Learning cell identity in immunology, neuroscience, and cancer.
Learning cell identity in immunology, neuroscience, and cancer.
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DOI：
10.1007/s00281-022-00976-y
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发表时间：
2023-01
影响因子：
9
通讯作者：
Irish, Jonathan M.
中科院分区：
文献类型：
--
作者：
Medina, Stephanie;Ihrie, Rebecca A.;Irish, Jonathan M.
悬浮液和成像细胞术技术可以同时测量数百种细胞特征，正在为细胞生物学的新时代提供动力，并改变我们对人体组织和肿瘤的理解。然而，一个核心的挑战仍然是在学习的身份意想不到的或新的细胞类型。可以帮助受训者（无论是人类还是机器）的细胞识别规则并不总是严格定义的，根据领域变化很大，并且不同地依赖于细胞内在测量、细胞外在组织测量或外部背景信息（如临床结果）。这种挑战在肿瘤的情况下尤其严重，其中细胞异常表达通常受时间、位置或细胞类型限制的发育程序。成熟的领域有着截然不同的细胞识别实践，这些实践既源于设计，也源于惯例和便利。例如，早期的免疫学专注于识别标记单个功能不同细胞的蛋白质特征的最小集合。在神经科学中，包括形态、发育和解剖位置在内的特征是定义细胞类型的典型起点。免疫学和神经科学现在都致力于将蛋白质或RNA的标准化测量与信息细胞功能联系起来，如电生理学，连接性，谱系电位，磷蛋白信号传导，细胞抑制和肿瘤细胞杀伤能力。用于学习细胞身份的自动化、机器驱动方法的扩展进一步迫切需要一个协调的框架来区分跨领域和技术平台的细胞身份。在这里，我们比较了免疫学和神经科学领域的实践，强调了可能在另一个领域工作得很好的概念，并提出了实施这些想法的方法，以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。
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.
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影响因子：
16.8
作者：
Bendall SC;Nolan GP;Roederer M;Chattopadhyay PK
通讯作者：
Chattopadhyay PK
DOI：
10.1073/pnas.0710000105
发表时间：
2008-01-22
影响因子：
11.1
作者：
Coskun, Volkan;Wu, Hao;Sun, Yi E.
通讯作者：
Sun, Yi E.
影响因子：
50.3
作者：
Chen, Ruihuan;Nishimura, Merry C.;Phillips, Heidi S.
通讯作者：
Phillips, Heidi S.
影响因子：
3.7
作者：
Brown DV;Filiz G;Daniel PM;Hollande F;Dworkin S;Amiridis S;Kountouri N;Ng W;Morokoff AP;Mantamadiotis T
通讯作者：
Mantamadiotis T
影响因子：
17.1
作者：
Gaudillière B;Fragiadakis GK;Bruggner RV;Nicolau M;Finck R;Tingle M;Silva J;Ganio EA;Yeh CG;Maloney WJ;Huddleston JI;Goodman SB;Davis MM;Bendall SC;Fantl WJ;Angst MS;Nolan GP
通讯作者：
Nolan GP