Lateral Diffusion on Tubular Membranes: Quantification of Measurements Bias
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{"title"=>"Lateral diffusion on tubular membranes: Quantification of measurements bias", "type"=>"journal", "authors"=>[{"first_name"=>"Marianne", "last_name"=>"Renner", "scopus_author_id"=>"23005830500"}, {"first_name"=>"Yegor", "last_name"=>"Domanov", "scopus_author_id"=>"6602503690"}, {"first_name"=>"Fanny", "last_name"=>"Sandrin", "scopus_author_id"=>"53865500700"}, {"first_name"=>"Ignacio", "last_name"=>"Izeddin", "scopus_author_id"=>"56085249800"}, {"first_name"=>"Patricia", "last_name"=>"Bassereau", "scopus_author_id"=>"6602905895"}, {"first_name"=>"Antoine", "last_name"=>"Triller", "scopus_author_id"=>"56084366000"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-80053321248", "doi"=>"10.1371/journal.pone.0025731", "sgr"=>"80053321248", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pmid"=>"21980531", "issn"=>"19326203", "pui"=>"362661083"}, "id"=>"e6ea1fc1-34b0-3366-a03e-b1414816483f", "abstract"=>"Single Particle Tracking (SPT) is a powerful technique for the analysis of the lateral diffusion of the lipid and protein components of biological membranes. In neurons, SPT allows the study of the real-time dynamics of receptors for neurotransmitters that diffuse continuously in and out synapses. In the simplest case where the membrane is flat and is parallel to the focal plane of the microscope the analysis of diffusion from SPT data is relatively straightforward. However, in most biological samples the membranes are curved, which complicates analysis and may lead to erroneous conclusions as for the mode of lateral diffusion. Here we considered the case of lateral diffusion in tubular membranes, such as axons, dendrites or the neck of dendritic spines. Monte Carlo simulations allowed us to evaluate the error in diffusion coefficient (D) calculation if the curvature is not taken into account. The underestimation is determined by the diameter of the tubular surface, the frequency of image acquisition and the degree of mobility itself. We found that projected trajectories give estimates that are 25 to 50% lower than the real D in case of 2D-SPT over the tubular surface. The use of 3D-SPT improved the measurements if the frequency of image acquisition was fast enough in relation to the mobility of the molecules and the diameter of the tube. Nevertheless, the calculation of D from the components of displacements in the axis of the tubular structure gave accurate estimate of D, free of geometrical artefacts. We show the application of this approach to analyze the diffusion of a lipid on model tubular membranes and of a membrane-bound GFP on neurites from cultured rat hippocampal neurons.", "link"=>"http://www.mendeley.com/research/lateral-diffusion-tubular-membranes-quantification-measurements-bias", "reader_count"=>65, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>16, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>16, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>3, "Student > Master"=>7, "Other"=>2, "Student > Bachelor"=>2, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>8, "Agricultural and Biological Sciences"=>28, "Medicine and Dentistry"=>2, "Neuroscience"=>8, "Physics and Astronomy"=>13, "Chemistry"=>3, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>8}, "Chemistry"=>{"Chemistry"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>13}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Austria"=>1, "United States"=>3, "United Kingdom"=>2, "France"=>2, "Germany"=>1, "India"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/730640"], "description"=>"<p><i>A</i>) The model system where a thin tubular tether is pulled by an optically trapped, streptavidin-coated bead (<i>right</i>) from a giant unilamellar vesicle (GUV) tagged with biotinylated lipids. Changing the aspiration pressure in the micropipette (<i>left</i>) holding the GUV allows us to vary and control the diameter of the tether. Scale bar: 5 µm. <i>B</i>) An example of connected trajectory obtained on a membrane tube. Several tens of trajectories were constructed on every tube for the MSD analysis. One pixel is 160 nm. <i>C</i>) An example of the QD positions extracted from a series of 1000 images (before trajectory reconnection). The direction of the tube is determined by a linear fit of all detected positions (<i>red dashed line</i>). For the analysis of the transverse distribution of the QD positions, the coordinate plane is further rotated (<i>not shown</i>) to align the X axis with the direction of the tube. <i>D</i>) Mathematically predicted profile for the projection of points uniformly distributed over the surface of a tube on a plane (<i>green solid line</i>, cross-section is shown, Eq. 1). A Gaussian profile with the same area is shown for comparison (<i>orange dashed line</i>, Eq. 2) as well as the result of convolution (<i>blue dotted line</i>) of the predicted profile with a Gaussian according to Eq. 3. <i>E</i>) An example of the transverse distribution of the QD positions extracted from the series of images. The distribution is fit by the convolution (<i>violet line</i>) of the tube projection profile and a Gaussian (see panel <i>D</i>) to extract the tube diameter and the smearing parameter (see text). <i>F</i>) Comparison of the tube diameters obtained from the analysis of transverse distribution of the QD positions and the diameters calculated based on the membrane tension and the pulling force (mean ± SD).</p>", "links"=>[], "tags"=>["allows", "simultaneous", "diffusion"], "article_id"=>400992, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g003", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SPT_allows_simultaneous_measurement_of_diffusion_and_tube_diameters_/400992", "title"=>"SPT allows simultaneous measurement of diffusion and tube diameters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:16:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/369365", "https://ndownloader.figshare.com/files/369405", "https://ndownloader.figshare.com/files/369434", "https://ndownloader.figshare.com/files/369468", "https://ndownloader.figshare.com/files/369501", "https://ndownloader.figshare.com/files/369552"], "description"=>"<div><p>Single Particle Tracking (SPT) is a powerful technique for the analysis of the lateral diffusion of the lipid and protein components of biological membranes. In neurons, SPT allows the study of the real-time dynamics of receptors for neurotransmitters that diffuse continuously in and out synapses. In the simplest case where the membrane is flat and is parallel to the focal plane of the microscope the analysis of diffusion from SPT data is relatively straightforward. However, in most biological samples the membranes are curved, which complicates analysis and may lead to erroneous conclusions as for the mode of lateral diffusion. Here we considered the case of lateral diffusion in tubular membranes, such as axons, dendrites or the neck of dendritic spines. Monte Carlo simulations allowed us to evaluate the error in diffusion coefficient (<em>D</em>) calculation if the curvature is not taken into account. The underestimation is determined by the diameter of the tubular surface, the frequency of image acquisition and the degree of mobility itself. We found that projected trajectories give estimates that are 25 to 50% lower than the real <em>D</em> in case of 2D-SPT over the tubular surface. The use of 3D-SPT improved the measurements if the frequency of image acquisition was fast enough in relation to the mobility of the molecules and the diameter of the tube. Nevertheless, the calculation of <em>D</em> from the components of displacements in the axis of the tubular structure gave accurate estimate of <em>D</em>, free of geometrical artefacts. We show the application of this approach to analyze the diffusion of a lipid on model tubular membranes and of a membrane-bound GFP on neurites from cultured rat hippocampal neurons.</p> </div>", "links"=>[], "tags"=>["lateral", "diffusion", "tubular", "quantification", "measurements"], "article_id"=>132987, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0025731.s001", "https://dx.doi.org/10.1371/journal.pone.0025731.s002", "https://dx.doi.org/10.1371/journal.pone.0025731.s003", "https://dx.doi.org/10.1371/journal.pone.0025731.s004", "https://dx.doi.org/10.1371/journal.pone.0025731.s005", "https://dx.doi.org/10.1371/journal.pone.0025731.s006"], "stats"=>{"downloads"=>9, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Lateral_Diffusion_on_Tubular_Membranes_Quantification_of_Measurements_Bias/132987", "title"=>"Lateral Diffusion on Tubular Membranes: Quantification of Measurements Bias", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-09-29 00:49:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/730244"], "description"=>"<p><i>A</i>) Example of a random trajectory simulated on a plane and the derived cylindrical and projected trajectories. <i>B</i>) Examples of MSD plots of the original trajectories (<i>MSD<sub>actual</sub>, left</i>), the trajectories on cylindrical surfaces (<i>MSD<sub>cyl</sub>, centre</i>) or projected (<i>MSD<sub>proj</sub>, right</i>) ones, for cylinders of the indicated diameters. Trajectories were simulated with a diffusivity of 1 µm<sup>2</sup>/s. <i>C</i>–<i>D</i>) Ratios of <i>D</i> calculated on trajectories on cylindrical surfaces (<i>C</i>, <i>D<sub>cyl</sub></i>) or projected (<i>D</i>, <i>D<sub>proj</sub></i>) trajectories to the real diffusion constant of the original trajectory in the plane (<i>D<sub>actual</sub></i>), as a function of the diameter of the cylinder. Each curve represents the mean ± SD values for 50 trajectories simulated to have the indicated diffusivities (0.001 to 1 µm<sup>2</sup>/s). <i>E</i>) The mean ratio <i>D<sub>proj</sub> / D<sub>actual</sub></i> as a function of the dimensionless parameter () incorporating the diffusion coefficient (<i>D<sub>actual</sub></i>), the image acquisition interval (<i>dt</i>) and the cylinder diameter (Ø).</p>", "links"=>[], "tags"=>["geometry", "diffusion", "cylindrical"], "article_id"=>400596, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_of_geometry_on_diffusion_measurements_on_cylindrical_structures_/400596", "title"=>"of geometry on diffusion measurements on cylindrical structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:09:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/730469"], "description"=>"<p><i>A</i>–<i>C</i>) Examples of MSD plots of projected trajectories (<i>A</i>) and their corresponding <i>MSD<sub>1Dtransv</sub></i> (<i>B</i>) and <i>MSD<sub>1Dlong</sub></i> (<i>C</i>) for the indicated cylinder diameters. Trajectories were simulated with a diffusivity of 1 µm<sup>2</sup>/s. Inset in A: the projection of each simulated trajectory was decomposed into two components: parallel and perpendicular to the cylinder axis. These components were used to calculate the longitudinal (<i>MSD<sub>1Dlong</sub></i>) and transversal (<i>MSD<sub>1Dtransv</sub></i>) MSD. <i>D</i>) Effect of cylinder diameter on the values of <i>D<sub>actual</sub></i>, <i>D<sub>proj</sub></i> or <i>D</i> calculated on <i>MSD<sub>1Dtransv</sub></i> (<i>D<sub>1Dlong</sub></i>), for trajectories simulated with a diffusivity of 1 µm<sup>2</sup>/s (mean ± SD, n = 50).</p>", "links"=>[], "tags"=>["displacements", "cylinder"], "article_id"=>400820, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g002", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unbiased_D_calculation_using_the_displacements_in_the_direction_of_the_cylinder_axis_/400820", "title"=>"Unbiased D calculation using the displacements in the direction of the cylinder axis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:13:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/730719"], "description"=>"<p><i>A,C)</i> Averaged MSD plots of projected 2D-trajectories (<i>A</i>) and of the longitudinal components (<i>C</i>) of the displacements. The diameter of the tubes varied between the indicated values. <i>B,D)</i> D calculated from the corresponding MSD plots on the left. The reference value of the diffusion coefficient measured on the GUV surface (quasi-planar membrane) is shown with dashed horizontal lines and open circles (mean ± SD).</p>", "links"=>[], "tags"=>["diffusion"], "article_id"=>401076, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analysis_of_diffusion_on_artificial_tubes_/401076", "title"=>"Analysis of diffusion on artificial tubes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:17:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/730815"], "description"=>"<p><i>A</i>) Neurites (axons) of hippocampal neurons transfected with GFP-GPI. Bar: 1 µm. <i>B</i>) A representative trajectory of GFP-GPI labeled with a QD on a neurite (white line). Epifluorescence image of GFP is shown in the background. Bar: 200 nm. <i>C,E,)</i> Examples of MSD of projected trajectories (<i>C</i>) and of longitudinal components (<i>E</i>) of the displacements for trajectories on neurites of the indicated diameters (mean ± errors calculated as in ref. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025731#pone.0025731-Goulian1\" target=\"_blank\">[18]</a>). <i>D,F)</i> The diffusion coefficients <i>D<sub>proj</sub> (D)</i> and <i>D<sub>1Dlong</sub> (F)</i> of GFP-GPI trajectories on neurites of different diameters. Pearson's correlation coefficients between the diffusion coefficients and diameter were 0.32667 (<i>D<sub>proj</sub></i>), and 0.13832 (<i>D<sub>1Dlong</sub></i>) (n = 49 trajectories on different neurites).</p>", "links"=>[], "tags"=>["diffusion"], "article_id"=>401175, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GFP_GPI_diffusion_on_neurites_/401175", "title"=>"GFP-GPI diffusion on neurites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:19:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/730894"], "description"=>"<p><i>A</i>) Fluorescence image of a portion of neurite of a GFP-GPI transfected neuron (<i>green</i>) overlaid with two GFP-bound quantum dots (GFPGPI-QD, <i>orange</i>). The shape of the QD depends on its position in the Z axis. <i>B</i>) Example of a 3D GFPGPI-QD trajectory. Bar: 200 nm. <i>C</i>) averaged MSD plot for trajectories obtained by 3D SPT (3D, <i>red</i>) and their projections in the plane (2D, <i>black</i>) (mean ± sem) (n = 26 trajectories on different axons) <i>D</i>) Ratio of <i>D</i> on neurites (black squares) calculated without (<i>D<sub>proj</sub></i>) or with (<i>D<sub>cyl</sub></i>) the displacements in Z (n = 26 trajectories on different axons), and the equivalent ratio of <i>D</i> of simulated trajectories with diffusivities between 0.001 and 1 µm<sup>2</sup>/s (<i>circles</i>, mean ± s.e.m., n = 200) versus the diameter of the neurites or cylinders. <i>E</i>) Ratio <i>D<sub>cyl</sub>/D<sub>proj</sub></i> versus the diameter calculated on the same neurites as D.</p>", "links"=>[], "tags"=>["tracking"], "article_id"=>401249, "categories"=>["Physiology", "Biochemistry", "Neuroscience", "Physics", "Biophysics"], "users"=>["Marianne Renner", "Yegor Domanov", "Fanny Sandrin", "Ignacio Izeddin", "Patricia Bassereau", "Antoine Triller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025731.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_3D_single_particle_tracking_of_GFP_GPI_/401249", "title"=>"3D single particle tracking of GFP-GPI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-29 00:20:49"}

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Relative Metric

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