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Specialized cortical ER architecture modulates Ca<sup>2+</sup> signaling dynamics.

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The authors' code

MATLAB · 172 lines · 6.9 KB · CC-BY-4.0

  1. %% Mesh Generation for the Less Tunnelling Calcium Model with 2 Subdomains (Cytoplasm and ER)
  2. % This is the code that generates the mesh of the Calcium Model (with
  3. % interior boundary). This mesh generation code follows MATLAB's
  4. % PDE Modeler User Guide.
  6. % Clear everything to ensure an empty environment before running
  7. clear all
  8. close all
  9. clc
  11. % Set Hmax, Hmin and Hgrad values (parameters that determine mesh size and
  12. % growth rate)
  13. Hmax_par = 70; % Maximum triangular mesh edge
  14. Hmin_par = 5; % Minimum triangular mesh edge
  15. Hgrad_par = 1.5; %Specify how fast the mesh size grow (between 1 and 2)
  16. Hedge_par = 5; %Specify how fine the mesh size is, override Hmin_par (use in cortical region)
  17. Hvertex_par = Hedge_par;
  19. %% Create geometry objects
  20. % Create an ER polygon as the base
  21. ER_geom = [2; 16; 900; 2150; 2150; 930; 930; 1850; 1850; 500; 470; 550; 550; 350; 350; 440; 500; 900;
  22. 200; 200; 400; 400; 770; 770; 800; 800; 860; 860; 885; 885; 860; 860; 770; 770;];
  23. % Full cell geom
  24. Full_geom = [2; 4; 0; 2350; 2350; 0;
  25. 0; 0; 900; 900;];
  26. Full_geom = [Full_geom; zeros(length(ER_geom) - length(Full_geom),1)];
  28. % Create a matrix to store the ER polygon
  29. ER_geom_matrix = ER_geom;
  30. % Create name for the ER geometry
  31. ER_geom_name = char('ER');
  32. ER_geom_name = ER_geom_name';
  33. % The formula for the resultant ER geometry
  34. ER_geom_formula = 'ER';
  35. % Create the ER geometry object
  36. [ER_dl,ER_bt] = decsg(ER_geom_matrix,ER_geom_formula,ER_geom_name);
  37. % Create ER PDE object
  38. ER_model = createpde();
  39. % Set the ER geometry into the ER PDE model
  40. geometryFromEdges(ER_model,ER_dl);
  41. % Generate the mesh of the ER geometry
  42. ER_mesh = generateMesh(ER_model,'Hmax',Hmax_par,...
  43. 'Hmin',Hmin_par,'Hedge',{[5 6 7 8 9 10 11 13 14 15 16],Hedge_par},...
  44. 'Hgrad',Hgrad_par,'GeometricOrder','linear');
  46. % Create a matrix to store the ER polygon and the full cell polygon
  47. geom_mat = [ER_geom, Full_geom];
  48. % Create name for the stored polygon
  49. geom_name = char('ER','Full');
  50. geom_name = geom_name';
  51. % The formula to get the cytoplasm geometry by deducting the ER poylgon
  52. % from the full cell polygon
  53. Cyto_geom = 'Full-ER';
  54. % Create the ER-PM junction cytoplasm geometry object
  55. [Cyto_dl,Cyto_bt] = decsg(geom_mat,Cyto_geom,geom_name);
  56. % Create ER-PM junction cytoplasm PDE object
  57. Cyto_model = createpde();
  58. % Set the ER-PM junction cytoplasm geometry into the ER PDE model
  59. geometryFromEdges(Cyto_model,Cyto_dl);
  60. % Generate the mesh of the ER-PM junction cytoplasm geometry
  61. Cyto_mesh = generateMesh(Cyto_model,'Hmax',Hmax_par,...
  62. 'Hmin',Hmin_par,'Hedge',{[4 5 6 7 8 9 10 13 14 15 16],Hedge_par},...
  63. 'Hgrad',Hgrad_par,'GeometricOrder','linear');
  65. %% Plot the resultant geometry object
  66. % Plot the resultant geometry object to view its edges and subdomains.
  67. % Great to check which edge is the interior boundary.
  68. figure(1)
  69. pdegplot(ER_dl,"EdgeLabels","on","VertexLabels","off")
  70. hold on
  71. pdegplot(Cyto_dl,"EdgeLabels","off","VertexLabels","off")
  72. xlim([-50 2500])
  73. ylim([-50 900])
  75. figure(2)
  76. pdegplot(ER_dl,"EdgeLabels","off","VertexLabels","off")
  77. hold on
  78. pdegplot(Cyto_dl,"EdgeLabels","on","VertexLabels","off")
  79. xlim([-50 2500])
  80. ylim([-50 950])
  82. % Plot the resultant geometry with the generated mesh
  83. figure(3)
  84. pdeplot(ER_model,NodeLabels="off");
  85. hold on
  86. pdeplot(Cyto_model,NodeLabels="off");
  87. xlim([-50 2500])
  88. ylim([-50 900])
  90. %% Extract the Point, Edge, and Connectivity matrix of the resultant geometric object
  91. [ER_p,ER_e,ER_t] = meshToPet(ER_model.Mesh);
  92. [Cyto_p,Cyto_e,Cyto_t] = meshToPet(Cyto_model.Mesh);
  94. % Round up the coordinates to 8 decimal points for easier match of the interior
  95. % shared boundaries/edges
  96. ER_p = round(ER_p,6);
  97. Cyto_p = round(Cyto_p,6);
  99. %% Extracting nodal info from Cytoplasm and ER regions which share the same edge
  100. for i = 1:size(Cyto_p,2)
  101. Cyto_p(3,i) = i;
  102. end
  104. for i = 1:size(ER_p,2)
  105. ER_p(3,i) = i;
  106. end
  108. % Extract the nodal info of the interior boundaries from ER
  109. inter_bound_ER_e_pointer = (ER_e(5,:) == 1 | ER_e(5,:) == 2 | ER_e(5,:) == 3 |...
  110. ER_e(5,:) == 6 | ER_e(5,:) == 7 | ER_e(5,:) == 8 | ER_e(5,:) == 9 |...
  111. ER_e(5,:) == 10 | ER_e(5,:) == 11 | ER_e(5,:) == 13 |...
  112. ER_e(5,:) == 14 | ER_e(5,:) == 15);
  113. inter_bound_ER_e = ER_e(1:2,inter_bound_ER_e_pointer);
  114. inter_bound_ER_e = reshape(inter_bound_ER_e,1,[]);
  115. inter_bound_ER_e = unique(inter_bound_ER_e);
  116. inter_bound_ER_p = zeros(3,size(inter_bound_ER_e,2));
  117. for i = 1:size(inter_bound_ER_e,2)
  118. inter_bound_ER_p(:,i) = ER_p(:,inter_bound_ER_e(1,i));
  119. end
  121. % Extract the nodal info of the interior boundaries from cytoplasm
  122. inter_bound_Cyto_e_pointer = (Cyto_e(5,:) == 1 | Cyto_e(5,:) == 2 | Cyto_e(5,:) == 17 |...
  123. Cyto_e(5,:) == 5 | Cyto_e(5,:) == 6 | Cyto_e(5,:) == 7 | Cyto_e(5,:) == 8 |...
  124. Cyto_e(5,:) == 9 | Cyto_e(5,:) == 10 | Cyto_e(5,:) == 13 |...
  125. Cyto_e(5,:) == 14 | Cyto_e(5,:) == 15);
  126. inter_bound_Cyto_e = Cyto_e(1:2,inter_bound_Cyto_e_pointer);
  127. inter_bound_Cyto_e = reshape(inter_bound_Cyto_e,1,[]);
  128. inter_bound_Cyto_e = unique(inter_bound_Cyto_e);
  129. inter_bound_Cyto_p = zeros(3,size(inter_bound_Cyto_e,2));
  130. for i = 1:size(inter_bound_Cyto_e,2)
  131. inter_bound_Cyto_p(:,i) = Cyto_p(:,inter_bound_Cyto_e(1,i));
  132. end
  134. % Check to ensure the all nodal info of the interior boundaries between ER and
  135. % cytoplasm match. If not, choose a different Hmax, Hmin, and Hgrad
  136. % parameter values to redefine the mesh in both ER and cytoplasm domains.
  137. inter_bound_p = inter_bound_Cyto_p;
  138. for i = 1:size(inter_bound_Cyto_p,2)
  139. inter_bound_p_pointer = (inter_bound_ER_p(1,:) == inter_bound_Cyto_p(1,i)) & ...
  140. (inter_bound_ER_p(2,:) == inter_bound_Cyto_p(2,i));
  141. inter_bound_p(4,i) = inter_bound_ER_p(3,inter_bound_p_pointer);
  142. end
  144. % Locate the nodal info where the cortical IP3R is placed
  145. n_nodal_cor_IPR_pointer = (Cyto_p(1,:) >= 1250 & Cyto_p(1,:) <= 1650 &...
  146. Cyto_p(2,:) == 800);
  147. n_nodal_cor_IPR = sum(n_nodal_cor_IPR_pointer);
  148. % Locate the nodal info where the SOCE is placed
  149. n_nodal_cor_SOCE_pointer = (Cyto_p(2,:) == 900 & ...
  150. Cyto_p(1,:) >= 400 & Cyto_p(1,:) <= 500);
  151. n_nodal_cor_SOCE = sum(n_nodal_cor_SOCE_pointer);
  152. % Locate the nodal info where the SERCA pumps are placed
  153. n_nodal_cor_SERCA_pointer = ((Cyto_p(1,:) >= 350 & Cyto_p(1,:) <= 800 &...
  154. Cyto_p(2,:) >= 770 & Cyto_p(2,:) < 885)) | ((Cyto_p(1,:) >= 350 &...
  155. Cyto_p(1,:) <= 400 & Cyto_p(2,:) == 885)) | ((Cyto_p(1,:) >= 500 &...
  156. Cyto_p(1,:) <= 550 & Cyto_p(2,:) == 885));
  157. n_nodal_cor_SERCA = sum(n_nodal_cor_SERCA_pointer);
  158. % Locate the nodal info where the PMCA is placed
  159. n_nodal_cor_PM_pointer = (Cyto_p(1,:) >= 0 & Cyto_p(1,:) <= 2350 &...
  160. Cyto_p(2,:) == 900);
  161. n_nodal_cor_PM = sum(n_nodal_cor_PM_pointer);
  162. % Locate the nodal info where the deep IP3R is placed
  163. n_nodal_deep_IPR_pointer = ((Cyto_p(1,:) >= 930 & Cyto_p(1,:) <= 2150 &...
  164. Cyto_p(2,:) == 200)) | ((Cyto_p(1,:) >= 930 & Cyto_p(1,:) <= 2150 &...
  165. Cyto_p(2,:) == 400)) | ((Cyto_p(1,:) == 2150 & Cyto_p(2,:) >= 200 &...
  166. Cyto_p(2,:) <= 400));
  167. n_nodal_deep_IPR = sum(n_nodal_deep_IPR_pointer);
  169. %% Important info to use
  170. % [ER_p,ER_e,ER_t], [Cyto_p,Cyto_e,Cyto_t], inter_bound_p
  172. save("Mesh_less_tunnel.mat")

Geom_Mesh_Less_Tunnel_v01.m, under CC-BY-4.0 · at the source

Overview

Authors: Raphael J Courjaret1,2, Lloyd Lee3, Hana Mohamed1, Fang Yu1,2, Lama Assaf1,4, Melanie Fisher5, David I Yule6, Mark Terasaki5, James Sneyd3, Khaled Machaca1,2
ORCID iDs: Khaled Machaca
  1. Ca2+ Signaling Group, Research Department, Weill Cornell Medicine Qatar, Qatar Foundation, Education City, Doha, Qatar
  2. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
  3. Department of Mathematics, University of Auckland, Auckland Central, Auckland 1142, New Zealand
  4. College of Health and Life Science, Hamad bin Khalifa University, Doha, Qatar
  5. Department of Cell Biology, UConn Health, Farmington, CT, USA
  6. Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, NY, USA
Institutions: Weill Cornell Medical College in Qatar (Qatar); Weill Cornell Medicine (United States); Qatar Foundation (Qatar); University of Auckland (New Zealand); Hamad bin Khalifa University (Qatar); UConn Health (United States); University of Rochester Medicine (United States); University of Rochester (United States)
Journal: iScience, volume 29, issue 8, article 116955
Dates: received 18 September 2025; accepted 10 July 2026; published online 7 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.116955 · PMID 42604072 · PMCID PMC13476580 · OpenAlex W7201838246
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cellular / molecular (subfield)
Methods: Statistics, Evoked potentials
Keywords: ER structure, calcium signaling, store-operated calcium entry, calcium tunneling, mathematical modeling
Topic: Calcium signaling and nucleotide metabolism (Physiology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIDCR NIH HHS (R01 DE019245); NIH; Qatar Research Development and Innovation Council (ARG02-0421-240243); Qatar Foundation
Citations: not cited yet (Europe PMC); 35 references in the paper
Research resources: Hela Cells (Female) RRID:CVCL_0030, Jurkat E6.1 cells (Male) RRID:CVCL_0367, RRID:SCR_011760, EMPIAR database RRID:SCR_019237

Abstract

Ca2+ signaling is ubiquitous and supports a multitude of cellular events. Specificity in Ca2+ signaling is encoded in its spatial and temporal dynamics. Ca2+ tunneling regulates the dynamics of Ca2+ signals downstream of store operated Ca2+ entry (SOCE). Here, we describe a novel cortical ER (cER) architectural feature, that we call the cER-basket, which underpins Ca2+ tunneling. We discovered the cER-basket through a combination of 3D reconstructions of the cortical ER at the ultrastructural level coupled to mathematical modeling of Ca2+ tunneling. Using insights from the modeling to inform experimental approaches and vice versa, we show that the detailed structural features of the cER-basket support Ca2+ tunneling. Therefore, we report a specific cortical ER structure, the cER-basket, which modulates cellular Ca2+ dynamics.

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

Repositories

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

Zenodo 20699141

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: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: zenodo.org/records/20699141

Zenodo 20699140

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: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source:

jsneyd/courjaret-et-al--2025

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 307130068a0be27e0716ebd47468c1e57a9857c6, 15 June 2026
Languages: MATLAB (9)
Size: 12 files, 9 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/jsneyd/Courjaret-et-al--2025/tree/Courjaret-et-al-2025

The paper's code and data availability statement is in the Data section.

Tracing map

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  • no match between paragraphs and code yet;
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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 and code availability

All data reported in this paper are included in the manuscript. Additional reasonable data requests should be directed to the lead contact.

All original code has been deposited at https://zenodo.org/records/20699141; https://doi.org/10.5281/zenodo.20699140.

Any additional information required is available from the lead contact upon request.

Reproduced under the paper's license (CC BY-NC), 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

  • Authors: added Khaled Machaca (0000-0001-6215-2411); removed Khaled Machaca

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 4 funders, 35 references, 4 RRIDs.

Cite

This paper

Courjaret, R. J., Lee, L., Mohamed, H., Yu, F., Assaf, L., Fisher, M., Yule, D. I., Terasaki, M., Sneyd, J., & Machaca, K. (2026). Specialized cortical ER architecture modulates Ca&lt;sup&gt;2+&lt;/sup&gt; signaling dynamics. iScience, 29(8), 116955. https://doi.org/10.1016/j.isci.2026.116955

BibTeX

@article{courjaret2026specialized,
author = {Courjaret, Raphael J and Lee, Lloyd and Mohamed, Hana and Yu, Fang and Assaf, Lama and Fisher, Melanie and Yule, David I and Terasaki, Mark and Sneyd, James and Machaca, Khaled},
title = {{Specialized cortical ER architecture modulates Ca\&lt;sup\&gt;2+\&lt;/sup\&gt; signaling dynamics}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {116955},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116955},
url = {https://doi.org/10.1016/j.isci.2026.116955},
pmid = {42604072},
pmcid = {PMC13476580}
}

RIS

TY - JOUR
AU - Courjaret, Raphael J
AU - Lee, Lloyd
AU - Mohamed, Hana
AU - Yu, Fang
AU - Assaf, Lama
AU - Fisher, Melanie
AU - Yule, David I
AU - Terasaki, Mark
AU - Sneyd, James
AU - Machaca, Khaled
TI - Specialized cortical ER architecture modulates Ca&lt;sup&gt;2+&lt;/sup&gt; signaling dynamics
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/07
VL - 29
IS - 8
SP - 116955
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116955
UR - https://doi.org/10.1016/j.isci.2026.116955
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Specialized cortical ER architecture modulates Ca&lt;sup&gt;2+&lt;/sup&gt; signaling dynamics",
"container-title": "iScience",
"author": [
{
"family": "Courjaret",
"given": "Raphael J"
},
{
"family": "Lee",
"given": "Lloyd"
},
{
"family": "Mohamed",
"given": "Hana"
},
{
"family": "Yu",
"given": "Fang"
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{
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"given": "Lama"
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{
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},
{
"family": "Terasaki",
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},
{
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{
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],
"container-title-short": "iScience",
"volume": "29",
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"page": "116955",
"DOI": "10.1016/j.isci.2026.116955",
"PMID": "42604072",
"PMCID": "PMC13476580",
"ISSN": "2589-0042",
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"URL": "https://doi.org/10.1016/j.isci.2026.116955",
"language": "en",
"issued": {
"date-parts": [
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7
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