OSCR

cGAS-mediated type I IFN signaling contributes to disease progression in drug-refractory epilepsy.

Code ↔ Paper

15 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 15 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Analysis of droplet-based snRNA-seq data ↔ Dravet_snRNAseq_data_processing/Step4_DVPV_annotation_both.R, lines 2–41 · score 0.97 · choroid plexus epithelial, Pla2g7, Slc17a7, Csf1r, P2ry12, Cx3cr1
  2. [2] § Methods › Analysis of droplet-based snRNA-seq data ↔ Dravet_TDI_snRNAseq_data_processing/Step4_LG815_TDI_annotation_mt5_subclustering.R, lines 52–123 · score 0.94 · Pla2g7, Slc17a7, Csf1r, P2ry12, Cx3cr1, Plpp3
  3. [3] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Fig. 3A,B N3 DS MG.R, lines 59–91 · score 0.84 · pie chart, Raet1e, H2 K1, H2 D1, MHC, UMAP
  4. [4] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Fig. 3A,B N3 DS MG.R, lines 59–91 · score 0.82 · Raet1e, H2 K1, H2 D1, cGAS, Ifngr2, Ctsl
  5. [5] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Fig. 3D DEG correlation.R, the whole file · a weak match · score 0.77 · H2 K1, DAM genes, H2 D1, overlapping DEGs, Ctsl, Ifnar2
  6. [6] § Results › Neuronal hyperexcitability activates microglial cGAS in vivo ↔ codes for figures/Fig 5F LG815 TDI MG subclustering.R, the whole file · a weak match · score 0.75 · Csf1r, P2ry12, Cx3cr1, Mrc1, Skap1, Macrophage
  7. [7] § Methods › Epileptiform activity analysis ↔ EEG_Seizure_Detector_Auto_V1.m, lines 53–112 · score 0.67 · peak width, upper threshold, lower threshold, algorithm, spikes, baseline
  8. [8] § Methods › Epileptiform activity analysis ↔ EEG_Discharge_Detector_V8_Auto.m, lines 53–112 · score 0.67 · peak width, upper threshold, lower threshold, algorithm, spikes, baseline
  9. [9] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Fig. 3D DEG correlation.R, the whole file · a weak match · score 0.66 · H2 K1, H2 D1, Ctsl, Ifnar2, Trem2, DAM
  10. [10] § Methods › Analysis of droplet-based snRNA-seq data ↔ codes for figures/Fig 1L hdWGCNA.R, lines 49–92 · score 0.65 · module eigengene, hdWGCNA, Seurat clusters, metacells, networks
  11. [11] § Results › Neuronal hyperexcitability activates microglial cGAS in vivo ↔ codes for figures/Fig. 3A,B N3 DS MG.R, lines 1–56 · score 0.58 · H2 K1, H2 D1, Stat1, Scn1a, Dravet, RNA
  12. [12] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Fig. 3J GSEA_SANKEY_PLOT_clusterprofiler.R, lines 1–66 · score 0.58 · protein catabolic process, glial cell, gliogenesis, GO, pathways, clusters
  13. [13] § Methods › Analysis of droplet-based snRNA-seq data ↔ Dravet_TDI_snRNAseq_data_processing/Step2_LG815_TDI_DF_2ndRound_mt5.R, lines 1–60 · score 0.57 · Cell Ranger, doublet cells, nuclei, neighbor, variable, Seurat
  14. [14] § Methods › Analysis of droplet-based snRNA-seq data ↔ Dravet_cGAS_snRNAseq_data_processing/Step2_LG815C_DF_2ndRound_mt5.R, lines 1–60 · score 0.57 · Cell Ranger, doublet cells, nuclei, neighbor, variable, Seurat
  15. [15] § Results › Genetic cGAS reduction rescues the DS-induced inflammatory signature in glial cells ↔ codes for figures/Extended Data Fig 5B,C clusterprofiler.R, the whole file · a weak match · score 0.56 · fold change, overlapping DEGs, GO, enriched, FC, pathway

Paper

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The authors' code

R · 91 lines · 3.1 KB · no license · 3 matches

  1. library(Seurat)
  2. library(ggplot2)
  3. library(dplyr)
  4. library(cowplot)
  5. library(reshape2)
  6. library(MAST)
  7. library(EnhancedVolcano)
  8. library(SCP)
  9. library(scCustomize)
  10. library(BiocParallel)
  11. library(RColorBrewer)
  12. register(MulticoreParam(workers = 8, progressbar = TRUE))
  16. ##### LG815C MG clustering
  17. DefaultAssay(MG) <- 'integrated'
  18. all.genes <- rownames(MG)
  19. MG<- ScaleData(MG, features = all.genes)
  20. MG<- FindVariableFeatures(object = MG)
  21. MG<- RunPCA(MG, features = VariableFeatures(object = MG))
  23. ElbowPlot(MG)
  24. MG <- FindNeighbors(MG, dims = 1:50)
  25. MG <- FindClusters(MG, resolution = 0.4)
  26. MG <- RunUMAP(MG, dims = 1:50)
  29. DimPlot(MG, reduction = "umap", split.by = "Condition", label = T, ncol = 2)
  30. DimPlot(MG, reduction = "umap", split.by = "Sample_Name", label = T, ncol = 2)
  32. #####remove very small clusters
  33. MG <- subset(MG, idents = c("0",'1','2','3','4','5'))
  34. MG <- FindNeighbors(MG, dims = 1:50)
  35. MG <- FindClusters(MG, resolution = 0.4)
  36. MG <- RunUMAP(MG, dims = 1:50)
  37. DimPlot(MG, reduction = "umap", split.by = "Condition", label = T, ncol = 2)
  40. MG2[["RNA3"]] <- as(object = MG2[["RNA"]], Class = "Assay")
  42. DefaultAssay(MG2) <- "RNA3"
  43. DefaultAssay(MG) <- 'RNA'
  44. DotPlot(object = MG, features = c('Stat1','Parp14','Rnf213','Ddx60','Trim30a','Cgas',"H2-D1", "H2-K1",'Ifnar1','Cd68'),scale.min = 0) + scale_colour_gradient2(low = "darkblue", mid = "white", high = "darkred")+theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))+ RotatedAxis()
  45. Idents(MG) <- 'seurat_clusters'
  46. MG_markers <- FindAllMarkers(MG, logfc.threshold = 0.1, test.use = "MAST", min.pct = 0.1, only.pos = T)
  47. write.csv(IFNARKO_OL_markers, "LG815_TDI_MG_markers_res0.15_RNAassay_logFC0.1.csv")
  50. Idents(MG) <-'Condition'
  51. DVKIvsCtrl <- FindMarkers(MG, ident.1 = 'Scn1a: +/-; cGAS: +/-', ident.2 = "Scn1a: +/-; cGAS: +/+", logfc.threshold = 0.15,min.pct = 0.1,
  52. test.use = "MAST", assay ='RNA')
  53. write.csv(DVKIvsCtrl, "Dravet_TDIvsDravet_DE_MMG_RNAassay_PV_pct0.1.csv")
  56. saveRDS(MG,"LG815C_MG_final_dim50_res0.4.rds")
  59. #### Fig 3a UMAP with pie chart
  60. CellDimPlot(
  61. srt = MG, group.by = c("seurat_clusters"),
  62. reduction = "UMAP", theme_use = "theme_classic"
  63. )
  65. stat.colors <- c("Scn1a: +/+; cGAS: +/+" = "#488CCA","Scn1a: +/+; cGAS: +/-" = "#7DD3F6","Scn1a: +/-; cGAS: +/+" ="#EE3425","Scn1a: +/-; cGAS: +/-" = "#F79420")
  67. CellDimPlot(MG, group.by = "seurat_clusters",
  68. reduction = "UMAP", stat.by = "Condition",
  69. theme_use = "theme_classic", legend.position="none",
  70. stat_palcolor = stat.colors,
  71. stat_plot_alpha = 3,
  72. stat_plot_label = FALSE,
  73. stat_plot_label_size = 1,)
  75. ##### Fig 3b
  76. ht <- GroupHeatmap(
  77. srt = MG,
  78. #cell_annotation = c("Condition"), cell_annotation_palette = c("Dark2"),
  79. features = c(
  80. "Trem2","Ctsl","Cd9", # stage-2 DAM
  81. 'Ifnar2',"Ifngr2", # Interferon
  82. "H2-K1", "H2-D1","Raet1e" # MHC-II
  83. ),
  84. group.by = c( "seurat_clusters","Condition"),
  85. heatmap_palette = "RdBu",
  86. show_row_names = FALSE, row_names_side = "left",
  87. add_dot = TRUE,dot_size=unit(8,"mm"),add_reticle = FALSE,
  88. add_bg = FALSE,flip = TRUE
  90. )
  91. print(ht$plot)

Fig. 3A,B N3 DS MG.R at commit eb26563, no license · at the source

Overview

Authors: Yige Huang1,2, Li Fan1, Man Ying Wong1, Zhuofan Lei3, Balaji Krishnamachary3, Daphne Zhu1, Mika P Cadiz1, Ravi Kumar Nagiri1, Pearly Ye1, Kendra Norman1, Maitreyee Bhagwat1, Young Jae Lee1, Hui Li3, Jingjie Zhu1, Sadaf Amin1, Kelli Lauderdale4, Hao Chen1, Wenjie Luo1, Shiaoching Gong1, Benjamin L Liechty5,6, Jorge J Palop4, Subhash C Sinha1, Junfang Wu3, Mingrui Zhao1, Li Gan1
ORCID iDs: Li Fan, Ravi Kumar Nagiri, Hao Chen, Wenjie Luo, Benjamin L Liechty, Subhash C Sinha, Junfang Wu, Li Gan
  1. Helen and Robert Appel Alzheimer’s Disease Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY USA
  2. Biochemistry, Structural Biology, Cell Biology, Developmental Biology and Molecular Biology Graduate Program, Weill Cornell Medicine, New York, NY USA
  3. Department of Anesthesiology and Shock, Trauma and Anesthesiology Research (STAR) Center, University of Maryland School of Medicine, Baltimore, MD USA
  4. Gladstone Institute of Neurological Disease, Department of Neurology, University of California, San Francisco, San Francisco, CA USA
  5. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY USA
  6. Department of Pathology and Laboratory Medicine, NewYork–Presbyterian Hospital, New York, NY USA
Institutions: Weill Cornell Medicine (United States); University of Maryland, Baltimore (United States); University of California, San Francisco (United States); NewYork–Presbyterian Hospital (United States)
Journal: Nature neuroscience, volume 29, issue 9, pages 2124-2138
Dates: received 29 May 2025; accepted 22 June 2026; published online 29 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02384-z · PMID 42527551 · PMCID PMC13533845 · OpenAlex W7171718356
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Epilepsy, Neuroimmunology
MeSH: Drug Resistant Epilepsy*, Interferon Type I*, Nucleotidyltransferases*, Animals, Brain, cGAS-STING Signaling Pathway, Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase, Disease Progression, Epilepsies, Myoclonic, Humans, Male, Mice, Microglia, Neurons, Signal Transduction (* major topic)
Topic: interferon and immune responses (Immunology, Immunology and Microbiology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01AG092683, R61AG094667); U.S. Department of Health &amp; Human Services | National Institutes of Health (R01NS145443, 1R01AG079291-01A1, R01AG074541, R01AG079557-01, R01AG072758, R01AG076448, K99AG078493); Daedalus Fund; U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (R61AG094667, R01AG092683); BrightFocus Foundation (BrightFocus) (A20201312F); NIA NIH HHS (K99 AG078493, R01 AG074541, R01 AG079291, R01 AG072758, R01 AG092683, R01 AG076448, R61 AG094667, RF1 AG079557); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (K99AG078493, R01AG079557-01, R01AG076448, 1R01AG079291-01A1, R01AG074541, R01AG072758, R01NS145443); the Rainwater Charitable Foundation Freedom Together Foundation Daedalus Fund; JumpStart Research Career Development Program; NINDS NIH HHS (R01 NS145443); Cure Alzheimer&apos;s Fund
Citations: cited by 2 papers (Europe PMC); 89 references in the paper

Abstract

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aertslab/SCENIC

License: GPL-3.0
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Commit: 7a74341745cecd3505310c6c5755cad456756cf9, 5 April 2024
Languages: R (32), Jupyter (2)
Size: 94 files, 34 scripts
Software Heritage: not archived
Found in: the text, “Analysis of droplet-based snRNA-seq data”
Holds: README, license file, environment (DESCRIPTION), documentation, 10 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: reshape2 (11 files), data.table (10 files), SingleCellExperiment (6 files), ComplexHeatmap (4 files), ggplot2 (3 files), Plotly (2 files), Seurat (2 files), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/aertslab/SCENIC

Jackson-Kyle-CCOM/Automated-EEG-Algorithm

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Commit: 9fd11a6a6ba76f18585b0cf98b1fa959c0e5f41e, 5 October 2023
Languages: MATLAB (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Epileptiform activity analysis”
Holds: README, license file
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Zenodo 20545168

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (20 files), ggplot2 (19 files), tidyverse (16 files), cowplot (14 files), reshape2 (11 files), patchwork (4 files), WGCNA (3 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), igraph (1 file), pheatmap (1 file)
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lifan36/Huang-Dravet-2026

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Commit: eb26563d0b7b3db8ed832f5d564db0d599cef9e1, 5 February 2026
Languages: R (25)
Size: 26 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (20 files), ggplot2 (19 files), tidyverse (16 files), cowplot (14 files), reshape2 (11 files), patchwork (4 files), WGCNA (3 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), igraph (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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Zenodo 20545167

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (20 files), ggplot2 (19 files), tidyverse (16 files), cowplot (14 files), reshape2 (11 files), patchwork (4 files), WGCNA (3 files), circlize (1 file), clusterProfiler (1 file), ComplexHeatmap (1 file), igraph (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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25 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02384-z.

Tracing map

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What the map holds:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 113 scripts, each with its path and the digest of its content;
  • 15 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02384-z.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 2 keywords, 15 MeSH terms, 11 funders, 89 references.

Cite

This paper

Huang, Y., Fan, L., Wong, M. Y., Lei, Z., Krishnamachary, B., Zhu, D., Cadiz, M. P., Nagiri, R. K., Ye, P., Norman, K., Bhagwat, M., Lee, Y. J., Li, H., Zhu, J., Amin, S., Lauderdale, K., Chen, H., Luo, W., Gong, S., . . . Gan, L. (2026). cGAS-mediated type I IFN signaling contributes to disease progression in drug-refractory epilepsy. Nature neuroscience, 29(9), 2124-2138. https://doi.org/10.1038/s41593-026-02384-z

BibTeX

@article{huang2026cgas,
author = {Huang, Yige and Fan, Li and Wong, Man Ying and Lei, Zhuofan and Krishnamachary, Balaji and Zhu, Daphne and Cadiz, Mika P and Nagiri, Ravi Kumar and Ye, Pearly and Norman, Kendra and Bhagwat, Maitreyee and Lee, Young Jae and Li, Hui and Zhu, Jingjie and Amin, Sadaf and Lauderdale, Kelli and Chen, Hao and Luo, Wenjie and Gong, Shiaoching and Liechty, Benjamin L and Palop, Jorge J and Sinha, Subhash C and Wu, Junfang and Zhao, Mingrui and Gan, Li},
title = {{cGAS-mediated type I IFN signaling contributes to disease progression in drug-refractory epilepsy}},
journal = {Nature neuroscience},
year = {2026},
month = jul,
volume = {29},
number = {9},
pages = {2124--2138},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02384-z},
url = {https://doi.org/10.1038/s41593-026-02384-z},
pmid = {42527551},
pmcid = {PMC13533845}
}

RIS

TY - JOUR
AU - Huang, Yige
AU - Fan, Li
AU - Wong, Man Ying
AU - Lei, Zhuofan
AU - Krishnamachary, Balaji
AU - Zhu, Daphne
AU - Cadiz, Mika P
AU - Nagiri, Ravi Kumar
AU - Ye, Pearly
AU - Norman, Kendra
AU - Bhagwat, Maitreyee
AU - Lee, Young Jae
AU - Li, Hui
AU - Zhu, Jingjie
AU - Amin, Sadaf
AU - Lauderdale, Kelli
AU - Chen, Hao
AU - Luo, Wenjie
AU - Gong, Shiaoching
AU - Liechty, Benjamin L
AU - Palop, Jorge J
AU - Sinha, Subhash C
AU - Wu, Junfang
AU - Zhao, Mingrui
AU - Gan, Li
TI - cGAS-mediated type I IFN signaling contributes to disease progression in drug-refractory epilepsy
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/07/29
VL - 29
IS - 9
SP - 2124
EP - 2138
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02384-z
UR - https://doi.org/10.1038/s41593-026-02384-z
LA - en
ER -

CSL-JSON

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In common: WGCNA, igraph, circlize, 11 other tools, mouse, cellular / molecular
[7] doi:10.1038/s41380-026-03629-w [code]
Maternal fasting during early gestation induces epigenetic alterations and schizophrenia-related phenotypes.
Journal: Molecular psychiatry
In common: WGCNA, igraph, circlize, 10 other tools, mouse, cellular / molecular, 1 reference
[8] doi:10.1038/s41586-026-10414-w [code]
Focal white matter lesions drive grey matter inflammation and synapse loss.
Journal: Nature
In common: SingleCellExperiment, igraph, circlize, 8 other tools, mouse, cellular / molecular, 3 references
[9] doi:10.1016/j.isci.2026.115573 [code]
Female cortical cellular mosaicism underlies shared MeCP2 and PCB impacted gene pathways.
Journal: iScience
In common: WGCNA, SingleCellExperiment, igraph, 9 other tools, mouse, cellular / molecular, 1 reference
[10] doi:10.1126/sciadv.aeg3223 [code]
The extreme diversity of retinal amacrine cells has deep evolutionary roots.
Journal: Science advances
In common: WGCNA, igraph, circlize, 10 other tools, cellular / molecular

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