OSCR

Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma.

Code ↔ Paper

6 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 6 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › MiroScape: an interactive portal for cross-species glioma transcriptomics ↔ src/components/PlotGenerators/CrossSpeciesQuerry.js, lines 63–128 · score 0.64 · log2 fold change, Human Bulk RNA, Cross Species, GTEx, TCGA, Mouse
  2. [2] § Results › MiroScape: an interactive portal for cross-species glioma transcriptomics ↔ src/components/MiroScripts.js, the whole file · a weak match · score 0.62 · Human Bulk RNA, Cross Species, miroScripts, snRNA, page, home
  3. [3] § Results › MiroScape: an interactive portal for cross-species glioma transcriptomics ↔ src/components/MiroScripts/MiroScriptsHome.js, the whole file · a weak match · score 0.60 · snRNA, download, vivo, ex, platforms, interactive
  4. [4] § Materials and Methods › Human bulk RNA-seq processing and differential expression analysis ↔ src/components/PlotGenerators/CrossSpeciesQuerry.js, lines 63–128 · score 0.56 · log2 fold change, DESeq2, RNA, human, bulk, gene
  5. [5] § Results › MiroScape: an interactive portal for cross-species glioma transcriptomics ↔ src/components/Introduction.js, the whole file · a weak match · score 0.53 · MiroScape, MiroScripts, surface, platform, proteomic, cross
  6. [6] § Materials and Methods › Web platform and data publication ↔ src/components/MiroScripts/MiroScriptsHome.js, the whole file · a weak match · score 0.51 · MiroScape, snRNA, platform, interactive, React, seq

Paper

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

The paper is loaded when this pane is shown.

The authors' code

JavaScript · 253 lines · 9.4 KB · no license · 2 matches

  1. import React, { useState, useEffect } from "react";
  3. export default function TestPage() {
  4. const [geneTableData, setGeneTableData] = useState(null);
  5. const [geneQuery, setGeneQuery] = useState("");
  6. const [excelData, setExcelData] = useState(null);
  7. const [loading, setLoading] = useState(false);
  8. const [allData, setAllData] = useState({
  9. tcga: null,
  10. deseq2: null,
  11. mouseSn: null,
  12. mousePseudobulk: null,
  13. mapping: null
  14. });
  15. const [error, setError] = useState(null);
  17. // Load all data files
  18. useEffect(() => {
  19. const loadData = async () => {
  20. setLoading(true);
  21. setError(null);
  22. try {
  23. const [tcga, deseq2, mouseSn, mousePseudobulk, mapping] = await Promise.all([
  24. fetch(`${process.env.PUBLIC_URL}/data/TCGA_GBM_vs_Brain.csv`).then(r => r.text()),
  25. fetch(`${process.env.PUBLIC_URL}/data/deseq2_results_with_plain.csv`).then(r => r.text()),
  26. fetch(`${process.env.PUBLIC_URL}/data/mouse_sn_DE.csv`).then(r => r.text()),
  27. fetch(`${process.env.PUBLIC_URL}/data/mouse_pseudobulk_de_results.csv`).then(r => r.text()),
  28. fetch(`${process.env.PUBLIC_URL}/data/human_mouse_presence_map.csv`).then(r => r.text())
  29. ]);
  31. setAllData({
  32. tcga: parseCSV(tcga),
  33. deseq2: parseCSV(deseq2),
  34. mouseSn: parseCSV(mouseSn),
  35. mousePseudobulk: parseCSV(mousePseudobulk),
  36. mapping: parseCSV(mapping)
  37. });
  38. } catch (err) {
  39. setError('Failed to load data files: ' + err.message);
  40. } finally {
  41. setLoading(false);
  42. }
  43. };
  45. loadData();
  46. }, []);
  48. // CSV parser
  49. const parseCSV = (csvText) => {
  50. const lines = csvText.trim().split('\n');
  51. const headers = lines[0].split(',').map(h => h.trim().replace(/^"|"$/g, ''));
  52. const data = [];
  54. for (let i = 1; i < lines.length; i++) {
  55. const values = lines[i].split(',').map(v => v.trim().replace(/^"|"$/g, ''));
  56. const row = {};
  57. headers.forEach((header, index) => {
  58. row[header] = values[index];
  59. });
  60. data.push(row);
  61. }
  63. return { headers, data };
  64. };
  66. // Search gene data
  67. const searchGene = () => {
  68. if (!allData.mapping || !geneQuery.trim()) {
  69. setError('Please enter a gene name');
  70. return;
  71. }
  73. const query = geneQuery.trim().toUpperCase();
  75. // Find gene in mapping
  76. const mappingEntry = allData.mapping.data.find(
  77. row => row.HumanGene?.toUpperCase() === query || row.MouseGene?.toUpperCase() === query
  78. );
  80. if (!mappingEntry) {
  81. setError('Gene not found in all datasets');
  82. setGeneTableData(null);
  83. return;
  84. }
  86. const humanGene = mappingEntry.HumanGene;
  87. const mouseGene = mappingEntry.MouseGene;
  89. // Build results table - always show 4 rows
  90. const results = [];
  92. // TCGA/GTEx
  93. let tcgaData = { dataset: 'TCGA/GTEx', gene: '-', log2FC: '-', avgExp: '-', pValue: '-', qValue: '-' };
  94. if (mappingEntry.in_tcga === '1') {
  95. const tcgaRow = allData.tcga.data.find(
  96. row => row.name?.toUpperCase() === humanGene?.toUpperCase()
  97. );
  98. if (tcgaRow) {
  99. tcgaData = {
  100. dataset: 'TCGA/GTEx',
  101. gene: tcgaRow.name,
  102. log2FC: parseFloat(tcgaRow.log2FC).toFixed(3),
  103. avgExp: parseFloat(tcgaRow.aveEXP).toFixed(3),
  104. pValue: parseFloat(tcgaRow['p-value']).toExponential(3),
  105. qValue: parseFloat(tcgaRow['q-value']).toExponential(3)
  106. };
  107. }
  108. }
  109. results.push(tcgaData);
  111. // Human Bulk RNA (DESeq2)
  112. let deseq2Data = { dataset: 'Human Bulk RNA', gene: '-', log2FC: '-', avgExp: '-', pValue: '-', qValue: '-' };
  113. if (mappingEntry.in_deseq2 === '1') {
  114. const deseq2Row = allData.deseq2.data.find(
  115. row => row.gene?.toUpperCase() === humanGene?.toUpperCase()
  116. );
  117. if (deseq2Row) {
  118. deseq2Data = {
  119. dataset: 'Human Bulk RNA',
  120. gene: deseq2Row.gene,
  121. log2FC: parseFloat(deseq2Row.log2FoldChange).toFixed(3),
  122. avgExp: parseFloat(deseq2Row.baseMean).toFixed(3),
  123. pValue: parseFloat(deseq2Row.pvalue).toExponential(3),
  124. qValue: parseFloat(deseq2Row.padj).toExponential(3)
  125. };
  126. }
  127. }
  128. results.push(deseq2Data);
  130. // Mouse snRNA-Seq
  131. let mouseSnData = { dataset: 'Mouse snRNA-Seq', gene: '-', log2FC: '-', avgExp: '-', pValue: '-', qValue: '-' };
  132. if (mappingEntry.in_mouse_sn === '1') {
  133. const mouseSnRow = allData.mouseSn.data.find(
  134. row => row.Gene?.toUpperCase() === mouseGene?.toUpperCase()
  135. );
  136. if (mouseSnRow) {
  137. mouseSnData = {
  138. dataset: 'Mouse snRNA-Seq',
  139. gene: mouseSnRow.Gene,
  140. log2FC: parseFloat(mouseSnRow.log2FC).toFixed(3),
  141. avgExp: '-',
  142. pValue: parseFloat(mouseSnRow.p_value).toExponential(3),
  143. qValue: parseFloat(mouseSnRow.q_value).toExponential(3)
  144. };
  145. }
  146. }
  147. results.push(mouseSnData);
  149. // Mouse Pseudobulk
  150. let mousePseudoData = { dataset: 'Mouse Pseudobulk', gene: '-', log2FC: '-', avgExp: '-', pValue: '-', qValue: '-' };
  151. if (mappingEntry.in_mouse_pseudobulk === '1') {
  152. const mousePseudoRow = allData.mousePseudobulk.data.find(
  153. row => row.Gene?.toUpperCase() === mouseGene?.toUpperCase()
  154. );
  155. if (mousePseudoRow) {
  156. mousePseudoData = {
  157. dataset: 'Mouse Pseudobulk',
  158. gene: mousePseudoRow.Gene,
  159. log2FC: parseFloat(mousePseudoRow.logFC_mouse).toFixed(3),
  160. avgExp: parseFloat(mousePseudoRow.AveExpr).toFixed(3),
  161. pValue: parseFloat(mousePseudoRow.PValue).toExponential(3),
  162. qValue: parseFloat(mousePseudoRow.padj || mousePseudoRow.FDR).toExponential(3)
  163. };
  164. }
  165. }
  166. results.push(mousePseudoData);
  168. setGeneTableData(results);
  169. setError(null);
  170. };
  172. return (
  173. <div style={{ padding: '20px', width: '100%' }}>
  174. <h2 style={{ fontSize: '22px' }}>Cross-Species Comparison</h2>
  176. <div style={{ marginBottom: '20px' }}>
  177. <input
  178. type="text"
  179. value={geneQuery}
  180. onChange={(e) => setGeneQuery(e.target.value)}
  181. onKeyPress={(e) => e.key === 'Enter' && searchGene()}
  182. placeholder="Enter gene name (e.g., PARP11 or Parp11)"
  183. style={{
  184. padding: '12px',
  185. fontSize: '18px',
  186. width: '400px',
  187. marginRight: '10px'
  188. }}
  189. />
  190. <button
  191. onClick={searchGene}
  192. disabled={loading}
  193. style={{
  194. padding: '12px 24px',
  195. fontSize: '18px',
  196. cursor: loading ? 'not-allowed' : 'pointer'
  197. }}
  198. >
  199. {loading ? 'Loading...' : 'Search'}
  200. </button>
  201. </div>
  203. {error && (
  204. <div style={{
  205. padding: '12px',
  206. backgroundColor: '#ffebee',
  207. color: '#c62828',
  208. borderRadius: '4px',
  209. marginBottom: '20px',
  210. fontSize: '16px'
  211. }}>
  212. {error}
  213. </div>
  214. )}
  216. {geneTableData && (
  217. <div>
  218. <h2 style={{ fontSize: '20px' }}>Results</h2>
  219. <table style={{
  220. width: '100%',
  221. borderCollapse: 'collapse',
  222. boxShadow: '0 2px 4px rgba(0,0,0,0.1)',
  223. fontSize: '16px',
  224. tableLayout: 'fixed'
  225. }}>
  226. <thead>
  227. <tr style={{ backgroundColor: '#f5f5f5' }}>
  228. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'left', fontSize: '18px', width: '20%' }}>Dataset</th>
  229. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'left', fontSize: '18px', width: '15%' }}>Gene</th>
  230. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'right', fontSize: '18px', width: '13%' }}>Log2 FC</th>
  231. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'right', fontSize: '18px', width: '17%' }}>Avg Expression</th>
  232. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'right', fontSize: '18px', width: '17%' }}>P-value</th>
  233. <th style={{ border: '1px solid #ddd', padding: '16px', textAlign: 'right', fontSize: '18px', width: '18%' }}>Q-value</th>
  234. </tr>
  235. </thead>
  236. <tbody>
  237. {geneTableData.map((row, index) => (
  238. <tr key={index} style={{ backgroundColor: index % 2 === 0 ? '#fff' : '#f9f9f9' }}>
  239. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px' }}>{row.dataset}</td>
  240. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px', fontStyle: row.gene === '-' ? 'italic' : 'normal', color: row.gene === '-' ? '#999' : '#000' }}>{row.gene}</td>
  241. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px', textAlign: 'right', color: row.log2FC === '-' ? '#999' : '#000' }}>{row.log2FC}</td>
  242. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px', textAlign: 'right', color: row.avgExp === '-' ? '#999' : '#000' }}>{row.avgExp}</td>
  243. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px', textAlign: 'right', color: row.pValue === '-' ? '#999' : '#000' }}>{row.pValue}</td>
  244. <td style={{ border: '1px solid #ddd', padding: '14px', fontSize: '16px', textAlign: 'right', color: row.qValue === '-' ? '#999' : '#000' }}>{row.qValue}</td>
  245. </tr>
  246. ))}
  247. </tbody>
  248. </table>
  249. </div>
  250. )}
  251. </div>
  252. );
  253. }

CrossSpeciesQuerry.js at commit 7973e06, no license · at the source

Overview

Authors: Zehui Du1, Menghan Li1, Brandon H Bergsneider1, Andy P Tsai2, Kwang Bog Cho1, Lily H Kim1, John Choi1, Gordon Li1, Tony Wyss-Coray2,3,4, Michael Lim1,4, Xinnan Wang1,4
ORCID iDs: Zehui Du, Menghan Li, Brandon H Bergsneider, Andy P Tsai, Tony Wyss-Coray, Michael Lim, Xinnan Wang
  1. Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
  2. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
  3. The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
  4. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
Institutions: Stanford Medicine (United States); Stanford University (United States)
Journal: Life science alliance, volume 9, issue 8, article e202603749
Dates: received 27 April 2026; accepted 30 April 2026; published online 13 May 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.26508/lsa.202603749 · PMID 42128668 · PMCID PMC13171295 · OpenAlex W7160981603
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
MeSH: Brain Neoplasms*, Glioma*, Macrophages*, Mitochondrial Proteins*, T-Lymphocytes*, Transcriptome*, Animals, Cell Line, Tumor, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Mice, Mitochondria, Tumor Microenvironment (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: NINDS (RO1NS128040); NIGMS (RO1GM143258, R35GM16151901); Stanford Wu Tsai Neurosciences Institute (Translate)
Citations: cited by 1 paper (Europe PMC); 60 references in the paper

Abstract

Mitochondrial regulators are increasingly recognized for their influence on immune signaling within the tumor microenvironment (TME). In glioma, where immunosuppression limits therapeutic efficacy, we investigate how targeting the mitochondrial protein MIRO1 alters the TME. We combine single-nucleus RNA sequencing of murine gliomas treated in vivo with an MIRO1-binding compound and bulk RNA sequencing of human glioma resections treated with the same compound ex vivo. Cross-species transcriptomic integration reveals an MIRO1-responsive program in the TME. Among shared targets, we identify PARP11/Parp11 as a consistently up-regulated gene in glioma, which is down-regulated after MIRO1-binding compound treatment in both human and mouse gliomas. Cell–cell communication analysis shows that a specific cluster of macrophages (MAC1), which exhibits robust Parp11 and Pdl1 (encoding PD-L1) expression, sends immunosuppressive signals to CD8+ cytotoxic T cells, and may receive prostaglandin E2 signals from another cluster of macrophages (MAC4). Targeting MIRO1 eliminates this cell circuitry and reduces the tumor cell population. Our study provides a transcriptomic framework for understanding mitochondria-immune crosstalk and nominates MIRO1-PARP11 as a potential effector axis of brain immune dysfunction.

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 6 matches between paragraphs and lines of code.

miroscape/MiroScape

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 7973e06f6ca9ce901d35e0af8151b35cc8b66bd0, 3 July 2026
Languages: JavaScript (24)
Size: 65 files, 24 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
25 files
At the source: github.com/miroscape/MiroScape

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

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;
  • 24 scripts, each with its path and the digest of its content;
  • 6 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

Processed data and analysis are available for download on our interactive platform, MiroScape, under the MiroScripts/Source Data tab. All original code is available on GitHub at https://github.com/miroscape/MiroScape. Raw sequencing data are at GEO repository GSE324505: GSE324504, GSE322979. TCGA (Weinstein et al, 2013) glioma samples and non-tumor brain cortex from the GTEx (The genotype-tissue expression project, 2013) dataset were analyzed with GEPIA3 (Kang et al, 2025) (https://gepia3.bioinfoliu.com/). Any additional information required to reanalyze the data reported in this paper is available from X Wang ().

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 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 14 MeSH terms, 3 funders, 60 references.

Cite

This paper

Du, Z., Li, M., Bergsneider, B. H., Tsai, A. P., Cho, K. B., Kim, L. H., Choi, J., Li, G., Wyss-Coray, T., Lim, M., & Wang, X. (2026). Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma. Life science alliance, 9(8), e202603749. https://doi.org/10.26508/lsa.202603749

BibTeX

@article{du2026cross,
author = {Du, Zehui and Li, Menghan and Bergsneider, Brandon H and Tsai, Andy P and Cho, Kwang Bog and Kim, Lily H and Choi, John and Li, Gordon and Wyss-Coray, Tony and Lim, Michael and Wang, Xinnan},
title = {{Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma}},
journal = {Life science alliance},
year = {2026},
month = may,
volume = {9},
number = {8},
pages = {e202603749},
publisher = {Life Science Alliance LLC},
issn = {2575-1077},
doi = {10.26508/lsa.202603749},
url = {https://doi.org/10.26508/lsa.202603749},
pmid = {42128668},
pmcid = {PMC13171295}
}

RIS

TY - JOUR
AU - Du, Zehui
AU - Li, Menghan
AU - Bergsneider, Brandon H
AU - Tsai, Andy P
AU - Cho, Kwang Bog
AU - Kim, Lily H
AU - Choi, John
AU - Li, Gordon
AU - Wyss-Coray, Tony
AU - Lim, Michael
AU - Wang, Xinnan
TI - Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma
T2 - Life science alliance
J2 - Life Sci Alliance
PY - 2026
DA - 2026/05/13
VL - 9
IS - 8
SP - e202603749
SN - 2575-1077
PB - Life Science Alliance LLC
DO - 10.26508/lsa.202603749
UR - https://doi.org/10.26508/lsa.202603749
LA - en
ER -

CSL-JSON

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"id": "10.26508/lsa.202603749",
"type": "article-journal",
"title": "Cross-species transcriptomic integration reveals a MIRO1-mediated macrophage-T cell axis in glioma",
"container-title": "Life science alliance",
"author": [
{
"family": "Du",
"given": "Zehui"
},
{
"family": "Li",
"given": "Menghan"
},
{
"family": "Bergsneider",
"given": "Brandon H"
},
{
"family": "Tsai",
"given": "Andy P"
},
{
"family": "Cho",
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},
{
"family": "Kim",
"given": "Lily H"
},
{
"family": "Choi",
"given": "John"
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{
"family": "Li",
"given": "Gordon"
},
{
"family": "Wyss-Coray",
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{
"family": "Lim",
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{
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"given": "Xinnan"
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],
"container-title-short": "Life Sci Alliance",
"volume": "9",
"issue": "8",
"page": "e202603749",
"DOI": "10.26508/lsa.202603749",
"PMID": "42128668",
"PMCID": "PMC13171295",
"ISSN": "2575-1077",
"publisher": "Life Science Alliance LLC",
"URL": "https://doi.org/10.26508/lsa.202603749",
"language": "en",
"issued": {
"date-parts": [
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13
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]
}
}

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

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