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Modeling Friedreich's ataxia with Bergmann glia-enriched human cerebellar organoids.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Time-course transcriptomic analysis of hCBOs from healthy donors and patients with FRDA ↔ bulk_rna_seq/ISB038/scripts/ISB038_DESeq_and_heatmaps.R, lines 30–110 · score 0.93 · POU5F1, PTF1A, CBLN1, GAD1, GRIN1, LHX1
  2. [2] § Methods › RNA bulk-sequencing analysis ↔ bulk_rna_seq/ISB038/quarto/SCTL_ISB038_DE.qmd, lines 157–239 · score 0.64 · lfcShrink, DESeq2, Bulk RNA Seq, genes, heatmaps
  3. [3] § Methods › RNA bulk-sequencing analysis ↔ bulk_rna_seq/ISB038/quarto/SCTL_ISB038_DE_files/libs/bootstrap/bootstrap.min.js, the whole file · a weak match · score 0.56 · Bulk RNA Seq, API, trimmed, ratios, sub, components

Paper

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The authors' code

R · 271 lines · 8 KB · MIT · 1 match

  1. library(DESeq2)
  2. library(data.table)
  3. library(RColorBrewer)
  4. library(stringr)
  5. library(pheatmap)
  6. library(apeglm)
  7. library(ggsci)
  8. library(dplyr)
  9. library(xfun)
  10. library(ComplexHeatmap)
  11. library(circlize)
  13. ### SETUP ###
  15. # Project name here:
  16. project_name <- "ISB038"
  18. # Set the output directory (to hold the files created by this script)
  19. output_dir <- file.path('')
  21. # input file from partek for all samples:
  22. combined_counts <- ".../ISB038/data/20250514_heatmap_data/Partek_ISB038-redo_Differential_analysis_filter_Ensembl_List_isb038_2 (1)/counts.txt"
  23. ### Heatmap code ###
  25. ## Setup z_score function for use in building heatmaps
  26. cal_z_score <- function(x) {
  27. (x - mean(x)) / sd(x)
  28. }
  30. # set this list of genes after examining the excel files (either directly:)
  31. goi_list <- c( "POU5F1",
  32. "NANOG",
  33. "OTX2",
  34. "GBX2",
  35. "FGF8",
  36. "EN2",
  37. "EN1",
  38. "WNT1",
  39. "NEUROD1",
  40. "ATOH1",
  41. "BARHL1",
  42. "CBLN1",
  43. "ZIC1",
  44. "PAX6",
  45. "SOX9",
  46. "PAX2",
  47. "NFIA",
  48. "MAP2",
  49. "PTF1A",
  50. "LHX1",
  51. "SKOR2",
  52. "S100B",
  53. "GFAP",
  54. "GAD1",
  55. "GRIN1",
  56. "PDE1C",
  57. "RELN",
  58. "CALB2",
  59. "GABRA6"
  60. )
  62. ##################################################
  63. ######## Reading in the files from partek #######
  64. ##################################################
  66. ########### Combined cell-lines:
  67. # Read in file
  68. input_data_2 <- read.csv( combined_counts,
  69. sep="\t")
  70. # give rownames from first column
  71. rownames(input_data_2) <- input_data_2$Feature
  72. # look at dims to get last column
  73. dim(input_data_2)
  74. # remove first column (rather, keep 2nd column through last column)
  75. input_data_2 <- input_data_2[,2:46]
  77. # now make the heatmap:
  78. heatmapValues2 <- input_data_2[goi_list,]
  79. heatmapData2 <- t(apply(heatmapValues2,1,cal_z_score))
  81. first_heatmapData2 <- t(heatmapData2)
  83. # Reorder rows so it's grouped by timepoint first, then GM23913, GM25256, and
  84. # NCRM within each timepoint:
  85. first_heatmapData2 <- first_heatmapData2[c(1,2,3,16,17,18,31,32,33,
  86. 4,5,6,19,20,21,34,35,36,
  87. 7,8,9,22,23,24,37,38,39,
  88. 10,11,12,25,26,27,40,41,42,
  89. 13,14,15,28,29,30,43,44,45),]
  91. col_fun = colorRamp2(c(-1.45,0,5.22), c("blue","white","red"))
  92. # Draw annotation blocks on timepoint first leaving space for cell line blocks:
  93. Heatmap(first_heatmapData2,
  94. name = "z_score",
  95. col = col_fun,
  96. left_annotation = rowAnnotation(
  97. timepoint = anno_block( gp = gpar(fill = c( "blue", "red", "orange", "green3", "purple")),
  98. labels = c( "day 0", "day 14", "day 21", "day 35", "day 60"),
  99. labels_gp = gpar( col = c("white", "white", "black", "white", "white"),
  100. fontsize = 10 )
  101. ),
  102. celline = anno_empty( border = FALSE )
  103. ),
  104. row_split = c(rep("day0",9),rep("day14",9), rep("day21",9), rep("day35", 9), rep("day60", 9)),
  105. row_order = rownames(first_heatmapData2),
  106. row_title = NULL,
  107. show_row_names = FALSE,
  108. column_order = colnames(first_heatmapData2),
  109. column_title = "All cell lines by day"
  110. )
  112. # Function to annotate a single timepoint block with cell line blocks:
  113. cell_line_anno <- function( group ) {
  115. # Select this group
  116. seekViewport( group )
  118. # Get coords of large box
  119. loc1 = deviceLoc( x = unit( 1, "npc" ), y = unit( 1, "npc" ) )
  120. loc2 = deviceLoc( x = unit( 0, "npc" ), y = unit( 0, "npc" ) )
  122. # find small block height
  123. small_height = ( loc2$y - loc1$y ) / 3
  124. small_width = loc2$x - loc1$x
  126. seekViewport( "global")
  128. # first box: (GM23913)
  129. grid.rect( loc1$x,
  130. loc1$y,
  131. width = small_width,
  132. height = small_height,
  133. just = c("left", "bottom"),
  134. gp = gpar( fill = "blue")
  135. )
  136. grid.text( "GM23913",
  137. x = (loc1$x + loc2$x) * 0.5,
  138. y = ( loc1$y + loc2$y) * 0.5 - small_height,
  139. rot = 60,
  140. gp = gpar( col = "white", fontsize = 8)
  141. )
  143. # second box: (GM25256)
  144. grid.rect( loc1$x,
  145. loc1$y + small_height,
  146. width = small_width,
  147. height = small_height,
  148. just = c("left", "bottom"),
  149. gp = gpar( fill = "red")
  150. )
  151. grid.text( "GM25256",
  152. x = (loc1$x + loc2$x) * 0.5,
  153. y = ( loc1$y + loc2$y) * 0.5,
  154. rot = 60,
  155. gp = gpar( col = "white", fontsize = 8)
  156. )
  158. # third box: (NCRM)
  159. grid.rect( loc1$x,
  160. loc1$y + (2 * small_height),
  161. width = small_width,
  162. height = small_height,
  163. just = c("left", "bottom"),
  164. gp = gpar( fill = "orange")
  165. )
  166. grid.text( "NCRM",
  167. x = (loc1$x + loc2$x) * 0.5,
  168. y = ( loc1$y + loc2$y) * 0.5 + small_height,
  169. rot = 60,
  170. gp = gpar( col = "black", fontsize = 8)
  171. )
  172. }
  174. # Draw the annotations
  175. cell_line_anno( "annotation_celline_1" )
  176. cell_line_anno( "annotation_celline_2" )
  177. cell_line_anno( "annotation_celline_3" )
  178. cell_line_anno( "annotation_celline_4" )
  179. cell_line_anno( "annotation_celline_5" )
  181. # EXPORT NOW, then:
  182. dev.off()
  185. ###################################################################
  186. #
  187. # pca plot
  188. #
  189. #
  190. library( plotly )
  191. library( ggfortify )
  192. library( tidyr )
  194. pca_data <- read.csv( combined_counts, sep="\t")
  196. # multistep transformation:
  197. genes <- pca_data$Feature
  198. df.data <- as.data.frame(t(pca_data[,-1]))
  199. colnames(df.data) <- genes
  200. df.data$sample <- gsub("\\.\\d+","",row.names(df.data))
  201. df.data$cell_line <- gsub("_day\\d+\\.\\d+","",row.names(df.data))
  202. df.data$timepoint <- gsub(".*(day\\d+).*","\\1",row.names(df.data))
  204. pca_counts <- df.data[,1:45]
  205. pca_res <- prcomp( pca_counts, scale. = FALSE )
  206. p <- autoplot( pca_res, data = df.data, colour = "timepoint", shape = "cell_line", size = 6)
  207. ggplotly(p)
  208. head(df.data)
  210. ## 3d
  211. prin_comp <- prcomp(pca_counts, rank. = 3 )
  212. components <- prin_comp[["x"]]
  213. components <- data.frame(components)
  214. components$PC2 <- components$PC2
  215. components$PC3 <- components$PC3
  216. components = cbind(components, df.data$timepoint)
  217. components = cbind(components, df.data$cell_line)
  218. tevr <- summary(prin_comp)[["importance"]]['Proportion of Variance',]
  219. tevr <- 100 * sum(tevr)
  221. tit <- paste0("Total Explained Variance = ", tevr )
  222. fig <- plot_ly( components,
  223. x = ~PC1,
  224. y = ~PC2,
  225. z = ~PC3,
  226. color = ~df.data$timepoint,
  227. colors = c("blue","red","orange","green","purple"),
  228. symbol = ~df.data$cell_line,
  229. symbols = c("cross","square","triangle-down")
  230. ) %>%
  231. add_markers( size = 12)
  233. fig <- fig %>%
  234. layout(
  235. title = tit,
  236. scene = list(bgcolor = "grey75")
  237. )
  239. fig
  241. #################### With my normalization from DESeq2
  242. # multistep transformation:
  244. normalized_goi <- normalizedCounts[goi_list,]
  245. df.data <- as.data.frame(t(normalizedCounts))
  246. df.data$sample <- gsub("\\.\\d+","",row.names(df.data))
  247. df.data$cell_line <- gsub("_day.*","",row.names(df.data))
  248. df.data$timepoint <- gsub(".*(day\\d+).*","\\1",row.names(df.data))
  249. dim(df.data)
  251. pca_counts <- df.data[,1:37877]
  252. pca_res <- prcomp( pca_counts, scale. = FALSE )
  253. p <- autoplot( pca_res, data = df.data, colour = "timepoint", shape = "cell_line", size = 6)
  254. ggplotly(p)
  256. # with ellipses:
  257. PC1 <- pca_res$x[,"PC1"]
  258. PC2 <- pca_res$x[,"PC2"]
  259. ggplot( df.data,
  260. aes( PC1,
  261. PC2,
  262. color = timepoint,
  263. shape = cell_line,
  264. group = timepoint
  265. )) +
  266. geom_point( size = 6) +
  267. theme_bw() +
  268. stat_ellipse() +
  269. xlab( "PC1 (42.9%)" ) +
  270. ylab( "PC2 (24.64%)")
  271. ## for 3d, run the 3d code above at this point.

ISB038_DESeq_and_heatmaps.R at commit dc91d1e, under MIT · at the source

Overview

Authors: Seungmi Ryu1, Jason Inman1, Hyenjong Hong1, Vukasin M Jovanovic1, Qiang Chen1, Yeliz Gedik1, Yogita Jethmalani1, Inae Hur1, Majid Harouni1, Ty Voss1, Justin Lack2, Jack Collins2, Pinar Ormanoglu1, Anton Simeonov1, Carlos A Tristan1, Ilyas Singeç1
ORCID iDs: Seungmi Ryu, Jason Inman, Vukasin M Jovanovic, Jack Collins, Anton Simeonov, Carlos A Tristan, Ilyas Singeç
  1. National Center for Advancing Translational Sciences (NCATS), Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), Rockville, MD USA
  2. National Institute of Allergy and Infectious Diseases (NIAID), Collaborative Bioinformatics Resource (NCBR), National Institutes of Health (NIH), Bethesda, MD USA
Institutions: National Institutes of Health (United States); National Center for Advancing Translational Sciences (United States); National Institute of Allergy and Infectious Diseases (United States)
Journal: Communications biology, volume 9, issue 1, article 1235
Dates: received 26 June 2025; accepted 23 April 2026; published online 8 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10180-5 · PMID 42098441 · PMCID PMC13598134 · OpenAlex W4410455320
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.16.654315
Status: code verified
Categories: human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Stem-cell differentiation, Disease model, Developmental neurogenesis, Neural stem cells
MeSH: Cerebellum*, Friedreich Ataxia*, Induced Pluripotent Stem Cells*, Neuroglia*, Organoids*, Cell Differentiation, Cell Movement, Humans, Neurons (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Intramural NIH HHS (ZIC TR000410)
Citations: cited by 1 paper (Europe PMC); 56 references in the paper

Abstract

The human cerebellum is implicated in various neurological and psychiatric diseases, but its complex development and cellular diversity have posed challenges for in vitro modeling. Here, we report the generation of human induced pluripotent stem cell (iPSC)-derived cerebellar organoids (hCBOs) that are characterized by induction of rhombomere 1 (R1) cellular identity and followed by derivation of typical neuronal and glial cell types of the cerebellum. In contrast to forebrain organoids with multiple neural rosettes and inside-out neuronal migration, hCBOs develop a germinal zone on the outermost surface of the organoids with outside-in neuronal migration. These hCBOs produce various neuronal cell types resembling granule neurons, Purkinje cells, Golgi neurons, and deep cerebellar nuclei. By using a glial induction strategy, we generate Bergmann glial cells (BGCs) that serve as scaffolds for migratory granule cells and enhance electrophysiological activity of the hCBOs. Furthermore, by generating hCBOs from patients with Friedreich’s ataxia (FRDA), we reveal disease-specific phenotypes that can be reversed by histone deacetylase (HDAC) inhibitors and gene editing by CRISPR-Cas9. Taken together, our advanced hCBO model provides new opportunities to investigate the mechanisms of cerebellar ontogenesis and utilize patient-derived iPSCs for translational research.

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

Repositories

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

ncats/cerebellar-organoid-paper-code

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: dc91d1e3bbb2900c780af98a158dc87cce12cfe6, 12 June 2026
Languages: Python (7), JavaScript (6), Quarto (1), R (1)
Size: 168 files, 15 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (_distance_analysis/requirements.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), pandas (3 files), scikit-image (3 files), data.table (2 files), DESeq2 (2 files), pheatmap (2 files), tidyverse (2 files), circlize (1 file), ComplexHeatmap (1 file), Matplotlib (1 file), Plotly (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
17 files
At the source: github.com/ncats/cerebellar-organoid-paper-code

Zenodo 19560381

License: MIT
State: the link answers, verified on 28 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: NumPy (5 files), pandas (3 files), scikit-image (3 files), data.table (2 files), DESeq2 (2 files), pheatmap (2 files), tidyverse (2 files), circlize (1 file), ComplexHeatmap (1 file), Matplotlib (1 file), Plotly (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
17 files
At the source:

Code availability

All custom code that supported the findings of this study is available at https://github.com/ncats/cerebellar-organoid-paper-code (https://github.com/ncats/cerebellar-organoid-paper-code.)56.

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 30 scripts, 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

Data availability

Additional data, including the uncropped and unedited blot/gel images, are provided in Supplementary Information. RNA-seq and whole genome sequencing data generated in this study can be found in the NCBI SRA under BioProject PRJNA1263825. Numerical source data for all graphs is included in Supplementary Data 1.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 4 keywords, 9 MeSH terms, 1 funder, 55 references.

Cite

This paper

Ryu, S., Inman, J., Hong, H., Jovanovic, V. M., Chen, Q., Gedik, Y., Jethmalani, Y., Hur, I., Harouni, M., Voss, T., Lack, J., Collins, J., Ormanoglu, P., Simeonov, A., Tristan, C. A., & Singeç, I. (2026). Modeling Friedreich's ataxia with Bergmann glia-enriched human cerebellar organoids. Communications biology, 9(1), 1235. https://doi.org/10.1038/s42003-026-10180-5

BibTeX

@article{ryu2026modeling,
author = {Ryu, Seungmi and Inman, Jason and Hong, Hyenjong and Jovanovic, Vukasin M and Chen, Qiang and Gedik, Yeliz and Jethmalani, Yogita and Hur, Inae and Harouni, Majid and Voss, Ty and Lack, Justin and Collins, Jack and Ormanoglu, Pinar and Simeonov, Anton and Tristan, Carlos A and Singeç, Ilyas},
title = {{Modeling Friedreich's ataxia with Bergmann glia-enriched human cerebellar organoids}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1235},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10180-5},
url = {https://doi.org/10.1038/s42003-026-10180-5},
pmid = {42098441},
pmcid = {PMC13598134}
}

RIS

TY - JOUR
AU - Ryu, Seungmi
AU - Inman, Jason
AU - Hong, Hyenjong
AU - Jovanovic, Vukasin M
AU - Chen, Qiang
AU - Gedik, Yeliz
AU - Jethmalani, Yogita
AU - Hur, Inae
AU - Harouni, Majid
AU - Voss, Ty
AU - Lack, Justin
AU - Collins, Jack
AU - Ormanoglu, Pinar
AU - Simeonov, Anton
AU - Tristan, Carlos A
AU - Singeç, Ilyas
TI - Modeling Friedreich's ataxia with Bergmann glia-enriched human cerebellar organoids
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/08
VL - 9
IS - 1
SP - 1235
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10180-5
UR - https://doi.org/10.1038/s42003-026-10180-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10180-5",
"type": "article-journal",
"title": "Modeling Friedreich's ataxia with Bergmann glia-enriched human cerebellar organoids",
"container-title": "Communications biology",
"author": [
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"family": "Ryu",
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{
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{
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{
"family": "Jethmalani",
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},
{
"family": "Hur",
"given": "Inae"
},
{
"family": "Harouni",
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"PMID": "42098441",
"PMCID": "PMC13598134",
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"language": "en",
"issued": {
"date-parts": [
[
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5,
8
]
]
}
}

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[10] doi:10.1038/s41593-026-02300-5 [code]
Integrated single-cell and spatial transcriptomic profiling in ALS uncovers peripheral-to-central immune infiltration and reprogramming.
Journal: Nature neuroscience
In common: circlize, DESeq2, ComplexHeatmap, 7 other tools, other condition

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