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ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A.

Code ↔ Paper

5 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 5 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] § STAR★Methods › Method details › Analysis of single-nuclei RNA-seq of human SNc ↔ import_data/build.qmd, lines 32–66 · score 0.71 · UMAP coordinates, MG, Olig, Astro, DA, Seurat
  2. [2] § STAR★Methods › Method details › LowC data analysis ↔ CUT_and_Tag/plot_hic_bw.R, lines 29–114 · score 0.70 · mDAN30, smNPC, Triangle, cool, plotgardener, smallest
  3. [3] § STAR★Methods › Method details › LowC data analysis ↔ Juicer_pipeline/scripts/Juicer_launcher.sh, the whole file · a weak match · score 0.61 · Juicer pipeline, smNPC, d30
  4. [4] § Results › ZFHX4 is a super-enhancer-controlled transcriptional regulator induced during dopaminergic neurogenesis ↔ ZFHX4_IHEC_analysis.Rmd, lines 55–142 · score 0.60 · neural progenitors, neural cell, hepatocytes, tissues, brain, gene
  5. [5] § STAR★Methods › Experimental model and study participant details › Cell lines ↔ ZFHX4_IHEC_analysis.Rmd, lines 55–142 · score 0.53 · induced pluripotent stem, gene, cell

Paper

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The authors' code

R Markdown · 144 lines · 5 KB · no license · 2 matches

  1. ---
  2. title: "Analysis"
  3. author: "Elena"
  4. date: "2024-02-01"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. ##In this analysis, the expression of ZFHX4 from the IHEC dataset will be examined together with the corresponding chromatin states across 41 human tissues. Source files are available upon request.
  14. ```{r cars}
  16. #Load packages
  18. library(tidyverse)
  19. library(dplyr)
  20. library(ggplot2)
  21. library(ggpubr)
  22. library(reshape2)
  23. library(pheatmap)
  24. library(RColorBrewer)
  26. ```
  28. ```{r pressure, echo=FALSE}
  30. #Load data
  32. Data <- readr::read_csv('ZFHX4_LIN28A_TPM_IHEC.csv')
  33. metadata <- readr::read_csv('metadata_v1.csv')
  34. ZFHX4 <- readr::read_csv('ZFHX4_chromgene_states.csv')
  35. ChromGene <- readr::read_csv('ZFHX4_LIN28A_ChromGene_signal.csv')
  37. ```
  39. ```{r, fig.height= 4}
  41. #Assign the correct labels
  43. Numbers <- c(1:12)
  44. States <- c ('strong_trans_enh', 'trans', 'trans_k36me3', 'trans_enh', 'trans_wk', 'ZNF','poised','PC_repr_wk', 'bivalent', 'quiescent', 'het', 'PC_repr')
  46. df <- data.frame(Values = Numbers, States = States)
  48. ZFHX4 <- ZFHX4 |>
  49. select(-1,-2,-3,-4,-5,-6,-7) |>
  50. pivot_longer(cols = everything(), names_to = "EpiRR", values_to = "Values")
  52. ```
  55. ```{r, fig.width=12, fig.height=4}
  57. merged <- merge(ZFHX4, df, by = 'Values')
  59. #Chrome_gene_states
  61. Data_ZFHX4 <- Data |>
  62. filter(id_col == "ENSG00000091656.16") |>
  63. full_join(merged, by = 'EpiRR') |>
  64. mutate(TPM = ifelse(TPM == 0, 0.01, TPM),
  65. log2 = log2(TPM)) |>
  66. group_by(harmonized_sample_ontology_intermediate) |>
  67. mutate(mean = mean(log2)) |>
  68. select(mean, harmonized_sample_ontology_intermediate) |>
  69. distinct() |>
  70. na.omit() |>
  71. pivot_wider(names_from = harmonized_sample_ontology_intermediate, values_from = mean)
  73. rownames(Data_ZFHX4)<- c('ZFHX4')
  75. Data_ZFHX4<- t(Data_ZFHX4)
  77. Data_ZFHX4 <- as.data.frame(Data_ZFHX4)
  79. Data_ZFHX4 <- Data_ZFHX4[order(desc(Data_ZFHX4$ZFHX4)), , drop = FALSE]
  81. annotations <- Data |>
  82. filter(id_col == "ENSG00000091656.16") |>
  83. full_join(merged, by = 'EpiRR') |>
  84. group_by(harmonized_sample_ontology_intermediate) |>
  85. count(States) |>
  86. top_n(1, n) |>
  87. distinct() |>
  88. select (harmonized_sample_ontology_intermediate, States) |>
  89. filter(harmonized_sample_ontology_intermediate != 'NA')
  92. annotations<- annotations[-10,]
  93. annotations <- as.data.frame(annotations)
  94. rownames(annotations)<- annotations$harmonized_sample_ontology_intermediate
  95. annotations <- annotations |> select(-harmonized_sample_ontology_intermediate)
  97. annotations$States[is.na(annotations$States)] <- "NA"
  99. # Desired order of row names
  100. desired_order <- c('germ line cell', 'neural progenitor cell', 'neural cell', 'muscle organ', 'brain', 'connective tissue cell', 'hepatocyte', 'melanocyte', 'stem cell derived cell line', 'liver', 'induced pluripotent stem cell', 'kidney', 'secretory cell', 'mammary gland epithelial cell', 'meso-epithelial cell', 'epithelial cell derived cell line', 'trophoblast', 'endoderm-derived structure', 'mole', 'placenta', 'epithelial cell of endometrial gland', 'extraembryonic cell', 'mesoderm-derived structure', 'mucosa', 'erythroid lineage cell', 'colon', 'dendritic cell', 'hematopoietic cell', 'myeloid cell', 'lymphocyte of B lineage', 'T cell', 'monocyte', 'macrophage', 'neutrophil', 'natural killer cell', 'eosinophil', 'venous blood', 'capillary blood', 'peripheral blood mononuclear cell', 'mononuclear cell', 'endo-epithelial cell')
  102. # Create an index to reorder the dataframe
  103. order_index <- match(desired_order, rownames(annotations))
  105. # Reorder the dataframe
  106. annotations_ordered <- annotations[order_index, , drop = FALSE]
  109. Data_ZFHX4<- t(Data_ZFHX4)
  112. # Define your colors
  113. mycolors <- c('trans_enh'= '#8B008B',
  114. 'poised'= '#C71585',
  115. 'trans_wk' = '#FF69B4',
  116. 'PC_repr'= '#D3D3D3',
  117. 'PC_repr_wk'= '#A9A9A9',
  118. 'bivalent'= '#808080',
  119. 'het'= '#696969',
  120. 'NA'= 'white')
  122. mycolors <- list(States = mycolors)
  125. heat_plot <- pheatmap(Data_ZFHX4, # choose a colour scale for your data
  126. cluster_rows = F, cluster_cols = T,
  127. annotation_col = annotations, # column (sample) annotations
  128. annotation_colors = mycolors, # colours for your annotations
  129. annotation_names_row = F,
  130. annotation_names_col = F,
  131. fontsize_row = 12,# row label font size
  132. fontsize_col = 12,
  133. fontsize_legend = 15,# col # legend customisation
  134. show_colnames = T, show_rownames = T,
  135. cellwidth = 15,
  136. angle_col = 270,
  137. cellheight = 12) # a title for our heatmap
  140. ggsave(plot = heat_plot, width = 15, height = 4, dpi = 300, filename = "heatmap.pdf")
  142. ```

ZFHX4_IHEC_analysis.Rmd at commit 460471b, no license · at the source

Overview

Authors: Elena Valceschini1, Borja Gomez Ramos1,2, Jochen Ohnmacht1,2,3, Aurelien Ginolhac1, Marie Catillon1, Deborah Gerard1,2, Anthoula Gaigneaux1, Dimitrios Kyriakis2, Kamil Grzyb2, Enrico Glaab2, Anne Grünewald2, Alexander Skupin2, Thomas Sauter1, Rejko Krüger2,3,4, Lasse Sinkkonen1
ORCID iDs: Lasse Sinkkonen
  1. Department of Health, Medicine and Life Sciences (DHML), University of Luxembourg, 4362 Belvaux, Luxembourg
  2. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 4362 Belvaux, Luxembourg
  3. Luxembourg Institute of Health (LIH), 1445 Strassen, Luxembourg
  4. Centre Hospitalier de Luxembourg (CHL), 1210 Belair, Luxembourg
Institutions: University of Luxembourg (Luxembourg); Luxembourg Centre for Systems Biomedicine (Luxembourg); Luxembourg Institute of Health (Luxembourg); Centre Hospitalier de Luxembourg (Luxembourg)
Journal: Stem cell reports, volume 21, issue 6, article 102930
Dates: received 28 July 2025; accepted 29 April 2026; published online 28 May 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.stemcr.2026.102930 · PMID 42208531 · PMCID PMC13261933 · OpenAlex W4412041075
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.03.662987
Status: code verified
Categories: genetics / omics (modality), human (organism), Parkinson's (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Evoked potentials
Keywords: dopaminergic neurons, ZFHX4, chromatin, super-enhancers, cell cycle, Parkinson's disease, transcription factors, neurodevelopment, LIN28
MeSH: Cell Cycle*, Cell Differentiation*, Dopaminergic Neurons*, RNA-Binding Proteins*, Transcription Factors*, Animals, Cell Proliferation, Humans, Protein Binding, Super Enhancers (* major topic)
Topic: Receptor Mechanisms and Signaling (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Fondation du Pélican de Mie et Pierre Hippert; Michael J. Fox Foundation for Parkinson’s Research; Aligning Science Across Parkinson’s; Weston Family Foundation
Citations: not cited yet (Europe PMC); 69 references in the paper
Research resources: Rabbit polyclonal anti-ZFHX4 RRID:AB_1234567, RRID:AB_144696, RRID:AB_162542, Rabbit polyclonal anti-H3K27ac RRID:AB_2118291, Chicken polyclonal anti-MAP2 RRID:AB_2138153, Rabbit polyclonal anti-SOX2 RRID:AB_2341193, RRID:AB_2534017, RRID:AB_2534098, RRID:AB_2535866, RRID:AB_2576217, RRID:AB_310177, Mouse monoclonal anti-Ki67 RRID:AB_393778, Mouse monoclonal anti-TH RRID:AB_795666, HEK 293T cells RRID:CVCL_0063, GraphPad Prism RRID:SCR_002798, ImageJ RRID:SCR_003070, RRID:SCR_006431, FlowJo RRID:SCR_008520

Abstract

Parkinson’s disease (PD) involves selective degeneration of midbrain dopaminergic neurons (mDANs), yet the regulatory networks governing their development remain incompletely understood. ZFHX4 has been linked to neurodevelopment across species and shows reduced expression in the PD midbrain. Through integrative analysis of our multiomic data of mDAN differentiation, we show that ZFHX4 is a super-enhancer-controlled transcription factor induced during mDAN specification. Importantly, ZFHX4 is necessary but not sufficient for mDAN differentiation. Genome-wide profiling of ZFHX4 binding revealed targeting to active promoters, and transcriptomic profiling after ZFHX4 depletion identified primary target genes enriched for cell-cycle regulation. Consistently, ZFHX4-depleted cells showed reduced proliferation and accumulated in G2 phase, impairing cell-cycle progression. LIN28A, an RNA-binding protein involved in stem-cell maintenance and microRNA maturation, is among the strongest upregulated genes upon ZFHX4 depletion, with direct ZFHX4 binding at the locus. Our findings indicate that ZFHX4 regulates mDAN maturation through a mechanism involving the LIN28A-miR-9 axis.

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

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sysbiolux/Valceschini_et_al_2025

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Commit: 460471b45c54edd8c4395ee30162cda6895d49ac, 22 May 2026
Languages: R (5), Quarto (1)
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At the source: github.com/sysbiolux/Valceschini_et_al_2025

uniluxembourg/fstm/dlsm/bioinfo/snakemake-rna_seqv0.2.3

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macs3-project/MACS

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hub.docker.com/layers/ginolhac

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sysbiolux/pd-rsnp_scarb2

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At the source: github.com/sysbiolux/PD-rSNP_SCARB2/tree/master/Juicer_pipeline

The paper's code and data availability statement is in the Data section.

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Data

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Data and code availability

• Gene expression data from PD case-control brain transcriptomics study were obtained from the Gene Expression Omnibus (GEO) under accession number GSE8397 (Tranchevent et al., 2023). • Gene expression data from single-nucleus RNA-seq analysis of human SNc (Kamath et al., 2022) were obtained from the Broad Institute Single Cell Portal (SCP1768 and SCP1769) and are publicly available via GEO under accession number GSE178265. Raw sequence-level data are available through dbGaP (phs002879.v1.p12). • Data used in the preparation of this article were obtained on May 22, 2023, from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data), RRID:SCR_006431 (https://scicrunch.org/resolver/SCR_006431). For up-to-date information on the study, visit http://www.ppmi-info.org. • The source RNA-seq, ATAC-seq, and ChIP-seq fastq files (Gomez Ramos et al., 2024) are available at https://ega-archive.org/, under the accession number EGAD00001009288. The LowC data are available under the accession number EGAC00001002822. • The newly generated RNA-seq and CUT&Tag fastq files have been deposited at https://ega-archive.org/, under the accession number EGAD50000001605. Additional intermediate files can be provided upon request. • The code used for the single-nuclei RNA-seq, RNA-seq, and CUT&Tag data analysis as well as the MATLAB code used to analyze images from the high-content imaging analysis and the codes for figure generation are available at https://github.com/sysbiolux/Valceschini_et_al_2025. The Snakemake used in this study for the RNA-seq analysis is available at https://gitlab.com/uniluxembourg/fstm/dlsm/bioinfo/snakemake-rna_seq v0.2.3.

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Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 9 keywords, 10 MeSH terms, 4 funders, 67 references, 18 RRIDs.

Cite

This paper

Valceschini, E., Gomez Ramos, B., Ohnmacht, J., Ginolhac, A., Catillon, M., Gerard, D., Gaigneaux, A., Kyriakis, D., Grzyb, K., Glaab, E., Grünewald, A., Skupin, A., Sauter, T., Krüger, R., & Sinkkonen, L. (2026). ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A. Stem cell reports, 21(6), 102930. https://doi.org/10.1016/j.stemcr.2026.102930

BibTeX

@article{valceschini2026zfhx4,
author = {Valceschini, Elena and Gomez Ramos, Borja and Ohnmacht, Jochen and Ginolhac, Aurelien and Catillon, Marie and Gerard, Deborah and Gaigneaux, Anthoula and Kyriakis, Dimitrios and Grzyb, Kamil and Glaab, Enrico and Grünewald, Anne and Skupin, Alexander and Sauter, Thomas and Krüger, Rejko and Sinkkonen, Lasse},
title = {{ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A}},
journal = {Stem cell reports},
year = {2026},
month = may,
volume = {21},
number = {6},
pages = {102930},
publisher = {Elsevier},
issn = {2213-6711},
doi = {10.1016/j.stemcr.2026.102930},
url = {https://doi.org/10.1016/j.stemcr.2026.102930},
pmid = {42208531},
pmcid = {PMC13261933}
}

RIS

TY - JOUR
AU - Valceschini, Elena
AU - Gomez Ramos, Borja
AU - Ohnmacht, Jochen
AU - Ginolhac, Aurelien
AU - Catillon, Marie
AU - Gerard, Deborah
AU - Gaigneaux, Anthoula
AU - Kyriakis, Dimitrios
AU - Grzyb, Kamil
AU - Glaab, Enrico
AU - Grünewald, Anne
AU - Skupin, Alexander
AU - Sauter, Thomas
AU - Krüger, Rejko
AU - Sinkkonen, Lasse
TI - ZFHX4 is necessary for dopaminergic neuron differentiation and controls cell cycle by regulating LIN28A
T2 - Stem cell reports
J2 - Stem Cell Reports
PY - 2026
DA - 2026/05/28
VL - 21
IS - 6
SP - 102930
SN - 2213-6711
PB - Elsevier
DO - 10.1016/j.stemcr.2026.102930
UR - https://doi.org/10.1016/j.stemcr.2026.102930
LA - en
ER -

CSL-JSON

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