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Synthetic lumen rounding directs neural progenitor division mode

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  1. [1] § Methods › Image analysis › Organoid 2D analysis ↔ Thickness-masks-measurements/Thickness-measurements-updated.py.R, lines 46–108 · score 0.57 · MajorAxis, largest, thickness, Fiji, circularity, masks

Paper

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The authors' code

R · 152 lines · 4.5 KB · no license · 1 match

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Created on Fri Mar 20 16:00:42 2026
  6. @author: marchenk
  7. """
  9. import math
  10. import numpy as np
  11. import tifffile as tif
  12. from skimage.measure import label, regionprops
  13. from skimage.morphology import remove_small_objects
  14. from scipy.ndimage import binary_fill_holes
  15. import os
  16. import pandas as pd
  17. from skimage.measure import regionprops_table
  19. def analyze_thickness_tiff(
  20. tiff_path,
  21. min_object_size=50,
  22. connectivity=2
  23. ):
  24. img = tif.imread(tiff_path).astype(float)
  26. mask = img > 0
  27. mask = remove_small_objects(mask, min_size=min_object_size)
  29. labeled = label(mask, connectivity=connectivity)
  31. measurements = []
  33. for region in regionprops(labeled, intensity_image=img):
  35. # -----------------------------
  36. # Thickness statistics
  37. # -----------------------------
  38. thickness = region.intensity_image[region.intensity_image > 0]
  39. if thickness.size == 0:
  40. continue
  42. mean_thickness = thickness.mean()
  43. std_thickness = thickness.std()
  44. cv_thickness = std_thickness / mean_thickness if mean_thickness > 0 else np.nan
  46. # -----------------------------
  47. # Area, perimeter & Roundness (FIJI MATCHING)
  48. # -----------------------------
  49. # 1. Area: The actual pixels of the ring (as you defined in Fiji)
  50. area = region.area
  52. # 2. Perimeter: Use standard perimeter (better Fiji match than Crofton)
  53. perimeter = region.perimeter_crofton
  55. # 3. Major Axis: To match Fiji, we find the axis of the FILLED shape
  56. # region.image is the ring; region.filled_image is the ring + lumen
  57. filled_label = label(region.filled_image.astype(int))
  58. filled_props = regionprops(filled_label)
  60. if filled_props:
  61. # We take the major axis of the solid version of this object
  62. fiji_major_axis = filled_props[0].major_axis_length
  63. else:
  64. fiji_major_axis = region.major_axis_length
  66. # Fiji Roundness: 4 * Area / (pi * (MajorAxis of filled shape)^2)
  67. circularity = (4 * area) / (np.pi * (fiji_major_axis**2)) if fiji_major_axis > 0 else np.nan
  69. # -----------------------------
  70. # Lumen (hole) perimeter
  71. # -----------------------------
  72. object_mask = region.image # binary mask in bounding box
  74. filled = binary_fill_holes(object_mask)
  75. holes = filled & (~object_mask)
  77. lumen_perimeter = np.nan
  79. if holes.any():
  80. hole_labels = label(holes, connectivity=connectivity)
  81. hole_regions = regionprops(hole_labels)
  83. if hole_regions:
  84. # Select largest hole = lumen
  85. lumen = max(hole_regions, key=lambda r: r.area)
  86. # Using standard perimeter for lumen too for consistency
  87. lumen_perimeter = lumen.perimeter
  89. measurements.append({
  90. "mean_thickness": mean_thickness,
  91. "std_thickness": std_thickness,
  92. "cv_thickness": cv_thickness,
  93. "area_pixels": area,
  94. "perimeter_pixels": perimeter,
  95. "circularity": circularity,
  96. "lumen_perimeter": lumen_perimeter
  97. })
  99. return measurements
  101. def process_folder(
  102. input_folder,
  103. output_folder,
  104. output_csv_name="thickness_measurements.csv",
  105. min_object_size=50,
  106. connectivity=2
  107. ):
  108. all_results = []
  110. # Filter for tiff files
  111. files = [f for f in os.listdir(input_folder) if f.lower().endswith((".tif", ".tiff"))]
  113. if not files:
  114. print(f"No TIFF files found in {input_folder}")
  115. return pd.DataFrame()
  117. for file_name in files:
  118. file_path = os.path.join(input_folder, file_name)
  120. object_measurements = analyze_thickness_tiff(
  121. file_path,
  122. min_object_size=min_object_size,
  123. connectivity=connectivity
  124. )
  126. for idx, obj in enumerate(object_measurements, start=1):
  127. all_results.append({
  128. "file_name": file_name,
  129. "object_id": idx,
  130. **obj
  131. })
  133. os.makedirs(output_folder, exist_ok=True)
  135. df = pd.DataFrame(all_results)
  136. output_path = os.path.join(output_folder, output_csv_name)
  137. df.to_csv(output_path, index=False)
  139. return df
  141. # --- Execution ---
  142. input_folder = r"" # Set your input path
  143. output_folder = r"" # Set your output path
  145. df = process_folder(
  146. input_folder=input_folder,
  147. output_folder=output_folder,
  148. output_csv_name="thickness_results.csv",
  149. )
  151. if not df.empty:
  152. print(df.head())

Thickness-measurements-updated.py.R at commit 20c58ed, no license · at the source

Overview

Authors: Marina Marchenko1,2, Guillermo Martínez Ara2, Juslina Pulikkal1, Keisuke Ishihara3, Miki Ebisuya1,2,4
ORCID iDs: Marina Marchenko, Guillermo Martínez Ara, Keisuke Ishihara, Miki Ebisuya
  1. Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany
  2. European Molecular Biology Laboratory, EMBL Barcelona, C/ Dr. Aiguader, 88, PRBB Building, 08003, Barcelona, Spain
  3. Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
  4. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
Institutions: Physics of Life (Germany); Technische Universität Dresden (Germany); European Molecular Biology Laboratory (Spain); University of Pittsburgh (United States); Max Planck Institute of Molecular Cell Biology and Genetics (Germany)
Journal: bioRxiv (preprint)
Dates: published online 1 April 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.03.30.715222 · OpenAlex W7147174344
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: developmental (subfield)
Methods: Statistics, Evoked potentials
Topic: Planarian Biology and Electrostimulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 49 references in the paper

Abstract

Although function often follows form, the causal role of tissue geometry is difficult to disentangle in complex embryonic development. Brain organoids generated using diverse protocols and species display striking morphological variability, particularly in lumen shape; however, whether and how lumen geometry influences neural development remains unclear. Here, we manipulate lumen sphericity in human cerebral organoids by acutely inducing apical constriction and reveal its impact on the division orientation of apical progenitors. Rapid protein stabilization or optogenetic reconstitution of the apical constriction regulator Shroom3 induces pronounced lumen rounding accompanied by a reduction in apical surface area. In organoids with rounded lumens, apical progenitor divisions shift toward horizontal cleavage planes compared with control organoids, consistent with geometric constraints from the reduced apical surface. Accordingly, rounded-lumen organoids exhibit increased cell delamination and an earlier emergence of basal progenitors in the abventricular region. These findings identify lumen geometry as an instructive regulator of progenitor division mode and lineage progression during early brain development.

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 1 match between paragraphs and lines of code.

mebisuya/BrainOrganoidShape

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 20c58ed341040a7d110ce214901f9cbf4de4f928, 22 March 2026
Languages: R (4), Jupyter (3)
Size: 16 files, 7 scripts
Software Heritage: not archived
Found in: “Data and materials availability”
Holds: environment (Angle-fusion-tif-main/env.yml), 3 notebooks
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (3 files), scikit-image (3 files), Matplotlib (2 files), SciPy (2 files), OpenCV (1 file), pandas (1 file), PyTorch (1 file), scikit-learn (1 file), seaborn (1 file), tifffile (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
7 files
At the source: github.com/mebisuya/BrainOrganoidShape

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

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

Data and materials availability

The custom scripts used are available from Github [https://github.com/mebisuya/BrainOrganoidShape].

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 2, 28 September 2026

  • Authors: added Marina Marchenko (0000-0002-0473-9430); Guillermo Martínez Ara (0000-0002-7480-5595); removed Marina Marchenko; Guillermo Martínez Ara

Version 1, 28 September 2026: the first record

Recorded: type, journal, dates, 5 authors, 47 references.

Cite

This paper

Marchenko, M., Martínez Ara, G., Pulikkal, J., Ishihara, K., & Ebisuya, M. (2026). Synthetic lumen rounding directs neural progenitor division mode. bioRxiv (preprint). https://doi.org/10.64898/2026.03.30.715222

BibTeX

@article{marchenko2026synthetic,
author = {Marchenko, Marina and Martínez Ara, Guillermo and Pulikkal, Juslina and Ishihara, Keisuke and Ebisuya, Miki},
title = {{Synthetic lumen rounding directs neural progenitor division mode}},
journal = {bioRxiv (preprint)},
year = {2026},
month = apr,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.30.715222},
url = {https://doi.org/10.64898/2026.03.30.715222}
}

RIS

TY - JOUR
AU - Marchenko, Marina
AU - Martínez Ara, Guillermo
AU - Pulikkal, Juslina
AU - Ishihara, Keisuke
AU - Ebisuya, Miki
TI - Synthetic lumen rounding directs neural progenitor division mode
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/04/01
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.30.715222
UR - https://doi.org/10.64898/2026.03.30.715222
ER -

CSL-JSON

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"type": "article",
"title": "Synthetic lumen rounding directs neural progenitor division mode",
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"given": "Juslina"
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{
"family": "Ishihara",
"given": "Keisuke"
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"given": "Miki"
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"container-title-short": "bioRxiv",
"DOI": "10.64898/2026.03.30.715222",
"ISSN": "2692-8205",
"publisher": "bioRxiv",
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"issued": {
"date-parts": [
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2026,
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1
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