The spatial subcellular proteome is a dynamic environment; one that can be perturbed by molecular cues and regulated by post‐translational modifications. Compartmentalization of this environment and management of these biomolecular dynamics allows for an array of ancillary protein functions. Profiling spatial proteomics has proved to be a powerful technique in identifying the primary subcellular localization of proteins. The approach has also been refashioned to study multi‐localization and localization dynamics. Here, the analytical approaches that have been applied to spatial proteomics thus far are critiqued, and challenges particularly associated with multi‐localization and dynamic relocalization is identified. To meet some of the current limitations in analytical processing, it is suggested that Bayesian modeling has clear benefits over the methods applied to date and should be favored whenever possible. Careful consideration of the limitations and challenges, and development of robust statistical frameworks, will ensure that profiling spatial proteomics remains a valuable technique as its utility is expanded.

中文翻译：

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移动分析空间蛋白质组学超越离散分类。

空间亚细胞蛋白质组是一个动态环境；一种可以被分子线索扰动并受翻译后修饰调节的分子。这种环境的划分和这些生物分子动力学的管理允许一系列辅助蛋白质功能。分析空间蛋白质组学已被证明是识别蛋白质主要亚细胞定位的强大技术。该方法也被重新设计以研究多本地化和本地化动态。在这里，对迄今为止应用于空间蛋白质组学的分析方法进行了批评，并确定了与多定位和动态重定位相关的挑战。为了满足当前分析处理的一些限制，建议贝叶斯建模与迄今为止应用的方法相比具有明显的优势，应尽可能受到青睐。仔细考虑局限性和挑战，并开发强大的统计框架，将确保剖析空间蛋白质组学在其效用得到扩展时仍然是一项有价值的技术。