Dataset Information

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

ABSTRACT: 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.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.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.

SUBMITTER: Kim J

PROVIDER: S-EPMC5670528 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

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

Kim Jiho J Tsoy Yury Y Persson Jan J Grailhe Regis R

Source code for biology and medicine 20171103

<h4>Background</h4>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.<h4>Findings</h4>Our developed open source software is dedicated to fluorescence lifetime imaging mi ...[more]

PMID: 29142589

Similar Datasets

Project description:FRET biosensors have proven very useful tools for studying the activation of specific signalling pathways in living cells. Most biosensors designed to date have been predicated on fluorescent protein pairs that were identified by, and for use in, intensity based measurements, however fluorescence lifetime provides a more reliable measurement of FRET. Both the technology and fluorescent proteins available for FRET have moved on dramatically in the last decade. Lifetime imaging systems have become increasingly accessible and user-friendly, and there is an entire field of biology dedicated to refining and adapting different characteristics of existing and novel fluorescent proteins. This growing pool of fluorescent proteins includes the long-lifetime green and cyan fluorescent proteins Clover and mTurquoise2, the red variant mRuby2, and the dark acceptor sREACh. Here, we have tested these donors and acceptors in appropriate combinations against the standard or recommended norms (EGFP and mTFP as donors, mCherry and either Ypet or Venus as acceptors) to determine if they could provide more reliable, reproducible and quantifiable FLIM-FRET data to improve on the dynamic range compared to other donors and breadth of application of biosensor technologies. These tests were performed for comparison on both a wide-field, frequency domain system and a multiphoton, TCSPC time domain FLIM system. Clover proved to be an excellent donor with extended dynamic range in combination with mCherry on both platforms, while mRuby2 showed a high degree of variability and poor FRET efficiencies in all cases. mTFP-Venus was the most consistent cyan-yellow pair between the two FLIM methodologies, but mTurquoise2 has better dynamic range and transfers energy consistently over time to the dark acceptor sRCh. Combination of mTFP-sRCh with Clover-mCherry would allow the simultaneous use of two FLIM-FRET biosensors within one sample by eliminating the crosstalk between the yellow acceptor and green donor emissions.

Project description:Background and objectiveEver since its discovery, calcium imaging has proven its worth in discovering new insights into the mechanisms of cellular communication. Yet, the analysis of the data generated by calcium imaging experiments demands a large amount of time from researchers. Tools enabling automated and semi-automated analysis are available, but often they allow automating only a part of the data analysis process. Therefore, we developed CALIMA (https://aethelraed.nl/calima), a free and open-source standalone software tool that provides an opportunity to quickly detect cells, to obtain the calcium spikes, and to determine the underlying network structure of neuronal cell cultures.MethodsOwing to the difference of Gaussians algorithm applied for the cell detection, CALIMA is able to detect regions of interest (ROIs) quickly. The z-scoring algorithm provides a means to set the requirements for spike detection, and the neuronal connections can be reconstructed by analyzing the cross-correlation between the cellular activity. We evaluated CALIMA's reliability, speed, and functionality with a special focus on neuronal cell detection and network reconstruction. The evaluation was performed by using real-life data such as a known example dataset (cultured primary rat cortical neurons, University of Pennsylvania) and by analyzing video graphic footage of in vitro brain cell samples (SH-SY5Y neuroblastoma cultures, one sample with synchronous neuron firing). The obtained results were compared to the corresponding outcomes observed on same datasets for other similar software solutions. Moreover, we compared the results of segmentation and peak detection analysis, the ones obtained using CALIMA and those acquired manually.ResultsCALIMA was able to detect the cells in the cultures within seconds. The average sensitivity was 82% across the datasets checked, comparing favorably with the alternative software solutions. Using the correct parameters, CALIMA's Ca-spikes detection sensitivity reached 96%. Lastly, neuronal networks were reconstructed by combining the data on the ROI's activity and the cell's positions, finding the most likely inter-cell connections.ConclusionsWe found that CALIMA proved to be a robust and fast tool to analyze the data of experiments for the digital reconstruction of the neuronal cellular network while being able to process the analysis steps with minimal user input required and in a time efficient manner.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets