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使用 IMARIS 和 MeshLab 优化 3D 免疫荧光分析和可视化。

Optimization of 3D Immunofluorescence Analysis and Visualization Using IMARIS and MeshLab.

机构信息

Medical Innovation Research Center, Shiga University of Medical Science, Seta Tsukinowa-cho, Otsu 520-2192, Japan.

Brigham and Women's Hospital, 75 Francis St., Boston, MA 02115, USA.

出版信息

Cells. 2023 Jan 4;12(2):218. doi: 10.3390/cells12020218.

DOI:10.3390/cells12020218
PMID:36672153
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9856541/
Abstract

The precision of colocalization analysis is enhanced by 3D and is potentially more accurate than 2D. Even though 3D improves the visualization of colocalization analysis, rendering a colocalization model may generate a model with numerous polygons. We developed a 3D colocalization model of FtMt/LC3 followed by simplification. Double immunofluorescence staining of FtMt and LC3 was conducted, and stacked images were acquired. We used IMARIS to render the 3D colocalization model of FtMt/LC3 and further processed it with MeshLab to decimate and generate a less complex colocalization model. We examined the available simplification algorithm using MeshLab in detail and evaluated the feasibility of each procedure in generating a model with less complexity. The quality of the simplified model was subsequently assessed. MeshLab's available shaders were scrutinized to facilitate the spatial colocalization determination. Finally, we showed that QECD was the most effective method for reducing the polygonal complexity of the colocalization model without compromising its quality. In addition, we would recommend implementing the x-ray shader, which we found useful for visualizing colocalization. As 3D was found to be more accurate in quantifying colocalization, our study provides a novel and dependable method for rendering 3D models for colocalization analysis.

摘要

三维(3D)共定位分析的精确性得到了提高,并且比二维(2D)更准确。尽管 3D 提高了共定位分析的可视化程度,但渲染共定位模型可能会生成具有大量多边形的模型。我们开发了一种 FtMt/LC3 的 3D 共定位模型,然后对其进行简化。对 FtMt 和 LC3 进行双重免疫荧光染色,并获取堆叠图像。我们使用 IMARIS 渲染 FtMt/LC3 的 3D 共定位模型,并使用 MeshLab 进一步处理它,以简化并生成一个不太复杂的共定位模型。我们详细检查了 MeshLab 中可用的简化算法,并评估了每个步骤在生成更简单模型方面的可行性。随后评估简化模型的质量。检查了 MeshLab 中可用的着色器,以方便确定空间共定位。最后,我们表明 QECD 是减少共定位模型多边形复杂性而不影响其质量的最有效方法。此外,我们建议实施 X 射线着色器,我们发现它对于可视化共定位很有用。由于 3D 在量化共定位方面更准确,因此我们的研究为渲染共定位分析的 3D 模型提供了一种新颖且可靠的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/d50f25f37ac4/cells-12-00218-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/6c1798a538d7/cells-12-00218-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/890067c8448d/cells-12-00218-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/1c1c84daa734/cells-12-00218-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/8796f118c8c9/cells-12-00218-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/2e7c865ad136/cells-12-00218-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/d50f25f37ac4/cells-12-00218-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/6c1798a538d7/cells-12-00218-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/890067c8448d/cells-12-00218-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/1c1c84daa734/cells-12-00218-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/8796f118c8c9/cells-12-00218-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/2e7c865ad136/cells-12-00218-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a46/9856541/d50f25f37ac4/cells-12-00218-g006.jpg

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