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Tau Aggregate Imaging and Transcriptomics of Alzheimer's Disease Brain at Different Stages of Disease.

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  1. [1] § Methods › Single Molecule Pull‐Down (SiMPull) › Super‐Resolution Image Acquisition and Analysis ↔ src/SR_analysis/SR_image_reconstruction.py, lines 1–20 · score 0.53 · Picasso functionality, rendered, reconstructed, precision, localisations, Super

Paper

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The authors' code

Python · 100 lines · 3.8 KB · MIT · 1 match

  1. # ## Manually export images using the picasso functions to render oversampled images with optional precision based blur.
  2. from configparser import Interpolation
  3. import os
  4. import numpy as np
  5. import pandas as pd
  6. from picasso import render, io
  7. import matplotlib.pyplot as plt
  8. from skimage.io import imread
  9. import matplotlib.patches as mpatches
  10. import napari
  11. import numpy as np
  12. from skimage import data, transform
  14. input_parameters = 'results/super-res/initial_cleanup/slide_parameters.csv'
  15. input_folder = 'results/super-res/localisation/'
  16. output_folder = 'results/super-res/rendering/'
  17. oversampling = 8
  19. if not os.path.exists(output_folder):
  20. os.makedirs(output_folder)
  22. # Compile list of files to be processed
  23. parameters = pd.read_csv(input_parameters)
  24. parameters.drop([col for col in parameters.columns.tolist() if 'Unnamed: ' in col], axis=1, inplace=True)
  25. parameters.dropna(subset=['keep'], inplace=True)
  27. # Read in localisations
  28. for filepath, well_info in parameters[['file_path', 'well_info']].values:
  29. filepath
  30. try:
  31. locs, info = io.load_locs(f'{input_folder}{well_info}_locs_corr_filt.hdf5')
  32. original = imread(filepath)
  33. except:
  34. continue
  36. # Get minimum / maximum localizations to define the ROI to be rendered
  37. x_min = np.min(locs.x)
  38. x_max = np.max(locs.x)
  39. y_min = np.min(locs.y)
  40. y_max = np.max(locs.y)
  42. viewport = (y_min, x_min), (y_max, x_max)
  43. oversampling = 8
  44. len_x, image = render.render(locs, viewport = viewport, oversampling=oversampling, blur_method='smooth')
  45. plt.imsave(f'{output_folder}{well_info}_smooth.png', image, cmap='hot', vmax=10)
  47. fig, axes = plt.subplots(1, 2, figsize=(30, 15))
  48. axes[0].imshow(np.max(original, axis=0), cmap='Greys_r')
  49. axes[0].set_title('Original')
  50. axes[1].imshow(image, cmap='hot', vmax=8)
  51. axes[1].set_title('Super-resolved')
  52. plt.savefig(f'{output_folder}{well_info}_sr.png')
  53. plt.show()
  57. # Cutom ROI with higher oversampling for a single image:
  58. # Open necessary components
  59. well = ''
  60. filepath, well_info = parameters[parameters['well_info'] == well][['file_path', 'well_info']].values[0]
  61. locs, info = io.load_locs(f'{input_folder}{well_info}_locs_corr_filt.hdf5')
  62. original = imread(filepath)
  63. original_image = transform.rescale(np.max(original, axis=0), 8, preserve_range=True)
  65. viewport = (np.min(locs.y), np.min(locs.x)), (np.max(locs.y), np.max(locs.x))
  66. viewport = (0, 0), (512, 512)
  67. oversampling = 8
  68. len_x, sr_image = render.render(locs, viewport = viewport, oversampling=oversampling, blur_method='smooth')
  69. # ---------
  72. with napari.gui_qt():
  73. # add the image
  74. viewer = napari.Viewer()
  75. viewer.add_image(original_image, name='Original', contrast_limits=(2, 65555))
  76. viewer.add_image(sr_image, name='Super resolved')
  77. shapes = viewer.add_shapes()
  78. napari.run()
  80. #[ystart, ystop, xstart, xstop],
  81. viewports = [[int(ystart/oversampling), int(ystop/oversampling), int(xstart/oversampling), int(xstop/oversampling)] for [[ystart, xstart], [_, _], [ystop, xstop], [_, _]] in shapes.data]
  83. zoomsampling = 20
  84. palette = {0: 'orange', 1: 'red', 2: 'rebeccapurple'}
  85. for x, chunk in enumerate(viewports):
  86. ystart, ystop, xstart, xstop = chunk
  87. len_x, zoom_image = render.render(locs, viewport = ((ystart, xstart), (ystop, xstop)), oversampling=zoomsampling, blur_method='smooth')
  88. plt.imsave(f'{output_folder}{well_info}_zoom_{x}_smooth.png', zoom_image, cmap='hot', vmax=10)
  90. fig, axes = plt.subplots(1, 2, figsize=(30, 15))
  91. axes[0].imshow(np.max(original[:, ystart: ystop, xstart: xstop], axis=0), cmap='Greys_r', interpolation='none')
  92. axes[0].set_title('Original')
  93. axes[0].axis('off')
  94. axes[1].imshow(zoom_image, cmap='hot', vmax=10)
  95. axes[1].set_title('Super-resolved')
  96. axes[1].axis('off')
  97. plt.savefig(f'{output_folder}{well_info}_zoom_{x}_sr.png')
  98. plt.show()

SR_image_reconstruction.py at commit 172aad8, under MIT · at the source

Overview

Authors: Elizabeth A English1,2, Dorothea Böken1,2, Matthew W Cotton1,2, Matthew R Cheetham1, Johanna S Jackson3,4, Georg Meisl1,2, John S H Danial5, Paul M Matthews3,4,6, Nurun N Fancy3,4, David Klenerman1,2
ORCID iDs: Elizabeth A English, Dorothea Böken, Matthew W Cotton, Matthew R Cheetham, Johanna S Jackson, Georg Meisl, Paul M Matthews, Nurun N Fancy, David Klenerman
  1. Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
  2. UK Dementia Research Institute at University of Cambridge, Cambridge, UK
  3. Department of Brain Sciences, Imperial College London, London, UK
  4. UK Dementia Research Institute, Imperial College London, London, UK
  5. School of Physics and Astronomy, University of St Andrews, St Andrews, UK
  6. Rosalind Franklin Institute, Didcot, UK
Institutions: University of Cambridge (United Kingdom); UK Dementia Research Institute (United Kingdom); Imperial College London (United Kingdom); University of St Andrews (United Kingdom); Rosalind Franklin Institute (United Kingdom)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), article e77453
Dates: received 6 February 2026; accepted 17 August 2026; published online 31 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.77453 · PMID 42671397 · PMCID PMC13528684 · OpenAlex W7204852976
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Alzheimer’s Research UK; Parkinson's UK; UK Dementia Research Institute; Alzheimer's Society (10); Edmond J. Safra Foundation; Medical Research Council; Royal Society
Citations: not cited yet (Europe PMC); 52 references in the paper

Abstract

Tau aggregation plays a critical role in the development and progression of Alzheimer's disease (AD). Tau aggregates of different sizes and shapes are formed, which ultimately lead to the deposition of fibrillar tangles. We used single‐molecule techniques to characterize tau aggregates in the middle temporal gyrus and somatosensory cortex in post‐mortem brain homogenates at different Braak stages from patients with AD. Total and phosphorylated tau aggregates increased dramatically in late Braak stages. The aggregates showed greater multi‐site phosphorylation with increased Braak stage, but there was only a moderate change in the aggregate size distribution. Paired single nuclei transcriptomic analyses provided evidence for greater pro‐inflammatory microglial and complement pathway activation with increasing phosphorylated tau aggregate concentration and length. Based on this correlation, we hypothesize a cascade of disease progression in which microglial inflammation induces tau aggregation in neighboring neurons that, in turn, further enhances inflammation and the spread of tau pathology to increase the concentration of small tau aggregates.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Zenodo 15635269

License: MIT
State: the link answers, verified on 27 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
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.15635269

dboeken/boeken_tauopathies

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 172aad8189c819db7c1606fa10931899cc9c255e, 8 March 2026
Languages: Python (9)
Size: 18 files, 9 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff, environment (environment.yml)
Not found: tests, continuous integration, documentation
Tools: NumPy (9 files), pandas (9 files), Matplotlib (6 files), scikit-image (6 files), seaborn (5 files), napari (3 files), h5py (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/dboeken/Boeken_Tauopathies/tree/1.0.0

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 9 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.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

The data set that supports these findings have been made publically available.

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

Data Availability Statement

All original code for the super‐resolution analysis has been deposited at Zenodo at https://doi.org/10.5281/zenodo.15635269 and is publicly available as of the date of publication. All data are available in the manuscript orthe supplementary material. while the transcriptomics data can be found at: https://www.synapse.org/Synapse:syn52658340. Raw data is available on written request to DK.

The data set that supports these findings have been made publically available.

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, 27 September 2026: the first record

Recorded: type, language, journal, pages, dates, 10 authors, 7 funders, 49 references.

Cite

This paper

English, E. A., Böken, D., Cotton, M. W., Cheetham, M. R., Jackson, J. S., Meisl, G., Danial, J. S. H., Matthews, P. M., Fancy, N. N., & Klenerman, D. (2026). Tau Aggregate Imaging and Transcriptomics of Alzheimer's Disease Brain at Different Stages of Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany), e77453. https://doi.org/10.1002/advs.77453

BibTeX

@article{english2026tau,
author = {English, Elizabeth A and Böken, Dorothea and Cotton, Matthew W and Cheetham, Matthew R and Jackson, Johanna S and Meisl, Georg and Danial, John S H and Matthews, Paul M and Fancy, Nurun N and Klenerman, David},
title = {{Tau Aggregate Imaging and Transcriptomics of Alzheimer's Disease Brain at Different Stages of Disease}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = aug,
pages = {e77453},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.77453},
url = {https://doi.org/10.1002/advs.77453},
pmid = {42671397},
pmcid = {PMC13528684}
}

RIS

TY - JOUR
AU - English, Elizabeth A
AU - Böken, Dorothea
AU - Cotton, Matthew W
AU - Cheetham, Matthew R
AU - Jackson, Johanna S
AU - Meisl, Georg
AU - Danial, John S H
AU - Matthews, Paul M
AU - Fancy, Nurun N
AU - Klenerman, David
TI - Tau Aggregate Imaging and Transcriptomics of Alzheimer's Disease Brain at Different Stages of Disease
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/08/31
SP - e77453
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.77453
UR - https://doi.org/10.1002/advs.77453
LA - en
ER -

CSL-JSON

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