Cargando…

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 syst...

Descripción completa

Detalles Bibliográficos
Autores principales: Murphy, Kathleen Z., Haile, Eyobel, McTigue, Anna, Pierce, Anne F., Donaldson, Zoe R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10054971/
https://www.ncbi.nlm.nih.gov/pubmed/36993180
http://dx.doi.org/10.1101/2023.03.14.532489
Descripción
Sumario: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 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 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 Installation Basic Protocol 2: GUI-driven Fiber Photometry Analysis Basic Protocol 3: Adding Modules