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利用遗传条码抗体进行高通量且定量的细胞表面蛋白分析。

Highly multiplexed and quantitative cell-surface protein profiling using genetically barcoded antibodies.

机构信息

Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94143.

Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.

出版信息

Proc Natl Acad Sci U S A. 2018 Mar 13;115(11):2836-2841. doi: 10.1073/pnas.1721899115. Epub 2018 Feb 23.

Abstract

Human cells express thousands of different surface proteins that can be used for cell classification, or to distinguish healthy and disease conditions. A method capable of profiling a substantial fraction of the surface proteome simultaneously and inexpensively would enable more accurate and complete classification of cell states. We present a highly multiplexed and quantitative surface proteomic method using genetically barcoded antibodies called phage-antibody next-generation sequencing (PhaNGS). Using 144 preselected antibodies displayed on filamentous phage (Fab-phage) against 44 receptor targets, we assess changes in B cell surface proteins after the development of drug resistance in a patient with acute lymphoblastic leukemia (ALL) and in adaptation to oncogene expression in a Myc-inducible Burkitt lymphoma model. We further show PhaNGS can be applied at the single-cell level. Our results reveal that a common set of proteins including FLT3, NCR3LG1, and ROR1 dominate the response to similar oncogenic perturbations in B cells. Linking high-affinity, selective, genetically encoded binders to NGS enables direct and highly multiplexed protein detection, comparable to RNA-sequencing for mRNA. PhaNGS has the potential to profile a substantial fraction of the surface proteome simultaneously and inexpensively to enable more accurate and complete classification of cell states.

摘要

人类细胞表达数千种不同的表面蛋白,可用于细胞分类,或区分健康和疾病状态。一种能够同时且经济高效地对大量表面蛋白质组进行分析的方法将能够更准确和完整地对细胞状态进行分类。我们提出了一种高度多重化和定量的表面蛋白质组学方法,使用称为噬菌体抗体下一代测序(PhaNGS)的遗传条形码抗体。我们使用针对 44 个受体靶标的 144 种预先选择的丝状噬菌体展示的抗体(Fab-phage),评估了急性淋巴细胞白血病(ALL)患者中耐药性发展和 Myc 诱导的 Burkitt 淋巴瘤模型中癌基因表达适应性后 B 细胞表面蛋白的变化。我们进一步表明 PhaNGS 可以应用于单细胞水平。我们的结果表明,一组常见的蛋白,包括 FLT3、NCR3LG1 和 ROR1,主导了 B 细胞对类似致癌扰动的反应。将高亲和力、选择性、遗传编码的结合物与 NGS 连接起来,可实现直接和高度多重化的蛋白质检测,与 RNA-seq 相比可用于检测 mRNA。PhaNGS 有可能同时且经济高效地对大量表面蛋白质组进行分析,从而能够更准确和完整地对细胞状态进行分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c629/5856557/ada29c40d22f/pnas.1721899115fig01.jpg

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