“Single-molecule FRET”的意思、由来-开放百科全书

Single molecule fluorescence resonance energy transfer (or smFRET) is a biophysical technique used to measure distances at the 1-10 nanometer scale in single molecules, typically biomolecules. It is an application of FRET wherein a pair of donor and acceptor fluorophores are excited and detected on a single molecule level. In contrast to "ensemble FRET" which provides the FRET signal of a high number of molecules, single-molecule FRET is able to resolve the FRET signal of each individual molecule.

Methodology

Single molecule FRET measurements are typically performed on fluorescence microscopes, either using surface-immobilized or freely-diffusing molecules. Single FRET pairs are illuminated using intense light sources, typically lasers, in order to generate sufficient fluorescence signal to enable single molecule detection. Wide-field multiphoton microscopy is typically combined with total internal reflection fluorescence microscope (TIRF). This selectively excites FRET pairs on the surface of the measurement chamber and rejects noise from the bulk of the sample. Conversely, confocal microscopy minimizes background by focusing the fluorescence light onto a pinhole to reject out of focus light.^[1] The confocal volume has a diameter of around 220 nm, and therefore it must be scanned across if an image of the sample is needed. With confocal excitation, it is possible to measure much deeper into the sample than when using TIRF. Fluorescence signal is detected either using ultra sensitive CCD or scientific CMOS cameras for wide field microscopy or SPADs for confocal microscopy.^[2] Once the single molecule intensities vs. time are available the FRET efficiency can be computed for each FRET pair as a function of time and thereby it is possible to follow kinetic events on the single molecule scale and to build FRET histograms showing the distribution of states in each molecule. However, data from many FRET pairs must be recorded and combined in order to obtain general information about a sample.^[3]

Surface-Immobilized

In surface-immobilized experiments, biomolecules labeled with fluorescent tags are bound to the surface of the coverglass and images of fluorescence are acquired (typically by a CCD or scientific CMOS cameras).^[4] Data collection with cameras will produce movies of the specimen which must be processed to derive the single molecule intensities with time.

An advantage of surface-immobilized experiments is that many molecules can be observed in parallel for an extended period of time until photobleaching

This allows to conveniently study transitions taking place on slow time scales. A disadvantage is represented by the additional biochemical modifications

needed to link molecules to the surface and the perturbations that the surface can potentially exert on the molecular activity.

In addition, the maximum time resolution of single-molecule intensities is limited by the camera acquisition time (> 1 ms).

Freely-Diffusing

SmFRET can also be used to study the conformations of molecules freely diffusing in a liquid sample. In freely-diffusing smFRET experiments (or diffusion-based smFRET), the same biomolecules are free to diffuse in solution while being excited by a small excitation volume (usually a diffraction-limited spot). Bursts of photons due a single-molecule crossing the excitation spot are acquired with SPAD detectors. The confocal spot is usually fixed in a given position (no scanning happens, and no image is acquired). Instead, the fluorescence photons emitted by individual molecules crossing the excitation volume are recorded and accumulated in order to build a distribution of different populations present in the sample. Depending on the complexity of this distribution, acquisition times varies from ~5 min to several hours.

A distinctive advantage of setups employing SPAD detectors is that they are not limited by a "frame rate" or a fixed integration time like when using cameras. In fact, unlike cameras, SPADs produce a pulse every time a photon is detected, while an additional electronics is needed to "timestamps" each pulse with 10-50 ns resolution. The high time resolution of confocal single-molecule FRET measurements allows to potentially detect dynamics on time scales as low as 10 μs. However, detecting "slow" transitions on timescales longer than the diffusion time (typically ~1 ms), is more difficult than in surface-immobilized experiments and generally requires much longer acquisitions.

Normally, the fluorescent emission of both donor and acceptor fluorophores is detected by two independent detectors and the FRET signal is computed from the ratio of intensities in the two channels. Some setup configurations further split each spectral channel (donor or acceptor) in two orthogonal polarizations (therefore requiring 4 detectors) and are able to measure both FRET and fluorescence anisotropy at the same time. In other configurations, 3 or 4 spectral channels are acquired at the same time in order to measure multiple FRET pairs at the same time.

Both CW or pulsed lasers can be used as excitation source. When using pulsed lasers, a suitable acquisition hardware can measure the photon arrival time with respect to the last laser pulse with picosecond resolution, in the so-called time-correlated single photon counting (TCSPC) acquisition. In this configuration each photon is characterized by a macro-time (i.e. a coarse 10-50 ns timestamp) and a micro-time (i.e. delay with respect the last laser pulse). The latter can be used to extract lifetime information and obtain the FRET signal.

SmFRET Data Analysis

Typical smFRET data of a two-dye system are time trajectories of the fluorescent emission intensities of the donor and the acceptor dye. Mainly two methods are used to obtain the emission of the two dyes: (1) accumulative measurement uses a fixed exposure time of the cameras for each frame, such as PMT, APD, EMCCD, and CMOS camera; (2) single photon arriving time sequence measured using PMT or APD cameras. The principle is to use optical filters to separate the emissions of the two dyes and measured in two channels. For example, a setup using two halves of a charge-coupled device (CCD) camera is explained in the literatur.^[5]

For a two-dye system, the emission signals are then used to calculate the FRET efficiency between the dyes over time. The FRET efficiency is the number of photons emitted from the acceptor dye over the sum of the emissions of the donor and the acceptor dye. Usually, only the donor dye is excited. Thus, the emission of the donor and acceptor dyes is just the number of photons collected for the two dyes divided by the photon collection efficiencies of the two channels respectively which are the functions of the collection efficiency, the filter and optical efficiency, and the camera efficiency of the two wavelength bands. These efficiencies can be calibrated for a given instrumental setup.

where FRET is the FRET efficiency of the two-dye system at a period of time,

and

are measured photon counts of the acceptor and donor channel respectively at the same period of time,

and

are the photon collection efficiencies of the two channels. If the photon collection efficiencies of the two channels are similar and the actual FRET distance is not important, one can set the two

= 1.

For the accumulative emission smFRET data, the time trajectories contain mainly the following information: (1) state transitions, (2) noise, (3) camera blurring, (4) photoblinking and photobleaching of the dyes. The state transition information is the information a typical measurement wants. However, the rest signals interfere with the data analysis thus have to be addressed. The noise signal of the dye emission typically contains camera readout noise, shot noise and white noise, and real-sample noise, that each follows a different noise distribution due to the different sources. The real-sample noise comes from the thermal disturbance of the system that results in the FRET distance broadening, uneven dye orientation distribution, and dye emission variations. The other noises are from the excitation path and the detection path especially the camera. In the end, the noise of the raw emission data is a combination of noises with Poisson distribution and Gaussian distribution. The noises in the two channels then are combined into the non-linear equation list above to calculate the FRET values. Thus, the noise on the smFRET trajectories is very complicated. The noise is asymmetric in above and below the mean FRET values. Most noises can be reduced by binning the data (see the Figure) with a cost of losing time resolution. The camera blurring signal comes from the discrete nature of the measurements. The emission signal has a mismatch with the real transition signal because both are stochastic. When a state transition happens between the readout of two emission reading intervals, the signal is the average of the two parts in the same measuring window, which then affects the state identification accuracy and eventually the rate constant analysis. This is less a problem when the measuring frequency is much faster than the transition rate but becomes a real problem when both approaching each other. The time trajectories also contain the photoblinking and photobleaching information of the two dyes. This information has to be removed which creates gaps in the time trajectory where the FRET information is lost. The photoblinking and photobleaching information can be removed for a typical dye system with relatively long photoblinking and photobleaching lifetimes that has been chosen in the measurement. Thus, they are less a problem for data analysis. However, it will become a big problem if a dye with short blinking lifetime is used.

Several data analysis methods have been developed to analyze the data, such as thresholding methods, Hidden Markov model (HMM) methods and transition point identification methods. Thresholding methods simply set a threshold between two adjacent states on the smFRET trajectories. The FRET values above the threshold belong to a state and the values below belong to another. This method works for the data with a very high signal to noise ratio thus have distinguishable FRET states. HMMs base on algorithms that statistically calculate probability functions of each state assignment, i.e. add penalties to a less probable assignment. The typical open source-code software packages can be found online such as HaMMy, vbFRET, ebFRET, SMART, SMACKS, MASH-FRET, and etc.^[6]^[7] Transition-point analysis or change-point analysis (CPA) use algorithms to identify when a transition happens over the time trajectory using statistical analysis. For example, CPA based on Fisher information theory and the Student's t-test method (STaSI, open-source) to identify state transitions and minimum description length to select the optimum number of states.

Once the states are identified, they can be used to calculate the Förster resonance energy transfer distances and transition rate constants between the states. For a parallel reaction matrix among the states, the rate constants of each transition cannot be pulled out from the average lifetimes of each transition, which is fixed the inverse sum of the rate constants. However, the rate constants can be calculated from the number of each transition over the total time of the state it transfers from.^[8]

where i is the initial state, f is the final state of the transition, N is the number of this transitions in the time trajectories, n is the total number of state, k is the rate constant, t is the time of each state before the transition happens. For example, one measures 130 second (s) of smFRET time trajectories. The total time of a molecule stay on state one is

second (s), state two

s, and state three is

s. Among the 100 s the molecule stays state one, it transfers to state two 70 times and transfer to state three 30 times, the rate constant of state 1 to state 2 is thus

, the rate constant of state 1 to state 3 is

. Typically the probabilities of the lengths of the 70 times or the 30 times transition (dwell times) are exponentially distributed (right figure). The average dwell times of the distributions, i.e. the lifetimes of the state one, are the same at 1 s for these two transitions (right figure).

The interpretation of the above equation is simply based on the assumption that each molecule is the same and the system is under the equilibrium. The existence of each state is just represented by its total time which is its "concentration". Thus, The rate of transition to any other state is the number of transition normalized by this concentration.

The camera blurring effect can be reduced via faster sampling frequency relies on the development of a more sensitive camera, special data analysis, or both. Traditionally in HMMs, the data point before and after the transition is specially assigned to reduce the wrong assignment rate of these data points to states in between of the transition. However, there is a limitation for this method to work. When the transition frequency is approaching the sampling frequency, too much data are blurred for this method to work. Thus, a two-step data analysis method has been reported to increase the analysis accuracy for such data. The idea is to simulate a trajectory with the Monte Carlo simulation method and compare it to the experimental data. At the right condition, both the simulation and the experimental data will contain the same degree of blurring information and noise. This simulated trajectory is a better answer than the raw experimental data. This method has open-source codes available as postFRET ^[9] and MASH-FRET.^[7] This method can also slightly correct the effect of the non-Gaussian noise that has caused trouble to accurately identify the states using the statistical methods.

The current data analysis for smFRET still requires great care and special training which is in a call for deep-learning algorithms to play a rule to free the labor in data analysis.

Advantages of smFRET

SmFRET allows for a more precise analysis of heterogeneous populations and has a few advantages when compared to ensemble FRET.

One benefit of studying distances in single molecules is that heterogeneous populations can be studied more accurately with values specific for each molecule rather than computing an average based on an ensemble. This allows for the study of specific homogeneous populations within a heterogeneous population. For example, if two existing homologous populations within a heterogeneous population have different FRET values, an ensemble FRET analysis will produce a weighted averaged FRET value to represent the population as a whole. Thus, the obtained FRET value does not produce data on the two distinct populations. In contrast, smFRET would be able to differentiate between the two populations and would allow analysis of the existing homologous populations.^[10]

SmFRET also provides dynamic temporal resolution of an individual molecule that cannot be accomplished through ensemble FRET measurements. Kinetic information in a system under equilibrium is lost at the ensemble level because none of the concentrations of the reactants and products change over time. However, at the single-molecule level, the transfer between the reactants and products can happen at a measurable high rate and balanced over time stochastically. Thus, tracing the time trajectory of a particular molecule enables the direct measurement of the rate constant of each transition step, including the intermediates that are hidden in the ensemble level due to its low concentrations. This allows smFRET to be used to study an RNA’s folding dynamics. Similar to protein folding, RNA folding goes through multiple interactions, folding pathways, and intermediates before reaching its native state. Ensemble FRET has the ability to detect well-populated transition states that accumulate in a population, but it lacks the ability to characterize intermediates that are short-lived and do not accumulate. This limit is addressed by smFRET which offers a direct way to observe the intermediates of single molecules regardless of accumulation. Therefore, smFRET demonstrates the ability to capture transient subpopulations in a heterogeneous environment.^[11]

SmFRET is also shown to utilize a three-color system better than ensemble FRET. Using two acceptor fluorophores rather than one, FRET can observe multiple sites for correlated movements and spatial changes in any complex molecule. This is shown in the research on the Holliday Junction. SmFRET with the three-color system offers insights on synchronized movements of junction's three helical sites and near non-existence of its parallel states. Ensemble FRET can use three-color system as well. However, any obvious advantages are outweighed by three-color system's requirements which includes a clear separation of fluorophore signals. For a clear distinction of signal, FRET overlaps must be small but that also weakens FRET strength. SmFRET corrects its overlap limitations by using band-pass filters and dichroic mirrors which further the signal between two fluorescence acceptors and solve for any bleed through effects.^[12]

Applications

A major application of smFRET is to analyze the minute biochemical nuances that facilitate protein folding. In recent years, multiple techniques have been developed to investigate single molecule interactions that are involved in protein folding and unfolding. Force-probe techniques, using atomic force microscopy and laser tweezers, have provided information on protein stability. smFRET allows researchers to investigate molecular interactions using fluorescence. Forster resonance energy transfer (FRET) was first applied to single molecules by Ha et al. and applied to protein folding in work by Hochstrasser, Weiss, et al. The benefit that smFRET as a whole has afforded to analyzing molecular interactions is the ability to test single molecule interactions directly without having to average ensembles of data. In protein folding analysis, ensemble experiments involve taking measurements of multiple proteins that are in various states of transition between their folded and unfolded state. When averaged, the protein structure that can be inferred from the ensemble of data only provides a rudimentary structural model of protein folding. However, true understanding of protein folding requires deciphering the sequence of structural events along the folding pathways between the folded and unfolded states. It is this particular branch of research that smFRET is highly applicable.

FRET studies calculate corresponding FRET efficiencies as a result of time-resolved observation of protein folding events. These FRET efficiencies can then be used to infer distances between molecules as a function against time. As the protein transitions between the folded and unfolded states, the corresponding distances between molecules can indicate the sequence of molecular interactions that lead to protein folding.^[13]

Another application of smFRET is for DNA and RNA folding dynamics.^[14] Typically, two different locations of a nucleotide are labeled with the donor and acceptor dyes. The change of the distance between the two locations changes over time due to the folding and unfolding of the nucleotide plus the random diffusion of the two points over time, within each measuring window and among different windows. Due to the complexity of the folding/unfolding trajectory, it is extremely difficult to measure the process at the ensemble level. Thus, smFRET becomes a key technique in this field. On top of the challenges of smFRET data analysis, one challenge is to label multiple positions of interest, another is from the two-point dynamics to calculate the overall folding pathways.

Single-molecule FRET can also be applied to study the conformational changes of the relevant channel motifs in certain channels. For example, labeled tetrameric KirBac potassium channels were labeled with donor and acceptor fluorophores at particular sites in order to understand the structural dynamics within the lipid membrane, thus allowing them to generalize similar dynamics for similar motifs in other eukaryotic Kir channels or even cation channels in general. The use of smFRET in this experiment allows for visualization of the conformational changes that cannot be seen if the macroscopic measurements are simply averaged. This will lead to ensemble analysis rather than analysis of individual molecules and the conformational changes within, allowing us to generalize similar dynamics for similar motifs in other eukaryotic channels.

The structural dynamics of the KirBac channel was thoroughly analyzed in both the open and closed states, dependent on the presence of the ligand PIP2. Part of the results based on smFRET demonstrated the structural rigidity of the extracellular region. The selectivity filter and the outer loop of the selectivity filter region was labeled with fluorophores and conformational coupling was observed. The individual smFRET trajectories strongly demonstrated a FRET efficiency of around 0.8 with no fluctuations, regardless of the state of the channel.^[15]

Limitations

Despite making approximate estimates, a limitation of smFRET is the difficulty of obtaining the correct distance involved in energy transfer. Requiring an accurate distance estimate gives rise to a major challenge because the fluorescence of the donor and acceptor fluorophores as well as the energy transfer is dependent on the environment and how the dyes are oriented, which can vary depending on the flexibility of where the fluorophores are bound. This issue, however, is not particularly relevant when the distance estimation of the two fluorophores does not need to be determined with exact and absolute precision.^[5]

Extracting kinetic information from a complicated biological system with transition rate around a few millisecond or below remains challenging. The current time resolutions of such measurements are typically at millisecond level with a few reports at microsecond level.

References

1. ^{{Cite journal| vauthors = Moerner WE, Fromm DP |date=2003-08-01|title=Methods of single-molecule fluorescence spectroscopy and microscopy|journal=Review of Scientific Instruments|volume=74|issue=8|pages=3597–3619|doi=10.1063/1.1589587|issn=0034-6748}}
2. ^{{cite journal | vauthors = Michalet X, Colyer RA, Scalia G, Ingargiola A, Lin R, Millaud JE, Weiss S, Siegmund OH, Tremsin AS, Vallerga JV, Cheng A, Levi M, Aharoni D, Arisaka K, Villa F, Guerrieri F, Panzeri F, Rech I, Gulinatti A, Zappa F, Ghioni M, Cova S | title = Development of new photon-counting detectors for single-molecule fluorescence microscopy | journal = Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences | volume = 368 | issue = 1611 | pages = 20120035 | date = February 2013 | pmid = 23267185 | pmc = 3538434 | doi = 10.1098/rstb.2012.0035 }}
3. ^{{cite journal | vauthors = Ha T, Enderle T, Ogletree DF, Chemla DS, Selvin PR, Weiss S | title = Probing the interaction between two single molecules: fluorescence resonance energy transfer between a single donor and a single acceptor | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 93 | issue = 13 | pages = 6264–8 | date = June 1996 | pmid = 8692803 | pmc = 39010 | doi = 10.1073/pnas.93.13.6264 }}
4. ^{{cite journal | vauthors = Kastantin M, Schwartz DK | title = Connecting rare DNA conformations and surface dynamics using single-molecule resonance energy transfer | journal = ACS Nano | volume = 5 | issue = 12 | pages = 9861–9 | date = December 2011 | pmid = 21942411 | pmc = 3246573 | doi = 10.1021/nn2035389 }}
5. ^¹{{cite journal | vauthors = Roy R, Hohng S, Ha T | title = A practical guide to single-molecule FRET | journal = Nature Methods | volume = 5 | issue = 6 | pages = 507–16 | date = June 2008 | pmid = 18511918 | pmc = 3769523 | doi = 10.1038/nmeth.1208 }}
6. ^{{cite journal | vauthors = Danial JS, García-Sáez AJ | title = Improving certainty in single molecule imaging | journal = Current Opinion in Structural Biology | volume = 46 | pages = 24–30 | date = October 2017 | pmid = 28482279 | doi = 10.1016/j.sbi.2017.04.007 }}
7. ^¹{{cite journal | vauthors = Börner R, Kowerko D, Hadzic MC, König SL, Ritter M, Sigel RK | title = Simulations of camera-based single-molecule fluorescence experiments | journal = PloS One | volume = 13 | issue = 4 | pages = e0195277 | year = 2018 | pmid = 29652886 | pmc = 5898730 | doi = 10.1371/journal.pone.0195277 }}
8. ^{{cite journal|url= https://pubs.acs.org/doi/suppl/10.1021/acs.jpcb.6b05697/suppl_file/jp6b05697_si_001.pdf|title=Supporting Information for: A Two-Step Method for smFRET Data Analysis|journal=The Journal of Physical Chemistry B|volume=120|issue=29|pages=7128–7132|doi=10.1021/acs.jpcb.6b05697|pmid=27379815|year=2016|last1=Chen|first1=Jixin|last2=Pyle|first2=Joseph R.|last3=Sy Piecco|first3=Kurt Waldo|last4=Kolomeisky|first4=Anatoly B.|last5=Landes|first5=Christy F.}}
9. ^{{cite journal | vauthors = Chen J, Pyle JR, Sy Piecco KW, Kolomeisky AB, Landes CF | title = A Two-Step Method for smFRET Data Analysis | journal = The Journal of Physical Chemistry B | volume = 120 | issue = 29 | pages = 7128–32 | date = July 2016 | pmid = 27379815 | doi = 10.1021/acs.jpcb.6b05697 }}
10. ^{{cite journal | vauthors = Ha T | title = Single-molecule fluorescence resonance energy transfer | journal = Methods | volume = 25 | issue = 1 | pages = 78–86 | date = September 2001 | pmid = 11558999 | doi = 10.1006/meth.2001.1217 }}
11. ^{{cite book | veditors = Hinterdorfer P, van Oijen A | date = 2009 | title = Handbook of single-molecule biophysics | edition = 1st | location = Dordrecht | publisher = Springer | isbn = 978-0-387-76497-9 }}
12. ^{{cite journal | vauthors = Hohng S, Joo C, Ha T | title = Single-molecule three-color FRET | journal = Biophysical Journal | volume = 87 | issue = 2 | pages = 1328–37 | date = August 2004 | pmid = 15298935 | pmc = 1304471 | doi = 10.1529/biophysj.104.043935 }}
13. ^{{cite journal | vauthors = Schuler B, Eaton WA | title = Protein folding studied by single-molecule FRET | journal = Current Opinion in Structural Biology | volume = 18 | issue = 1 | pages = 16–26 | date = February 2008 | pmid = 18221865 | pmc = 2323684 | doi = 10.1016/j.sbi.2007.12.003 | series = Folding and Binding / Protein-nucleic acid interactions }}
14. ^{{cite journal | vauthors = Chen J, Poddar NK, Tauzin LJ, Cooper D, Kolomeisky AB, Landes CF | title = Single-molecule FRET studies of HIV TAR–DNA hairpin unfolding dynamics | journal = The Journal of Physical Chemistry B | volume = 118 | issue = 42 | pages = 12130–12139 | date = September 25, 2014 | pmid = 25254491 | pmc = 4207534 | doi = 10.1021/jp507067p}}
15. ^{{cite journal | vauthors = Wang S, Vafabakhsh R, Borschel WF, Ha T, Nichols CG | title = Structural dynamics of potassium-channel gating revealed by single-molecule FRET | journal = Nature Structural & Molecular Biology | volume = 23 | issue = 1 | pages = 31–36 | date = January 2016 | pmid = 26641713 | pmc = 4833211 | doi = 10.1038/nsmb.3138 }}