Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks
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{"title"=>"Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks", "type"=>"journal", "authors"=>[{"first_name"=>"Niclas", "last_name"=>"Thomas", "scopus_author_id"=>"57190856911"}, {"first_name"=>"Lenka", "last_name"=>"Matejovicova", "scopus_author_id"=>"55366913200"}, {"first_name"=>"Wichat", "last_name"=>"Srikusalanukul", "scopus_author_id"=>"9246684000"}, {"first_name"=>"John", "last_name"=>"Shawe-Taylor", "scopus_author_id"=>"7003290763"}, {"first_name"=>"Benny", "last_name"=>"Chain", "scopus_author_id"=>"35509213600"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84866702627", "sgr"=>"84866702627", "pui"=>"365718469", "isbn"=>"19326203", "pmid"=>"23028891", "doi"=>"10.1371/journal.pone.0045262"}, "id"=>"3fba9527-7f1e-37ee-abb7-da25620d883b", "abstract"=>"Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. We examine how long different T cells may spend in an individual lymph node by examining data from long term cannulation of blood and efferent lymphatics of a single lymph node in the sheep. We determine empirically the distribution of transit times of migrating T cells by applying the Least Absolute Shrinkage & Selection Operator (LASSO) or regularised S-LASSO to fit experimental data describing the proportion of labelled infused cells in blood and efferent lymphatics over time. The optimal inferred solution reveals a distribution with high variance and strong skew. The mode transit time is typically between 10 and 20 hours, but a significant number of cells spend more than 70 hours before exiting. We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node insilico. On the basis of previous two photon analysis of lymphocyte movement, we optimised distributions which describe the transit times (first passage times) of discrete one dimensional and continuous (Brownian) three dimensional random walks with drift. The optimal fit is obtained when drift is small, i.e. the ratio of probabilities of migrating forward and backward within the node is close to one. These distributions are qualitatively similar to the inferred empirical distribution, with high variance and strong skew. In contrast, an optimised normal distribution of transit times (symmetrical around mean) fitted the data poorly. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes, and significant probabilities of long transit times. We discuss how this pattern of migration may contribute to facilitating interactions between low frequency T cells and antigen presenting cells carrying cognate antigen.", "link"=>"http://www.mendeley.com/research/directional-migration-recirculating-lymphocytes-through-lymph-nodes-via-random-walks", "reader_count"=>22, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Researcher"=>9, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>2, "Student > Master"=>2, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Mathematics"=>1, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>5, "Physics and Astronomy"=>1, "Psychology"=>1, "Computer Science"=>2, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>2}, "Mathematics"=>{"Mathematics"=>1}}, "reader_count_by_country"=>{"Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/574089"], "description"=>"<p>SSE on a test set of eight sheep from three different distributions.</p>", "links"=>[], "tags"=>["ovine", "ln", "times", "goodness-of-fit", "distributions"], "article_id"=>244584, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.t003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_ovine_LN_migration_times_and_goodness_of_fit_of_individual_distributions_to_data_/244584", "title"=>"Average ovine LN migration times and goodness-of-fit of individual distributions to data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-20 01:16:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/573463"], "description"=>"<p>a) Flow cytometry histogram showing CFSE positive and negative cells in lymphatic fluid. % CFSE in blood (b) and lymphatic fluid (c) at different times post infusion. (d and e) Actual and interpolated % CFSE values in blood (d) and lymphatic fluid (e) over the first 100 hours, using either linear or cubic spline interpolation to infer missing time points.</p>", "links"=>[], "tags"=>["cannulation"], "article_id"=>243959, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g001", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Results_from_a_representative_cannulation_experiment_R705_/243959", "title"=>"Results from a representative cannulation experiment (R705).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:05:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/574163"], "description"=>"<p>The mean lifetime of labeled T cells in blood before trans-endothelial migration to LN, as predicted by fitting the equation .</p>", "links"=>[], "tags"=>["labeled", "cells"], "article_id"=>244657, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mean_lifetime_of_labeled_T_cells_in_blood_/244657", "title"=>"Mean lifetime of labeled T cells in blood.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-20 01:17:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/574129"], "description"=>"<p>Median and mean migration times in seventeen individual prescapular and popliteal ovine lymph nodes, as calculated by .</p>", "links"=>[], "tags"=>["ovine", "ln", "times", "goodness-of-fit", "distributions"], "article_id"=>244623, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.t002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_ovine_LN_migration_times_and_goodness_of_fit_of_individual_distributions_to_data_/244623", "title"=>"Average ovine LN migration times and goodness-of-fit of individual distributions to data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-20 01:17:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/573918"], "description"=>"<p>The parameters , and for a discrete random walk model (fig. 4a), the two parameters describing the inverse Gaussian distribution or the normal distributions were optimised as described in the text. The optimum probability distributions were then used to predict efflux. The results for a representative experiment (R797) are shown.</p>", "links"=>[], "tags"=>["predictive", "probability"], "article_id"=>244413, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g006", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_comparison_of_the_predictive_accuracy_of_different_transit_time_probability_distributions_/244413", "title"=>"A comparison of the predictive accuracy of different transit time probability distributions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:13:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/574015"], "description"=>"<p>The solid blue line represents the optimal combination of parameter values, whose distribution of migration times best fits the LN efflux data. Parameter combinations which poorly predict LN efflux (dashed line with crosses and dashed line with squares) predict unrealsitic accumulation near HEVs.</p>", "links"=>[], "tags"=>["lymphocytes", "ln", "steady-state", "parameter", "combinations"], "article_id"=>244505, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g007", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_distribution_of_T_lymphocytes_across_the_LN_under_steady_state_conditions_as_predicted_by_various_parameter_combinations_in_the_random_walk_model_/244505", "title"=>"The distribution of T lymphocytes across the LN under steady-state conditions, as predicted by various parameter combinations in the random walk model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:15:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/573834"], "description"=>"<p>The heat map shows the goodness-of-fit as (the probability of remaining at a node) (y axis) and (a measure of directional movement)(x axis) are varied. Red regions represent a high SSE and blue regions a low SSE at each point in the parameter space. Best fits clearly occur when , (i.e. ), indicating quasi-undirected T cell migration within the LN.</p>", "links"=>[], "tags"=>["directional", "parameters", "shown"], "article_id"=>244330, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g005", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_influence_of_directional_parameters_drift_on_the_the_model_shown_in_fig_4a_/244330", "title"=>"The influence of directional parameters (drift) on the the model shown in fig. 4a.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:12:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/573754"], "description"=>"<p>a) Schematic of Markov chain model. In each time step, a cell may move to the next vertex with probability , the previous vertex with probability or remain at the same vertex with probability . b) The optimal probability distribution of migration times as found by the model shown in a). Parameter values are . E(t)  = 28.0 hrs.</p>", "links"=>[], "tags"=>["markov", "lymphocyte"], "article_id"=>244247, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Discrete_Markov_chain_random_walk_model_of_lymphocyte_migration_/244247", "title"=>"Discrete Markov chain random walk model of lymphocyte migration.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:10:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/573674"], "description"=>"<p>Each sampled training set is optimised over to determine the necessary extent of smoothing, and this sampling is repeated fifty times. The optimal distribution in each case is that which gives the lowest SSE on the test set out of all fifty samples. The optimal distributions shown here are based on sampling (a) three and (b) nine data sets to form the training set. (c and d) Piecewise 95% confidence intervals for optimal distributions of migration times. Each bar gives a 95% confidence interval for the mean probability of migration occurring within each two-hour interval based on fifty random samples of (c) three and (d) nine concatenated data sets to form the training set. (e) Similar analysis showing inferred smoothed distribution using CD4+ cells only (four samples).</p>", "links"=>[], "tags"=>["smoothed", "distributions", "times"], "article_id"=>244165, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inferred_smoothed_distributions_of_migration_times_obtained_by_on_combined_data_sets_/244165", "title"=>"Inferred smoothed distributions of migration times obtained by on combined data sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:09:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/304599"], "description"=>"<div><p>Naive T lymphocytes exhibit extensive antigen-independent recirculation between blood and lymph nodes, where they may encounter dendritic cells carrying cognate antigen. We examine how long different T cells may spend in an individual lymph node by examining data from long term cannulation of blood and efferent lymphatics of a single lymph node in the sheep. We determine empirically the distribution of transit times of migrating T cells by applying the Least Absolute Shrinkage & Selection Operator () or regularised to fit experimental data describing the proportion of labelled infused cells in blood and efferent lymphatics over time. The optimal inferred solution reveals a distribution with high variance and strong skew. The mode transit time is typically between 10 and 20 hours, but a significant number of cells spend more than 70 hours before exiting. We complement the empirical machine learning based approach by modelling lymphocyte passage through the lymph node . On the basis of previous two photon analysis of lymphocyte movement, we optimised distributions which describe the transit times (first passage times) of discrete one dimensional and continuous (Brownian) three dimensional random walks with drift. The optimal fit is obtained when drift is small, i.e. the ratio of probabilities of migrating forward and backward within the node is close to one. These distributions are qualitatively similar to the inferred empirical distribution, with high variance and strong skew. In contrast, an optimised normal distribution of transit times (symmetrical around mean) fitted the data poorly. The results demonstrate that the rapid recirculation of lymphocytes observed at a macro level is compatible with predominantly randomised movement within lymph nodes, and significant probabilities of long transit times. We discuss how this pattern of migration may contribute to facilitating interactions between low frequency T cells and antigen presenting cells carrying cognate antigen.</p> </div>", "links"=>[], "tags"=>["directional", "recirculating", "lymphocytes", "lymph", "nodes", "walks"], "article_id"=>120079, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Directional_Migration_of_Recirculating_Lymphocytes_through_Lymph_Nodes_via_Random_Walks/120079", "title"=>"Directional Migration of Recirculating Lymphocytes through Lymph Nodes via Random Walks", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 00:01:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/573544"], "description"=>"<p>The distribution of migration times calculated using (a) and predicted and actual LN efflux based on this distribution (b) in a representative sheep (R797). Arrows indicate the possible existence of multiple peaks in the distribution of migration times. (c) Piecewise confidence intervals for individual distributions of migration times. Each bar gives a 95% confidence interval for the mean probability of migration occurring within each two-hour interval in seventeen individuals.</p>", "links"=>[], "tags"=>["probability", "times", "corresponding", "efflux"], "article_id"=>244036, "categories"=>["Physiology", "Biological Sciences", "Immunology"], "users"=>["Niclas Thomas", "Lenka Matejovicova", "Wichat Srikusalanukul", "John Shawe-Taylor", "Benny Chain"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045262.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Inferred_probability_distribution_of_migration_times_and_corresponding_predicted_efflux_profiles_/244036", "title"=>"Inferred probability distribution of migration times and corresponding predicted efflux profiles.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-20 01:07:16"}

PMC Usage Stats | Further Information

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Relative Metric

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