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Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization.

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3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Methods › Mass Spectrometry › Evaluation of proteomic results ↔ src/compute/dimred.cpp, lines 47–175 · score 0.60 · distributed stochastic neighbor, perplexity, SNE, embedding, weight
  2. [2] § Results › Synaptic adhesion molecules are embedded into extended protein networks ↔ src/compute/dimred.cpp, lines 47–175 · score 0.53 · distributed stochastic neighbor, diagonal, embedding, SNE
  3. [3] § Methods › Mass Spectrometry › Evaluation of proteomic results ↔ src/compute/dimred.cpp, lines 16–45 · score 0.52 · stochastic neighborhood embedding, perplexity, SNE

Paper

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The authors' code

C++ · 177 lines · 5.9 KB · GPL-3.0 · 3 matches

  1. #include "dimred.h"
  2. #include <tapkee/tapkee.hpp> // includes Eigen
  3. #include <tapkee/callbacks/precomputed_callbacks.hpp>
  4. #include <tapkee/utils/logging.hpp>
  5. #include <opencv2/core.hpp>
  6. #include <opencv2/imgproc.hpp> // for EMD
  7. #include <tbb/parallel_for.h>
  9. #include <map>
  10. #include <iostream>
  12. using namespace tapkee;
  14. namespace dimred {
  16. const std::vector<dimred::Method> &availableMethods()
  17. {
  18. static std::vector<dimred::Method> ret{
  19. {"PCA", "PCA/12", "Principal Component Analysis"},
  20. #ifdef EXPERIMENTAL
  21. {"kPCA EMD", "kPCA EMD/12", "Kernel-PCA, EMD"},
  22. {"kPCA L1", "kPCA L1/12", "Kernel-PCA, Manhattan"},
  23. {"kPCA L2", "kPCA L2/12", "Kernel-PCA, Euclidean"},
  24. {"MDS L1", "MDS L1/12", "Multi-dimensional Scaling, Manhattan"},
  25. {"MDS NL2", "MDS NL2/12", "Multi-dimensional Scaling, Normalized L2"},
  26. {"MDS EMD", "MDS EMD/12", "Multi-dimensional Scaling, EMD"},
  27. #endif
  28. {"tSNE", "tSNE", "t-distributed stochastic neighbor embedding"},
  29. #ifdef EXPERIMENTAL
  30. {"tSNE 10", "tSNE 10", "t-SNE with perplexity 10"},
  31. {"tSNE 20", "tSNE 20", "t-SNE with perplexity 20"},
  32. #endif
  33. {"tSNE 30", "tSNE 30", "t-SNE with perplexity 30"},
  34. {"tSNE 40", "tSNE 40", "t-SNE with perplexity 40"},
  35. {"tSNE 50", "tSNE 50", "t-SNE with perplexity 50"},
  36. {"tSNE 60", "tSNE 60", "t-SNE with perplexity 60"},
  37. {"tSNE 70", "tSNE 70", "t-SNE with perplexity 70"},
  38. {"Diffusion Map", "Diffusion Map", "Diffusion Map"},
  39. #ifdef EXPERIMENTAL
  40. {"Diff. Map L1", "Diff. Map L1", "Diffusion Map, Manhattan"},
  41. {"Diff. Map EMD", "Diff. Map EMD", "Diffusion Map, EMD"},
  42. #endif
  43. };
  44. return ret;
  45. }
  47. QMap<QString, QVector<QPointF>> compute(QString m, const std::vector<std::vector<double>> &features)
  48. {
  49. if (features.empty() || features.front().size() < 3)
  50. return {};
  51. std::cout << "Computing " << m.toStdString() << std::endl;
  53. // setup some logging
  54. tapkee::LoggingSingleton::instance().enable_info();
  55. tapkee::LoggingSingleton::instance().enable_benchmark();
  57. // perform dimensionality reduction
  58. ParametersSet p;
  59. if (m.startsWith("PCA") || m.startsWith("kPCA") || m.startsWith("MDS")) {
  60. p, method=(m == "PCA" ? PCA : (m.startsWith("kPCA") ? KernelPCA : MultidimensionalScaling));
  61. p, target_dimension=std::min(size_t(3), features.front().size() - 1);
  62. }
  63. if (m.startsWith("tSNE")) {
  64. p, method=tDistributedStochasticNeighborEmbedding, target_dimension=2;
  65. double perp = m.split(' ').back().toDouble();
  66. if (perp > 0.)
  67. p, sne_perplexity=perp;
  68. // also available: sne_theta
  69. }
  70. if (m.startsWith("Diff")) {
  71. p, method=DiffusionMap, target_dimension=2;
  72. }
  73. auto parametrized = initialize().withParameters(p);
  74. auto nFeat = features.size();
  76. std::map<QString, std::function<double(size_t, size_t)>> distFun = {
  77. {"L1", [&features] (size_t i, size_t j) {
  78. return cv::norm(features[i], features[j], cv::NORM_L1);
  79. }},
  80. {"L2", [&features] (size_t i, size_t j) {
  81. return cv::norm(features[i], features[j], cv::NORM_L2);
  82. }},
  83. {"NL2", [&features] (size_t i, size_t j) {
  84. cv::Mat1d mi(features[i]), mj(features[j]);
  85. mi /= cv::norm(mi);
  86. mj /= cv::norm(mj);
  87. return cv::norm(mi, mj); // TODO: use cv::NORM_L2SQR?
  88. }},
  89. {"COS", [&features] (size_t i, size_t j) {
  90. cv::Mat1d mi(features[i]), mj(features[j]);
  91. return mi.dot(mj) / (cv::norm(mi) * cv::norm(mj));
  92. }},
  93. {"EMD", [&features] (size_t i, size_t j) {
  94. cv::Mat1f mi(features[i].size(), 1 + 1, 1.f); // weight + value
  95. cv::Mat1f mj(features[j].size(), 1 + 1, 1.f); // weight + value
  96. std::copy(features[i].cbegin(), features[i].cend(), mi.col(1).begin());
  97. std::copy(features[j].cbegin(), features[j].cend(), mj.col(1).begin());
  98. // use L1 here as we have scalar inputs anyway
  99. return cv::EMD(mi, mj, cv::DIST_L1);
  100. }},
  101. };
  103. auto precomputeDistances = [&] (auto callback, bool kernel = false) {
  104. std::vector<IndexType> indices((unsigned)nFeat);
  105. DenseMatrix distances(nFeat, nFeat);
  107. std::cerr << "computing distances for " << nFeat << " points" << std::endl;
  108. tbb::parallel_for(size_t(0), nFeat, [&] (size_t i) {
  109. if (i % 10)
  110. std::cerr << ".";
  111. indices[i] = i;
  113. // fill diagonal
  114. distances(i, i) = 0;
  116. for (size_t j = i + 1; j < nFeat; ++j) {
  118. auto dist = callback(i, j);
  119. // fill symmetrically
  120. distances(j, i) = distances(i, j) = dist;
  121. }
  122. });
  123. std::cerr << " done" << std::endl;
  124. if (kernel) {
  125. auto imean = -1. / distances.mean();
  126. distances = (distances.array() * imean).exp();
  127. }
  128. return std::make_pair(indices, distances);
  129. };
  131. TapkeeOutput output;
  132. // custom distance
  133. if (m.startsWith("MDS") || m.startsWith("Diff. Map")) {
  134. auto [indices, distances] = precomputeDistances(distFun[m.split(" ").last()]);
  135. precomputed_distance_callback d(distances);
  136. output = parametrized.withDistance(d).embedUsing(indices);
  137. // custom kernel
  138. } else if (m.startsWith("kPCA")) {
  139. auto [indices, distances] = precomputeDistances(distFun[m.split(" ").last()], true);
  140. precomputed_kernel_callback k(distances);
  141. output = parametrized.withKernel(k).embedUsing(indices);
  142. // plain work on features
  143. } else {
  144. // setup feature matrix
  145. IndexType nrows = features[0].size();
  146. DenseMatrix featmat(nrows, nFeat);
  147. for (size_t i = 0; i < nFeat; ++i)
  148. featmat.col(i) = Eigen::Map<const DenseVector>(features[i].data(), nrows);
  150. output = parametrized.embedUsing(featmat);
  151. }
  153. // store result chart-readable: 3D → 2D
  154. if (output.embedding.cols() == 3) {
  155. // hack: ensure the name does not yet contain dimension markers
  156. m = m.split("/").first();
  157. std::map<QString, std::pair<int, int>> map = {
  158. {{m + "/12"}, {0, 1}}, {{m + "/13"}, {0, 2}}, {{m + "/23"}, {1, 2}}
  159. };
  160. QMap<QString, QVector<QPointF>> ret;
  161. for (const auto& [name, cols] : map) {
  162. auto points = QVector<QPointF>(nFeat);
  163. for (size_t i = 0; i < nFeat; ++i)
  164. points[i] = {output.embedding(i, cols.first), output.embedding(i, cols.second)};
  165. ret.insert(name, points);
  166. }
  167. return ret;
  168. }
  170. // plain 2D
  171. QVector<QPointF> points(nFeat);
  172. for (size_t i = 0; i < nFeat; ++i)
  173. points[i] = {output.embedding(i, 0), output.embedding(i, 1)};
  174. return {{m, points}};
  175. }
  177. }

dimred.cpp at commit 0fa2736, under GPL-3.0 · at the source

Overview

Authors: Spyros Thivaios1, Jochen Schwenk1, Aline Brechet1, Sami Boudkkazi1, Nithya Sethumadhavan1, Phil Henneken1, Eriko Miura2, Ayumi Hayashi2, Maciej K. Kocylowski1, Alexander Haupt1, Debora Kaminski3,4, Dietmar Schreiner5, Agata Nowacka5, Jean-Baptiste van den Broucke1, Akos Kulik1, Uwe Schulte1,6, Fredrik H. Sterky3,4,7, Michisuke Yuzaki2, Peter Scheiffele5, Bernd Fakler1,8,9
ORCID iDs: Spyros Thivaios, Jochen Schwenk, Eriko Miura, Maciej K. Kocylowski, Alexander Haupt, Agata Nowacka, Jean-Baptiste van den Broucke, Uwe Schulte, Fredrik H. Sterky, Michisuke Yuzaki, Peter Scheiffele, Bernd Fakler
  1. Institute of Physiology, Faculty of Medicine, University of Freiburg, Hermann-Herder-Str. 7,Freiburg, Germany
  2. Department of Neurophysiology, Keio University School of Medicine,Tokyo, Japan
  3. Department of Laboratory Medicine, Institute for Biomedicine, Sahlgrenska Academy, University of Gothenburg,Gothenburg, Sweden
  4. Department of Clinical Chemistry, Sahlgrenska University Hospital,Gothenburg, Sweden
  5. Biozentrum of the University of Basel,Basel, Switzerland
  6. Logopharm GmbH, Schlossstr. 14,March-Buchheim, Germany
  7. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg,Gothenburg, Sweden
  8. Signalling Research Centres BIOSS and CIBSS, Schänzlestr. 18,Freiburg, Germany
  9. Center for Basics in NeuroModulation, Breisacherstr. 4, Freiburg, Germany
Institutions: University of Freiburg (Germany); Keio University (Japan); University of Gothenburg (Sweden); Sahlgrenska University Hospital (Sweden); University of Basel (Switzerland); Logopharm (Germany) (Germany)
Journal: Nature communications, volume 17, issue 1, article 9509
Dates: received 13 July 2025; accepted 19 August 2026; published online 5 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-77377-4 · PMID 42701130 · PMCID PMC13546301 · OpenAlex W4400830032
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.07.16.603715
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials
Keywords: Molecular neuroscience, Protein-protein interaction networks
MeSH: Neurexins*, Neurons*, Receptor-Like Protein Tyrosine Phosphatases, Class 2*, Synapses*, Animals, Brain, Mice, Neural Cell Adhesion Molecules (* major topic)
Topic: RNA regulation and disease (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (Fa 332/15-1)
Citations: not cited yet (Europe PMC); 111 references in the paper
Research resources: anti-guinea pig IgG-Alexa Fluor 488 RRID:AB_2340472, anti-rabbit IgG-Cy5 RRID:AB_2340607, anti-PSD95 RRID:AB_2571612, anti-pan Neurexin RRID:AB_2571817, anti-rat IgG-Alexa Fluor 555 RRID:AB_2910652, anti-HA RRID:AB_390918, CD1) were obtained from JAX RRID:IMSR_JAX:036906

Abstract

Synapses, prototypic sites for neuronal communication, are key to brain function. Their organization and properties are instructed by synaptic cell adhesion molecules (sCAMs) that may operate independently or in coordination through yet unknown linker proteins. Here, we used multi-epitope affinity-purifications combined with quantitative mass spectrometry and immuno-EM to comprehensively map synaptic protein networks in the mouse brain. We identify a pre-synaptic core-module assembled from the major sCAMs, Neurexins1-3 and LAR-type receptor protein-tyrosine-phosphatases (PTPRs), and the previously uncharacterized tetraspanins T178A/B. These ternary Neurexin-T178-PTPR complexes form through their trans-membrane domains and assemble during biogenesis in the ER. Loss of T178B leads to module destabilization, accompanied by strong reduction of LAR-PTPRs and re-distribution of synaptic Neurexins. At synapses, the Neurexin-T178-PTPR module recruits stable trans-synaptic protein networks thereby interlinking machineries of the pre-synaptic active zone and establishing stable associations with post-synaptic neurotransmitter receptors. This work uncovers a widely distributed core-module for synaptic adhesion and trans-synaptic signaling in the mammalian brain.

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 3 matches between paragraphs and lines of code.

phys2/belki

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 0fa27367a490fdbd67dd987257f9165d7f4347d4, 1 January 2021
Languages: C++ (49), C/C++ (47)
Size: 259 files, 96 scripts
Software Heritage: archived
Found in: the text, “Evaluation of proteomic results”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
98 files
At the source: github.com/phys2/belki

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 96 scripts, each with its path and the digest of its content;
  • 3 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

Mass spectrometry data from AP-MS analyses, membrane proteomics and organellar proteomics have been deposited in PRIDE (PXD054017 and 10.6019/PXD054017). The meAP-MS data reconstituting the network illustrated in Fig. 6 are provided as an interactive version (https://phys2.github.io/synaptic-connectome). All other data reported in this work are available from the corresponding authors upon request. Source data are provided with this paper.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 20 authors, 2 keywords, 8 MeSH terms, 1 funder, 110 references, 7 RRIDs.

Cite

This paper

Thivaios, S., Schwenk, J., Brechet, A., Boudkkazi, S., Sethumadhavan, N., Henneken, P., Miura, E., Hayashi, A., Kocylowski, M. K., Haupt, A., Kaminski, D., Schreiner, D., Nowacka, A., van den Broucke, J.-B., Kulik, A., Schulte, U., Sterky, F. H., Yuzaki, M., Scheiffele, P., & Fakler, B. (2026). Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization. Nature communications, 17(1), 9509. https://doi.org/10.1038/s41467-026-77377-4

BibTeX

@article{thivaios2026ternary,
author = {Thivaios, Spyros and Schwenk, Jochen and Brechet, Aline and Boudkkazi, Sami and Sethumadhavan, Nithya and Henneken, Phil and Miura, Eriko and Hayashi, Ayumi and Kocylowski, Maciej K. and Haupt, Alexander and Kaminski, Debora and Schreiner, Dietmar and Nowacka, Agata and van den Broucke, Jean-Baptiste and Kulik, Akos and Schulte, Uwe and Sterky, Fredrik H. and Yuzaki, Michisuke and Scheiffele, Peter and Fakler, Bernd},
title = {{Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization}},
journal = {Nature communications},
year = {2026},
month = sep,
volume = {17},
number = {1},
pages = {9509},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-77377-4},
url = {https://doi.org/10.1038/s41467-026-77377-4},
pmid = {42701130},
pmcid = {PMC13546301}
}

RIS

TY - JOUR
AU - Thivaios, Spyros
AU - Schwenk, Jochen
AU - Brechet, Aline
AU - Boudkkazi, Sami
AU - Sethumadhavan, Nithya
AU - Henneken, Phil
AU - Miura, Eriko
AU - Hayashi, Ayumi
AU - Kocylowski, Maciej K.
AU - Haupt, Alexander
AU - Kaminski, Debora
AU - Schreiner, Dietmar
AU - Nowacka, Agata
AU - van den Broucke, Jean-Baptiste
AU - Kulik, Akos
AU - Schulte, Uwe
AU - Sterky, Fredrik H.
AU - Yuzaki, Michisuke
AU - Scheiffele, Peter
AU - Fakler, Bernd
TI - Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/09/05
VL - 17
IS - 1
SP - 9509
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-77377-4
UR - https://doi.org/10.1038/s41467-026-77377-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-77377-4",
"type": "article-journal",
"title": "Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization",
"container-title": "Nature communications",
"author": [
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"given": "Spyros"
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{
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{
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{
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{
"family": "Schreiner",
"given": "Dietmar"
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{
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"given": "Agata"
},
{
"family": "van den Broucke",
"given": "Jean-Baptiste"
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{
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{
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"given": "Uwe"
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{
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"given": "Fredrik H."
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{
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{
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},
{
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}
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9509",
"DOI": "10.1038/s41467-026-77377-4",
"PMID": "42701130",
"PMCID": "PMC13546301",
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5
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}
}

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