OSCR

CSF1R+ macrophage and osteoclast depletion impairs neural crest proliferation and craniofacial morphogenesis.

Code ↔ Paper

2 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 2 matches
  1. [1] § MATERIALS AND METHODS › Single-cell RNA sequencing data analysis ↔ E12_5 and E13_5 craniofacial mesenchyme Csf1r cell expression.R, lines 44–98 · score 0.84 · quality cells, DimPlot, FindClusters, FindNeighbors, variable features, Gene
  2. [2] § RESULTS › Gestational exposure to PLX5622 alters cytokine and chemokine secretion from CSF1R+ cells ↔ E12_5 and E13_5 craniofacial mesenchyme Csf1r cell expression.R, lines 100–153 · score 0.56 · expressing cells, Lyve1, Ptprc, Tnf, Ctsk, Ccl12

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 · 153 lines · 7.4 KB · no license · 2 matches

  1. #https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
  3. #Load packages
  4. library(dplyr)
  5. library(Seurat)
  6. library(patchwork)
  7. library(clustree)
  9. #Load datasets
  10. angelozzi_e12.5.data <- Read10X(data.dir = "~/Angelozzi et al 10.1016j.celrep.2022.111045/E12.5")
  11. angelozzi_e13.5.data <- Read10X(data.dir = "~/Angelozzi et al 10.1016j.celrep.2022.111045/E13.5")
  12. rajderkar <- readRDS(file = "~/Rajderkar et al 10.1038s41467-024-46396-4/GSE235753_2021-05-23_seurat.face.rds")
  14. #Create Seurat object from counts
  15. angelozzi_e12.5 <- CreateSeuratObject(counts = angelozzi_e12.5.data, project = "angelozzi_e12.5", min.cells = 3, min.features = 200)
  16. angelozzi_e13.5 <- CreateSeuratObject(counts = angelozzi_e13.5.data, project = "angelozzi_e13.5", min.cells = 3, min.features = 200)
  18. #Check metadata labels for RDS file
  19. head([email hidden], 1)
  21. #Find unique values in "stage" metadata
  22. unique([email hidden]$stage)
  24. #Subset E12.5 untransfected cells (no cells found for E13.5)
  25. rajderkar_e12.5 <- subset(rajderkar, subset = stage == "e12.5")
  26. rajderkar_e12.5 <- subset(rajderkar, subset = element_transfected == "none")
  28. #Reset previous UMAP and analyses from RDS file that will not be used
  29. DefaultAssay(object = rajderkar_e12.5) <- "RNA"
  30. rajderkar_e12.5[["SCT"]] <- NULL
  31. rajderkar_e12.5[["HTO"]] <- NULL
  32. rajderkar_e12.5[["integrated"]] <- NULL
  33. DietSeurat(rajderkar_e12.5,
  34. dimreducs = NULL,
  35. graphs = NULL,
  36. misc = TRUE)
  37. rajderkar_e12.5[["RNA"]]$data <- NULL
  38. Idents(rajderkar_e12.5) <- "rajderkar_e12.5"
  40. #Calculate mtDNA percent for raw data
  41. angelozzi_e12.5[["percent.mt"]] <- PercentageFeatureSet(angelozzi_e12.5, pattern = "^mt-")
  42. angelozzi_e13.5[["percent.mt"]] <- PercentageFeatureSet(angelozzi_e13.5, pattern = "^mt-")
  44. #Plot Feature Scatter for counts, features, mtDNA to check QC
  45. plot1 <- FeatureScatter(angelozzi_e12.5, feature1 = "nCount_RNA", feature2 = "percent.mt")
  46. plot2 <- FeatureScatter(angelozzi_e12.5, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
  47. plot1 + plot2
  49. plot1 <- FeatureScatter(angelozzi_e13.5, feature1 = "nCount_RNA", feature2 = "percent.mt")
  50. plot2 <- FeatureScatter(angelozzi_e13.5, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
  51. plot1 + plot2
  53. #Filter out poor quality cells based on Feature Scatter
  54. angelozzi_e12.5 <- subset(angelozzi_e12.5, subset = nFeature_RNA < 6000 & nCount_RNA < 20000 & percent.mt < 20)
  55. angelozzi_e13.5 <- subset(angelozzi_e13.5, subset = nFeature_RNA < 7000 & nCount_RNA < 35000 & percent.mt < 15)
  56. rajderkar_e12.5 <- subset(rajderkar_e12.5, subset = nFeature_RNA > 1500 & nFeature_RNA < 7500)
  58. #Normalize datasets
  59. angelozzi_e12.5 <- NormalizeData(angelozzi_e12.5)
  60. angelozzi_e13.5 <- NormalizeData(angelozzi_e13.5)
  61. rajderkar_e12.5 <- NormalizeData(rajderkar_e12.5)
  63. #Subset out Csf1r cells
  64. angelozzi_e12.5 <- subset(angelozzi_e12.5, subset = Csf1r > 0)
  65. angelozzi_e13.5 <- subset(angelozzi_e13.5, subset = Csf1r > 0)
  66. rajderkar_e12.5 <- subset(rajderkar_e12.5, subset = Csf1r > 0)
  68. #Merge datasets
  69. e12.5_13.5_Csf1r <- merge(angelozzi_e12.5, y = c(angelozzi_e13.5, rajderkar_e12.5), add.cell.ids = c("angelozzi_e12.5", "angelozzi_e13.5", "rajderkar_e12.5"), project = "e12.5_13.5_Csf1r")
  71. #Normalize data again
  72. e12.5_13.5_Csf1r <- NormalizeData(e12.5_13.5_Csf1r)
  74. #Scale data
  75. all.genes <- rownames(e12.5_13.5_Csf1r)
  76. e12.5_13.5_Csf1r <- ScaleData(e12.5_13.5_Csf1r, features = all.genes)
  78. #Find variable features, run PCA and elbow plot
  79. e12.5_13.5_Csf1r <- FindVariableFeatures(e12.5_13.5_Csf1r, selection.method = "vst", nfeatures = 2000)
  80. e12.5_13.5_Csf1r <- RunPCA(e12.5_13.5_Csf1r, features = VariableFeatures(object = e12.5_13.5_Csf1r))
  81. ElbowPlot(e12.5_13.5_Csf1r)
  83. #Find neighbors, test UMAP clustering, and use clustree
  84. e12.5_13.5_Csf1r <- FindNeighbors(e12.5_13.5_Csf1r, dims = 1:15)
  85. # Clustering with a few different resolutions
  86. for (res in c(0.4, 0.5, 0.6, 0.7, 0.8)) {
  87. e12.5_13.5_Csf1r <- FindClusters(e12.5_13.5_Csf1r, graph.name = "RNA_snn", resolution = res)}
  88. # Visualize different resolutions
  89. e12.5_13.5_Csf1r <- RunUMAP(e12.5_13.5_Csf1r, dims = 1:15)
  90. wrap_plots(
  91. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.4", label=T) + ggtitle("res_0.4"),
  92. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.5", label=T) + ggtitle("res_0.5"),
  93. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.6", label=T) + ggtitle("res_0.6"),
  94. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.7", label=T) + ggtitle("res_0.7"),
  95. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.8", label=T) + ggtitle("res_0.8"),
  96. ncol = 3
  97. )
  98. clustree([email hidden], prefix = "RNA_snn_res.")
  100. #Select resolution
  101. e12.5_13.5_Csf1r <- FindClusters(e12.5_13.5_Csf1r, resolution = 0.6)
  102. DimPlot(e12.5_13.5_Csf1r, reduction = "umap",label=T)
  104. #Join layers
  105. e12.5_13.5_Csf1r[["RNA"]] <- JoinLayers(e12.5_13.5_Csf1r[["RNA"]])
  107. #Find markers for every cluster compared to all remaining cells, top 30
  108. e12.5_13.5_Csf1r.markers <- FindAllMarkers(e12.5_13.5_Csf1r, only.pos = TRUE, min.pct=0.25)
  109. e12.5_13.5_Csf1r.markers %>%
  110. group_by(cluster) %>%
  111. dplyr::filter(avg_log2FC > 1)
  112. max.clusters <- max(as.numeric(as.character(e12.5_13.5_Csf1r.markers$cluster)))
  113. for(x in 0:max.clusters) {
  114. print(head(e12.5_13.5_Csf1r.markers[e12.5_13.5_Csf1r.markers$cluster == x,],30))}
  116. #Subset out hemoglobin-expressing cells that are likely contamination
  117. e12.5_13.5_Csf1r <- subset(e12.5_13.5_Csf1r, subset = RNA_snn_res.0.5 == 0, invert = TRUE)
  119. #Normalize data again
  120. e12.5_13.5_Csf1r <- NormalizeData(e12.5_13.5_Csf1r)
  122. #Scale data
  123. all.genes <- rownames(e12.5_13.5_Csf1r)
  124. e12.5_13.5_Csf1r <- ScaleData(e12.5_13.5_Csf1r, features = all.genes)
  126. #Find variable features, run PCA and elbow plot
  127. e12.5_13.5_Csf1r <- FindVariableFeatures(e12.5_13.5_Csf1r, selection.method = "vst", nfeatures = 2000)
  128. e12.5_13.5_Csf1r <- RunPCA(e12.5_13.5_Csf1r, features = VariableFeatures(object = e12.5_13.5_Csf1r))
  129. ElbowPlot(e12.5_13.5_Csf1r)
  131. #Find neighbors, test UMAP clustering, and use clustree
  132. e12.5_13.5_Csf1r <- FindNeighbors(e12.5_13.5_Csf1r, dims = 1:15)
  133. # Clustering with a few different resolutions
  134. for (res in c(0.1, 0.2, 0.3, 0.4, 0.5)) {
  135. e12.5_13.5_Csf1r <- FindClusters(e12.5_13.5_Csf1r, graph.name = "RNA_snn", resolution = res)}
  136. # Visualize different resolutions
  137. e12.5_13.5_Csf1r <- RunUMAP(e12.5_13.5_Csf1r, dims = 1:15)
  138. wrap_plots(
  139. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.1", label=T) + ggtitle("res_0.1"),
  140. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.2", label=T) + ggtitle("res_0.2"),
  141. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.3", label=T) + ggtitle("res_0.3"),
  142. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.4", label=T) + ggtitle("res_0.4"),
  143. DimPlot(e12.5_13.5_Csf1r, reduction = "umap", group.by = "RNA_snn_res.0.5", label=T) + ggtitle("res_0.5"),
  144. ncol = 3)
  145. clustree([email hidden], prefix = "RNA_snn_res.")
  147. #Select resolution
  148. e12.5_13.5_Csf1r <- FindClusters(e12.5_13.5_Csf1r, resolution = 0.2)
  149. e12.5_13.5_Csf1r$seurat_clusters <- as.factor(as.numeric(as.character(e12.5_13.5_Csf1r$seurat_clusters)) + 1)
  150. DimPlot(e12.5_13.5_Csf1r, reduction = "umap",label=T)
  152. #Violin plots
  153. VlnPlot(e12.5_13.5_Csf1r, features = c("Csf1r", "Lyve1", "Ptprc","Ctsk","Ccl2","Ccl3","Ccl4","Ccl12","Cxcl2","Cxcl10","Il16","Tnf"))

E12_5 and E13_5 craniofacial mesenchyme Csf1r cell expression.R at commit 14c46de, no license · at the source

Overview

Authors: Felix Ma1,2, Rose Ru Jing Zhou2,3, Matthew Rosin2,4, Iris Zhou2,3, Sabrina Ownsworth2,3, Rouzbeh Ostadsharif Memar1,2,5, Vincent B. Wong2,6, Jessica M. Rosin2,4
ORCID iDs: Felix Ma, Jessica M. Rosin
  1. Craniofacial Science Graduate Program, Faculty of Dentistry, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
  2. Life Sciences Institute, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
  3. Doctor of Dental Medicine Program, Faculty of Dentistry, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
  4. Department of Oral Biological and Medical Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
  5. Division of Periodontics and Dental Hygiene, Faculty of Dentistry, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
  6. Neuroscience Program, Faculty of Science, McGill University, Montreal, QC H3A 1A1, Canada
Institutions: University of British Columbia (Canada); McGill University (Canada)
Journal: Development (Cambridge, England), volume 153, issue 16, article dev205423
Dates: received 4 December 2025; accepted 3 March 2026; published online 7 May 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1242/dev.205423 · PMID 41891183 · PMCID PMC13200734 · OpenAlex W7141593066
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), developmental (subfield)
Methods: Statistics, Evoked potentials
Keywords: Macrophage, Osteoclast, Neural crest, Embryogenesis, Craniofacial morphogenesis, Colony stimulating factor-1 receptor (CSF1R), Mouse
MeSH: Macrophages*, Morphogenesis*, Neural Crest*, Osteoclasts*, Receptors, Granulocyte-Macrophage Colony-Stimulating Factor*, Skull*, Animals, Cell Differentiation, Cell Proliferation, Female, Male, Mice, Organic Chemicals, Pregnancy, Signal Transduction (* major topic)
Topic: Craniofacial Disorders and Treatments (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Natural Sciences and Engineering Research Council of Canada (579343-2023, RGPIN-2022-03718); University of British Columbia; Michael Smith Health Research BC; Canada Research Chairs
Citations: cited by 3 papers (Europe PMC); 214 references in the paper
Research resources: goat anti-CCL4 RRID:AB_2071055, mouse MF20 RRID:AB_2147781, mouse anti-SOX10 RRID:AB_2195180, exposed to goat anti-PDGFRα RRID:AB_2236897, rabbit anti-cathepsin K RRID:AB_2261274, rabbit anti-SOX2 RRID:AB_2286686, rabbit anti-Sp7 RRID:AB_2892207, goat anti-CCL3 RRID:AB_354492, and/or mouse anti-Ki67 RRID:AB_396287, rabbit anti-active caspase 3 RRID:AB_397274, mouse 2H3 RRID:AB_531793, exposed to sheep anti-CSF1R RRID:AB_884158, CD1 RRID:IMSR_CRL:022, Charles River) and C57BL/6 RRID:IMSR_JAX:005304, RRID:IMSR_JAX:007914, Wnt1Cre mice RRID:IMSR_JAX:022501, 2015) in RStudio RRID:SCR_000432, RRID:SCR_001905, images were adjusted by Fiji v2.16 RRID:SCR_002285, RRID:SCR_002798, 2012) with ImageJ v1.54 RRID:SCR_003070, RRID:SCR_013672, 2025) and/or Adobe Photoshop CC RRID:SCR_014199, RRID:SCR_016341, RRID:SCR_017217, RRID:SCR_027412

Abstract

Despite a wealth of knowledge about the mechanisms underlying craniofacial morphogenesis during gestation, the roles of fetal macrophages and osteoclasts during this process remain less well characterized. Here, we used the pharmacological inhibitor PLX5622 to disrupt colony stimulating factor 1 receptor (CSF1R) signaling, which is essential for macrophage and osteoclast proliferation, differentiation and survival. Prenatal PLX5622 exposure in mouse resulted in ∼50% depletion of CSF1R+ macrophages, with complete loss of osteoclasts. While there were no notable changes in craniofacial nerve or muscle development, prenatal exposure to PLX5622 resulted in skull doming and cranial suture impairments, in addition to disruptions to development of the premaxilla, mandible, ear ossicles, palate and cranial base. In response to PLX5622 exposure, cytokine and chemokine signaling was altered and neural crest proliferation was impaired. Our data also highlight sex- and strain-specific differences in PLX5622 phenotypes and together demonstrate that CSF1R+ macrophages and osteoclasts are essential for craniofacial morphogenesis.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

RosinLabUBC/scRNAseq-code-FM

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 14c46de63ca7298c1a511312a8e69c465717b35e, 23 March 2026
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the text, “Single-cell RNA sequencing data analysis”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: patchwork (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
At the source: github.com/RosinLabUBC/scRNAseq-code-FM

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;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

Datasets cited

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 2, 28 September 2026

  • Publisher: n/a → The Company of Biologists

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 keywords, 15 MeSH terms, 4 funders, 211 references, 26 RRIDs.

Cite

This paper

Ma, F., Zhou, R. R. J., Rosin, M., Zhou, I., Ownsworth, S., Memar, R. O., Wong, V. B., & Rosin, J. M. (2026). CSF1R+ macrophage and osteoclast depletion impairs neural crest proliferation and craniofacial morphogenesis. Development (Cambridge, England), 153(16), dev205423. https://doi.org/10.1242/dev.205423

BibTeX

@article{ma2026csf1r,
author = {Ma, Felix and Zhou, Rose Ru Jing and Rosin, Matthew and Zhou, Iris and Ownsworth, Sabrina and Memar, Rouzbeh Ostadsharif and Wong, Vincent B. and Rosin, Jessica M.},
title = {{CSF1R+ macrophage and osteoclast depletion impairs neural crest proliferation and craniofacial morphogenesis}},
journal = {Development (Cambridge, England)},
year = {2026},
month = may,
volume = {153},
number = {16},
pages = {dev205423},
publisher = {The Company of Biologists},
issn = {0950-1991},
doi = {10.1242/dev.205423},
url = {https://doi.org/10.1242/dev.205423},
pmid = {41891183},
pmcid = {PMC13200734}
}

RIS

TY - JOUR
AU - Ma, Felix
AU - Zhou, Rose Ru Jing
AU - Rosin, Matthew
AU - Zhou, Iris
AU - Ownsworth, Sabrina
AU - Memar, Rouzbeh Ostadsharif
AU - Wong, Vincent B.
AU - Rosin, Jessica M.
TI - CSF1R+ macrophage and osteoclast depletion impairs neural crest proliferation and craniofacial morphogenesis
T2 - Development (Cambridge, England)
J2 - Development
PY - 2026
DA - 2026/05/06
VL - 153
IS - 16
SP - dev205423
SN - 0950-1991
PB - The Company of Biologists
DO - 10.1242/dev.205423
UR - https://doi.org/10.1242/dev.205423
LA - en
ER -

CSL-JSON

{
"id": "10.1242/dev.205423",
"type": "article-journal",
"title": "CSF1R+ macrophage and osteoclast depletion impairs neural crest proliferation and craniofacial morphogenesis",
"container-title": "Development (Cambridge, England)",
"author": [
{
"family": "Ma",
"given": "Felix"
},
{
"family": "Zhou",
"given": "Rose Ru Jing"
},
{
"family": "Rosin",
"given": "Matthew"
},
{
"family": "Zhou",
"given": "Iris"
},
{
"family": "Ownsworth",
"given": "Sabrina"
},
{
"family": "Memar",
"given": "Rouzbeh Ostadsharif"
},
{
"family": "Wong",
"given": "Vincent B."
},
{
"family": "Rosin",
"given": "Jessica M."
}
],
"container-title-short": "Development",
"volume": "153",
"issue": "16",
"page": "dev205423",
"DOI": "10.1242/dev.205423",
"PMID": "41891183",
"PMCID": "PMC13200734",
"ISSN": "0950-1991",
"publisher": "The Company of Biologists",
"URL": "https://doi.org/10.1242/dev.205423",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
6
]
]
}
}

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/s41467-026-74434-w [code]
The alx gene family confers segmental identity to frontonasal cranial neural crest cells.
Journal: Nature communications
In common: Seurat, patchwork, tidyverse, 4 references
[2] 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: Seurat, patchwork, tidyverse, mouse, 4 references
[3] doi:10.1016/j.isci.2026.117228 [code]
Single-nucleus transcriptomics reveals cell type-specific remodeling and epilepsy-associated microglia.
Journal: iScience
In common: Seurat, patchwork, tidyverse, 4 references
[4] doi:10.3389/fncel.2026.1713437 [code]
Single-nucleus transcriptomics identifies cell cycle and synaptic pathway dysregulation during OPC-to-glioma progression.
Journal: Frontiers in cellular neuroscience
In common: Seurat, tidyverse, 5 references
[5] doi:10.1038/s41597-026-07185-4 [code]
A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex.
Journal: Scientific data
In common: Seurat, patchwork, tidyverse, mouse, 3 references
[6] doi:10.1016/j.celrep.2026.117411
Enhancing the specificity of microglia genetic targeting using a CSF1R inhibitor.
Journal: Cell reports
In common: mouse, 4 references
[7] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: Seurat, patchwork, tidyverse, mouse, 2 references
[8] doi:10.1038/s41586-026-10490-y [code]
Lineage and organ signals sequentially build organ intrinsic nervous systems.
Journal: Nature
In common: Seurat, patchwork, tidyverse, developmental, mouse, 2 references
[9] doi:10.1371/journal.pbio.3003931 [code]
A single-cell transcriptomic atlas reveals the emergence of medusa-specific cell states in the scyphozoan Aurelia coerulea.
Journal: PLoS biology
In common: Seurat, patchwork, tidyverse, 3 references
[10] 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: Seurat, patchwork, tidyverse, developmental, 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.