Spectral/FLIM strategies for FRET based analysis of signal transduction pathways

University Of Wisconsin-Madison, Madison WI

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Abstract

DESCRIPTION (provided by applicant): To understand how metazoan cells function in the context of the tissue in which they reside requires an understanding of the myriad signal transduction pathways that operate both within and between cells. Classical biochemical techniques have revealed many of the signaling elements and their mode of action within a pathway;however, such techniques are unable to visualize the activation state of a signaling pathway in vivo. Recently, the combined use of advanced optical techniques, used in conjunction with engineered fluorescent protein probes, have opened up the possibility of directly visualizing the activation state of a signaling pathway. We propose to develop a generally applicable toolkit for studying the activation of Rho-family GTPase pathways in the context of intact tissues or organisms. The tools consist of a set of optimized fluorescent probes for visualizing when a GTPase becomes activated, when it binds to a cognate effector and how a signaling complex may be formed. Binding is detected optically with multiphoton microscopy by fluorescence lifetime analysis to reveal the presence of Fvrster resonant energy transfer (FRET). Analysis algorithms for obtaining spatial lifetime maps indicating localized regions of signal activation will be generated and refined. Novel techniques using polarization anisotropy decay measurements for detecting associations of GTPases upon activation and also the formation of large signaling complexes will be developed. We anticipate that the methods we are proposing will facilitate the application and reproducibility of FRET measurements for many in vivo signaling studies. PUBLIC HEALTH RELEVANCE: The development of robust FRET-based advanced imaging approaches could enhance considerably our knowledge of inter- and intra-cellular signaling particularly in regards to cancers such as breast cancer. FRET-based methods could help elucidate underlying mechanisms behind cancer onset and progression and help identify future targets for treatment.