Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data

Matthias Meyer-Bender

doi:10.1038/s41592-026-03155-1

2026-07-24

Matthias Meyer-Bender *

Harald Vöhringer

Christina Schniederjohann

DOI：10.1038/s41592-026-03155-1

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摘要

En 中文

高倍多重免疫荧光成像在完整组织中实现了低成本、高可扩展性的单细胞分辨率蛋白质水平可视化和定量。这些数据的分析涉及多个步骤和多种方法及参数选择，必须适应数据和解析目标。目前迫切需要一个能够灵活覆盖整个工作流程的工具箱。本文介绍了‘spatialproteomics’，一个解决这些挑战的Python软件包。Spatialproteomics能够处理和分析大型成像数据，包括分割、图像处理和细胞类型分类等步骤，同时同步跨数据模式的共享坐标。我们在132例患者的反应性淋巴结和B细胞非霍奇金淋巴瘤图像上展示了spatialproteomics的应用。我们演示了从原始图像到统计分析的端到端分析，揭示了惰性和侵袭性淋巴瘤中细胞类型组成和空间分布的变化。此外，我们还展示了spatialproteomics如何处理千兆像素的全玻片图像。Spatialproteomics是一个基于Python的工具箱，支持高倍多重成像数据的端到端分析。

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Nature Methods

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32.1

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7.2K

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European Molecular Biology Laboratory

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401

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university of heidelberg

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university hospital düsseldorf

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125

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