SH2域
全内反射荧光显微镜
生物物理学
锡克
细胞生物学
细胞信号
化学
细胞膜
血浆蛋白结合
信号转导
生物
膜
酪氨酸磷酸化
生物化学
酪氨酸激酶
作者
Megan V. Farrell,Andrea C. Nunez,Zhengmin Yang,Pablo Pérez-Ferreros,Katharina Gaus,Jesse Goyette
出处
期刊:Science Signaling
[American Association for the Advancement of Science]
日期:2022-02-01
卷期号:15 (719)
被引量:18
标识
DOI:10.1126/scisignal.abg9782
摘要
Superresolution techniques have advanced our understanding of complex cellular structures and processes but require the attachment of fluorophores to targets through tags or antibodies, which can be bulky and result in underlabeling. To overcome these limitations, we developed a technique to visualize the nanoscale binding locations of signaling proteins by taking advantage of their native interaction domains. Here, we demonstrated that pPAINT (protein point accumulation in nanoscale topography) is a new, single-molecule localization microscopy (SMLM) technique and used it to investigate T cell signaling by visualizing the Src homology 2 (SH2) domain, which is common in signaling molecules. When SH2 domain–containing proteins relocate to the plasma membrane, the domains selectively, transiently, and reversibly bind to preferred phosphorylated tyrosine residues on receptors. This transient binding yields the stochastic blinking events necessary for SMLM when observed with total internal reflection microscopy and enables quantification of binding coefficients in intact cells. We used pPAINT to reveal the binding sites of several T cell receptor–proximal signaling molecules, including Zap70, PI3K, Grb2, Syk, Eat2, and SHP2, and showed that the probes could be multiplexed. We showed that the binding half-life of the tandem SH2 domain of PI3K correlated with binding site cluster size at the immunological synapses of T cells, but that longer binding lifetimes were associated with smaller clusters for the monovalent SH2 domain of Eat2. These results demonstrate the potential of pPAINT for investigating phosphotyrosine-mediated signaling processes at the plasma membrane.
科研通智能强力驱动
Strongly Powered by AbleSci AI