1
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Schirripa Spagnolo C, Luin S. Impact of temporal resolution in single particle tracking analysis. DISCOVER NANO 2024; 19:87. [PMID: 38724858 PMCID: PMC11082114 DOI: 10.1186/s11671-024-04029-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/29/2024] [Indexed: 05/12/2024]
Abstract
Temporal resolution is a key parameter in the observation of dynamic processes, as in the case of single molecules motions visualized in real time in two-dimensions by wide field (fluorescence) microscopy, but a systematic investigation of its effects in all the single particle tracking analysis steps is still lacking. Here we present tools to quantify its impact on the estimation of diffusivity and of its distribution using one of the most popular tracking software for biological applications on simulated data and movies. We found important shifts and different widths for diffusivity distributions, depending on the interplay of temporal sampling conditions with various parameters, such as simulated diffusivity, density of spots, signal-to-noise ratio, lengths of trajectories, and kind of boundaries in the simulation. We examined conditions starting from the ones of experiments on the fluorescently labelled receptor p75NTR, a relatively fast-diffusing membrane receptor (diffusivity around 0.5-1 µm2/s), visualized by TIRF microscopy on the basal membrane of living cells. From the analysis of the simulations, we identified the best conditions in cases similar to these ones; considering also the experiments, we could confirm a range of values of temporal resolution suitable for obtaining reliable diffusivity results. The procedure we present can be exploited in different single particle/molecule tracking applications to find an optimal temporal resolution.
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Affiliation(s)
| | - Stefano Luin
- NEST Laboratory, Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy.
- NEST Laboratory, Istituto Nanoscienze-CNR, Piazza San Silvestro 12, 56127, Pisa, Italy.
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2
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Roudot P, Legant WR, Zou Q, Dean KM, Isogai T, Welf ES, David AF, Gerlich DW, Fiolka R, Betzig E, Danuser G. u-track3D: Measuring, navigating, and validating dense particle trajectories in three dimensions. CELL REPORTS METHODS 2023; 3:100655. [PMID: 38042149 PMCID: PMC10783629 DOI: 10.1016/j.crmeth.2023.100655] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/10/2023] [Accepted: 11/09/2023] [Indexed: 12/04/2023]
Abstract
We describe u-track3D, a software package that extends the versatile u-track framework established in 2D to address the specific challenges of 3D particle tracking. First, we present the performance of the new package in quantifying a variety of intracellular dynamics imaged by multiple 3D microcopy platforms and on the standard 3D test dataset of the particle tracking challenge. These analyses indicate that u-track3D presents a tracking solution that is competitive to both conventional and deep-learning-based approaches. We then present the concept of dynamic region of interest (dynROI), which allows an experimenter to interact with dynamic 3D processes in 2D views amenable to visual inspection. Third, we present an estimator of trackability that automatically defines a score for every trajectory, thereby overcoming the challenges of trajectory validation by visual inspection. With these combined strategies, u-track3D provides a complete framework for unbiased studies of molecular processes in complex volumetric sequences.
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Affiliation(s)
- Philippe Roudot
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA; Aix Marseille University, CNRS, Centrale Marseille, I2M, Turing Centre for Living Systems, Marseille, France.
| | - Wesley R Legant
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, North Carolina State University, Chapel Hill, NC, USA; Department of Pharmacology, University of North Carolina, Chapel Hill, NC, USA
| | - Qiongjing Zou
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Kevin M Dean
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Tadamoto Isogai
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Erik S Welf
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Ana F David
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria
| | - Daniel W Gerlich
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria
| | - Reto Fiolka
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Eric Betzig
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
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3
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Abstract
Arc is one of the genes that are rapidly transcribed by neuronal activity and thus used as a marker for memory trace or engram cells. However, the dynamics of engram cell populations is not well-known because of the difficulty in monitoring the rapid and transient gene expression in live animals. Using a mouse model in which endogenous Arc messenger RNA (mRNA) is fluorescently labeled, we demonstrate that Arc-expressing neuronal populations have distinct dynamics in different brain regions and that only a small subpopulation that consistently expresses Arc during both memory encoding and retrieval exhibits context-specific calcium activity. This live-animal RNA-imaging technique will offer a powerful tool for connecting gene expression to neuronal activity patterns and to behavior. Memories are thought to be encoded in populations of neurons called memory trace or engram cells. However, little is known about the dynamics of these cells because of the difficulty in real-time monitoring of them over long periods of time in vivo. To overcome this limitation, we present a genetically encoded RNA indicator (GERI) mouse for intravital chronic imaging of endogenous Arc messenger RNA (mRNA)—a popular marker for memory trace cells. We used our GERI to identify Arc-positive neurons in real time without the delay associated with reporter protein expression in conventional approaches. We found that the Arc-positive neuronal populations rapidly turned over within 2 d in the hippocampal CA1 region, whereas ∼4% of neurons in the retrosplenial cortex consistently expressed Arc following contextual fear conditioning and repeated memory retrievals. Dual imaging of GERI and a calcium indicator in CA1 of mice navigating a virtual reality environment revealed that only the population of neurons expressing Arc during both encoding and retrieval exhibited relatively high calcium activity in a context-specific manner. This in vivo RNA-imaging approach opens the possibility of unraveling the dynamics of the neuronal population underlying various learning and memory processes.
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4
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Maris JJE, Rabouw FT, Weckhuysen BM, Meirer F. Classification-based motion analysis of single-molecule trajectories using DiffusionLab. Sci Rep 2022; 12:9595. [PMID: 35689015 PMCID: PMC9187757 DOI: 10.1038/s41598-022-13446-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 05/24/2022] [Indexed: 11/25/2022] Open
Abstract
Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available.
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Affiliation(s)
- J J Erik Maris
- Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG, Utrecht, The Netherlands
| | - Freddy T Rabouw
- Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG, Utrecht, The Netherlands.,Soft Condensed Matter and Biophysics, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CC, Utrecht, The Netherlands
| | - Bert M Weckhuysen
- Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG, Utrecht, The Netherlands
| | - Florian Meirer
- Inorganic Chemistry and Catalysis, Debye Institute for Nanomaterials Science, Utrecht University, 3584 CG, Utrecht, The Netherlands.
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5
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Gabriel L, Almeida H, Avelar M, Sarmento B, das Neves J. MPTHub: An Open-Source Software for Characterizing the Transport of Particles in Biorelevant Media. NANOMATERIALS 2022; 12:nano12111899. [PMID: 35683754 PMCID: PMC9182034 DOI: 10.3390/nano12111899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/04/2023]
Abstract
The study of particle transport in different environments plays an essential role in understanding interactions with humans and other living organisms. Importantly, obtained data can be directly used for multiple applications in fields such as fundamental biology, toxicology, or medicine. Particle movement in biorelevant media can be readily monitored using microscopy and converted into time-resolved trajectories using freely available tracking software. However, translation into tangible and meaningful parameters is time consuming and not always intuitive. We developed new software—MPTHub—as an open-access, standalone, user-friendly tool for the rapid and reliable analysis of particle trajectories extracted from video microscopy. The software was programmed using Python and allowed to import and analyze trajectory data, as well as to export relevant data such as individual and ensemble time-averaged mean square displacements and effective diffusivity, and anomalous transport exponent. Data processing was reliable, fast (total processing time of less than 10 s), and required minimal memory resources (up to a maximum of around 150 MB in random access memory). Demonstration of software applicability was conducted by studying the transport of different polystyrene nanoparticles (100–200 nm) in mucus surrogates. Overall, MPTHub represents a freely available software tool that can be used even by inexperienced users for studying the transport of particles in biorelevant media.
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Affiliation(s)
- Leandro Gabriel
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (L.G.); (H.A.); (M.A.); (B.S.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- FEUP—Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
| | - Helena Almeida
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (L.G.); (H.A.); (M.A.); (B.S.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, 4050-313 Porto, Portugal
| | - Marta Avelar
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (L.G.); (H.A.); (M.A.); (B.S.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- FEUP—Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal
- ICBAS—Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, 4050-313 Porto, Portugal
| | - Bruno Sarmento
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (L.G.); (H.A.); (M.A.); (B.S.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- IUCS—Instituto Universitário de Ciências da Saúde, CESPU, 4585-116 Gandra, Portugal
| | - José das Neves
- i3S—Instituto de Investigação e Inovação em Saúde, Universidade do Porto, 4200-135 Porto, Portugal; (L.G.); (H.A.); (M.A.); (B.S.)
- INEB—Instituto de Engenharia Biomédica, Universidade do Porto, 4200-135 Porto, Portugal
- IUCS—Instituto Universitário de Ciências da Saúde, CESPU, 4585-116 Gandra, Portugal
- Correspondence: ; Tel.: +351-220-408-800
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6
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Sikes JC, Wonner K, Nicholson A, Cignoni P, Fritsch I, Tschulik K. Characterization of Nanoparticles in Diverse Mixtures Using Localized Surface Plasmon Resonance and Nanoparticle Tracking by Dark-Field Microscopy with Redox Magnetohydrodynamics Microfluidics. ACS PHYSICAL CHEMISTRY AU 2022; 2:289-298. [PMID: 35915589 PMCID: PMC9335947 DOI: 10.1021/acsphyschemau.1c00046] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
![]()
Redox magnetohydrodynamics
(RMHD) microfluidics is coupled with
dark-field microscopy (DFM) to offer high-throughput single-nanoparticle
(NP) differentiation in situ and operando in a flowing mixture by localized surface plasmon resonance (LSPR)
and tracking of NPs. The color of the scattered light allows visualization
of the NPs below the diffraction limit. Their Brownian motion in 1-D
superimposed on and perpendicular to the RMHD trajectory yields their
diffusion coefficients. LSPR and diffusion coefficients provide two
orthogonal modalities for characterization where each depends on a
particle’s material composition, shape, size, and interactions
with the surrounding medium. RMHD coupled with DFM was demonstrated
on a mixture of 82 ± 9 nm silver and 140 ± 10 nm gold-coated
silica nanospheres. The two populations of NPs in the mixture were
identified by blue/green and orange/red LSPR and their scattering
intensity, respectively, and their sizes were further evaluated based
on their diffusion coefficients. RMHD microfluidics facilitates high-throughput
analysis by moving the sample solution across the wide field of view
absent of physical vibrations within the experimental cell. The well-controlled
pumping allows for a continuous, reversible, and uniform flow for
precise and simultaneous NP tracking of the Brownian motion. Additionally,
the amounts of nanomaterials required for the analysis are minimized
due to the elimination of an inlet and outlet. Several hundred individual
NPs were differentiated from each other in the mixture flowing in
forward and reverse directions. The ability to immediately reverse
the flow direction also facilitates re-analysis of the NPs, enabling
more precise sizing.
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Affiliation(s)
- Jazlynn C. Sikes
- University of Arkansas Department of Chemistry and Biochemistry, Fayetteville, Arkansas 72701, United States
| | - Kevin Wonner
- Ruhr University Bochum, Faculty of Chemistry and Biochemistry, Chair of Analytical Chemistry II, Bochum 44801, Germany
| | - Aaron Nicholson
- University of Arkansas Department of Chemistry and Biochemistry, Fayetteville, Arkansas 72701, United States
| | - Paolo Cignoni
- Ruhr University Bochum, Faculty of Chemistry and Biochemistry, Chair of Analytical Chemistry II, Bochum 44801, Germany
| | - Ingrid Fritsch
- University of Arkansas Department of Chemistry and Biochemistry, Fayetteville, Arkansas 72701, United States
| | - Kristina Tschulik
- Ruhr University Bochum, Faculty of Chemistry and Biochemistry, Chair of Analytical Chemistry II, Bochum 44801, Germany
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7
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Oviedo-Bocanegra LM, Hinrichs R, Rotter DAO, Dersch S, Graumann PL. Single molecule/particle tracking analysis program SMTracker 2.0 reveals different dynamics of proteins within the RNA degradosome complex in Bacillus subtilis. Nucleic Acids Res 2021; 49:e112. [PMID: 34417617 PMCID: PMC8565344 DOI: 10.1093/nar/gkab696] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/29/2021] [Indexed: 01/06/2023] Open
Abstract
Single-molecule (particle) tracking is a powerful method to study dynamic processes in cells at highest possible spatial and temporal resolution. We have developed SMTracker, a graphical user interface for automatic quantifying, visualizing and managing of data. Version 2.0 determines distributions of positional displacements in x- and y-direction using multi-state diffusion models, discriminates between Brownian, sub- or superdiffusive behaviour, and locates slow or fast diffusing populations in a standardized cell. Using SMTracker, we show that the Bacillus subtilis RNA degradosome consists of a highly dynamic complex of RNase Y and binding partners. We found similar changes in molecule dynamics for RNase Y, CshA, PNPase and enolase, but not for phosphofructokinase, RNase J1 and J2, to inhibition of transcription. However, the absence of PfkA or of RNase J2 affected molecule dynamics of RNase Y-mVenus, indicating that these two proteins are indeed part of the degradosome. Molecule counting suggests that RNase Y is present as a dimer in cells, at an average copy number of about 500, of which 46% are present in a slow-diffusive state and thus likely engaged within degradosomes. Thus, RNase Y, CshA, PNPase and enolase likely play central roles, and RNase J1, J2 and PfkA more peripheral roles, in degradosome architecture.
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Affiliation(s)
- Luis M Oviedo-Bocanegra
- Centre for Synthetic Microbiology (SYNMIKRO) and Fachbereich Chemie, Philipps-Universität Marburg, 35032 Marburg, Germany
| | - Rebecca Hinrichs
- Centre for Synthetic Microbiology (SYNMIKRO) and Fachbereich Chemie, Philipps-Universität Marburg, 35032 Marburg, Germany
| | - Daniel Andreas Orlando Rotter
- Centre for Synthetic Microbiology (SYNMIKRO) and Fachbereich Chemie, Philipps-Universität Marburg, 35032 Marburg, Germany
| | - Simon Dersch
- Centre for Synthetic Microbiology (SYNMIKRO) and Fachbereich Chemie, Philipps-Universität Marburg, 35032 Marburg, Germany
| | - Peter L Graumann
- Centre for Synthetic Microbiology (SYNMIKRO) and Fachbereich Chemie, Philipps-Universität Marburg, 35032 Marburg, Germany
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8
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Kuhn T, Hettich J, Davtyan R, Gebhardt JCM. Single molecule tracking and analysis framework including theory-predicted parameter settings. Sci Rep 2021; 11:9465. [PMID: 33947895 PMCID: PMC8096815 DOI: 10.1038/s41598-021-88802-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/16/2021] [Indexed: 02/02/2023] Open
Abstract
Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.
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Affiliation(s)
- Timo Kuhn
- grid.6582.90000 0004 1936 9748Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Johannes Hettich
- grid.6582.90000 0004 1936 9748Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
| | - Rubina Davtyan
- grid.6582.90000 0004 1936 9748Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany ,grid.4514.40000 0001 0930 2361Present Address: NanoLund and Solid State Physics, Lund University, Box 118, 22100 Lund, Sweden
| | - J. Christof M. Gebhardt
- grid.6582.90000 0004 1936 9748Institute of Biophysics, Ulm University, Albert-Einstein-Allee 11, 89081 Ulm, Germany
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9
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Vink JNA, Brouns SJJ, Hohlbein J. Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis. Biophys J 2020; 119:1970-1983. [PMID: 33086040 DOI: 10.1016/j.bpj.2020.09.033] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/29/2020] [Accepted: 09/29/2020] [Indexed: 10/23/2022] Open
Abstract
Single-particle tracking is an important technique in the life sciences to understand the kinetics of biomolecules. The analysis of apparent diffusion coefficients in vivo, for example, enables researchers to determine whether biomolecules are moving alone, as part of a larger complex, or are bound to large cellular components such as the membrane or chromosomal DNA. A remaining challenge has been to retrieve quantitative kinetic models, especially for molecules that rapidly switch between different diffusional states. Here, we present analytical diffusion distribution analysis (anaDDA), a framework that allows for extracting transition rates from distributions of apparent diffusion coefficients calculated from short trajectories that feature less than 10 localizations per track. Under the assumption that the system is Markovian and diffusion is purely Brownian, we show that theoretically predicted distributions accurately match simulated distributions and that anaDDA outperforms existing methods to retrieve kinetics, especially in the fast regime of 0.1-10 transitions per imaging frame. AnaDDA does account for the effects of confinement and tracking window boundaries. Furthermore, we added the option to perform global fitting of data acquired at different frame times to allow complex models with multiple states to be fitted confidently. Previously, we have started to develop anaDDA to investigate the target search of CRISPR-Cas complexes. In this work, we have optimized the algorithms and reanalyzed experimental data of DNA polymerase I diffusing in live Escherichia coli. We found that long-lived DNA interaction by DNA polymerase are more abundant upon DNA damage, suggesting roles in DNA repair. We further revealed and quantified fast DNA probing interactions that last shorter than 10 ms. AnaDDA pushes the boundaries of the timescale of interactions that can be probed with single-particle tracking and is a mathematically rigorous framework that can be further expanded to extract detailed information about the behavior of biomolecules in living cells.
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Affiliation(s)
- Jochem N A Vink
- Department of Bionanoscience, Delft University of Technology, HZ Delft, the Netherlands; Kavli Institute of Nanoscience, Delft, the Netherlands
| | - Stan J J Brouns
- Department of Bionanoscience, Delft University of Technology, HZ Delft, the Netherlands; Kavli Institute of Nanoscience, Delft, the Netherlands.
| | - Johannes Hohlbein
- Laboratory of Biophysics, Wageningen University & Research, Wageningen, the Netherlands; Microspectroscopy Reasearch Facility, Wageningen University & Research, Wageningen, the Netherlands.
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10
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Shaban HA, Seeber A. Monitoring the spatio-temporal organization and dynamics of the genome. Nucleic Acids Res 2020; 48:3423-3434. [PMID: 32123910 PMCID: PMC7144944 DOI: 10.1093/nar/gkaa135] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 02/17/2020] [Accepted: 02/23/2020] [Indexed: 12/22/2022] Open
Abstract
The spatio-temporal organization of chromatin in the eukaryotic cell nucleus is of vital importance for transcription, DNA replication and genome maintenance. Each of these activities is tightly regulated in both time and space. While we have a good understanding of chromatin organization in space, for example in fixed snapshots as a result of techniques like FISH and Hi-C, little is known about chromatin dynamics in living cells. The rapid development of flexible genomic loci imaging approaches can address fundamental questions on chromatin dynamics in a range of model organisms. Moreover, it is now possible to visualize not only single genomic loci but the whole genome simultaneously. These advances have opened many doors leading to insight into several nuclear processes including transcription and DNA repair. In this review, we discuss new chromatin imaging methods and how they have been applied to study transcription.
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Affiliation(s)
- Haitham A Shaban
- Center for Advanced Imaging, Harvard University, Cambridge, MA 02138, USA
- Spectroscopy Department, Physics Division, National Research Centre, Dokki, 12622 Cairo, Egypt
| | - Andrew Seeber
- Center for Advanced Imaging, Harvard University, Cambridge, MA 02138, USA
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11
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Kim SH, Vieira M, Shim JY, Choi H, Park HY. Recent progress in single-molecule studies of mRNA localization in vivo. RNA Biol 2019; 16:1108-1118. [PMID: 30336727 PMCID: PMC6693552 DOI: 10.1080/15476286.2018.1536592] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/28/2018] [Accepted: 10/08/2018] [Indexed: 12/26/2022] Open
Abstract
From biogenesis to degradation, mRNA goes through diverse types of regulation and interaction with other biomolecules. Uneven distribution of mRNA transcripts and the diverse isoforms and modifications of mRNA make us wonder how cells manage the complexity and keep the functional integrity for the normal development of cells and organisms. Single-molecule microscopy tools have expanded the scope of RNA research with unprecedented spatiotemporal resolution. In this review, we highlight the recent progress in the methods for labeling mRNA targets and analyzing the quantitative information from fluorescence images of single mRNA molecules. In particular, the MS2 system and its various applications are the main focus of this article. We also review how recent studies have addressed biological questions related to the significance of mRNA localization in vivo. Efforts to visualize the dynamics of single mRNA molecules in live cells will push forward our knowledge on the nature of heterogeneity in RNA sequence, structure, and distribution as well as their molecular function and coordinated interaction with RNA binding proteins.
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Affiliation(s)
- Songhee H. Kim
- Department of Physics and Astronomy, Seoul National University, Seoul, Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Korea
| | - Melissa Vieira
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Korea
| | - Jae Youn Shim
- Department of Physics and Astronomy, Seoul National University, Seoul, Korea
| | - Hongyoung Choi
- Department of Physics and Astronomy, Seoul National University, Seoul, Korea
| | - Hye Yoon Park
- Department of Physics and Astronomy, Seoul National University, Seoul, Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Korea
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul, Korea
- Institute of Applied Physics, Seoul National University, Seoul, Korea
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12
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Visualization of Single mRNAs in Live Neurons. Methods Mol Biol 2019. [PMID: 31407277 DOI: 10.1007/978-1-4939-9674-2_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Transcription and post-transcriptional regulations are critical in gene expression. To study the spatiotemporal regulation of RNA inside a cell, techniques for high-resolution imaging of RNA have been developed. In this chapter, we describe RNA fluorescent labeling methods using MS2 and PP7 systems to detect single RNA molecules in live neurons. We use hippocampal neurons cultured from knock-in mouse models in which β-actin or Arc mRNAs are tagged with MS2 or PP7 stem-loops. Adeno-associated virus (AAV) or lentiviral vectors are used to express MS2 or PP7 capsid proteins fused with GFP in those neurons. Then, GFP-labeled RNAs in live neurons can be detected by epifluorescence microscopy, and their moving pathways can be analyzed using single-particle tracking software. For these processes, we introduce protocols for neuron culture, transfection, imaging, and particle tracking methods.
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13
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Jakobs MA, Dimitracopoulos A, Franze K. KymoButler, a deep learning software for automated kymograph analysis. eLife 2019; 8:42288. [PMID: 31405451 PMCID: PMC6692109 DOI: 10.7554/elife.42288] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 07/11/2019] [Indexed: 12/11/2022] Open
Abstract
Kymographs are graphical representations of spatial position over time, which are often used in biology to visualise the motion of fluorescent particles, molecules, vesicles, or organelles moving along a predictable path. Although in kymographs tracks of individual particles are qualitatively easily distinguished, their automated quantitative analysis is much more challenging. Kymographs often exhibit low signal-to-noise-ratios (SNRs), and available tools that automate their analysis usually require manual supervision. Here we developed KymoButler, a Deep Learning-based software to automatically track dynamic processes in kymographs. We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of different biological systems. The software was packaged in a web-based ‘one-click’ application for use by the wider scientific community (http://kymobutler.deepmirror.ai). Our approach significantly speeds up data analysis, avoids unconscious bias, and represents another step towards the widespread adaptation of Machine Learning techniques in biological data analysis. Many molecules and structures within cells have to move about to do their job. Studying these movements is important to understand many biological processes, including the development of the brain or the spread of viruses. Kymographs are images that represent the movement of particles in time and space. Unfortunately, tracing the lines that represent movement in kymographs of biological particles is hard to do automatically, so currently this analysis is done by hand. Manually annotating kymographs is tedious, time-consuming and prone to the researcher’s unconscious bias. In an effort to simplify the analysis of kymographs, Jakobs et al. have developed KymoButler, a software tool that can do it automatically. KymoButler uses artificial intelligence to trace the lines in a kymograph and extract the information about particle movement. It speeds up analysis of kymographs by between 50 and 250 times, and comparisons show that it is as reliable as manual analysis. KymoButler is also significantly more effective than any previously existing automatic kymograph analysis programme. To make KymoButler accessible, Jakobs et al. have also created a website with a drag-and-drop facility that allows researchers to easily use the tool. KymoButler has been tested in many areas of biological research, from quantifying the movement of molecules in neurons to analysing the dynamics of the scaffolds that help cells keep their shape. This variety of applications showcases KymoButler’s versatility, and its potential applications. Jakobs et al. are further contributing to the field of machine learning in biology with ‘deepmirror.ai’, an online hub with the goal of accelerating the adoption of artificial intelligence in biology.
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Affiliation(s)
- Maximilian Ah Jakobs
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Andrea Dimitracopoulos
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Kristian Franze
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
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Kim SH, Vieira M, Kim HJ, Kesawat MS, Park HY. MS2 Labeling of Endogenous Beta-Actin mRNA Does Not Result in Stabilization of Degradation Intermediates. Mol Cells 2019; 42:356-362. [PMID: 30841028 PMCID: PMC6530646 DOI: 10.14348/molcells.2019.2398] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 01/17/2019] [Accepted: 02/05/2019] [Indexed: 11/27/2022] Open
Abstract
The binding of MS2 bacteriophage coat protein (MCP) to MS2 binding site (MBS) RNA stem-loop sequences has been widely used to label mRNA for live-cell imaging at single-molecule resolution. However, concerns have been raised recently from studies with budding yeast showing aberrant mRNA metabolism following the MS2-GFP labeling. To investigate the degradation pattern of MS2-GFP-labeled mRNA in mammalian cells and tissues, we used Northern blot analysis of β-actin mRNA extracted from the Actb-MBS knock-in and MBS×MCP hybrid mouse models. In the immortalized mouse embryonic cell lines and various organ tissues derived from the mouse models, we found no noticeable accumulation of decay products of β-actin mRNA compared with the wild-type mice. Our results suggest that accumulation of MBS RNA decay fragments does not always happen depending on the mRNA species and the model organisms used.
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Affiliation(s)
- Songhee H. Kim
- Department of Physics and Astronomy, Seoul National University, Seoul 08826,
Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826,
Korea
| | - Melissa Vieira
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826,
Korea
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826,
Korea
| | - Hye-Jin Kim
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826,
Korea
| | - Mahipal Singh Kesawat
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826,
Korea
| | - Hye Yoon Park
- Department of Physics and Astronomy, Seoul National University, Seoul 08826,
Korea
- Institute of Molecular Biology and Genetics, Seoul National University, Seoul 08826,
Korea
- Interdisciplinary Program in Neuroscience, Seoul National University, Seoul 08826,
Korea
- Institute of Applied Physics, Seoul National University, Seoul 08826,
Korea
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