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Early fate diversification of radial glial progenitors during corticogenesis.

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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.

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  1. [1] § MATERIALS AND METHODS › Analysis of the single-cell RNA seq dataset ↔ Varela-Martínez_RNAseq analysis.R, lines 46–86 · score 0.99 · nearest neighbors, FindClusters, FindNeighbors, FindVariableFeatures, RunUMAP, nCount_RNA
  2. [2] § RESULTS › POU3F transcription factors drive IT-PN fate specification ↔ Varela-Martínez_RNAseq analysis.R, lines 89–136 · score 0.68 · Pou3f3, Pou3f1, Pou3f2, UMAP, clustered, genes

Paper

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The authors' code

R · 188 lines · 5.9 KB · CC-BY-4.0 · 2 matches

  1. ################################################################################
  2. # Importación de paquetes
  4. library(dplyr)
  5. library(Seurat)
  6. library(readr)
  7. library(ggplot2)
  8. library(URD)
  9. library(SingleCellExperiment)
  10. library(destiny)
  11. library(tidyverse)
  12. library(dplyr)
  13. library(monocle3)
  14. library(SeuratWrappers)
  16. # Guardo count matrix
  17. matriz <- Read10X(data.dir = '/Users/irene/Desktop/Pruebita/matrix')
  19. # Creo objeto seurat filtrado para muestras que tienen más de 500 genes.
  20. # min.features = 500, en verdad no es necesario, ya que la matriz publicada ya está filtrada
  22. data <- CreateSeuratObject(matriz, project = 'Prueba', min.features = 500)
  24. # Me quedo solo con muestras de estadios E12-E15
  25. data <- subset (data, orig.ident == 'E12' | orig.ident == 'E13' |
  26. orig.ident == 'E14' | orig.ident == 'E15')
  28. # Cargo los metadatos asoaciados a las muestras.
  29. metadatos <- read_tsv('/Users/irene/Desktop/Pruebita/matrix/metaData.tsv')
  31. # Filtro metadatos por estadio (E12- E15)
  32. metadatos <- subset (metadatos, orig_ident == 'E12' | orig_ident == 'E13' |
  33. orig_ident == 'E14' | orig_ident == 'E15')
  38. ### QUALITY CONTROL ### --> LOS DATOS DISPONIBLES YA ESTÁN FILTRADOS POR MEDIDAS DE CALIDAD.
  40. # Porcentaje de counts originados a partir de genes mitocondriales
  41. # Al estar normalizada la matriz de recuentos, me salen distintos porcentajes mitocondriales.
  42. # data[["percent.mt"]] <- PercentageFeatureSet(data, pattern = "^mt-")
  43. #data <- subset(data, subset = nFeature_RNA > 500 & percent.mt < 7.5)
  46. ### NORMALIZACIÓN ###
  47. # Intuyo que los datos disponibles (la matriz de recuentos) ya está normalizada, por lo que da igual realizar este paso
  49. data <- NormalizeData(data, normalization.method = "LogNormalize", scale.factor = 10000)
  51. ### FEATURE SELECTION ###
  53. # Selección de las 3000 features más variables
  54. data <- FindVariableFeatures(data, selection.method = 'vst', nfeatures = 3000)
  57. ### ESCALADO Y REGRESIÓN ###
  58. # En el paper aplican regresión a anotaciones más: cc.diference y percent.mito.
  60. data <- ScaleData(data, vars.to.regress = c('nCount_RNA', 'nFeature_RNA'), do.centre = TRUE, do.scale = TRUE)
  62. #SIN ESCALADO:
  63. #data <- ScaleData(data, vars.to.regress = c('nCount_RNA', 'nFeature_RNA'), do.centre = FALSE, do.scale = FALSE)
  66. ### REDUCCIÓN POR PCA (50 principal components) ###
  67. data <- RunPCA (data, npcs = 50)
  69. # Contruir el grafo de k-nearest-neighbours en el espacio PCA
  70. data <- FindNeighbors(data, reduction = "pca", dims = 1:50)
  72. # Establezco la semilla para reproducibilidad
  73. set.seed(1234) # Puedes usar cualquier número como semilla
  75. # clustering mediante el algoritmo de Louvain (optimización de la modularidad)
  76. data <- FindClusters(data, resolution = 0.7)
  78. # UMAP
  79. data <- RunUMAP(data, dims = 1:15)
  81. ### PLOTS ###
  83. # Genes más variables
  84. hvgplot <-VariableFeaturePlot(data)
  85. hvgtop10 <- head(VariableFeatures(data), 10)
  86. hvgtop10plot<- LabelPoints(plot = hvgplot, points = hvgtop10, repel = TRUE)
  89. #PCA plot
  90. plotPCA <- DimPlot( data, reduction = 'pca')
  93. # Añado las anotaciones de estadio y tipo celular para plotear sobre el UMAP
  94. metadatos$orig_ident -> data[['orig_ident']]
  95. metadatos$New_cellType -> data[['New_cellType']]
  97. DimPlot(data, group.by = "orig.ident", dims = c(2,1), label = T )
  98. DimPlot(data, group.by = "New_cellType", dims = c(2,1), label = T )
  99. DimPlot(data, group.by = "seurat_clusters", dims = c(2,1), label = T )
  101. gene_to_plot <- "Pou3f1"
  102. feature_plot <- FeaturePlot(data, features = gene_to_plot, dims = c(2,1), label = T )
  103. feature_plot
  105. gene_to_plot <- "Pou3f2"
  106. feature_plot <- FeaturePlot(data, features = gene_to_plot, dims = c(2,1), label = T )
  107. feature_plot
  109. gene_to_plot <- "Pou3f3"
  110. feature_plot <- FeaturePlot(data, features = gene_to_plot, dims = c(2,1), label = T )
  111. feature_plot
  114. tibble(
  115. cluster = data$seurat_clusters,
  116. cell_type = data$New_cellType
  117. ) %>%
  118. group_by(cluster,cell_type) %>%
  119. dplyr::count() %>%
  120. group_by(cluster) %>%
  121. mutate(
  122. percent=(100*n)/sum(n)
  123. ) %>%
  124. ungroup() %>%
  125. mutate(
  126. cluster=paste("Cluster",cluster)
  127. ) %>%
  128. ggplot(aes(x="",y=percent, fill=cell_type)) +
  129. geom_col(width=1) +
  130. coord_polar("y", start=0) +
  131. facet_wrap(vars(cluster)) +
  132. theme(axis.text.x=element_blank()) +
  133. xlab(NULL) +
  134. ylab(NULL) -> plot
  136. plot
  140. # Sacar GENES MARCADORES de cada uno de los clusters mediante Wilcoxon Rank Sum con Bonferroni
  141. # en genes que estén presentes en un mínimo del 25% de células
  142. #con un p-valor ajustado < 0.05 y al menos 0.25-fold difference
  144. data.markers <- FindAllMarkers(data, only.pos = TRUE, min.pct = 0.25,
  145. logfc.threshold = 0.25, return.thresh = 0.05)
  146. data.markers %>%
  147. group_by(cluster) %>%
  148. slice_max(n = 10, order_by = avg_log2FC) -> markers
  151. #Genes marcadores de cada uno de los clusters
  153. subset(markers, cluster == 2) -> markers2
  154. subset(markers, cluster == 0) -> markers0
  155. subset(markers, cluster == 1) -> markers1
  156. subset(markers, cluster == 3) -> markers3
  157. subset(markers, cluster == 4) -> markers4
  158. subset(markers, cluster == 5) -> markers5
  159. subset(markers, cluster == 6) -> markers6
  160. subset(markers, cluster == 7) -> markers7
  161. subset(markers, cluster == 8) -> markers8
  162. subset(markers, cluster == 9) -> markers9
  163. subset(markers, cluster == 10) -> markers10
  164. subset(markers, cluster == 11) -> markers11
  165. subset(markers, cluster == 12) -> markers12
  166. subset(markers, cluster == 13) -> markers13
  167. subset(markers, cluster == 14) -> markers14
  168. subset(markers, cluster == 15) -> markers15
  169. subset(markers, cluster == 16) -> markers16
  170. subset(markers, cluster == 17) -> markers17
  171. subset(markers, cluster == 18) -> markers18
  172. subset(markers, cluster == 19) -> markers19
  173. subset(markers, cluster == 20) -> markers20
  174. subset(markers, cluster == 21) -> markers21
  175. subset(markers, cluster == 22) -> markers22
  176. subset(markers, cluster == 23) -> markers23
  177. subset(markers, cluster == 24) -> markers24
  181. #Guardo el objeto seurat data
  182. saveRDS (data, file = "/bhome/sarapascuale/resultsR/data.rds")

Varela-Martínez_RNAseq analysis.R, under CC-BY-4.0 · at the source

Overview

Authors: Irene Varela-Martínez1, Ana Villalba2, Jorge García-Marqués1, Alfonso Aguilera1, Diogo S. Castro3, Simon Hippenmeyer2, Marta Nieto1
ORCID iDs: Irene Varela-Martínez, Ana Villalba, Jorge García-Marqués, Alfonso Aguilera, Diogo S. Castro, Simon Hippenmeyer, Marta Nieto
  1. Department of Molecular and Cellular Biology, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid 28049, Spain
  2. Institute of Science and Technology Austria (ISTA), Am Campus 1, 3400 Klosterneuburg, Austria
  3. i3S Instituto de Investigação e Inovação em Saúde da Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal
Institutions: Consejo Superior de Investigaciones Científicas (Spain); Centro Nacional de Biotecnología (Spain); Institute of Science and Technology Austria (Austria); i3S - Instituto de Investigação e Inovação em Saúde, Universidade do Porto (Portugal)
Journal: Science advances, volume 12, issue 32, article eadw5487
Dates: received 7 February 2025; accepted 1 July 2026; published online 5 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.adw5487 · PMID 42555737 · PMCID PMC13440428 · OpenAlex W7172541480
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, Machine learning, fMRI & imaging
MeSH: Cell Lineage*, Cerebral Cortex*, Ependymoglial Cells*, Neural Stem Cells*, Neurogenesis*, Neuroglia*, Animals, Cell Differentiation, Gene Expression Regulation, Developmental, Mice, Neurodevelopment, Neurons (* major topic)
Journal subjects: Biomedicine and Life Sciences, Developmental Neuroscience
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación) (PRE-2018-083376, PID2020-112831GB-I00, 10.13039/501100011033); MCIN/AEI /10.13039/501100011033 and by “ERDF A way of making Europe” (PID2023-146322NB-I00); 2023 EMBO scientific exchange (grant 10214)
Citations: not cited yet (Europe PMC); 77 references in the paper

Abstract

Radial glial progenitors (RGPs) generate all projection neurons (PNs) in the cerebral cortex through incompletely understood processes. We combined Mosaic Analysis with Double Markers at embryonic stages (E)12.5 and E13.5 with early postnatal callosal tracing to dissect RGP lineage progression. We find that multipotent RGPs generate all extra-telencephalic (ET) and intra-telencephalic (IT) PNs via parallel sublineages that emerge simultaneously at neurogenesis onset. ET-PN production progresses exclusively via small, self-consuming lineages; IT-PN lineages feature RGPs generating large translaminar outputs. The early emergence of IT-PN–fated RGPs, coinciding with a switch to direct neurogenesis, contributes to the stereotyped population-level progression of the multipotent lineage. We also identify POU3F transcription factors as candidate regulators of IT-PN fate via noncanonical mitotic chromatin binding. The results support a model whereby IT- and ET-PNs arise from an early bifurcation and parallel specification within the multipotent RGP lineage.

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.

Zenodo 14609057

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 3 files, 3 scripts
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (3 files), tidyverse (2 files), Monocle 3 (1 file), Seurat (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source:

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;
  • 3 scripts, 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

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Source data underlying all figures (including the clonal atlas) are provided in the Supplementary Materials. RNA-seq analysis code has been deposited in Zenodo (DOI: 10.5281/zenodo.14609057 (http://dx.doi.org/10.5281/zenodo.14609057)). This study did not generate new materials.

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, 7 authors, 12 MeSH terms, 3 funders, 77 references.

Cite

This paper

Varela-Martínez, I., Villalba, A., García-Marqués, J., Aguilera, A., Castro, D. S., Hippenmeyer, S., & Nieto, M. (2026). Early fate diversification of radial glial progenitors during corticogenesis. Science advances, 12(32), eadw5487. https://doi.org/10.1126/sciadv.adw5487

BibTeX

@article{varelamartinez2026early,
author = {Varela-Martínez, Irene and Villalba, Ana and García-Marqués, Jorge and Aguilera, Alfonso and Castro, Diogo S. and Hippenmeyer, Simon and Nieto, Marta},
title = {{Early fate diversification of radial glial progenitors during corticogenesis}},
journal = {Science advances},
year = {2026},
month = aug,
volume = {12},
number = {32},
pages = {eadw5487},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.adw5487},
url = {https://doi.org/10.1126/sciadv.adw5487},
pmid = {42555737},
pmcid = {PMC13440428}
}

RIS

TY - JOUR
AU - Varela-Martínez, Irene
AU - Villalba, Ana
AU - García-Marqués, Jorge
AU - Aguilera, Alfonso
AU - Castro, Diogo S.
AU - Hippenmeyer, Simon
AU - Nieto, Marta
TI - Early fate diversification of radial glial progenitors during corticogenesis
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/08/05
VL - 12
IS - 32
SP - eadw5487
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.adw5487
UR - https://doi.org/10.1126/sciadv.adw5487
LA - en
ER -

CSL-JSON

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