OSCR

Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue.

Code ↔ Paper

29 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 29 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Cell-type annotation ↔ scripts/03_celltype_annotation.R, lines 185–248 · score 1.00 · intermediate progenitor subtypes, C1QA, C1QB, SLC17A7, TOP2A, radial glia subtype
  2. [2] § Materials and methods › Cell-type annotation ↔ scripts/07_make_supplementary_tables.R, lines 246–314 · score 1.00 · intermediate progenitor subtypes, C1QA, C1QB, SLC17A7, TOP2A, apical radial glia
  3. [3] § Materials and methods › Single-cell RNA-seq datasets ↔ scripts/07_make_supplementary_tables.R, lines 58–104 · score 0.91 · interferon beta stimulation, FSS13025 isolate, Brazilian ZIKV exposure, Brazilian ZIKV BR, human fetal brain, derived neural tissue
  4. [4] § Materials and methods › Single-cell RNA-seq datasets ↔ scripts/07_make_supplementary_tables.R, lines 107–155 · score 0.85 · GEO sample, primary human monocyte, monocyte derived dendritic, cell system, derived dendritic cells, sample IDs
  5. [5] § Materials and methods › Single-cell RNA-seq datasets ↔ scripts/01_prepare_10x_data.R, lines 179–217 · score 0.83 · exposed ex vivo, single cell RNA, derived neural tissue, human fetal brain, ZIKV BR, ZIKV FSS
  6. [6] § Materials and methods › Single-cell RNA-seq datasets ↔ scripts/01_prepare_10x_data.R, lines 1–48 · score 0.83 · monocyte derived dendritic, primary human, single cell, Vero cells, Gene Expression, Seurat
  7. [7] § Results › Comparative alignment between ZIKV-associated and IFNβ-associated transcriptional programs ↔ scripts/06_ZIKV_IFNb_correlation_sensitivity.R, lines 745–832 · score 0.80 · primary exploratory DE, gene classes, correlation summaries, Canonical ISGs, gene universe, cycling NPC
  8. [8] § Results › Cellular architecture of fetal neural tissue under ZIKV exposure ↔ scripts/03_celltype_annotation.R, lines 185–248 · score 0.79 · oligodendrocyte progenitor, excitatory neurons, inhibitory neurons, progenitor enriched radial, neuronal, cycling NPCs
  9. [9] § Materials and methods › ZIKV–IFNβ transcriptional similarity analysis ↔ scripts/06_ZIKV_IFNb_correlation_sensitivity.R, lines 835–877 · score 0.77 · shared ISG induction, IFN stimulated programs, Spearman correlations, genes excluding, ZIKV associated, ISG genes
  10. [10] § Results › Independent bulk RNA-seq contextual analysis ↔ scripts/07_make_supplementary_tables.R, lines 214–243 · score 0.77 · bulk RNA seq, human cerebral organoid, contextual bulk transcriptomic, ZIKV exposed, interferon associated transcriptional, GSE97919
  11. [11] § Materials and methods › Independent bulk RNA-seq contextual analysis ↔ scripts/09_bulk_RNAseq_contextual_GSE97919.R, lines 1–78 · score 0.76 · bulk RNA seq, DESeq2, IFI6, IFIT1, IFITM1, IRF7
  12. [12] § Materials and methods › Interferon-stimulated gene (ISG) signature analysis ↔ scripts/05_progenitor_volcano_ISG_heatmap.R, lines 477–537 · score 0.73 · heatmaps summarizing, cell intrinsic antiviral, viral RNA burden, IFN production, infection frequency, transcriptional IFN
  13. [13] § Materials and methods › Exploratory differential expression analysis ↔ scripts/04_exploratory_celltype_DE.R, lines 1–49 · score 0.72 · FindMarkers, biological sample, biological replicates, FDR, rank, Wilcoxon
  14. [14] § Materials and methods › Independent bulk RNA-seq contextual analysis ↔ scripts/07_make_supplementary_tables.R, lines 214–243 · score 0.71 · bulk RNA seq, human cerebral organoid, bulk transcriptomic, interferon associated transcriptional, lineage resolved, GSE97919
  15. [15] § Results › Lineage-specific transcriptional remodeling in response to viral and cytokine stimulation ↔ scripts/07_make_supplementary_tables.R, lines 246–314 · score 0.71 · inhibitory neurons, Progenitor enriched radial, interferon stimulated genes, canonical ISG, cycling NPC, excitatory
  16. [16] § Materials and methods › Software ↔ scripts/06_ZIKV_IFNb_correlation_sensitivity.R, lines 46–110 · score 0.70 · correlation sensitivity, gene universes, seed, tibble, purrr, tidyr
  17. [17] § Materials and methods › Contextual comparison using the Vero/moDC dataset ↔ scripts/01_prepare_10x_data.R, lines 268–306 · score 0.69 · human monocyte derived, cellular systems, dendritic cells, Vero cells, GSE230571
  18. [18] § Materials and methods › Preprocessing and quality control ↔ scripts/02_qc_normalization_umap_clustering.R, lines 1–66 · score 0.69 · nCount_RNA, quality control, scRNA, Seurat, Raw, seq
  19. [19] § Materials and methods › Normalization, dimensionality reduction, and clustering ↔ scripts/02_qc_normalization_umap_clustering.R, lines 1–66 · score 0.68 · UMAP embeddings, regression, Diagnostic, variable, PCA, resolution
  20. [20] § Materials and methods › Interferon-stimulated gene (ISG) signature analysis ↔ scripts/08_transcriptional_response_summary.R, lines 467–540 · score 0.66 · ISG summaries, OAS2, OAS3, IFI6, IFIT1, IFITM1
  21. [21] § Results › Global transcriptional structure and dataset integrity ↔ scripts/01_prepare_10x_data.R, lines 1–48 · score 0.65 · monocyte derived dendritic, derived neural tissue, human fetal brain, dendritic cells, ZIKV BR, ZIKV FSS
  22. [22] § Materials and methods › Software ↔ install_packages.R, the whole file · a weak match · score 0.63 · DESeq2, magick, repository, tibble, purrr, tidyr
  23. [23] § Results › Transcriptional response magnitude across developmental identities ↔ scripts/08_transcriptional_response_summary.R, lines 625–711 · score 0.61 · transcriptional response magnitude, antiviral efficacy, viral RNA burden, IFN production, infection frequency, ISG responsiveness
  24. [24] § Results › Transcriptional response magnitude across developmental identities ↔ scripts/08_transcriptional_response_summary.R, lines 585–623 · score 0.60 · moDCs, ISG module score, ZIKV exposed Vero, core ISG, transcriptional, cells
  25. [25] § Results › Comparative alignment between ZIKV-associated and IFNβ-associated transcriptional programs ↔ scripts/06_ZIKV_IFNb_correlation_sensitivity.R, lines 745–832 · score 0.59 · primary DE, ISG genes, canonical ISGs, cycling NPC, radial glia, ZIKV BR
  26. [26] § Materials and methods › Pseudobulk construction ↔ scripts/03_celltype_annotation.R, lines 475–545 · score 0.58 · AggregateExpression, pseudobulk, Seurat, biological sample, biological replicates, matrix
  27. [27] § Results › Exploratory strain-associated transcriptional patterns in progenitor populations ↔ scripts/05_progenitor_volcano_ISG_heatmap.R, lines 477–537 · score 0.55 · ISG heatmap, cell intrinsic antiviral, infection frequency, progenitor, initiation, susceptibility
  28. [28] § Results › Global transcriptional structure and dataset integrity ↔ scripts/03_celltype_annotation.R, lines 1–72 · score 0.54 · dimensionless embedding units, UMAP coordinates, RNA seq, fetal neural, cluster, Seurat
  29. [29] § Results › Exploratory strain-associated transcriptional patterns in progenitor populations ↔ scripts/04_exploratory_celltype_DE.R, lines 1–49 · score 0.54 · statistically powered comparisons, biological sample, ZIKV BR, ZIKV FSS, fetal neural, Exploratory

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 375 lines · 13 KB · no license · 6 matches

  1. ############################################################
  2. # Zika scRNA-seq project – STEP 7
  3. # Supplementary Tables 2 and 3
  4. #
  5. # Purpose:
  6. # Generate reproducibility tables documented in the analysis:
  7. # - Supplementary Table 2: sample mapping and dataset-use labels
  8. # - Supplementary Table 3: cell-type marker genes and curated ISG panel
  9. #
  10. # Analysis notes:
  11. # - GSE238140 is the primary fetal neural scRNA-seq dataset.
  12. # - GSE230571 is used only as a contextual Vero/moDC comparison.
  13. # - GSE97919 is included as contextual bulk RNA-seq information.
  14. # - The Vero/moDC and bulk datasets are not used as validation of fetal
  15. # neural lineage-specific responses.
  16. # - FSS13025 is annotated as a Cambodian 2010 Asian-lineage isolate.
  17. #
  18. # Inputs:
  19. # Optional:
  20. # results/dc_step2_norm_qc.rds
  21. #
  22. # Outputs:
  23. # supplementary_tables/Supplementary_Table_2_sample_mapping.csv
  24. # supplementary_tables/Supplementary_Table_3_gene_lists.csv
  25. # supplementary_tables/Supplementary_Tables_2_3.xlsx if writexl is installed
  26. # session_info/STEP7_sessionInfo.txt
  27. ############################################################
  30. ## ==========================================================
  31. ## 0. Project root and packages
  32. ## ==========================================================
  34. project_root <- getwd()
  35. message("[STEP7] Project root: ", project_root)
  37. required_packages <- c("dplyr", "tibble", "readr")
  39. missing_packages <- required_packages[
  40. !vapply(required_packages, requireNamespace, logical(1), quietly = TRUE)
  41. ]
  43. if (length(missing_packages) > 0) {
  44. stop(
  45. "[STEP7] Missing required package(s): ",
  46. paste(missing_packages, collapse = ", "),
  47. "\nInstall them before running STEP 7."
  48. )
  49. }
  51. suppressPackageStartupMessages({
  52. library(dplyr)
  53. library(tibble)
  54. library(readr)
  55. })
  58. ## ==========================================================
  59. ## 1. Directories
  60. ## ==========================================================
  62. results_dir <- file.path(project_root, "results")
  63. supp_dir <- file.path(project_root, "supplementary_tables")
  64. session_dir <- file.path(project_root, "session_info")
  66. dir.create(results_dir, recursive = TRUE, showWarnings = FALSE)
  67. dir.create(supp_dir, recursive = TRUE, showWarnings = FALSE)
  68. dir.create(session_dir, recursive = TRUE, showWarnings = FALSE)
  71. ## ==========================================================
  72. ## 2. Supplementary Table 2 – sample mapping
  73. ## ==========================================================
  75. ## -------------------------------
  76. ## 2A. GSE238140 fetal neural scRNA-seq
  77. ## -------------------------------
  79. supp_table2_brain <- tibble(
  80. dataset = "Fetal neural scRNA-seq",
  81. geo_accession = "GSE238140",
  82. geo_sample_id = c("GSM7659279", "GSM7659280", "GSM7659281", "GSM7659282"),
  83. cell_system = "Human fetal brain-derived neural tissue",
  84. condition = c("Mock", "ZIKV-BR", "ZIKV-FSS/FSS13025", "IFNβ"),
  85. analysis_label = c("Mock", "ZIKV_BR", "ZIKV_FSS", "IFNb"),
  86. study_use = c(
  87. "Primary fetal neural dataset",
  88. "Primary fetal neural dataset",
  89. "Primary fetal neural dataset",
  90. "Primary fetal neural dataset"
  91. ),
  92. interpretation_scope = c(
  93. "Reference/control condition",
  94. "Exploratory viral exposure condition",
  95. "Exploratory viral exposure condition",
  96. "Cytokine stimulation reference condition"
  97. ),
  98. notes = c(
  99. "Mock control",
  100. "Brazilian ZIKV exposure condition, referred to as ZIKV-BR",
  101. "FSS13025 isolate; Cambodian 2010 Asian-lineage isolate, referred to as ZIKV-FSS",
  102. "Recombinant interferon-beta stimulation, referred to as IFNβ"
  103. )
  104. )
  107. ## -------------------------------
  108. ## 2B. GSE230571 Vero/moDC scRNA-seq
  109. ## -------------------------------
  111. dc_rds_path <- file.path(results_dir, "dc_step2_norm_qc.rds")
  113. if (file.exists(dc_rds_path)) {
  115. message("[STEP7] Reading dc object for sample IDs: ", dc_rds_path)
  117. dc <- readRDS(dc_rds_path)
  119. if (!inherits(dc, "Seurat")) {
  120. warning("[STEP7] dc_step2_norm_qc.rds exists but is not a Seurat object. Creating summary-level GSE230571 rows.")
  122. supp_table2_dc <- tibble(
  123. dataset = "Vero/moDC scRNA-seq",
  124. geo_accession = "GSE230571",
  125. geo_sample_id = c("ZIKV-Vero-cells", "p22086 panel"),
  126. cell_system = c("Vero cells", "Primary human monocyte-derived dendritic cells"),
  127. condition = c("ZIKV-exposed", "moDC condition panel"),
  128. analysis_label = c("ZIKV_Vero_cells", "moDC_panel"),
  129. study_use = "Contextual comparison only",
  130. interpretation_scope = "Broad IFN/ISG-associated contextual comparison; not evidence for fetal neural lineage-specific responses",
  131. notes = "Used only for contextual cross-system comparison"
  132. )
  134. } else {
  136. dc_meta <- [email hidden] %>%
  137. as.data.frame() %>%
  138. tibble::rownames_to_column("cell_barcode")
  140. if (!"sample_id" %in% colnames(dc_meta)) {
  141. warning("[STEP7] dc object does not contain sample_id metadata. Creating summary-level GSE230571 rows.")
  143. supp_table2_dc <- tibble(
  144. dataset = "Vero/moDC scRNA-seq",
  145. geo_accession = "GSE230571",
  146. geo_sample_id = c("ZIKV-Vero-cells", "p22086 panel"),
  147. cell_system = c("Vero cells", "Primary human monocyte-derived dendritic cells"),
  148. condition = c("ZIKV-exposed", "moDC condition panel"),
  149. analysis_label = c("ZIKV_Vero_cells", "moDC_panel"),
  150. study_use = "Contextual comparison only",
  151. interpretation_scope = "Broad IFN/ISG-associated contextual comparison; not evidence for fetal neural lineage-specific responses",
  152. notes = "Used only for contextual cross-system comparison"
  153. )
  155. } else {
  157. supp_table2_dc <- dc_meta %>%
  158. distinct(sample_id) %>%
  159. mutate(
  160. dataset = "Vero/moDC scRNA-seq",
  161. geo_accession = "GSE230571",
  162. geo_sample_id = sample_id,
  163. cell_system = case_when(
  164. grepl("ZIKV-Vero-cells", sample_id) ~ "Vero cells",
  165. grepl("p22086", sample_id) ~ "Primary human monocyte-derived dendritic cells",
  166. TRUE ~ "Unknown"
  167. ),
  168. condition = case_when(
  169. grepl("ZIKV-Vero-cells", sample_id) ~ "ZIKV-exposed",
  170. grepl("p22086", sample_id) ~ "moDC condition panel",
  171. TRUE ~ "Unknown"
  172. ),
  173. analysis_label = case_when(
  174. grepl("ZIKV-Vero-cells", sample_id) ~ "ZIKV_Vero_cells",
  175. grepl("p22086", sample_id) ~ "moDC_panel",
  176. TRUE ~ "Unknown"
  177. ),
  178. study_use = "Contextual comparison only",
  179. interpretation_scope = "Broad IFN/ISG-associated contextual comparison; not evidence for fetal neural lineage-specific responses",
  180. notes = "Used only for contextual cross-system comparison"
  181. ) %>%
  182. select(
  183. dataset,
  184. geo_accession,
  185. geo_sample_id,
  186. cell_system,
  187. condition,
  188. analysis_label,
  189. study_use,
  190. interpretation_scope,
  191. notes
  192. )
  193. }
  194. }
  196. } else {
  198. message("[STEP7] dc_step2_norm_qc.rds not found. Creating summary-level GSE230571 rows.")
  200. supp_table2_dc <- tibble(
  201. dataset = "Vero/moDC scRNA-seq",
  202. geo_accession = "GSE230571",
  203. geo_sample_id = c("ZIKV-Vero-cells", "p22086 panel"),
  204. cell_system = c("Vero cells", "Primary human monocyte-derived dendritic cells"),
  205. condition = c("ZIKV-exposed", "moDC condition panel"),
  206. analysis_label = c("ZIKV_Vero_cells", "moDC_panel"),
  207. study_use = "Contextual comparison only",
  208. interpretation_scope = "Broad IFN/ISG-associated contextual comparison; not evidence for fetal neural lineage-specific responses",
  209. notes = "Used only for contextual cross-system comparison"
  210. )
  211. }
  214. ## -------------------------------
  215. ## 2C. GSE97919 bulk RNA-seq
  216. ## -------------------------------
  218. supp_table2_bulk <- tibble(
  219. dataset = "Bulk RNA-seq",
  220. geo_accession = "GSE97919",
  221. geo_sample_id = "GSM-level samples available in GEO metadata",
  222. cell_system = "Human cerebral organoid / neural bulk RNA-seq samples",
  223. condition = "ZIKV-exposed/control",
  224. analysis_label = "bulk_ZIKV_vs_control",
  225. study_use = "Contextual bulk RNA-seq analysis only",
  226. interpretation_scope = "Contextual support for broad interferon-associated transcriptional activation; not lineage-resolved evidence",
  227. notes = "Used only as contextual bulk transcriptomic comparison because experimental system and modality differ from fetal neural scRNA-seq"
  228. )
  231. ## -------------------------------
  232. ## 2D. Combine and save
  233. ## -------------------------------
  235. supp_table2 <- bind_rows(
  236. supp_table2_brain,
  237. supp_table2_dc,
  238. supp_table2_bulk
  239. )
  241. supp_table2_path <- file.path(supp_dir, "Supplementary_Table_2_sample_mapping.csv")
  242. readr::write_csv(supp_table2, supp_table2_path)
  243. message("[STEP7] Saved: ", supp_table2_path)
  246. ## ==========================================================
  247. ## 3. Supplementary Table 3 – marker genes and ISG panel
  248. ## ==========================================================
  250. supp_table3 <- tibble(
  251. gene_list_category = c(
  252. rep("Cell-type marker", 7),
  253. "Contextual progenitor subtype marker",
  254. "Contextual progenitor subtype marker",
  255. "Contextual progenitor subtype marker",
  256. "ISG signature"
  257. ),
  258. cell_type_or_signature = c(
  259. "Progenitor-enriched radial glia-like",
  260. "Cycling NPC",
  261. "Excitatory neuron",
  262. "Inhibitory neuron",
  263. "Astrocyte",
  264. "Microglia",
  265. "OPC",
  266. "Apical radial glia context",
  267. "Outer/basal radial glia context",
  268. "Intermediate progenitor context",
  269. "Canonical ISG panel"
  270. ),
  271. genes = c(
  272. "SOX2, PAX6, NES",
  273. "MKI67, TOP2A, HMGB2",
  274. "SLC17A7, NEUROD6",
  275. "GAD1, GAD2, DLX1",
  276. "GFAP, AQP4",
  277. "C1QA, C1QB, TYROBP",
  278. "PDGFRA, OLIG1, OLIG2",
  279. "NOTCH1, HES1, HES5",
  280. "HOPX, PTPRZ1, FAM107A",
  281. "EOMES",
  282. "IFITM1, MX1, OAS1, OAS2, OAS3, IFIT1, IFI6, ISG15, STAT1, IRF7, RSAD2"
  283. ),
  284. use_in_this_study = c(
  285. "Cell-type annotation",
  286. "Cell-type annotation",
  287. "Cell-type annotation",
  288. "Cell-type annotation",
  289. "Cell-type annotation",
  290. "Cell-type annotation",
  291. "Cell-type annotation",
  292. "Contextual marker set used to support cautious annotation discussion",
  293. "Contextual marker set used to support cautious annotation discussion",
  294. "Contextual marker set used to support cautious annotation discussion",
  295. "ISG heatmaps, ISG summaries, AddModuleScore, and ZIKV–IFNβ correlation sensitivity analyses"
  296. ),
  297. notes = c(
  298. "Interpreted cautiously as progenitor-enriched radial glia-like because SOX2, PAX6, and NES are shared across multiple progenitor states",
  299. "Cycling progenitor-associated markers",
  300. "Excitatory neuron-associated markers",
  301. "Inhibitory neuron-associated markers",
  302. "Astrocyte-associated markers",
  303. "Microglia-associated markers",
  304. "Oligodendrocyte precursor-associated markers",
  305. "Not used to claim a resolved apical radial glia subtype; included to address marker-resolution limitations",
  306. "Not used to claim a resolved outer/basal radial glia subtype; included to address marker-resolution limitations",
  307. "Not used to claim a resolved intermediate progenitor subtype; included to address marker-resolution limitations",
  308. "Curated canonical interferon-stimulated gene panel"
  309. )
  310. )
  312. supp_table3_path <- file.path(supp_dir, "Supplementary_Table_3_gene_lists.csv")
  313. readr::write_csv(supp_table3, supp_table3_path)
  314. message("[STEP7] Saved: ", supp_table3_path)
  317. ## ==========================================================
  318. ## 4. Optional Excel output
  319. ## ==========================================================
  321. if (requireNamespace("writexl", quietly = TRUE)) {
  323. xlsx_path <- file.path(supp_dir, "Supplementary_Tables_2_3.xlsx")
  325. writexl::write_xlsx(
  326. list(
  327. "Supp_Table_2_sample_mapping" = supp_table2,
  328. "Supp_Table_3_gene_lists" = supp_table3
  329. ),
  330. path = xlsx_path
  331. )
  333. message("[STEP7] Saved Excel file: ", xlsx_path)
  335. } else {
  336. message("[STEP7] Package 'writexl' is not installed. CSV files were saved only.")
  337. message("[STEP7] Optional Excel output can be enabled with: install.packages('writexl')")
  338. }
  341. ## ==========================================================
  342. ## 5. Save reproducibility information
  343. ## ==========================================================
  345. interpretation_note <- c(
  346. "STEP 7 supplementary table interpretation note",
  347. "",
  348. "Supplementary Table 2 provides dataset/sample mapping and analysis labels.",
  349. "Supplementary Table 3 provides cell-type marker genes and the curated canonical ISG panel.",
  350. "The fetal neural dataset GSE238140 is the primary dataset.",
  351. "GSE230571 and GSE97919 are contextual comparison datasets only.",
  352. "They are not used as independent evidence for fetal neural lineage-specific responses.",
  353. "FSS13025 is annotated as a Cambodian 2010 Asian-lineage isolate and referred to as ZIKV-FSS."
  354. )
  356. writeLines(
  357. interpretation_note,
  358. file.path(results_dir, "step7_supplementary_tables_interpretation_note.txt")
  359. )
  361. sink(file.path(session_dir, "STEP7_sessionInfo.txt"))
  362. sessionInfo()
  363. sink()
  365. message("\n[STEP7] Supplementary Table 2 preview:")
  366. print(supp_table2)
  368. message("\n[STEP7] Supplementary Table 3 preview:")
  369. print(supp_table3)
  371. message("\n[STEP7] Done.")
  373. ############################################################
  374. # End of STEP 7
  375. ############################################################

07_make_supplementary_tables.R at commit 2b0871a, no license · at the source

Overview

Authors: Zahra Abedi1, Mohammad Ali Sheikh Beig Goharrizi2, Amirreza Abbasi3, Negar Sadat Soleimani Zakeri4, Helia Jangi5, Alireza Susanabadi Farahani6
ORCID iDs: Zahra Abedi, Mohammad Ali Sheikh Beig Goharrizi, Amirreza Abbasi, Negar Sadat Soleimani Zakeri, Helia Jangi, Alireza Susanabadi Farahani
  1. Dina Pharmed Exir Salamat, Pharmaceutical Co., Tehran, 574768 Iran
  2. Department of Medical Biotechnology, School of Biotechnology, College of Science, University of Tehran, Tehran, Iran
  3. Department of Biology, Azad University of Tabriz, Tabriz, Iran
  4. Department of Software Engineering, Engineering and Architecture Faculty, Istanbul Nişantaşi University, Istanbul, Turkey
  5. Department of Biology, Ferdowsi University of Mashhad, Mashhad, Iran
  6. Department of Anesthesia and Pain, Arak University of Medical Sciences, Arak, Iran
Institutions: University of Tehran (Iran); Islamic Azad University of Tabriz (Iran); İstanbul Nişantaşı Üniversitesi (Türkiye); Ferdowsi University of Mashhad (Iran); Arak University of Medical Sciences (Iran)
Journal: Genomics & informatics, volume 24, issue 1, article 14
Dates: received 24 April 2026; accepted 2 July 2026; published online 9 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s44342-026-00076-5 · PMID 42426912 · PMCID PMC13352994 · OpenAlex W7167792823
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: Fetal brain development, Interferon-stimulated genes, Neural progenitors, Single-cell RNA sequencing, Zika virus
Topic: Mosquito-borne diseases and control (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 34 references in the paper

Abstract

Zika virus (ZIKV) infection affects early human brain development, but it is still not fully clear how different developmental cell lineages shape antiviral transcriptional responses. In this study, we carried out a secondary descriptive analysis using publicly available single-cell transcriptomic datasets from human fetal brain-derived neural tissue exposed ex vivo to Brazilian ZIKV-BR, the Cambodian 2010 Asian-lineage FSS13025 isolate, referred to here as ZIKV-FSS, or interferon-beta stimulation (IFNβ). Across neural lineages, radial glia-like cells enriched in progenitors and cycling NPC populations showed strong transcriptional IFN/ISG responsiveness and a higher number of genes that met exploratory differential expression criteria compared with several other cell types. Exploratory comparisons suggested strain-associated differences in response structure. In selected progenitor-focused and ISG-related summaries, the patterns linked to ZIKV-FSS showed more descriptive similarity to IFNβ-related profiles, while ZIKV-BR exposure was linked to broader and more varied gene expression changes. Sensitivity analyses showed that the transcriptional similarity between ZIKV and IFNβ depended on the gene universe used, was strongest among canonical interferon-stimulated genes (ISGs), and was more conservative across broader non-ISG and all-expressed gene sets. Contextual analyses of an independent human cerebral organoid bulk RNA-seq dataset and a Vero/moDC single-cell dataset supported the general presence of IFN/ISG-related transcriptional activation across ZIKV-exposed systems, but these were not interpreted as validation of fetal neural lineage-specific responses. Overall, these results provide an exploratory, lineage-resolved descriptive reference of antiviral transcriptional states in developing human neural tissue. Since the main fetal neural dataset included only one biological sample per condition, these findings should be interpreted as exploratory strain-associated patterns rather than definitive evidence for strain-dependent mechanisms.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s44342-026-00076-5.

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 29 matches between paragraphs and lines of code.

abedizahra/Zika-scrnaseq-antiviral-response-code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2b0871acd66ea544a4483540dfdc5bd6508d767f, 23 June 2026
Languages: R (11)
Size: 22 files, 11 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), ggplot2 (8 files), patchwork (7 files), Seurat (7 files), data.table (2 files), DESeq2 (2 files), pheatmap (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/abedizahra/Zika-scrnaseq-antiviral-response-code

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;
  • 11 scripts, each with its path and the digest of its content;
  • 29 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

All datasets analyzed in this study are publicly available from the Gene Expression Omnibus under accession numbers GSE238140 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE238140), GSE230571 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE230571), and GSE97919 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97919). The R analysis scripts, curated sample metadata tables, gene lists, and documentation required to reproduce the analyses have been deposited in a public GitHub repository at https://github.com/abedizahra/Zika-scrnaseq-antiviral-response-code. Raw sequencing data were not deposited in the repository because they are publicly available through GEO. Derived results can be regenerated by running the scripts according to the workflow described in the repository README.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 33 references.

Cite

This paper

Abedi, Z., Sheikh Beig Goharrizi, M. A., Abbasi, A., Soleimani Zakeri, N. S., Jangi, H., & Susanabadi Farahani, A. (2026). Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue. Genomics & informatics, 24(1), 14. https://doi.org/10.1186/s44342-026-00076-5

BibTeX

@article{abedi2026exploratory,
author = {Abedi, Zahra and Sheikh Beig Goharrizi, Mohammad Ali and Abbasi, Amirreza and Soleimani Zakeri, Negar Sadat and Jangi, Helia and Susanabadi Farahani, Alireza},
title = {{Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue}},
journal = {Genomics \& informatics},
year = {2026},
month = jul,
volume = {24},
number = {1},
pages = {14},
publisher = {BMC},
issn = {1598-866X},
doi = {10.1186/s44342-026-00076-5},
url = {https://doi.org/10.1186/s44342-026-00076-5},
pmid = {42426912},
pmcid = {PMC13352994}
}

RIS

TY - JOUR
AU - Abedi, Zahra
AU - Sheikh Beig Goharrizi, Mohammad Ali
AU - Abbasi, Amirreza
AU - Soleimani Zakeri, Negar Sadat
AU - Jangi, Helia
AU - Susanabadi Farahani, Alireza
TI - Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue
T2 - Genomics & informatics
J2 - Genomics Inform
PY - 2026
DA - 2026/07/09
VL - 24
IS - 1
SP - 14
SN - 1598-866X
PB - BMC
DO - 10.1186/s44342-026-00076-5
UR - https://doi.org/10.1186/s44342-026-00076-5
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s44342-026-00076-5",
"type": "article-journal",
"title": "Exploratory strain-associated patterns of antiviral transcriptional responses to Zika virus exposure in developing human neural tissue",
"container-title": "Genomics & informatics",
"author": [
{
"family": "Abedi",
"given": "Zahra"
},
{
"family": "Sheikh Beig Goharrizi",
"given": "Mohammad Ali"
},
{
"family": "Abbasi",
"given": "Amirreza"
},
{
"family": "Soleimani Zakeri",
"given": "Negar Sadat"
},
{
"family": "Jangi",
"given": "Helia"
},
{
"family": "Susanabadi Farahani",
"given": "Alireza"
}
],
"container-title-short": "Genomics Inform",
"volume": "24",
"issue": "1",
"page": "14",
"DOI": "10.1186/s44342-026-00076-5",
"PMID": "42426912",
"PMCID": "PMC13352994",
"ISSN": "1598-866X",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s44342-026-00076-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
9
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s44318-026-00806-z [code]
Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis.
Journal: The EMBO journal
In common: DESeq2, pheatmap, Seurat, 4 other tools, 3 references
[2] doi:10.1038/s41467-026-70232-6 [code]
Gene expression dynamics of human and mouse craniofacial development at the single-cell level.
Journal: Nature communications
In common: DESeq2, pheatmap, Seurat, 4 other tools, genetics / omics, 2 references
[3] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: DESeq2, pheatmap, Seurat, 4 other tools, genetics / omics, 2 references
[4] doi:10.1093/brain/awaf440 [code]
Cerebrospinal fluid-driven ependymal motile cilia defects are implicated in multiple sclerosis.
Journal: Brain : a journal of neurology
In common: DESeq2, Seurat, data.table, 3 other tools, genetics / omics, 3 references
[5] doi:10.1038/s41467-026-71803-3 [code]
Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.
Journal: Nature communications
In common: DESeq2, pheatmap, Seurat, 4 other tools, genetics / omics, 1 reference
[6] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: DESeq2, pheatmap, Seurat, 4 other tools, genetics / omics, 1 reference
[7] doi:10.1038/s41467-026-74320-5 [code]
Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development.
Journal: Nature communications
In common: pheatmap, Seurat, data.table, 2 other tools, genetics / omics, 3 references
[8] doi:10.1038/s41467-026-73305-8 [code]
Comparative analysis of the cellular landscape in mammalian striatum.
Journal: Nature communications
In common: DESeq2, pheatmap, Seurat, 4 other tools, genetics / omics, 1 reference
[9] doi:10.1038/s41514-026-00397-3 [code]
Nasal administration of Protollin enhances monocyte phagocytosis and decreases CD8&lt;sup&gt;+&lt;/sup&gt; T cell cytotoxicity in subjects with early Alzheimer's disease: a Phase 1 clinical trial.
Journal: npj aging
In common: DESeq2, pheatmap, Seurat, 4 other tools, 1 reference
[10] doi:10.1016/j.isci.2026.117228 [code]
Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.
Journal: iScience
In common: DESeq2, pheatmap, Seurat, 3 other tools, genetics / omics, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.