In vivo detection of GPCR-dependent signaling using fiber photometry and FRET-based biosensors

PhAT: A Flexible Open-Source GUI-Driven Toolkit for Photometry Analysis

Photometry approaches detect sensor-mediated changes in fluorescence as a proxy for rapid molecular changes within the brain. As a flexible technique with a relatively low cost to implement, photometry is rapidly being incorporated into neuroscience laboratories. Yet, although multiple data acquisition systems for photometry now exist, robust analytical pipelines for the resulting data remain limited. Here we present the Photometry Analysis Toolkit (PhAT)-a free open-source analysis pipeline that provides options for signal normalization, incorporation of multiple data streams to align photometry data with behavior and other events, calculation of event-related changes in fluorescence, and comparison of similarity across fluorescent traces. A graphical user interface (GUI) enables use of this software without prior coding knowledge. In addition to providing foundational analytical tools, PhAT is designed to readily incorporate community-driven development of new modules for more bespoke analyses, and enables data to be easily exported to enable subsequent statistical testing and/or code-based analyses. In addition, we provide recommendations regarding technical aspects of photometry experiments, including sensor selection and validation, reference signal considerations, and best practices for experimental design and data collection. We hope that the distribution of this software and protocols will lower the barrier to entry for new photometry users and improve the quality of collected data, increasing transparency and reproducibility in photometry analyses. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Software and environment installation Alternate Protocol 1: Software and environment update Basic Protocol 2: GUI-driven fiber photometry analysis Support Protocol 1: Examining signal quality Support Protocol 2: Interacting with graphs Basic Protocol 3: Adding modules to PhAT Alternate Protocol 2: Creating functions for use in Jupyter Notebook.

PhAT: A flexible open-source GUI-driven toolkit for photometry analysis

Photometry approaches detect sensor-mediated changes in fluorescence as a proxy for rapid molecular changes within the brain. As a flexible technique with a relatively low cost to implement, photometry is rapidly being incorporated into neuroscience laboratories. While multiple data acquisition systems for photometry now exist, robust analytical pipelines for the resulting data remain limited. Here we present the Ph otometry A nalysis T oolkit (PhAT) - a free open source analysis pipeline that provides options for signal normalization, incorporation of multiple data streams to align photometry data with behavior and other events, calculation of event-related changes in fluorescence, and comparison of similarity across fluorescent traces. A graphical user interface (GUI) enables use of this software without prior coding knowledge. In addition to providing foundational analytical tools, PhAT is designed to readily incorporate community-driven development of new modules for more bespoke analyses, and data can be easily exported to enable subsequent statistical testing and/or code-based analyses. In addition, we provide recommendations regarding technical aspects of photometry experiments including sensor selection and validation, reference signal considerations, and best practices for experimental design and data collection. We hope that the distribution of this software and protocol will lower the barrier to entry for new photometry users and improve the quality of collected data, increasing transparency and reproducibility in photometry analyses. Basic Protocol 1: Software Environment InstallationBasic Protocol 2: GUI-driven Fiber Photometry AnalysisBasic Protocol 3: Adding Modules.

Biosensor-based profiling to track cellular signalling in patient-derived models of dilated cardiomyopathy

Dilated cardiomyopathies (DCM) represent a diverse group of cardiovascular diseases impacting the structure and function of the myocardium. To better treat these diseases, we need to understand the impact of such cardiomyopathies on critical signalling pathways that drive disease progression downstream of receptors we often target therapeutically. Our understanding of cellular signalling events has progressed substantially in the last few years, in large part due to the design, validation and use of biosensor-based approaches to studying such events in cells, tissues and in some cases, living animals. Another transformative development has been the use of human induced pluripotent stem cells (hiPSCs) to generate disease-relevant models from individual patients. We highlight the importance of going beyond monocellular cultures to incorporate the influence of paracrine signalling mediators. Finally, we discuss the recent coalition of these approaches in the context of DCM. We discuss recent work in generating patient-derived models of cardiomyopathies and the utility of using signalling biosensors to track disease progression and test potential therapeutic strategies that can be later used to inform treatment options in patients.