OSCR

Venezuelan equine encephalitis virus infection causes chronic neurobehavioral outcomes, cellular remodeling, and hippocampal single-cell transcriptomic changes.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › VEEV infection significantly impacts single cell gene expression within the hippocampus at 7 DPI, including multiple immune and antiviral genes ↔ scRNA-seq_code.R, lines 167–233 · score 0.83 · endothelial cells, progenitor cells, immune cell, inhibitory neurons, excitatory neurons, UMAP
  2. [2] § Materials and methods › scRNA-seq sample purification, processing, and analysis ↔ scRNA-seq_code.R, lines 126–165 · score 0.77 · low quality cells, quality control, nFeature_RNA, PCA, harmony, variable
  3. [3] § Results › VEEV infection significantly impacts single cell gene expression within the hippocampus at 7 DPI, including multiple immune and antiviral genes ↔ scRNA-seq_code.R, lines 167–233 · score 0.74 · endothelial cells, progenitor cells, inhibitory neurons, excitatory neurons, UMAP, oligodendrocytes

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 · 238 lines · 11 KB · no license · 3 matches

  1. options(stringAsFactors=F)
  2. library(dplyr)
  3. library(Seurat)
  4. library(patchwork)
  5. library(stringr)
  6. library(harmony)
  7. #### read 10X genomics scRNAseq data
  8. MOCK_106_DPI_Spike <- Read10X("MOCK-90-DPI_results_FRP/sample_filtered_feature_bc_matrix/sample_filtered_feature_bc_matrix/")
  9. VEEV_106_DPI_Spike <- Read10X("VEEV-90-DPI_results_FRP/sample_filtered_feature_bc_matrix/sample_filtered_feature_bc_matrix/")
  10. VEEV_neuro_106_DPI_Spike <- Read10X("VEEV-neuro-90-DPI_results_FRP/sample_filtered_feature_bc_matrix/sample_filtered_feature_bc_matrix/")
  11. MOCK_7_DPI_Spike <- Read10X("MOCK-7-DPI_results_FRP/sample_filtered_feature_bc_matrix/sample_filtered_feature_bc_matrix/")
  12. VEEV_7_DPI_Spike <- Read10X("VEEV-7-DPI_results_FRP/sample_filtered_feature_bc_matrix/sample_filtered_feature_bc_matrix/")
  13. VEEV_neuro_106_DPI_v2_Spike <- Read10X("VEEV-neuro-90-DPI_results_FRP_additional/sample_filtered_feature_bc_matrix/")
  15. MOCK_106_DPI_Spike.obj <- CreateSeuratObject(counts = MOCK_106_DPI_Spike, project = "MOCK_106_DPI_Spike", min.cells = 0, min.features = 0)
  16. VEEV_106_DPI_Spike.obj <- CreateSeuratObject(counts = VEEV_106_DPI_Spike, project = "VEEV_106_DPI_Spike", min.cells = 0, min.features = 0)
  17. VEEV_neuro_106_DPI_Spike.obj <- CreateSeuratObject(counts = VEEV_neuro_106_DPI_Spike, project = "VEEV_neuro_106_DPI_Spike", min.cells = 0, min.features = 0)
  18. MOCK_7_DPI_Spike.obj <- CreateSeuratObject(counts = MOCK_7_DPI_Spike, project = "MOCK_7_DPI_Spike", min.cells = 0, min.features = 0)
  19. VEEV_7_DPI_Spike.obj <- CreateSeuratObject(counts = VEEV_7_DPI_Spike, project = "VEEV_7_DPI_Spike", min.cells = 0, min.features = 0)
  20. VEEV_neuro_106_DPI_v2_Spike.obj <- CreateSeuratObject(counts = VEEV_neuro_106_DPI_v2_Spike, project = "VEEV_neuro_106_DPI_v2_Spike", min.cells = 0, min.features = 0)
  23. merge_obj <- merge(x=MOCK_106_DPI_Spike.obj,
  24. y = c(VEEV_106_DPI_Spike.obj,
  25. VEEV_neuro_106_DPI_Spike.obj,
  26. MOCK_7_DPI_Spike.obj,
  27. VEEV_7_DPI_Spike.obj,
  28. VEEV_neuro_106_DPI_v2_Spike.obj
  29. ),
  30. add.cell.ids = c("Study-2-MOCK-90-DPI",
  31. "Study-2-VEEV-90-DPI",
  32. "Study-2-VEEV-neuro-90-DPI",
  33. "Study-3-MOCK-7-DPI",
  34. "Study-3-VEEV-7-DPI",
  35. "VEEV_neuro_106_DPI_add"
  36. ) )
  37. merge_obj <- JoinLayers(merge_obj)
  39. #################################################
  40. ######################## merge two batches of VEEV_neuro_106_DPI
  42. length(unique(c(colnames(VEEV_neuro_106_DPI_Spike) , colnames(VEEV_neuro_106_DPI_v2_Spike))))
  43. all(rownames(VEEV_neuro_106_DPI_Spike) == rownames(VEEV_neuro_106_DPI_v2_Spike))
  44. VEEV_neuro_106_DPI_merge_Spike <- VEEV_neuro_106_DPI_v2_Spike
  46. cells <- intersect(colnames(VEEV_neuro_106_DPI_Spike) , colnames(VEEV_neuro_106_DPI_v2_Spike))
  47. VEEV_neuro_106_DPI_merge_Spike <- VEEV_neuro_106_DPI_v2_Spike
  48. VEEV_neuro_106_DPI_merge_Spike[,cells] <- VEEV_neuro_106_DPI_merge_Spike[,cells] + VEEV_neuro_106_DPI_Spike[,cells]
  49. VEEV_neuro_106_DPI_merge_Spike <- cbind(VEEV_neuro_106_DPI_merge_Spike,
  50. VEEV_neuro_106_DPI_Spike[,-which(colnames(VEEV_neuro_106_DPI_Spike) %in% cells)]
  51. )
  53. VEEV_neuro_106_DPI_merge_Spike.obj <- CreateSeuratObject(counts = VEEV_neuro_106_DPI_merge_Spike, project = "VEEV_neuro_106_DPI_merge_Spike", min.cells = 0, min.features = 0)
  55. merge_obj <- merge(x=MOCK_106_DPI_Spike.obj,
  56. y = c(VEEV_106_DPI_Spike.obj,
  57. VEEV_neuro_106_DPI_merge_Spike.obj,
  58. MOCK_7_DPI_Spike.obj,
  59. VEEV_7_DPI_Spike.obj
  60. ),
  61. add.cell.ids = c("Study-2-MOCK-90-DPI",
  62. "Study-2-VEEV-90-DPI",
  63. "Study-2-VEEV-neuro-90-DPI",
  64. "Study-3-MOCK-7-DPI",
  65. "Study-3-VEEV-7-DPI"
  66. ) )
  67. merge_obj <- JoinLayers(merge_obj)
  68. #######################################################################
  69. ##### summary parameters after merge two batches
  70. table(merge_obj$orig.ident)
  71. tapply(merge_obj$nCount_RNA,merge_obj$orig.ident,median)
  72. tapply(merge_obj$nFeature_RNA,merge_obj$orig.ident,median)
  74. length(which(rowSums(MOCK_106_DPI_Spike)>0))
  75. length(which(rowSums(VEEV_106_DPI_Spike)>0))
  76. length(which(rowSums(VEEV_neuro_106_DPI_merge_Spike)>0))
  77. length(which(rowSums(MOCK_7_DPI_Spike)>0))
  78. length(which(rowSums(VEEV_7_DPI_Spike)>0))
  80. ###########################################
  81. ## summary infected cells
  83. merge_obj$probe1=merge_obj[["RNA"]]$counts["Probe1",]
  84. merge_obj$probe2=merge_obj[["RNA"]]$counts["Probe2",]
  85. merge_obj$probe3=merge_obj[["RNA"]]$counts["Probe3",]
  86. merge_obj$probe4=merge_obj[["RNA"]]$counts["Probe4",]
  87. merge_obj$probe5=merge_obj[["RNA"]]$counts["Probe5",]
  88. merge_obj$probe6=merge_obj[["RNA"]]$counts["Probe6",]
  89. merge_obj$probe7=merge_obj[["RNA"]]$counts["Probe7",]
  91. merge_obj$probe9=merge_obj[["RNA"]]$counts["Probe9",]
  92. merge_obj$probe10=merge_obj[["RNA"]]$counts["Probe10",]
  93. merge_obj$probe11=merge_obj[["RNA"]]$counts["Probe10",]
  94. merge_obj$probe12=merge_obj[["RNA"]]$counts["Probe12",]
  97. probe_mat <- data.frame(probe1=merge_obj[["RNA"]]$counts["Probe1",],
  98. probe2=merge_obj[["RNA"]]$counts["Probe2",],
  99. probe3=merge_obj[["RNA"]]$counts["Probe3",],
  100. probe4=merge_obj[["RNA"]]$counts["Probe4",],
  101. probe5=merge_obj[["RNA"]]$counts["Probe5",],
  102. probe6=merge_obj[["RNA"]]$counts["Probe6",],
  103. probe7=merge_obj[["RNA"]]$counts["Probe7",],
  104. probe9=merge_obj[["RNA"]]$counts["Probe9",],
  105. probe10=merge_obj[["RNA"]]$counts["Probe10",],
  106. probe11=merge_obj[["RNA"]]$counts["Probe11",],
  107. probe12=merge_obj[["RNA"]]$counts["Probe12",]
  108. )
  111. index <- apply(probe_mat,1,function(x){return(all(x>0))} )
  113. merge_obj$infected2 <- as.numeric(0)
  114. merge_obj$infected2[which(index == TRUE )] <- 1
  116. ##############################################
  117. ######### quality control
  119. merge_obj[["percent.mt"]] <- PercentageFeatureSet(merge_obj, pattern = "mt-")
  120. # Visualize QC metrics as a violin plot
  121. VlnPlot(merge_obj, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
  122. plot1 <- FeatureScatter(merge_obj, feature1 = "nCount_RNA", feature2 = "percent.mt")
  123. plot2 <- FeatureScatter(merge_obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
  124. plot1 + plot2
  126. ####################################
  127. ######### filter low quality cells
  128. mt.cutoff <- 10
  129. merge_obj <- merge_obj[,which(merge_obj$percent.mt <= mt.cutoff)]
  130. merge_obj <- merge_obj[,which(merge_obj$nFeature_RNA >= 200)]
  133. #######################################################################
  134. ##### summary parameters after quality control
  135. table(merge_obj$orig.ident)
  136. tapply(merge_obj$nCount_RNA,merge_obj$orig.ident,median)
  137. tapply(merge_obj$nFeature_RNA,merge_obj$orig.ident,median)
  139. #######################################################################
  140. ## clustering
  142. merge_obj <- NormalizeData(merge_obj)
  143. merge_obj <- FindVariableFeatures(merge_obj, selection.method = "vst", nfeatures = 2000)
  144. top10 <- head(VariableFeatures(merge_obj), 10)
  146. # plot variable features with and without labels
  147. plot1 <- VariableFeaturePlot(merge_obj)
  148. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  149. plot1 + plot2
  151. all.genes <- rownames(merge_obj)
  153. merge_obj <- ScaleData(merge_obj, features = all.genes)
  154. merge_obj <- RunPCA(merge_obj, features = VariableFeatures(object = merge_obj))
  155. merge_obj <- RunHarmony(merge_obj, group.by.vars = "orig.ident")
  157. DimPlot(merge_obj, reduction = "pca") + NoLegend()
  158. DimHeatmap(merge_obj, dims = 1:3, cells = 500, balanced = TRUE)
  159. ElbowPlot(merge_obj,ndims =30)
  160. merge_obj <- FindNeighbors(merge_obj, dims = 1:20,reduction='harmony')
  161. merge_obj <- FindClusters(merge_obj, resolution = 0.2)
  162. merge_obj <- RunUMAP(merge_obj, dims = 1:20,reduction = "harmony")
  164. DimPlot(merge_obj, reduction = "umap",label = T,pt.size=0.5)
  165. save(merge_obj,file=merge_obj,cluster.Rdata)
  167. #################################################################
  168. ############################## cell type annotation
  170. merge_obj$celltype <- ""
  171. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(0,11,14))] = "Inhibitory_neurons"
  172. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(4:6,13))] = "Excitatory_neurons"
  173. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(1))] = "Oligodendrocytes"
  174. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(2))] = "Microglia"
  175. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(3))] = "Astrocytes"
  176. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(7))] = "Pericytes "
  177. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(8,16))] = "Immune_cells"
  178. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(9))] = "Endothelial_cells"
  179. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(10))] = "OPCs"
  180. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(12))] = "Neural_progenitor_cell"
  181. merge_obj$celltype[which(merge_obj$seurat_clusters %in% c(15))] = "Ependymal_cells"
  183. ###### plot cell type markers and UMAP
  184. library(ggplot2)
  185. colors <- c( "#FB8072", "#45BFC0", "#96B85D", "#C790E0","#DEB068", "#4EAEE3","#FF8DC6", "#52B77C", "#7DACD8", "#FFB090", "#B89FC8"
  186. )
  188. DimPlot(merge_obj,group.by = "celltype", cols = colors,label=F )
  190. selected_features = c(
  191. "Aqp4", "Slc1a3", ## Astrocytes
  192. "Slc17a7","Slc17a6", ## EXC neuron
  193. "Gad1", "Gad2", ## INH neuron
  194. "Ttr","Ecrg4", ## NPC
  195. "Itgam","Aif1", ## Microglia
  196. "Mog","Mag","Ermn", ## Oligodendrocytes
  197. "Cspg4", "Vcan", ## OPC
  198. "Cd3e" , "Nkg7" , ## Immune cells
  199. "Pecam1", "Cldn5" , ## Endothelial
  200. "Col1a2","Pdgfrb", ## Pericytes
  201. "Foxj1", "Ccdc153","Cfap65","Pax6","Hes5" ## Ependymal cells
  202. )
  205. merge_obj$celltype <- factor(merge_obj$celltype,
  206. levels = c("Astrocytes",
  207. "Excitatory_neurons",
  208. "Inhibitory_neurons",
  209. "NPC",
  210. "Microglia",
  211. "Oligodendrocytes",
  212. "OPCs",
  213. "Immune_cells",
  214. "Endothelial_cells",
  215. "Pericytes",
  216. "Ependymal_cells"
  217. ))
  220. p1 <- DoHeatmap(merge_obj, features = selected_features,group.by="celltype",
  221. group.colors = colors
  222. )+
  223. scale_fill_gradientn(colors = c("white","grey","firebrick3"))
  225. VlnPlot(merge_obj,
  226. features = selected_features,group.by="celltype",raster=T,
  227. flip = F,stack = T,fill.by ="ident", cols = rev(colors))
  229. p2 <- DotPlot(object = merge_obj,
  230. features = selected_features, cols =c("white","firebrick3"),
  231. group.by = 'celltype',scale.min=30) + RotatedAxis()
  233. cowplot::plot_grid(p1,p2,ncol=1)

scRNA-seq_code.R at commit 22f1db8, no license · at the source

Overview

Authors: Morgen VanderGiessen1,2, Elizabeth Harris1, Liduo Yin1, Brittany Heath1,2, Shannon K Carney1,2, Caitlin M Woodson1, Xiaowei Wu3, Erik Johnson4, Hehuang Xie1, Michelle Theus1, Kylene Kehn-Hall1,2
ORCID iDs: Hehuang Xie, Kylene Kehn-Hall
  1. Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
  2. Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
  3. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
  4. United States Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, Maryland, United States of America
Institutions: Virginia–Maryland College of Veterinary Medicine (United States); Virginia Tech (United States); United States Army (United States)
Journal: PLoS pathogens, volume 22, issue 4, article e1014115
Dates: received 17 September 2025; accepted 24 March 2026; published online 8 April 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1371/journal.ppat.1014115 · PMID 41950299 · PMCID PMC13089902 · OpenAlex W7151776809
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Machine learning, Physiology & signal measures
MeSH: Encephalitis Virus, Venezuelan Equine*, Encephalomyelitis, Venezuelan Equine*, Hippocampus*, Transcriptome*, Animals, Male, Mice, Mice, Inbred C57BL, Neurons, Single-Cell Analysis (* major topic)
Topic: Mosquito-borne diseases and control (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Funding: Australian Government; Defense Threat Reduction Agency (HDTRA1‐22-1‐0009)
Citations: not cited yet (Europe PMC); 121 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Gavin-Yinld/VEEV_scRNA-seq

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 22f1db873ab296d9fb35877330a427335e41cde6, 14 February 2025
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), ggplot2 (1 file), Harmony (1 file), patchwork (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
2 files
At the source: github.com/Gavin-Yinld/VEEV_scRNA-seq

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;
  • 1 script, each with its path and the digest of its content;
  • 3 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

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1371/journal.ppat.1014115.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 10 MeSH terms, 2 funders, 117 references.

Cite

This paper

VanderGiessen, M., Harris, E., Yin, L., Heath, B., Carney, S. K., Woodson, C. M., Wu, X., Johnson, E., Xie, H., Theus, M., & Kehn-Hall, K. (2026). Venezuelan equine encephalitis virus infection causes chronic neurobehavioral outcomes, cellular remodeling, and hippocampal single-cell transcriptomic changes. PLoS pathogens, 22(4), e1014115. https://doi.org/10.1371/journal.ppat.1014115

BibTeX

@article{vandergiessen2026venezuelan,
author = {VanderGiessen, Morgen and Harris, Elizabeth and Yin, Liduo and Heath, Brittany and Carney, Shannon K and Woodson, Caitlin M and Wu, Xiaowei and Johnson, Erik and Xie, Hehuang and Theus, Michelle and Kehn-Hall, Kylene},
title = {{Venezuelan equine encephalitis virus infection causes chronic neurobehavioral outcomes, cellular remodeling, and hippocampal single-cell transcriptomic changes}},
journal = {PLoS pathogens},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e1014115},
publisher = {PLOS},
issn = {1553-7366},
doi = {10.1371/journal.ppat.1014115},
url = {https://doi.org/10.1371/journal.ppat.1014115},
pmid = {41950299},
pmcid = {PMC13089902}
}

RIS

TY - JOUR
AU - VanderGiessen, Morgen
AU - Harris, Elizabeth
AU - Yin, Liduo
AU - Heath, Brittany
AU - Carney, Shannon K
AU - Woodson, Caitlin M
AU - Wu, Xiaowei
AU - Johnson, Erik
AU - Xie, Hehuang
AU - Theus, Michelle
AU - Kehn-Hall, Kylene
TI - Venezuelan equine encephalitis virus infection causes chronic neurobehavioral outcomes, cellular remodeling, and hippocampal single-cell transcriptomic changes
T2 - PLoS pathogens
J2 - PLoS Pathog
PY - 2026
DA - 2026/04/08
VL - 22
IS - 4
SP - e1014115
SN - 1553-7366
PB - PLOS
DO - 10.1371/journal.ppat.1014115
UR - https://doi.org/10.1371/journal.ppat.1014115
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.ppat.1014115",
"type": "article-journal",
"title": "Venezuelan equine encephalitis virus infection causes chronic neurobehavioral outcomes, cellular remodeling, and hippocampal single-cell transcriptomic changes",
"container-title": "PLoS pathogens",
"author": [
{
"family": "VanderGiessen",
"given": "Morgen"
},
{
"family": "Harris",
"given": "Elizabeth"
},
{
"family": "Yin",
"given": "Liduo"
},
{
"family": "Heath",
"given": "Brittany"
},
{
"family": "Carney",
"given": "Shannon K"
},
{
"family": "Woodson",
"given": "Caitlin M"
},
{
"family": "Wu",
"given": "Xiaowei"
},
{
"family": "Johnson",
"given": "Erik"
},
{
"family": "Xie",
"given": "Hehuang"
},
{
"family": "Theus",
"given": "Michelle"
},
{
"family": "Kehn-Hall",
"given": "Kylene"
}
],
"container-title-short": "PLoS Pathog",
"volume": "22",
"issue": "4",
"page": "e1014115",
"DOI": "10.1371/journal.ppat.1014115",
"PMID": "41950299",
"PMCID": "PMC13089902",
"ISSN": "1553-7366",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.ppat.1014115",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
8
]
]
}
}

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

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/s41467-026-70232-6 [code]
Gene expression dynamics of human and mouse craniofacial development at the single-cell level.
Journal: Nature communications
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, mouse, cellular / molecular, 2 references
[2] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: Harmony, Seurat, cowplot, 3 other tools, other, genetics / omics, mouse, 1 other category, 1 reference
[3] doi:10.1002/ctm2.70683 [code]
Niacin promotes motor function recovery after spinal cord injury via Hcar2-dependent microglia immunometabolic regulation.
Journal: Clinical and translational medicine
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, other condition, mouse, 1 other category, 1 reference
[4] 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: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, mouse, cellular / molecular, 2 references
[5] doi:10.1016/j.cell.2026.05.026 [code]
The critical role of the endogenous immune compartment after CAR T cell therapy in recurrent GBM.
Journal: Cell
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, other condition, 2 references
[6] doi:10.1371/journal.pcbi.1014424 [code]
Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.
Journal: PLoS computational biology
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, mouse, 2 references
[7] doi:10.1038/s41398-026-04200-5 [code]
Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder.
Journal: Translational psychiatry
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, cellular / molecular, 2 references
[8] doi:10.1016/j.celrep.2026.117500 [code]
Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain.
Journal: Cell reports
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, cellular / molecular, 2 references
[9] doi:10.1038/s41467-026-76232-w [code]
Th17 effector cytokines induce shared and distinct microglial and endothelial cell responses in a mouse model for post-streptococcal encephalitis.
Journal: Nature communications
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, other condition, mouse, 1 other category, 1 reference
[10] doi:10.1016/j.stemcr.2026.102967 [code]
Single-cell multiomic approaches define a gradual, spatially regulated epigenetic and transcriptional transition from embryonic to adult neural stem cells.
Journal: Stem cell reports
In common: Harmony, Seurat, cowplot, 3 other tools, genetics / omics, mouse, 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.