱图像,以创建荧光图像和共聚焦反射显微镜图像的 Z 堆栈,并将图像保存为 16 位 tiff 格式。要进行细胞分割和单细胞先天荧光特征的重建,请打开适当的图像分析软件程序,然后双击以打开提供的脚本之一。

在 editor 选项卡下,单击 run.将出现一个文件夹选择窗口。选择保存 Z-Stack 图像的目录,然后单击 open。

将自动出现一个对话框,提示输入分段参数。将图像二值化阈值设置为 0-1,将图像二值化设置为 0.45,将像元区域的阈值上限设置为 200,将像元区域的阈值下限设置为 10,将检测器数设置为 32。将出现一个对话框,要求输入激发波长的数量。

输入用于图像采集的波长数,然后单击 Ok。将出现一个对话框,请求输入激发波长。按从最短到最长的顺序输入激发波长,然后单击 okay。

将出现一个显示共聚焦反射显微镜图像的新图像窗口。选择一个任意的背景区域用于背景扣除,并在共聚焦反射显微镜图像中绘制一个矩形。在所选区域内双击以确认选择并找到名为 signature 的新目录。

要执行降维技术,请创建一个空目录并将该目录命名为 parent_directory。将两个细胞群的荧光特征分别存储到两个单独的目录中,并打开工作站的命令行界面。输入命令。

显示选择目标目录时,选择parent_directory。然后在 parent_directory 文件夹中,找到 PCA。PNG 文件,该文件将包含生成的主成分分析图解。

这里显示了细菌细胞的典型单细胞荧光特征,以传统光谱图和热图表示。在该图中,可以观察到叠加在土壤细菌种群的原始共聚焦反射显微镜图像上的不准确的 2D 细胞分割。由此产生的种群的先天荧光特征以热图的形式呈现。

请注意,在成功进行细胞分割后,群体内变异性相对较小。在这里,一个不准确的细胞分割示例被叠加到相同的恶臭假单胞菌群体上,如前所述。不准确的细胞分割对群体先天荧光特征的影响从相应的热图中观察到的大量异常值中显而易见。

与准确细胞分割后获得的类型簇相比,不准确的细胞分割会导致主成分分析后出现更松散的簇。尽管在单个细菌菌株中观察到的先天荧光特征变化很小,但每个群体在主成分分析图上形成一个不同的簇。在该图中,可以观察到不准确的 3D 细胞分割叠加在出芽酵母 YM4271 群体的原始共聚焦反射显微镜图像上。

请注意,缺乏异常值和由此产生的种群的先天荧光特征。必须通过共聚焦显微镜获取尽可能干净的图像,并避免信号强度饱和,因为噪声会破坏后续分析的准确性。您可以使用固有的 Western 特征数据集训练机器学习模型,以用于分类或预测任务,从而提供无标签的群体分析和表型预测。

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