OSCR

Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.

Code ↔ Paper

11 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 11 matches
  1. [1] § STAR★Methods › Method details › Sequencing data processing ↔ preprocessing.R, lines 50–126 · score 0.97 · nCount_RNA, nFeature_RNA, scDblFinder, QC metrics, RunUMAP, SoupX
  2. [2] § STAR★Methods › Method details › Cell-cell communication inference ↔ fig8/fig8-CellChat.Rmd, lines 366–428 · score 0.94 · CellChatDB.mouse, aggregateNet, computeCommunProb, filterCommunication, identifyOverExpressedGenes, population.size
  3. [3] § STAR★Methods › Method details › Cell-cell communication inference ↔ fig3/fig3-CellChat.R, lines 18–41 · score 0.89 · aggregateNet, computeCommunProb, filterCommunication, identifyOverExpressedGenes, population.size, subsetData
  4. [4] § STAR★Methods › Method details › Cell type annotation ↔ fig8/fig8-CellChat.Rmd, lines 49–151 · score 0.65 · TransferData, imDG, subtypes, subclass, Allen, excitatory
  5. [5] § STAR★Methods › Method details › Cell-type proportion comparisons and network inference ↔ fig2/fig2cde.R, lines 92–131 · score 0.65 · updateF, ivar, propd, disjointed, propr, CLR
  6. [6] § STAR★Methods › Method details › Cell-type proportion comparisons and network inference ↔ fig2/fig2cde.R, lines 133–204 · score 0.60 · CLR transformed, glasso, huge, Adjacency, network, compositional
  7. [7] § STAR★Methods › Method details › Cell-type proportion comparisons and network inference ↔ fig2/fig2ab.R, lines 36–78 · score 0.58 · propeller.ttest, transformed proportions, treatment, timepoint, epilepsy
  8. [8] § STAR★Methods › Method details › Cell-type proportion comparisons and network inference ↔ fig5/fig5.R, lines 39–81 · score 0.58 · propeller.ttest, transformed proportions, treatment, timepoint, epilepsy
  9. [9] § Results › EAM are predicted to engage DG and glial populations ↔ fig3/fig3-CellChat.R, lines 43–110 · score 0.53 · diffOPC, ProS, imDG, CR, incoming, Interaction
  10. [10] § Results › A single-nucleus transcriptomic atlas of the hippocampus during epileptogenesis ↔ fig2/fig2cde.R, lines 1–37 · score 0.53 · diffOPC, ProS, imDG, CR, SUB, weeks
  11. [11] § STAR★Methods › Method details › Comparison to other disease models ↔ fig5/fig5.R, lines 365–445 · score 0.51 · SuperExactTest, background, union, TBI, overlapping, microglia

Paper

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The authors' code

R · 364 lines · 13 KB · no license · 3 matches

  1. library(Seurat)
  2. library(tidyverse)
  3. library(ggrepel)
  4. library(compositions)
  5. library(propr)
  6. library(RColorBrewer)
  7. library(pheatmap)
  8. library(huge)
  9. library(igraph)
  10. library(ggraph)
  11. library(patchwork)
  12. set.seed(123)
  15. seu.meta <- [email hidden]
  17. # Calculate proportions per sample
  18. prop_df <- seu.meta %>%
  19. group_by(sample_ID, weeks, celltype) %>%
  20. summarise(count = n(), .groups = "drop") %>%
  21. group_by(sample_ID) %>%
  22. mutate(prop = count / sum(count)) %>%
  23. ungroup() %>%
  24. dplyr::select(sample_ID, weeks, celltype, prop) %>%
  25. pivot_wider(names_from = celltype, values_from = prop, values_fill = 0)
  27. write.csv(prop_df, file = "proportions.csv")
  29. # PROPORTIONALILTY
  31. celltype.order <- c(
  32. "CA1","CA2-IG-FC","CA3-do","CA3-ve","DG","imDG","Mossy","CR","ProS-SUB",
  33. "Sst","Pvalb","Pvalb Vipr2","Cck","Lamp5","Lamp5 Lhx6","Vip",
  34. "astrocytes","microglia","OPC","diffOPC","oligo","fibroblast","vascular","unassigned"
  35. )
  37. # Pairwise proportionality (rho)
  39. # get proportions from Seurat object
  40. props_from_seurat <- function(seu, state_value) {
  41. [email hidden] %>%
  42. filter(state == state_value) %>%
  43. count(sample_ID, celltype, name = "n") %>%
  44. group_by(sample_ID) %>% mutate(prop = n / sum(n)) %>% ungroup() %>%
  45. dplyr::select(sample_ID, celltype, prop) %>%
  46. pivot_wider(names_from = celltype, values_from = prop, values_fill = 0) %>%
  47. column_to_rownames("sample_ID") %>% as.matrix()
  48. }
  50. control.mat <- props_from_seurat(seu, "control")
  51. epilepsy.mat <- props_from_seurat(seu, "epilepsy")
  53. # arrange cell type order
  54. control.mat <- control.mat[, celltype.order, drop = FALSE]
  55. epilepsy.mat <- epilepsy.mat[, celltype.order, drop = FALSE]
  57. # bulid propr objects
  58. prop_ctrl <- propr(counts = control.mat, metric = "rho", p=2000)
  59. prop_epi <- propr(counts = epilepsy.mat, metric = "rho", p=2000)
  61. # permute FDR
  62. prop_ctrl <- updateCutoffs(prop_ctrl, tails = "both", number_of_cutoffs = 200)
  63. prop_epi <- updateCutoffs(prop_epi, tails = "both", number_of_cutoffs = 200)
  65. # extract propr matrices
  66. rho_ctrl <- getMatrix(prop_ctrl)
  67. rho_epi <- getMatrix(prop_epi)
  69. # build a label matrix from a propr object at a chosen FDR
  70. sig_labels_from_propr <- function(pr_obj, stat_mat, fdr = 0.05, mark = c("stars","sign")) {
  71. mark <- match.arg(mark)
  72. # Get logical adjacency of significant pairs at the requested FDR
  73. adj <- getAdjacencyFDR(pr_obj, fdr = fdr)
  74. adj <- as.matrix(adj)
  75. adj <- adj != 0
  76. # Initialize blank label matrix
  77. lab <- matrix("", nrow = nrow(stat_mat), ncol = ncol(stat_mat),
  78. dimnames = dimnames(stat_mat))
  79. # Ensure dims match (they should if you used the same celltype order)
  80. stopifnot(identical(dimnames(adj), dimnames(stat_mat)))
  82. if (mark == "stars") {
  83. lab[adj] <- "*"
  84. } else { # "sign": show direction of association for 'rho' (or 'pcor')
  85. lab[ adj & stat_mat > 0 ] <- "+"
  86. lab[ adj & stat_mat < 0 ] <- "–"
  87. }
  88. diag(lab) <- "" # don't annotate the diagonal
  89. lab
  90. }
  92. # inspect the cutoffs that correspond to your FDR levels
  93. cut_ctrl_05 <- getCutoffFDR(prop_ctrl, fdr = 0.05)
  94. cut_epi_05 <- getCutoffFDR(prop_epi, fdr = 0.05)
  96. # build the display label matrices (choose one style) ---
  97. sig_ctrl <- sig_labels_from_propr(prop_ctrl, rho_ctrl, fdr = 0.05, mark = "stars")
  98. sig_epi <- sig_labels_from_propr(prop_epi, rho_epi, fdr = 0.05, mark = "stars")
  100. # consistent colors/breaks for both heatmaps
  101. col_fun <- colorRampPalette(rev(brewer.pal(11, "RdBu")))(100)
  102. brks <- seq(-1, 1, length.out = 101)
  104. # plot with overlays
  106. p1 <- pheatmap(rho_ctrl,
  107. cluster_rows = FALSE, cluster_cols = FALSE, treeheight_row = 0, treeheight_col = 0,
  108. display_numbers = sig_ctrl, number_color = "black", fontsize_number = 12,
  109. main = sprintf("Proportionality ρ – Control (FDR ≤ 0.05; |ρ| ≥ %.2f)", cut_ctrl_05),
  110. color = col_fun, breaks = brks, fontsize = 10)
  112. ggsave(p1, filename = "/Users/vho/Library/CloudStorage/Box-Box/0-LAB/EXPERIMENTS/Sequencing/acala-scripts-202505/manuscript-aug2025/proportion-allcells/propr/rho-control.pdf", height = 6, width = 7)
  114. p2 <- pheatmap(rho_epi,
  115. cluster_rows = FALSE, cluster_cols = FALSE, treeheight_row = 0, treeheight_col = 0,
  116. display_numbers = sig_epi, number_color = "black", fontsize_number = 12,
  117. main = sprintf("Proportionality ρ – Epilepsy (FDR ≤ 0.05; |ρ| ≥ %.2f)", cut_epi_05),
  118. color = col_fun, breaks = brks, fontsize = 10)
  120. ggsave(p2, filename = "/Users/vho/Library/CloudStorage/Box-Box/0-LAB/EXPERIMENTS/Sequencing/acala-scripts-202505/manuscript-aug2025/proportion-allcells/propr/rho-epilepsy.pdf", height = 6, width = 7)
  122. # differential proportionality with propd
  124. counts_all <- rbind(control.mat, epilepsy.mat)
  125. group <- factor(c(rep("control", nrow(control.mat)),
  126. rep("epilepsy", nrow(epilepsy.mat))),
  127. levels = c("control","epilepsy"))
  129. pd <- propd(counts_all, group, p = 2000, weighted = TRUE)
  130. setDisjointed(pd)
  131. pd <- updateF(pd, moderated = FALSE, ivar = "clr")
  133. # theta_d for "disjointed"
  134. setActive(pd, "theta_d")
  136. # Significant pairs (FDR on the F-test)
  137. adj_diff <- getAdjacencyFstat(pd, fdr = 0.05) |> as.matrix() |> {\(m) m != 0}()
  139. rho_diff <- rho_epi - rho_ctrl
  141. # Build label matrix
  142. lab <- matrix("", nrow = nrow(rho_diff), ncol = ncol(rho_diff),
  143. dimnames = dimnames(rho_diff))
  144. lab[adj_diff] <- "*"
  145. diag(lab) <- ""
  147. # fixed plotting order
  148. ord <- celltype.order
  149. rho_diff_ord <- rho_diff[ord, ord, drop = FALSE]
  150. lab_ord <- lab[ord, ord, drop = FALSE]
  152. p3 <- pheatmap(rho_diff_ord,
  153. cluster_rows = FALSE, cluster_cols = FALSE,
  154. display_numbers = lab_ord, number_color = "black", fontsize_number = 12,
  155. main = "Δρ (Epilepsy − Control) * mark FDR≤0.05 differential proportionality (θᵈ)",
  156. color = colorRampPalette(rev(brewer.pal(11, "PRGn")))(100),
  157. breaks = seq(-1, 1, length.out = 101),
  158. treeheight_row = 0, treeheight_col = 0, fontsize = 10
  159. )
  161. ggsave(p3, filename = "/Users/vho/Library/CloudStorage/Box-Box/0-LAB/EXPERIMENTS/Sequencing/acala-scripts-202505/manuscript-aug2025/proportion-allcells/propr/rho_diff_epilepsy-control.pdf", height = 6, width = 7)
  163. # NETWORK
  165. prop_df <- seu.meta %>%
  166. group_by(sample_ID, weeks, state, celltype) %>%
  167. summarise(count = n(), .groups = "drop_last") %>%
  168. mutate(prop = count / sum(count)) %>%
  169. ungroup() %>%
  170. dplyr::select(any_of(c("sample_ID", "weeks", "state", "celltype", "prop"))) %>%
  171. pivot_wider(names_from = celltype, values_from = prop, values_fill = 0) %>%
  172. mutate(weeks = readr::parse_number(weeks))
  174. meta_cols <- intersect(c("sample_ID","weeks","state"), names(prop_df))
  175. celltypes <- setdiff(names(prop_df), meta_cols)
  177. X_all <- prop_df[, celltypes, drop = FALSE]
  178. rownames(X_all) <- prop_df$sample_ID
  179. grp <- factor(prop_df$state, levels = c("control", "epilepsy"))
  181. # CLR transform
  182. eps <- 1e-6
  183. X_clr_all <- compositions::clr(as.matrix(X_all) + eps)
  184. Xc <- X_clr_all[grp == "control", , drop = FALSE]
  185. Xe <- X_clr_all[grp == "epilepsy", , drop = FALSE]
  187. # pooled lambda path + STARS
  189. X_pool_std <- scale(X_clr_all)
  190. fit_pool <- huge(X_pool_std, method = "glasso", nlambda = 40)
  191. sel_pool <- huge.select(fit_pool, criterion = "stars")
  192. lam_seq <- fit_pool$lambda
  193. k_star <- sel_pool$opt.index
  195. # fit group paths using SAME lambda sequence
  196. fit_ctrl <- huge(scale(Xc), method = "glasso", lambda = lam_seq)
  197. fit_epi <- huge(scale(Xe), method = "glasso", lambda = lam_seq)
  199. # use the SAME λ index k* (take from the PATH, not $refit)
  200. adj_ctrl <- as.matrix(fit_ctrl$path[[k_star]])
  201. adj_epi <- as.matrix(fit_epi$path[[k_star]])
  203. dimnames(adj_ctrl) <- list(celltypes, celltypes)
  204. dimnames(adj_epi) <- list(celltypes, celltypes)
  206. # centrality tables
  207. to_hub_table <- function(adj) {
  208. g <- igraph::graph_from_adjacency_matrix(adj, mode = "undirected", diag = FALSE)
  209. tibble(
  210. celltype = colnames(adj),
  211. degree = igraph::degree(g),
  212. betweenness = igraph::betweenness(g),
  213. closeness = igraph::closeness(g, normalized = TRUE)
  214. ) %>%
  215. mutate(closeness = ifelse(is.finite(closeness), closeness, 0)) %>%
  216. arrange(desc(degree), desc(betweenness))
  217. }
  219. hub_ctrl <- to_hub_table(adj_ctrl) %>% mutate(group = "control")
  220. hub_epi <- to_hub_table(adj_epi) %>% mutate(group = "epilepsy")
  222. # compare hubs
  223. hub_compare <- dplyr::full_join(
  224. hub_ctrl %>%
  225. dplyr::transmute(celltype,
  226. degree_ctrl = degree,
  227. betw_ctrl = betweenness,
  228. close_ctrl = closeness),
  229. hub_epi %>%
  230. dplyr::transmute(celltype,
  231. degree_epi = degree,
  232. betw_epi = betweenness,
  233. close_epi = closeness),
  234. by = "celltype"
  235. ) %>%
  236. dplyr::mutate(dplyr::across(dplyr::everything(), ~tidyr::replace_na(., 0))) %>%
  237. dplyr::mutate(delta_degree = degree_epi - degree_ctrl) %>%
  238. dplyr::arrange(dplyr::desc(delta_degree), dplyr::desc(degree_epi))
  240. cat("\n=== CONTROL ===\n"); print(hub_ctrl %>% head(24), n = 24)
  241. cat("\n=== EPILEPSY ===\n"); print(hub_epi %>% head(24), n = 24)
  242. cat("\n=== Δdegree (E - C) ranking ===\n"); print(hub_compare, n = nrow(hub_compare))
  244. epi_only_hubs <- hub_compare %>% filter(degree_epi > 0 & degree_ctrl == 0) %>% arrange(desc(degree_epi))
  245. cat("\n=== Hubs present only in epilepsy ===\n"); print(epi_only_hubs, n = nrow(epi_only_hubs))
  247. # epilepsy-only edges
  248. only_epi_edges <- function(adj_e, adj_c) {
  249. p <- ncol(adj_e); nm <- colnames(adj_e)
  250. idx <- which(upper.tri(adj_e) & adj_e == 1 & adj_c == 0, arr.ind = TRUE)
  251. if (nrow(idx) == 0) tibble(ct1 = character(), ct2 = character())
  252. else tibble(ct1 = nm[idx[,1]], ct2 = nm[idx[,2]])
  253. }
  254. epi_only <- only_epi_edges(adj_epi, adj_ctrl)
  255. cat("\n=== Edges present only in epilepsy ===\n"); print(epi_only, n = nrow(epi_only))
  257. # layout
  258. adj_union <- (adj_ctrl + adj_epi) > 0
  259. g_union <- igraph::graph_from_adjacency_matrix(adj_union * 1, mode = "undirected", diag = FALSE)
  260. lay_mat <- igraph::layout_with_fr(g_union)
  261. coords <- tibble(celltype = igraph::V(g_union)$name, x = lay_mat[,1], y = lay_mat[,2])
  263. # node metadata
  264. cell_class <- tibble(
  265. celltype = celltypes,
  266. class = case_when(
  267. celltype %in% c("microglia","astrocytes","oligo","diffOPC","OPC","fibroblast","vascular") ~ "Glia",
  268. celltype %in% c("CA1","CA2-IG-FC","CA3-do","CA3-ve","DG","Mossy","ProS-SUB","imDG") ~ "Excitatory",
  269. celltype %in% c("Sst","Cck","Vip","Lamp5","Lamp5 Lhx6","CR","Pvalb","Pvalb Vipr2") ~ "Inhibitory",
  270. TRUE ~ "Other"
  271. )
  272. )
  274. class_cols <- c(Glia="#6A9FB5", Excitatory="#E28F41", Inhibitory="#8C6BB1", Other="#7F7F7F")
  276. deg_ctrl <- igraph::degree(igraph::graph_from_adjacency_matrix(adj_ctrl, mode = "undirected", diag = FALSE))
  277. deg_epi <- igraph::degree(igraph::graph_from_adjacency_matrix(adj_epi, mode = "undirected", diag = FALSE))
  279. node_df <- tibble(celltype = celltypes) %>%
  280. left_join(cell_class, by="celltype") %>%
  281. mutate(
  282. class = factor(class, levels=c("Glia","Excitatory","Inhibitory","Other")),
  283. deg_ctrl = deg_ctrl[celltype],
  284. deg_epi = deg_epi[celltype]
  285. )
  287. edges_tidy <- function(g) {
  288. as_tibble(igraph::as_edgelist(g), .name_repair="minimal") %>%
  289. rename(from = 1, to = 2)
  290. }
  291. E_ctrl <- edges_tidy(igraph::graph_from_adjacency_matrix(adj_ctrl, mode="undirected", diag=FALSE))
  292. E_epi <- edges_tidy(igraph::graph_from_adjacency_matrix(adj_epi, mode="undirected", diag=FALSE))
  294. # plotting
  295. plot_panel <- function(edge_df, title, deg_vec) {
  296. edges_plot <- edge_df %>%
  297. left_join(coords %>% rename(from = celltype, x = x, y = y), by="from") %>%
  298. rename(x_from = x, y_from = y) %>%
  299. left_join(coords %>% rename(to = celltype, x = x, y = y), by="to") %>%
  300. rename(x_to = x, y_to = y)
  302. nodes_plot <- node_df %>%
  303. left_join(coords, by="celltype") %>%
  304. mutate(size_deg = scales::rescale(deg_vec[celltype], to=c(3,10)))
  306. ggplot() +
  307. geom_segment(
  308. data = edges_plot,
  309. aes(x = x_from, y = y_from, xend = x_to, yend = y_to),
  310. color = "grey70", linewidth = 0.4, alpha = 0.9
  311. ) +
  312. geom_point(
  313. data = nodes_plot,
  314. aes(x = x, y = y, color = class, size = size_deg)
  315. ) +
  316. ggrepel::geom_text_repel(
  317. data = nodes_plot,
  318. aes(x = x, y = y, label = celltype),
  319. size = 3, max.overlaps = 100, segment.alpha = 0.3
  320. ) +
  321. scale_color_manual(values = class_cols, name = "Class") +
  322. guides(size = "none") +
  323. coord_equal() +
  324. theme_void(base_size = 11) +
  325. theme(
  326. legend.position = "right",
  327. plot.title = element_text(hjust=0.5, face="bold")
  328. ) +
  329. ggtitle(title)
  330. }
  332. p_ctrl <- plot_panel(E_ctrl, "Control network", deg_ctrl)
  333. p_epi <- plot_panel(E_epi, "Epilepsy network", deg_epi)
  335. combined <- p_ctrl + p_epi + plot_layout(guides = "collect") &
  336. theme(legend.position = "right")
  338. print(combined)
  340. # Save to PDF
  341. ggsave(
  342. filename = "networks_control_vs_epilepsy.pdf",
  343. plot = combined,
  344. width = 12, height = 6, units = "in",
  345. device = cairo_pdf
  346. )
  349. # print all edges and weights
  350. edges_ctrl <- which(adj_ctrl != 0, arr.ind = TRUE)
  351. edges_ctrl <- tibble(
  352. from = rownames(adj_ctrl)[edges_ctrl[, 1]],
  353. to = colnames(adj_ctrl)[edges_ctrl[, 2]]
  354. ) %>%
  355. filter(from < to) # keep upper triangle to avoid duplicates
  356. print(edges_ctrl, n = nrow(edges_ctrl))
  358. edges_epi <- which(adj_epi != 0, arr.ind = TRUE)
  359. edges_epi <- tibble(
  360. from = rownames(adj_epi)[edges_epi[, 1]],
  361. to = colnames(adj_epi)[edges_epi[, 2]]
  362. ) %>%
  363. filter(from < to)
  364. print(edges_epi, n = nrow(edges_epi))

fig2cde.R at commit 71822cb, no license · at the source

Overview

Authors: Victoria Ho1,2, Ruth Tjondropurnomo1, Jennifer Nguyen1, Eszter Balkó3,4, Samantha Depew1, Xing Chen1, Radhika Singh1, J. Edward van Veen5, Bence Rácz3,4, Peyman Golshani1,2,6,7
ORCID iDs: Victoria Ho, Xing Chen
  1. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
  2. Greater Los Angeles VA Medical Center, Los Angeles, CA, USA
  3. National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine Budapest, Budapest, Hungary
  4. Department of Anatomy and Histology, University of Veterinary Medicine, Budapest, Hungary
  5. Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
  6. Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
  7. Intellectual and Developmental Disability Research Center, University of California, Los Angeles, Los Angeles, CA, USA
Institutions: University of California, Los Angeles (United States); VA Greater Los Angeles Healthcare System (United States); University of Veterinary Medicine (Hungary)
Journal: iScience, volume 29, issue 9, article 117228
Dates: received 27 February 2026; accepted 21 July 2026; published online 19 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1016/j.isci.2026.117228 · PMID 42662868 · PMCID PMC13521094 · OpenAlex W7134045176
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.64898/2026.03.04.709339
Status: code verified
Categories: genetics / omics (modality), epilepsy (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: temporal lobe epilepsy, TLE, epileptogenesis, hippocampus
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: NIH; NINDS (K08 NS123509); American Academy of Neurology Neuroscience Research Training Scholarship; National Recovery Fund Recovery and Resilience Facility (RRF-2.3.1-21-2022-00001)
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Temporal lobe epilepsy (TLE) is the most common acquired epilepsy, causing refractory seizures and cognitive deficits. We performed single-nucleus RNA sequencing on hippocampal tissue from mice 3 and 6 weeks following pilocarpine-induced status epilepticus, a robust model of TLE. Epilepsy samples showed reductions in Cck and Lamp5-Lhx6 interneuron subclusters, alongside increases in Cajal-Retzius cells, dentate granule (DG) cell precursors, and a mature DG cell subcluster. Among glia, an astrocyte subcluster and a markedly expanded microglia sublcuster were increased. We term this microglia population epilepsy-associated microglia (EAM). The transcriptomic profile of EAM overlaps with microglia described in models of Alzheimer’s disease and traumatic brain injury, including enrichment of Myo1e and Igf1. EAM display amoeboid morphology, can be found in clumps around pyramidal and granule cell body layers, and exhibit enlarged vesicles and mitochondria. Cell-cell interaction analysis predicts DG cells as their primary interaction partners. This dataset defines transcriptomic programs underlying key cellular alterations in TLE, enabling mechanistic dissection of epileptogenesis.

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

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VictoriaHo-Lab/snRNAseq-pilo

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State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 71822cbe3eb3d8a7c48c1fb49b3825e3e997ed7d, 23 January 2026
Languages: R (9)
Size: 20 files, 9 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (9 files), tidyverse (9 files), patchwork (5 files), ggplot2 (4 files), DESeq2 (3 files), pheatmap (3 files), Harmony (2 files), cowplot (1 file), igraph (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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10 files
At the source: github.com/VictoriaHo-Lab/snRNAseq-pilo

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

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  • 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 and code availability

• Raw FASTQ files and processed data are deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE306031. • This paper does not report original code. Scripts used to perform analyses in this paper can be found at https://github.com/VictoriaHo-Lab/snRNAseq-pilo. • 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 Victoria Ho (0000-0003-4159-1356); Xing Chen (0009-0009-0228-5867); removed Victoria Ho; Xing Chen

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 4 funders, 66 references.

Cite

This paper

Ho, V., Tjondropurnomo, R., Nguyen, J., Balkó, E., Depew, S., Chen, X., Singh, R., van Veen, J. E., Rácz, B., & Golshani, P. (2026). Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia. iScience, 29(9), 117228. https://doi.org/10.1016/j.isci.2026.117228

BibTeX

@article{ho2026single,
author = {Ho, Victoria and Tjondropurnomo, Ruth and Nguyen, Jennifer and Balkó, Eszter and Depew, Samantha and Chen, Xing and Singh, Radhika and van Veen, J. Edward and Rácz, Bence and Golshani, Peyman},
title = {{Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117228},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117228},
url = {https://doi.org/10.1016/j.isci.2026.117228},
pmid = {42662868},
pmcid = {PMC13521094}
}

RIS

TY - JOUR
AU - Ho, Victoria
AU - Tjondropurnomo, Ruth
AU - Nguyen, Jennifer
AU - Balkó, Eszter
AU - Depew, Samantha
AU - Chen, Xing
AU - Singh, Radhika
AU - van Veen, J. Edward
AU - Rácz, Bence
AU - Golshani, Peyman
TI - Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/19
VL - 29
IS - 9
SP - 117228
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117228
UR - https://doi.org/10.1016/j.isci.2026.117228
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.117228",
"type": "article-journal",
"title": "Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia",
"container-title": "iScience",
"author": [
{
"family": "Ho",
"given": "Victoria"
},
{
"family": "Tjondropurnomo",
"given": "Ruth"
},
{
"family": "Nguyen",
"given": "Jennifer"
},
{
"family": "Balkó",
"given": "Eszter"
},
{
"family": "Depew",
"given": "Samantha"
},
{
"family": "Chen",
"given": "Xing"
},
{
"family": "Singh",
"given": "Radhika"
},
{
"family": "van Veen",
"given": "J. Edward"
},
{
"family": "Rácz",
"given": "Bence"
},
{
"family": "Golshani",
"given": "Peyman"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "9",
"page": "117228",
"DOI": "10.1016/j.isci.2026.117228",
"PMID": "42662868",
"PMCID": "PMC13521094",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.117228",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
19
]
]
}
}

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