OSCR

Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease.

Code ↔ Paper

16 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 16 matches
  1. [1] § Methods › Transcriptional age calculation ↔ 2_Scripts/2. RNAAgeCalc.R, lines 170–216 · score 0.92 · predict_age, DESeq2, transcript length, gene length, RNAAgeCalc, exprtype
  2. [2] § Results › Descriptive statistics ↔ 2_Scripts/3. Deconvolution.R, lines 43–88 · score 0.86 · CERAD neuritic plaque, Thal phase, CERAD score, PD Braak, AD Braak, allele
  3. [3] § Methods › Deconvolution ↔ 2_Scripts/3. Deconvolution.R, lines 90–170 · score 0.86 · music2_prop_t_statistics, RNA seq, deconvolution, astrocytes, endothelia, microglia
  4. [4] § Methods › Weighted gene co-expression network analysis ↔ 2_Scripts/8. WGCNA.R, lines 227–273 · score 0.79 · dynamic tree cutting, module eigengene, dissimilarity, dendrogram, WGCNA, distance
  5. [5] § Results › Descriptive statistics ↔ 2_Scripts/1. Data exploration and pre-processing.R, lines 106–147 · score 0.67 · Thal phase, AD Braak, genotype, Fisher, APOE, females
  6. [6] § Results › Common DEGs are linked to neurological diseases ↔ 2_Scripts/7. Functional pathway analyses.R, lines 963–1043 · score 0.66 · DisGeNet, gene disease, common DEGs, vocabulary, curated, interaction
  7. [7] § Results › Chromatin organization module is altered in dementia ↔ 2_Scripts/8. WGCNA.R, lines 137–176 · score 0.66 · soft thresholding, adjacency matrix, negative correlation, power, WGCNA
  8. [8] § Methods › Differential gene expression ↔ 2_Scripts/5. DEGs.R, lines 44–86 · score 0.65 · continuous variables, DESeq2, RNAAge, model, covariates, filtered
  9. [9] § Methods › Differential gene expression ↔ 2_Scripts/5.1 DEGs demented versus non-demented.R, lines 43–81 · score 0.65 · continuous variables, DESeq2, RNAAge, model, covariates, filtered
  10. [10] § Results › Gene ontology reveals common molecular functions and cellular components among the dementias ↔ 2_Scripts/7. Functional pathway analyses.R, lines 392–433 · score 0.64 · Kyoto Encyclopedia, molecular function, Gene Ontology, MF, Genomes, KEGG
  11. [11] § Results › Common DEGs are linked to neurological diseases ↔ 2_Scripts/6. Common expressed genes.R, lines 146–210 · score 0.62 · Venn diagram, upregulated genes, downregulated genes, expressed genes, DEGs, PDD
  12. [12] § Results › Common DEGs are linked to neurological diseases ↔ 2_Scripts/7. Functional pathway analyses.R, lines 963–1043 · score 0.60 · Gene Disease class, DisGeNet, Common DEGs, curated, database, Heatmap
  13. [13] § Methods › Covariate selection ↔ 2_Scripts/1. Data exploration and pre-processing.R, lines 401–473 · score 0.60 · edgeR, Trimmed, composition, TMM, cpm, sequencing
  14. [14] § Methods › RNA-sequencing and data quality control ↔ 2_Scripts/3. Deconvolution.R, lines 43–88 · score 0.52 · NovaSeq, Bio, Illumina, sequencing, library, RNA
  15. [15] § Methods › Covariate selection ↔ 2_Scripts/3. Deconvolution.R, lines 1–41 · score 0.51 · edgeR, deconvolution, location, cpm, cell, sex
  16. [16] § Results › Transcriptional aging is accelerated in the hippocampus of Down syndrome with dementia ↔ 2_Scripts/2. RNAAgeCalc.R, lines 170–216 · score 0.50 · RNAAgeCalc, chronological age, Caucasian, predicts, database, tissue

Paper

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The authors' code

R · 170 lines · 8.5 KB · no license · 4 matches

  1. # 3. MuSiC2: Cell type deconvolution for multi-condition bulk RNA-seq data
  2. # Load libraries
  3. library(clusterProfiler)
  4. library(MuSiC)
  5. library(MuSiC2) # Deconvolution method
  6. library(scRNAseq) # To load the snRNA-seq data from Darmanis et., 2015 for brain data
  7. library(edgeR)
  8. library(ggpubr)
  10. # Load phenotype data
  11. phenoHEROES <- read.delim("NPJ_dementia/1_Datasets/PhenoData/phenoHEROES_RNAAge_FPKM.csv", sep = ",", dec = ",", header = TRUE)
  12. row.names(phenoHEROES) <- phenoHEROES$Tube_code
  13. phenoHEROES$X <- NULL
  15. # Check samples
  16. table(phenoHEROES$Brain_region, factor(phenoHEROES$Status, levels = c("Control", "PDD", "AD", "DSD")))
  17. table(phenoHEROES$Sex, factor(phenoHEROES$Status, levels = c("Control", "PDD", "AD", "DSD")))
  19. # Load the original Counts Matrix
  20. countsHERO <- read.delim("NPJ_dementia/1_Datasets/Counts.matrix.csv", sep = ",", header = TRUE)
  21. countsHERO <- cbind(countsHERO$X, countsHERO[, phenoHEROES$Tube_code])
  22. row.names(countsHERO) <- countsHERO[,1]
  23. countsHEROES <- countsHERO[,2:21]
  25. # Place bulk RNA-seq in Expression object and snRNA-seq data in SingleCellExperiment object for MuSiC2 deconvolution
  26. # Step 1: Darmanis et al., 2015 -- > The counts of all genes for any given cell where converted
  27. # to counts per million (CPM) by diving with the total number of reads and multiplying
  28. # by 10^6 followed by conversion to a log base 10.
  29. sn_RNA_seq <- DarmanisBrainData(ensembl = FALSE,
  30. location = TRUE,
  31. remove.htseq = TRUE,
  32. legacy = FALSE)
  34. # Step 2: Make my own data in an ExpressionSet object
  35. # Phenotype data for Demented for the brain regions Hippocampus
  36. phenoHEROES_DEMENTED_HC <- phenoHEROES
  38. # Make assaydata objects
  39. countsHEROES_DEMENTED_HC <- countsHEROES
  40. countsHEROES_DEMENTED_HC <- edgeR::cpm(countsHEROES_DEMENTED_HC, log = FALSE)
  41. summary(countsHEROES_DEMENTED_HC)
  43. # Check for same names in counts matrices and phenotype data frame
  44. all(rownames(phenoHEROES_DEMENTED_HC) == colnames(countsHEROES_DEMENTED_HC))
  45. colnames(countsHEROES_DEMENTED_HC)
  46. rownames(phenoHEROES_DEMENTED_HC)
  48. # Create the metadata
  49. metadata <- data.frame(labelDescription = c("Case/control status", "Case number", "Chronological age", "Biological age",
  50. "Difference between Biological age and Chronological age", "Sex of the subject",
  51. "APOE alleles", "APOE4 status",
  52. "BioBank ID", "Biobank", "Batch number", "Postmortem interval in hours",
  53. "Braak and Braak NFT stage", "Thal Pahse for Aβ plaques", "CERAD neuritic plaque score", "Spread of Lewy pathology (α-synuclein protein)",
  54. "Brain region", "Labelled code", "Sequencing library",
  55. "RNA intergrity number",
  56. "RNA concentration", "Condition of the patient",
  57. "PC1", "PC2", "PC3"),
  58. row.names = c("Status", "Case", "ChronAge", "RNAAge",
  59. "AgeDif", "Sex",
  60. "APOE", "APOE4", "BrainID", "BrainBank",
  61. "Batch", "PMD", "AD_Braak", "Thal_phase", "CERAD_score", "PD_Braak",
  62. "Brain_region", "Tube_code", "Library_code",
  63. "RIN", "Concentration", "Condition",
  64. "The first principal component",
  65. "The second principal component",
  66. "The third principal component"))
  68. # Create AnnotatedDataFrame
  69. phenoData_DEMENTED_HC <- new("AnnotatedDataFrame", data = phenoHEROES_DEMENTED_HC, varMetadata = metadata)
  71. # Create annotation
  72. annotation <- "NovaSeq 6000 Sequencing System (Illumina)"
  74. experimentData <- new("MIAME",
  75. name = "René A.J. Crans",
  76. lab="Cellular & Systems Neurobiology Lab",
  77. contact = "[email hidden]",
  78. title = "Hippocampal brain samples of control and demented individuals",
  79. abstract = "ExpressionSet",
  80. url = "https://www.crg.eu/en/programmes-groups/dierssen-lab")
  82. # ExpressionSet
  83. DEMENTED_HC_Set <- ExpressionSet(assayData = countsHEROES_DEMENTED_HC,
  84. phenoData = phenoData_DEMENTED_HC,
  85. experimentData = experimentData,
  86. annotation = "NovaSeq 6000 Sequencing System")
  88. exprs(DEMENTED_HC_Set)
  90. # Step 3: MuSiC2 deconvolution
  91. # Use T-statistics, because it is more robust when dealing with smaller sample sizes compared to TOAST.
  92. unique(colData(sn_RNA_seq)$cell.type) # Cell-types present in the snRNA-seq experiment
  93. counts(sn_RNA_seq) <- edgeR::cpm(counts(sn_RNA_seq), log = FALSE)
  95. # Demented subjects -----------------------------------------------
  96. bulk_control_DEMENTED_HC <- exprs(DEMENTED_HC_Set)[, DEMENTED_HC_Set$Status == "Control"]
  97. bulk_case_DEMENTED_HC <- exprs(DEMENTED_HC_Set)[, DEMENTED_HC_Set$Status != "Control"]
  99. set.seed(1234)
  100. est_DEMENTED_HC <- music2_prop_t_statistics(bulk.control.mtx = bulk_control_DEMENTED_HC,
  101. bulk.case.mtx = bulk_case_DEMENTED_HC,
  102. sc.sce = sn_RNA_seq, clusters = 'cell.type', samples = 'experiment_sample_name',
  103. select.ct = c("astrocytes", "endothelial", "microglia", "neurons", "oligodendrocytes"),
  104. n_resample = 1000, sample_prop = 0.5, cutoff_c = 0.05, cutoff_r = 0.01)
  106. est.prop_HC <- est_DEMENTED_HC$Est.prop
  108. # Plot estimated cell type proportions
  109. prop_HC <- cbind("proportion" = c(est.prop_HC),
  110. "sampleID" = rep(rownames(est.prop_HC),
  111. times=ncol(est.prop_HC)),
  112. "celltype" = rep(colnames(est.prop_HC),
  113. each = nrow(est.prop_HC)))
  115. prop_HC <- as.data.frame(prop_HC)
  116. prop_HC$proportion <- as.numeric(as.character(prop_HC$proportion))
  118. CONTROL_DEMENTED_HC <- phenoHEROES_DEMENTED_HC$Status
  120. prop_HC$group <- rep(CONTROL_DEMENTED_HC, 5) # As I will select 5 cell-types
  121. prop_HC$group <- factor(prop_HC$group, levels = c("Control", "PDD", "AD", "DSD"))
  122. cols <-c("astrocytes" = "cadetblue2",
  123. "endothelial" = "lightsalmon1",
  124. "microglia" = "palegreen2",
  125. "neurons" = "goldenrod1",
  126. "oligodendrocytes" = "steelblue3")
  128. # Plot samples cell deconvolution
  129. stat.test_DEMENTED_HC <- prop_HC %>%
  130. group_by(celltype) %>%
  131. tukey_hsd(proportion ~ group)
  132. stat.test_DEMENTED_HC
  134. png(filename="NPJ_dementia/3_Figures/3_Deconvolution/MuSiC2_deconvolution.png",
  135. width = 30,
  136. height = 15,
  137. units = "cm",
  138. res = 1200,
  139. pointsize = 4)
  141. ggplot(prop_HC, aes(x = group, y = proportion, colour = celltype, fill = celltype)) + xlab('')+
  142. geom_violin(width = 0.5, alpha = 0.75, size = 5) +
  143. geom_jitter(position = position_dodge2(width = 0.5, preserve = "total"), size = 4 , alpha = 0.5, color = "black") +
  144. stat_summary(fun = mean,
  145. geom = "crossbar", width = 0.5,size=0.5,color='gray36') +
  146. theme_bw() +
  147. facet_grid(.~celltype, scales = "free_y",
  148. labeller = labeller(celltype = c(astrocytes = "Astrocytes",
  149. endothelial = "Endothelia",
  150. microglia = "Microglia",
  151. neurons = "Neurons",
  152. oligodendrocytes = "Oligodendrocytes"))) +
  153. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  154. axis.text.x = element_text(size = 12, angle = 0, hjust = 0.5, face = "bold"),
  155. axis.text.y = element_text(size = 14, face = "bold"),
  156. axis.title.x = element_text(size = 12),
  157. axis.title.y = element_text(size = 18, face = "bold"),
  158. axis.line = element_line(colour = "black"),
  159. strip.text.x = element_text(size = 16, face = "bold"),
  160. panel.grid.major = element_blank(),
  161. panel.grid.minor = element_blank(),
  162. panel.background = element_blank(),
  163. legend.position = 'none')+
  164. scale_color_manual(values = cols) +
  165. scale_fill_manual(values = cols) +
  166. ylab('Cell-type proportions') +
  167. ggtitle(paste("MuSiC2 cell-type deconvolution")) +
  168. stat_pvalue_manual(stat.test_DEMENTED_HC, "p = {p.adj}", hide.ns = TRUE, y.position = 0.15, size = 5)
  170. dev.off()

3. Deconvolution.R at commit 7a0f2a6, no license · at the source

Overview

Authors: René A. J. Crans1, Marta Fructuoso2, Karen Bascón-Cardozo1, Hatice Recaioglu1, Jesus Sotelo-Fonseca1, Yannick Vermeiren3,4, André Strydom5, Debby Van Dam3,6, Peter P. De Deyn3,6,7, Bernardo Rodríguez-Martín1, Marie-Claude Potier2, Mara Dierssen1,8,9
ORCID iDs: René A. J. Crans, Yannick Vermeiren
  1. Center for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology,Barcelona, Spain
  2. Paris Brain Institute ICM, Salpêtrière Hospital,Paris, France
  3. Department of Biomedical Sciences, Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp,Antwerp, Belgium
  4. Present Address: Division of Human Nutrition and Health, Chair Group Nutritional Biology, Wageningen University and Research (WUR),Wageningen, The Netherlands
  5. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London,London, United Kingdom
  6. Department of Neurology and Alzheimer Research Center, University of Groningen and University Medical Center Groningen,Groningen, The Netherlands
  7. Department of Neurology and Memory Clinic, Middelheim General Hospital (ZNA),Antwerp, Belgium
  8. Department of Experimental and Health Sciences, University Pompeu Fabra,Barcelona, Spain
  9. Biomedical Research Networking Center for Rare Diseases (CIBERER),Barcelona, Spain
Institutions: Barcelona Institute of Science and Technology (Spain); Institut du Cerveau (France); University of Antwerp (Belgium); Wageningen University & Research (Netherlands); King's College London (United Kingdom); University Medical Center Groningen (Netherlands); Universitat Pompeu Fabra (Spain); Centre for Biomedical Network Research on Rare Diseases (Spain)
Journal: NPJ dementia, volume 2, issue 1, article 32
Dates: received 24 September 2025; accepted 5 March 2026; published online 29 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44400-026-00075-x · PMID 42078116 · PMCID PMC13128439 · OpenAlex W4415587479
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-7703742/v1
Status: code verified
Categories: other condition (population), Alzheimer's / dementia (population), Parkinson's (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Spectral & time-frequency
Keywords: Biomarkers, Diseases, Neurology, Neuroscience
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Jérôme-Lejeune Fondation (15_PDC-2021, 12_PDC-2019); ISCIII Sello Excelencia ISCIII-Health (IHMC22/00026); Ministerio de Ciencia, Innovación y Universidades (RTC2019-007230-1, RTC2019-007329-1, and CPP2022-009659); Joint Program Neurodegenerative Diseases Agence Nationale de la Recherche (JPND ANR-17-JPCD-0003 HEROES and JPND HEROES AC170006); Horizon 2020 (848077 and 899986); Medical Research Council (MR/R024901/1); ZonMW project (733051072); Centre Of Excellence Neurodegenerative Diseases Agence Nationale de la Recherche (COEN-0002 and COEN-4024); Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación) (PID2019-110755RB-I00/AEI/10.13039/501100011033 and PID2022-141900OB-I00 INTO-DS)
Citations: not cited yet (Europe PMC); 87 references in the paper

Abstract

Extensive evidence suggests overlapping pathological mechanisms in the brain of individuals with Parkinson’s disease dementia, Down syndrome dementia, and Alzheimer’s disease. For these neurodegenerative dementias, we observed that the chronological age did not align with their biological age, which was determined based on hippocampal transcript levels (i.e., transcriptional age). Subsequently, we performed a transcriptomic analysis that corrected for the transcriptional age in the hippocampus of affected individuals, highlighting common underlying pathogenic mechanisms. There were 45 common differentially expressed genes (DEGs), whereas enriched functional terms were related to lysine N-methyltransferase activity and intermediate filament. Co-expression network analysis displayed a module that was significantly downregulated in the non-demented control group only. This module identified EHMT2 and LMNB2 as hub genes, which were also common DEGs. Overall, these findings uncover shared functional insights in the hippocampus, while specifically highlighting EHMT2 and LMNB2 as potential universal biomarkers or disease-altered targets across neurodegenerative dementias.

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 16 matches between paragraphs and lines of code.

renealbertjohan/NPJ_dementia

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7a0f2a6a8fed940ee1723ebb8af0486612924698, 23 February 2026
Languages: R (10)
Size: 150 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), car (4 files), ggplot2 (4 files), ggpubr (4 files), DESeq2 (3 files), edgeR (3 files), pheatmap (3 files), rstatix (3 files), clusterProfiler (2 files), limma (1 file), reshape2 (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/renealbertjohan/NPJ_dementia

Code availability

The R code and instructions for full reproduction of the results are available at https://github.com/renealbertjohan/NPJ_dementia.

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

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;
  • 10 scripts, each with its path and the digest of its content;
  • 16 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

The RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus repository with the series record GSE318560.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 4 keywords, 9 funders, 86 references.

Cite

This paper

Crans, R. A. J., Fructuoso, M., Bascón-Cardozo, K., Recaioglu, H., Sotelo-Fonseca, J., Vermeiren, Y., Strydom, A., Van Dam, D., De Deyn, P. P., Rodríguez-Martín, B., Potier, M.-C., & Dierssen, M. (2026). Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease. NPJ dementia, 2(1), 32. https://doi.org/10.1038/s44400-026-00075-x

BibTeX

@article{crans2026common,
author = {Crans, René A. J. and Fructuoso, Marta and Bascón-Cardozo, Karen and Recaioglu, Hatice and Sotelo-Fonseca, Jesus and Vermeiren, Yannick and Strydom, André and Van Dam, Debby and De Deyn, Peter P. and Rodríguez-Martín, Bernardo and Potier, Marie-Claude and Dierssen, Mara},
title = {{Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease}},
journal = {NPJ dementia},
year = {2026},
month = apr,
volume = {2},
number = {1},
pages = {32},
publisher = {Springer Science+Business Media},
issn = {3005-1940},
doi = {10.1038/s44400-026-00075-x},
url = {https://doi.org/10.1038/s44400-026-00075-x},
pmid = {42078116},
pmcid = {PMC13128439}
}

RIS

TY - JOUR
AU - Crans, René A. J.
AU - Fructuoso, Marta
AU - Bascón-Cardozo, Karen
AU - Recaioglu, Hatice
AU - Sotelo-Fonseca, Jesus
AU - Vermeiren, Yannick
AU - Strydom, André
AU - Van Dam, Debby
AU - De Deyn, Peter P.
AU - Rodríguez-Martín, Bernardo
AU - Potier, Marie-Claude
AU - Dierssen, Mara
TI - Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease
T2 - NPJ dementia
J2 - NPJ Dement
PY - 2026
DA - 2026/04/29
VL - 2
IS - 1
SP - 32
SN - 3005-1940
PB - Springer Science+Business Media
DO - 10.1038/s44400-026-00075-x
UR - https://doi.org/10.1038/s44400-026-00075-x
LA - en
ER -

CSL-JSON

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"id": "10.1038/s44400-026-00075-x",
"type": "article-journal",
"title": "Common pathogenic mechanisms in the hippocampus across neurodegenerative dementias: Alzheimer's disease, Down syndrome, and Parkinson's disease",
"container-title": "NPJ dementia",
"author": [
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{
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{
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"given": "Jesus"
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{
"family": "Vermeiren",
"given": "Yannick"
},
{
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"given": "André"
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{
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"given": "Debby"
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{
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"given": "Peter P."
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{
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],
"container-title-short": "NPJ Dement",
"volume": "2",
"issue": "1",
"page": "32",
"DOI": "10.1038/s44400-026-00075-x",
"PMID": "42078116",
"PMCID": "PMC13128439",
"ISSN": "3005-1940",
"publisher": "Springer Science+Business Media",
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"language": "en",
"issued": {
"date-parts": [
[
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}

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[9] doi:10.1016/j.isci.2026.115196 [code]
Transcriptional and cellular maturation of the chick spinal cord in the context of distinct neuromuscular circuits.
Journal: iScience
In common: WGCNA, edgeR, limma, 6 other tools, 2 references
[10] doi:10.1038/s41467-026-74455-5 [code]
Neuron type-specific translatomes in dorsal hippocampus during early memory consolidation.
Journal: Nature communications
In common: WGCNA, edgeR, DESeq2, 4 other tools, 6 references

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