OSCR

Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease.

Code ↔ Paper

17 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.

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  1. [1] § METHODS › Stimulation of DA neurons and microglia › Bioinformatic analysis of stimulated DA neurons and microglia ↔ code/ASAP_invitro/processing/gene_DEA.Rmd, lines 328–421 · score 0.94 · OrgDb, keyType, maxGSSize, minGSSize, pAdjustMethod, pvalueCutoff
  2. [2] § METHODS › Bioinformatic analysis of snRNA-seq › Gene differential expression and GSEAs ↔ code/ASAP_invitro/processing/gene_DEA.Rmd, lines 328–421 · score 0.94 · OrgDb, keyType, maxGSSize, minGSSize, pAdjustMethod, pvalueCutoff
  3. [3] § METHODS › Bioinformatic analysis of snRNA-seq › Gene differential expression and GSEAs ↔ code/ASAP_PMDBS_snRNAseq/processing/gene_DEA_GSEA_batch_sex.Rmd, lines 88–129 · score 0.94 · OrgDb, keyType, maxGSSize, minGSSize, pAdjustMethod, pvalueCutoff
  4. [4] § METHODS › Bioinformatic analysis of snRNA-seq › Validation of differential expression analyses ↔ code/ASAP_PMDBS_snRNAseq/processing/gene_DEA_GSEA_batch_sex.Rmd, lines 88–129 · score 0.94 · OrgDb, keyType, maxGSSize, minGSSize, pAdjustMethod, pvalueCutoff
  5. [5] § RESULTS › PD microglia display an interferon response that correlates with TE expression ↔ code/ASAP_PMDBS_snRNAseq/processing/gene_DEA_GSEA_batch_sex.Rmd, lines 179–220 · score 0.71 · related GO terms, IFN related genes, core enrichment, PD SN, SN microglia, log2FC
  6. [6] § METHODS › Bioinformatic analysis of snRNA-seq › Validation of differential expression analyses ↔ code/ASAP_PMDBS_snRNAseq/processing/HERV_expression_PDvsCtl_batch_sex.Rmd, lines 36–150 · score 0.67 · covariate variance, fitExtractVarPartModel, voom, limma, genes
  7. [7] § RESULTS › PD microglia display an interferon response that correlates with TE expression ↔ code/ASAP_PMDBS_snRNAseq/processing/gene_DEA_GSEA.Rmd, lines 302–356 · score 0.67 · core enrichment genes, IFN related genes, related GO, Violin, log2FC, Interferon
  8. [8] § RESULTS › Detection of unique TE loci expression at single-cell type resolution in the human brain ↔ code/ASAP_PMDBS_snRNAseq/processing/TE_expression_celltype_markers.Rmd, lines 38–58 · score 0.64 · kbp L1HS PA3, kbp L1HS L1PA3, UMAP, TE expression, L1s, rows
  9. [9] § METHODS › Bioinformatic analysis of snRNA-seq › Gene and TE expression quantification using pseudobulks ↔ code/ASAP_PMDBS_bulkRNAseq/preprocessing/Scripts_Unique_STARalignment/UniMap_ASAP156_PD_NP21-57_PFC_bulk.sh, the whole file · a weak match · score 0.63 · genomeDir, sjdbGTFfile, outFilterMismatchNoverLmax, gencode, mapping, STAR
  10. [10] § METHODS › Bioinformatic analysis of snRNA-seq › Gene and TE expression quantification using pseudobulks ↔ code/ASAP_PMDBS_bulkRNAseq/preprocessing/Scripts_Unique_STARalignment/UniMap_ASAP157_PD_NP21-208_PFC_bulk.sh, the whole file · a weak match · score 0.63 · genomeDir, sjdbGTFfile, outFilterMismatchNoverLmax, gencode, mapping, STAR
  11. [11] § RESULTS › Detection of TE expression in the human brain ↔ src/truster/r_scripts/plot_TEexpression.R, lines 39–100 · score 0.62 · L1PA2, L1PA3, L1HS, TEs
  12. [12] § RESULTS › PD microglia display an interferon response that correlates with TE expression ↔ code/ASAP_PMDBS_snRNAseq/processing/microglia_subclustering_gene_DEA.Rmd, lines 227–276 · score 0.59 · interferon response genes, PFC microglia, related genes, SN microglia, PUT microglia, saw
  13. [13] § METHODS › Bioinformatic analysis of snRNA-seq › Cell type characterization ↔ code/ASAP_PMDBS_snRNAseq/processing/ASAP_PMDBS_snRNAseq_PD_Ctl_DEA_wilcox.ipynb, lines 74–106 · score 0.58 · rank genes, Scanpy, Wilcoxon, tl, leiden, cluster
  14. [14] § METHODS › Visualization of TE expression using pseudobulks ↔ code/ASAP_PMDBS_snRNAseq/processing/TE_expression_heatmaps.Rmd, lines 803–884 · score 0.57 · kbp L1HS PA3, TE expression, heatmaps, boxplots, pseudobulks, nuclei
  15. [15] § RESULTS › Detection of unique TE loci expression at single-cell type resolution in the human brain ↔ code/ASAP_PMDBS_bulkRNAseq/processing/FL_L1HS_L1PA3_expressed.Rmd, lines 86–127 · score 0.56 · kbp L1HS L1PA3, kbp L1HS PA3, rows, strand, Heatmaps, TE
  16. [16] § RESULTS › Increased TE expression in PD brains ↔ code/ASAP_PMDBS_snRNAseq/processing/TE_expression_celltype_markers.Rmd, lines 38–58 · score 0.54 · kbp L1HS PA3, snRNA, L1PA3, rows, Heatmap, pseudobulk
  17. [17] § METHODS › Visualization of TE expression using pseudobulks ↔ code/ASAP_PMDBS_bulkRNAseq/processing/FL_L1HS_L1PA3_expressed.Rmd, lines 86–127 · score 0.52 · kbp L1HS PA3, pheatmap, heatmaps, TE

Paper

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The authors' code

R Markdown · 220 lines · 9.8 KB · MIT · 3 matches

  1. ---
  2. title: "Microglia Gene Expression Analysis: batch and sex covariates"
  3. author: "Raquel Garza"
  4. output:
  5. html_document:
  6. df_print: paged
  7. ---
  8. ## Overview
  10. This notebook is a repetition of gene_DEA_GSEA.Rmd to verify that the results hold when adjusting for batch and sex effects in the design formula of the differential expression analyses.
  12. ### Inputs
  13. - trusTEr output files with gene counts per cluster/region
  14. - Sample metadata: ASAP_samplesheet.xlsx
  16. ### Outputs
  17. - Differential expression results in microglia (excel files)
  18. - Gene set enrichment analyses results (excel files)
  19. - Figures showing:
  20. - PD vs Control effects
  21. - IFN enrichment across clusters
  23. ### Notes
  24. - Only expressed genes (row sums > 0) are tested.
  26. ## Setup: Load libraries and helper functions
  27. Load all required libraries and custom DEA functions
  28. ```{r class.source = 'fold-hide'}
  29. library(DESeq2)
  30. library(data.table)
  31. library(ggpubr)
  32. library(ggplot2)
  33. library(tidyverse)
  34. library(openxlsx)
  35. library(pheatmap)
  36. library(clusterProfiler)
  37. library(org.Hs.eg.db)
  38. library(EnhancedVolcano)
  41. source("TE_DEA_functions.R")
  42. ```
  44. ## Characterize PD effect per cluster
  45. Differential expression analyses for PD vs Control pseudobulks (adjusting for batch and sex) for each region in microglia (cluster 4).
  46. Save results (one excel with region per sheet).
  47. ```{r}
  48. samplesheet <- read.xlsx("/Volumes/MyPassport/ASAP/code/ASAP_PMDBS_snRNAseq/original/ASAP_samplesheet.xlsx")
  49. gene_dds_list <- list()
  50. gene_exp_list <- list()
  51. gene_res_list <- list()
  52. gene_res_list_df <- list()
  53. regions <- c("SN", "PUT", "AMY", "PFC")
  54. cluster <- "4" # Microglia cluster
  55. gene_dds_list[[cluster]] <- list()
  56. gene_exp_list[[cluster]] <- list()
  57. gene_res_list[[cluster]] <- list()
  58. gene_res_list_df[[cluster]] <- list()
  59. for(region in regions){
  60. print(cluster)
  61. print(region)
  62. gene_counts <- list()
  63. gene_counts[[region]] <- fread(paste("~/inbox/ASAP/trusTEr_output_vs2/", region,"/gene_counts_", region,"_",cluster,".csv", sep=""), sep = ",", data.table = F)
  64. rownames(gene_counts[[region]]) <- gene_counts[[region]]$Geneid
  66. samplesheet_list <- split(samplesheet, f = samplesheet$Region)
  68. rownames(samplesheet_list[[region]]) <- samplesheet_list[[region]]$Sample
  69. samples_cluster <- paste(rownames(samplesheet_list[[region]]), cluster, sep="_")
  70. samples_cluster <- samples_cluster[which(samples_cluster %in% colnames(gene_counts[[region]]))]
  71. rownames(samplesheet_list[[region]]) <- paste(rownames(samplesheet_list[[region]]), cluster, sep="_")
  72. rownames(gene_counts[[region]]) <- gene_counts[[region]]$Geneid
  74. expressed_genes <- rownames(gene_counts[[region]])[which(rowSums(gene_counts[[region]][,samples_cluster]) > 0)]
  75. gene_dds_list[[cluster]][[region]] <- DESeqDataSetFromMatrix((gene_counts[[region]][expressed_genes,samples_cluster]+1), samplesheet_list[[region]][samples_cluster,], design = ~ Seqnum + Sex + Dx) # Covariates with batch and sex
  76. gene_dds_list[[cluster]][[region]]$Dx <- relevel(gene_dds_list[[cluster]][[region]]$Dx, "Ctl")
  77. gene_dds_list[[cluster]][[region]] <- DESeq(gene_dds_list[[cluster]][[region]])
  78. gene_res_list[[cluster]][[region]] <- results(gene_dds_list[[cluster]][[region]], )
  79. gene_res_list_df[[cluster]][[region]] <- as.data.frame(gene_res_list[[cluster]][[region]])
  80. gene_res_list_df[[cluster]][[region]]$gene_id <- rownames(gene_res_list_df[[cluster]][[region]])
  81. print(names(gene_res_list_df[[cluster]]))
  82. }
  83. openxlsx::write.xlsx(gene_res_list_df[[cluster]], paste("/Volumes/MyPassport/ASAP/data/ASAP_PMDBS_snRNAseq/results/tables/gene_cluster_", cluster, "_PD_DEA_batch_sex_snRNAseq_res.xlsx", sep=""))
  86. ```
  88. ## Some helper functions for Gene Set Enrichment Analyses
  89. ```{r}
  90. set.seed(10)
  91. gse_dotplot <- function(df){
  92. genelist <- df[which(!is.na(df$log2FoldChange)),c("log2FoldChange", "gene_id"), drop=F]
  93. genelist <- genelist[order(genelist$log2FoldChange, decreasing = T),]
  95. genelist_FC <- genelist$log2FoldChange
  96. names(genelist_FC) <- genelist$gene_id
  97. gse <- gseGO(geneList=genelist_FC,
  98. ont ="ALL",
  99. keyType = "SYMBOL",
  100. minGSSize = 3,
  101. maxGSSize = 800,
  102. seed = T,
  103. pvalueCutoff = 0.05,
  104. verbose = TRUE,
  105. OrgDb = org.Hs.eg.db,
  106. pAdjustMethod = "BH")
  107. return(gse)
  108. }
  110. gse_pretty_dotplot <- function(gse, topn = 10, terms = NA){
  111. gse <- gse@result
  112. # gse <- gse[which(gse$ONTOLOGY == "BP"),]
  113. gse$sign <- ifelse(gse$enrichmentScore > 0, "Activated", "Supressed")
  114. gse$num_genes <- sapply(str_split(gse$core_enrichment, "/"), length)
  115. gse$gene_ratio <- gse$num_genes / gse$setSize
  116. gse$Description <- factor(gse$Description, levels = gse[order(gse$gene_ratio), "Description"])
  118. if(!all(is.na(terms))){
  119. gse <- gse %>%
  120. drop_na() %>%
  121. filter(ID %in% terms)
  122. }else{
  123. gse <- gse[order(gse$NES, -log10(gse$p.adjust), decreasing = T),]
  124. }
  125. gse %>%
  126. drop_na() %>%
  127. ggplot(aes(x=Description, y=gene_ratio, size = num_genes, fill = p.adjust)) + geom_point(shape=21, colour="lightgrey") + facet_wrap(.~sign) + coord_flip() + theme_pubr(legend = "right", border = T) + labs(x="", fill="Padj", y = "Gene ratio", size = "Num genes") + scale_fill_gradientn(colors = c("firebrick1", "gray94")) + scale_size_continuous(range = c(2,8)) + theme(axis.text.y = element_text(size=10), strip.text.x = element_text(size = 10),axis.text.x = element_text(size=10),legend.text = element_text(size=10),legend.title = element_text(size=10))
  128. }
  129. ```
  131. ## Gene Set Enrichment Analyses (GSEA)
  133. For some reason the seed is not taken in by clusterProfiler, so the results from here onwards will be using the original run I did on this code (same but ever so slightly different numbers, loaded here at the end of the chunk). The clean data provided will be the original run I did.
  135. * Test whether there is enrichment of GO-terms within the DEA results (log2FC based).
  136. * Save results to excel
  137. ```{r}
  138. gse_list <- list()
  139. gse_list_df <- list()
  140. gse_plot_list <- list()
  141. gse_list[[cluster]] <- list()
  142. gse_list_df[[cluster]] <- list()
  143. gse_plot_list[[cluster]] <- list()
  144. for(region in regions){
  145. print(cluster)
  146. print(region)
  147. gse_list[[cluster]][[region]] <- gse_dotplot(df = gene_res_list_df[[cluster]][[region]])
  148. gse_list_df[[cluster]][[region]] <- gse_list[[cluster]][[region]]@result
  149. if(!is.null(gse_plot_list[[cluster]][[region]])){
  150. # gse_plot_list[[cluster]][[region]] <- gse_pretty_dotplot(gse = gse_list[[cluster]][[region]], topn = 30) + ggtitle(paste(region, cluster, sep=" cluster: "))
  151. }
  152. }
  153. if(length(gse_list_df[[cluster]]) > 0){
  154. openxlsx::write.xlsx(gse_list_df[[cluster]], paste("/Volumes/MyPassport/ASAP/data/ASAP_PMDBS_snRNAseq/results/tables/gse_cluster", cluster, "PD_GSEA_batch_sex_snRNAseq_res.xlsx", sep="_"))
  155. }
  157. ```
  159. ## IFN and viral related terms in microglia
  160. ```{r}
  161. cluster = "4"
  162. region = "SN"
  163. ifn_viral_related_go <- gse_list_df[[cluster]][[region]][which(grepl("interferon|vir", gse_list_df[[cluster]][[region]]$Description)), "ID"]
  164. gse_pretty_dotplot(gse = gse_list[[cluster]][[region]], topn = 10, terms = c(ifn_viral_related_go)) + ggtitle(paste(region, cluster, sep=" cluster: "))
  166. region = "PUT"
  167. ifn_viral_related_go <- gse_list_df[[cluster]][[region]][which(grepl("interferon|vir", gse_list_df[[cluster]][[region]]$Description)), "ID"]
  168. gse_pretty_dotplot(gse = gse_list[[cluster]][[region]], topn = 10, terms = c(ifn_viral_related_go)) + ggtitle(paste(region, cluster, sep=" cluster: "))
  170. region = "AMY"
  171. ifn_viral_related_go <- gse_list_df[[cluster]][[region]][which(grepl("interferon|vir", gse_list_df[[cluster]][[region]]$Description)), "ID"]
  172. gse_pretty_dotplot(gse = gse_list[[cluster]][[region]], topn = 10, terms = c(ifn_viral_related_go)) + ggtitle(paste(region, cluster, sep=" cluster: "))
  174. region = "PFC"
  175. ifn_viral_related_go <- gse_list_df[[cluster]][[region]][which(grepl("interferon|vir", gse_list_df[[cluster]][[region]]$Description)), "ID"]
  176. ifn_viral_related_go
  177. ```
  179. ## IFN related genes avg log2FC heatmaps
  181. IFN related genes that were up in PD SN microglia
  182. ```{r}
  183. gene_res_list_df$`4`$SN$region <- "SN"
  184. gene_res_list_df$`4`$PUT$region <- "PUT"
  185. gene_res_list_df$`4`$PFC$region <- "PFC"
  186. gene_res_list_df$`4`$AMY$region <- "AMY"
  188. ifn_related_go <- gse_list_df[["4"]][["SN"]][which(grepl("interferon", gse_list_df[["4"]][["SN"]]$Description)), "Description"]
  189. tmp <- sapply(sapply(gse_list_df[["4"]][["SN"]][which(gse_list_df[["4"]][["SN"]]$Description %in% ifn_related_go), "core_enrichment"], str_split, "/"), unlist)
  190. names(tmp) <- NULL
  191. tmp <- unique(unlist(tmp))
  193. tmp_res <- rbind(gene_res_list_df$`4`$SN[tmp,],
  194. gene_res_list_df$`4`$PUT[tmp,],
  195. gene_res_list_df$`4`$PFC[tmp,],
  196. gene_res_list_df$`4`$AMY[tmp,])
  198. tmp_res <- unique(tmp_res)
  200. tmp_heatmap_log2FC <- reshape2::dcast(tmp_res, gene_id~region, value.var = "log2FoldChange")
  201. rownames(tmp_heatmap_log2FC) <- tmp_heatmap_log2FC$gene_id
  202. tmp_heatmap_pvalue <- reshape2::dcast(tmp_res, gene_id~region, value.var = "pvalue")
  203. tmp_heatmap_pvalue[is.na(tmp_heatmap_pvalue)] <- 1
  204. rownames(tmp_heatmap_pvalue) <- tmp_heatmap_pvalue$gene_id
  205. tmp_heatmap_pvalue <- tmp_heatmap_pvalue[,-1]
  206. tmp_heatmap_pvalue <- ifelse(tmp_heatmap_pvalue > 0.05, "", ifelse(tmp_heatmap_pvalue < 0.001, "***", ifelse(tmp_heatmap_pvalue < 0.01, "**", "*")))
  208. bk_neg <- seq(-3,0, length.out=50)
  209. bk_pos <- seq(0,3, length.out=50)
  210. bk <- c(bk_neg, bk_pos)
  211. bk <- unique(bk)
  212. color_pos <- colorRampPalette(c("white", "red"))(50)
  213. color_neg <- colorRampPalette(c("blue", "white"))(50)
  214. tmp_heatmap_log2FC[is.na(tmp_heatmap_log2FC)] <- 0
  216. pheatmap::pheatmap(tmp_heatmap_log2FC[,-1], breaks = bk, color = c(color_neg, color_pos), cluster_cols = F, main = "Core enrichment of enriched IFN-related\nGO terms in SN microglia", show_rownames = T, border_color = F, display_numbers = tmp_heatmap_pvalue)
  218. pheatmap::pheatmap(tmp_heatmap_log2FC[,-1], breaks = bk, color = c(color_neg, color_pos), cluster_cols = F, main = "Core enrichment of enriched IFN-related\nGO terms in SN microglia", show_rownames = F, border_color = F, display_numbers = tmp_heatmap_pvalue)
  220. ```

gene_DEA_GSEA_batch_sex.Rmd at commit 63d638f, under MIT · at the source

Overview

Authors: Raquel Garza1,2, Anita Adami1,2, Arun Thiruvalluvan1,3, Sasvi Wijesinghe1,4,5,6,7, Annabel Curle1,8,9, Oliver Tam1,10, Talitha Forcier1,10, Danai A Lagka1,3, Nina L Kazakou1,3, Diahann A M Atacho1,2, Yogita Sharma1,2, Vivien Horvath1,2, Sara Bermudez1,2, Jenny Johansson1,2, Daniel B Rainbow1,8, Laura Castilla-Vallmanya1,2, Joanne L Jones1,8, Annelies Quaegebeur1,4,5,11, Molly Gale Hammell1,10, Agnete Kirkeby1,3, Roger A Barker1,4, Johan Jakobsson1,2
ORCID iDs: Raquel Garza, Anita Adami, Arun Thiruvalluvan, Sasvi Wijesinghe, Annabel Curle, Oliver Tam, Talitha Forcier, Danai A Lagka, Nina L Kazakou, Diahann A M Atacho, Yogita Sharma, Vivien Horvath, Sara Bermudez, Jenny Johansson, Daniel B Rainbow, Laura Castilla-Vallmanya, Joanne L Jones, Annelies Quaegebeur, Molly Gale Hammell, Agnete Kirkeby, Roger A Barker, Johan Jakobsson
  1. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
  2. Department of Experimental Medical Science, Wallenberg Neuroscience Center and Lund Stem Cell Center, BMC A11, Lund University, 221 84 Lund, Sweden
  3. Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Department of Biomedical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
  4. John van Geest Centre for Brain Repair, Department of Clinical Neurosciences and Cambridge Stem Cell Institute, University of Cambridge, Forvie Site, Cambridge CB2 2PY, UK
  5. Cambridge University Hospitals NHS Foundation Trust, Cambridge CB20YY, UK
  6. Department of Brain Sciences, Imperial College London, London, UK
  7. UK Dementia Research Institute, Imperial College London, London, UK
  8. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
  9. Ann Romney Center for Neurologic Diseases, Department of Neurology, Mass General Brigham and Harvard Medical School, Boston, MA 02115, USA
  10. Institute for Systems Genetics, Department of Neuroscience and Physiology, NYU Langone Health, New York, NY 10016, USA
  11. Department of Imaging and Pathology, University of Leuven, 3000 Leuven, Belgium
Institutions: Lund University (Sweden); Aligning Science Across Parkinson's (United States); University of Copenhagen (Denmark); Novo Nordisk Foundation Center for Stem Cell Medicine (Denmark); Wellcome/MRC Cambridge Stem Cell Institute (United Kingdom); University of Cambridge (United Kingdom); Cambridge University Hospitals NHS Foundation Trust (United Kingdom); UK Dementia Research Institute (United Kingdom); Imperial College London (United Kingdom); Harvard University (United States); Mass General Brigham (United States); NYU Langone Health (United States); New York University (United States); KU Leuven (Belgium)
Journal: Science advances, volume 12, issue 36, article eaed2952
Dates: received 22 October 2025; accepted 20 July 2026; published online 2 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aed2952 · PMID 42685224 · PMCID PMC13537272 · OpenAlex W4414042582
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.03.673956
Status: code verified
Categories: human (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
MeSH: DNA Transposable Elements*, Interferons*, Parkinson Disease*, Brain, Female, Humans, Male, Microglia, Neurons (* major topic)
Topic: RNA regulation and disease (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR203312)
Citations: not cited yet (Europe PMC); 72 references in the paper
Research resources: hES RRID:CVCL_9773, hiPSCs RRID:CVCL_B5P3, Human embryonic stem cells RRID:CVCL_L206, RRID:SCR_002105, RRID:SCR_004463, RRID:SCR_010943, RRID:SCR_011793, RRID:SCR_012919, RRID:SCR_015687, RRID:SCR_016366, RRID:SCR_016418, RRID:SCR_016884, RRID:SCR_017344, RRID:SCR_019204, RRID:SCR_023923

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder involving a neuroinflammatory response, the cause of which remains unclear. Transposable elements (TEs) have been linked to inflammation, but their potential role in PD remains unexplored. Using bulk- and single-nuclei RNA-seq of postmortem brain tissue from four brain regions, we studied TE transcription and its correlation with PD neuroinflammation. Over a thousand TEs, including LINE-1 s and ERVs, were expressed in a cell type– and region-specific manner in the human brain. Increased TE expression was found in microglia and neurons in the substantia nigra and putamen of PD brains, but not amygdala or prefrontal cortex, compared to controls. This TE activation correlated with an innate immune response in the same brain regions. The link between an interferon response and TE activation was mechanistically confirmed using human pluripotent stem cell–derived microglia and neurons. Our findings provide insights into TE transcription in the PD brain and suggest that TEs may contribute to neuroinflammation and pathological progression in PD.

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

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Zenodo 7589548

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Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Gene and TE expression quantification using pseu”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: patchwork (5 files), Seurat (5 files), tidyverse (5 files), data.table (4 files), pandas (3 files), pysam (3 files), reshape2 (2 files), anndata (1 file), Biopython (1 file), ggplot2 (1 file), ggpubr (1 file), pheatmap (1 file), scVelo (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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19 files
At the source: dx.doi.org/10.5281/zenodo.7589548

molecular-neurogenetics.github.io/truster

License: none: the authors keep all their rights
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Software Heritage: not checked
Found in: the text, “Gene and TE expression quantification using pseu”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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At the source: molecular-neurogenetics.github.io/truster/

Zenodo 17434560

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.17434560

molecular-neurogenetics/pd_asap_garza2025

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 63d638f3d1d648231c870170f27a80f48f034109, 23 September 2026
Languages: Shell (341), R (20), Python (8), Jupyter (4)
Size: 566 files, 373 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, 24 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: deepTools (136 files), Subread (featureCounts) (69 files), SAMtools (67 files), STAR (67 files), data.table (20 files), tidyverse (20 files), ggplot2 (19 files), ggpubr (16 files), DESeq2 (14 files), pandas (12 files), pheatmap (10 files), reshape2 (10 files), anndata (4 files), Matplotlib (4 files), Scanpy (4 files), clusterProfiler (3 files), BEDTools (2 files), seaborn (2 files), limma (1 file), NumPy (1 file), SciPy (1 file), UMAP (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
375 files
At the source: github.com/Molecular-Neurogenetics/PD_ASAP_Garza2025/tree/v1.0

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 391 scripts, each with its path and the digest of its content;
  • 17 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, code, and materials availability

The RC17 cell line can be provided by ROSLIN CELLS LIMITED pending scientific review and execution of a completed material transfer agreement. The KOLF2.1 cell line can be provided by the Jackson Laboratory pending scientific review and execution of a completed material transfer agreement. The H9 cell line can be provided by WiCell Research Institute pending scientific review and execution of a completed material transfer agreement. Human material can be provided by the Cambridge Brain Bank pending scientific review and execution of a completed material transfer agreement. Requests should be directed to the corresponding authors, who will facilitate contact with the appropriate providing institution. All the data, code (https://doi.org/10.5281/zenodo.17434560), and protocols generated and used in this study needed to evaluate and reproduce the results in the paper have been deposited in public repositories. They are listed, together with used key laboratory materials, alongside their persistent identifiers in a Key Resource Table at https://doi.org/10.5281/zenodo.18863546. Data used in the preparation of this article were obtained from the following collections from the Aligning Science Across Parkinson’s Collaborative Research Network Cloud (ASAP CRN Cloud) (RRID:SCR_023923): “Single nuclei sequencing of brain regions from healthy and Parkinson’s Disease individuals” (https://doi.org/10.5281/zenodo.15162834), “Deep bulk RNAseq of neurological controls and PD brains” (https://doi.org/10.5281/zenodo.16929448), “Bulk RNAseq of dopaminergic neurons in vitro cultures” (https://doi.org/10.5281/zenodo.17149267), and “Bulk RNAseq of microglia in vitro cultures” (https://zenodo.org/records/17496865). The data are controlled. Researchers can register for access to these data by submitting a Data Use Application through the ASAP CRN Cloud website (https://cloud.parkinsonsroadmap.org/collections). Data dictionaries, README files, protocols used to collect the data, and data processing pipelines are openly available at https://cloud.parkinsonsroadmap.org/collections.

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 3, 28 September 2026

  • Authors: added Danai A Lagka (0009-0007-9252-3667); removed Danai A Lagka

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 22 authors, 9 MeSH terms, 1 funder, 72 references, 15 RRIDs.

Cite

This paper

Garza, R., Adami, A., Thiruvalluvan, A., Wijesinghe, S., Curle, A., Tam, O., Forcier, T., Lagka, D. A., Kazakou, N. L., Atacho, D. A. M., Sharma, Y., Horvath, V., Bermudez, S., Johansson, J., Rainbow, D. B., Castilla-Vallmanya, L., Jones, J. L., Quaegebeur, A., Hammell, M. G., . . . Jakobsson, J. (2026). Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease. Science advances, 12(36), eaed2952. https://doi.org/10.1126/sciadv.aed2952

BibTeX

@article{garza2026activation,
author = {Garza, Raquel and Adami, Anita and Thiruvalluvan, Arun and Wijesinghe, Sasvi and Curle, Annabel and Tam, Oliver and Forcier, Talitha and Lagka, Danai A and Kazakou, Nina L and Atacho, Diahann A M and Sharma, Yogita and Horvath, Vivien and Bermudez, Sara and Johansson, Jenny and Rainbow, Daniel B and Castilla-Vallmanya, Laura and Jones, Joanne L and Quaegebeur, Annelies and Hammell, Molly Gale and Kirkeby, Agnete and Barker, Roger A and Jakobsson, Johan},
title = {{Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease}},
journal = {Science advances},
year = {2026},
month = sep,
volume = {12},
number = {36},
pages = {eaed2952},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aed2952},
url = {https://doi.org/10.1126/sciadv.aed2952},
pmid = {42685224},
pmcid = {PMC13537272}
}

RIS

TY - JOUR
AU - Garza, Raquel
AU - Adami, Anita
AU - Thiruvalluvan, Arun
AU - Wijesinghe, Sasvi
AU - Curle, Annabel
AU - Tam, Oliver
AU - Forcier, Talitha
AU - Lagka, Danai A
AU - Kazakou, Nina L
AU - Atacho, Diahann A M
AU - Sharma, Yogita
AU - Horvath, Vivien
AU - Bermudez, Sara
AU - Johansson, Jenny
AU - Rainbow, Daniel B
AU - Castilla-Vallmanya, Laura
AU - Jones, Joanne L
AU - Quaegebeur, Annelies
AU - Hammell, Molly Gale
AU - Kirkeby, Agnete
AU - Barker, Roger A
AU - Jakobsson, Johan
TI - Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/09/02
VL - 12
IS - 36
SP - eaed2952
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aed2952
UR - https://doi.org/10.1126/sciadv.aed2952
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aed2952",
"type": "article-journal",
"title": "Activation of transposable elements is linked to a region- and cell type-specific interferon response in Parkinson's disease",
"container-title": "Science advances",
"author": [
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"family": "Garza",
"given": "Raquel"
},
{
"family": "Adami",
"given": "Anita"
},
{
"family": "Thiruvalluvan",
"given": "Arun"
},
{
"family": "Wijesinghe",
"given": "Sasvi"
},
{
"family": "Curle",
"given": "Annabel"
},
{
"family": "Tam",
"given": "Oliver"
},
{
"family": "Forcier",
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},
{
"family": "Lagka",
"given": "Danai A"
},
{
"family": "Kazakou",
"given": "Nina L"
},
{
"family": "Atacho",
"given": "Diahann A M"
},
{
"family": "Sharma",
"given": "Yogita"
},
{
"family": "Horvath",
"given": "Vivien"
},
{
"family": "Bermudez",
"given": "Sara"
},
{
"family": "Johansson",
"given": "Jenny"
},
{
"family": "Rainbow",
"given": "Daniel B"
},
{
"family": "Castilla-Vallmanya",
"given": "Laura"
},
{
"family": "Jones",
"given": "Joanne L"
},
{
"family": "Quaegebeur",
"given": "Annelies"
},
{
"family": "Hammell",
"given": "Molly Gale"
},
{
"family": "Kirkeby",
"given": "Agnete"
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{
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{
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],
"container-title-short": "Sci Adv",
"volume": "12",
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"page": "eaed2952",
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"PMID": "42685224",
"PMCID": "PMC13537272",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aed2952",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
2
]
]
}
}

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

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