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Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels.

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The authors' code

MATLAB · 276 lines · 13 KB · MIT

  1. %% set up workspace
  2. clear
  3. clc
  5. working_path = '/Users/maddyammon/Documents/grad school work/sai lab rotation/minflux data/untreated/';
  6. contents = dir([working_path, '*.tif']);
  8. %import pixel size information
  9. file_name = 'untreated.xlsx';
  10. datainfo = readtable([working_path, file_name]);
  11. pixelsize = table2array(datainfo(:,3));
  12. filename = datainfo(:,1);
  13. Folder = pwd;
  15. nbits = 8; % bit depth of input images
  16. cutoff_multiplier = 7; % Clusters with area > median + cutoff_multiplier * std will be discarded
  18. nfiles = length(contents); % # of files
  19. cluster_mass_results = cell(nfiles,1); % Declare array to collect results
  20. cluster_mass_values = [];
  21. cluster_area_results = cell(nfiles,1); % Declare array to collect results
  22. cluster_area_values = [];
  23. cluster_density_results = cell(nfiles,1); % Declare array to collect results
  24. cluster_density_values = [];
  25. max_intensity = 2^nbits - 1;
  26. xmax_mass = 50;% X axis limit for histograms
  27. xmax_area = 10000;
  28. xmax_density = .1;
  29. bin_width = 1; % Bin width for histograms
  30. bin_width_density = .001;
  31. cluster_per_area = zeros(nfiles,1);
  33. %% calculate cluster sizes
  34. for counter1 = 1 : nfiles % Loop through files
  35. fn = contents(counter1).name; % Current file name
  36. cal = pixelsize(counter1); % Current calibration factor (nm)
  37. calibration = cal^2; % Area calibration factor (nm^2)
  38. im1 = imread([working_path, fn]); % Read image
  39. im1 = im1(:, :, 1); % Pull out only the red layer
  40. im1_binary = or(or(logical(im1 == 85), logical(im1 == 170)), logical(im1 == 255)); % Pixels with intensity values of 85, 170, 255 are considered signal-positive.
  41. im1_counts = zeros(size(im1));
  42. im1_counts(im1 == 85) = 1;
  43. im1_counts(im1 == 170) = 2;
  44. im1_counts(im1 == 255) = 3;
  46. cc = bwconncomp(im1_binary); % Perform KNN clustering
  48. nclusters = cc.NumObjects; % # of clusters
  49. pixels = cc.PixelIdxList; % pixel lists for clusters
  50. % Declare array to collect results
  51. cluster_mass = zeros(nclusters, 1); % Cluster Mass - total number of localizations in current clusters
  52. cluster_area = zeros(nclusters, 1); % Cluster Area - number of pixels in cluster * calibration factor
  53. % cluster_density = zeros(nclusters, 1); % Cluster Density - cluster mass normalized to cluster area
  54. for counter2 = 1 : nclusters % Loop through clusters
  55. pixel_values = im1_counts(pixels{counter2}); % Isolate, normalize values of pixels in current clusters
  56. cluster_mass(counter2) = sum(pixel_values); % Calculate mass of current cluster
  57. cluster_area(counter2) = numel(pixel_values) * calibration; % Cluster area
  58. end
  59. cluster_density = cluster_mass ./ cluster_area;
  61. cluster_mass_results{counter1} = cluster_mass; % store current image results in cell array
  62. cluster_mass_values = [cluster_mass_values; cluster_mass]; %#ok<AGROW>
  63. cluster_area_results{counter1} = cluster_area; % store current image results in cell array
  64. cluster_area_values = [cluster_area_values; cluster_area]; %#ok<AGROW>
  65. cluster_density_results{counter1} = cluster_density; % store current image results in cell array
  66. cluster_density_values = [cluster_density_values; cluster_density]; %#ok<AGROW>
  68. end
  70. % Remove statistical outliers
  71. cutoff_mass = median(cluster_mass_values) + cutoff_multiplier * std(cluster_mass_values); % Max acceptable cluster mass
  72. cutoff_area = median(cluster_area_values) + cutoff_multiplier * std(cluster_area_values); % Max acceptable cluster mass
  73. cutoff_density = median(cluster_density_values) + cutoff_multiplier * std(cluster_density_values); % Max acceptable cluster mass
  74. to_include_mass = logical(cluster_mass_values <= cutoff_mass);
  75. to_include_area = logical(cluster_area_values <= cutoff_area);
  76. to_include_density = logical(cluster_density_values <= cutoff_density);
  78. cluster_mass_values = cluster_mass_values(to_include_mass); % Remove outliers from master list
  79. cluster_area_values = cluster_area_values(to_include_area); % Remove outliers from master list
  80. cluster_density_values = cluster_density_values(to_include_density); % Remove outliers from master list
  82. % set up histogram bins
  83. bin_edges_mass = 0 : bin_width : xmax_mass;
  84. bin_edges_area = 0 : bin_width : xmax_area;
  85. bin_edges_density = 0 : bin_width_density : xmax_density;
  86. [bin_counts_mass, bin_edges_mass] = histcounts(cluster_mass_values, bin_edges_mass, 'Normalization','cdf');
  87. [bin_counts_area, bin_edges_area] = histcounts(cluster_area_values, bin_edges_area, 'Normalization','cdf');
  88. [bin_counts_density, bin_edges_density] = histcounts(cluster_density_values, bin_edges_density, 'Normalization','cdf');
  89. bin_centers_mass = bin_edges_mass(1 : end-1) + bin_width/2;
  90. bin_centers_area = bin_edges_area(1 : end-1) + bin_width/2;
  91. bin_centers_density = bin_edges_density(1 : end-1) + bin_width_density/2;
  93. %% cdf plots
  94. figure(1)
  95. subplot(3,1,1)
  96. plot(bin_centers_mass, bin_counts_mass)
  97. set(gca, 'xlim', [0 xmax_mass])
  98. xlabel('Cluster Mass [# localizations]')
  99. ylabel('Cumulative Probability [0-1]')
  100. title('Cluster Mass');
  102. subplot(3,1,2)
  103. plot(bin_centers_area, bin_counts_area)
  104. set(gca, 'xlim', [0 xmax_area])
  105. xlabel('Cluster Area [nm^2]')
  106. ylabel('Cumulative Probability [0-1]')
  107. title('Cluster Area');
  109. subplot(3,1,3)
  110. plot(bin_centers_density, bin_counts_density)
  111. set(gca, 'xlim', [0 xmax_density])
  112. xlabel('Cluster Density [# localizations / nm^2]')
  113. ylabel('Cumulative Probability [0-1]')
  114. title('Cluster Density');
  115. exportgraphics(gcf, 'cluster_mass_CDF.pdf', 'ContentType','vector');
  117. %% running k-means for mass (k=2)
  119. idxk2 = kmeans(cluster_mass_values, 2); %run k-means and get a vector of values
  120. indexk2_1 = logical(idxk2==1); %find all of the group 1 indices
  121. indexk2_2 = logical(idxk2==2); %find all of the group 2 indices
  123. [bin_counts_mass_all, ~] = histcounts(cluster_mass_values, bin_edges_mass, 'Normalization','cdf');
  125. k1_max_val = max(cluster_mass_values(indexk2_1)); % Max cluster mass in k_1 group
  126. k2_max_val = max(cluster_mass_values(indexk2_2)); % Max cluster mass in k_2 group
  128. x_axis_breaks = sort([k1_max_val, k2_max_val]);
  129. x_axis_break_k1 = find(bin_centers_mass <= x_axis_breaks(1), 1, 'last');
  130. % x_axis_break_k2 = find(bin_centers_mass <= x_axis_breaks(2), 1, 'last');
  132. %plot histogram for k means
  133. figure(2)
  134. histogram(cluster_mass_values(indexk2_1), 'BinEdges', bin_edges_mass);
  135. hold on;
  136. histogram(cluster_mass_values(indexk2_2), 'BinEdges', bin_edges_mass);
  137. title('Cluster Mass, Kmeans(k = 2)')
  138. xlabel('Cluster Mass [# localizations]')
  139. hold off;
  140. exportgraphics(gcf, 'cluster_mass_histogram.pdf', 'ContentType','vector');
  142. figure(3)
  143. plot(bin_centers_mass(1 : x_axis_break_k1), bin_counts_mass_all(1 : x_axis_break_k1), '-ok')
  144. hold on
  145. plot(bin_centers_mass(x_axis_break_k1 : end), bin_counts_mass_all(x_axis_break_k1 : end), '-or')
  146. hold off
  147. title('Cluster Mass, Kmeans(k = 2)')
  148. xlabel('Cluster Mass [# localizations]')
  149. ylabel('Cumulative Probability [0-1]')
  150. exportgraphics(gcf, 'cluster_mass_CDF_k_means_k2.pdf', 'ContentType','vector');
  153. %% running k-means for mass (k=3)
  155. idxk3 = kmeans(cluster_mass_values, 3);
  157. indexk3_1 = logical(idxk3==1); %find all of the group 1 indices
  158. indexk3_2 = logical(idxk3==2); %find all of the group 2 indices
  159. indexk3_3 = logical(idxk3==3); %find all of the group 3 indices
  161. [bin_counts_mass_all, ~] = histcounts(cluster_mass_values, bin_edges_mass, 'Normalization','cdf');
  163. k1_max_val = max(cluster_mass_values(indexk3_1)); % Max cluster mass in k_1 group
  164. k2_max_val = max(cluster_mass_values(indexk3_2)); % Max cluster mass in k_2 group
  165. k3_max_val = max(cluster_mass_values(indexk3_3)); % Max cluster mass in k_3 group
  167. x_axis_breaks = sort([k1_max_val, k2_max_val, k3_max_val]);
  168. x_axis_break_k1 = find(bin_centers_mass <= x_axis_breaks(1), 1, 'last');
  169. x_axis_break_k2 = find(bin_centers_mass <= x_axis_breaks(2), 1, 'last');
  171. %plot histogram for k means
  172. figure(4)
  173. histogram(cluster_mass_values(indexk3_1), 'BinEdges', bin_edges_mass);
  174. hold on;
  175. histogram(cluster_mass_values(indexk3_2), 'BinEdges', bin_edges_mass);
  176. histogram(cluster_mass_values(indexk3_3), 'BinEdges', bin_edges_mass);
  177. title('Cluster Mass, Kmeans(k = 3)')
  178. xlabel('Cluster Mass [# localizations]')
  179. hold off;
  180. exportgraphics(gcf, 'cluster_mass_histogram_kmeans_k3.pdf', 'ContentType','vector');
  183. figure(5)
  184. plot(bin_centers_mass(1 : x_axis_break_k1), bin_counts_mass_all(1 : x_axis_break_k1), '-ok')
  185. hold on
  186. plot(bin_centers_mass(x_axis_break_k1 : x_axis_break_k2), bin_counts_mass_all(x_axis_break_k1 : x_axis_break_k2), '-or')
  187. plot(bin_centers_mass(x_axis_break_k2 : end), bin_counts_mass_all(x_axis_break_k2 : end), '-om')
  188. hold off
  189. title('Cluster Mass, Kmeans(k = 3)')
  190. xlabel('Cluster Mass [# localizations]')
  191. ylabel('Cumulative Probability [0-1]')
  192. exportgraphics(gcf, 'cluster_mass_CDF_kmeans_k3.pdf', 'ContentType','vector');
  196. %% running k-means for density (k=2)
  198. idxk2_density = kmeans(cluster_density_values, 2); %run k-means and get a vector of values
  199. indexk2_density_1 = logical(idxk2_density==1); %find all of the group 1 indices
  200. indexk2_density_2 = logical(idxk2_density==2); %find all of the group 2 indices
  202. [bin_counts_density_all, ~] = histcounts(cluster_density_values, bin_edges_density, 'Normalization','cdf');
  204. k1_density_max_val = max(cluster_density_values(indexk2_density_1)); % Max cluster density in k_1 group
  205. k2_density_max_val = max(cluster_density_values(indexk2_density_2)); % Max cluster density in k_2 group
  207. x_axis_breaks_density = sort([k1_density_max_val, k2_density_max_val]);
  208. x_axis_break_k1_density = find(bin_centers_density <= x_axis_breaks_density(1), 1, 'last');
  210. %plot histogram for k means
  211. figure(6)
  212. histogram(cluster_density_values(indexk2_density_1), 'BinEdges', bin_edges_mass);
  213. hold on;
  214. histogram(cluster_density_values(indexk2_density_2), 'BinEdges', bin_edges_mass);
  215. title('Cluster Density, Kmeans(k = 2)')
  216. xlabel('Cluster Density [# localizations / nm^2]')
  217. hold off;
  218. exportgraphics(gcf, 'cluster_density_histogram_kmeans_k2.pdf', 'ContentType','vector');
  220. figure(7)
  221. plot(bin_centers_density(1 : x_axis_break_k1_density), bin_counts_density_all(1 : x_axis_break_k1_density), '-ok')
  222. hold on
  223. plot(bin_centers_density(x_axis_break_k1_density : end), bin_counts_density_all(x_axis_break_k1_density : end), '-or')
  224. hold off
  225. title('Cluster Density, Kmeans(k = 2)')
  226. xlabel('Cluster Density [# localizations / nm^2]')
  227. ylabel('Cumulative Probability [0-1]')
  228. exportgraphics(gcf, 'cluster_density_CDF_kmeans_k2.pdf', 'ContentType','vector');
  231. %% running k-means for density (k=3)
  233. idxk3_density = kmeans(cluster_density_values, 3);
  235. indexk3_density_1 = logical(idxk3_density==1); %find all of the group 1 indices
  236. indexk3_density_2 = logical(idxk3_density==2); %find all of the group 2 indices
  237. indexk3_density_3 = logical(idxk3_density==3); %find all of the group 3 indices
  239. [bin_counts_density_all, ~] = histcounts(cluster_density_values, bin_edges_density, 'Normalization','cdf');
  241. k1_density_max_val = max(cluster_density_values(indexk3_density_1)); % Max cluster mass in k_1 group
  242. k2_density_max_val = max(cluster_density_values(indexk3_density_2)); % Max cluster mass in k_2 group
  243. k3_density_max_val = max(cluster_density_values(indexk3_density_3)); % Max cluster mass in k_3 group
  245. x_axis_breaks_density = sort([k1_density_max_val, k2_density_max_val, k3_density_max_val]);
  246. x_axis_break_k1_density = find(bin_centers_density <= x_axis_breaks_density(1), 1, 'last');
  247. x_axis_break_k2_density = find(bin_centers_density <= x_axis_breaks_density(2), 1, 'last');
  249. %plot histogram for k means
  250. figure(8)
  251. histogram(cluster_density_values(indexk3_density_1), 'BinEdges', bin_edges_density);
  252. hold on;
  253. histogram(cluster_density_values(indexk3_density_2), 'BinEdges', bin_edges_density);
  254. histogram(cluster_density_values(indexk3_density_3), 'BinEdges', bin_edges_density);
  255. title('Cluster Density, Kmeans(k = 3)')
  256. xlabel('Cluster Density [# localizations / nm^2]')
  257. hold off;
  258. exportgraphics(gcf, 'cluster_density_histogram_kmeans_k3.pdf', 'ContentType','vector');
  260. figure(9)
  261. plot(bin_centers_density(1 : x_axis_break_k1_density), bin_counts_density_all(1 : x_axis_break_k1_density), '-ok')
  262. hold on
  263. plot(bin_centers_density(x_axis_break_k1_density : x_axis_break_k2_density), bin_counts_density_all(x_axis_break_k1_density : x_axis_break_k2_density), '-or')
  264. plot(bin_centers_density(x_axis_break_k2_density : end), bin_counts_density_all(x_axis_break_k2_density : end), '-om')
  265. hold off
  266. title('Cluster Density, Kmeans(k = 3)')
  267. xlabel('Cluster Density [# localizations / nm^2]')
  268. ylabel('Cumulative Probability [0-1]')
  269. exportgraphics(gcf, 'cluster_density_CDF_kmeans_k3.pdf', 'ContentType','vector');
  271. %% save results
  272. save('results.mat', "cluster_mass_results", "cluster_mass_values", "cutoff_mass",...
  273. "cluster_area_results", "cluster_area_values", "cluster_density_results", "cutoff_area",...
  274. "cluster_density_values", "cutoff_density");
  276. save('clusterPerArea.mat', "cluster_per_area");

cluster analysis MINFLUX.m, under MIT · at the source

Overview

Authors: Mikhail Tarasov1,2, Madison Ammon1,3, Jan Otto Wirth4, Christopher Hampton1,3, Zoja Selimi1,2, Rengasayee Veeraraghavan1,3, Przemysław B Radwański1,2
ORCID iDs: Mikhail Tarasov, Madison Ammon, Jan Otto Wirth, Christopher Hampton, Zoja Selimi, Przemysław B Radwański
  1. The Frick Center for Heart Failure and Arrhythmia, Dorothy M. Davis Heart and Lung Research Institute, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH USA
  2. Division of Pharmaceutics and Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH USA
  3. Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH USA
  4. Abberior Instruments GmbH, Göttingen, Germany
Institutions: The Ohio State University Wexner Medical Center (United States); The Ohio State University (United States)
Journal: Nature communications, volume 17, issue 1, article 6218
Dates: received 9 June 2025; accepted 14 April 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72387-8 · PMID 42098094 · PMCID PMC13369509 · OpenAlex W7160556730
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials, Machine learning, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Cardiovascular biology, Ion channels, Ion channels in the nervous system, Permeation and transport, Single-channel recording
MeSH: Cell Membrane*, Voltage-Gated Sodium Channels*, Animals, Biophysical Phenomena, Biophysics, HEK293 Cells, Humans, Ion Channel Gating, Membrane Potentials, Sodium (* major topic)
Topic: Ion channel regulation and function (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NHLBI NIH HHS (R01 HL165751, R01 HL155378); U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) (R01HL165751); NINDS NIH HHS (R01 NS121234); U.S. Department of Health &amp; Human Services | NIH | National Heart, Lung, and Blood Institute (R01HL165751)
Citations: not cited yet (Europe PMC); 105 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above.

Zenodo 18499384

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
3 files
At the source:

tarasov4/analysis-and-modeling-of-voltage-gated-sodium-channels-clusters

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b289712fbd948d2c56b2ee5e24ddf52bc4f5da82, 5 February 2026
Languages: MATLAB (1)
Size: 11 files, 1 script
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/tarasov4/Analysis-and-modeling-of-voltage-gated-sodium-channels-clusters/tree/single_ion_channels

Code availability statement

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  • it points to the authors' code: Zenodo 18499384

Read it in the paper: doi.org/10.1038/s41467-026-72387-8.

Tracing map

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

Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-72387-8.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 10 MeSH terms, 4 funders, 97 references.

Cite

This paper

Tarasov, M., Ammon, M., Wirth, J. O., Hampton, C., Selimi, Z., Veeraraghavan, R., & Radwański, P. B. (2026). Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels. Nature communications, 17(1), 6218. https://doi.org/10.1038/s41467-026-72387-8

BibTeX

@article{tarasov2026nanoscale,
author = {Tarasov, Mikhail and Ammon, Madison and Wirth, Jan Otto and Hampton, Christopher and Selimi, Zoja and Veeraraghavan, Rengasayee and Radwański, Przemysław B},
title = {{Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6218},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72387-8},
url = {https://doi.org/10.1038/s41467-026-72387-8},
pmid = {42098094},
pmcid = {PMC13369509}
}

RIS

TY - JOUR
AU - Tarasov, Mikhail
AU - Ammon, Madison
AU - Wirth, Jan Otto
AU - Hampton, Christopher
AU - Selimi, Zoja
AU - Veeraraghavan, Rengasayee
AU - Radwański, Przemysław B
TI - Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/08
VL - 17
IS - 1
SP - 6218
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72387-8
UR - https://doi.org/10.1038/s41467-026-72387-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72387-8",
"type": "article-journal",
"title": "Nanoscale organization in the cell membrane dynamically modulates the biophysics of voltage-gated sodium channels",
"container-title": "Nature communications",
"author": [
{
"family": "Tarasov",
"given": "Mikhail"
},
{
"family": "Ammon",
"given": "Madison"
},
{
"family": "Wirth",
"given": "Jan Otto"
},
{
"family": "Hampton",
"given": "Christopher"
},
{
"family": "Selimi",
"given": "Zoja"
},
{
"family": "Veeraraghavan",
"given": "Rengasayee"
},
{
"family": "Radwański",
"given": "Przemysław B"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6218",
"DOI": "10.1038/s41467-026-72387-8",
"PMID": "42098094",
"PMCID": "PMC13369509",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72387-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
8
]
]
}
}

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