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使用基于色相-饱和度-亮度的表面创建来简化体积多通道图像细胞术

Streamlining volumetric multi-channel image cytometry using hue-saturation-brightness-based surface creation.

作者信息

Tan Yingrou, Li Jackson Liang Yao, Goh Chi Ching, Lee Bernett Teck Kwong, Kwok Immanuel Weng Han, Ng Wei Jie, Evrard Maximilien, Poidinger Michael, Tey Hong Liang, Ng Lai Guan

机构信息

Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, 138648, Singapore, Singapore.

National Skin Centre, 1 Mandalay Road, 308205, Singapore, Singapore.

出版信息

Commun Biol. 2018 Sep 6;1:136. doi: 10.1038/s42003-018-0139-y. eCollection 2018.

DOI:10.1038/s42003-018-0139-y
PMID:30272015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6127105/
Abstract

Image cytometry is the process of converting image data to flow cytometry-style plots, and it usually requires computer-aided surface creation to extract out statistics for cells or structures. One way of dealing with structures stained with multiple markers in three-dimensional images, is carrying out multiple rounds of channel co-localization and image masking before surface creation, which is cumbersome and laborious. We propose the application of the hue-saturation-brightness color space to streamline this process, which produces complete surfaces, and allows the user to have a global view of the data before flexibly defining cell subsets. Spectral compensation can also be performed after surface creation to accurately resolve different signals. We demonstrate the utility of this workflow in static and dynamic imaging datasets of a needlestick injury on the mouse ear, and we believe this scalable and intuitive approach will improve the ease of performing histocytometry on biological samples.

摘要

图像细胞术是将图像数据转换为流式细胞术风格图表的过程，通常需要借助计算机辅助创建表面来提取细胞或结构的统计数据。处理三维图像中用多种标记物染色的结构的一种方法是，在创建表面之前进行多轮通道共定位和图像掩膜，这既繁琐又费力。我们建议应用色相-饱和度-亮度颜色空间来简化这一过程，该方法能生成完整的表面，并允许用户在灵活定义细胞亚群之前对数据有一个全局的了解。在创建表面之后还可以进行光谱补偿，以准确解析不同的信号。我们在小鼠耳部针刺损伤的静态和动态成像数据集中展示了此工作流程的实用性，并且我们相信这种可扩展且直观的方法将提高对生物样品进行组织细胞计数的简便性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/d6e41cbb0dd7/42003_2018_139_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/46d908a3cb14/42003_2018_139_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/28f3481e6e45/42003_2018_139_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/339870278bbe/42003_2018_139_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/b7e38d3580e7/42003_2018_139_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/989c3e719b5e/42003_2018_139_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/d6e41cbb0dd7/42003_2018_139_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/46d908a3cb14/42003_2018_139_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/28f3481e6e45/42003_2018_139_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/339870278bbe/42003_2018_139_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/b7e38d3580e7/42003_2018_139_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/989c3e719b5e/42003_2018_139_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97d5/6127105/d6e41cbb0dd7/42003_2018_139_Fig6_HTML.jpg

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