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Source Code for Biology and Medicine

Open Access

FLIM-FRET analyzer: open source software for automation of lifetime-based FRET analysis

Source Code for Biology and Medicine201712:7

https://doi.org/10.1186/s13029-017-0067-0

Received: 11 January 2017

Accepted: 24 October 2017

Published: 3 November 2017

Abstract

Background

Despite the broad use of FRET techniques, available methods for analyzing protein-protein interaction are subject to high labor and lack of systematic analysis. We propose an open source software allowing the quantitative analysis of fluorescence lifetime imaging (FLIM) while integrating the steady-state fluorescence intensity information for protein-protein interaction studies.

Findings

Our developed open source software is dedicated to fluorescence lifetime imaging microscopy (FLIM) data obtained from Becker & Hickl SPC-830. FLIM-FRET analyzer includes: a user-friendly interface enabling automated intensity-based segmentation into single cells, time-resolved fluorescence data fitting to lifetime value for each segmented objects, batch capability, and data representation with donor lifetime versus acceptor/donor intensity quantification as a measure of protein-protein interactions.

Conclusions

The FLIM-FRET analyzer software is a flexible application for lifetime-based FRET analysis. The application, the C#. NET source code, and detailed documentation are freely available at the following URL: http://FLIM-analyzer.ip-korea.org.

Keywords

Fluorescence lifetime imaging microscopy (FLIM)Fluorescence resonance energy transfer (FRET) analysisIntensity-based segmentationBatch processing

Introduction

Over the past decades, light microscopy has been customized to facilitate the investigation of protein assembly into macromolecular complexes in living cells. Calculating the efficiency of the Förster/fluorescence resonance energy transfer (FRET) allows characterization of protein-protein interactions or changes in protein conformation, because the efficiency varies as the inverse sixth power of distance between fluorophores, typically reaching 50% at 2–8 nm [1]. Among the few existing approaches toward detecting and quantifying FRET, fluorescent lifetime imaging (FLIM) has a number of advantages. The changes in fluorescence lifetime are independent of fluorophore concentration, simplifying the process of filtering out artifacts introduced by variations in fluorophore concentration and emission intensity across the sample. In contrast to intensity-based FRET measurements, FLIM measurements are relatively robust under conditions where spectral crosstalk is present. As a result, FLIM experiments do not require spectral calibration measurements. Fluorescence lifetime can also be used to distinguish different fluorophores with similar spectral properties [2] and report variations in the fluorophore’s local environment [3]. Although FLIM is widely used, it is subject to major limitations. A few softwares are available for batching the analysis of FLIM data [46], but none provide automated segmentation function for single cell lifetime analysis. Such limitations make FLIM data analysis extremely time intensive, which is a major drawback for protein-protein interaction studies. We therefore have developed a single cell image segmentation software called FLIM-FRET analyzer, which separates objects of interest from the background to delineate whole cells, facilitating image segmentation into single cells followed by donor lifetime and donor/acceptor fluorescence intensity quantification.

Implementation

FLIM-FRET analyzer was designed to automate processing of intensity-based cell image segmentation, fluorescence lifetime fitting, and FRET analysis of entire data sets. FLIM-FRET analyzer was designed to automate processing of intensity-based cell image segmentation, fluorescence lifetime fitting, and FRET analysis of entire data sets. This software consists of two distinct processes: The first part is to create a FRET collection which associates fluorescent intensity and fluorescence decay image datasets. To create a FRET collection, the user imports the donor and acceptor fluorescent channels (.tiff files) and the donor fluorescence decay curves (.sdt files), which in our use-case were obtained with a Single Photon Counting module (SPC-830; Becker & Hickl GmbH). During step 1 of data analysis, the .sdt or .tiff file is selected, and the cell or organelles are segmented based on size and fluorescence intensity (Fig. 1a-b). After cell segmentation, the lifetime fitting is performed using the exponentially modified Gaussian curve [7]. During step 2, binning is set to select the optimal tradeoff between spatial resolution and the number of photons acquired per pixel. A color-coded lifetime image (red to blue) is displayed after the fitting process is complete. The lifetime calculation step produces two output tabs. The first tab (Fig. 1c, inset) contains the lifetime histogram, which illustrates the lifetime distribution for the selected images. The second tab (Fig. 1d) contains the fluorescence decay curve raw data and the fitted trace for the selected pixel (the quality of fit is determined by the chi-square value (χ2). Step 3 consists of the single cell lifetime calculation. Choosing “Run FRET analysis” enables the quantification of the acceptor/donor intensity ratio and the average lifetime for each segmented single cell. A plot of average lifetime versus acceptor/donor intensity for each single cell is automatically displayed, as seen Fig. 1e. This plot is very useful for visualization of the expected correlation between FRET and acceptor/donor intensities. It is also possible to launch all three described steps for all data within the stack by choosing “Run all steps for the whole collection”. The FRET analysis results appear as a pop-up window as shown in Fig. 1f. In batch analysis, step 4 involves summarizing the data in a table as well as presenting a scatterplot of lifetime versus the acceptor/donor intensity ratio.
Fig. 1

Illustration of the features of FLIM-FRET analyzer. a Multistep menu for single cell segmentation (step 1), whole image lifetime calculation (step 2), average lifetime single cell calculation (step 3), and batched multi FLIM image experiments (step 4). b Fluorescence-based (white) single cell segmentations (red). c Lifetime map. d Instrument response function (blue), Lifetime raw data (red), Lifetime fitting (green). e Average lifetime calculation at the single cell level. f Batched analysis of multiple experiments and the average single cell FLIM measurement display

Results and discussion

We tested the performance of FLIM-FRET analyzer against that of the standard SPCImage (version 5.4) software, which requires manual segmentation of single cells, using HEK-293 cells expressing fluorescent proteins. Both methods yielded comparable results (Pearson r: 0.9925), suggesting that FLIM-FRET analyzer software is a robust and accurate tool for analyzing protein-protein interactions using fluorescence assays (Additional file 1: Figure S1). Last, we illustrate the application of FLIM-FRET Analyzer upon lifetime quantification of single cell expressing FRET-capable pairs of fluorescent proteins (Additional file 1: Figure S2).

Conclusion

The stand-alone software described here aims to simplify and accelerate the process of analyzing multivariate FLIM data sets for single cell lifetime quantification. The available full C# source code (http://FLIM-analyzer.ip-korea.org.) will allow the user to adapt or extend the currently provided version of the application. A tutorial (Additionnal file 2) can also be downloaded at the same URL.

Declarations

Acknowledgements

Not applicable

Funding

This work was supported by the National Research foundation of Korea (NRF) grant funded by the Korea government (MSIP)(NRF-2014K1A4A7A01074642; NRF-2017M3A9G6068257), Gyeonggi-do. This work was also funded by the NRF individual scientist support program (NRF-2012R1A1A2004980/ NRF-2015R1D1A1A09057239).

Availability of data and materials

The application, the C#. Net source code, and detailed documentation are freely available at following URL: http://FLIM-analyzer.ip-korea.org

Authors’ contributions

Study conception and design: JK and RG (Leading FLIM-FRET analyzer software development). Acquisition of data: JK (Design the layout of FLIM-FRET analyzer software, supervise the lifetime fitting process, and validate the software), YT (Development and programming) and JP (Develop the software architecture, user interface and workflow). Analysis and interpretation of data: JK, YT and RG. Drafting of manuscript: JK and RG, Critical revision: RG. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Technology Development Platform, Institut Pasteur Korea
(2)
Image Mining, Institut Pasteur Korea

References

  1. Vogel SS, Thaler C, Koushik SV. Fanciful FRET. Sci STKE. 2006;2006:re2.PubMedGoogle Scholar
  2. Kim J, Kwon D, Lee J, Pasquier H, Grailhe R. The use of cyan fluorescent protein variants with a distinctive lifetime signature. Mol BioSyst. 2009;5:151–3.View ArticlePubMedGoogle Scholar
  3. Suhling K, Siegel J, Phillips D, French PMW, Lévêque-Fort S, Webb SED, Davis DM. Imaging the environment of green fluorescent protein. Biophys J. 2002;83:3589–95.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Warren SC, Margineanu A, Alibhai D, Kelly DJ, Talbot C, Alexandrov Y, Munro I, Katan M, Dunsby C, French PMW. Rapid global fitting of large fluorescence lifetime imaging microscopy datasets. PLoS One. 2013 Aug 5;8(8):e70687.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Barber PR, Tullis IDC, Pierce GP, Newman RG, Prentice J, Rowley MI, Matthews DR, Ameer-Beg SM, Vojnovic B. The gray institute “open” high-content, fluorescence lifetime microscopes. J Microsc. 2013 Aug;251(2):154–67.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Gmbh H: SPCImage 4.0. 2013(February).Google Scholar
  7. Laptenok SP, Mullen KM, Borst J. Fluorescence lifetime imaging microscopy (FLIM) data analysis with TIMP. J Stat Softw. 2007;18:1–20.View ArticleGoogle Scholar

Copyright

© The Author(s). 2017

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