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Glucocorticoid Signaling in PSC-Derived Neural Systems to Elucidate Mechanisms of Stress-Induced Psychiatric Vulnerability.

Code ↔ Paper

6 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 6 matches
  1. [1] § Glucocorticoid Modulation in Human Neural in vitro Models › Integrative Analysis of Global Effects of DEX ↔ volcano.py, lines 90–105 · score 0.80 · PIK3R1, HIF3A, TSC22D3, CHST15, LIFR, RASSF4
  2. [2] § Glucocorticoid Modulation in Human Neural in vitro Models › Neural Progenitors › Exposure Duration on Glucocorticoid Responses ↔ correlation_matrix.py, lines 36–68 · score 0.76 · Dony_FOK4, Dony_409b, Babaniyi_H9, Babaniyi_H1, correlation, Cruceanu
  3. [3] § Glucocorticoid Modulation in Human Neural in vitro Models › Neural Progenitors › Exposure Duration on Glucocorticoid Responses ↔ scatter.py, lines 14–32 · score 0.62 · Babaniyi_H9, Babaniyi_H1, log2FC, Scatter, fok4, Cruceanu
  4. [4] § Glucocorticoid Modulation in Human Neural in vitro Models › Neural Progenitors › Exposure Duration on Glucocorticoid Responses ↔ correlation_matrix.py, lines 36–68 · score 0.56 · Babaniyi_H9, Babaniyi_H1, Correlation, Krontira, Dony, models
  5. [5] § Glucocorticoid Modulation in Human Neural in vitro Models › Integrative Analysis of Global Effects of DEX ↔ correlation_matrix.py, lines 25–34 · score 0.52 · log2 fold change, b2, H1, H9, matrices, neurons
  6. [6] § Glucocorticoid Modulation in Human Neural in vitro Models › Integrative Analysis of Global Effects of DEX ↔ heatmap.py, lines 90–99 · score 0.51 · functional pathways, regulated genes, heatmap, enriched

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 119 lines · 4.5 KB · no license · 3 matches

  1. import os
  2. import pandas as pd
  3. import seaborn as sns
  4. import matplotlib.pyplot as plt
  6. # =========================================================
  7. # CONFIGURATION & PATHS
  8. # =========================================================
  9. base_path = "data"
  10. results_path = "results"
  12. # Ensure the results directory exists
  13. if not os.path.exists(results_path):
  14. os.makedirs(results_path)
  16. # Input files mapping
  17. files = {
  18. "babaniyi_H1": "babaniyih1.xlsx",
  19. "babaniyi_H9": "babaniyih9.xlsx",
  20. "cruceanu_dn": "cruceanu_dn.xlsx",
  21. "dony": "donysemfc.xlsx",
  22. "krontira_dn": "krontira_dn.xlsx"
  23. }
  25. # Metadata for gene columns and Fold Change columns
  26. meta = {
  27. "babaniyi_H1": ("hgnc_symbol", ["log2FoldChange"]),
  28. "babaniyi_H9": ("hgnc_symbol", ["log2FoldChange"]),
  29. "cruceanu_dn": ("gene", ["log2FC"]),
  30. "dony": ("gene", ["log2FC_Line409b2", "log2FC_LineFOK4"]),
  31. "krontira_dn": ("gene", ["log2FoldChange"]),
  32. }
  34. dony_models = ["ChP", "Ex.Neurons", "Imm.ChP", "Inh.Neurons", "RGS5Neurons"]
  36. # =========================================================
  37. # PROCESSING FUNCTIONS
  38. # =========================================================
  39. def expand_long_format_and_count(df, gene_col, fc_cols, tag):
  40. """Filters, cleans, and prepares the data for correlation."""
  41. if tag == "cruceanu_dn" and "model" in df.columns:
  42. df = df[df["model"].str.strip().str.upper() == "NEURONS"]
  43. print(f"📌 Cruceanu filtered for 'Neurons', {df.shape[0]} genes.")
  44. elif tag == "dony" and "model" in df.columns:
  45. df = df[df["model"].isin(dony_models)]
  46. print(f"📌 Dony filtered for models {dony_models}, {df.shape[0]} genes.")
  48. df = df[[gene_col] + fc_cols].copy()
  49. df.columns = ["gene"] + fc_cols
  50. df[fc_cols] = df[fc_cols].apply(pd.to_numeric, errors='coerce')
  51. df = df[df[fc_cols].notna().any(axis=1)].copy()
  52. df["gene"] = df["gene"].astype(str).str.upper().str.strip()
  54. long_frames = []
  55. counts = {}
  56. for col in fc_cols:
  57. col_name = (
  58. "Dony_409b" if col == "log2FC_Line409b2" else
  59. "Dony_FOK4" if col == "log2FC_LineFOK4" else
  60. "Babaniyi_H9" if tag == "babaniyi_H9" else
  61. "Babaniyi_H1" if tag == "babaniyi_H1" else
  62. "Krontira" if tag == "krontira_dn" else
  63. "Cruceanu" if tag == "cruceanu_dn" else tag
  64. )
  65. subset = df[["gene", col]].dropna()
  66. long_frames.append(subset.rename(columns={col: "logFC"}).assign(dataset=col_name))
  67. counts[col_name] = subset.shape[0]
  68. return pd.concat(long_frames, ignore_index=True), counts
  70. # =========================================================
  71. # DATA LOADING & MERGING
  72. # =========================================================
  73. frames = []
  74. counts_dict = {}
  76. for tag, fname in files.items():
  77. gene_col, fc_cols = meta[tag]
  78. path = os.path.join(base_path, fname)
  80. # Reading Excel files from the /data folder
  81. df = pd.read_excel(path, engine="openpyxl")
  82. expanded_df, counts = expand_long_format_and_count(df, gene_col, fc_cols, tag)
  83. frames.append(expanded_df)
  84. counts_dict.update(counts)
  86. # Gene counts summary
  87. counts_df = pd.DataFrame(list(counts_dict.items()), columns=["Dataset", "Num_genes"])
  88. print("\n📌 Number of genes considered per dataset/lineage:")
  89. print(counts_df)
  91. # Concatenate all data into a wide matrix
  92. full_df = pd.concat(frames, ignore_index=True)
  93. wide_df = full_df.pivot_table(index='gene', columns='dataset', values='logFC', aggfunc='first')
  95. # =========================================================
  96. # STATISTICAL ANALYSIS: PEARSON CORRELATION
  97. # =========================================================
  98. corr_matrix = wide_df.corr(method='pearson')
  100. print("\n📌 Pearson Correlation Matrix:")
  101. print(corr_matrix)
  103. # =========================================================
  104. # EXPORTING RESULTS
  105. # =========================================================
  106. # Save Correlation Matrix to Excel
  107. corr_out_path = os.path.join(results_path, "acute_vs_chronic_correlation.xlsx")
  108. corr_matrix.to_excel(corr_out_path)
  109. print(f"\n✓ Correlation matrix saved to: {corr_out_path}")
  111. # Save Correlation Heatmap to PNG
  112. heatmap_out_path = os.path.join(results_path, "acute_vs_chronic_heatmap.png")
  113. plt.figure(figsize=(8,6))
  114. sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", square=True)
  115. plt.tight_layout()
  116. plt.savefig(heatmap_out_path, dpi=600)
  117. plt.show()
  119. print(f"✓ Heatmap saved to: {heatmap_out_path}")

correlation_matrix.py at commit 018ac94, no license · at the source

Overview

Authors: Eloiza Adriane Dal Molin1, Manuella Pinto Kaster1, Juliana Minardi Nascimento1
ORCID iDs: Eloiza Adriane Dal Molin, Manuella Pinto Kaster, Juliana Minardi Nascimento
  1. Department of Biochemistry, School of Biological Sciences, Federal University of Santa Catarina, Florianopolis, SC Brazil
Institutions: Universidade Federal de Santa Catarina (Brazil)
Journal: Molecular neurobiology, volume 63, issue 1, article 748
Dates: received 12 November 2025; accepted 26 June 2026; published online 7 July 2026; in print 2026
Type: Review · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12035-026-06041-1 · PMID 42412256 · PMCID PMC13341972 · OpenAlex W7167573010
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Glucocorticoid signaling, Cortisol, Dexamethasone, HiPSC-derived neural models, Transcriptomic reanalysis, Stress-related psychiatric disorders
MeSH: Glucocorticoids*, Induced Pluripotent Stem Cells*, Mental Disorders*, Neurons*, Signal Transduction*, Stress, Physiological*, Stress, Psychological*, Animals, Humans (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: Universidade Federal De Santa Catarina
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Glucocorticoids are major regulators of human neural development and stress adaptation, yet their transcriptional effects across experimental paradigms remain poorly integrated. This review synthesizes evidence from 13 studies employing human induced pluripotent stem cell (hiPSC)-derived neural models exposed to cortisol or dexamethasone, including neural progenitors, neurons, astrocytes, and brain organoids. When available, transcriptomic datasets from these studies were reanalyzed under standardized criteria to directly compare acute and chronic GC exposure. This comparative approach revealed that GCs modulate shared pathways related to neurogenesis, cytoskeletal organization, immune signaling, and stress response, while the duration of exposure critically shapes the underlying transcriptional architecture. Acute stimulation predominantly upregulated canonical GR targets such as FKBP5, ZBTB16, and TSC22D3 involved in early stress response and feedback control, whereas chronic exposure induced sustained remodeling of genes including MT2A, RASFAF4, and DPYSL5 linked to oxidative stress regulation and neuronal structure. A conserved downregulated core comprising NFIA, NFIB, and CCL2 was shared across paradigms, reflecting persistent suppression of glial differentiation and inflammatory signaling. Together, these findings delineate distinct yet convergent transcriptional programs governed by GC exposure, providing mechanistic insight into how temporal dynamics of glucocorticoid signaling may contribute to altered neurodevelopmental trajectories and increased vulnerability to stress-related psychiatric disorders.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12035-026-06041-1.

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 6 matches between paragraphs and lines of code.

eloizadalmolin/DEX_neural_models

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 018ac94d28883b84f1615628d3ffefde1b2fb008, 15 January 2026
Languages: Python (4)
Size: 14 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (4 files), pandas (4 files), NumPy (2 files), seaborn (2 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/eloizadalmolin/DEX_neural_models

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;
  • 4 scripts, each with its path and the digest of its content;
  • 6 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 Availability

The datasets reanalyzed in this study were obtained from publicly available repositories cited in the reviewed articles. All processed data and original Python scripts used for comparative transcriptomic integration, including functional enrichment and correlation analyses, are openly available in the GitHub repository: (https://github.com/eloizadalmolin/DEX_neural_models).

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 9 MeSH terms, 1 funder, 92 references.

Cite

This paper

Dal Molin, E. A., Kaster, M. P., & Nascimento, J. M. (2026). Glucocorticoid Signaling in PSC-Derived Neural Systems to Elucidate Mechanisms of Stress-Induced Psychiatric Vulnerability. Molecular neurobiology, 63(1), 748. https://doi.org/10.1007/s12035-026-06041-1

BibTeX

@article{dalmolin2026glucocorticoid,
author = {Dal Molin, Eloiza Adriane and Kaster, Manuella Pinto and Nascimento, Juliana Minardi},
title = {{Glucocorticoid Signaling in PSC-Derived Neural Systems to Elucidate Mechanisms of Stress-Induced Psychiatric Vulnerability}},
journal = {Molecular neurobiology},
year = {2026},
month = jul,
volume = {63},
number = {1},
pages = {748},
publisher = {Springer Science+Business Media},
issn = {0893-7648},
doi = {10.1007/s12035-026-06041-1},
url = {https://doi.org/10.1007/s12035-026-06041-1},
pmid = {42412256},
pmcid = {PMC13341972}
}

RIS

TY - JOUR
AU - Dal Molin, Eloiza Adriane
AU - Kaster, Manuella Pinto
AU - Nascimento, Juliana Minardi
TI - Glucocorticoid Signaling in PSC-Derived Neural Systems to Elucidate Mechanisms of Stress-Induced Psychiatric Vulnerability
T2 - Molecular neurobiology
J2 - Mol Neurobiol
PY - 2026
DA - 2026/07/07
VL - 63
IS - 1
SP - 748
SN - 0893-7648
PB - Springer Science+Business Media
DO - 10.1007/s12035-026-06041-1
UR - https://doi.org/10.1007/s12035-026-06041-1
LA - en
ER -

CSL-JSON

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"id": "10.1007/s12035-026-06041-1",
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"title": "Glucocorticoid Signaling in PSC-Derived Neural Systems to Elucidate Mechanisms of Stress-Induced Psychiatric Vulnerability",
"container-title": "Molecular neurobiology",
"author": [
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"family": "Dal Molin",
"given": "Eloiza Adriane"
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{
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"container-title-short": "Mol Neurobiol",
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"page": "748",
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"PMID": "42412256",
"PMCID": "PMC13341972",
"ISSN": "0893-7648",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s12035-026-06041-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
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7
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]
}
}

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