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Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience.

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The authors' code

Python · 188 lines · 5.5 KB · no license

  1. # Import required packages
  2. from flask import Flask, jsonify, render_template,request
  3. from flask_sqlalchemy import SQLAlchemy
  4. from init import db
  5. import pandas as pd
  6. # Define app framework
  7. app = Flask(__name__)
  8. app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///db.sqlite3'
  9. app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
  10. db.init_app(app)
  11. # -- Navigation routes --
  12. @app.route('/')
  13. def index():
  14. return render_template("index.html")
  15. @app.route('/legal')
  16. def legal():
  17. return render_template("legal.html")
  18. # -- Database routes to fetch data --
  19. # .. Route to fetch large table from database ..
  20. @app.route("/fetch_large_table")
  21. def fetch_large_table():
  22. try:
  23. entries = LargeTable.query.all()
  24. entries_dict = [row.to_dict() for row in entries]
  25. if not entries:
  26. return jsonify({"error": "No entries found"}), 404
  27. return jsonify(entries_dict), 200
  28. except Exception as e:
  29. return jsonify({"error": str(e)}), 500
  30. # .. Route that fetches list of unique values for a specific column string ..
  31. @app.route("/fetch_unique_entries")
  32. def fetch_unique_entries():
  33. try:
  34. col = request.args.get('col')
  35. entries = [entry[0] for entry in LargeTable.query.with_entities(getattr(LargeTable, col)).distinct().all()]
  36. if not entries:
  37. return jsonify({"error": "No entries found"}), 404
  38. return jsonify(entries), 200
  39. except Exception as e:
  40. return jsonify({"error": str(e)}), 500
  41. # .. Route to fetch abbreviations from database ..
  42. @app.route("/fetch_abbreviations")
  43. def fetch_abbreviations():
  44. try:
  45. abbreviations = Abbreviations.query.order_by(Abbreviations.abbreviation).all()
  46. grouped_abbr = {}
  47. for abbreviation in abbreviations:
  48. key = abbreviation.abbreviation[0].lower()
  49. grouped_abbr.setdefault(key, []).append(abbreviation.to_dict())
  50. if not grouped_abbr:
  51. return jsonify({"error": "No entries found"}), 404
  52. return jsonify(grouped_abbr), 200
  53. except Exception as e:
  54. return jsonify({"error": str(e)}), 500
  55. # Define the LargeTable (main table) model class
  56. class LargeTable(db.Model):
  57. id = db.Column(db.Integer, primary_key=True)
  58. group = db.Column(db.String, nullable=False)
  59. subgroup = db.Column(db.String, nullable=False)
  60. focus = db.Column(db.String)
  61. title = db.Column(db.String)
  62. journal = db.Column(db.String)
  63. year = db.Column(db.Integer, nullable=False)
  64. doi = db.Column(db.String)
  65. model = db.Column(db.String, nullable=False)
  66. cell_origin = db.Column(db.String, nullable=False)
  67. application = db.Column(db.String)
  68. advantages = db.Column(db.String)
  69. limitations = db.Column(db.String)
  70. # Initialization of a db entry
  71. def __init__(self, group, subgroup, focus, title, journal, year, doi, model, cell_origin, application, advantages, limitations):
  72. self.group = group
  73. self.subgroup = subgroup
  74. self.focus = focus
  75. self.title = title
  76. self.journal = journal
  77. self.year = year
  78. self.doi = doi
  79. self.model = model
  80. self.cell_origin = cell_origin
  81. self.application = application
  82. self.advantages = advantages
  83. self.limitations = limitations
  84. # Conversion to dictionary
  85. def to_dict(self):
  86. return {
  87. 'id': self.id,
  88. 'group': self.group,
  89. 'subgroup': self.subgroup,
  90. 'focus' : self.focus,
  91. 'title': self.title,
  92. 'journal' : self.journal,
  93. 'year': self.year,
  94. 'doi': self.doi,
  95. 'model': self.model,
  96. 'cell_origin': self.cell_origin,
  97. 'application': self.application,
  98. 'advantages': self.advantages,
  99. 'limitations': self.limitations
  100. }
  101. # Define the Abbreviations model
  102. class Abbreviations(db.Model):
  103. id = db.Column(db.Integer, primary_key=True)
  104. abbreviation = db.Column(db.String(100), nullable=False)
  105. description = db.Column(db.String(100), nullable=False)
  106. # Initialization of db entry
  107. def __init__(self, abbreviation, description):
  108. self.abbreviation = abbreviation
  109. self.description = description
  110. # Conversion to dictionary
  111. def to_dict(self):
  112. return {
  113. 'id': self.id,
  114. 'abbreviation': self.abbreviation,
  115. 'description': self.description
  116. }
  117. # Create the database in app context
  118. with app.app_context():
  119. db.create_all()
  120. # Function for converting the abbreviations table to a database
  121. def addLargeTable(excel_path):
  122. df = pd.read_excel(excel_path, sheet_name='Map')
  123. for _, row in df.iterrows():
  124. entry = LargeTable(group=row['Group'], subgroup=row['Subgroup'], focus=row['Focus'], title=row['Title'],
  125. journal=row['Journal'], year=row['Year'], cell_origin=row['Cell origin'], doi=row['DOI'],
  126. model=row['Model'], application=row['Application'], advantages=row['Advantages'],
  127. limitations=row['Limitations'])
  128. db.session.add(entry)
  129. db.session.commit()
  130. # Function for converting the abbreviations table to a database
  131. def addAbbreviations(excel_path):
  132. df = pd.read_excel(excel_path, sheet_name='Abbreviations')
  133. for _, row in df.iterrows():
  134. entry = Abbreviations(abbreviation=row['Abbreviation'], description=row['Description'])
  135. db.session.add(entry)
  136. db.session.commit()
  137. if __name__ == "__main__":
  138. app.run(debug=True)

app.py at commit 6f90302, no license · at the source

Overview

Authors: Anna Wolfram1,2,3,4,5, Vanessa Arnold1,2,3, Maik Wolfram‐Schauerte6, Anastassiya Moskalchuk1,2, Caroline Trust6, Marie Vontz1,2, Mona Scheurenbrand1,2, Vijayasarathy Sampath‐Kumar1,2,3, Sogand Ahari1,2,3, Carlos Romero‐Nieto4,5, Lisa Sevenich1,2,7
  1. Department of Neurology and Interdisciplinary Neurooncology, Hertie Institute for Clinical Brain Research, University Hospital Tübingen, Tübingen, Germany
  2. M3 Research Center For Malignome, Metabolome and Microbiome, University Hospital Tübingen, Tübingen, Germany
  3. Graduate Training Centre of Neuroscience, University of Tübingen, Tübingen, Germany
  4. Faculty of Pharmacy, University of Castilla‐La Mancha, Albacete, Spain
  5. Instituto Regional de Investigación Científica Aplicada (IRICA), University of Castilla‐La Mancha, Ciudad Real, Spain
  6. Institute for Bioinformatics and Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
  7. Cluster of Excellence iFIT (EXC 2180) 'Image‐Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
Journal: Advanced healthcare materials, volume 15, issue 18, article e04889
Dates: received 2 October 2025; accepted 30 January 2026; published online 11 March 2026; in print 15 May 2026
Type: Review · Language: English
License: CC BY
Identifiers: DOI 10.1002/adhm.202504889 · PMID 41811100 · PMCID PMC13176544 · OpenAlex W7134959821
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), developmental (subfield)
Methods: Physiology & signal measures
Keywords: CNS, ex vivo models, neurodevelopment, neurodegeneration, neurooncology, organoid database
MeSH: Brain*, Models, Biological*, Neurosciences*, Organoids*, Animals, Humans, Induced Pluripotent Stem Cells (* major topic)
Topic: Pluripotent Stem Cells Research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (CRC1479, INST 39/1479-1, INST 39/1479‐1); Friedrich-Ebert-Stiftung; Joachim Herz Stiftung; Kickstart Fund; Hector Stiftung
Citations: not cited yet (Europe PMC); 844 references in the paper

Abstract

Cerebral organoids are complex, three‐dimensional (3D) dynamic models that recapitulate key features of brain development and disease. These systems serve as bioengineerable platforms with diverse architectures and customizable properties, enabling advances in both basic and translational neuroscience. Despite rapid adoption across neurodevelopment, neurodegeneration, and neuro‐oncology, the field remains fragmented, with substantial methodological variability and no standardized framework for model selection. A systematic review of 738 original studies published between 2014 and 2024, drawn from 3631 articles across PubMed, Semantic Scholar, and OpenAlex, reveals that human induced pluripotent stem cell‐derived cerebral organoids and neurodevelopmental studies dominate the field. In contrast, applications in neurodegeneration, brain metastases, and non‐human systems remain limited, narrowing the translational scope. To address the challenge of navigating this expanding literature, OrganoidMap is introduced—an open‐access, interactive web platform for exploring, filtering, and comparing cerebral organoid models across disease areas, cell sources, and methodological features. OrganoidMap enables the identification of appropriate models for specific experimental goals and reveals underexplored research areas. This synthesis establishes a scalable foundation for enhancing transparency, reproducibility, and model selection in organoid‐based research, setting a new benchmark for how neuroscience and biomaterials communities organize, share, and advance cerebral organoid science.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

MaikTungsten/OrganoidMap

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6f903025f463b373e60bb537e9dbf197f7222863, 29 January 2026
Languages: Python (3034), Fortran (60), C (53), C/C++ (28), JavaScript (4), C++ (1)
Size: 8,104 files, 3,180 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (Dockerfile)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (1,205 files), pandas (1,087 files), Matplotlib (24 files), Numba (16 files), SciPy (10 files), xarray (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2,000 files

Zenodo 18420242

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1,205 files), pandas (1,087 files), Matplotlib (24 files), Numba (16 files), SciPy (10 files), xarray (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2,000 files

organoidmap.cs.uni-tuebingen.de

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3,998 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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.

Dataset Finalization and Public Accessibility

Data extraction was completed on October 9, 2024. Following this, efforts transitioned toward the development of OrganoidMap, an open‐access, interactive platform designed to present, filter, and explore the curated dataset. The dataset remains fixed as of the extraction date, ensuring consistency between the publication and the public‐facing resource.

Although the current review reflects the literature up to October 2024, we acknowledge the importance of continued engagement. We plan to publish regular follow‐up reports and dataset expansions to reflect future research developments, user feedback, and enhancements to the OrganoidMap platform.

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

Data Availability Statement

The OrganoidMap web application is available at organoidmap.cs.uni‐tuebingen.de. The corresponding code is available at https://github.com/MaikTungsten/OrganoidMap and archived at https://zenodo.org/records/18420242. All required software is containerized in a Docker image that can be built with all information from the GitHub repository.

Data extraction for this systematic review was completed on October 9, 2024. The dataset used in this manuscript and presented through the OrganoidMap platform is fixed as of this date to ensure consistency between the publication and the public resource. The full curated dataset is available as an Excel file in the Supplementary Material. The OrganoidMap web application is publicly accessible at https://organoidmap.cs.uni‐tuebingen.de (https://organoidmap.cs.uni-tuebingen.de). The source code is hosted on GitHub (https://github.com/MaikTungsten/OrganoidMap) and has been archived on Zenodo (https://zenodo.org/records/18420242)for long‐term preservation. All necessary software dependencies are provided via Docker and can be built using the configuration files available in the GitHub repository. Future updates to the OrganoidMap platform and dataset are planned to incorporate newly published studies, user feedback, and technical improvements.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 6 keywords, 7 MeSH terms, 5 funders, 840 references.

Cite

This paper

Wolfram, A., Arnold, V., Wolfram‐Schauerte, M., Moskalchuk, A., Trust, C., Vontz, M., Scheurenbrand, M., Sampath‐Kumar, V., Ahari, S., Romero‐Nieto, C., & Sevenich, L. (2026). Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience. Advanced healthcare materials, 15(18), e04889. https://doi.org/10.1002/adhm.202504889

BibTeX

@article{wolfram2026mapping,
author = {Wolfram, Anna and Arnold, Vanessa and Wolfram‐Schauerte, Maik and Moskalchuk, Anastassiya and Trust, Caroline and Vontz, Marie and Scheurenbrand, Mona and Sampath‐Kumar, Vijayasarathy and Ahari, Sogand and Romero‐Nieto, Carlos and Sevenich, Lisa},
title = {{Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience}},
journal = {Advanced healthcare materials},
year = {2026},
month = mar,
volume = {15},
number = {18},
pages = {e04889},
publisher = {Wiley},
issn = {2192-2640},
doi = {10.1002/adhm.202504889},
url = {https://doi.org/10.1002/adhm.202504889},
pmid = {41811100},
pmcid = {PMC13176544}
}

RIS

TY - JOUR
AU - Wolfram, Anna
AU - Arnold, Vanessa
AU - Wolfram‐Schauerte, Maik
AU - Moskalchuk, Anastassiya
AU - Trust, Caroline
AU - Vontz, Marie
AU - Scheurenbrand, Mona
AU - Sampath‐Kumar, Vijayasarathy
AU - Ahari, Sogand
AU - Romero‐Nieto, Carlos
AU - Sevenich, Lisa
TI - Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience
T2 - Advanced healthcare materials
J2 - Adv Healthc Mater
PY - 2026
DA - 2026/03/11
VL - 15
IS - 18
SP - e04889
SN - 2192-2640
PB - Wiley
DO - 10.1002/adhm.202504889
UR - https://doi.org/10.1002/adhm.202504889
LA - en
ER -

CSL-JSON

{
"id": "10.1002/adhm.202504889",
"type": "article-journal",
"title": "Mapping the Cerebral Organoid Landscape: A Systematic Review of Preclinical 3D Models in Neuroscience",
"container-title": "Advanced healthcare materials",
"author": [
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"family": "Wolfram",
"given": "Anna"
},
{
"family": "Arnold",
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{
"family": "Wolfram‐Schauerte",
"given": "Maik"
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{
"family": "Moskalchuk",
"given": "Anastassiya"
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{
"family": "Trust",
"given": "Caroline"
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{
"family": "Vontz",
"given": "Marie"
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{
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"given": "Vijayasarathy"
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"container-title-short": "Adv Healthc Mater",
"volume": "15",
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"page": "e04889",
"DOI": "10.1002/adhm.202504889",
"PMID": "41811100",
"PMCID": "PMC13176544",
"ISSN": "2192-2640",
"publisher": "Wiley",
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"issued": {
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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