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整合基因组特征以进行免疫发现。

Integrating genomic signatures for immunologic discovery.

机构信息

Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA.

出版信息

Immunity. 2010 Feb 26;32(2):152-61. doi: 10.1016/j.immuni.2010.02.001.

Abstract

Understanding heterogeneity in adaptive immune responses is essential to dissect pathways of memory B and T cell differentiation and to define correlates of protective immunity. Traditionally, immunologists have deconvoluted this heterogeneity with flow cytometry--with combinations of markers to define signatures that represent specific lineages, differentiation states, and functions. Genome-scale technologies have become widely available and provide the ability to define expression signatures--sets of genes--that represent discrete biological properties of cell populations. Because genomic signatures can serve as surrogates of a phenotype, function, or cell state, they can integrate phenotypic information between experiments, cell types, and species. Here, we discuss how integration of well-defined expression signatures across experimental conditions together with functional analysis of their component genes could provide new opportunities to dissect the complexity of the adaptive immune response and map the immune response to vaccines and pathogens.

摘要

理解适应性免疫反应的异质性对于解析记忆 B 和 T 细胞分化的途径以及定义保护性免疫的相关因素至关重要。传统上,免疫学家通过流式细胞术来剖析这种异质性——使用组合标记物来定义代表特定谱系、分化状态和功能的特征。基因组规模的技术已经广泛可用,并提供了定义表达特征(基因集)的能力,这些特征代表细胞群体的离散生物学特性。由于基因组特征可以作为表型、功能或细胞状态的替代物,它们可以在实验、细胞类型和物种之间整合表型信息。在这里,我们讨论了如何将定义明确的表达特征整合到实验条件中,并对其组成基因进行功能分析,这为剖析适应性免疫反应的复杂性以及将免疫反应映射到疫苗和病原体提供了新的机会。

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