1
|
Sgouralis I, Xu LWQ, Jalihal AP, Kilic Z, Walter NG, Pressé S. BNP-Track: a framework for superresolved tracking. Nat Methods 2024; 21:1716-1724. [PMID: 39039336 PMCID: PMC11399105 DOI: 10.1038/s41592-024-02349-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/03/2024] [Indexed: 07/24/2024]
Abstract
Superresolution tools, such as PALM and STORM, provide nanoscale localization accuracy by relying on rare photophysical events, limiting these methods to static samples. By contrast, here, we extend superresolution to dynamics without relying on photodynamics by simultaneously determining emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution on immobilized emitters under similar imaging conditions (≈50 nm). We demonstrate our Bayesian nonparametric track (BNP-Track) framework on both in cellulo and synthetic data. BNP-Track develops a joint (posterior) distribution that learns and quantifies uncertainty over emitter numbers and their associated tracks propagated from shot noise, camera artifacts, pixelation, background and out-of-focus motion. In doing so, we integrate spatiotemporal information into our distribution, which is otherwise compromised by modularly determining emitter numbers and localizing and linking emitter positions across frames. For this reason, BNP-Track remains accurate in crowding regimens beyond those accessible to other single-particle tracking tools.
Collapse
Affiliation(s)
- Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, TN, USA
| | - Lance W Q Xu
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Ameya P Jalihal
- Department of Cell Biology, Duke University, Durham, NC, USA
| | - Zeliha Kilic
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Nils G Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA.
- Department of Physics, Arizona State University, Tempe, AZ, USA.
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
| |
Collapse
|
2
|
Dhar M, Berg MA. Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Photon and photon-counting noise. J Chem Phys 2024; 161:034116. [PMID: 39028845 DOI: 10.1063/5.0212157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/28/2024] [Indexed: 07/21/2024] Open
Abstract
A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region.
Collapse
Affiliation(s)
- Mainak Dhar
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, USA
| | - Mark A Berg
- Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, USA
| |
Collapse
|
3
|
Fazel M, Grussmayer KS, Ferdman B, Radenovic A, Shechtman Y, Enderlein J, Pressé S. Fluorescence Microscopy: a statistics-optics perspective. ARXIV 2023:arXiv:2304.01456v3. [PMID: 37064525 PMCID: PMC10104198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Modeling these features is ever more important in quantitatively interpreting microscopy images collected at scales on par or smaller than light's wavelength. Here we review the optics responsible for generating fluorescent images, fluorophore properties, microscopy modalities leveraging properties of both light and fluorophores, in addition to the necessarily probabilistic modeling tools imposed by the stochastic nature of light and measurement.
Collapse
Affiliation(s)
- Mohamadreza Fazel
- Department of Physics, Arizona State University, Tempe, Arizona, USA
- Center for Biological Physics, Arizona State University, Tempe, Arizona, USA
| | - Kristin S Grussmayer
- Department of Bionanoscience, Faculty of Applied Science and Kavli Institute for Nanoscience, Delft University of Technology, Delft, Netherlands
| | - Boris Ferdman
- Russel Berrie Nanotechnology Institute and Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Aleksandra Radenovic
- Laboratory of Nanoscale Biology, Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yoav Shechtman
- Russel Berrie Nanotechnology Institute and Department of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Jörg Enderlein
- III. Institute of Physics - Biophysics, Georg August University, Göttingen, Germany
| | - Steve Pressé
- Department of Physics, Arizona State University, Tempe, Arizona, USA
- Center for Biological Physics, Arizona State University, Tempe, Arizona, USA
| |
Collapse
|
4
|
Xu (徐伟青) LW, Jazani S, Kilic Z, Pressé S. Single-Molecule Reaction-Diffusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.05.556378. [PMID: 37732202 PMCID: PMC10508780 DOI: 10.1101/2023.09.05.556378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
We propose to capture reaction-diffusion on a molecule-by-molecule basis from the fastest acquirable timescale, namely individual photon arrivals. We illustrate our method on intrinsically disordered human proteins, the linker histone H1.0 as well as its chaperone prothymosin α , as these diffuse through an illuminated confocal spot and interact forming larger ternary complexes on millisecond timescales. Most importantly, single-molecule reaction-diffusion, smRD, reveals single molecule properties without trapping or otherwise confining molecules to surfaces. We achieve smRD within a Bayesian paradigm and term our method Bayes-smRD. Bayes-smRD is further free of the average, bulk, results inherent to the analysis of long photon arrival traces by fluorescence correlation spectroscopy. In learning from thousands of photon arrivals continuous spatial positions and discrete conformational and photophysical state changes, Bayes-smRD estimates kinetic parameters on a molecule-by-molecule basis with two to three orders of magnitude less data than tools such as fluorescence correlation spectroscopy thereby also dramatically reducing sample photodamage.
Collapse
Affiliation(s)
- Lance W.Q. Xu (徐伟青)
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Sina Jazani
- Department of Biophysics and Biophysical Chemistry, Johns Hopkins Medicine, Baltimore, MD 21205, USA
| | - Zeliha Kilic
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
- School of Molecular Science, Arizona State University, Tempe, AZ 85287, USA
| |
Collapse
|
5
|
Sgouralis I, Xu (徐伟青) LW, Jalihal AP, Walter NG, Pressé S. BNP-Track: A framework for superresolved tracking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.03.535459. [PMID: 37066320 PMCID: PMC10104004 DOI: 10.1101/2023.04.03.535459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below light's diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions (≈50nm). We demonstrate our results for both in cellulo data and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.
Collapse
Affiliation(s)
- Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, TN 37996, USA
| | - Lance W.Q. Xu (徐伟青)
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
| | - Ameya P. Jalihal
- Department of Cell Biology, Duke University, Durham, NC 27710, USA
| | - Nils G. Walter
- Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ 85287, USA
- Department of Physics, Arizona State University, Tempe, AZ 85287, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| |
Collapse
|
6
|
Saurabh A, Fazel M, Safar M, Sgouralis I, Pressé S. Single-photon smFRET. I: Theory and conceptual basis. BIOPHYSICAL REPORTS 2023; 3:100089. [PMID: 36582655 PMCID: PMC9793182 DOI: 10.1016/j.bpr.2022.100089] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022]
Abstract
We present a unified conceptual framework and the associated software package for single-molecule Förster resonance energy transfer (smFRET) analysis from single-photon arrivals leveraging Bayesian nonparametrics, BNP-FRET. This unified framework addresses the following key physical complexities of a single-photon smFRET experiment, including: 1) fluorophore photophysics; 2) continuous time kinetics of the labeled system with large timescale separations between photophysical phenomena such as excited photophysical state lifetimes and events such as transition between system states; 3) unavoidable detector artefacts; 4) background emissions; 5) unknown number of system states; and 6) both continuous and pulsed illumination. These physical features necessarily demand a novel framework that extends beyond existing tools. In particular, the theory naturally brings us to a hidden Markov model with a second-order structure and Bayesian nonparametrics on account of items 1, 2, and 5 on the list. In the second and third companion articles, we discuss the direct effects of these key complexities on the inference of parameters for continuous and pulsed illumination, respectively.
Collapse
Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Matthew Safar
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee Knoxville, Knoxville, Tennesse
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
- School of Molecular Sciences, Arizona State University, Tempe, Arizona
| |
Collapse
|
7
|
Saurabh A, Safar M, Fazel M, Sgouralis I, Pressé S. Single-photon smFRET: II. Application to continuous illumination. BIOPHYSICAL REPORTS 2023; 3:100087. [PMID: 36582656 PMCID: PMC9792399 DOI: 10.1016/j.bpr.2022.100087] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/01/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022]
Abstract
Here we adapt the Bayesian nonparametrics (BNP) framework presented in the first companion article to analyze kinetics from single-photon, single-molecule Förster resonance energy transfer (smFRET) traces generated under continuous illumination. Using our sampler, BNP-FRET, we learn the escape rates and the number of system states given a photon trace. We benchmark our method by analyzing a range of synthetic and experimental data. Particularly, we apply our method to simultaneously learn the number of system states and the corresponding kinetics for intrinsically disordered proteins using two-color FRET under varying chemical conditions. Moreover, using synthetic data, we show that our method can deduce the number of system states even when kinetics occur at timescales of interphoton intervals.
Collapse
Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Matthew Safar
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona
| | - Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
- School of Molecular Sciences, Arizona State University, Tempe, Arizona
| |
Collapse
|
8
|
Kilic Z, Schweiger M, Moyer C, Shepherd D, Pressé S. Gene expression model inference from snapshot RNA data using Bayesian non-parametrics. NATURE COMPUTATIONAL SCIENCE 2023; 3:174-183. [PMID: 38125199 PMCID: PMC10732567 DOI: 10.1038/s43588-022-00392-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2023]
Abstract
Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models-composed of gene states, their connectivities and associated parameters-are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.
Collapse
Affiliation(s)
- Zeliha Kilic
- Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA
- These authors contributed equally: Zeliha Kilic, Max Schweiger
| | - Max Schweiger
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
- These authors contributed equally: Zeliha Kilic, Max Schweiger
| | - Camille Moyer
- Center for Biological Physics, ASU, Tempe, AZ, USA
- School of Mathematics and Statistical Sciences, ASU, Tempe, AZ, USA
| | - Douglas Shepherd
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
| | - Steve Pressé
- Center for Biological Physics, ASU, Tempe, AZ, USA
- Department of Physics, ASU, Tempe, AZ, USA
- School of Molecular Sciences, ASU, Tempe, AZ, USA
| |
Collapse
|
9
|
Safar M, Saurabh A, Sarkar B, Fazel M, Ishii K, Tahara T, Sgouralis I, Pressé S. Single-photon smFRET. III. Application to pulsed illumination. BIOPHYSICAL REPORTS 2022; 2:100088. [PMID: 36530182 PMCID: PMC9747580 DOI: 10.1016/j.bpr.2022.100088] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Förster resonance energy transfer (FRET) using pulsed illumination has been pivotal in leveraging lifetime information in FRET analysis. However, there remain major challenges in quantitative single-photon, single-molecule FRET (smFRET) data analysis under pulsed illumination including 1) simultaneously deducing kinetics and number of system states; 2) providing uncertainties over estimates, particularly uncertainty over the number of system states; and 3) taking into account detector noise sources such as cross talk and the instrument response function contributing to uncertainty; in addition to 4) other experimental noise sources such as background. Here, we implement the Bayesian nonparametric framework described in the first companion article that addresses all aforementioned issues in smFRET data analysis specialized for the case of pulsed illumination. Furthermore, we apply our method to both synthetic as well as experimental data acquired using Holliday junctions.
Collapse
Affiliation(s)
- Matthew Safar
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Mathematics and Statistical Science, Arizona State University, Tempe, Arizona
| | - Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Bidyut Sarkar
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan
- Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama, Japan
| | - Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
| | - Kunihiko Ishii
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan
- Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama, Japan
| | - Tahei Tahara
- Molecular Spectroscopy Laboratory, RIKEN, 2-1 Hirosawa, Wako, Saitama, Japan
- Ultrafast Spectroscopy Research Team, RIKEN Center for Advanced Photonics (RAP), 2-1 Hirosawa, Wako, Saitama, Japan
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee Knoxville, Knoxville, Tennessee
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, Arizona
- Department of Physics, Arizona State University, Tempe, Arizona
- School of Molecular Sciences, Arizona State University, Phoenix, Arizona
| |
Collapse
|
10
|
Makarov DE, Berezhkovskii A, Haran G, Pollak E. The Effect of Time Resolution on Apparent Transition Path Times Observed in Single-Molecule Studies of Biomolecules. J Phys Chem B 2022; 126:7966-7974. [PMID: 36194758 PMCID: PMC9574923 DOI: 10.1021/acs.jpcb.2c05550] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/20/2022] [Indexed: 11/28/2022]
Abstract
Single-molecule experiments have now achieved a time resolution allowing observation of transition paths, the brief trajectory segments where the molecule undergoing an unfolding or folding transition enters the energetically or entropically unfavorable barrier region from the folded/unfolded side and exits to the unfolded/folded side, thereby completing the transition. This resolution, however, is yet insufficient to identify the precise entrance/exit events that mark the beginning and the end of a transition path: the nature of the diffusive dynamics is such that a molecular trajectory will recross the boundary between the barrier region and the folded/unfolded state, multiple times, at a time scale much shorter than that of the typical experimental resolution. Here we use theory and Brownian dynamics simulations to show that, as a result of such recrossings, the apparent transition path times are generally longer than the true ones. We quantify this effect using a simple model where the observed dynamics is a moving average of the true dynamics and discuss experimental implications of our results.
Collapse
Affiliation(s)
- Dmitrii E. Makarov
- Depatment
of Chemistry and Oden Institute for Computational Engineering and
Sciences, University of Texas at Austin, Austin, Texas78712, United States
| | - Alexander Berezhkovskii
- Eunice
Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland20892, United States
| | - Gilad Haran
- Department
of Chemical and Biological Physics, Weizmann
Institute of Science, Rehovot76100, Israel
| | - Eli Pollak
- Department
of Chemical and Biological Physics, Weizmann
Institute of Science, Rehovot76100, Israel
| |
Collapse
|
11
|
Heterogeneous migration routes of DNA triplet repeat slip-outs. BIOPHYSICAL REPORTS 2022; 2:None. [PMID: 36299495 PMCID: PMC9586884 DOI: 10.1016/j.bpr.2022.100070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022]
Abstract
It is unclear how the length of a repetitive DNA tract determines the onset and progression of repeat expansion diseases, but the dynamics of secondary DNA structures formed by repeat sequences are believed to play an important role. It was recently shown that three-way DNA junctions containing slip-out hairpins of CAG or CTG repeats and contiguous triplet repeats in the adjacent duplex displayed single-molecule FRET (smFRET) dynamics that were ascribed to both local conformational motions and longer-range branch migration. Here we explore these so-called "mobile" slip-out structures through a detailed kinetic analysis of smFRET trajectories and coarse-grained modeling. Despite the apparent structural simplicity, with six FRET states resolvable, most smFRET states displayed biexponential dwell-time distributions, attributed to structural heterogeneity and overlapping FRET states. Coarse-grained modeling for a (GAC)10 repeat slip-out included trajectories that corresponded to a complete round of branch migration; the structured free energy landscape between slippage events supports the dynamical complexity observed by smFRET. A hairpin slip-out with 40 CAG repeats, which is above the repeat length required for disease in several triplet repeat disorders, displayed smFRET dwell times that were on average double those of 3WJs with 10 repeats. The rate of secondary-structure rearrangement via branch migration, relative to particular DNA processing pathways, may be an important factor in the expansion of triplet repeat expansion diseases.
Collapse
|
12
|
Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap. iScience 2022; 25:104731. [PMID: 36034218 PMCID: PMC9400092 DOI: 10.1016/j.isci.2022.104731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/27/2022] [Accepted: 07/02/2022] [Indexed: 11/22/2022] Open
Abstract
While particle trajectories encode information on their governing potentials, potentials can be challenging to robustly extract from trajectories. Measurement errors may corrupt a particle’s position, and sparse sampling of the potential limits data in higher energy regions such as barriers. We develop a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials. As an alternative to Gaussian process priors over potentials, we introduce structured kernel interpolation to the Natural Sciences which allows us to extend our analysis to large datasets. Structured-Kernel-Interpolation Priors for Potential Energy Reconstruction (SKIPPER) is validated on 1D and 2D experimental trajectories for particles in a feedback trap. A feedback trap was used to generate noisy Langevin microbead trajectories The potential energy surface is recovered using a Bayesian formulation The formulation uses a structured-kernel-interpolation Gaussian process (SKI-GP) to tractably approximate Gaussian process regression for larger datasets Thanks to our adaptation of SKI-GP, we have broadened the use of Gaussian processes for natural science applications
Collapse
|
13
|
Saurabh A, Niekamp S, Sgouralis I, Pressé S. Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores. J Phys Chem B 2022; 126:10.1021/acs.jpcb.2c01889. [PMID: 35649158 PMCID: PMC9712593 DOI: 10.1021/acs.jpcb.2c01889] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Quantitative fluorescence analysis is often used to derive chemical properties, including stoichiometries, of biomolecular complexes. One fundamental underlying assumption in the analysis of fluorescence data─whether it be the determination of protein complex stoichiometry by super-resolution, or step-counting by photobleaching, or the determination of RNA counts in diffraction-limited spots in RNA fluorescence in situ hybridization (RNA-FISH) experiments─is that fluorophores behave identically and do not interact. However, recent experiments on fluorophore-labeled DNA origami structures such as fluorocubes have shed light on the nature of the interactions between identical fluorophores as these are brought closer together, thereby raising questions on the validity of the modeling assumption that fluorophores do not interact. Here, we analyze photon arrival data under pulsed illumination from fluorocubes where distances between dyes range from 2 to 10 nm. We discuss the implications of non-additivity of brightness on quantitative fluorescence analysis.
Collapse
Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Stefan Niekamp
- Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, United States
| | - Ioannis Sgouralis
- Department of Mathematics, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Steve Pressé
- Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
- School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| |
Collapse
|