OSCR

Structural basis of IgLON5 autoantibody recognition in autoimmune encephalitis

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  1. [1] § Materials and Methods › Paired BCR repertoire ↔ scripts/BCR_analysis_D1_PUB.Rmd, lines 345–399 · score 0.94 · species richness, clonalRarefaction, n.boots, Shannon diversity, scRepertoire, evenness
  2. [2] § Results › Characterization of the B cell receptor repertoire in anti-IgLON5 disease ↔ scripts/BCR_analysis_D1_PUB.Rmd, lines 558–649 · score 0.71 · Games Howell, mutation frequency, zero, variable, BCR, D1

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The authors' code

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  1. ---
  2. title: "BCR analysis - IgLON5 - donor D1"
  3. author: "Mathilde Foglierini"
  4. output: html_document
  5. date: "`r format(Sys.time(), '%a %d %B %Y %X')`"
  6. ---
  8. # Analysis script for BCR repertoire study in anti-IgLON5 disease
  10. Publication: "Structural basis for antibody-mediated IgLON5 receptor clustering and endocytosis in autoimmune encephalitis"
  11. Repository: https://github.com/MathildeFogPerez/manuscript-iglon5-D1
  12. Zenodo dataset: https://doi.org/10.5281/zenodo.18925481
  14. ### Description
  16. This script contains the code used to perform the analyses and generate the figures presented in the publication "Structural basis for antibody-mediated IgLON5 receptor clustering and endocytosis in autoimmune encephalitis".
  17. The analyses are based on processed B cell receptor (BCR) repertoire datasets generated from single-cell V(D)J sequencing experiments.
  19. ### Input data
  21. The input files required to run this script are available in the Zenodo repository: Zenodo: https://doi.org/10.5281/zenodo.18925481
  23. Specifically, this script uses the files contained in the folder:
  24. files_for_repertoire_analysis/
  26. This folder contains, for each donor:
  27. BCR_paired.csv – paired heavy and light chain BCR sequences
  28. airr_afterchangeo.csv – AIRR-formatted repertoire datasets generated after processing with the Immcantation/Change-O pipeline
  30. ```{r global_options, include=FALSE}
  31. knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE, results ='asis')
  33. setwd("/users/mfoglier/SCRIPTS/R_workspace/IgLON5/code")
  34. renv::status()
  35. ```
  37. ```{r load_libraries}
  38. # Core data manipulation
  39. library(tidyverse)
  41. # Single-cell analysis frameworks
  42. library(Seurat)
  43. library(scRepertoire)
  45. # Statistical analysis
  46. library(rstatix)
  47. library(gtsummary)
  48. library(corrplot)
  49. library(ggpubr)
  52. # Visualization - base plotting extensions
  53. library(gridExtra)
  54. library(patchwork)
  55. library(circlize)
  57. # Visualization - colors and palettes
  58. library(RColorBrewer)
  59. library(viridis)
  61. # Data output
  62. library(knitr)
  63. library(openxlsx)
  64. ```
  67. ```{r setting_variables }
  68. datapath='data/'
  70. donor="D1"
  71. date_for_output="2026.02.11"
  72. myOut=paste0(date_for_output,"_out")
  73. outpath=paste0(datapath,donor,'/',myOut,"/")
  74. dir.create(outpath, showWarnings = FALSE, recursive = TRUE)
  75. figpath=paste0(outpath,'figures/')
  76. dir.create(figpath, showWarnings = FALSE, recursive = TRUE)
  77. rdsfolder=paste0(outpath,'/RDS_files/')
  78. dir.create(rdsfolder, showWarnings = FALSE, recursive = TRUE)
  80. meta <- read.csv(paste0(datapath,"/metadata_D1_H1_H2.csv"), header=T)
  81. allDonors=unique(meta$donor)
  82. allAgreg=unique(meta$agreg)
  84. processedDonors=c("D1","H1","H2")
  85. iglonDonors=c("D1")
  86. sample_order <- c("NS1","NS2","SI1","SI2", "H1","H2")
  89. donor_cols <- c(
  90. D1 = "#d3d3d3",
  91. H1="#8b8b8c",
  92. H2="#8b8b8c"
  93. )
  96. all_isotypes_sorted <- c(
  97. "IGHA1", "IGHA2", "IGHD",
  98. "IGHG1", "IGHG2", "IGHG3", "IGHG4",
  99. "IGHM","Unknown"
  100. )
  102. isotype_colors <- c(
  103. "IGHA1" = alpha("#4F6980" ,alpha = 0.6),
  104. "IGHA2" = alpha("#849DB1",alpha = 0.6),
  105. "IGHD" = alpha("#76B782",alpha = 0.6),
  106. "IGHE" = alpha("black",alpha = 0.6),
  107. "IGHG1" = alpha("#FBB04E",alpha = 0.8),
  108. "IGHG2" = alpha( "#F0B381",alpha = 0.8),
  109. "IGHG3" = alpha("#CE988D",alpha = 0.8),
  110. "IGHG4" = alpha("#F1560E",alpha = 0.8),
  111. "IGHM" = alpha("#D7CE9F",alpha = 0.6),
  112. "Unknown" = alpha("grey80",alpha = 0.4)
  114. )
  117. # Mapping of GEM group → sample name per experiment
  118. sample_map <- list(
  119. Exp1 = c(
  120. "1" = "SI1",
  121. "2" = "NS1"
  122. ),
  123. Exp2 = c(
  124. "1" = "SI2",
  125. "2" = "NS2"
  126. )
  127. )
  129. group_map <- c(
  130. Exp1 = "Iglon",
  131. Exp2 = "Iglon"
  132. )
  134. #Sample SI2 is the IgLON5-sorted B cells
  135. iglonspe_map <- list(
  136. Exp1 = c(
  137. "SI1" ="unknown",
  138. "NS1" ="unknown"
  139. ),
  140. Exp2 = c(
  141. "SI2"="IgLON5",
  142. "NS2"="unknown"
  143. )
  144. )
  145. ```
  147. ```{r load_all_paired_df}
  149. all.paired.df =readRDS(paste0(rdsfolder,"all_paired_df.Rds"))
  150. d1=all.paired.df[all.paired.df$donor=="D1",]
  152. combined.BCR= readRDS(paste0(rdsfolder,"combined.BCR.Rds"))
  154. ```
  156. # Get and process data
  158. ```{r functions}
  160. #This function is use to remove the allele or the multi gene assignment from changeO in order to have nice graph from scRepertoire
  161. simplify_ig <- function(x) {
  162. blocks <- strsplit(x, "_", fixed = TRUE)[[1]]
  163. result <- vapply(blocks, function(chain) {
  164. parts <- strsplit(chain, "\\.", fixed = FALSE)[[1]]
  165. parts <- vapply(parts, function(p) {
  166. if (p == "") return("") # keep empty blocks
  167. p <- sub(",.*$", "", p) # keep first candidate
  168. p <- sub("\\*.*$", "", p) # remove allele
  169. p
  170. }, character(1))
  171. paste(parts, collapse = ".")
  172. }, character(1))
  174. paste(result, collapse = "_") # Rejoin with underscore
  175. }
  176. ```
  178. ```{r pairedBCR, echo=F ,fig.height=3,fig.width=6}
  180. #For each donor load immcantation object
  181. imm.contigs=list()
  182. imm.contigs.v2=list()
  183. i=1
  184. for (don in processedDonors){
  185. immcantationOutPath=paste0(datapath,'files_for_repertoire_analysis/',don,'/')
  186. imm.contigs[[i]]<- read.csv(paste0(immcantationOutPath,don,"_airr_afterchangeo.csv"))
  187. dtomod= imm.contigs[[i]]
  188. dtomod$barcode=paste0(don,"_", sub("_contig_[0-9]+$", "", dtomod$sequence_id))
  189. imm.contigs.v2[[i]]<-dtomod
  190. i=i+1
  191. }
  192. BCR.contigs <- loadContigs(imm.contigs, format = "Immcantation")
  193. combined.BCR <- combineBCR(BCR.contigs,samples=processedDonors, removeNA = FALSE,removeMulti = TRUE,
  194. sequence = "aa", call.related.clones=FALSE) # we use Immcantation DefineClones.py instead
  195. all.paired.list <- list()
  196. i=1
  198. for (nm in names(combined.BCR)) {
  199. df <- combined.BCR[[nm]]
  200. #IgLON5 donors: rename samples according to gem well
  201. if(nm %in% iglonDonors){
  202. # extract experiment (Exp1 / Exp2 )
  203. exp <- sub(".*_(Exp[0-9]+).*", "\\1", df$barcode)
  204. # extract GEM well (number after last hyphen)
  205. gem_group <- sub(".*-", "", df$barcode)
  206. # remap sample
  207. df$sample <- mapply(
  208. function(e, g) sample_map[[e]][g],
  209. exp, gem_group
  210. )
  211. df$group <- mapply(
  212. function(e, g) group_map[[e]],
  213. exp
  214. )
  215. #infer agSpe from Exp+ sample
  216. sample <- df$sample
  217. df$agSpe <- mapply(
  218. function(exp, samp) iglonspe_map[[exp]][samp],
  219. exp,sample
  220. )
  221. }else{
  222. df$group="Healthy"
  223. df$agSpe=NA
  224. }
  225. #Keep only the clonotypes as in _paired_bcrs.csv file, and add sequence_id and h_clone_id
  226. immcantationOutPath=paste0(datapath,'files_for_repertoire_analysis/',nm,'/')
  227. paired.df=read.csv(file=paste0(immcantationOutPath,nm,'_paired_bcrs.csv'))
  228. paired.df$donor=nm
  229. paired.df$h_cell_id=paste0(nm,"_",paired.df$h_cell_id)
  230. cat("For donor ",nm," number of df clonotypes: ",nrow(df),"\n")
  231. cat(" ",nm," number of paired.df clonotypes: ",nrow(paired.df),"\n")
  232. featToAdd=paired.df[,c("sequence_id","h_cell_id","h_clone_id","h_c_call")]
  233. df=merge(df,featToAdd, by.x="barcode",by.y="h_cell_id")
  234. cat(" after merging number of clonotypes: ",nrow(df),"\n")
  235. #simplify gene names
  236. df$IGH <- vapply(df$IGH, simplify_ig, character(1), USE.NAMES = FALSE)
  237. df$CTgene.al <- df$CTgene
  238. #store the first version of CTgene (with allele) in case we need it for one scRepertoire function
  239. df$CTgene <- vapply(df$CTgene, simplify_ig, character(1), USE.NAMES = FALSE)
  240. df$CTgene.wo.al <- df$CTgene
  241. df$IGLC <- vapply(df$IGLC, simplify_ig, character(1), USE.NAMES = FALSE)
  242. combined.BCR[[nm]] <- df
  243. all.paired.list[[i]] <- paired.df
  244. i <- i + 1
  245. }
  246. # find shared columns
  247. common_cols <- Reduce(intersect, lapply(all.paired.list, colnames))
  248. # keep only shared columns
  249. all.paired.list <- lapply(all.paired.list, function(df) {
  250. df[common_cols]
  251. })
  252. # bind
  253. all.paired.df <- do.call(rbind, all.paired.list)
  255. #Add the back the donor as variable
  256. combined.BCR <- addVariable(combined.BCR,
  257. variable.name = "donor",
  258. variables =processedDonors)
  261. combined.BCR <- lapply(combined.BCR, function(df) {
  262. df$sample <- factor(df$sample, levels = sample_order)
  263. df
  264. })
  266. #count and plot the number of clonotypes by donor and samples
  267. sample_counts <- table(
  268. unlist(lapply(combined.BCR, function(df) df$sample))
  269. )
  270. sample_counts
  272. sample_cols <- c(
  273. # Donor D1 (blue / lavender)
  274. NS1 = "#80B1D3FF",
  275. NS2 = "#6B9AC4FF",
  276. SI1 = "#C6CDF7FF",
  277. SI2 = "#E3E7FFFF",
  278. H1="grey70",
  279. H2="grey80"
  280. )
  282. clonalQuant(combined.BCR,
  283. cloneCall="aa",
  284. chain="both",
  285. scale=FALSE, exportTable=FALSE,
  286. group.by="sample") +
  287. scale_fill_manual(values = sample_cols, breaks = sample_order)
  289. #saveRDS(combined.BCR,paste0(rdsfolder,"combined.BCR.Rds"))
  290. #saveRDS(all.paired.df,paste0(rdsfolder,"all_paired_df.Rds"))
  291. ```
  294. ```{r add_col_to_paired_data}
  295. idx_special <- all.paired.df$donor %in% c("H1", "H2")
  296. idx_normal <- !idx_special
  298. # init outputs
  299. all.paired.df$sample <- NA_character_
  300. all.paired.df$agSpe <- NA_character_
  303. # SPECIAL: sample = donor,agSpe = NA
  304. all.paired.df$sample[idx_special] <- all.paired.df$donor[idx_special]
  305. all.paired.df$agSpe[idx_special] <- NA
  308. # NORMAL: do Exp/GEM parsing only for normal rows
  309. exp <- rep(NA_character_, nrow(all.paired.df))
  310. gem_group <- rep(NA_character_, nrow(all.paired.df))
  312. exp[idx_normal] <- sub(".*_(Exp[0-9]+).*", "\\1", all.paired.df$h_cell_id[idx_normal])
  313. gem_group[idx_normal] <- sub(".*-", "", all.paired.df$h_cell_id[idx_normal])
  315. # map sample for normal rows
  316. all.paired.df$sample[idx_normal] <- mapply(
  317. function(e, g) sample_map[[e]][g],
  318. exp[idx_normal], gem_group[idx_normal]
  319. )
  321. # map agSpe for normal rows
  322. all.paired.df$agSpe[idx_normal] <- mapply(
  323. function(e, samp) iglonspe_map[[e]][samp],
  324. exp[idx_normal], all.paired.df$sample[idx_normal]
  325. )
  327. #Save all_paired_df to load later
  329. saveRDS(all.paired.df,paste0(rdsfolder,"all_paired_df.Rds"))
  330. ```
  332. ```{r 3_donors_count}
  333. clonalQuant(combined.BCR,
  334. cloneCall="aa",
  335. chain="both",
  336. scale=FALSE, exportTable=FALSE,
  337. group.by="donor") +
  338. scale_fill_manual(values = donor_cols)
  340. ```
  345. ## Extended data Fig 1: rarefaction plots
  347. ```{r ext_dat_fig1}
  349. # "Rarefaction analysis was performed using the clonalRarefaction function from scRepertoire v2, implementing iNEXT-based Hill number estimation with 50 bootstrap replicates."
  351. donor_cols_sup <- c("D1" = "#F1560E",
  352. "H1" = "grey50",
  353. "H2" = "grey30")
  355. #Panel A — Sample quality & coverage
  356. # Plot type 2 : shows how well each donor was sampled
  357. p1 <-clonalRarefaction(combined.BCR,
  358. plot.type = 2,
  359. hill.numbers = 0,
  360. n.boots = 50,
  361. group.by = "donor" ) + scale_color_manual(values = donor_cols_sup) +
  362. scale_fill_manual(values = donor_cols_sup)+
  363. ggtitle("A Sample completeness") +NoLegend()
  365. #Panel B — Species richness (Hill 0)
  366. # Total unique clones — directly relevant before showing VH usage.
  367. p2 <-clonalRarefaction(combined.BCR,
  368. plot.type = 3,
  369. hill.numbers = 0,
  370. n.boots = 50,
  371. group.by = "donor")+ scale_color_manual(values = donor_cols_sup) +
  372. scale_fill_manual(values = donor_cols_sup)+
  373. ggtitle("B Species richness (Hill 0)") +NoLegend()
  375. #Panel C — Shannon diversity (Hill 1)
  376. #Evenness of repertoire — relevant before showing isotype proportions.
  377. p3 <-clonalRarefaction(combined.BCR,
  378. plot.type = 3,
  379. hill.numbers = 1,
  380. n.boots = 50,
  381. group.by = "donor")+ scale_color_manual(values = donor_cols_sup) +
  382. scale_fill_manual(values = donor_cols_sup)+
  383. ggtitle("C Shannon diversity (Hill 1)") +NoLegend()
  386. combined <- plot_grid(p1, p2, p3,
  387. ncol = 2,
  388. labels = c("a", "b", "c"),
  389. label_size = 12)
  391. pdf(paste0(figpath,"/supplementary_rarefaction.pdf"),
  392. width = 8,
  393. height = 6)
  395. print(combined)
  396. dev.off()
  399. ```
  402. ## Figure 1A. VH gene usage
  404. ```{r vh}
  405. p=vizGenes(combined.BCR,
  406. x.axis = "IGHV",
  407. y.axis = NULL,
  408. plot = "barplot",
  409. summary.fun = "proportion") + theme(axis.text.x = element_text(size=7),
  410. axis.text.y = element_text(size=7),
  411. strip.text = element_text(size=8))
  412. print(p)
  413. pdf(file=paste0(figpath,"VH_distribution_byDonor.pdf"), width=5.2, height=3.5)
  414. print(p)
  415. invisible(dev.off())
  417. ```
  421. ## Extended data Fig 2 : VL/VK usage
  423. ```{r ext_dat_fig1}
  425. p=vizGenes(combined.BCR,
  426. x.axis = "IGLV",
  427. y.axis = NULL,
  428. plot = "barplot",
  429. summary.fun = "proportion") + theme(axis.text.x = element_text(size=7),
  430. axis.text.y = element_text(size=7),
  431. strip.text = element_text(size=8))
  432. print(p)
  434. pdf(file=paste0(figpath,"VL_distribution_byDonor.pdf"), width=5.2, height=3.5)
  435. print(p)
  436. invisible(dev.off())
  438. ```
  440. ## Figure 1B. Isotype distribution
  442. ```{r isotpye}
  444. all_isotypes <- c(
  445. "IGHA1", "IGHA2", "IGHD", "IGHE",
  446. "IGHG1", "IGHG2", "IGHG3", "IGHG4",
  447. "IGHM"
  448. )
  450. plotlist=list()
  451. i=1
  452. for (don in c("D1","H1","H2")){
  453. df=all.paired.df[all.paired.df$donor==don,]
  454. tot=nrow(df)
  455. df=df[!nchar(df$h_c_call)==0,]
  456. # Calculate Frequencies
  457. # Force factor levels
  458. df$h_c_call <- factor(df$h_c_call, levels = all_isotypes)
  459. freq_df <- as.data.frame(prop.table(table(df$h_c_call)))
  460. colnames(freq_df) <- c("Isotype", "Frequency")
  462. # Create Bar Plot for Frequency
  463. p=ggplot(freq_df, aes(x = Isotype, y = Frequency, fill = Isotype)) +
  464. geom_bar(stat = "identity", width = 0.7) +
  465. geom_text(
  466. data = subset(freq_df, Isotype == "IGHG4"),
  467. aes(label = scales::percent(Frequency, accuracy = 0.1)),
  468. vjust = -0.3,
  469. size = 2.5
  470. )+
  471. scale_y_continuous(labels = scales::percent, limits = c(0, 0.65)) + # Convert to percentage
  472. labs(title = paste0(don," (n=",tot,")"),
  473. x = "",
  474. y = "Frequency (%)") +
  475. scale_fill_manual(values = isotype_colors) +
  476. theme_classic() + NoLegend()+
  477. theme(axis.text.x = element_text(angle = 45, hjust = 1,size=6.5),
  478. axis.text.y = element_text(size=6),
  479. title = element_text(size=7))
  480. if (i<3){
  481. p <- p + theme(axis.text.x = element_blank())
  482. }
  483. plotlist[[i]]=p
  484. i=i+1
  485. }
  487. wrap_plots(plotlist, ncol = 1)
  489. pdf(file=paste0(figpath,"Isotype_distribution.pdf"), width=3, height=3.5)
  490. wrap_plots(plotlist, ncol = 1)
  491. invisible(dev.off())
  493. #number of IGHG4
  494. all.paired.df %>%
  495. count(donor, h_c_call == "IGHG4") %>%
  496. group_by(donor) %>%
  497. mutate(
  498. total = sum(n),
  499. prop = (n / total) * 100
  500. ) %>%
  501. filter(`h_c_call == "IGHG4"`)
  503. ```
  505. ## Figure 1C. CDRH3 length distribution
  507. ```{r cdrh3_length}
  509. # Compute medians
  510. median_df <- all.paired.df %>%
  511. group_by(donor) %>%
  512. summarise(median_length = median(HCDR3.length, na.rm = TRUE))
  514. p <-ggplot(all.paired.df, aes(x = HCDR3.length, fill = donor)) +
  515. geom_histogram(binwidth = 1, boundary = 0,
  516. color = "black", show.legend = FALSE) +
  517. scale_fill_manual(values = donor_cols) +
  518. facet_wrap(~ donor, nrow = 1) +
  520. # Median vertical dotted line
  521. geom_vline(data = median_df,
  522. aes(xintercept = median_length, color = donor),
  523. linetype = "dashed",
  524. linewidth = 0.5) +
  526. # Median value text
  527. geom_text(data = median_df,
  528. aes(x = median_length,
  529. y = Inf,
  530. label = paste0(median_length)),
  531. vjust = 1.5,hjust = -0.2,
  532. size = 2.8) +
  533. theme_classic(base_size = 8) +
  534. theme(
  535. axis.line = element_line(linewidth = 0.6),
  536. axis.ticks = element_line(linewidth = 0.6),
  537. axis.title.x = element_text(size = 8), axis.title.y = element_text(size = 7),
  538. axis.text.y = element_text(size = 7), axis.text.x = element_text(size = 8),
  539. strip.text = element_text(size=8)
  540. )+
  541. labs(
  542. x = "CDRH3 length (aa)",
  543. y = "Count"
  544. )+
  545. scale_color_manual(values = c(D1 = "#F1560E",
  546. H1 = "grey70",
  547. H2 = "grey70")) +NoLegend()
  549. print(p)
  551. somePDFPath = file.path(paste0(figpath,'CDRH3_length.pdf'))
  552. pdf(file=somePDFPath, width=5, height=1.9)
  553. print(p)
  554. dev.off()
  556. ```
  558. ## Figure 1D. Mutation frequency
  560. ```{r mutation_freq}
  562. #### Check how many have 0 freq.mut
  563. all.paired.df %>%
  564. group_by(donor) %>%
  565. summarise(
  566. n_total = n(),
  567. n_zero = sum(mut.freq == 0),
  568. pct_zero = round(100 * n_zero / n_total, 1)
  569. )
  570. # donor n_total n_zero pct_zero
  571. # <chr> <int> <int> <dbl>
  572. # 1 D1 9577 2399 25
  573. # 2 H1 7263 244 3.4
  574. # 3 H2 7539 422 5.6
  576. #We will remove them
  577. all.paired.df2 <- all.paired.df[all.paired.df$mut.freq>0,]
  579. all.paired.df2 %>%
  580. group_by(donor) %>%
  581. get_summary_stats(mut.freq, type = "mean_sd")
  582. # donor variable n mean sd
  583. # <chr> <fct> <dbl> <dbl> <dbl>
  584. # 1 D1 mut.freq 7178 0.121 0.061
  585. # 2 H1 mut.freq 7019 0.096 0.05
  586. # 3 H2 mut.freq 7117 0.098 0.053
  589. #homogeneity of variance
  590. levene_test(mut.freq ~ donor, data = all.paired.df2)
  591. # Variances are clearly not equal across donors. -> no ANOVA
  592. #we do welch_anova instead
  594. wel.anov = welch_anova_test(mut.freq ~ donor, data = all.paired.df2) %>%
  595. add_significance()
  596. # There is a statistically significant difference in mutation frequency across donors.
  598. #Next identify which donor differ
  599. pwc <- all.paired.df2 %>%
  600. games_howell_test(mut.freq ~ donor,)
  602. # Report effect size: ges (generalized eta squared), using ANOVA
  603. #If η² < 0.01 → difference is statistically significant but practically very small (not the case here)
  605. anova_test(data = all.paired.df2, dv = mut.freq, between = donor)
  606. #ANOVA Table (type II tests)
  607. # Effect DFn DFd F p p<.05 ges
  608. # 1 donor 2 21311 448.736 1.28e-191 * 0.04
  611. # Visualisation : Boxplots with p-values
  612. p2 <- ggplot(all.paired.df2, aes(x = donor, y = mut.freq))+
  613. geom_jitter(aes(y=mut.freq,group=donor),width = 0.2, size = 0.5, alpha = 0.2)+
  614. geom_boxplot(aes(fill=donor, color = donor),alpha=0.6,outlier.shape = NA)+
  615. stat_pvalue_manual(pwc, tip.length = 0, hide.ns = TRUE, y.position = c(0.45,0.5),size=2.5) +
  616. labs(
  617. color="donor",
  618. y="Mutation frequency",
  619. x=""
  620. )+
  621. scale_color_manual(values = c("#F1560E","grey50","grey50"))+
  622. scale_fill_manual(values =donor_cols) +
  623. scale_y_continuous(breaks =c(0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5) ,limits = c(0,0.5))+
  624. theme_classic() +
  625. theme(
  626. axis.line = element_line(linewidth = 0.6),
  627. axis.ticks = element_line(linewidth = 0.6),
  628. axis.title = element_text(size = 6),
  629. axis.text.y = element_text(size = 6),axis.text.x = element_text(size = 8)
  630. )+ NoLegend()
  632. print(p2)
  635. pdf(file = paste0(figpath, "/Mutation_frequency.pdf"), width = 2.3, height =1.7)
  636. p2
  637. invisible(dev.off())
  639. ggsave(
  640. filename = paste0(figpath, "/Mutation_frequency.tiff"),
  641. plot = p2,
  642. device = "tiff",
  643. width = 2.9,
  644. height = 1.9,
  645. units = "in",
  646. dpi = 600,
  647. compression = "lzw"
  648. )
  649. ```
  653. ## Figure 1E .Clonal network
  655. ```{r clonalNetwork, eval=T}
  656. #actual is 2.6.2
  657. ##just for this figure we downgrade to a previous version of scRepertoire 2.2.1
  658. ##renv::install("BorchLab/scRepertoire@v2.2.1")
  659. ##then restart R
  661. #To visualize the clones
  663. #To get the cluster as the clone_id defined by changeO, we replace the VH gene by the h_clone_id
  664. temp.bcrData= combined.BCR$D1
  666. # Replace anything between 'IGHV' and the first '*' with h_clone_id/CTgene
  667. temp.bcrData$CTgene <- mapply(function(ctgene, hclone) {
  668. sub("(?<=IGHV)[^\\*^.]+", paste0(as.character(hclone),"-1*01"), ctgene, perl = TRUE)
  669. }, temp.bcrData$CTgene, gsub("_","",temp.bcrData$h_clone_id))
  671. ## We will get the cluster with at least 2 clonotypes
  672. igraph.object <- clonalCluster(temp.bcrData,
  673. chain = "IGH",
  674. sequence = "aa",
  675. threshold = 0.81,
  676. exportGraph = TRUE)
  678. igraph::V(igraph.object)$degrees <- igraph::degree(igraph.object)
  680. #saveRDS(igraph.object,paste0(rdsfolder,"igraph.object.Rds"))
  683. # Build a lookup: vertex name
  684. meta=temp.bcrData
  685. v_names <- igraph::V(igraph.object)$name
  686. meta <- meta[meta$cdr3_aa1 %in% v_names,] #barcode here in new version but does not work (cdr3_aa1)
  687. # # then match order to v_names
  688. iso <- meta$h_c_call[match(v_names, meta$cdr3_aa1)]
  689. iso[iso == ""] <- "Unknown"
  690. iso_lab <- iso
  692. iso_fac <- factor(iso_lab, levels = all_isotypes_sorted)
  693. iso_cols <- isotype_colors
  695. col_samples <- unname(isotype_colors[as.character(iso_fac)])
  696. igraph::V(igraph.object)$color <- col_samples
  697. color.legend <- factor(unique(iso_fac), levels=all_isotypes_sorted)
  699. edge_alpha_color <- adjustcolor("gray", alpha.f = 0.3)
  702. somePDFPath = file.path(paste0(figpath,'Network_clusters_plot_D1.pdf'))
  703. pdf(file=somePDFPath, width=10, height=10)
  706. set.seed(1)
  707. layout_fixed <- igraph::layout_nicely(igraph.object)
  708. plot(igraph.object,
  709. layout = layout_fixed,
  710. vertex.label = NA,
  711. vertex.size = 2*sqrt(igraph::V(igraph.object)$degrees),
  712. vertex.color = col_samples,
  713. vertex.frame.color = "white",
  714. edge.color = "black",
  715. edge.arrow.size = 0,
  716. edge.curved = 0.3,
  717. margin = -0.1)
  718. legend("topleft", legend =all_isotypes_sorted, pch = 16, col = isotype_colors[all_isotypes_sorted], bty = "n")
  720. invisible(dev.off())
  722. ```
  724. ## Figure 1F. Rank–abundance / rank–size plot
  726. ```{r}
  728. threshold <- 5
  730. d1=all.paired.df[all.paired.df$donor=="D1",]
  732. # 1) Compute clone sizes (members per h_clone_id)
  733. clone_sizes <- d1 %>%
  734. filter(!is.na(h_clone_id)) %>%
  735. count(h_clone_id, name = "clone_size") %>%
  736. arrange(desc(clone_size)) %>%
  737. mutate(rank = row_number(),
  738. above = clone_size > threshold)
  740. # stats
  741. total_families <- nrow(clone_sizes)
  742. families_above <- sum(clone_sizes$above)
  743. pct_cells_in_above <- 100 * sum(clone_sizes$clone_size[clone_sizes$above]) / sum(clone_sizes$clone_size)
  745. # 2) Plot
  746. p <- ggplot(clone_sizes, aes(x = rank, y = clone_size)) +
  747. geom_point(
  748. shape = 21, fill = "white", color = "grey20", stroke = 0.6, size = 2
  749. ) +
  750. geom_hline(yintercept = threshold, linetype = "dashed", color = "red",linewidth = 0.5) +
  751. scale_y_log10(
  752. expand = expansion(add = c(0.1, 0.2)), #to add 10 % below and 20% above the highest value
  753. breaks = c(0,1, 2, 5, 10, 20, 30, 50, 80, 100),
  754. labels = scales::label_number()
  755. )+
  756. scale_x_log10() +
  757. labs(
  758. x = "Rank by family size",
  759. y = "Size of clonal families"
  760. ) +
  761. annotate(
  762. "text",
  763. x = 1.5, y = max(clone_sizes$clone_size) * 0.9,
  764. hjust = 0,
  765. label = paste0(
  766. "Total families, n = ", format(total_families, big.mark = ","), "\n",
  767. "Families (size > ", threshold, " cells),\n",
  768. "n = ", format(families_above, big.mark = ","), " (",
  769. sprintf("%.1f", pct_cells_in_above), "% of cells)"
  770. ),
  771. size = 2.8
  772. ) +
  773. theme_classic(base_size = 8) +
  774. theme(
  775. axis.line = element_line(linewidth = 0.6),
  776. axis.ticks = element_line(linewidth = 0.6),
  777. axis.title = element_text(size = 9),
  778. axis.text = element_text(size = 8)
  779. )
  781. print(p)
  783. somePDFPath = file.path(paste0(figpath,'Rank_abundance_size_plot_D1.pdf'))
  784. pdf(file=somePDFPath, width=3, height=2)
  785. print(p)
  786. dev.off()
  788. ```
  791. # Figure 1G. Circos plot V-J usage IgG4 (chord diagram)
  793. ```{r circos_g4}
  795. #simplify j
  796. d1$jh.gene <-sub("\\*.*$", "", d1$h_j_call)
  797. #remove IGH
  798. d1$jh.gene <-sub("^IGH", "", d1$jh.gene)
  799. d1$vh.gene <-sub("^IGH", "", d1$vh.gene)
  801. d1.g4 <- d1 %>%
  802. filter(h_c_call =="IGHG4")
  803. nrow(d1.g4)
  805. # make a matrix gor IgH4
  806. mat <- table(d1.g4$vh.gene, d1.g4$jh.gene)
  808. # ordering matrices
  809. row_order <- names(sort(rowSums(mat), decreasing = TRUE)) # Ordering j_gene
  810. col_order <- names(sort(colSums(mat), decreasing = TRUE)) # Ordering V_gene
  811. # Reorder matrix
  812. mat <- mat[row_order, col_order]
  814. # make a matrix for alal
  815. mat.all <- table(d1$vh.gene, d1$jh.gene)
  817. # ordering matrices
  818. row_order <- names(sort(rowSums(mat.all), decreasing = TRUE)) # Ordering j_gene
  819. col_order <- names(sort(colSums(mat.all), decreasing = TRUE)) # Ordering V_gene
  820. # Reorder matrix
  821. mat.all <- mat.all[row_order, col_order]
  823. # Generate Chord Diagram
  824. par(mfrow = c(1, 1),cex = 1) # 1 circos
  825. set.seed(8389)
  826. library(randomcoloR)
  827. # Get unique names for both rows and columns
  828. all_labels <- unique(c(rownames(mat), colnames(mat), rownames(mat.all), colnames(mat.all)))
  829. colors <- setNames(distinctColorPalette(length(all_labels)), all_labels)
  831. circos.clear() # Clear any previous plots
  832. circos.par(start.degree = -90, clock.wise = TRUE)
  833. chordDiagram(mat, grid.col = colors,
  834. annotationTrack = "grid", # Adds grid annotations
  835. preAllocateTracks = list(track.height = 0.5)) # Space for labels
  837. # Rotate labels
  838. circos.track(track.index = 1, panel.fun = function(x, y) {
  839. circos.text(CELL_META$xcenter, CELL_META$ylim[1],
  840. CELL_META$sector.index, facing = "clockwise", niceFacing = T,
  841. adj = c(0, 0.5), cex = 0.65) # Adjust label size if needed
  842. }, bg.border = NA)
  844. text(0, 0, paste0("IGHG4\n(n=",nrow(d1.g4),")"), cex = 1.5, font = 1, col = "black")
  847. somePDFPath = file.path(paste0(figpath,'circos_IGHG4_D1.pdf'))
  848. pdf(file=somePDFPath, width=4, height=4)
  849. circosplot=chordDiagram(circles, self.link = 1, grid.col = grid.cols )
  850. invisible(dev.off())
  851. ```
  853. # Extended data Fig 3 : IgLON5 specific BCRs vs unknown specificity plots
  855. ```{r isotype_Iglon5_spe}
  859. ################## a. VH usage #########################
  861. p=vizGenes(combined.BCR$D1,
  862. x.axis = "IGHV",group.by = "agSpe", order.by = c("IgLON5","unknown"),
  863. y.axis = NULL, # No specific y-axis variable, will group all samples
  864. plot = "barplot",
  865. summary.fun = "proportion") + theme(axis.text.x = element_text(size=7),
  866. axis.text.y = element_text(size=7),
  867. strip.text = element_text(size=8))
  868. pdf(file=paste0(figpath,"VH_distribution_D1.pdf"), width=5.2, height=2.5)
  869. print(p)
  870. invisible(dev.off())
  872. ################## b. VL usage #########################
  874. p=vizGenes(combined.BCR$D1,
  875. x.axis = "IGLV",group.by = "agSpe", order.by = c("IgLON5","unknown"),
  876. y.axis = NULL,
  877. plot = "barplot",
  878. summary.fun = "proportion") + theme(axis.text.x = element_text(size=7),
  879. axis.text.y = element_text(size=7),
  880. strip.text = element_text(size=8))
  881. print(p)
  883. pdf(file=paste0(figpath,"VL_distribution_D1.pdf"), width=5.2, height=2.5)
  884. print(p)
  885. invisible(dev.off())
  887. ###### c .Isotype ###########################
  889. all_isotypes <- c(
  890. "IGHA1", "IGHA2", "IGHD", "IGHE",
  891. "IGHG1", "IGHG2", "IGHG3", "IGHG4",
  892. "IGHM"
  893. )
  895. plotlist=list()
  896. i=1
  897. paired.df.d1 <- all.paired.df[all.paired.df$donor=="D1",]
  898. for (agsp in unique(paired.df.d1$agSpe)){
  899. df=paired.df.d1[paired.df.d1$agSpe==agsp,]
  900. tot=nrow(df)
  901. df=df[!nchar(df$h_c_call)==0,]
  902. # Calculate Frequencies
  903. # Force factor levels
  904. df$h_c_call <- factor(df$h_c_call, levels = all_isotypes)
  905. freq_df <- as.data.frame(prop.table(table(df$h_c_call)))
  906. colnames(freq_df) <- c("Isotype", "Frequency")
  908. # Create Bar Plot for Frequency
  909. p=ggplot(freq_df, aes(x = Isotype, y = Frequency, fill = Isotype)) +
  910. geom_bar(stat = "identity", width = 0.7) +
  911. geom_text(
  912. data = subset(freq_df, Isotype == "IGHG4"),
  913. aes(label = scales::percent(Frequency, accuracy = 0.1)),
  914. vjust = -0.3,
  915. size = 2.5
  916. )+
  917. scale_y_continuous(labels = scales::percent, limits = c(0, 0.65)) + # Convert to percentage
  918. labs(title = paste0("D1 ",agsp," (n=",tot,")"),
  919. x = "",
  920. y = "Frequency (%)") +
  921. scale_fill_manual(values = isotype_colors) +
  922. theme_classic() + NoLegend()+
  923. theme(axis.text.x = element_text(angle = 45, hjust = 1,size=6.5),
  924. axis.text.y = element_text(size=6),
  925. title = element_text(size=7))
  926. if (i<2){
  927. p <- p + theme(axis.text.x = element_blank())
  928. }
  929. plotlist[[i]]=p
  930. i=i+1
  931. }
  933. wrap_plots(plotlist, ncol = 1)
  935. pdf(file=paste0(figpath,"Isotype_distribution_D1.pdf"), width=3, height=2.5)
  936. wrap_plots(plotlist, ncol = 1)
  937. invisible(dev.off())
  939. ###### d CDRH3 ###########################
  941. paired.df.d1 <- paired.df.d1 %>%
  942. mutate(agSpe = factor(agSpe, levels = c("unknown", "IgLON5")))
  944. # Compute medians
  945. median_df <- paired.df.d1 %>%
  946. group_by(agSpe) %>%
  947. summarise(median_length = median(HCDR3.length, na.rm = TRUE))
  949. p <- ggplot(paired.df.d1, aes(x = HCDR3.length, fill = agSpe)) +
  950. geom_histogram(aes(y = after_stat(count / sum(count))),
  951. binwidth = 1, boundary = 0,
  952. color = "black", alpha = 0.7, position = "identity") +
  953. scale_fill_manual(values = c(IgLON5 = "#F1560E", unknown = "grey70")) +
  955. # Median vertical dotted line
  956. geom_vline(data = median_df,
  957. aes(xintercept = median_length, color = agSpe),
  958. linetype = "dashed",
  959. linewidth = 0.5) +
  961. # Median value text
  962. geom_text(data = median_df,
  963. aes(x = median_length,
  964. y = Inf,
  965. color = agSpe,
  966. label = paste0(median_length)),
  967. vjust = 1.5, hjust = -0.2,
  968. size = 2.8) +
  969. scale_color_manual(values = c(IgLON5 = "#F1560E", unknown = "grey70")) +
  970. theme_classic(base_size = 8) +
  971. theme(
  972. axis.line = element_line(linewidth = 0.6),
  973. axis.ticks = element_line(linewidth = 0.6),
  974. axis.title.x = element_text(size = 8), axis.title.y = element_text(size = 7),
  975. axis.text.y = element_text(size = 7), axis.text.x = element_text(size = 8),
  976. legend.title = element_blank(),
  977. legend.key.size = unit(0.2, "cm"),
  978. legend.text = element_text(size = 6),
  979. legend.position = c(0.85, 0.65)
  980. ) +
  981. labs(
  982. x = "CDRH3 length (aa)",
  983. y = "Frequency"
  984. )
  986. somePDFPath = file.path(paste0(figpath,'CDRH3_length_D1.pdf'))
  987. pdf(file=somePDFPath, width=2.5, height=1.9)
  988. print(p)
  989. dev.off()
  991. ###### e Mutation Freq ###########################
  992. #### Check how many have 0 freq.mut
  993. paired.df.d1 %>%
  994. group_by(agSpe) %>%
  995. summarise(
  996. n_total = n(),
  997. n_zero = sum(mut.freq == 0),
  998. pct_zero = round(100 * n_zero / n_total, 1)
  999. )
  1000. # agSpe n_total n_zero pct_zero
  1001. # <fct> <int> <int> <dbl>
  1002. # 1 unknown 8609 2285 26.5
  1003. # 2 IgLON5 968 114 11.8
  1005. #We will remove them
  1006. all.paired.df2 <- paired.df.d1[paired.df.d1$mut.freq>0,]
  1008. all.paired.df2 %>%
  1009. group_by(agSpe) %>%
  1010. get_summary_stats(mut.freq, type = "mean_sd")
  1011. # agSpe variable n mean sd
  1012. # <fct> <fct> <dbl> <dbl> <dbl>
  1013. # 1 unknown mut.freq 6324 0.121 0.061
  1014. # 2 IgLON5 mut.freq 854 0.121 0.058
  1017. #not sure there is normality here
  1018. ggqqplot(all.paired.df2, "mut.freq", ggtheme = theme_bw())
  1020. #homogeneity of variance
  1021. levene_test(mut.freq ~ donor, data = all.paired.df2)
  1022. # no sign diff between variance p>0.05 -> homogeneity -> we can do wilcoxon test
  1025. pwc <- all.paired.df2 %>%
  1026. wilcox_test(mut.freq ~ agSpe)
  1028. # Visualisation : Boxplots avec p-values
  1030. p2 <- ggplot(all.paired.df2, aes(x = agSpe, y = mut.freq))+
  1031. geom_jitter(aes(y=mut.freq,group=agSpe),width = 0.2, size = 0.5, alpha = 0.2)+
  1032. geom_boxplot(aes(fill=agSpe, color = agSpe),alpha=0.6,outlier.shape = NA)+
  1033. stat_pvalue_manual(pwc, tip.length = 0, hide.ns = FALSE, y.position = 0.45,size=2.5) +
  1034. labs(
  1035. color="agSpe",
  1036. y="Mutation frequency",
  1037. x=""
  1038. )+
  1039. scale_color_manual(values = c("grey50","#F1560E"))+
  1040. scale_fill_manual(values =donor_cols) +
  1041. scale_y_continuous(breaks =c(0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5) ,limits = c(0,0.5))+
  1042. theme_classic() +
  1043. theme(
  1044. axis.line = element_line(linewidth = 0.6),
  1045. axis.ticks = element_line(linewidth = 0.6),
  1046. axis.title = element_text(size = 6),
  1047. axis.text.y = element_text(size = 6),axis.text.x = element_text(size = 8)
  1048. )+ NoLegend()
  1050. print(p2)
  1053. pdf(file = paste0(figpath, "/Mutation_frequency_D1.pdf"), width = 1.8, height =1.7)
  1054. p2
  1055. invisible(dev.off())
  1057. ggsave(
  1058. filename = paste0(figpath, "/Mutation_frequency_D1.tiff"),
  1059. plot = p2,
  1060. device = "tiff",
  1061. width = 1.8,
  1062. height = 1.9,
  1063. units = "in",
  1064. dpi = 600,
  1065. compression = "lzw"
  1066. )
  1068. ```
  1071. ```{r, eval=F}
  1072. sessionInfo()
  1073. ```

BCR_analysis_D1_PUB.Rmd at commit 23551bd, under MIT · at the source

Overview

Authors: Laurent Perez1, Angelique Roux Roux1, Rachel Schelling2, David Vinyals-Sales1, Lidia Sabater3, Rahel Winiger1, Ilayda Senyuz1, Alison Lin1, Amandine Mathias4, Renaud DU Pasquier4, Carles Gaig5, Josep Dalmau6, Mathilde Foglierini2
ORCID iDs: Laurent Perez, Rachel Schelling, Amandine Mathias, Renaud DU Pasquier, Josep Dalmau, Mathilde Foglierini
  1. Lausanne University Hospital and University of Lausanne
  2. CHUV
  3. Hospital Clínic de Barcelona
  4. Laboratory of Neuroimmunology, Neuroscience Research Centre, Department of Clinical Neurosciences, University Hospital and University of Lausanne
  5. Hospital Clinic of Barcelona
  6. ICREA-Institut d’Investigacions Biomèdiques August Pi i Sunyer
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); Hospital Clínic de Barcelona (Spain); Institució Catalana de Recerca i Estudis Avançats (Spain)
Journal: Research Square (preprint)
Dates: published online 27 April 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.21203/rs.3.rs-9427214/v1 · OpenAlex W7156132166
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: IgLON5, Neurodegeneration, CryoEM, Immune repertoire, antigen-antibody complex
Topic: Autoimmune Neurological Disorders and Treatments (Neurology, Medicine), according to OpenAlex
Funding: National Science Foundation; École Polytechnique Fédérale de Lausanne; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (10005403); Université de Genève; Université de Lausanne
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Anti-IgLON5 disease is a rare neurological disorder at the interface of autoimmunity and neurodegeneration. It is characterized by autoantibodies against the neuronal adhesion molecule IgLON5 and is associated with profound brain dysfunction and tau pathology. Despite its severe clinical manifestations, the molecular basis of antibody recognition and its contribution to disease pathogenesis remain unclear. Here, we profile the B cell receptor repertoire of a patient with anti-IgLON5 disease and identify a highly polyclonal response lacking dominant clonal expansion. We isolate a human monoclonal IgG4 antibody that binds IgLON5 with high affinity and determine its structure in complex with IgLON5 by cryo–electron microscopy. Biochemical and structural analyses show that antibody binding preserves IgLON5 adhesion interfaces while promoting higher-order clustering of IgLON5 dimers. These findings provide mechanistic insight into autoantibody recognition of neuronal surface proteins and establish a framework for understanding antibody-mediated neurodegeneration in anti-IgLON5 disease.

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 2 matches between paragraphs and lines of code.

MathildeFogPerez/manuscript-iglon5-D1

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 23551bd9ba3c38857783c639862206a92eb6ff10, 16 September 2026
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), circlize (1 file), ggpubr (1 file), igraph (1 file), patchwork (1 file), rstatix (1 file), Seurat (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files
At the source: github.com/MathildeFogPerez/manuscript-iglon5-D1

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data and code availability

The associated scripts are available on https://github.com/MathildeFogPerez/manuscript-iglon5-D1. Raw sequencing data files for single-cell VDJ sequencing are available at ENA database: PRJEB109754. Processed data files (BCR repertoires) are available at Zenodo: https://doi.org/10.5281/zenodo.18925481. Cryo-EM map and PDB Model were deposited on EMDB: and PDB with the following accession numbers EMD-57080 and EMD-57198 with PDB-ID:29CP and PDB-ID: 29IN

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, journal, dates, 13 authors, 5 keywords, 5 funders, 60 references.

Cite

This paper

Perez, L., Roux, A. R., Schelling, R., Vinyals-Sales, D., Sabater, L., Winiger, R., Senyuz, I., Lin, A., Mathias, A., Pasquier, R. D., Gaig, C., Dalmau, J., & Foglierini, M. (2026). Structural basis of IgLON5 autoantibody recognition in autoimmune encephalitis. Research Square (preprint). https://doi.org/10.21203/rs.3.rs-9427214/v1

BibTeX

@article{perez2026structural,
author = {Perez, Laurent and Roux, Angelique Roux and Schelling, Rachel and Vinyals-Sales, David and Sabater, Lidia and Winiger, Rahel and Senyuz, Ilayda and Lin, Alison and Mathias, Amandine and Pasquier, Renaud DU and Gaig, Carles and Dalmau, Josep and Foglierini, Mathilde},
title = {{Structural basis of IgLON5 autoantibody recognition in autoimmune encephalitis}},
journal = {Research Square (preprint)},
year = {2026},
month = apr,
publisher = {Research Square},
issn = {2693-5015},
doi = {10.21203/rs.3.rs-9427214/v1},
url = {https://doi.org/10.21203/rs.3.rs-9427214/v1}
}

RIS

TY - JOUR
AU - Perez, Laurent
AU - Roux, Angelique Roux
AU - Schelling, Rachel
AU - Vinyals-Sales, David
AU - Sabater, Lidia
AU - Winiger, Rahel
AU - Senyuz, Ilayda
AU - Lin, Alison
AU - Mathias, Amandine
AU - Pasquier, Renaud DU
AU - Gaig, Carles
AU - Dalmau, Josep
AU - Foglierini, Mathilde
TI - Structural basis of IgLON5 autoantibody recognition in autoimmune encephalitis
T2 - Research Square (preprint)
J2 - Res Sq
PY - 2026
DA - 2026/04/27
SN - 2693-5015
PB - Research Square
DO - 10.21203/rs.3.rs-9427214/v1
UR - https://doi.org/10.21203/rs.3.rs-9427214/v1
ER -

CSL-JSON

{
"id": "10.21203/rs.3.rs-9427214/v1",
"type": "article",
"title": "Structural basis of IgLON5 autoantibody recognition in autoimmune encephalitis",
"container-title": "Research Square (preprint)",
"author": [
{
"family": "Perez",
"given": "Laurent"
},
{
"family": "Roux",
"given": "Angelique Roux"
},
{
"family": "Schelling",
"given": "Rachel"
},
{
"family": "Vinyals-Sales",
"given": "David"
},
{
"family": "Sabater",
"given": "Lidia"
},
{
"family": "Winiger",
"given": "Rahel"
},
{
"family": "Senyuz",
"given": "Ilayda"
},
{
"family": "Lin",
"given": "Alison"
},
{
"family": "Mathias",
"given": "Amandine"
},
{
"family": "Pasquier",
"given": "Renaud DU"
},
{
"family": "Gaig",
"given": "Carles"
},
{
"family": "Dalmau",
"given": "Josep"
},
{
"family": "Foglierini",
"given": "Mathilde"
}
],
"container-title-short": "Res Sq",
"DOI": "10.21203/rs.3.rs-9427214/v1",
"ISSN": "2693-5015",
"publisher": "Research Square",
"URL": "https://doi.org/10.21203/rs.3.rs-9427214/v1",
"issued": {
"date-parts": [
[
2026,
4,
27
]
]
}
}

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