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Transcriptomic signatures of synaptic loss in Alzheimer's disease.

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2 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 2 matches
  1. [1] § STAR★Methods › Method details › AD-related genes in PLS1 significant genes ↔ Analysis of AD-related gene from Genecards.py, lines 11–21 · score 0.70 · AD related gene, GeneCards, relevance score, overlapped, PLS1
  2. [2] § Results › Genes associated with SV2A alterations in AD ↔ Analysis of AD-related gene from Genecards.py, lines 11–21 · score 0.52 · AD related genes, GeneCards, scores, PLS1

Paper

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

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The authors' code

Python · 41 lines · 1.1 KB · no license · 2 matches

  2. import pandas as pd
  3. from scipy.stats import pearsonr
  5. # Background gene
  6. df1 = pd.read_excel(path+'expression_L.xlsx')
  8. # PLS1 significant genes
  9. df2 = pd.read_excel(path+'PLS_res.xlsx')
  11. # AD-related genes from GeneCards
  12. df3 = pd.read_csv(path+'GeneCards-SearchResults.csv')
  13. df3 = df3[df3['Relevance score']>7]
  15. bgge = df1.columns
  16. opge0 = list( set(bgge)&set(list(df3['Gene Symbol'])) )
  17. opge = list( set(opge0)&set(list(df2['Genes'])) )
  19. print("-Number of genes overlapping with background genes：",len(opge0))
  20. print('-Number of genes overlapping with PLS1 significant genes',len(opge))
  21. print(df2[df2['Genes'].isin(opge)])
  23. # t-map correlation analysis
  24. df4 = pd.read_excel(path+'t_map_pval.xlsx')
  25. t_stats = list(df4['t_stats'])
  26. t_stats = t_stats[0:41]
  28. exp_opge = df1[opge]
  29. corr_res = pd.DataFrame()
  30. ps=[];cors=[]
  31. for ge in opge:
  32. corr, p_value = pearsonr(exp_opge[ge], t_stats)
  33. ps.append(p_value)
  34. cors.append(corr)
  36. corr_res['Genes'] = opge
  37. corr_res['Correlation'] = cors
  38. corr_res['p-value'] = ps
  39. corr_res = corr_res.sort_values(by='Correlation')
  40. print(corr_res)

Analysis of AD-related gene from Genecards.py at commit cd661bb, no license · at the source

Overview

Authors: Lipeng Sun1, Xinyuan Yang1, Xiaomeng Xu1, Lin Kang2, Yingting Zheng2, Qi Huang3, Wei Xu2, Yihui Guan3, Yiwen Shen4, Jun Liu2, Yulei Deng1,2, Shu Liu5, Junfang Zhang6, Fang Xie3, Binyin Li1,2
ORCID iDs: Fang Xie, Binyin Li
  1. Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai 200020, China
  2. Department of Neurology & Institute of Neurology, Ruijin Hospital Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  3. Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai 200040, China
  4. Department of Radiology, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai 200020, China
  5. State Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
  6. Department of Neurology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
Institutions: Shanghai Jiao Tong University (China); Ruijin Hospital (China); Fudan University (China); Huashan Hospital (China); Kunming Institute of Zoology (China); Chinese Academy of Sciences (China); Shanghai Sixth People's Hospital (China)
Journal: iScience, volume 29, issue 7, article 116336
Dates: received 9 December 2025; accepted 26 May 2026; published online 11 June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.116336 · PMID 42317388 · PMCID PMC13272548 · OpenAlex W7164382104
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), PET / SPECT (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: biological sciences
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (81901180, 82271441, 82171473, 82501588); National Key Research and Development Program of China (2022ZD0213800)
Citations: not cited yet (Europe PMC); 91 references in the paper

Abstract

Synaptic loss is a major pathological cause of cognitive impairment in Alzheimer’s disease (AD). We integrated in vivo synaptic density imaging using synaptic vesicle glycoprotein 2A positron emission tomography (PET) from a prospective AD cohort with brain transcriptomic data. Partial least squares analysis identified 1,233 genes associated with synaptic loss, enriched for synaptic organization, Tau phosphorylation, cytoskeletal integrity, and ubiquitin-mediated protein degradation. Cell-type enrichment showed downregulation in glutamatergic and GABAergic neurons and upregulation in oligodendrocytes and endothelial cells. Stratification by Tau Braak staging suggested stage-dependent transcriptional programs involved in myelination and inflammation. These associations were supported by retest PET data and longitudinal analyses, which further linked progressive synaptic decline to DNA repair and telomere pathways. Proteomic profiling further highlighted mitochondrial dysfunction. Together, these findings delineate transcriptional signatures underlying synaptic decline in AD with consistency evidence across cross-sectional, retest, longitudinal, and proteomic analyses underscoring their mechanistic relevance and biomarker potential.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Sahuhu-Sun/Imaging-transcriptomics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cd661bbfc3b58ec31500e08e2ac36b4291cb516c, 19 October 2025
Languages: Python (3), R (1)
Size: 5 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Additional resources”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (3 files), NumPy (2 files), SciPy (2 files), statsmodels (2 files), ggplot2 (1 file), scikit-learn (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/Sahuhu-Sun/Imaging-transcriptomics

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;
  • 4 scripts, each with its path and the digest of its content;
  • 2 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

Datasets cited

Data and code availability

The processed SV2A PET data and all public transcriptomic datasets used in this study are publicly available as of the date of publication. Accession numbers are listed in the key resources table.

All original analysis code has been deposited in GitHub and is publicly available as of the date of publication. Accession numbers are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper 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 2, 28 September 2026

  • Authors: added Fang Xie (0000-0003-2667-281X); Binyin Li (0000-0003-1953-382X); removed Fang Xie; Binyin Li

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 1 keyword, 2 funders, 88 references.

Cite

This paper

Sun, L., Yang, X., Xu, X., Kang, L., Zheng, Y., Huang, Q., Xu, W., Guan, Y., Shen, Y., Liu, J., Deng, Y., Liu, S., Zhang, J., Xie, F., & Li, B. (2026). Transcriptomic signatures of synaptic loss in Alzheimer's disease. iScience, 29(7), 116336. https://doi.org/10.1016/j.isci.2026.116336

BibTeX

@article{sun2026transcriptomic,
author = {Sun, Lipeng and Yang, Xinyuan and Xu, Xiaomeng and Kang, Lin and Zheng, Yingting and Huang, Qi and Xu, Wei and Guan, Yihui and Shen, Yiwen and Liu, Jun and Deng, Yulei and Liu, Shu and Zhang, Junfang and Xie, Fang and Li, Binyin},
title = {{Transcriptomic signatures of synaptic loss in Alzheimer's disease}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {116336},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116336},
url = {https://doi.org/10.1016/j.isci.2026.116336},
pmid = {42317388},
pmcid = {PMC13272548}
}

RIS

TY - JOUR
AU - Sun, Lipeng
AU - Yang, Xinyuan
AU - Xu, Xiaomeng
AU - Kang, Lin
AU - Zheng, Yingting
AU - Huang, Qi
AU - Xu, Wei
AU - Guan, Yihui
AU - Shen, Yiwen
AU - Liu, Jun
AU - Deng, Yulei
AU - Liu, Shu
AU - Zhang, Junfang
AU - Xie, Fang
AU - Li, Binyin
TI - Transcriptomic signatures of synaptic loss in Alzheimer's disease
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/11
VL - 29
IS - 7
SP - 116336
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116336
UR - https://doi.org/10.1016/j.isci.2026.116336
LA - en
ER -

CSL-JSON

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