OSCR

Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Image Analysis ↔ src/Colocalisation_analysis/2_count_spots.py, lines 102–132 · score 0.59 · localized spots, detected spots, ComDet, channel, threshold
  2. [2] § Methods › Image Analysis ↔ src/SR_analysis/SR_image_reconstruction.py, lines 1–20 · score 0.50 · Picasso functionality, rendered, precision, reconstructed, Super

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Python · 149 lines · 5.1 KB · MIT · 1 match

  1. import functools
  2. import os
  3. import matplotlib.pyplot as plt
  4. import numpy as np
  5. import pandas as pd
  6. import seaborn as sns
  7. from skimage import io
  8. from loguru import logger
  10. from smma.src.utilities import comdet
  11. import smma.src.utilities as utilities
  13. logger.info('Import OK')
  15. # Remember to add napari.run() calls after each loop!
  16. # SETTINGS.application.ipy_interactive = False
  17. np.set_printoptions(suppress=True) # stop scientific notion for printing
  19. image_id = ''
  20. input_folder = utilities.locate_raw_drive_files(input_path='raw_data/raw_data.txt')
  21. image_folder = f'{input_folder}/{image_id}/'
  22. input_path = 'results/spot_detection/initial_cleanup/slide_parameters.csv'
  23. output_folder = 'results/spot_detection/count_spots/'
  25. control_image = ''
  26. test_image = ''
  28. if not os.path.exists(output_folder):
  29. os.makedirs(output_folder)
  32. # Read in file directory
  33. slide_params = pd.read_csv(f'{input_path}')
  34. slide_params.drop([col for col in slide_params.columns.tolist()
  35. if 'Unnamed: ' in col], axis=1, inplace=True)
  36. # remove discard images
  37. slide_params.dropna(subset=['keep'], inplace=True)
  39. # ----------Determine optimal parameters----------
  40. #Generate projection image - in this case, maximum
  42. max_pixels = 10
  43. max_threshold = 20
  45. control_spots = []
  46. for image_name in [control_image, test_image]:
  47. image = io.imread(
  48. f'{image_folder}{image_name}.tif')
  49. mean_image = np.mean(image, axis=0)
  50. for pixel_size in range(2, max_pixels+1):
  51. for threshold in range(3, max_threshold+1):
  52. visualise = False if threshold == 18 else False
  53. measurements = comdet(image=mean_image.astype(
  54. float), sigma_threshold=threshold, particle_guess=pixel_size, visualise=visualise)
  55. logger.info(f'{len(measurements)} spots detected in {control_image} with particle guess {pixel_size} and threshold {threshold}')
  57. measurements['particle_guess'] = pixel_size
  58. measurements['sigma_threshold'] = threshold
  59. measurements['image_name'] = image_name
  60. control_spots.append(measurements)
  61. spots = pd.concat(control_spots)
  64. spots_per_fov = spots.groupby(['particle_guess', 'sigma_threshold', 'image_name']).count(
  65. )['label'].reset_index().rename(columns={'label': 'spots_count'})
  67. fig, axes = plt.subplots(1, 2, figsize=(10, 5))
  68. for x, image_name in enumerate([control_image, test_image]):
  69. sns.lineplot(
  70. data=spots_per_fov[spots_per_fov['image_name'] == image_name],
  71. x='sigma_threshold',
  72. y='spots_count',
  73. hue='particle_guess',
  74. ax=axes[x]
  75. )
  76. axes[x].set_title(image_name)
  77. plt.tight_layout()
  78. plt.show()
  80. comparison = pd.pivot(spots_per_fov, index=['particle_guess', 'sigma_threshold'], columns=['image_name'], values='spots_count').reset_index()
  81. comparison['proportion'] = comparison[control_image] / comparison[test_image]
  82. fig, ax = plt.subplots()
  83. sns.lineplot(
  84. data=comparison,
  85. x='sigma_threshold',
  86. y='proportion',
  87. hue='particle_guess',
  88. palette='tab10'
  89. )
  90. sns.scatterplot(
  91. data=comparison[comparison['proportion'] < 0.1].sort_values('sigma_threshold').drop_duplicates(subset=['particle_guess']),
  92. x='sigma_threshold',
  93. y='proportion',
  94. hue='particle_guess',
  95. palette='tab10'
  96. )
  98. plt.legend(bbox_to_anchor=(1.0, 1.0))
  99. plt.tight_layout()
  100. plt.show()
  102. # -----------------localize_spots-----------------
  103. thresholds = {
  104. '641': [8, 4],
  105. '488': [5, 4],
  106. }
  108. slide_details = dict()
  109. # Detect spots
  110. spots = []
  111. for x, (layout, image_path, slide_name) in enumerate(slide_params[['layout', 'file_path', 'well_info']].values):
  112. channel = slide_name.split('_')[-1]
  113. sigma_threshold, particle_guess = thresholds[channel]
  115. # Generate projection image - in this case, maximum
  116. image = io.imread(image_path)
  117. mean_image = np.mean(image[10:, :, :], axis=0)
  119. visualise = f'{output_folder}{slide_name}_spots.png' if x % 16 == 0 else False
  120. measurements = comdet(image=mean_image.astype(
  121. float), sigma_threshold=sigma_threshold, particle_guess=particle_guess, visualise=visualise)
  122. logger.info(f'{len(measurements)} spots detected in {slide_name}')
  124. measurements['layout'] = layout
  125. measurements['well_info'] = slide_name
  126. measurements['file_path'] = image_path
  128. spots.append(measurements)
  129. spots = pd.concat(spots)
  131. spots_per_fov = spots.groupby(['file_path', 'well_info', 'layout']).count(
  132. )['label'].reset_index().rename(columns={'label': 'spots_count'})
  134. # Add sample info
  135. spots_per_fov = functools.reduce(lambda left, right: pd.merge(
  136. left, right, on=['file_path', 'well_info', 'layout'], how='outer'), [slide_params, spots_per_fov])
  138. # Save to csv
  139. spots_per_fov.to_csv(f'{output_folder}spots_per_fov.csv')
  140. spots.to_csv(f'{output_folder}compiled_spots.csv')
  143. # Add sample info
  144. spot_stats = spots.groupby(['file_path', 'well_info', 'layout']).mean(
  145. ).reset_index()
  146. spot_stats = functools.reduce(lambda left, right: pd.merge(
  147. left, right, on=['file_path', 'well_info', 'layout'], how='outer'), [slide_params, spot_stats])
  148. spot_stats.drop([col for col in spot_stats.columns.tolist() if 'Unnamed: ' in col], axis=1, inplace=True)
  149. spot_stats.to_csv(f'{output_folder}compiled_stats.csv')

2_count_spots.py at commit 44a90a7, under MIT · at the source

Overview

Authors: Dorothea Böken1,2, Paula Beltran Lobo3, Yunzhao Wu1, Lyla A. Rowe4, Emre Fertan1,2,5, Cara L. Croft4, Maria Jimenez‐Sanchez3, Dezerae Cox1,2,6, David Klenerman1,2
ORCID iDs: Dorothea Böken, Paula Beltran Lobo, Emre Fertan, Cara L. Croft, Maria Jimenez‐Sanchez, David Klenerman
  1. Yusuf Hamied Department of Chemistry University of Cambridge Cambridge UK
  2. UK Dementia Research Institute University of Cambridge Cambridge UK
  3. Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute King's College London London UK
  4. Centre For Neuroscience, Surgery and Trauma, The Blizard Institute Queen Mary University of London London UK
  5. Department of Clinical Neurosciences University of Cambridge Cambridge UK
  6. Molecular Horizons, School of Sciences University of Wollongong Wollongong New South Wales Australia
Institutions: University of Cambridge (United Kingdom); UK Dementia Research Institute (United Kingdom); King's College London (United Kingdom); Queen Mary University of London (United Kingdom); Blizard Institute (United Kingdom); University of Wollongong (Australia)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), article e77822
Dates: received 16 January 2026; accepted 8 September 2026; published online 16 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.77822 · PMID 42750204 · PMCID PMC13583081 · OpenAlex W7213472695
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Machine learning
Keywords: Alzheimer's disease, interactome, proteomics, single‐molecule, Tau
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Royal Society funded Professorship; UK Dementia Research Institute (DRI‐PRO202332); MND Association UK ((Cox971‐799)); Department of Education and Training | Australian Research Council (ARC) (DE240100707); Race Against Dementia Alzheimer’s Research UK fellowship (ARUK‐RADF2019A‐003); RCUK | Medical Research Council (MRC) (MR/V036947/1)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Tau aggregation is a central pathological feature of Alzheimer's disease, yet how different forms of tau—ranging from monomers to small soluble aggregates and mature fibrils—interact with the cellular environment remains poorly understood. Here, we combine immunoaffinity proteomics with single‐molecule techniques and super‐resolution microscopy to systematically map the tau interactome across defined aggregation states, spanning monomeric tau, nanoscopic soluble aggregates, and fibrillar species. Using post‐mortem Alzheimer's disease brain tissue, we identify distinct functional modules associated with different aggregation states: while proteostasis factors and immune‐related proteins preferentially associate with nanoscopic aggregates (oligomers), cytoskeletal, metabolic, and RNA‐binding proteins are enriched for mature fibrillar tau. Single‐molecule microscopy directly confirms this conformation‐dependent recruitment for key interactors including Hsp70‐2, ENO1, hnRNPA1, APP, EAAT4, and ubiquitin. A primary‐neuron system with accelerated tau aggregation is used to model these findings in a controlled system, showing striking similarities to the brain samples. Finally, pseudotime analysis reconstructs a progressive remodelling of the tau interactome across disease progression, revealing stage‐specific pathway vulnerabilities. Together, these results establish a temporally resolved framework for tau pathology shaped by protein interactions and identify potential therapeutic intervention points for investigation across stages of disease.

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 2 matches between paragraphs and lines of code.

Zenodo 17990783

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
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 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.17990783

dboeken/boeken_tau_aggregate_interactome

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 44a90a7dd6f13b813f20666faa896d156f765e32, 15 May 2026
Languages: Python (17)
Size: 26 files, 17 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff, environment (environment_proteomics.yml, environment_smma.yml)
Not found: tests, continuous integration, documentation
Tools: pandas (17 files), NumPy (16 files), Matplotlib (9 files), seaborn (8 files), scikit-image (6 files), napari (3 files), SciPy (3 files), h5py (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
19 files
At the source: github.com/dboeken/Boeken_Tau_aggregate_interactome/tree/v1.0.0

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;
  • 17 scripts, each with its path and the digest of its content;
  • 2 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 Statement

All original code has been deposited at Zenodo at https://doi.org/10.5281/zenodo.17990783 and is publicly available as of the date of publication. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [37] partner repository with the dataset identifier PXD072117. The PRIDE dataset includes the raw mass‐spectrometry files, the MaxQuant parameter file, and the corresponding MaxQuant output files. All data are available in the manuscript or the supplementary material. Raw data available on written request to DK.

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, 9 authors, 5 keywords, 6 funders, 42 references.

Cite

This paper

Böken, D., Lobo, P. B., Wu, Y., Rowe, L. A., Fertan, E., Croft, C. L., Jimenez‐Sanchez, M., Cox, D., & Klenerman, D. (2026). Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease. Advanced science (Weinheim, Baden-Wurttemberg, Germany), e77822. https://doi.org/10.1002/advs.77822

BibTeX

@article{boken2026temporal,
author = {Böken, Dorothea and Lobo, Paula Beltran and Wu, Yunzhao and Rowe, Lyla A. and Fertan, Emre and Croft, Cara L. and Jimenez‐Sanchez, Maria and Cox, Dezerae and Klenerman, David},
title = {{Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = sep,
pages = {e77822},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.77822},
url = {https://doi.org/10.1002/advs.77822},
pmid = {42750204},
pmcid = {PMC13583081}
}

RIS

TY - JOUR
AU - Böken, Dorothea
AU - Lobo, Paula Beltran
AU - Wu, Yunzhao
AU - Rowe, Lyla A.
AU - Fertan, Emre
AU - Croft, Cara L.
AU - Jimenez‐Sanchez, Maria
AU - Cox, Dezerae
AU - Klenerman, David
TI - Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/09/16
SP - e77822
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.77822
UR - https://doi.org/10.1002/advs.77822
LA - en
ER -

CSL-JSON

{
"id": "10.1002/advs.77822",
"type": "article-journal",
"title": "Temporal Trajectories of the Tau Aggregate Interactome Reveal Stage-Specific Vulnerabilities in Alzheimer's Disease",
"container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
"author": [
{
"family": "Böken",
"given": "Dorothea"
},
{
"family": "Lobo",
"given": "Paula Beltran"
},
{
"family": "Wu",
"given": "Yunzhao"
},
{
"family": "Rowe",
"given": "Lyla A."
},
{
"family": "Fertan",
"given": "Emre"
},
{
"family": "Croft",
"given": "Cara L."
},
{
"family": "Jimenez‐Sanchez",
"given": "Maria"
},
{
"family": "Cox",
"given": "Dezerae"
},
{
"family": "Klenerman",
"given": "David"
}
],
"container-title-short": "Adv Sci (Weinh)",
"page": "e77822",
"DOI": "10.1002/advs.77822",
"PMID": "42750204",
"PMCID": "PMC13583081",
"ISSN": "2198-3844",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/advs.77822",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
16
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1021/acs.analchem.6c03315 [code]
Profiling Protein Aggregate Size Using Single-Molecule Array Technology.
Journal: Analytical chemistry
In common: pandas, NumPy, Alzheimer's / dementia, cellular / molecular, 6 references, 3 authors
[2] doi:10.1002/advs.77453 [code]
Tau Aggregate Imaging and Transcriptomics of Alzheimer's Disease Brain at Different Stages of Disease.
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
In common: napari, scikit-image, h5py, 4 other tools, Alzheimer's / dementia, genetics / omics, cellular / molecular, 4 references, 2 authors
[3] doi: [code]
Real-time closed-loop feedback system for mouse mesoscale cortical signal and movement control
Journal: eLife
In common: napari, scikit-image, h5py, 5 other tools
[4] doi:10.1038/s41467-026-75352-7 [code]
Mechanosensory encoding of surface mechanics optimizes locomotion.
Journal: Nature communications
In common: napari, scikit-image, seaborn, 4 other tools, cellular / molecular
[5] doi:10.1126/sciadv.aed5548 [code]
In situ proteomics unveils specialized domains for extrasynaptic signaling on neuronal cilia.
Journal: Science advances
In common: napari, scikit-image, SciPy, 2 other tools, genetics / omics, cellular / molecular, 1 reference
[6] doi:10.1093/neuonc/noag128 [code]
Spatially-resolved single-cell imaging of melanoma brain metastases identifies localized immune patterns predictive of immune checkpoint blockade response.
Journal: Neuro-oncology
In common: napari, scikit-image, h5py, 3 other tools, genetics / omics
[7] doi:10.1038/s41467-026-73045-9 [code]
Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.
Journal: Nature communications
In common: napari, scikit-image, pandas, 3 other tools, histology / microscopy, cellular / molecular
[8] doi:10.1371/journal.pcbi.1014571 [code]
SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.
Journal: PLoS computational biology
In common: napari, scikit-image, seaborn, 4 other tools
[9] doi:10.1038/s41467-026-73770-1 [code]
Non-coding structural variants disrupt FOXG1 transcriptional regulation in early neurodevelopment.
Journal: Nature communications
In common: napari, scikit-image, seaborn, 4 other tools
[10] doi:10.1016/j.celrep.2026.117420 [code]
Neural population dynamics of direct electrical stimulation of neocortex.
Journal: Cell reports
In common: napari, scikit-image, seaborn, 4 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.