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Flame Photometry: Overview

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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...

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Updated: Jul 30, 2025

Automated Two-dimensional Spatiotemporal Analysis of Mobile Single-molecule FRET Probes

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Published on: November 23, 2021

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

Kathleen Z Murphy¹, Eyobel D Haile², Anna D McTigue³

¹Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, Colorado.

Current Protocols

|May 15, 2023

Summary

This summary is machine-generated.

This study introduces the Photometry Analysis Toolkit (PhAT), a free, open-source software for analyzing fiber photometry data. PhAT simplifies complex analyses, enhancing reproducibility in neuroscience research.

Keywords:

analysis open-source photometry software

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Area of Science:

Neuroscience
Biotechnology
Data Science

Background:

Fiber photometry is a valuable technique for measuring molecular changes in the brain via fluorescence.
Existing data analysis pipelines for photometry are limited, hindering widespread adoption.
There is a need for accessible, robust tools to analyze photometry data.

Purpose of the Study:

To introduce the Photometry Analysis Toolkit (PhAT), an open-source software pipeline.
To provide a user-friendly solution for analyzing fiber photometry data.
To improve the quality, transparency, and reproducibility of photometry experiments.

Main Methods:

Development of PhAT, a free, open-source analysis pipeline with a graphical user interface (GUI).
PhAT offers signal normalization, multi-stream data integration, and event-related fluorescence analysis.
Includes protocols for installation, usage, module addition, and best practices for photometry experiments.

Main Results:

PhAT provides a comprehensive suite of tools for photometry data analysis, accessible via a GUI.
The toolkit facilitates signal normalization, alignment with behavioral data, and similarity comparisons.
PhAT is designed for extensibility, allowing community contributions and easy data export for further analysis.

Conclusions:

PhAT lowers the barrier to entry for fiber photometry research.
The software enhances data quality, transparency, and reproducibility in neuroscience.
This toolkit supports both novice and expert users in advancing brain research.