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Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway.

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  1. [1] § Methods › STORM Experiments ↔ ColocalizationTubulins.m, lines 142–279 · score 0.68 · alphaShape, colocalization cluster, reference cluster, overlap, channel, threshold
  2. [2] § Methods › STORM Experiments ↔ Santiago-Ruiz_et_al_Coloc_Pipeline/FindBorders.m, the whole file · a weak match · score 0.62 · border point, alphaShape, written, MATLAB, threshold, cells

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 419 lines · 20 KB · GPL-3.0 · 1 match

  1. %% Data Construction
  2. % Load the data
  3. [file,path] = uigetfile('*.mat','Select the file you want to load'); % Open a dialog so you can specify which file you want to load
  4. selectedfile = fullfile(path,file); % Create the name for the file to load
  5. load(selectedfile); % Load the file
  6. % Clear up the work space
  7. clear file path
  9. % Select the reference and the co-localization channels
  10. List = cellfun(@(x) x.name,data,'UniformOutput',false);
  11. RefIndex = [];
  12. ColocIndex = [];
  13. while isempty(RefIndex) || ~(numel(RefIndex) == numel(ColocIndex))
  14. % Reference channel selection
  15. [RefIndex,tf] = listdlg('Name','Select reference channel(s)','PromptString','Select the channel or channels that are refering to the reference data.','ListString',List,'ListSize',[500 300]);
  16. % Stop the loop if ESC/cancel/X is pressed
  17. if tf == 0
  18. break
  19. end
  21. % Reference channel selection
  22. [ColocIndex,tf] = listdlg('Name','Select co-localization channel(s)','PromptString',{'Select the channel or channels that are refering to the co-localization data.',['Please select ' num2str(numel(RefIndex)) ' values.']},'ListString',List,'ListSize',[500 300]);
  23. % Stop the loop if ESC/cancel/X is pressed
  24. if tf == 0
  25. break
  26. end
  28. % Check if the amount of reference and co-localization channels
  29. % selected is the same
  30. if ~(numel(RefIndex) == numel(ColocIndex))
  31. uiwait(msgbox({'The number of reference channels selected is not equal to the number of co-localization channels selected. Please redo the selection.','','Press OK to continue.'}, 'Error','error'));
  32. end
  33. end
  34. % Stop the program if ESC/cancel/X was pressed
  35. if tf == 0
  36. clear;clc
  37. error('The program was cancelled prematurely.')
  38. end
  40. % Clear up the work space
  41. clear List tf
  43. % Show an input dialog for the parameters that can change
  44. input_values = inputdlg('Overlap Percentage Threshold:','',1,{'75'}); % Show up an input dialog
  45. % Extract the input values from the dialog window
  46. ThresholdOverlap = str2double(input_values{1})/100; % The minimum overlap (in %) for it to be co-localized
  47. % Clear up the work space
  48. clear input_values
  50. % Show a wait bar, we start filtering/selection/calculations here
  51. f = waitbar(0,'Step 1 of 3: Extracting reference & co-localization clusters...','Name','Progress of the co-localization process');
  52. set(findall(f),'Units','normalized');
  53. set(f,'Position',[0.3 0.45 0.35 0.075])
  54. pause(0.1)
  56. % Extract the data for the reference channel and co-localziation channel
  57. % Pre-allocate for speed reasons
  58. ClustersRef = cell(numel(RefIndex),1);
  59. % Reference data
  60. for i = 1:numel(RefIndex)
  61. % Update the wait bar
  62. waitbar(i/(numel(RefIndex)*2),f, 'Step 1 of 3: Extracting reference & co-localization clusters...');
  64. DataRef = horzcat(data{1,RefIndex(i)}.x_data,data{1,RefIndex(i)}.y_data, data{1,RefIndex(i)}.area); % Set up the reference data
  65. Groups = findgroups(DataRef(:,3)); % Find unique groups and their number
  66. ClustersRef{i,:} = splitapply(@(x){(x)},DataRef(:,1:3),Groups);
  67. end
  68. ClustersRefComplete = ClustersRef;
  69. % Clear up the work space
  70. clear i DataRef Groups Ref
  72. % Pre-allocate for speed reasons
  73. ClustersColoc = cell(numel(ColocIndex),1);
  74. % Co-localization data
  75. for i = 1:numel(ColocIndex)
  76. % Update the wait bar
  77. waitbar(0.5+i/(numel(ColocIndex)*2),f, 'Step 1 of 3: Extracting reference & co-localization clusters...');
  79. DataColoc = horzcat(data{1,ColocIndex(i)}.x_data,data{1,ColocIndex(i)}.y_data, data{1,ColocIndex(i)}.area); % Extract the data
  80. Groups = findgroups(DataColoc(:,3)); % Find unique groups and their number
  81. ClustersColoc{i,:} = splitapply(@(x){(x)},DataColoc(:,1:3),Groups); % Split the clusters into the corresponding groups
  82. end
  83. % Clear up the work space
  84. clear i DataColoc Groups
  86. % Update the wait bar
  87. waitbar(1,f, 'Step 1 of 3: Extracting reference & co-localization clusters... Complete');
  89. %% Filtering step (remove all clusters outside the reference cluster boundaries)
  90. % Update the wait bar
  91. pause(0.2)
  92. waitbar(0,f,'Step 2 of 3: Filtering co-localization clusters...');
  94. % Pre-allocate for speed reasons
  95. PolyLowResRefExpanded = cell(numel(RefIndex),1);
  96. FilteredColoc = cell(numel(RefIndex),1);
  97. SelectedColoc = cell(numel(RefIndex),1);
  98. % Loop over all the reference/co-localization pairs and filter the
  99. % co-localization clusters
  100. for i = 1:numel(RefIndex)
  101. % Extract the colocalization channel cluster centers
  102. ColocCenter = cell2mat(cellfun(@(x) mean(x(:,1:2)), ClustersColoc{i},'UniformOutput',false));
  104. % Pre-allocate and initialize for speed and convenience reasons
  105. PolyLowResRefExpanded{i} = cell(size(ClustersRef{i},1),1);
  106. PolygonRef = polyshape();
  107. % Create a set of low-resolution coordinates of all the reference cluster
  108. % coordinates
  109. warning('off','all') % Turn off warnings for the creation of the polygons
  110. for j = 1:size(ClustersRef{i},1)
  111. % Update the wait bar
  112. waitbar((i-1)/numel(RefIndex)+(j/size(ClustersRef{i},1))/numel(RefIndex),f,'Step 2 of 3: Filtering co-localization clusters...');
  114. LowResCoords = unique(round(ClustersRef{i}{j}(:,1:2)),'rows'); % Create a low-res version of the reference coordinates
  115. LowResBoundary = boundary(LowResCoords,1); % Calculate the boundary
  116. LowResBoundaryCoords = LowResCoords(LowResBoundary,:); % Extract the coordinates from the boundary
  117. PolyLowResRef = polyshape(LowResBoundaryCoords); % Create a polygon from these coordinates
  118. PolyLowResRefExpanded{i}{j} = polybuffer(PolyLowResRef,5); % Expand the polygon with 5 pixels
  119. PolygonRef = union(PolygonRef,PolyLowResRefExpanded{i}{j}); % Create one polygon only
  120. end
  121. warning('on','all') % Turn the warnings back on
  122. % Clear up the work space
  123. clear j LowResCoords LowResBoundary LowResBoundaryCoords PolyLowResRef
  125. % Update the wait bar
  126. waitbar(i/numel(RefIndex),f,'Step 2 of 3: Filtering co-localization clusters... This might take a minute...');
  128. % Check if the coordinates of the colocalization channel are inside this
  129. % polygon
  130. IsInside = inpolygon(ColocCenter(:,1),ColocCenter(:,2),PolygonRef.Vertices(:,1),PolygonRef.Vertices(:,2));
  131. FilteredColoc{i} = {ClustersColoc{i}{~IsInside}}';
  132. SelectedColoc{i} = {ClustersColoc{i}{IsInside}}';
  133. % Clear up the work space
  134. clear IsInside PolygonRef ColocCenter
  135. end
  136. % Clear up the work space
  137. clear i ClustersColoc
  139. % Update the wait bar
  140. waitbar(1,f,'Step 2 of 3: Filtering co-localization clusters... Complete');
  142. %% Calculate the overlap between the reference clusters and the co-localization clusters
  143. % Update the wait bar
  144. pause(0.2)
  145. waitbar(0,f,'Step 3 of 3: Calculating overlap...');
  147. % Pre-allocate for speed reasons
  148. PercentageInside = cell(numel(RefIndex),1);
  149. ColocalizedClusters = cell(numel(RefIndex),1);
  150. NotColocalizedClusters = cell(numel(RefIndex),1);
  151. IdxClusters = cell(numel(RefIndex),1);
  152. % Loop over all the reference/co-localization pairs to find all the
  153. % co-localization clusters that potentially overlap with the reference
  154. % clusters and calculate the actual overlap percentage
  155. for i = 1:numel(RefIndex)
  156. % Determine the possible reference clusters related to the co-localization
  157. % clusters
  158. ColocCenter = cell2mat(cellfun(@(x) mean(x(:,1:2)),SelectedColoc{i},'UniformOutput',false));
  159. IsInside = cellfun(@(x) inpolygon(ColocCenter(:,1),ColocCenter(:,2),x.Vertices(:,1),x.Vertices(:,2)),PolyLowResRefExpanded{i},'UniformOutput',false);
  160. Idx = cellfun(@(x) find(x), IsInside,'UniformOutput',false);
  161. % Clear up the work space
  162. clear ColocCenter IsInside
  164. % Clean up the reference clusters that do not have any potential
  165. % co-localization clusters associated to them
  166. SelectRefClusters = cell2mat(cellfun(@(x) ~isempty(x),Idx,'UniformOutput',false));
  167. ClustersRef{i} = {ClustersRef{i}{SelectRefClusters}}';
  168. IdxClusters{i} = {Idx{SelectRefClusters}}';
  169. % Clear up the work space
  170. clear SelectRefClusters Idx
  172. % Pre-allocate for speed reasons
  173. PercentageInside{i} = cell(size(ClustersRef{i},1),1);
  174. % Create alpha shapes of the reference clusters and then check if the
  175. % coordinates of the selected clusters are inside these.
  176. for j = 1:size(ClustersRef{i},1)
  177. % Extract the coordinates of the reference cluster and make an
  178. % 'alphashape' of it. This alphashape will keep into account the
  179. % holes inside the reference clusters.
  180. alpha = 0.8; % This was set with Qing that looked good for her tubulins data
  181. xRef = ClustersRef{i}{j}(:,1);
  182. yRef = ClustersRef{i}{j}(:,2);
  183. shp = alphaShape(xRef,yRef,alpha);
  184. % Clear up the work space
  185. clear alpha xRef yRef
  187. % Pre-allocate for speed reasons
  188. PercentageInside{i}{j} = zeros(size(IdxClusters{i}{j},1),1);
  189. % Loop over the different potential overlapping clusters and
  190. % calculate their overlap with the reference cluster
  191. for k = 1:size(IdxClusters{i}{j},1)
  192. % Update the wait bar
  193. waitbar((i-1)/numel(RefIndex)+(j/size(ClustersRef{i},1))/numel(RefIndex),f,['Step 3 of 3: Calculating overlap... - Data set ' num2str(i) ' of ' num2str(numel(RefIndex)) ', Reference cluster ' num2str(j) ' of ' num2str(size(ClustersRef{i},1))]);
  195. % Calculate the actual overlap
  196. ColocCoords = SelectedColoc{i}{IdxClusters{i}{j}(k)}(:,1:2); % Extract the coordinates for the potentially interesting cluster
  197. tf = inShape(shp,ColocCoords(:,1),ColocCoords(:,2)); % Calculate the overlap between reference cluster and co-localization channel cluster
  198. total = numel(tf); % Determine the total number of coordinates
  199. PercentageInside{i}{j}(k) = sum(tf) / total; % The percentage of overlap is calculated as the number of coordinate pairs that overlap with the reference cluster divided by the total number of coordinate pairs
  200. % Clear up the work space
  201. clear ColocCoords tf total
  202. end
  203. % Clear up the work space
  204. clear k shp
  205. end
  206. % Clear up the work space
  207. clear j
  209. % Check if any co-localization cluster got assigned to multiple
  210. % reference clusters. This is possible due to the expanded boundary
  211. % region of the reference clusters
  212. [C,~,IC] = unique(vertcat(IdxClusters{i}{:})); % Find the unique values
  213. IC = accumarray(IC,1); % Count how many times each unique value occurs
  214. C(IC==1,:) = []; % Remove the cluster Ids that only occur once
  215. % Clear up the work space
  216. clear IC
  217. % Only do this if there are duplicate assignments
  218. if ~isempty(C)
  219. % Find out where the duplicates are, to what reference cluster they
  220. % are associated and then remove the unimportant contributions
  221. for j = 1:numel(C)
  222. % For each duplicate assignment, find out to which reference
  223. % cluster is was associated
  224. ClustersWithDuplicates = cellfun(@(x) find(x==C(j)),IdxClusters{i},'UniformOutput',false);
  225. DuplicateIdx = find(~cellfun(@(x) isempty(x), ClustersWithDuplicates));
  226. % Extract the percentages of overlap so that only the maximum
  227. % one can be kept
  228. Percentages = [];
  229. for k = 1:numel(DuplicateIdx)
  230. Percentages(k,:) = [PercentageInside{i}{DuplicateIdx(k)}(IdxClusters{i}{DuplicateIdx(k)}==C(j))];
  231. end
  232. [~,Idx] = max(Percentages);
  233. DuplicateIdx(Idx) = [];
  234. % Remove the co-localization cluster id from the calculations.
  235. % Only 1 assignment per co-localization cluster
  236. for k = 1:numel(DuplicateIdx)
  237. PercentageInside{i}{DuplicateIdx(k)}(IdxClusters{i}{DuplicateIdx(k)}==C(j)) = [];
  238. IdxClusters{i}{DuplicateIdx(k)}(IdxClusters{i}{DuplicateIdx(k)}==C(j)) = [];
  239. end
  240. % Clear up the work space
  241. clear k ClustersWithDuplicates DuplicateIdx Idx Percentages
  242. end
  243. % Clear up the work space
  244. clear j C
  245. % Clean up the reference clusters that do not have any
  246. % co-localization clusters associated to them anymore
  247. SelectRefClusters = cell2mat(cellfun(@(x) ~isempty(x),IdxClusters{i},'UniformOutput',false));
  248. ClustersRef{i} = {ClustersRef{i}{SelectRefClusters}}';
  249. PercentageInside{i} = {PercentageInside{i}{SelectRefClusters}}';
  250. IdxClusters{i} = {IdxClusters{i}{SelectRefClusters}}';
  251. % Clear up the work space
  252. clear SelectRefClusters
  253. end
  255. % Determine whether or not the co-localization clusters pass the
  256. % threshold to be considered co-localized and then filter the ones that
  257. % were from the ones that were not.
  258. ColocalizedOrNot = cellfun(@(x) x>=ThresholdOverlap, PercentageInside{i}, 'UniformOutput',false); % Perform the thresholding to select the ones that were.
  260. % Pre-allocate for speed reasons
  261. ColocClusterIds = cell(size(IdxClusters{i},1),1);
  262. % Loop over the different reference clusters to extract the ones that
  263. % contain co-localized clusters
  264. for j = 1:size(IdxClusters{i},1)
  265. ColocClusterIds{j} = IdxClusters{i}{j}(ColocalizedOrNot{j});
  266. end
  267. ColocClusterIds = vertcat(ColocClusterIds{:});
  268. ColocalizedClusters{i} = {SelectedColoc{i}{ColocClusterIds}}';
  269. NotColocClusterIds = setdiff(1:size(SelectedColoc{i},1),ColocClusterIds)';
  270. NotColocalizedClusters{i} = vertcat(FilteredColoc{i},{SelectedColoc{i}{NotColocClusterIds}}');
  271. % Clear up the work space
  272. clear ColocalizedOrNot ColocClusterIds j NotColocClusterIds
  273. end
  274. % Update the wait bar and then delete it
  275. waitbar(1,f,'Step 3 of 3: Calculating overlap... Complete');
  276. pause(1)
  277. delete(f)
  278. % Clear up the work space
  279. clear i PolyLowResRefExpanded f
  281. %% Plot some figures
  282. % Loop until the user is happy with the threshold
  283. Continue = 1;
  284. while Continue
  285. % Show a histogram of the co-localization percentages with the current
  286. % threshold indicated and a scatterplot of the reference data with the
  287. % co-localized and isolated clusters indicated
  288. for i = 1:numel(RefIndex)
  289. % Extract the individual percentages for each data set
  290. UnfoldedPercentages = cell2mat(PercentageInside{i});
  291. % Do the actual plotting
  292. Fig = figure(i);
  293. set(Fig,'Name',data{1,ColocIndex(i)}.name);
  294. subplot(1,2,1);
  295. histogram(UnfoldedPercentages*100,100,'BinLimits',[0 100],'Normalization','cdf');
  296. axis([0 100 0 1]);axis square;
  297. xlabel('co-localization overlap [%]','FontWeight','bold');
  298. ylabel('Cumulative distribution function [-]','FontWeight','bold');
  299. line([ThresholdOverlap*100 ThresholdOverlap*100],[0 1],'Color','r','LineWidth',2);
  300. text(ThresholdOverlap*100+0.5,0.975,['Threshold: ' num2str(ThresholdOverlap*100)],'Color','r');
  301. set(gca,'FontWeight','bold');
  302. % Clean up the workspace
  303. clear UnfoldedPercentages
  305. % Unfold all cluster coordinates for plotting
  306. ReferenceCoordinates = cell2mat(ClustersRefComplete{i});
  307. ColocalizedCoordinates = cell2mat(ColocalizedClusters{i});
  308. NotColocalizedCoordinates = cell2mat(NotColocalizedClusters{i});
  309. % Do the actual plotting
  310. figure(i);
  311. subplot(1,2,2);
  312. plot(ReferenceCoordinates(:,1),ReferenceCoordinates(:,2),'.','MarkerSize',2);
  313. hold on;
  314. if ~isempty(ColocalizedCoordinates)
  315. plot(ColocalizedCoordinates(:,1),ColocalizedCoordinates(:,2),'.g');
  316. end
  317. if ~isempty(NotColocalizedCoordinates)
  318. plot(NotColocalizedCoordinates(:,1),NotColocalizedCoordinates(:,2),'.r');
  319. end
  320. xlabel('Pixels in x [-]','FontWeight','bold');
  321. ylabel('Pixels in y [-]','FontWeight','bold');
  322. set(gca,'FontWeight','bold');
  323. title('Blue: Reference data; Green: co-localized clusters; Red: non co-localized clusters','FontWeight','bold');
  324. axis square;
  325. hold off;
  326. % Clean up the workspace
  327. clear ReferenceCoordinates ColocalizedCoordinates NotColocalizedCoordinates
  328. end
  330. if numel(RefIndex) ~= 1
  331. % Unfold all overlaps into a single data set
  332. UnfoldedPercentages = cell2mat(vertcat(PercentageInside{:}));
  333. % Plot a global histogram for completeness
  334. Fig = figure(i+1);
  335. set(Fig,'Name','Histogram for all opened data sets together');
  336. histogram(UnfoldedPercentages*100,100,'Normalization','cdf');
  337. axis([0 100 0 1]);axis square;
  338. xlabel('co-localization overlap [%]','FontWeight','bold')
  339. ylabel('Cumulative distribution function [-]','FontWeight','bold')
  340. line([ThresholdOverlap*100 ThresholdOverlap*100],[0 1],'Color','r','LineWidth',2)
  341. text(ThresholdOverlap*100+0.5,0.975,['Threshold: ' num2str(ThresholdOverlap*100)],'Color','r')
  342. set(gca,'FontWeight','bold')
  343. % Clean up the workspace
  344. clear UnfoldedPercentages
  345. end
  347. uiwait(msgbox({'Press OK after inspecting the plots to continue.'}, 'Press OK to continue','help'));
  349. % Ask to keep or change the overlap threshold percentage
  350. input_values = inputdlg('Overlap Percentage Threshold (cancel to keep current one):','',1,{num2str(ThresholdOverlap*100)}); % Show up an input dialog
  352. % If the current one is kept (i.e., cancel is pressed), then break out
  353. % of the loop
  354. if isempty(input_values)
  355. break
  356. end
  357. % If a value ie selected, extract the input values from the dialog
  358. % window
  359. ThresholdOverlap = str2double(input_values{1})/100;
  361. % Redo the co-localization selection
  362. for i = 1:numel(RefIndex)
  363. ColocalizedOrNot = cellfun(@(x) x>=ThresholdOverlap, PercentageInside{i}, 'UniformOutput',false); % Perform the thresholding to select the ones that were.
  365. ColocClusterIds = cell(size(IdxClusters{i},1),1);
  366. for j = 1:size(IdxClusters{i},1)
  367. ColocClusterIds{j} = IdxClusters{i}{j}(ColocalizedOrNot{j});
  368. end
  369. ColocClusterIds = vertcat(ColocClusterIds{:});
  370. ColocalizedClusters{i} = {SelectedColoc{i}{ColocClusterIds}}';
  371. NotColocClusterIds = setdiff(1:size(SelectedColoc{i},1),ColocClusterIds)';
  372. NotColocalizedClusters{i} = vertcat(FilteredColoc{i},{SelectedColoc{i}{NotColocClusterIds}}');
  373. clear ColocalizedOrNot ColocClusterIds j NotColocClusterIds
  374. end
  375. clear i
  376. end
  377. % Clear up the work space
  378. clear i input_values Continue FilteredColoc IdxClusters SelectedColoc ClustersRef
  380. % Store the co-localized and isolated clusters for each data set in the
  381. % session that was loaded before
  382. for i = 1:numel(ColocIndex)
  383. % Unfold the co-localized and isolated clusters to matrices
  384. Colocalized = cell2mat(ColocalizedClusters{i});
  385. NonColocalized = cell2mat(NotColocalizedClusters{i});
  386. % Append the co-localized clusters to the current data set
  387. data{1,end+1} = data{1,ColocIndex(i)};
  388. if ~isempty(Colocalized)
  389. data{1,end}.x_data = Colocalized(:,1);
  390. data{1,end}.y_data = Colocalized(:,2);
  391. data{1,end}.area = Colocalized(:,3);
  392. else
  393. data{1,end}.x_data = [];
  394. data{1,end}.y_data = [];
  395. data{1,end}.area = [];
  396. end
  397. data{1,end}.name = strcat(data{1,end}.name,'_ColocalizedClusters_',num2str(ThresholdOverlap*100),'prOverlap');
  398. % Append the isolated clusters to the current data set
  399. data{1,end+1} = data{1,ColocIndex(i)};
  400. if ~isempty(NonColocalized)
  401. data{1,end}.x_data = NonColocalized(:,1);
  402. data{1,end}.y_data = NonColocalized(:,2);
  403. data{1,end}.area = NonColocalized(:,3);
  404. else
  405. data{1,end}.x_data = [];
  406. data{1,end}.y_data = [];
  407. data{1,end}.area = [];
  408. end
  409. data{1,end}.name = strcat(data{1,end}.name,'_NonColocalizedClusters_',num2str(ThresholdOverlap*100),'prOverlap');
  410. % Clear up the workspace
  411. clear Colocalized NonColocalized
  412. end
  413. clearvars -except data selectedfile
  415. % Save the file with a new name
  416. SaveFile = [extractBefore(selectedfile,'.mat') '_Colocalized.mat'];
  417. save(SaveFile,'data');
  418. % Clear up the workspace
  419. clear selectedfile SaveFile

ColocalizationTubulins.m at commit ddaea08, under GPL-3.0 · at the source

Overview

Authors: Victoria Hannett1,2, A Alejandro Vilcaes1,3, Siewert Hugelier1, Qing Tang4, Melike Lakadamyali1,5, Natali L Chanaday1,6
ORCID iDs: A Alejandro Vilcaes, Siewert Hugelier, Melike Lakadamyali, Natali L Chanaday
  1. Department of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  2. Master of Biotechnology Program, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  3. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba, Córdoba, Argentina
  4. Department of Biochemistry, Institute of Agriculture and Natural Resources, College of Arts and Sciences, University of Nebraska‐Lincoln, Lincoln, Nebraska, USA
  5. Penn Muscle Institute and Epigenetics Institute, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  6. Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
Institutions: University of Pennsylvania (United States); Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina); Universidad Nacional de Córdoba (Argentina); University of Nebraska–Lincoln (United States); Institute of Agriculture and Natural Resources
Journal: Journal of neurochemistry, volume 170, issue 9, article e70551
Dates: received 20 November 2025; accepted 4 September 2026; published online 11 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/jnc.70551 · PMID 42725393 · PMCID PMC13563309 · OpenAlex W7212287678
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), cellular / molecular (subfield)
Methods: Evoked potentials, Statistics, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: dynamin, endocytosis, extracellular vesicle, neuron, synaptic vesicle recycling, synaptobrevin‐2
MeSH: Dynamins*, Extracellular Vesicles*, Neurons*, Vesicle-Associated Membrane Protein 2*, Animals, Cells, Cultured, Endocytosis, Hippocampus, Rats, Rats, Sprague-Dawley, Signal Transduction, Synaptic Vesicles (* major topic)
Topic: Cellular transport and secretion (Cell Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIGMS NIH HHS (GM152111, RM1 GM136511, GM136511, R35 GM152111); Margaret Q. Landenberger Research Foundation
Citations: not cited yet (Europe PMC); 98 references in the paper
Research resources: Syn1 (1:500 RRID:AB_10805139, and/or MAP2 (1:1000 RRID:AB_2138181, primary antibodies against GFP (1:150 RRID:AB_221569, Syp1 (1:500 RRID:AB_2622239, RRID:CVCL_0045

Abstract

Synaptobrevin‐2 (Syb2) is a SNARE protein essential for neurotransmitter release and communication in the nervous system. We previously showed that Syb2 is also exchanged among neurons via extracellular vesicles (EVs). Host neurons rapidly integrate exogenous Syb2 into their synaptic vesicle cycle to support neurotransmitter release. However, the endocytic mechanism by which neurons incorporate Syb2‐containing EVs is unknown. Here, we use a fusion of Syb2 with the pH‐sensitive GFP (Syb2‐pHluorin) to track the incorporation of Syb2‐containing EVs into rat hippocampal and cortical neurons and the trafficking of exogenous Syb2 to synaptic vesicles at synapses. We determined that Syb2‐containing EVs are endocytosed via a Dynamin‐dependent pathway, largely independently of macropinocytosis. The same endocytic route is used in the somatodendritic and axonal compartment and it occurs very rapidly, with the majority of Syb2‐pHluorin residing in internal acidic organelles at 30 min after EV addition, including synaptic vesicles at synapses. Leveraging this finding, we used EVs to sparsely deliver Syb2‐pHluorin to synapses and track the fusion and endocytosis of single synaptic vesicles. The results indicate that Syb2‐pHluorin‐positive synaptic vesicles are endocytosed with either ultrafast (< 1 s) or fast (~1–3 s) kinetics during synaptic transmission, suggesting limited diffusion and high fidelity in the fast retrieval of synaptic vesicle molecules immediately after fusion. Our findings expand our understanding of the mechanisms EVs use to enter neurons and open the door for future applications of EVs as vehicles to deliver fluorescent molecules in a neuron‐specific, targeted manner.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

melikelakadamyali/StormAnalysisSoftware

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ddaea080d42c03647204e470611ba94146259352, 5 June 2024
Languages: MATLAB (238), Python (3)
Size: 248 files, 241 scripts
Software Heritage: not archived
Found in: the text, “STORM Experiments”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (37 files), Image Processing Toolbox (8 files), Curve Fitting Toolbox (4 files), Optimization Toolbox (3 files), SciPy (3 files), NumPy (2 files), Matplotlib (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
243 files
At the source: github.com/melikelakadamyali/StormAnalysisSoftware

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 241 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

All data and resources generated during this work are available upon reasonable request to the corresponding author (NLC—).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 12 MeSH terms, 2 funders, 97 references, 5 RRIDs.

Cite

This paper

Hannett, V., Vilcaes, A. A., Hugelier, S., Tang, Q., Lakadamyali, M., & Chanaday, N. L. (2026). Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway. Journal of neurochemistry, 170(9), e70551. https://doi.org/10.1111/jnc.70551

BibTeX

@article{hannett2026synaptobrevin,
author = {Hannett, Victoria and Vilcaes, A Alejandro and Hugelier, Siewert and Tang, Qing and Lakadamyali, Melike and Chanaday, Natali L},
title = {{Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway}},
journal = {Journal of neurochemistry},
year = {2026},
month = sep,
volume = {170},
number = {9},
pages = {e70551},
publisher = {Wiley},
issn = {0022-3042},
doi = {10.1111/jnc.70551},
url = {https://doi.org/10.1111/jnc.70551},
pmid = {42725393},
pmcid = {PMC13563309}
}

RIS

TY - JOUR
AU - Hannett, Victoria
AU - Vilcaes, A Alejandro
AU - Hugelier, Siewert
AU - Tang, Qing
AU - Lakadamyali, Melike
AU - Chanaday, Natali L
TI - Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway
T2 - Journal of neurochemistry
J2 - J Neurochem
PY - 2026
DA - 2026/09/01
VL - 170
IS - 9
SP - e70551
SN - 0022-3042
PB - Wiley
DO - 10.1111/jnc.70551
UR - https://doi.org/10.1111/jnc.70551
LA - en
ER -

CSL-JSON

{
"id": "10.1111/jnc.70551",
"type": "article-journal",
"title": "Synaptobrevin-2 Containing Extracellular Vesicles Are Rapidly Incorporated Into Mammalian Neurons via a Dynamin-Dependent Pathway",
"container-title": "Journal of neurochemistry",
"author": [
{
"family": "Hannett",
"given": "Victoria"
},
{
"family": "Vilcaes",
"given": "A Alejandro"
},
{
"family": "Hugelier",
"given": "Siewert"
},
{
"family": "Tang",
"given": "Qing"
},
{
"family": "Lakadamyali",
"given": "Melike"
},
{
"family": "Chanaday",
"given": "Natali L"
}
],
"container-title-short": "J Neurochem",
"volume": "170",
"issue": "9",
"page": "e70551",
"DOI": "10.1111/jnc.70551",
"PMID": "42725393",
"PMCID": "PMC13563309",
"ISSN": "0022-3042",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/jnc.70551",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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