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Intracellular neuronal recordings across DNA tiles.

Code ↔ Paper

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  1. [1] § Methods › Statistics and reproducibility ↔ excitability_nanopore.m, lines 1–73 · score 0.64 · Post hoc, pass filtered, pairwise, ANOVA, treatment, dendrites

Paper

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The authors' code

MATLAB · 219 lines · 8.7 KB · no license · 1 match

  1. %% cell viability_with cholesterol
  2. clear all
  3. close all
  5. addpath(genpath(fullfile(pwd,'main')))
  6. addpath(genpath(fullfile(pwd,'plotting')))
  8. M = readtable('E:\data\nanopore\pool\DNAnanopole_cell_viability.xlsx', 'Sheet','with_cholesterol');
  9. % M = readtable('E:\data\dendritic patch\pool\pass_filter.xlsx', 'Sheet','morph');
  10. base_path = 'E:\data\nanopore';
  11. save_path = 'E:\data\nanopore\with_cholesterol';
  12. if ~exist(save_path, 'dir')
  13. mkdir(save_path)
  14. end
  15. Rm = [];
  16. RMP = [];
  17. Ihold = [];
  18. Rm_fit = [];
  19. Cm = [];
  20. f_pre = [];
  21. f_post = [];
  22. AP_amp = [];
  23. AP_width = [];
  24. I = [];
  26. idx_rmv = [2,6,7,10,11,18];
  27. M(idx_rmv,:) = [];
  28. for i = 1:size(M, 1)
  29. data_path = base_path;
  30. [params_pre_temp, ~, f_pre_temp, I_temp, AP_amp_pre_temp, AP_width_pre_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_before(i),0,1,save_path);
  31. [params_post_temp, ~, f_post_temp, I_temp, AP_amp_post_temp, AP_width_post_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_after(i),0,1,save_path);
  32. Rm = [Rm;[params_pre_temp(1), params_post_temp(1)]];
  33. Cm = [Cm;[params_pre_temp(5), params_post_temp(5)]];
  34. RMP = [RMP;[params_pre_temp(8), params_post_temp(8)]];
  35. AP_amp = [AP_amp; [median(AP_amp_pre_temp), median(AP_amp_post_temp)]];
  36. AP_width = [AP_width; [median(AP_width_pre_temp), median(AP_width_post_temp)]];
  37. Rm_fit = [Rm_fit;[params_pre_temp(6), params_post_temp(6)]];
  38. Ihold = [Ihold;[params_pre_temp(7), params_post_temp(7)]];
  39. f_pre = [f_pre,f_pre_temp'];
  40. f_post = [f_post,f_post_temp'];
  41. I = [I, I_temp'];
  42. end
  44. % idx_rmv = find(abs(diff(Ihold'))>200);
  45. % Rm(idx_rmv,:) = [];
  46. % Cm(idx_rmv,:) = [];
  47. % Rm_fit(idx_rmv,:) = [];
  48. % Ihold(idx_rmv,:) = [];
  49. % f_pre(:,idx_rmv) = [];
  50. % f_post(:,idx_rmv) = [];
  51. % AP_amp(idx_rmv,:) = [];
  53. colors = [[0,0,0];[255, 0, 0]/255];
  54. boxplot_pairwise(Rm, colors)
  55. % boxplot_pairwise(Rm_fit.*Cm/1e3)
  56. boxplot_pairwise(Cm, colors)
  57. barplot_pairwise(AP_amp((sum(isnan(AP_amp),2)==0),:), colors), ylim([50,100])
  58. barplot_pairwise(AP_width((sum(isnan(AP_width),2)==0),:), colors), ylim([1,3.5])
  59. barplot_pairwise(-Ihold, colors)
  60. lineplot_with_shaded_errorbar(I_temp, {f_pre(:,find(M.QX314==0)),f_post(:,find(M.QX314==0))}, colors), xlim([0,350])
  61. [data_table, within_design] = gen_table_for_ranova(I_temp((I_temp>0)&(I_temp<=350)), {f_pre((I_temp>0)&(I_temp<=350),find(M.QX314==0)),f_post((I_temp>0)&(I_temp<=350),find(M.QX314==0))});
  63. rm = fitrm(data_table,'measurements1-measurements14 ~ 1', 'WithinDesign', within_design);
  64. AT = ranova(rm, 'WithinModel','treatment*voltage');
  65. anova_table = anovaTable(AT, 'DV');
  66. disp(anova_table);
  67. % postHoc = multcompare(rm,'treatment');
  68. % postHoc_g = postHoc.pValue(1);
  69. p = zeros(1, length(I_temp));
  70. for i = 1:length(I_temp)
  71. [p(i),~] = signrank(f_pre(i,find(M.QX314==0))',f_post(i,find(M.QX314==0))');
  72. end
  73. save(fullfile(save_path,'excitability_with_cholesterol.mat'),'RMP','Rm','Cm','AP_amp','AP_width', 'Ihold','I_temp','f_pre', 'f_post', 'anova_table', 'p', 'M')
  74. %% cell viability_without cholesterol
  75. clear all
  76. close all
  78. addpath(genpath(fullfile(pwd,'main')))
  79. addpath(genpath(fullfile(pwd,'plotting')))
  81. M = readtable('E:\data\nanopore\pool\DNAnanopole_cell_viability.xlsx', 'Sheet','wo_cholesterol');
  82. % M = readtable('E:\data\dendritic patch\pool\pass_filter.xlsx', 'Sheet','morph');
  83. base_path = 'E:\data\nanopore';
  84. save_path = 'E:\data\nanopore\wo_cholesterol';
  85. if ~exist(save_path, 'dir')
  86. mkdir(save_path)
  87. end
  88. Rm = [];
  89. RMP = [];
  90. Ihold = [];
  91. Rm_fit = [];
  92. Cm = [];
  93. f_pre = [];
  94. f_post = [];
  95. AP_amp = [];
  96. AP_width = [];
  97. I = [];
  99. idx_rmv = [1, 6];
  100. M(idx_rmv,:) = [];
  101. for i = 1:size(M, 1)
  102. data_path = base_path;
  103. [params_pre_temp, ~, f_pre_temp, I_temp, AP_amp_pre_temp, AP_width_pre_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_before(i),0,1,save_path);
  104. [params_post_temp, ~, f_post_temp, I_temp, AP_amp_post_temp, AP_width_post_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_after(i),0,1,save_path);
  105. Rm = [Rm;[params_pre_temp(1), params_post_temp(1)]];
  106. Cm = [Cm;[params_pre_temp(5), params_post_temp(5)]];
  107. RMP = [RMP;[params_pre_temp(8), params_post_temp(8)]];
  108. AP_amp = [AP_amp; [median(AP_amp_pre_temp), median(AP_amp_post_temp)]];
  109. AP_width = [AP_width; [median(AP_width_pre_temp), median(AP_width_post_temp)]];
  110. Rm_fit = [Rm_fit;[params_pre_temp(6), params_post_temp(6)]];
  111. Ihold = [Ihold;[params_pre_temp(7), params_post_temp(7)]];
  112. f_pre = [f_pre,f_pre_temp'];
  113. f_post = [f_post,f_post_temp'];
  114. I = [I, I_temp'];
  115. end
  117. % idx_rmv = find(abs(diff(Ihold'))>200);
  118. % Rm(idx_rmv,:) = [];
  119. % Cm(idx_rmv,:) = [];
  120. % Rm_fit(idx_rmv,:) = [];
  121. % Ihold(idx_rmv,:) = [];
  122. % f_pre(:,idx_rmv) = [];
  123. % f_post(:,idx_rmv) = [];
  124. % AP_amp(idx_rmv,:) = [];
  126. colors = [[0,0,0];[128, 128, 128]/255];
  127. boxplot_pairwise(Rm, colors)
  128. % boxplot_pairwise(Rm_fit.*Cm/1e3)
  129. boxplot_pairwise(Cm, colors)
  130. barplot_pairwise(AP_amp((sum(isnan(AP_amp),2)==0),:), colors), ylim([50,100])
  131. barplot_pairwise(AP_width((sum(isnan(AP_width),2)==0),:), colors), ylim([1,3.5])
  132. barplot_pairwise(-Ihold, colors)
  133. lineplot_with_shaded_errorbar(I_temp, {f_pre(:,find(M.QX314==0)),f_post(:,find(M.QX314==0))}, colors), xlim([0,350])
  134. [data_table, within_design] = gen_table_for_ranova(I_temp((I_temp>0)&(I_temp<=350)), {f_pre((I_temp>0)&(I_temp<=350),find(M.QX314==0)),f_post((I_temp>0)&(I_temp<=350),find(M.QX314==0))});
  136. rm = fitrm(data_table,'measurements1-measurements14 ~ 1', 'WithinDesign', within_design);
  137. AT = ranova(rm, 'WithinModel','treatment*voltage');
  138. anova_table = anovaTable(AT, 'DV');
  139. disp(anova_table);
  140. % postHoc = multcompare(rm,'treatment');
  141. % postHoc_g = postHoc.pValue(1);
  142. p = zeros(1, length(I_temp));
  143. for i = 1:length(I_temp)
  144. [p(i),~] = signrank(f_pre(i,find(M.QX314==0))',f_post(i,find(M.QX314==0))');
  145. end
  146. save(fullfile(save_path,'excitability_wo_cholesterol.mat'),'RMP','Rm','Cm','AP_amp','AP_width', 'Ihold','I_temp','f_pre', 'f_post', 'anova_table', 'p', 'M')
  147. %% cell viability_broken
  148. clear all
  149. close all
  151. addpath(genpath(fullfile(pwd,'main')))
  152. addpath(genpath(fullfile(pwd,'plotting')))
  154. M = readtable('E:\data\nanopore\pool\DNAnanopole_cell_viability.xlsx', 'Sheet','broken');
  155. % M = readtable('E:\data\dendritic patch\pool\pass_filter.xlsx', 'Sheet','morph');
  156. base_path = 'E:\data\nanopore';
  157. save_path = 'E:\data\nanopore\broken';
  158. if ~exist(save_path, 'dir')
  159. mkdir(save_path)
  160. end
  161. Rm = [];
  162. RMP = [];
  163. Ihold = [];
  164. Rm_fit = [];
  165. Cm = [];
  166. f_pre = [];
  167. f_post = [];
  168. AP_amp = [];
  169. AP_width = [];
  170. I = [];
  172. idx_rmv = [];
  173. M(idx_rmv,:) = [];
  174. for i = 1:size(M, 1)
  175. data_path = base_path;
  176. [params_pre_temp, ~, f_pre_temp, I_temp, AP_amp_pre_temp, AP_width_pre_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_before(i),0,1,save_path);
  177. [params_post_temp, ~, f_post_temp, I_temp, AP_amp_post_temp, AP_width_post_temp] = sub_and_supra_classify(data_path, M.date{i}, M.cell(i), M.idx_after(i),0,1,save_path);
  178. Rm = [Rm;[params_pre_temp(1), params_post_temp(1)]];
  179. Cm = [Cm;[params_pre_temp(5), params_post_temp(5)]];
  180. RMP = [RMP;[params_pre_temp(8), params_post_temp(8)]];
  181. AP_amp = [AP_amp; [median(AP_amp_pre_temp), median(AP_amp_post_temp)]];
  182. AP_width = [AP_width; [median(AP_width_pre_temp), median(AP_width_post_temp)]];
  183. Rm_fit = [Rm_fit;[params_pre_temp(6), params_post_temp(6)]];
  184. Ihold = [Ihold;[params_pre_temp(7), params_post_temp(7)]];
  185. f_pre = [f_pre,f_pre_temp'];
  186. f_post = [f_post,f_post_temp'];
  187. I = [I, I_temp'];
  188. end
  190. % idx_rmv = find(abs(diff(Ihold'))>200);
  191. % Rm(idx_rmv,:) = [];
  192. % Cm(idx_rmv,:) = [];
  193. % Rm_fit(idx_rmv,:) = [];
  194. % Ihold(idx_rmv,:) = [];
  195. % f_pre(:,idx_rmv) = [];
  196. % f_post(:,idx_rmv) = [];
  197. % AP_amp(idx_rmv,:) = [];
  199. colors = [[0,0,0];[128, 128, 128]/255];
  200. boxplot_pairwise(Rm, colors)
  201. % boxplot_pairwise(Rm_fit.*Cm/1e3)
  202. boxplot_pairwise(Cm, colors)
  203. barplot_pairwise(AP_amp((sum(isnan(AP_amp),2)==0),:), colors), ylim([50,100])
  204. barplot_pairwise(AP_width((sum(isnan(AP_width),2)==0),:), colors), ylim([1,3.5])
  205. barplot_pairwise(-Ihold, colors)
  206. lineplot_with_shaded_errorbar(I_temp, {f_pre(:,find(M.QX314==0)),f_post(:,find(M.QX314==0))}, colors), xlim([0,350])
  207. [data_table, within_design] = gen_table_for_ranova(I_temp((I_temp>0)&(I_temp<=350)), {f_pre((I_temp>0)&(I_temp<=350),find(M.QX314==0)),f_post((I_temp>0)&(I_temp<=350),find(M.QX314==0))});
  209. rm = fitrm(data_table,'measurements1-measurements14 ~ 1', 'WithinDesign', within_design);
  210. AT = ranova(rm, 'WithinModel','treatment*voltage');
  211. anova_table = anovaTable(AT, 'DV');
  212. disp(anova_table);
  213. % postHoc = multcompare(rm,'treatment');
  214. % postHoc_g = postHoc.pValue(1);
  215. p = zeros(1, length(I_temp));
  216. for i = 1:length(I_temp)
  217. [p(i),~] = signrank(f_pre(i,find(M.QX314==0))',f_post(i,find(M.QX314==0))');
  218. end
  219. save(fullfile(save_path,'excitability_broken.mat'),'RMP','Rm','Cm','AP_amp','AP_width', 'Ihold','I_temp','f_pre', 'f_post', 'anova_table', 'p', 'M')

excitability_nanopore.m at commit cffe25d, no license · at the source

Overview

Authors: Shulan Xiao1, Sang Hoon Um1, Meng Xu2,3, Derrick Dankwa1, Seongmin Seo4, Jong Hyun Choi4, Aleksei Aksimentiev2,3,5, Leopold N. Green1, Krishna Jayant1,6
ORCID iDs: Shulan Xiao, Sang Hoon Um, Meng Xu, Derrick Dankwa, Seongmin Seo, Jong Hyun Choi, Aleksei Aksimentiev, Krishna Jayant
  1. Weldon School of Biomedical Engineering, Purdue University,West Lafayette, IN USA
  2. Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign,Urbana, IL USA
  3. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign,Urbana, IL USA
  4. School of Mechanical Engineering, Purdue University,West Lafayette, IN USA
  5. Department of Physics, University of Illinois at Urbana-Champaign,Urbana, IL USA
  6. Purdue Institute for Integrative Neuroscience, Purdue University,West Lafayette, IN USA
Institutions: Purdue University West Lafayette (United States); University of Illinois Urbana-Champaign (United States)
Journal: Nature nanotechnology, volume 21, issue 6, pages 859-868
Dates: received 27 December 2024; accepted 15 April 2026; published online 1 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41565-026-02180-7 · PMID 42225982 · PMCID PMC13293863 · OpenAlex W7163063769
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: intracellular / patch clamp (modality), none (in silico) (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Nanostructures, Characterization and analytical techniques, Drug delivery
MeSH: DNA*, DNA Nanostructures*, Neurons*, Animals, Cell Membrane, Ion Transport, Molecular Dynamics Simulation, Patch-Clamp Techniques (* major topic)
Topic: Nanopore and Nanochannel Transport Studies (Biomedical Engineering, Engineering), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER) (DP2MH136494); NSF (2120200, 23057066, 2411133); Leslie A. Geddes Named Professorship; United States Department of Defense | United States Navy | ONR | Office of Naval Research Global (ONR Global) (N00014-21-1-2624); supercomputer time via ACCESS allocation MCA05S028; U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award No. DE-SC0020673
Citations: not cited yet (Europe PMC); 93 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, with 1 match between paragraphs and lines of code.

shulanx1/dnatiles

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0dfd989e250c8cee30045a2aa8d04bd1506007f7, 30 January 2026
Languages: Python (4), MATLAB (2)
Size: 9 files, 6 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, continuous integration
Not found: license file, CITATION.cff, environment file, tests, documentation
Tools: NumPy (4 files), SciPy (4 files), Curve Fitting Toolbox (2 files), Matplotlib (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
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7 files
At the source: github.com/shulanx1/dnatiles

shulanx1/DNAtiles_analysis

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Commit: cffe25d6a977587d5b6d6e1c26ad6abfe36a0b6c, 30 January 2026
Languages: MATLAB (63)
Size: 68 files, 63 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (18 files), Signal Processing Toolbox (11 files), Curve Fitting Toolbox (3 files), Image Processing Toolbox (3 files), Violinplot-Matlab (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
64 files
At the source: github.com/shulanx1/DNAtiles_analysis

Zenodo 18702671

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
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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: NumPy (4 files), SciPy (4 files), Curve Fitting Toolbox (2 files), Matplotlib (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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7 files
At the source:

Zenodo 18702664

License: CC-BY-4.0
State: the link answers, verified on 27 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: Statistics and Machine Learning Toolbox (18 files), Signal Processing Toolbox (11 files), Curve Fitting Toolbox (3 files), Image Processing Toolbox (3 files), Violinplot-Matlab (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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64 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41565-026-02180-7.

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 138 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

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

  • it points to a dataset: Zenodo 14783746
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41565-026-02180-7.

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 8 MeSH terms, 6 funders, 88 references.

Cite

This paper

Xiao, S., Um, S. H., Xu, M., Dankwa, D., Seo, S., Choi, J. H., Aksimentiev, A., Green, L. N., & Jayant, K. (2026). Intracellular neuronal recordings across DNA tiles. Nature nanotechnology, 21(6), 859-868. https://doi.org/10.1038/s41565-026-02180-7

BibTeX

@article{xiao2026intracellular,
author = {Xiao, Shulan and Um, Sang Hoon and Xu, Meng and Dankwa, Derrick and Seo, Seongmin and Choi, Jong Hyun and Aksimentiev, Aleksei and Green, Leopold N. and Jayant, Krishna},
title = {{Intracellular neuronal recordings across DNA tiles}},
journal = {Nature nanotechnology},
year = {2026},
month = jun,
volume = {21},
number = {6},
pages = {859--868},
publisher = {Nature Portfolio},
issn = {1748-3387},
doi = {10.1038/s41565-026-02180-7},
url = {https://doi.org/10.1038/s41565-026-02180-7},
pmid = {42225982},
pmcid = {PMC13293863}
}

RIS

TY - JOUR
AU - Xiao, Shulan
AU - Um, Sang Hoon
AU - Xu, Meng
AU - Dankwa, Derrick
AU - Seo, Seongmin
AU - Choi, Jong Hyun
AU - Aksimentiev, Aleksei
AU - Green, Leopold N.
AU - Jayant, Krishna
TI - Intracellular neuronal recordings across DNA tiles
T2 - Nature nanotechnology
J2 - Nat Nanotechnol
PY - 2026
DA - 2026/06/01
VL - 21
IS - 6
SP - 859
EP - 868
SN - 1748-3387
PB - Nature Portfolio
DO - 10.1038/s41565-026-02180-7
UR - https://doi.org/10.1038/s41565-026-02180-7
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41565-026-02180-7",
"type": "article-journal",
"title": "Intracellular neuronal recordings across DNA tiles",
"container-title": "Nature nanotechnology",
"author": [
{
"family": "Xiao",
"given": "Shulan"
},
{
"family": "Um",
"given": "Sang Hoon"
},
{
"family": "Xu",
"given": "Meng"
},
{
"family": "Dankwa",
"given": "Derrick"
},
{
"family": "Seo",
"given": "Seongmin"
},
{
"family": "Choi",
"given": "Jong Hyun"
},
{
"family": "Aksimentiev",
"given": "Aleksei"
},
{
"family": "Green",
"given": "Leopold N."
},
{
"family": "Jayant",
"given": "Krishna"
}
],
"container-title-short": "Nat Nanotechnol",
"volume": "21",
"issue": "6",
"page": "859-868",
"DOI": "10.1038/s41565-026-02180-7",
"PMID": "42225982",
"PMCID": "PMC13293863",
"ISSN": "1748-3387",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41565-026-02180-7",
"language": "en",
"issued": {
"date-parts": [
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]
}
}

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