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图形化与交互式空间蛋白质组学图像分析工作流程

Graphical and Interactive Spatial Proteomics Image Analysis Workflow.

作者信息

Singh Pritpal, Wright Jocelyn H, Smythe Kimberly S, Fukuda Bryce, Hung Ling-Hong, Yeung Cecilia Cs, Yeung Ka Yee

机构信息

School of Engineering and Technology, University of Washington Tacoma, WA.

Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA.

出版信息

bioRxiv. 2025 May 27:2025.05.23.655879. doi: 10.1101/2025.05.23.655879.


DOI:10.1101/2025.05.23.655879
PMID:40501614
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12154974/
Abstract

Spatial proteomics provides a spatially resolved view of protein expression and localization within cells and tissues by mapping the location and abundance of proteins. There is a need for containerized end-to-end imaging workflows for spatial proteomic analysis that are flexible, high-throughput, and support graphical and interactive visualizations. We present a modular and interactive spatial proteomics imaging workflow that empowers biomedical researchers to reproducibly execute and customize complex analyses. Our workflow consists of cell segmentation, unsupervised clustering, validation of clusters on the image, and cell type clustering results visualization. Users can utilize a form-based graphical interface to execute and customize multi-step workflows with a single click or interactively adjust image processing steps within the workflow, apply workflows to various datasets, and modify input parameters as needed. We illustrated the functionality of our workflow using a cancer imaging dataset consisting of a tissue microarray (TMA) stained by high-plex immunohistochemistry. This TMA contained a variety of cancer and tissue cell types to assess the broad applicability of this workflow to different biopsy types.

摘要

空间蛋白质组学通过绘制蛋白质的位置和丰度,提供细胞和组织内蛋白质表达和定位的空间分辨视图。需要用于空间蛋白质组学分析的容器化端到端成像工作流程,这些流程灵活、高通量,并支持图形化和交互式可视化。我们提出了一种模块化和交互式的空间蛋白质组学成像工作流程,使生物医学研究人员能够可重复地执行和定制复杂分析。我们的工作流程包括细胞分割、无监督聚类、图像上聚类的验证以及细胞类型聚类结果可视化。用户可以使用基于表单的图形界面,通过单击执行和定制多步骤工作流程,或在工作流程中交互式调整图像处理步骤,将工作流程应用于各种数据集,并根据需要修改输入参数。我们使用由高多重免疫组织化学染色的组织微阵列(TMA)组成的癌症成像数据集说明了我们工作流程的功能。该TMA包含多种癌症和组织细胞类型,以评估该工作流程对不同活检类型的广泛适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20c/12261883/0cd041a6e46d/nihpp-2025.05.23.655879v2-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20c/12261883/0cd041a6e46d/nihpp-2025.05.23.655879v2-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20c/12261883/0cd041a6e46d/nihpp-2025.05.23.655879v2-f0001.jpg

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本文引用的文献

[1]
SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis.

Gigascience. 2025-1-6

[2]
Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks.

Sci Rep. 2022-9-2

[3]
CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues.

Cell Rep. 2020-4-21

[4]
Building Containerized Workflows Using the BioDepot-Workflow-Builder.

Cell Syst. 2019-9-11

[5]
QuPath: Open source software for digital pathology image analysis.

Sci Rep. 2017-12-4

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