悬浮和成像细胞学技术可以同时测量数百种细胞特征，推动了细胞生物学的新时代，并且改变了我们对人类组织和肿瘤的认识。然而，一个核心难题在于学习意外或新型细胞类型的身份。细胞识别准则可以帮助人类或机器学习者，但它们并非总是经过严谨定义，而是因领域不同而差异很大，且不同程度地依赖于细胞内在测量、细胞外在组织测量或外部情境信息，例如临床结果。在肿瘤环境下，这个挑战尤为严峻，因为细胞异常表达普通情况下仅限于时间、位置或细胞类型的发育程序。而已经确立的领域对细胞身份有着不同的实践方法，这些方法既有传统的惯例，也有便捷的设计。例如，早期的免疫学侧重于确定标记个体、功能不同的细胞最小蛋白特征集。在神经科学中，形态、发育和解剖位置都是定义细胞类型的典型起点。现在，免疫学和神经科学都致力于将蛋白或RNA的标准测量与相关细胞功能（如电生理学、连接性、祖细胞潜力、磷酸化蛋白信号、细胞抑制和肿瘤细胞杀伤能力）联系起来。自动化、机器驱动的细胞身份学习方法的扩展进一步创建了一种跨领域和技术平台区分细胞身份的统一框架的紧迫需求。在这里，我们比较免疫学和神经科学领域的实践方法，强调从每个领域中获得的概念，以及建议实施这些想法来研究脑肿瘤和相关模型系统中的神经和免疫细胞相互作用。 ©2022年。作者独家许可，授权给施普林格-费尔拉格德公司德国公司，隶属施普林格自然出版集团。

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.