OSCR

Spinal cord regeneration deploys cell-type specific developmental and non-developmental strategies to restore neuron diversity.

Code ↔ Paper

17 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 17 matches
  1. [1] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Interneurons_Fig2D,2E.Rmd, lines 206–218 · score 0.86 · slc6a4, slc6a2, slc6a3, pkd1l2, GABAergic, foxa2
  2. [2] § Results › Emergence of Xenopus transient regeneration-specific neurons at 1 dpa ↔ Fig6A,6B,6C,6E,6F, S7, S10.Rmd, lines 553–558 · score 0.85 · semaphorin plexin signaling, enriched GO term, nervous system development, cell adhesion, axon guidance, pathway
  3. [3] § Results › Emergence of Xenopus transient regeneration-specific neurons at 1 dpa ↔ Fig6A,6B,6C,6E,6F, S7, S10.Rmd, lines 276–324 · score 0.70 · ppp1r17, rtn4rl1, Neural Progenitor Cells, sfrp4, gap43, unknown
  4. [4] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Figure_S8A-E.Rmd, lines 27–102 · score 0.67 · slc6a2, slc6a3, catecholaminergic, serotonergic, tph2, gad2
  5. [5] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Neuron_neurotransmitters.Rmd, lines 24–56 · score 0.67 · Kolmer Agduhr interneurons, Rohon Beard neurons, en1, neurotransmitter, neural cell, vsx2
  6. [6] § Methods › scRNA-sequencing processing ↔ FigS8_F-K.Rmd, lines 37–72 · score 0.66 · CellChat, neural subset, ortholog, interactions, db, mouse
  7. [7] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Fig2A,2B,2C,3A,S3A.Rmd, lines 75–123 · score 0.64 · Kolmer Agduhr interneurons, Rohon Beard neurons, neural cell, excitatory, vsx2, inhibitory
  8. [8] § Results › scRNA-seq shows dynamic changes in cell type abundance over time ↔ Seurat_tail_annotations_final.Rmd, lines 132–193 · score 0.63 · basal cells, goblet cells, immune cells, syndetome, sclerotome, tail
  9. [9] § Results › scRNA-seq shows dynamic changes in cell type abundance over time ↔ Fig1B,1C,1D,S2,S3.Rmd, lines 23–58 · score 0.62 · basal cells, goblet cells, immune cells, syndetome, sclerotome, tail
  10. [10] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Interneurons_Fig2D,2E.Rmd, lines 175–204 · score 0.62 · glycinergic interneuron, KA interneuron, intermediate KA, UMAP, V2b, V1
  11. [11] § Results › An expanded atlas of Xenopus neuron types based on conserved markers ↔ Fig2A,2B,2C,3A,S3A.Rmd, lines 75–123 · score 0.62 · Excitatory interneurons, inhibitory interneurons, glycinergic interneuron, UMAP, Figure 2, cluster
  12. [12] § Methods › scRNA-sequencing processing ↔ Seurat_tail_cell_cycle_scoring_final.Rmd, lines 19–37 · score 0.61 · Cell Cycle Scoring, Seurat, gene
  13. [13] § Methods › scRNA-sequencing pipeline ↔ Seurat_tail_QC_final.Rmd, lines 84–184 · score 0.60 · processed Seurat, scRNA, singlets, seq
  14. [14] § Results › scRNA-seq shows dynamic changes in cell type abundance over time ↔ Fig1B,1C,1D,S2,S3.Rmd, lines 23–58 · score 0.59 · basal cells, goblet cells, immune cells, syndetome, sclerotome, Figure 1
  15. [15] § Methods › scRNA-sequencing processing ↔ Seurat_tail_QC_final.Rmd, lines 43–82 · score 0.57 · nFeature_RNA, cutoff, mitochondrial, CellRanger, UMI, Seurat
  16. [16] § Results › Emergence of Xenopus transient regeneration-specific neurons at 1 dpa ↔ FigS8_F-K.Rmd, lines 127–200 · score 0.56 · L1CAM, NCAM, NRG, NRXN, SEMA3, lep
  17. [17] § Results › Proliferative neurogenesis drives neuron repopulation from 3 to 7 dpa ↔ Seurat_tail_cell_cycle_scoring_final.Rmd, lines 19–37 · score 0.53 · cell cycle, Seurat, G2M, score, phase, genes

Paper

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The authors' code

R Markdown · 218 lines · 9.8 KB · no license · 2 matches

  1. ---
  2. title: "Motor_Neurons"
  3. output: html_document
  4. date: "2025-08-21"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = TRUE)
  9. ```
  11. ```{r}
  12. library(ggplot2)
  13. library(dplyr)
  14. library(Seurat)
  15. library(scCustomize)
  16. ```
  18. Subset dataset and run processing on Interneurons
  19. ```{r}
  20. neural <- LoadSeuratRds(file = "neural_final.rds")
  21. INs <- subset(x = neural, subset = ([email hidden]$neural_cell_types == "Glutamatergic Interneurons" |
  22. [email hidden]$neural_cell_types == "GABA/Glycinergic Interneurons" |
  23. [email hidden]$neural_cell_types == "Kolmer-Agduhr Interneurons" |
  24. [email hidden]$neural_cell_types == "vsx2+ Interneurons" |
  25. [email hidden]$neural_cell_types == "Differentiating Interneurons"))
  27. # Normalize, etc REGRESS OUT SAMPLE AND NFEAT_RNA
  28. INs <- NormalizeData(INs)
  29. INs <- ScaleData(INs, vars.to.regress = c("Sample", "nFeature_RNA"))
  30. INs <- FindVariableFeatures(INs, selection.method = "vst", nfeatures = 2000)
  31. INs <- RunPCA(INs, features = VariableFeatures(object = INs))
  32. INs <- FindNeighbors(INs, dims=1:50, reduction = "integrated.rpca")
  33. INs <- FindClusters(INs, resolution = 4, reduction.type = "integrated.rpca")
  34. INs <- RunUMAP(INs, dims = 1:50, reduction = "integrated.rpca")
  36. # Look at basic parameters
  37. DimPlot(INs, reduction = "umap", group.by = "neural_cell_types")
  38. DimPlot(INs, reduction = "umap", group.by = "neural_cell_types", split.by = "Condition")
  39. DimPlot(INs, reduction = "umap", group.by = "seurat_clusters", label = TRUE)
  41. # Re load
  42. INs <- LoadSeuratRds(file = "INs.rds")
  43. #neural <- LoadSeuratRds(file = "neural_final.rds")
  44. ```
  46. Looking at some markers to identify cardinal neuron types
  47. ```{r}
  48. # dI6
  49. FeaturePlot(INs, features = c("lhx1", "dmrt3", "lbx1", "dbx2"))
  51. # V0
  52. FeaturePlot(INs, features = c("evx1", "lhx1", "nrn1", "dbx1", "cbln2"))
  54. # V1
  55. FeaturePlot(INs, features = c("pax2", "lhx1", "pnoc", "sp9", "foxd3", "en1", "gbx2", "otp"))
  57. # KA'
  58. FeaturePlot(INs, features = c("pkd1l2", "th", "tal1", "foxa2", "nkx6-2", "ptchd", "chgb", "sox1"))
  60. # KA''
  61. FeaturePlot(INs, features = c("pkd1l2", "cdx4", "cfap210", "sall2", "daw1", "tal1", "sox1", "kctd8"))
  63. # V2A
  64. FeaturePlot(INs, features = c("vsx2", "nrn1", "nkx6-2", "lhx3", "sox21", "sox14", "prdm8", "chat"))
  66. # V2B
  67. FeaturePlot(INs, features = c("gata2", "gata3" ,"lhx1", "sox1", "tal1", "slc6a5"))
  69. # mystery clusters
  70. FeaturePlot(INs, features = c("megf11", "pax2", "foxp2", "slc6a5"))
  71. FeaturePlot(INs, features = c("sim1"))
  73. ```
  75. Labelling clusters
  76. ```{r}
  77. DimPlot(INs, group.by = "seurat_clusters")
  79. [email hidden]$INs_names <- as.character([email hidden]$seurat_clusters)
  80. [email hidden]$INs_names =
  81. dplyr::recode([email hidden]$INs_names,
  82. "0" = "dI6 Glycinergic Interneurons",
  83. "1" = "V0 Glutamatergic Interneurons",
  84. "2" = "V0 Glutamatergic Interneurons",
  85. "3" = "V1 GABAergic/Glycinergic Interneurons",
  86. "4" = "V2a/b Glutamatergic/Cholinergic Interneurons",
  87. "5" = "V0 Glutamatergic Interneurons",
  88. "6" = "KA'' GABAergic Interneurons",
  89. "7" = "Differentiating Interneurons",
  90. "8" = "Intermediate KA Interneurons",
  91. "9" = "Differentiating Interneurons",
  92. "10" = "KA'' GABAergic Interneurons",
  93. "11" = "V2b/s GABAergic/Glycinergic Interneurons",
  94. "12" = "V0 Glutamatergic Interneurons",
  95. "13" = "Differentiating Interneurons",
  96. "14" = "KA' GABAergic/Dopaminergic Interneurons",
  97. "15" = "KA'' GABAergic Interneurons",
  98. "16" = "V0 Glutamatergic Interneurons",
  99. "17" = "V0 Glutamatergic Interneurons",
  100. "18" = "V2a Glutamatergic/Cholinergic Interneurons",
  101. "19" = "KA'' GABAergic Interneurons",
  102. "20" = "unknown Glycinergic Interneurons",
  103. "21" = "Differentiating Interneurons",
  104. "22" = "V2b GABAergic/Glycinergic Interneurons",
  105. "23" = "V0 Glutamatergic Interneurons")
  107. [email hidden]$INs_names <- factor([email hidden]$INs_names, levels = c(
  108. "Differentiating Interneurons",
  109. "V0 Glutamatergic Interneurons",
  110. "V1 GABAergic/Glycinergic Interneurons",
  111. "unknown Glycinergic Interneurons",
  112. "dI6 Glycinergic Interneurons",
  113. "V2a Glutamatergic/Cholinergic Interneurons",
  114. "V2b GABAergic/Glycinergic Interneurons",
  115. "KA'' GABAergic Interneurons",
  116. "Intermediate KA Interneurons",
  117. "KA' GABAergic/Dopaminergic Interneurons"))
  119. DimPlot(INs, group.by = "INs_names")
  120. DimPlot(INs, group.by = "INs_names", split.by = "Sample")
  121. ```
  123. Some of the mystery cells between dI6 and V1 won't cluster, so let's select them instead
  124. ```{r}
  125. unique([email hidden]$INs_names)
  126. barcodes <- xSelectCells::xSelectCells(INs)
  127. INs$cells_of_interest <- ifelse(rownames([email hidden]) %in% barcodes, "yes", "not")
  128. [email hidden] <- [email hidden] %>% mutate(info=paste(INs_names, cells_of_interest, sep = "_"))
  129. [email hidden]$INs_names =
  130. dplyr::recode([email hidden]$info,
  131. "KA'' GABAergic Interneurons_not" = "KA'' GABAergic Interneurons",
  132. "V1 GABAergic/Glycinergic Interneurons_not" = "V1 GABAergic/Glycinergic Interneurons",
  133. "V0 Glutamatergic Interneurons_not" = "V0 Glutamatergic Interneurons",
  134. "V2a Glutamatergic/Cholinergic Interneurons_not" = "V2a Glutamatergic/Cholinergic Interneurons",
  135. "dI6 Glycinergic Interneurons_not" = "dI6 Glycinergic Interneurons",
  136. "V2b GABAergic/Glycinergic Interneurons_not" = "V2b GABAergic/Glycinergic Interneurons",
  137. "KA' GABAergic/Dopaminergic Interneurons_not" = "KA' GABAergic/Dopaminergic Interneurons",
  138. "Intermediate KA Interneurons_not" = "Intermediate KA Interneurons",
  139. "unknown Glycinergic Interneurons_not" = "unknown Glycinergic Interneurons",
  140. "unknown Glycinergic Interneurons_yes" = "unknown Glycinergic Interneurons",
  141. "V1 GABAergic/Glycinergic Interneurons_yes" = "unknown Glycinergic Interneurons",
  142. "dI6 Glycinergic Interneurons_yes" = "unknown Glycinergic Interneurons",
  143. "Differentiating Interneurons_not" = "Differentiating Interneurons")
  145. unique([email hidden]$INs_names)
  146. DimPlot(INs, group.by = "INs_names")
  148. [email hidden]$INs_names <- factor([email hidden]$INs_names, levels = c(
  149. "Differentiating Interneurons",
  150. "dI6 Glycinergic Interneurons",
  151. "Regenerating V0 Glutamatergic Interneurons",
  152. "V0 Glutamatergic Interneurons",
  153. "V1 GABAergic/Glycinergic Interneurons",
  154. "V2a Glutamatergic/Cholinergic Interneurons",
  155. "Regenerating V2a Glutamatergic/Cholinergic Interneurons",
  156. "V2b GABAergic/Glycinergic Interneurons",
  157. "KA' GABAergic/Dopaminergic Interneurons",
  158. "KA'' GABAergic Interneurons",
  159. "Intermediate KA Interneurons",
  160. "unknown Glycinergic Interneurons"))
  162. saveRDS(INs, file = "INs.rds")
  163. ```
  165. Double check markers
  166. ```{r}
  167. markers <- FindAllMarkers(INs, only.pos = TRUE, group.by = "INs_names")
  168. markers %>%
  169. group_by(cluster) %>%
  170. dplyr::filter(avg_log2FC > 1) %>%
  171. slice_head(n = 3) %>%
  172. ungroup() -> top3
  173. ```
  175. Figures 2D, E
  176. ```{r}
  177. INspalette <- c("KA'' GABAergic Interneurons" = "steelblue1",
  178. "Differentiating Interneurons" = "grey",
  179. "V1 GABAergic/Glycinergic Interneurons" = "mediumturquoise",
  180. "V0 Glutamatergic Interneurons" = "coral1",
  181. "Regenerating V0 Glutamatergic Interneurons" = "coral1",
  182. "dI6 Glycinergic Interneurons" = "#FFD62B",
  183. "V2a Glutamatergic/Cholinergic Interneurons" = "palevioletred1",
  184. "Regenerating V2a Glutamatergic/Cholinergic Interneurons" = "palevioletred1",
  185. "V2b GABAergic/Glycinergic Interneurons" = "olivedrab3",
  186. "KA' GABAergic/Dopaminergic Interneurons" = "dodgerblue4",
  187. "Intermediate KA Interneurons" = "dodgerblue3",
  188. "unknown Glycinergic Interneurons" = "sienna")
  190. # make a heatmap for markers
  191. DoHeatmap(INs, features = top3$gene, group.by = "INs_names", group.colors = INspalette, size = 3, draw.lines = FALSE) + scale_fill_gradientn(colors = c("grey", "white", "navyblue")) + theme(text=element_text(size=7), plot.margin = margin(0.5, 0.75, 0, 0, "in"), legend.position="none")
  192. ggsave("2D.png", width = 4.5, height = 4.5, units = c("in"), dpi = 300)
  194. # make a umap
  195. DimPlot(INs, group.by = "INs_names", label = FALSE, label.size = 3, repel = TRUE, cols = INspalette, alpha = 1) + theme(axis.ticks=element_blank(), axis.text=element_blank(), plot.title=element_blank(), axis.line = element_blank(), axis.title = element_blank()) +
  196. scale_y_continuous(limits = c(-8, 9)) + scale_x_continuous(limits = c(-17, 10)) +
  197. NoLegend() +
  198. annotate("segment",
  199. x = -17, xend = -17 + c(3, 0),
  200. y = -8, yend = -8 + c(0, 3),
  201. arrow = arrow(type = "closed", length = unit(10, 'pt')))
  203. ggsave("2E.png", width = 5.5, height = 3.5, units = c("in"), dpi = 300)
  204. ```
  206. #Figure 2F KA neurons
  207. ```{r}
  208. KAs <- subset(x = INs, subset = ([email hidden]$INs_names == "KA'' GABAergic Interneurons" |
  209. [email hidden]$INs_names == "KA' GABAergic/Dopaminergic Interneurons" |
  210. [email hidden]$INs_names == "Intermediate KA Interneurons"))
  211. DotPlot_scCustom(KAs,
  212. features = c("gad2", "pkd1l2", "tal1", "nkx6-2", "foxa2", "ptchd", "th", "slc6a2", "slc6a4", "tph2", "slc6a3"),
  213. group.by = "INs_names",
  214. colors_use = viridis_light_high) +
  215. coord_flip()
  217. ggsave("2F.png", width = 3.5, height = 3.5, units = c("in"), dpi = 300)
  218. ```

Interneurons_Fig2D,2E.Rmd at commit e121c96, no license · at the source

Overview

Authors: Avery Angell Swearer1,2, Samuel B. Perkowski2,3, Iba Husain2, Thiago A. Figueiredo2, Morgan E. McCartney2,4, Andrea E. Wills1,2
ORCID iDs: Avery Angell Swearer, Samuel B. Perkowski, Andrea E. Wills
  1. Program in Molecular and Cellular Biology, University of Washington,Seattle, WA USA
  2. Department of Biochemistry, University of Washington School of Medicine,Seattle, WA USA
  3. Committee on Development, Regeneration, and Stem Cell Biology, University of Chicago,Chicago, IL USA
  4. Program in Molecular and Cell Biology, University of California,Berkeley, CA USA
Institutions: University of Washington (United States); University of Chicago (United States); University of California, Berkeley (United States)
Journal: Nature communications, volume 17, issue 1, article 8866
Dates: received 3 November 2025; accepted 26 June 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-75273-5 · PMID 42477343 · PMCID PMC13500724 · OpenAlex W4416221656
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.10.687703
Status: code verified
Categories: other (organism), other condition (population), developmental (subfield)
Methods: Evoked potentials
Keywords: Developmental neurogenesis, Regeneration
MeSH: Neurons*, Spinal Cord*, Spinal Cord Injuries*, Spinal Cord Regeneration*, Animals, Cell Differentiation, Neurogenesis, Single-Cell Analysis, Xenopus (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS (R01NS099124)
Citations: cited by 1 paper (Europe PMC); 116 references in the paper
Research resources: RRID:NXR_1, RRID:SCR_013731

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

angellswearera/scRNAseq_xenopus_regen_wills

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e121c96bec8aa44c78068cdcfb122a7b328a2e74, 4 June 2026
Languages: R (19)
Size: 21 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 19 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (19 files), tidyverse (18 files), ggplot2 (15 files), car (3 files), clusterProfiler (3 files), rstatix (3 files), Monocle 3 (2 files), circlize (1 file), patchwork (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
20 files
At the source: github.com/angellswearera/scRNAseq_xenopus_regen_wills

Zenodo 20549290

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: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (19 files), tidyverse (18 files), ggplot2 (15 files), car (3 files), clusterProfiler (3 files), rstatix (3 files), Monocle 3 (2 files), circlize (1 file), patchwork (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
20 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-75273-5.

Tracing map

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

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-75273-5.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 9 MeSH terms, 1 funder, 114 references, 2 RRIDs.

Cite

This paper

Angell Swearer, A., Perkowski, S. B., Husain, I., Figueiredo, T. A., McCartney, M. E., & Wills, A. E. (2026). Spinal cord regeneration deploys cell-type specific developmental and non-developmental strategies to restore neuron diversity. Nature communications, 17(1), 8866. https://doi.org/10.1038/s41467-026-75273-5

BibTeX

@article{angellswearer2026spinal,
author = {Angell Swearer, Avery and Perkowski, Samuel B. and Husain, Iba and Figueiredo, Thiago A. and McCartney, Morgan E. and Wills, Andrea E.},
title = {{Spinal cord regeneration deploys cell-type specific developmental and non-developmental strategies to restore neuron diversity}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {8866},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75273-5},
url = {https://doi.org/10.1038/s41467-026-75273-5},
pmid = {42477343},
pmcid = {PMC13500724}
}

RIS

TY - JOUR
AU - Angell Swearer, Avery
AU - Perkowski, Samuel B.
AU - Husain, Iba
AU - Figueiredo, Thiago A.
AU - McCartney, Morgan E.
AU - Wills, Andrea E.
TI - Spinal cord regeneration deploys cell-type specific developmental and non-developmental strategies to restore neuron diversity
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/20
VL - 17
IS - 1
SP - 8866
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75273-5
UR - https://doi.org/10.1038/s41467-026-75273-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-75273-5",
"type": "article-journal",
"title": "Spinal cord regeneration deploys cell-type specific developmental and non-developmental strategies to restore neuron diversity",
"container-title": "Nature communications",
"author": [
{
"family": "Angell Swearer",
"given": "Avery"
},
{
"family": "Perkowski",
"given": "Samuel B."
},
{
"family": "Husain",
"given": "Iba"
},
{
"family": "Figueiredo",
"given": "Thiago A."
},
{
"family": "McCartney",
"given": "Morgan E."
},
{
"family": "Wills",
"given": "Andrea E."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8866",
"DOI": "10.1038/s41467-026-75273-5",
"PMID": "42477343",
"PMCID": "PMC13500724",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-75273-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
20
]
]
}
}

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Journal: Cell reports. Medicine
In common: Monocle 3, car, circlize, 6 other tools, other condition, 2 references
[2] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: Monocle 3, car, circlize, 6 other tools, other condition
[3] doi:10.1371/journal.pcbi.1014573 [code]
Cell-type-specific m1A dynamics are associated with microglial phenotypic transition and neuronal metabolic adaptation during spinal cord injury.
Journal: PLoS computational biology
In common: Monocle 3, rstatix, clusterProfiler, 5 other tools, other condition, 1 reference
[4] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: Monocle 3, rstatix, circlize, 6 other tools
[5] 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: Monocle 3, circlize, clusterProfiler, 5 other tools, developmental, 1 reference
[6] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Monocle 3, circlize, clusterProfiler, 5 other tools, 1 reference
[7] doi:10.1126/sciadv.aeb8034 [code]
Deciphering the ancestral mechanisms of neural regeneration through single-cell analyses of jellyfish rhopalium repair.
Journal: Science advances
In common: Monocle 3, Seurat, tidyverse, other, 5 references
[8] doi:10.1186/s13059-026-04177-w [code]
Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: Monocle 3, rstatix, circlize, 4 other tools, 2 references
[9] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: rstatix, circlize, clusterProfiler, 5 other tools, other
[10] doi:10.1038/s41467-026-69944-6 [code]
Multi-modal dissection of cell-type specific TDP-43 pathology in the motor cortex.
Journal: Nature communications
In common: Monocle 3, circlize, clusterProfiler, 5 other tools, other condition

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