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Fazel M, Jazani S, Scipioni L, Vallmitjana A, Zhu S, Gratton E, Digman MA, Pressé S. Building Fluorescence Lifetime Maps Photon-by-Photon by Leveraging Spatial Correlations. ACS PHOTONICS 2023; 10:3558-3569. [PMID: 38406580 PMCID: PMC10890823 DOI: 10.1021/acsphotonics.3c00595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Fluorescence lifetime imaging microscopy (FLIM) has become a standard tool in the quantitative characterization of subcellular environments. However, quantitative FLIM analyses face several challenges. First, spatial correlations between pixels are often ignored as signal from individual pixels is analyzed independently thereby limiting spatial resolution. Second, existing methods deduce photon ratios instead of absolute lifetime maps. Next, the number of fluorophore species contributing to the signal is unknown, while excited state lifetimes with <1 ns difference are difficult to discriminate. Finally, existing analyses require high photon budgets and often cannot rigorously propagate experimental uncertainty into values over lifetime maps and number of species involved. To overcome all of these challenges simultaneously and self-consistently at once, we propose the first doubly nonparametric framework. That is, we learn the number of species (using Beta-Bernoulli process priors) and absolute maps of these fluorophore species (using Gaussian process priors) by leveraging information from pulses not leading to observed photon. We benchmark our framework using a broad range of synthetic and experimental data and demonstrate its robustness across a number of scenarios including cases where we recover lifetime differences between species as small as 0.3 ns with merely 1000 photons.
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Affiliation(s)
- Mohamadreza Fazel
- Center for Biological Physics and Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Sina Jazani
- Center for Biological Physics and Department of Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Lorenzo Scipioni
- Department of Biomedical Engineering, University of California Irvine, Irvine, California 92697, United States; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California, Irvine, California 92697, United States
| | - Alexander Vallmitjana
- Department of Biomedical Engineering, University of California Irvine, Irvine, California 92697, United States; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California, Irvine, California 92697, United States
| | - Songning Zhu
- Department of Biomedical Engineering, University of California Irvine, Irvine, California 92697, United States; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California, Irvine, California 92697, United States
| | - Enrico Gratton
- Department of Biomedical Engineering, University of California Irvine, Irvine, California 92697, United States; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California, Irvine, California 92697, United States
| | - Michelle A Digman
- Department of Biomedical Engineering, University of California Irvine, Irvine, California 92697, United States; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California, Irvine, California 92697, United States
| | - Steve Pressé
- Center for Biological Physics and Department of Physics, Arizona State University, Tempe, Arizona 85287, United States; School of Molecular Science, Arizona State University, Tempe, Arizona 85287, United States
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2
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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.
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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
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3
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Mo Y, Zhou H, Xu J, Chen X, Li L, Zhang S. Genetically encoded fluorescence lifetime biosensors: overview, advances, and opportunities. Analyst 2023; 148:4939-4953. [PMID: 37721109 DOI: 10.1039/d3an01201h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Genetically encoded biosensors based on fluorescent proteins (FPs) are powerful tools for tracking analytes and cellular events with high spatial and temporal resolution in living cells and organisms. Compared with intensiometric readout and ratiometric readout, fluorescence lifetime readout provides absolute measurements, independent of the biosensor expression level and instruments. Thus, genetically encoded fluorescence lifetime biosensors play a vital role in facilitating accurate quantitative assessments within intricate biological systems. In this review, we first provide a concise description of the categorization and working mechanism of genetically encoded fluorescence lifetime biosensors. Subsequently, we elaborate on the combination of the fluorescence lifetime imaging technique and lifetime analysis methods with fluorescence lifetime biosensors, followed by their application in monitoring the dynamics of environment parameters, analytes and cellular events. Finally, we discuss worthwhile considerations for the design, optimization and development of fluorescence lifetime-based biosensors from three representative cases.
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Affiliation(s)
- Yidan Mo
- State Key Laboratory of Precision Spectroscopy, East China Normal University, No. 500, Dongchuan Rd, Shanghai 200241, China
| | - Huangmei Zhou
- State Key Laboratory of Precision Spectroscopy, East China Normal University, No. 500, Dongchuan Rd, Shanghai 200241, China
| | - Jinming Xu
- State Key Laboratory of Precision Spectroscopy, East China Normal University, No. 500, Dongchuan Rd, Shanghai 200241, China
| | - Xihang Chen
- State Key Laboratory of Precision Spectroscopy, East China Normal University, No. 500, Dongchuan Rd, Shanghai 200241, China
| | - Lei Li
- School of Science, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu, 214122, China.
| | - Sanjun Zhang
- State Key Laboratory of Precision Spectroscopy, East China Normal University, No. 500, Dongchuan Rd, Shanghai 200241, China
- Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi 030006, China
- NYU-ECNU Institute of Physics at NYU Shanghai, No. 3663, North Zhongshan Rd, Shanghai 200062, China.
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4
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Chen P, Kang Q, Niu J, Jing Y, Zhang X, Yu B, Qu J, Lin D. Fluorescence lifetime tracking and imaging of single moving particles assisted by a low-photon-count analysis algorithm. BIOMEDICAL OPTICS EXPRESS 2023; 14:1718-1731. [PMID: 37078048 PMCID: PMC10110318 DOI: 10.1364/boe.485729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 05/03/2023]
Abstract
Fluorescence lifetime imaging microscopy (FLIM) has been widely used in the field of biological research because of its high specificity, sensitivity, and quantitative ability in the sensing cellular microenvironment. The most commonly used FLIM technology is based on time-correlated single photon counting (TCSPC). Although the TCSPC method has the highest temporal resolution, the data acquisition time is usually long, and the imaging speed is slow. In this work, we proposed a fast FLIM technology for fluorescence lifetime tracking and imaging of single moving particles, named single particle tracking FLIM (SPT-FLIM). We used feedback-controlled addressing scanning and Mosaic FLIM mode imaging to reduce the number of scanned pixels and the data readout time, respectively. Moreover, we developed a compressed sensing analysis algorithm based on alternating descent conditional gradient (ADCG) for low-photon-count data. We applied the ADCG-FLIM algorithm on both simulated and experimental datasets to evaluate its performance. The results showed that ADCG-FLIM could achieve reliable lifetime estimation with high accuracy and precision in the case of a photon count less than 100. By reducing the photon count requirement for each pixel from, typically, 1000 to 100, the acquisition time for a single frame lifetime image could be significantly shortened, and the imaging speed could be improved to a great extent. On this basis, we obtained lifetime trajectories of moving fluorescent beads using the SPT-FLIM technique. Overall, our work offers a powerful tool for fluorescence lifetime tracking and imaging of single moving particles, which will promote the application of TCSPC-FLIM in biological research.
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Fazel M, Vallmitjana A, Scipioni L, Gratton E, Digman MA, Pressé S. Fluorescence lifetime: Beating the IRF and interpulse window. Biophys J 2023; 122:672-683. [PMID: 36659850 PMCID: PMC9989884 DOI: 10.1016/j.bpj.2023.01.014] [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: 09/09/2022] [Revised: 11/29/2022] [Accepted: 01/11/2023] [Indexed: 01/20/2023] Open
Abstract
Fluorescence lifetime imaging captures the spatial distribution of chemical species across cellular environments employing pulsed illumination confocal setups. However, quantitative interpretation of lifetime data continues to face critical challenges. For instance, fluorescent species with known in vitro excited-state lifetimes may split into multiple species with unique lifetimes when introduced into complex living environments. What is more, mixtures of species, which may be both endogenous and introduced into the sample, may exhibit 1) very similar lifetimes as well as 2) wide ranges of lifetimes including lifetimes shorter than the instrumental response function or whose duration may be long enough to be comparable to the interpulse window. By contrast, existing methods of analysis are optimized for well-separated and intermediate lifetimes. Here, we broaden the applicability of fluorescence lifetime analysis by simultaneously treating unknown mixtures of arbitrary lifetimes-outside the intermediate, Goldilocks, zone-for data drawn from a single confocal spot leveraging the tools of Bayesian nonparametrics (BNP). We benchmark our algorithm, termed BNP lifetime analysis, using a range of synthetic and experimental data. Moreover, we show that the BNP lifetime analysis method can distinguish and deduce lifetimes using photon counts as small as 500.
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Affiliation(s)
- Mohamadreza Fazel
- Center for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona
| | - Alexander Vallmitjana
- Department of Biomedical Engineering, University of California Irvine, Irvine, California; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California Irvine, Irvine, California
| | - Lorenzo Scipioni
- Department of Biomedical Engineering, University of California Irvine, Irvine, California; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California Irvine, Irvine, California
| | - Enrico Gratton
- Department of Biomedical Engineering, University of California Irvine, Irvine, California; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California Irvine, Irvine, California
| | - Michelle A Digman
- Department of Biomedical Engineering, University of California Irvine, Irvine, California; Laboratory of Fluorescence Dynamics, The Henry Samueli School of Engineering, University of California Irvine, Irvine, California
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona; School of Molecular Science, Arizona State University, Tempe, Arizona.
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6
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Luo Y, Chang J, Yang D, Bryan JS, MacIsaac M, Pressé S, Wong WP. Resolving Molecular Heterogeneity with Single-Molecule Centrifugation. J Am Chem Soc 2023; 145:3276-3282. [PMID: 36716175 PMCID: PMC9936575 DOI: 10.1021/jacs.2c11450] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
For many classes of biomolecules, population-level heterogeneity is an essential aspect of biological function─from antibodies produced by the immune system to post-translationally modified proteins that regulate cellular processes. However, heterogeneity is difficult to fully characterize for multiple reasons: (i) single-molecule approaches are needed to avoid information lost by ensemble-level averaging, (ii) sufficient statistics must be gathered on both a per-molecule and per-population level, and (iii) a suitable analysis framework is required to make sense of a potentially limited number of intrinsically noisy measurements. Here, we introduce an approach that overcomes these difficulties by combining three techniques: a DNA nanoswitch construct to repeatedly interrogate the same molecule, a benchtop centrifuge force microscope (CFM) to obtain thousands of statistics in a highly parallel manner, and a Bayesian nonparametric (BNP) inference method to resolve separate subpopulations with distinct kinetics. We apply this approach to characterize commercially available antibodies and find that polyclonal antibody from rabbit serum is well-modeled by a mixture of three subpopulations. Our results show how combining a spatially and temporally multiplexed nanoswitch-CFM assay with BNP analysis can help resolve complex biomolecular interactions in heterogeneous samples.
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Affiliation(s)
- Yi Luo
- Program
in Cellular and Molecular Medicine, Boston
Children’s Hospital, Boston, Massachusetts 02115, United States,Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States,Department
of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jeffrey Chang
- Department
of Physics, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Darren Yang
- Program
in Cellular and Molecular Medicine, Boston
Children’s Hospital, Boston, Massachusetts 02115, United States,Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States,Department
of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - J. Shepard Bryan
- Department
of Physics, Arizona State University, Tempe, Arizona 85287, United States,Center
for
Biological Physics, Arizona State University, Tempe, Arizona 85287, United States
| | - Molly MacIsaac
- Program
in Cellular and Molecular Medicine, Boston
Children’s Hospital, Boston, Massachusetts 02115, United States,Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States,Department
of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Steve Pressé
- Department
of Physics, Arizona State University, Tempe, Arizona 85287, United States,Center
for
Biological Physics, Arizona State University, Tempe, Arizona 85287, United States,School
of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Wesley P. Wong
- Program
in Cellular and Molecular Medicine, Boston
Children’s Hospital, Boston, Massachusetts 02115, United States,Wyss
Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts 02115, United States,Department
of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts 02115, United States,
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7
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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.
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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
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8
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Saurabh A, Fazel M, Safar M, Sgouralis I, Pressé S. Single-photon smFRET. I: Theory and conceptual basis. BIOPHYSICAL REPORTS 2022; 3:100089. [PMID: 36582655 PMCID: PMC9793182 DOI: 10.1016/j.bpr.2022.100089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [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.
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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,Corresponding author
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9
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Fazel M, Wester MJ, Schodt DJ, Cruz SR, Strauss S, Schueder F, Schlichthaerle T, Gillette JM, Lidke DS, Rieger B, Jungmann R, Lidke KA. High-precision estimation of emitter positions using Bayesian grouping of localizations. Nat Commun 2022; 13:7152. [PMID: 36418347 PMCID: PMC9684143 DOI: 10.1038/s41467-022-34894-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/10/2022] [Indexed: 11/25/2022] Open
Abstract
Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.
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Affiliation(s)
- Mohamadreza Fazel
- Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
| | - Michael J Wester
- Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
| | - David J Schodt
- Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA
| | - Sebastian Restrepo Cruz
- Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA
| | - Sebastian Strauss
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Florian Schueder
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Thomas Schlichthaerle
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jennifer M Gillette
- Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA
| | - Diane S Lidke
- Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA
| | - Bernd Rieger
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands
| | - Ralf Jungmann
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Keith A Lidke
- Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA.
- Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA.
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