OSCR

Synthetic super-enhancers enable precision viral immunotherapy.

Code ↔ Paper

8 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] § Discovery of functional GSC enhancers ↔ chip-seq/scripts/R/Figure2.Rmd, lines 109–166 · score 0.82 · SOX2 SOX9 peaks, Venn diagram, co bound, consensus SOX2, ChIP seq, Genome
  2. [2] § Methods › SOX2 and SOX9 ChIP–seq library preparation and analysis ↔ chip-seq/scripts/R/Figure2.Rmd, lines 109–166 · score 0.80 · circular permutation, co bound, SOX2 SOX9, SOX9 peaks, ChIP, consensus
  3. [3] § Methods › SCENIC analysis of scRNA-seq data ↔ scRNA-seq/7_visualization_SSE.R, lines 94–173 · score 0.79 · HIF1A, ETS1, IRF9, MAF, SMAD1, SOX8
  4. [4] § Methods › scRNA-seq analysis ↔ scRNA-seq/2_umap_doublets_ambientR_SSE.R, lines 51–104 · score 0.75 · decontX, doublets rate, contaminated, Ambient, RNA, clusters
  5. [5] § Methods › scRNA-seq analysis ↔ scRNA-seq/1_data_filtration_SSE.R, lines 44–124 · score 0.73 · bGHployA, HSV TK, UMI, mCherry, filtration, RNA
  6. [6] § Methods › scRNA-seq analysis ↔ scRNA-seq/3_annotation_SSE.R, lines 79–138 · score 0.66 · CellCycleScoring, Seurat, predicted, subtypes, genes, SSE
  7. [7] § Methods › SCENIC analysis of scRNA-seq data ↔ scRNA-seq/5_pyscenic.sh, the whole file · a weak match · score 0.62 · v10 clust, pyscenic, modules, hg38, Motif, activated
  8. [8] § Discovery of functional GSC enhancers ↔ chip-seq/scripts/R/Figure2.Rmd, lines 208–262 · score 0.58 · super enhancers, co bound, enriched, ChIP seq, bp, overlapped

Paper

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The authors' code

R Markdown · 320 lines · 14 KB · MIT · 3 matches

  1. ---
  2. title: "Steps taken to generate Figure 2 of Koeber et al, Nature 2026"
  3. author: Alhafidz Hamdan
  4. output: html_document
  5. date: "2026-02-01"
  6. editor_options:
  7. chunk_output_type: console
  8. ---
  10. # This document details the methods/steps used to generate figures used in Figure 2 (Koeber et al. Nature, 2026):
  11. ## Load required libraries
  12. ```{r}
  13. .p <- c("tidyverse", "ggpubr", "lemon", "data.table", "regioneR", "VennDiagram", "parallel", "regioneR", "rtracklayer")
  14. lapply(.p, require, character.only =T)
  16. theme_colplot <- theme(legend.position = "right",
  17. legend.title = element_text(size=13, face = "bold"),
  18. legend.text = element_text(size=12),
  19. axis.ticks.length = unit(.2, "cm"),
  20. plot.margin = margin(0,0,0,0, "cm"),
  21. plot.background = element_blank(),
  22. panel.background = element_blank(),
  23. plot.title = element_text(size=12, face = "bold", vjust = 1),
  24. axis.text = element_text(size=13),
  25. axis.title = element_text(size=14))
  27. ```
  29. ## Figure A is created outside of R, using Adobe Illustrator + BioRender
  31. ## Figure B (left): Venn diagram of SOX2 and SOX9 overlap for a representative sample E28:
  32. ```{r}
  33. peak_dir = "../../processed_data/"
  34. this_sample = "E28"
  35. this_sample_sox2 <- import(paste0(peak_dir, "/", this_sample, "_Sox2_peaks.narrowPeak"))
  36. this_sample_sox9 <- import(paste0(peak_dir, "/", this_sample, "_Sox9_peaks.narrowPeak"))
  38. ## Plot venn diagram of overlap:
  39. overlap <- subsetByOverlaps(this_sample_sox2, this_sample_sox9)
  40. sox2_exclusive <- subsetByOverlaps(this_sample_sox2, this_sample_sox9, invert = T)
  41. sox9_exclusive <- subsetByOverlaps(this_sample_sox9, this_sample_sox2, invert = T)
  43. pdf(paste0("Figures/", this_sample, "_sox2_sox9_overlap_venn_plot.pdf"), width = 4, height = 4, useDingbats = T, onefile = F)
  44. draw.pairwise.venn(area1 = length(sox2_exclusive) + length(overlap),
  45. area2 = length(sox9_exclusive) + length(overlap),
  46. cross.area = length(overlap), scaled =T,
  47. fill = c("grey","darkred"),
  48. alpha = c(0.6,0.6), fontfamily = rep("sans",3))
  49. dev.off()
  51. ```
  53. ## Figure B (right): SOX2 and SOX9 signal intensity heatmaps for E28, generated in bash/05_submit_deeptools_heatmap.sh
  54. ## Figure C: Bar charts of SOX2 and SOX9 peak overlaps across 7 GSC samples:
  55. ```{r}
  56. sox2_sox9_chipseq_samples = c("E17","E21","E27","E28","E31","E34","E37")
  57. peak_dir = "../../processed_data/macs2"
  58. list.files(peak_dir)
  60. ## Generate sets of overlapping and exclusive peaks for each sample:
  61. merged_peak_stats <- mclapply(seq_along(sox2_sox9_chipseq_samples), function(x) {
  63. this_sample <- sox2_sox9_chipseq_samples[x]
  64. this_sample_sox2 <- import(paste0(peak_dir, "/", this_sample, "_Sox2_peaks.narrowPeak"))
  65. this_sample_sox9 <- import(paste0(peak_dir, "/", this_sample, "_Sox9_peaks.narrowPeak"))
  67. overlap <- subsetByOverlaps(this_sample_sox2, this_sample_sox9)
  68. overlap %>% as.data.frame() %>%
  69. fwrite(paste0("../../processed_data/", sox2_sox9_chipseq_samples[x], "_Sox2_Sox9_overlap.bed"), quote=F, sep = "\t", row.names = F, col.names = F)
  70. sox2_only <- subsetByOverlaps(this_sample_sox2, this_sample_sox9, invert = T)
  71. sox2_only %>% as.data.frame() %>%
  72. fwrite(paste0("../../processed_data/", sox2_sox9_chipseq_samples[x], "_Sox2_exclusive.bed"), quote=F, sep = "\t", row.names = F)
  73. sox9_only <- subsetByOverlaps(this_sample_sox9, this_sample_sox2, invert = T)
  74. sox9_only %>% as.data.frame() %>%
  75. fwrite(paste0("../../processed_data/", sox2_sox9_chipseq_samples[x], "_Sox9_exclusive.bed"), quote=F, sep = "\t", row.names = F)
  77. dat <- data.frame(sample = this_sample,
  78. total_sox2 = length(this_sample_sox2),
  79. total_sox9 = length(this_sample_sox9),
  80. sox2_only = length(sox2_only),
  81. sox9_only = length(sox9_only),
  82. overlap = length(overlap)) %>%
  83. dplyr::mutate(perc_sox2_overlap = overlap*100/total_sox2,
  84. perc_sox9_overlap = overlap*100/total_sox9)
  86. return(dat)
  88. }) %>% rbindlist()
  90. pdf("Figures/co_binding_bar_plot.pdf", width = 4.2, height = 3.1, useDingbats = T, onefile = F)
  91. merged_peak_stats %>%
  92. dplyr::select(sample, perc_sox2_overlap, perc_sox9_overlap) %>%
  93. pivot_longer(!sample) %>%
  94. dplyr::mutate(TF = ifelse(str_detect(name, "sox2"), "SOX2", "SOX9")) %>%
  95. ggplot(aes(x=sample, y=value, fill=TF)) +
  96. geom_col(position = "dodge") +
  97. scale_y_continuous(limits=c(0,100), expand = c(0.04,0)) +
  98. scale_x_discrete(expand=c(0.09,0)) +
  99. scale_fill_manual(values = c("SOX2" = "grey", "SOX9" = "#AD6363")) +
  100. labs(x="Samples", y="% of co-bound peaks", fill = "TF peaks") +
  101. theme_classic() +
  102. lemon::coord_capped_cart(left = "both", bottom = "none") +
  103. theme_colplot +
  104. theme(legend.position = "top", plot.margin = margin(0, 0, 0, 0, "cm"))
  105. dev.off()
  107. ```
  109. ## Figure D (left): Venn diagram of consensus SOX2 and SOX9 overlap:
  110. ```{r}
  111. ## Use bedtools multiinter to generate calls for SOX2/SOX9 overlap, SOX2 exclusive and SOX9 exclusive sets:
  112. ## For consensus overlap, use union of 5 out of 7 calls
  113. # bedtools multiinter -header -i ChIP-seq/processed_data/*_Sox2_Sox9_overlap.bed > ChIP-seq/processed_data/Merged_rep_Consensus5_overlap.bed
  114. # bedtools multiinter -header -i ChIP-seq/processed_data/*_Sox2_exclusive.bed > ChIP-seq/processed_data/Merged_rep_consensus5_Sox2_exclusive.bed
  115. # bedtools multiinter -header -i ChIP-seq/processed_data/*_Sox9_exclusive.bed > ChIP-seq/processed_data/Merged_rep_consensus5_Sox9_exclusive.bed
  117. ## Load consensus sets:
  118. ### Co-bound set:
  119. co_bound_consensus <- fread("../../processed_data/Merged_rep_Consensus5_overlap.bed") %>%
  120. toGRanges() %>%
  121. reduce()
  122. length(co_bound_consensus)
  124. ## SOX2 exclusive:
  125. Sox2_consensus <- fread("../../processed_data/Merged_rep_consensus5_Sox2_exclusive.bed") %>%
  126. toGRanges() %>%
  127. reduce()
  128. length(Sox2_consensus)
  130. ## SOX9 exclusive:
  131. Sox9_consensus <- fread("../../processed_data/Merged_rep_consensus5_Sox9_exclusive.bed") %>%
  132. toGRanges() %>%
  133. reduce()
  134. length(Sox9_consensus)
  136. pdf("/Figures/venn_diag_co_bound.pdf", height = 3, width = 3)
  137. draw.pairwise.venn(area1 = length(Sox2_consensus) + length(co_bound_consensus),
  138. area2 = length(Sox9_consensus) + length(co_bound_consensus),
  139. cross.area = length(co_bound_consensus), scaled =T,
  140. fill = c("darkgrey","brown"),
  141. cex = c(1.5,1.5,1.5),
  142. alpha = c(0.5,0.5), fontfamily = rep("sans",3))
  144. dev.off()
  146. ## Circular permutation test:
  147. ### Get all SOX2 peaks:
  148. sox2_peaks <- c(Sox2_consensus, co_bound_consensus)
  150. ### Create all SOX9 peaks:
  151. sox9_peaks <- c(Sox9_consensus, co_bound_consensus)
  153. ### Perform overlap test:
  154. set.seed(999)
  155. perm_test_co_bound <- permTest(A = sox2_peaks,
  156. B = sox9_peaks,
  157. ntimes = 10000,
  158. randomize.function=circularRandomizeRegions,
  159. evaluate.function=numOverlaps,
  160. count.once=TRUE,
  161. genome="hg38",
  162. mc.set.seed=FALSE,
  163. mc.cores=20)
  164. perm_test_co_bound
  166. ```
  168. ## Figure D (right): Venn diagram of consensus co-bound SOX2/SOX9 peaks vs super-enhancers in GSC:
  169. ```{r}
  170. ## Load consensus SE set: SEs occuring in >1 GSC
  171. consensus_SE <- fread("../../processed_data/Consensus_SEs.bed") %>%
  172. toGRanges() %>%
  173. reduce()
  174. length(consensus_SE)
  176. ## Load consensus co-bound SOX2 and SOX9 set:
  177. co_bound_consensus <- fread("../../processed_data/Merged_rep_Consensus5_overlap.bed") %>%
  178. toGRanges() %>%
  179. reduce()
  180. length(co_bound_consensus)
  182. co_bound_SOX_SEs <- subsetByOverlaps(co_bound_consensus, consensus_SE)
  183. co_bound_SOX_not_SE <- subsetByOverlaps(co_bound_consensus, consensus_SE, invert = T)
  184. SE_not_co_bound <- subsetByOverlaps(consensus_SE, co_bound_consensus, invert = T)
  186. pdf("Figures/venn_diag_co_bound_SEs.pdf", height = 3, width = 3)
  187. draw.pairwise.venn(area2 = length(co_bound_SOX_not_SE) +length(co_bound_SOX_SEs),
  188. area1 = length(SE_not_co_bound) + length(co_bound_SOX_SEs),
  189. cross.area = length(co_bound_SOX_SEs), scaled =T,
  190. cex = c(1.5,1.5,1.5),
  191. fill = c("lightblue","brown"), alpha = c(0.5,0.5), inverted = T, fontfamily = rep("sans",3))
  192. dev.off()
  194. ### Perform overlap test:
  195. set.seed(999)
  196. perm_test_co_bound_SE <- permTest(A = co_bound_consensus,
  197. B = consensus_SE,
  198. ntimes = 10000,
  199. randomize.function=circularRandomizeRegions,
  200. evaluate.function=numOverlaps,
  201. count.once=TRUE,
  202. genome="hg38",
  203. mc.set.seed=FALSE,
  204. mc.cores=20)
  206. ```
  208. ## Figure E: GO terms for co-bound SOX2/SOX9 sites at SEs:
  209. ```{r}
  210. ### First overlap co-bound SOX2/SOX9 sets with consensus SEs and get gene annotations using GREAT via the online tool
  211. ### Then perform GO Biological term analysis using clusterProfiler.
  212. library(clusterProfiler)
  213. library(org.Hs.eg.db)
  214. library(DOSE)
  216. # # Convert to ENTREZID:
  217. # this_set_entrezid <- bitr(co_bound_SE_genes, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.eg.db) ## For DO terms
  218. # nrow(this_set_entrezid)
  219. #
  220. # ## GO and DO tests:
  221. # go_bp_terms <- enrichGO(gene = this_set_entrezid$SYMBOL,
  222. # OrgDb = org.Hs.eg.db,
  223. # keyType = 'SYMBOL',
  224. # ont = "BP",
  225. # pAdjustMethod = "BH",
  226. # pvalueCutoff = 0.01,
  227. # qvalueCutoff = 0.05) %>% as.data.table() %>%
  228. # dplyr::mutate(term = "GO Biological Process", set = "co_bound_SE_genes")
  229. #
  230. # go_bp_terms %>% fwrite("../../processed_data/gene_set_enrichment_biological_processes_co-bound_sox_in_SEs.tsv", quote = F, sep = "\t", row.names = F)
  231. go_bp_terms = fread("../../processed_data/gene_set_enrichment_biological_processes_co-bound_sox_in_SEs.tsv")
  233. pdf("Figures/cobound_overlap_SEs_go_biological_terms.pdf", width = 4.2, height = 3.1, useDingbats = T, onefile = F)
  234. go_bp_terms %>%
  235. slice_head(n=10) %>%
  236. ungroup() %>%
  237. separate(GeneRatio, into = c("GeneCount", "GeneTotal"), sep = "/") %>%
  238. separate(BgRatio, into = c("BgCount", "BgTotal"), sep = "/") %>%
  239. dplyr::mutate(FC = (as.numeric(GeneCount)/as.numeric(GeneTotal))/(as.numeric(BgCount)/as.numeric(BgTotal))) %>%
  240. ggplot(aes(x = -log10(qvalue), y=reorder(Description, -qvalue), size= FC)) +
  241. geom_segment(aes(xend = 0, yend = Description), color = "grey", size=0.2) +
  242. geom_point(aes(color = set)) +
  243. labs(y =NULL, title = "Super-enhancers", x = NULL) +
  244. scale_color_manual(values = c("co_bound_SE_genes" = "#7570B3")) +
  245. scale_size_binned(limits=c(1,10)) +
  246. scale_x_reverse(expand=c(0.01,0.1), n.breaks=5, limits=c(8,0)) +
  247. scale_y_discrete(position = "right") +
  248. theme_classic() +
  249. coord_capped_cart(right = "none", bottom = "both") +
  250. theme_colplot +
  251. theme(strip.text = element_text(size=12, hjust=0),
  252. legend.position = "none",
  253. axis.ticks.y = element_line(linewidth = 0.2),
  254. axis.line.x = element_line(colour = "transparent"),
  255. axis.ticks.x = element_blank(),
  256. axis.text.x = element_blank(),
  257. plot.title = element_text(face = "plain"),
  258. axis.line.y = element_line(linewidth = 0.2),
  259. axis.text.y = element_text(size=10))
  260. dev.off()
  262. ```
  264. ## Figure F: SOX2/SOX9 expression correlation across TCGA cancers:
  265. ```{r}
  266. ## From TCGA RNA-seq data (cbioportal):
  267. TCGA <- read.delim("/Users/alhafidzhamdan/PhD/Data/ChIP-seq/Sox2_Sox9/External_data/cbioportal/tcga_pan_can_atlas_2018/sox_genes/cbioportal_sox2_sox9.txt", header=T) %>%
  268. select(Sample = Sample.Id,
  269. Cancer_type = TCGA.PanCanAtlas.Cancer.Type.Acronym,
  270. Sox2_expression = SOX2..mRNA.Expression..RSEM..Batch.normalized.from.Illumina.HiSeq_RNASeqV2...log2.value...1..,
  271. Sox9_expression = SOX9..mRNA.Expression..RSEM..Batch.normalized.from.Illumina.HiSeq_RNASeqV2...log2.value...1..)
  273. pdf("Figures/TCGA_SOX2_SOX9_exp_corr.pdf", height = 4.9, width = 6)
  274. TCGA_stats %>%
  275. ggplot(aes(x=reorder(Cancer_type,cor), fill=-log10(p.adj), y = cor)) +
  276. geom_col() +
  277. scale_fill_viridis_c(option = "magma") +
  278. theme_classic() +
  279. lemon::coord_capped_cart(left = "both") +
  280. labs(
  281. # title = "SOX2 and SOX9 expression correlation analysis",
  282. y="SOX2/SOX9\nexpression correlation", x = "\nTCGA cancer types", fill = expression("-log"[10]*"Q-value")) +
  283. scale_y_continuous(breaks = c(-0.5,-0.25,0,0.25,0.5), limits = c(-0.5,0.5)) +
  284. theme(axis.text.y = element_text(size=12),
  285. axis.title = element_text(size=13),
  286. legend.position = "bottom",
  287. plot.title = element_text(size=13, face = "bold"),
  288. legend.title = element_text(size=13, face = "bold", vjust = 0.9),
  289. legend.text = element_text(size=11),
  290. axis.text.x = element_text(size=12, angle = 90, hjust=0.9, vjust=0.5))
  291. dev.off()
  293. ```
  295. ## Figure G: Inverted palindromic motif spacing at co-bound SOX2/SOX9 sites:
  296. ```{r}
  297. pdf("Figures/Thesis/chapter_5/sox_monomer_pairing_inverted_palindrome_gaps.pdf", width = 2, height = 3, useDingbats = F, onefile = F)
  298. fread("../../processed_data/SOX_monomer_at_SOX2_co_bound_ENH_spamo.tsv") %>%
  299. dplyr::filter(str_detect(sec_alt, "Sox|SRY")) %>%
  300. dplyr::filter(orient == "0" | orient == "1") %>%
  301. group_by(gap) %>%
  302. dplyr::summarise(total = sum(count)) %>%
  303. ggplot(aes(x=gap, y=total)) +
  304. geom_col() +
  305. theme_classic() +
  306. coord_capped_cart(left = "both", bottom = "none") +
  307. labs(x= "Inverted palindromic\n SOX motif gap", y = "Count") +
  308. theme_colplot +
  309. scale_y_continuous(limits=c(0,150), expand = c(0.02,0.05)) +
  310. scale_x_continuous(expand = c(0.02,0.01))
  311. dev.off()
  313. ```
  315. ## Figure H: Coverage tracks of SOX2 and SOX9 near CDK6 and PTRPRZ1, USCS browser at https://genome.ucsc.edu/s/alhafidzhamdan/co_bound_SOX2_SOX9_peaks
  316. ## Figure I: Run within the MEME-Suite tool via the online platform - https://meme-suite.org/meme/tools/meme-chip (Classic mode)

Figure2.Rmd at commit 15a21c1, under MIT · at the source

Overview

Authors: Ute Koeber1,2,3, Mantas Matjusaitis1,2,4, Neza Alfazema1,2, Katharine Furlong1, Zeyu Wang1,2, Rachel White1,2,5, Alhafidz Hamdan1,2,6, Pooran Dewari1,2, Gregoire Morisse2,7, Mariela Navarette8, Rosie Willis1,2, Jin Wang2,7, Michelle P Clark1,2,5, Carla Jacinto de Sousa1,2,5, Hei Ip Hong1,2, Shahida Sheraz1,2, Ben Southgate1,2,5, Justyna Cholewa-Waclaw1,2, Sabine Gogolok1,2, Gillian M Morrison1,2
and 9 other authorsFelipe Galvez Cancino8,9, Faye Robertson1,2, Anna Williams1, Susan J Rosser10, Paul M Brennan1,2,11, Dirk Sieger2,7, Abdenour Soufi1, Sergio A Quezada8, Steven M Pollard1,2
ORCID iDs: Mantas Matjusaitis, Katharine Furlong, Alhafidz Hamdan, Michelle P Clark, Justyna Cholewa-Waclaw, Faye Robertson, Anna Williams, Paul M Brennan, Abdenour Soufi, Sergio A Quezada, Steven M Pollard
  1. Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
  2. Cancer Research UK Scotland Centre, Edinburgh, UK
  3. Present Address: IQVIA RDS, Frankfurt am Main, Germany
  4. Present Address: Plurify, Cambridge, UK
  5. Present Address: Trogenix, Edinburgh, UK
  6. Edinburgh Pathology, Royal Infirmary Edinburgh, NHS Lothian, Edinburgh, UK
  7. Institute for Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, UK
  8. Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London, UK
  9. Present Address: Laboratory of Immune Regulation, NDM Centre for Immuno-Oncology, University of Oxford, Oxford, UK
  10. School of Biological Sciences, University of Edinburgh, Edinburgh, UK
  11. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
Institutions: Cancer Research UK (United Kingdom); Edinburgh Cancer Research (United Kingdom); MRC Centre for Regenerative Medicine (United Kingdom); University of Edinburgh (United Kingdom); NHS Lothian (United Kingdom); Edinburgh Royal Infirmary (United Kingdom); University College London (United Kingdom); University of Oxford (United Kingdom)
Journal: Nature, volume 653, issue 8113, pages 232-241
Dates: received 7 December 2023; accepted 26 February 2026; published online 8 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10329-6 · PMID 41951744 · PMCID PMC13149004 · OpenAlex W7152014901
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials
Keywords: Cancer immunotherapy, Targeted gene repair
MeSH: Dependovirus*, Enhancer Elements, Genetic*, Genetic Therapy*, Glioblastoma*, Immunotherapy*, Precision Medicine*, Animals, Cell Line, Tumor, Female, Ganciclovir, Gene Regulatory Networks, Genetic Vectors, Humans, Interleukin-12, Male, Mice, Neoplastic Stem Cells, Promoter Regions, Genetic, SOX9 Transcription Factor, SOXB1 Transcription Factors (* major topic)
Topic: Virus-based gene therapy research (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/M018040/1)
Citations: cited by 6 papers (Europe PMC); 55 references in the paper

Abstract

Cell-type-specific promoters are used in gene therapy to restrict expression of the therapeutic payload. However, these promoters often have suboptimal strength, selectivity and size. Here, leveraging recent insights into the function of enhancers, we developed synthetic super-enhancers (SSEs) by assembling functionally validated enhancer fragments into multipart arrays. Focusing on the core SOX2-driven and SOX9-driven transcriptional regulatory network in glioblastoma stem cells (GSCs)1, we engineered SSEs with robust activity and high selectivity. Single-cell profiling, biochemical analyses and genome-binding data indicated that SSEs integrate neurodevelopmental and signalling-state transcription factors to trigger the formation of large multimeric complexes of transcription factors. Moreover, GSC-selective expression of a combination of cytotoxic (HSV-TK and ganciclovir) and immunomodulatory (IL-12) payloads, delivered using adeno-associated virus vectors, as a single treatment led to curative outcomes in a mouse model of aggressive glioblastoma. Notably, IL-12 induced an immunological memory that prevented tumour recurrence. The activity and selectivity of the adeno-associated virus and SSE were validated using primary human glioblastoma tissue and normal cortex samples. In summary, SSEs harness the unique core transcriptional programs that define the GSC phenotype and enable precision immune activation. This approach may have broader applications in other contexts when precise control of transgene expression in specific cell states is necessary.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

alhafidzhamdan/sse_gene_therapy

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 15a21c1106841b2f04541b08d8b0f7b3438e79fc, 14 August 2026
Languages: R (8), Shell (6)
Size: 40 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), Seurat (7 files), ggplot2 (3 files), patchwork (3 files), SAMtools (3 files), BEDTools (1 file), clusterProfiler (1 file), deepTools (1 file), emmeans (1 file), pheatmap (1 file), reshape2 (1 file), reticulate (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
16 files
At the source: github.com/alhafidzhamdan/sse_gene_therapy

Zenodo 18676096

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), Seurat (7 files), ggplot2 (3 files), patchwork (3 files), SAMtools (3 files), BEDTools (1 file), clusterProfiler (1 file), deepTools (1 file), emmeans (1 file), pheatmap (1 file), reshape2 (1 file), reticulate (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
16 files
At the source:

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:

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

The scRNA-seq data have been deposited at the European Nucleotide Archive (ENA) (accession number: PRJEB81816 (https://www.ebi.ac.uk/ena/data/view/PRJEB81816)). The project number for ChIP–seq data is PRJEB107008 (https://www.ebi.ac.uk/ena/data/view/PRJEB107008). For ChIP–seq, raw (fastq) and processed (bigwig and narrowPeak) files have been deposited and made public (https://www.ebi.ac.uk/ena/browser/view/PRJEB107008). The ENA does not take processed files; therefore, these have been uploaded to BioStudies, which links to the same ENA accession (PRJEB107008 (https://www.ebi.ac.uk/ena/browser/view/PRJEB107008)), otherwise they are at https://www.ebi.ac.uk/biostudies/studies/S-BSST2733. All the codes and processing steps for scRNA-seq, SCENIC analysis and ChIP–seq data are available at GitHub (https://github.com/alhafidzhamdan/sse_gene_therapy). The GitHub repository includes some processed files, including all the narrowPeak files and consensus sets; using these in conjunction with the .Rmd document for reproduction of Fig. 2. This also includes a link to the SOX2 and SOX9 tracks on the UCSC browser. The linked and minted a repo is available at Zenodo (https://zenodo.org/records/18676096)55. Source data is provided with this paper.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 29 authors, 2 keywords, 20 MeSH terms, 1 funder, 55 references.

Cite

This paper

Koeber, U., Matjusaitis, M., Alfazema, N., Furlong, K., Wang, Z., White, R., Hamdan, A., Dewari, P., Morisse, G., Navarette, M., Willis, R., Wang, J., Clark, M. P., Jacinto de Sousa, C., Hong, H. I., Sheraz, S., Southgate, B., Cholewa-Waclaw, J., Gogolok, S., . . . Pollard, S. M. (2026). Synthetic super-enhancers enable precision viral immunotherapy. Nature, 653(8113), 232-241. https://doi.org/10.1038/s41586-026-10329-6

BibTeX

@article{koeber2026synthetic,
author = {Koeber, Ute and Matjusaitis, Mantas and Alfazema, Neza and Furlong, Katharine and Wang, Zeyu and White, Rachel and Hamdan, Alhafidz and Dewari, Pooran and Morisse, Gregoire and Navarette, Mariela and Willis, Rosie and Wang, Jin and Clark, Michelle P and Jacinto de Sousa, Carla and Hong, Hei Ip and Sheraz, Shahida and Southgate, Ben and Cholewa-Waclaw, Justyna and Gogolok, Sabine and Morrison, Gillian M and Cancino, Felipe Galvez and Robertson, Faye and Williams, Anna and Rosser, Susan J and Brennan, Paul M and Sieger, Dirk and Soufi, Abdenour and Quezada, Sergio A and Pollard, Steven M},
title = {{Synthetic super-enhancers enable precision viral immunotherapy}},
journal = {Nature},
year = {2026},
month = apr,
volume = {653},
number = {8113},
pages = {232--241},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10329-6},
url = {https://doi.org/10.1038/s41586-026-10329-6},
pmid = {41951744},
pmcid = {PMC13149004}
}

RIS

TY - JOUR
AU - Koeber, Ute
AU - Matjusaitis, Mantas
AU - Alfazema, Neza
AU - Furlong, Katharine
AU - Wang, Zeyu
AU - White, Rachel
AU - Hamdan, Alhafidz
AU - Dewari, Pooran
AU - Morisse, Gregoire
AU - Navarette, Mariela
AU - Willis, Rosie
AU - Wang, Jin
AU - Clark, Michelle P
AU - Jacinto de Sousa, Carla
AU - Hong, Hei Ip
AU - Sheraz, Shahida
AU - Southgate, Ben
AU - Cholewa-Waclaw, Justyna
AU - Gogolok, Sabine
AU - Morrison, Gillian M
AU - Cancino, Felipe Galvez
AU - Robertson, Faye
AU - Williams, Anna
AU - Rosser, Susan J
AU - Brennan, Paul M
AU - Sieger, Dirk
AU - Soufi, Abdenour
AU - Quezada, Sergio A
AU - Pollard, Steven M
TI - Synthetic super-enhancers enable precision viral immunotherapy
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/04/08
VL - 653
IS - 8113
SP - 232
EP - 241
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10329-6
UR - https://doi.org/10.1038/s41586-026-10329-6
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41586-026-10329-6",
"type": "article-journal",
"title": "Synthetic super-enhancers enable precision viral immunotherapy",
"container-title": "Nature",
"author": [
{
"family": "Koeber",
"given": "Ute"
},
{
"family": "Matjusaitis",
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},
{
"family": "Alfazema",
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{
"family": "Furlong",
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{
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{
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{
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},
{
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"given": "Pooran"
},
{
"family": "Morisse",
"given": "Gregoire"
},
{
"family": "Navarette",
"given": "Mariela"
},
{
"family": "Willis",
"given": "Rosie"
},
{
"family": "Wang",
"given": "Jin"
},
{
"family": "Clark",
"given": "Michelle P"
},
{
"family": "Jacinto de Sousa",
"given": "Carla"
},
{
"family": "Hong",
"given": "Hei Ip"
},
{
"family": "Sheraz",
"given": "Shahida"
},
{
"family": "Southgate",
"given": "Ben"
},
{
"family": "Cholewa-Waclaw",
"given": "Justyna"
},
{
"family": "Gogolok",
"given": "Sabine"
},
{
"family": "Morrison",
"given": "Gillian M"
},
{
"family": "Cancino",
"given": "Felipe Galvez"
},
{
"family": "Robertson",
"given": "Faye"
},
{
"family": "Williams",
"given": "Anna"
},
{
"family": "Rosser",
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{
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},
{
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}
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"volume": "653",
"issue": "8113",
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"PMID": "41951744",
"PMCID": "PMC13149004",
"ISSN": "0028-0836",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41586-026-10329-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
8
]
]
}
}

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

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