scholarly journals Systematic Assessment of Burst Impurity in Confocal-Based Single-Molecule Fluorescence Detection Using Brownian Motion Simulations

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2557 ◽  
Author(s):  
Dolev Hagai ◽  
Eitan Lerner

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.

Author(s):  
Dolev Hagai ◽  
Eitan Lerner

Single-molecule fluorescence detection (SMFD) experiments are useful in distinguishing between sub-populations of molecular species in measurements of heterogeneous samples. One of the experimental platforms for SMFD is based on a confocal microscope setup, where molecules in the solution randomly traverse an effective detection volume, formed by a tightly focused laser beam. 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, single-molecule fluorescence burst analysis is frequently used. Yet, the relation between the results of burst analyses and the underlying spatial 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 from which the molecules emitted photons that were detected and 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.


2021 ◽  
Author(s):  
Mattia Fontana ◽  
Ŝarūnė Ivanovaitė ◽  
Simon Lindhoud ◽  
Willy van den Berg ◽  
Dolf Weijers ◽  
...  

Single-molecule fluorescence detection offers powerful ways to study biomolecules and their complex interactions. Here, we combine nanofluidic devices and camera-based, single-molecule Foerster resonance energy transfer (smFRET) detection to study the interactions between plant transcription factors of the Auxin response family (ARF) and DNA oligonucleotides that contain target DNA response elements. In particular, we show that the binding of the unlabelled ARF DNA binding domain (ARF-DBD) to donor and acceptor labelled DNA oligonucleotides can be detected by changes in the FRET efficiency and changes in the diffusion coefficient of the DNA. In addition, our data on fluorescently labelled ARF-DBDs suggest that, at nanomolar concentrations, ARF-DBDs are exclusively present as monomers. In general, the fluidic framework of freely diffusing molecules minimizes potential surface-induced artefacts, enables high-throughput measurements and proved to be instrumental in shedding more light on the interactions between ARF-DBDs monomers and between ARF-DBDs and their DNA response element.


2018 ◽  
Vol 90 (10) ◽  
pp. 6109-6115 ◽  
Author(s):  
Aaron M. Keller ◽  
Matthew S. DeVore ◽  
Dominik G. Stich ◽  
Dung M. Vu ◽  
Timothy Causgrove ◽  
...  

2019 ◽  
Author(s):  
Abhishek Mazumder ◽  
Miaoxin Lin ◽  
Achillefs N. Kapanidis ◽  
Richard H. Ebright

The RNA polymerase (RNAP) trigger loop (TL) is a mobile structural element of the RNAP active center that, based on crystal structures, has been proposed to cycle between an “unfolded”/“open” state that allows an NTP substrate to enter the active center and a “folded”/“closed” state that holds the NTP substrate in the active center. Here, by quantifying single-molecule fluorescence resonance energy transfer between a first fluorescent probe in the TL and a second fluorescent probe elsewhere in RNAP or in DNA, we detect and characterize TL closing and opening in solution. We show that the TL closes and opens on the millisecond timescale; we show that TL closing and opening provides a checkpoint for NTP complementarity, NTP ribo/deoxyribo identity, and NTP tri/di/monophosphate identity, and serves as a target for inhibitors; and we show that one cycle of TL closing and opening typically occurs in each nucleotide addition cycle in transcription elongation.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jieming Li ◽  
Leyou Zhang ◽  
Alexander Johnson-Buck ◽  
Nils G. Walter

AbstractTraces from single-molecule fluorescence microscopy (SMFM) experiments exhibit photophysical artifacts that typically necessitate human expert screening, which is time-consuming and introduces potential for user-dependent expectation bias. Here, we use deep learning to develop a rapid, automatic SMFM trace selector, termed AutoSiM, that improves the sensitivity and specificity of an assay for a DNA point mutation based on single-molecule recognition through equilibrium Poisson sampling (SiMREPS). The improved performance of AutoSiM is based on accepting both more true positives and fewer false positives than the conventional approach of hidden Markov modeling (HMM) followed by hard thresholding. As a second application, the selector is used for automated screening of single-molecule Förster resonance energy transfer (smFRET) data to identify high-quality traces for further analysis, and achieves ~90% concordance with manual selection while requiring less processing time. Finally, we show that AutoSiM can be adapted readily to novel datasets, requiring only modest Transfer Learning.


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