Reconstruction, analysis, and segmentation of LA-ICP-MS imaging data using Python for the identification of sub-organ regions in tissues
Reconstruction, analysis, and segmentation of LA-ICP-MS imaging data using Python for the identification of sub-organ regions in tissues
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DOI：
10.1039/c9an02472g
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发表时间：
2020-05-21
期刊：
影响因子：
4.2
通讯作者：
Vachet, Richard W.
中科院分区：
文献类型：
--
作者：
Castellanos-Garcia, Laura J.;Elci, S. Gokhan;Vachet, Richard W.
激光烧蚀电感耦合等离子体质谱（LA-ICP-MS）成像已被广泛用于确定生物组织中金属的分布，用于各种各样的应用。为了用于识别金属生物分布，需要将采集的原始数据重建成二维图像。已经开发了几种方法用于LA-ICP-MS图像重建，但是较少关注用于成像数据的更深入统计处理的软件。然而，改进的图像处理可以允许更好地理解组织中金属分布的生物学分支。在这项工作中，我们描述了用Python编写的软件，该软件可以自动重建，分析和分割LA-ICP-MS成像数据中的图像。使用来自生物金属Fe和Zn的LA-ICP-MS信号与k均值聚类一起实现图像分割，以自动识别不同组织中的子器官区域。空间感知还可以通过邻近像素评估纳入图像中，从而识别处于LA-ICP-MS成像分辨率极限的感兴趣区域。所描述的算法的值被证明为LA-ICP-MS图像的纳米材料生物分布。开发的图像重建和处理方法揭示了纳米材料根据其化学和物理性质分布在不同的子器官区域，为了解此类纳米材料在体内的影响开辟了新的可能性。
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) imaging has been extensively used to determine the distributions of metals in biological tissues for a wide variety of applications. To be useful for identifying metal biodistributions, the acquired raw data needs to be reconstructed into a two-dimensional image. Several approaches have been developed for LA-ICP-MS image reconstruction, but less focus has been placed on software for more in-depth statistical processing of the imaging data. Yet, improved image processing can allow the biological ramifications of metal distributions in tissues to be better understood. In this work, we describe software written in Python that automatically reconstructs, analyzes, and segments images from LA-ICP-MS imaging data. Image segmentation is achieved using LA-ICP-MS signals from the biological metals Fe and Zn together with k-means clustering to automatically identify sub-organ regions in different tissues. Spatial awareness also can be incorporated into the images through a neighboring pixel evaluation that allows regions of interest to be identified that are at the limit of the LA-ICP-MS imaging resolution. The value of the described algorithms is demonstrated for LA-ICP-MS images of nanomaterial biodistributions. The developed image reconstruction and processing approach reveals that nanomaterials distribute in different sub-organ regions based on their chemical and physical properties, opening new possibilities for understanding the impact of such nanomaterials in vivo.