Project

Open Computational Tools for Single-Molecule Spectroscopy

Goal: In the spirit of open science, we develop open source computational tools for solution-based single-molecule spectroscopy. We employ Jupyter notebooks to facilitate automatic recording of all the steps of the analysis, improving transparency and reproducibility. At the same time, by providing open source tools that anybody can use study and modify, we aim to render solution-based single-molecule techniques more accessible to new researchers and research groups. The development of these tools takes place on GitHub, where participation from any member of the community is welcome.

At the core of the project stands Photon-HDF5, a file format for publication and long term archival of timestamp-based single-molecule fluorescence data.

Another tool is FRETBursts, a burst analysis software for single-molecule FRET experiments.

Finally, PyBroMo is a simulator for freely-diffusing particles excited by a confocal excitation volume. PyBroMo can simulate single-molecule FRET experiments.

Updates
0 new
19
Recommendations
0 new
7
Followers
0 new
44
Reads
0 new
931

Project log

Eitan Lerner
added a research item
Single-molecule spectroscopy has revolutionized molecular biophysics and provided means to probe how structural moieties within biomolecules spatially reorganize at different timescales. There are several single-molecule methodologies that probe local structural dynamics in the vicinity of a single dye-labeled residue, which rely on fluorescence lifetimes as readout. Nevertheless, an analytical framework to quantify dynamics in such single-molecule single-dye fluorescence bursts, at timescales of microseconds to milliseconds, has not yet been demonstrated. Here, we suggest an analytical framework for identifying and quantifying within-burst lifetime-based dynamics, such as conformational dynamics recorded in single-molecule photo-isomerization related fluorescence enhancement. After testing the capabilities of the analysis on simulations, we proceed to exhibit within-burst millisecond local structural dynamics in the unbound α-synuclein monomer. The analytical framework provided in this work paves the way for extracting a full picture of the energy landscape for the coordinate probed by fluorescence-lifetime based single-molecule measurements.
Eitan Lerner
added a research item
Single-molecule Förster resonance energy transfer (smFRET) is a unique biophysical approach for studying conformational dynamics in biomacromolecules. Photon-by-photon hidden Markov modelling (H2MM) is an analysis tool that can quantify FRET dynamics of single biomolecules, even if they occur on the sub-millisecond timescale. However, dye photophysical transitions intertwined with FRET dynamics may introduce artefacts. Here, we introduce multi-parameter H2MM (mpH2MM), which assists in identifying FRET dynamics based on simultaneous observation of multiple experimentally-derived parameters. We show the importance of using mpH2MM to decouple FRET dynamics caused by conformational changes from photophysical transitions in confocal-based smFRET measurements of a DNA hairpin, the maltose binding protein, MalE, and the type-III secretion system effector, YopO, from Yersinia species all exhibiting conformational dynamics ranging from the sub-second to microsecond timescales. Overall, we show that using mpH2MM facilitates the identification and quantification of biomolecular sub-populations and their origin.
Eitan Lerner
added a research item
The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze microsecond alternating laser excitation (usALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a global Gamma correction, assuming the Gamma correction factor is constant for all FRET populations, based on the procedure from Lee et al. 2005. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the FCS (a bit irrelevant here, due to the lasr alternation in microeconds), 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 50 pM of hairpin 3 presented in Tsukanov et al. 2013, in [NaCl]=300 mM, labeled with ATTO dyes (ATTO 550 & ATTO 647N as donor and acceptor dyes, respectively) - 532 & 640 nm cw excitation, with an alternation period of 50 microseconds. Everything is dposited in Zenodo (https://doi.org/10.5281/zenodo.3630474)
Eitan Lerner
added a research item
The herein Python notebook uses FRETbursts (download from here: https://github.com/tritemio/FRETBursts) to show how to analyze pulsed-interleaved excitation (PIE) / nanosecond alternating laser excitation (nsALEX) confocal-based FRET measurements of freely diffusing single molecules. It includes a step-by-step calculation and implementation of correction factors, donor fluorescence leakage to the acceptor detection channel (Lk), acceptor fluorescence caused by acceptor excitation by the laser intended for donor excitation (Dir), the imbalance in acceptor/donor fluorecence quantum yields and detection efficiencies (Gamma) and the imbalance in donor/acceptor excitation yields (Beta). The notebook implments a Gamma correction per each FRET population, based on both the un-corrected mean of the FRET ratio, and the donor fluorescence mean nanotimes. Burst search for showing the FRET population is a dual-channel burst search. After correction, the corrected FRET histogram is presented (after burst selection takes into account Beta & Gamma corrected burst sizes). We also present analysis of bursts from recurring molecules, as well as the MFD, FCS, 2CDE & BVA plots, helping in identifying whether a FRET population is a time average of FRET states, occurring faster then molecular diffusion time, or wheather the FRET population is static and represents a single conformational state. The sample data is a result of measurement of 33 pM dsDNA labeled with ATTO dyes (ATTO 488 & ATTO 647N as donor and acceptor dyes, respectively) - 470 & 640 nm pulsed excitation, operating at 20 MHz. Everything is dposited in Zenodo (https://doi.org/10.5281/zenodo.3630498). MFD FRET lines were calculated using the code deposited by Thomas Peulen in https://github.com/Fluorescence-Tools/ChiSurf/blob/master/docs/notebooks/fret_lines.ipynb
Eitan Lerner
added a research item
Single-molecule fluorescence detection (SMFD) experiments are useful in distinguishing sub-populations of molecular species when measuring heterogeneous samples. One experimental platform for SMFD is based on a confocal microscope, where molecules randomly traverse an effective detection volume. The non-uniformity of the excitation profile and the random nature of Brownian motion, produce fluctuating fluorescence signals. For these signals to be distinguished from the background, burst analysis is frequently used. Yet, the relation between the results of burst analyses and the underlying information of the diffusing molecules is still obscure and requires systematic assessment. In this work we performed three-dimensional Brownian motion simulations of SMFD, and tested the positions at which molecules emitted photons that passed the burst analysis criteria for different values of burst analysis parameters. The results of this work verify which of the burst analysis parameters and experimental conditions influence both the position of molecules in space when fluorescence is detected and taken into account, and whether these bursts of photons arise purely from single molecules, or not entirely. Finally, we show, as an example, the effect of bursts that are not purely from a single molecule on the accuracy in single-molecule Förster resonance energy transfer measurements.
Eitan Lerner
added a research item
Single-molecule fluorescence detection (SMFD) experiments are useful in distinguishing sub-populations of molecular species when measuring heterogeneous samples. One experimental platform for SMFD is based on a confocal microscope, where molecules randomly traverse an effective detection volume. The non-uniformity of the excitation profile and the random nature of Brownian motion, produce fluctuating fluorescence signals. For these signals to be distinguished from the background, burst analysis is frequently used. Yet, the relation between the results of burst analyses and the underlying information of the diffusing molecules is still obscure and requires systematic assessment. In this work we performed three-dimensional Brownian motion simulations of SMFD, and tested the positions at which molecules emitted photons that passed the burst analysis criteria for different values of burst analysis parameters. The results of this work verify which of the burst analysis parameters and experimental conditions influence both the position of molecules in space when fluorescence is detected and taken into account, and whether these bursts of photons arise purely from single molecules, or not entirely. Finally, we show, as an example, the effect of bursts that are not purely from a single molecule on the accuracy in single-molecule Förster resonance energy transfer measurements.
Eitan Lerner
added an update
We are happy to announce the release of phconvert 0.9, a library that reads a large number of data file formats and converts them to Photon-HDF5.
  • This version improves support for decoding PicoQuant files (including the .ptu file format).
  • In this version we also dropped support for Python 2.7, which is approaching the end of its life.
For more details see release notes:
This release has been sponsored by Eitan Lerner. Phconvert homepage: https://photon-hdf5.github.io/phconvert/
 
Eitan Lerner
added a research item
Single-molecule Förster Resonance Energy Transfer (smFRET) is utilized to study the structure and dynamics of many bio-molecules, such as proteins, DNA and their various complexes. The structural assessment is based on the well-known Förster relationship between the measured efficiency of energy transfer between a donor (D) and an acceptor (A) dye and the distance between them. Classical smFRET analysis methods called photon distribution analysis (PDA) take into account photon shot-noise, D-A distance distribution and, more recently, interconversion between states in order to extract accurate distance information. It is known that rapid D-A distance fluctuations on the order of the D lifetime (or shorter) can increase the measured mean FRET efficiency and thus decrease the estimated D-A distance. Nonetheless, this effect has been so far neglected in smFRET experiments, potentially leading to biases in estimated distances. Here we introduce a PDA approach dubbed Monte-Carlo-diffusion-enhanced photon inference (MC-DEPI). MC-DEPI recolor detected photons of smFRET experiments taking into account dynamics of D-A distance fluctuations, multiple interconverting states and photo-blinking. Using this approach, we show how different underlying conditions may yield identical FRET histograms and how the additional information from fluorescence decays helps distinguishing between the different conditions. We also introduce a machine learning fitting approach for retrieving the D-A distance distribution, decoupled from the above-mentioned effects. We show that distance interpretation of smFRET experiments of even the simplest dsDNA is nontrivial and requires decoupling the effects of rapid D-A distance fluctuations on FRET in order to avoid systematic biases in the estimation of the D-A distance distribution.
Eitan Lerner
added a research item
Single-molecule Förster Resonance Energy Transfer (smFRET) is utilized to study the structure and dynamics of many bio-molecules, such as proteins, DNA and their various complexes. The structural assessment is based on the well-known Förster relationship between the measured efficiency of energy transfer between a donor (D) and an acceptor (A) dye and the distance between them. Classical smFRET analysis methods called photon distribution analysis (PDA) take into account photon shot-noise, D-A distance distribution and, more recently, interconversion between states in order to extract accurate distance information. It is known that rapid D-A distance fluctuations on the order of the D lifetime (or shorter) can increase the measured mean FRET efficiency and thus decrease the estimated D-A distance. Nonetheless, this effect has been so far neglected in smFRET experiments, potentially leading to biases in estimated distances. Here we introduce a PDA approach dubbed Monte-Carlo-diffusion-enhanced photon inference (MC-DEPI). MC-DEPI recolor detected photons of smFRET experiments taking into account dynamics of D-A distance fluctuations, multiple interconverting states and photo-blinking. Using this approach, we show how different underlying conditions may yield identical FRET histograms and how the additional information from fluorescence decays helps distinguishing between the different conditions. We also introduce a machine learning fitting approach for retrieving the D-A distance distribution, decoupled from the above-mentioned effects. We show that distance interpretation of smFRET experiments of even the simplest dsDNA is nontrivial and requires decoupling the effects of rapid D-A distance fluctuations on FRET in order to avoid systematic biases in the estimation of the D-A distance distribution.
Antonino Ingargiola
added an update
We are proud to announce the release of FRETBursts 0.7. This is an incremental update with better import and export functions and many bug fixes.
For more details see the release notes:
 
Antonino Ingargiola
added an update
We created a new organization on GitHub called OpenSMFS (Open Single-Molecule Fluorescence Spectroscopy) as a central hub for public development our open source tools for single molecule spectroscopy. The organization will make it easier to find and track the projects we are working on and will facilitate collaborations (repositories are not in personal user accounts anymore).
We currently have 3 projects in the organization: FRETBursts (smFRET burst analysis), PyBroMo (smFRET simulator), pycorrelate (FCS calculations). The documentation remains on ReadTheDocs and has not changed URL, while the repositories and landing pages have moved.
The repositories related to the Photon-HDF5 file format remain their Photon-HDF5 organization.
Let us know if you find some outdated link or have any issue with the transition.
Here the new official URLs:
FRETBursts Home Page
PyBroMo Home Page
Pycorrelate Home Page
Photon-HDF5
 
Antonino Ingargiola
added an update
Pycorrelate 0.3 has been released. This version includes and example notebook showing how to compute FCS curves and fit a simple model to it. We also expanded the documentation and introduced unit testing and continuous integration.
Homepage:
FCS Notebook:
Cross-correlation algorithm:
 
Antonino Ingargiola
added an update
We released a new version of PyBroMo, the freely-diffusing single-molecule FRET simulator.
This version adds the capability to simulate 2-state dynamics in addition to arbitrary mixtures of static FRET populations.
Release notes:
PyBroMo Homepage:
 
Antonino Ingargiola
added an update
We are happy to announce the released of phconvert 0.8, a library that reads a large number of data file formats (including PicoQuant and Becker Hickl formats) and converts them to Photon-HDF5.
This new version saves files in the new Photon-HDF5 0.5 format and adds support for two input file formats: PT3 and T3R both from PicoQuant.
See the release notes here:
Phconvert homepage:
 
Antonino Ingargiola
added an update
We are pleased to announce the first pre-release of Photon-HDF5 0.5rc1.
Photon-HDF5 is an open file format for timestamp-based data, such as diffusion-based smFRET, FCS and related techniques. Photon-HDF5 allows including all the experimental details needed to analyze the data, plus sample and authorship information in a single compressed file that is suitable for sharing and long-term archival.
The new 0.5 version of Photon-HDF5 introduces a new "generic" measurement type making it easier to describe setup variations. To read more about what's new in version 0.5 see:
You can find the official 0.5 documentation at:
We also updated phconvert, the official software for creating Photon-HDF5 files:
As always, feedback is welcome. Please feel free to comment/ask questions here or on GitHub Issues:
Happy data sharing!
 
Antonino Ingargiola
added an update
First release of pycorrelate, a package for computing FCS/FCCS curves from photon timestamps.
The package implements the fast cross-correlation algorithm from Laurence et al. Optics letters 2006 (doi: 10.1364/OL.31.000829) and can compute cross-correlation efficiently on log-spaced bins, or on arbitrary-spaced bins.
Homepage:
 
Antonino Ingargiola
added an update
We are please to announce the availability of FRETBursts version 0.6.4.
This is an incremental release which adds support for PAX and better support for 48-spot measurements, on top of a few minor bug fixes.
For the details see:
To install FRETBursts see:
 
Antonino Ingargiola
added 6 research items
We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long-term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode, photomultiplier tube, or arrays of such detectors. The format is based on HDF5, a widely used platform- and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc.) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. The format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference Python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. To encourage adoption by the academic and commercial communities, all software is released under the MIT open source license.
Example data files in different formats that can be converted to Photon-HDF5 format using phconvert.
We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode (SPAD), photomultiplier tube (PMT) or arrays of such detectors. The format is based on HDF5, a widely used platform-and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc…) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. The format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference Python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. To encourage adoption by the academic and commercial communities, all software is released under the MIT open source license.
Antonino Ingargiola
added a project goal
In the spirit of open science, we develop open source computational tools for solution-based single-molecule spectroscopy. We employ Jupyter notebooks to facilitate automatic recording of all the steps of the analysis, improving transparency and reproducibility. At the same time, by providing open source tools that anybody can use study and modify, we aim to render solution-based single-molecule techniques more accessible to new researchers and research groups. The development of these tools takes place on GitHub, where participation from any member of the community is welcome.
At the core of the project stands Photon-HDF5, a file format for publication and long term archival of timestamp-based single-molecule fluorescence data.
Another tool is FRETBursts, a burst analysis software for single-molecule FRET experiments.
Finally, PyBroMo is a simulator for freely-diffusing particles excited by a confocal excitation volume. PyBroMo can simulate single-molecule FRET experiments.