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TissUUmaps3中用于大规模多重组织分析的可视化和质量控制工具。

Visualization and quality control tools for large-scale multiplex tissue analysis in TissUUmaps3.

作者信息

Behanova Andrea, Avenel Christophe, Andersson Axel, Chelebian Eduard, Klemm Anna, Wik Lina, Östman Arne, Wählby Carolina

机构信息

Department of Information Technology and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden.

Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden.

出版信息

Biol Imaging. 2023 Feb 20;3:e6. doi: 10.1017/S2633903X23000053. eCollection 2023.

Abstract

Large-scale multiplex tissue analysis aims to understand processes such as development and tumor formation by studying the occurrence and interaction of cells in local environments in, for example, tissue samples from patient cohorts. A typical procedure in the analysis is to delineate individual cells, classify them into cell types, and analyze their spatial relationships. All steps come with a number of challenges, and to address them and identify the bottlenecks of the analysis, it is necessary to include quality control tools in the analysis workflow. This makes it possible to optimize the steps and adjust settings in order to get better and more precise results. Additionally, the development of automated approaches for tissue analysis requires visual verification to reduce skepticism with regard to the accuracy of the results. Quality control tools could be used to build users' trust in automated approaches. In this paper, we present three plugins for visualization and quality control in large-scale multiplex tissue analysis of microscopy images. The first plugin focuses on the quality of cell staining, the second one was made for interactive evaluation and comparison of different cell classification results, and the third one serves for reviewing interactions of different cell types.

摘要

大规模多重组织分析旨在通过研究例如患者队列组织样本中局部环境中细胞的出现和相互作用,来理解诸如发育和肿瘤形成等过程。分析中的一个典型程序是勾勒单个细胞,将它们分类为细胞类型,并分析它们的空间关系。所有步骤都面临许多挑战,为了解决这些挑战并识别分析的瓶颈,有必要在分析工作流程中纳入质量控制工具。这使得优化步骤和调整设置成为可能,以便获得更好、更精确的结果。此外,组织分析自动化方法的开发需要视觉验证,以减少对结果准确性的怀疑。质量控制工具可用于建立用户对自动化方法的信任。在本文中,我们展示了用于显微镜图像大规模多重组织分析的可视化和质量控制的三个插件。第一个插件关注细胞染色质量,第二个插件用于对不同细胞分类结果进行交互式评估和比较,第三个插件用于审查不同细胞类型的相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/348d/10936381/3902a10418c9/S2633903X23000053_fig1.jpg

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