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. 2016 Aug 17;11(8):e0160716.
doi: 10.1371/journal.pone.0160716. eCollection 2016.

FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET

Antonino Ingargiola  1 , Eitan Lerner  1 , SangYoon Chung  1 , Shimon Weiss  1 , Xavier Michalet  1
Affiliations

FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET

Antonino Ingargiola et al. PLoS One. .

Abstract

Single-molecule Förster Resonance Energy Transfer (smFRET) allows probing intermolecular interactions and conformational changes in biomacromolecules, and represents an invaluable tool for studying cellular processes at the molecular scale. smFRET experiments can detect the distance between two fluorescent labels (donor and acceptor) in the 3-10 nm range. In the commonly employed confocal geometry, molecules are free to diffuse in solution. When a molecule traverses the excitation volume, it emits a burst of photons, which can be detected by single-photon avalanche diode (SPAD) detectors. The intensities of donor and acceptor fluorescence can then be related to the distance between the two fluorophores. While recent years have seen a growing number of contributions proposing improvements or new techniques in smFRET data analysis, rarely have those publications been accompanied by software implementation. In particular, despite the widespread application of smFRET, no complete software package for smFRET burst analysis is freely available to date. In this paper, we introduce FRETBursts, an open source software for analysis of freely-diffusing smFRET data. FRETBursts allows executing all the fundamental steps of smFRET bursts analysis using state-of-the-art as well as novel techniques, while providing an open, robust and well-documented implementation. Therefore, FRETBursts represents an ideal platform for comparison and development of new methods in burst analysis. We employ modern software engineering principles in order to minimize bugs and facilitate long-term maintainability. Furthermore, we place a strong focus on reproducibility by relying on Jupyter notebooks for FRETBursts execution. Notebooks are executable documents capturing all the steps of the analysis (including data files, input parameters, and results) and can be easily shared to replicate complete smFRET analyzes. Notebooks allow beginners to execute complex workflows and advanced users to customize the analysis for their own needs. By bundling analysis description, code and results in a single document, FRETBursts allows to seamless share analysis workflows and results, encourages reproducibility and facilitates collaboration among researchers in the single-molecule community.

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Conflict of interest statement

Competing Interests: Dr. Weiss discloses equity in Nesher Technologies and intellectual property used in the research reported here. The work at UCLA was conducted in Dr. Weiss’s Laboratory. The declared conflict of interest of Prof. Shimon Weiss does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. In particular, all data and software used in this paper is freely and permanently available accessible online under an open license.

Figures

Fig 1
Fig 1. Inter-photon delays fitted with and exponential function.
Experimental distributions of inter-photon delays (dots) and corresponding fits of the exponential tail (solid lines). (Panel a) An example of inter-photon delays distribution (red dots) and an exponential fit of the tail of the distribution (black line). (Panel b) Inter-photon delays distribution and exponential fit for different photon streams as obtained with dplot(d, hist_bg). The dots represent the experimental histogram for the different photon streams. The solid lines represent the corresponding exponential fit of the tail of the distributions. The legend shows abbreviations of the photon streams and the fitted background rates.
Fig 2
Fig 2. Background rates as a function of time.
Estimated background rate as a function of time for two μs-ALEX measurements. Different colors represent different photon streams. (Panel a) A measurement performed with a sealed sample chamber exhibiting constant a background as a function of time. (Panel b) A measurement performed on an unsealed sample exhibiting significant background variations due to sample evaporation and/or photobleaching (likely impurities on the cover-glass). These plots are produced by the command dplot(d, timetrace_bg) after estimation of background. Each data point in these figures is computed for a 30 s time window.
Fig 3
Fig 3. Alternation histograms for μs-ALEX and ns-ALEX measurements.
Histograms used for the selection/determination of the alternation periods for two typical smFRET-ALEX experiments. Distributions of photons detected by donor channel are in green, and by acceptor channel in red. The light green and red shaded areas indicate the donor and acceptor period definitions. (a) μs-ALEX alternation histogram, i.e. histogram of timestamps modulo the alternation period for a smFRET measurement (in timestamp clock unit). (b) ns-ALEX TCSPC nanotime histogram for a smFRET measurement (in TDC or TAC bin unit). Both plots have been generated by the same plot function (plot_alternation_hist()). Additional information on these specific measurements can be found in the attached notebook (link).
Fig 4
Fig 4. E-S histogram showing FRET, D-only and A-only populations.
A 2-D ALEX histogram and marginal E and S histograms for a 40-bp dsDNA with D-A distance of 17 bases (Donor dye: ATTO550, Acceptor dye: ATTO647N). Bursts are selected with a size-threshold of 30 photons, including Aex photons. The plot is obtained with alex_jointplot(ds). The 2D E-S distribution plot (join plot) is an histogram with hexagonal bins, which reduce the binning artifacts (compared to square bins) and naturally resembles a scatter-plot when the burst density is low (see S4 Appendix). Three populations are visible: FRET population (middle), D-only population (top left) and A-only population (bottom, S < 0.2). Compare with Fig 5 where the FRET population has been isolated.
Fig 5
Fig 5. E-S histogram after filtering out D-only and A-only populations.
2-D ALEX histogram after selection of FRET population using the composition of two burst selection filters: (1) selection of bursts with counts in Dex stream larger than 15; (2) selection of bursts with counts in AexAem stream larger than 15. Compare to Fig 4 where all burst populations (FRET, D-only and A-only) are reported.
Fig 6
Fig 6. FRET histogram fitted with two Gaussians.
Example of a FRET histogram fitted with a 2-Gaussian model. After performing the fit (see main text), the plot is generated with dplot(ds, hist_fret, show_model = True).
Fig 7
Fig 7. BVA distribution for a static mixture sample.
The left panel shows the E-S histogram for a mixture of single stranded DNA (20dT) and double stranded DNA (20dT-20dA) molecules in 200 mM MgCl2. The right panel shows the corresponding BVA plot. Since both 20dT and 20dT-20dA are stable and have no dynamics, the BVA plots shows sE peaks lying on the static standard deviation curve (red curve).
Fig 8
Fig 8. BVA distribution for a hairpin sample undergoing dynamics.
The left panel shows the E-S histogram for a single stranded DNA sample (A31-TA, see text), designed to form a transient hairpin in 400mM NaCl. The right panel shows the corresponding BVA plot. Since the transition between hairpin and open structure causes a significant change in FRET efficiency, sE lies largely above the static standard deviation curve (red curve).
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