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Opioid receptor distribution in the claustrum-dorsal endopiriform complex.

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Paper

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

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The authors' code

Jupyter notebook · 110 lines · 3.8 KB · BSD-3-Clause

  1. # %% [markdown]
  2. # **In case of problems or questions, please first check the list of [Frequently Asked Questions (FAQ)](https://stardist.net/docs/faq.html).**
  4. # %%
  5. from __future__ import print_function, unicode_literals, absolute_import, division
  6. import numpy as np
  7. import matplotlib
  8. matplotlib.rcParams["image.interpolation"] = 'none'
  9. import matplotlib.pyplot as plt
  10. %matplotlib inline
  11. %config InlineBackend.figure_format = 'retina'
  13. from glob import glob
  14. from tqdm import tqdm
  15. from tifffile import imread
  16. from csbdeep.utils import Path, download_and_extract_zip_file
  18. from stardist import fill_label_holes, relabel_image_stardist, random_label_cmap
  19. from stardist.matching import matching_dataset
  21. np.random.seed(42)
  22. lbl_cmap = random_label_cmap()
  24. # %% [markdown]
  25. # # Data
  26. #
  27. # This notebook demonstrates how the training data for *StarDist* should look like and whether the annotated objects can be appropriately described by star-convex polygons.
  28. #
  29. # <div class="alert alert-block alert-info">
  30. # The training data that needs to be provided for StarDist consists of corresponding pairs of raw images and pixelwise annotated ground truth images (masks), where every pixel has a unique integer value indicating the object id (or 0 for background).
  31. # </div>
  32. #
  33. # For this demo we will download the file `dsb2018.zip` that contains the respective train and test images with associated ground truth labels as used in [our paper](https://arxiv.org/abs/1806.03535).
  34. # They are a subset of the `stage1_train` images from the Kaggle 2018 Data Science Bowl, which are [available in full](https://data.broadinstitute.org/bbbc/BBBC038/) from the [Broad Bioimage Benchmark Collection](https://data.broadinstitute.org/bbbc/).
  36. # %%
  37. download_and_extract_zip_file(
  38. url = 'https://github.com/stardist/stardist/releases/download/0.1.0/dsb2018.zip',
  39. targetdir = 'data',
  40. verbose = 1,
  41. )
  43. # %%
  44. X = sorted(glob('data/dsb2018/train/images/*.tif'))
  45. Y = sorted(glob('data/dsb2018/train/masks/*.tif'))
  46. assert all(Path(x).name==Path(y).name for x,y in zip(X,Y))
  48. # %% [markdown]
  49. # Load only a small subset
  51. # %%
  52. X, Y = X[:10], Y[:10]
  54. # %%
  55. X = list(map(imread,X))
  56. Y = list(map(imread,Y))
  58. # %% [markdown]
  59. # # Example image
  61. # %%
  62. i = min(4, len(X)-1)
  63. img, lbl = X[i], fill_label_holes(Y[i])
  64. assert img.ndim in (2,3)
  65. img = img if img.ndim==2 else img[...,:3]
  66. # assumed axes ordering of img and lbl is: YX(C)
  68. # %%
  69. plt.figure(figsize=(16,10))
  70. plt.subplot(121); plt.imshow(img,cmap='gray'); plt.axis('off'); plt.title('Raw image')
  71. plt.subplot(122); plt.imshow(lbl,cmap=lbl_cmap); plt.axis('off'); plt.title('GT labels')
  72. None;
  74. # %% [markdown]
  75. # # Fitting ground-truth labels with star-convex polygons
  77. # %%
  78. n_rays = [2**i for i in range(2,8)]
  79. scores = []
  80. for r in tqdm(n_rays):
  81. Y_reconstructed = [relabel_image_stardist(lbl, n_rays=r) for lbl in Y]
  82. mean_iou = matching_dataset(Y, Y_reconstructed, thresh=0, show_progress=False).mean_true_score
  83. scores.append(mean_iou)
  85. # %%
  86. plt.figure(figsize=(8,5))
  87. plt.plot(n_rays, scores, 'o-')
  88. plt.xlabel('Number of rays for star-convex polygon')
  89. plt.ylabel('Reconstruction score (mean intersection over union)')
  90. plt.title("Accuracy of ground truth reconstruction (should be > 0.8 for a reasonable number of rays)")
  91. None;
  93. # %%
  94. plt.figure(figsize=(8,5))
  95. plt.plot(n_rays, scores, 'o-')
  96. plt.xlabel('Number of rays for star-convex polygon')
  97. plt.ylabel('Reconstruction score (mean intersection over union)')
  98. plt.title("Accuracy of ground truth reconstruction (should be > 0.8 for a reasonable number of rays)")
  99. None;
  101. # %% [markdown]
  102. # ## Example image reconstructed with various number of rays
  104. # %%
  105. fig, ax = plt.subplots(2,3, figsize=(16,11))
  106. for a,r in zip(ax.flat,n_rays):
  107. a.imshow(relabel_image_stardist(lbl, n_rays=r), cmap=lbl_cmap)
  108. a.set_title('Reconstructed (%d rays)' % r)
  109. a.axis('off')
  110. plt.tight_layout();

1_data.ipynb at commit e80c6de, under BSD-3-Clause · at the source

Overview

Authors: Matthew Bolger1, Jesse Jackson2,3, Anna M.W. Taylor1,2,4,5
ORCID iDs: Anna M.W. Taylor
  1. Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
  2. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
  3. Department of Physiology, University of Alberta, Edmonton, Alberta, Canada
  4. Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, Alberta, Canada
  5. Department of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, Alberta, Canada
Institutions: University of Alberta (Canada); Neuroscience and Mental Health Institute (Canada)
Journal: iScience, volume 29, issue 6, article 116213
Dates: received 26 November 2025; accepted 18 May 2026; published online 2 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116213 · PMID 42291192 · PMCID PMC13254841 · OpenAlex W4416329631
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.18.687877
Status: code verified
Categories: pain (population), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: biological sciences
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: Alberta Cancer Foundation; Natural Sciences and Engineering Research Council of Canada (RGPIN 2018-04010); Canadian Institutes of Health Research (PJT 192040, PJT 195864); University of Alberta Faculty of Medicine & Dentistry Cell Imaging Core; University Hospital Foundation
Citations: cited by 1 paper (Europe PMC); 32 references in the paper

Abstract

The claustrum and dorsal endopiriform form a subcortical structure reciprocally connected to the neocortex and enriched in opioid receptors. However, the precise cellular distribution of opioid receptors within this region remains unclear. Using multiplexed fluorescent in situ hybridization we mapped the expression of mu (Oprm), delta (Oprd), kappa (Oprk), and ORL1 (Oprl) opioid receptors in the mouse claustrum-dorsal endopiriform. Oprk expression was restricted to excitatory neurons and enriched in a single population of Synpr+ claustrum core projection cells. In contrast, Oprd, Oprm, and Oprl genes were more broadly distributed across excitatory and inhibitory populations. Spatial mapping further confirmed Oprk expression was restricted within the claustrum core. Analysis of a publicly available snRNA-seq dataset of the macaque claustrum also revealed a similar receptor distribution. This demonstrates an evolutionarily conserved, cell-type-specific organization of opioid receptor expression in the claustrum-dorsal endopiriform and suggests nuanced roles for other opioid receptors in modulating claustrocortical circuits.

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

Repository

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

stardist/stardist

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e80c6de700693bc228ed3c9ba1dc19c3785667ee, 14 February 2026
Languages: Python (40), C/C++ (38), C++ (26), C (15), Jupyter (14)
Size: 201 files, 133 scripts
Software Heritage: archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (pyproject.toml, setup.cfg, setup.py, docker/Dockerfile), tests, continuous integration, 14 notebooks
Not found: CITATION.cff, documentation
Tools: NumPy (19 files), Matplotlib (14 files), tifffile (14 files), scikit-image (7 files), imageio (2 files), Numba (1 file), SciPy (1 file), TensorFlow (1 file), xarray (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
59 files
At the source: github.com/stardist/stardist

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;
  • 57 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

Datasets cited

Data and code availability

• mFISH image analysis datasets generated in this study can be found at https://doi.org/10.5281/zenodo.17633672. • This study does not report original code. • Any additional information required to reanalyze the data reported in this study is available from lead contact upon request.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 1 keyword, 5 funders, 32 references.

Cite

This paper

Bolger, M., Jackson, J., & Taylor, A. M. (2026). Opioid receptor distribution in the claustrum-dorsal endopiriform complex. iScience, 29(6), 116213. https://doi.org/10.1016/j.isci.2026.116213

BibTeX

@article{bolger2026opioid,
author = {Bolger, Matthew and Jackson, Jesse and Taylor, Anna M.W.},
title = {{Opioid receptor distribution in the claustrum-dorsal endopiriform complex}},
journal = {iScience},
year = {2026},
month = jun,
volume = {29},
number = {6},
pages = {116213},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116213},
url = {https://doi.org/10.1016/j.isci.2026.116213},
pmid = {42291192},
pmcid = {PMC13254841}
}

RIS

TY - JOUR
AU - Bolger, Matthew
AU - Jackson, Jesse
AU - Taylor, Anna M.W.
TI - Opioid receptor distribution in the claustrum-dorsal endopiriform complex
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/06/02
VL - 29
IS - 6
SP - 116213
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116213
UR - https://doi.org/10.1016/j.isci.2026.116213
LA - en
ER -

CSL-JSON

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