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Profiling Protein Aggregate Size Using Single-Molecule Array Technology.

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The authors' code

Python · 127 lines · 5 KB · MIT

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Wed Sep 18 15:30:53 2024
  5. @author: Trevor Wu
  6. """
  8. import pandas as pd
  9. import os
  10. import numpy as np
  12. path = r'C:\Users\Trevor Wu\OneDrive - University of Cambridge\2025_Ongoing_Projects\HT7_intensity_paper\Data\20260417_HT7_lysate\2026-04-16 Run 2_20260416151310'
  13. saveto = r'C:\Users\Trevor Wu\OneDrive - University of Cambridge\2025_Ongoing_Projects\HT7_intensity_paper\Data\20260417_HT7_lysate\20260416_lysate_analysed'
  14. all_wells = True
  15. woi = ['D06', 'C07'] # wells of interest, use 'E03' instead of 'E3'
  16. intensity_range = [-1000, 19000]
  17. bin_size = 100
  18. intensity_cutoff = 110
  20. #for new AEB and integrated intensity calculation
  21. intensity_CO_start = 0
  22. intensity_CO_end = 20000
  23. intensity_CO_step = 10
  25. def getListFiles(path, kwd = ''):
  26. filelist = []
  27. for root, dirs, files in os.walk(path):
  28. for filespath in files:
  29. if kwd in filespath:
  30. filelist.append(os.path.join(root,filespath))
  31. return filelist
  33. def generate_allwells(path):
  34. if all_wells:
  35. filelist= getListFiles(path, kwd = '.csv')
  36. ffs = []
  37. for f in filelist:
  38. ff = f.split('\\')[-1].split('_')[1].split('.ipl')[0]
  39. ffs.append(ff)
  40. ffs = sorted(list(set(ffs)))
  41. return ffs
  43. def generate_bins(intensity_range, bin_size):
  44. bins = np.arange(intensity_range[0], intensity_range[1]+1, bin_size)
  45. bin_centers = bins + bin_size/2
  46. bin_centers = np.array(bin_centers[:-1], dtype = np.int64)
  47. return bins, bin_centers
  49. def get_histo(w, welldata_list, bins, bin_centers, intensity_cutoff):
  50. wname = [well for well in welldata_list if w in well][0]
  51. wdata = pd.read_csv(wname)
  52. reso_intensity = np.asarray(wdata['Resorufin Growth'])
  53. histo = np.histogram(reso_intensity, bins)[0]
  54. num_mw_overthres = len(reso_intensity[reso_intensity>intensity_cutoff])
  55. nor_histo = histo/num_mw_overthres
  56. return histo, nor_histo, num_mw_overthres
  58. def process_folder(path, woi, intensity_range, bin_size, intensity_cutoff):
  59. welldata_list = getListFiles(path, 'Well Data.csv')
  60. bins, bin_centers = generate_bins(intensity_range, bin_size)
  61. histos = [bin_centers]
  62. nor_histos = [bin_centers]
  63. num_wells = []
  64. for w in woi:
  65. histo, nor_histo, num_mw_overthres = get_histo(w, welldata_list, bins, bin_centers, intensity_cutoff)
  66. histos.append(histo)
  67. nor_histos.append(nor_histo)
  68. num_wells.append(num_mw_overthres)
  69. histos_pd = pd.DataFrame(histos).T
  70. norhistos_pd = pd.DataFrame(nor_histos).T
  71. wells_pd = pd.DataFrame(num_wells).T
  72. wells_pd.columns = woi
  73. woii = woi.copy()
  74. woii.insert(0, 'Bin_center')
  75. histos_pd.columns = woii
  76. norhistos_pd.columns = woii
  77. return histos_pd, norhistos_pd, wells_pd
  79. def cal_AEB(list_CO, intensity_well):
  80. aeb_int = []
  81. for c in list_CO[:, 0]:
  82. num_pos = len(intensity_well[intensity_well>c])
  83. fon = num_pos/len(intensity_well)
  84. aeb_int.append([-np.log(1-fon), np.sum(intensity_well[intensity_well>c])])
  85. return np.asarray(aeb_int)[:,0], np.asarray(aeb_int)[:,1]
  87. def analyse_welldata(w, welldata_list, list_CO):
  88. wname = [well for well in welldata_list if w in well][0]
  89. wdata = pd.read_csv(wname)['Resorufin Growth']
  90. aeb, integrated_intensity = cal_AEB(list_CO, wdata)
  91. return aeb.reshape((len(aeb), 1)), integrated_intensity.reshape((len(integrated_intensity), 1))
  93. def output_aeb_int(path, saveto, woi, intensity_CO_start, intensity_CO_end, intensity_CO_step):
  94. list_CO = np.arange(intensity_CO_start, intensity_CO_end, intensity_CO_step)
  95. list_CO = list_CO.reshape((len(list_CO), 1))
  96. filelist = getListFiles(path, kwd='Well Data.csv')
  98. aebs = [list_CO]
  99. ints = [list_CO]
  101. for w in woi:
  102. aeb, integrated_intensity = analyse_welldata(w, filelist, list_CO)
  103. aebs.append(aeb)
  104. ints.append(integrated_intensity)
  105. aebs = np.concatenate(aebs, axis = 1)
  106. ints = np.concatenate(ints, axis = 1)
  107. aebs_df = pd.DataFrame(aebs)
  108. ints_df = pd.DataFrame(ints)
  109. woii = woi.copy()
  110. woii.insert(0, 'Cutoff')
  111. aebs_df.columns = woii
  112. ints_df.columns = woii
  113. return aebs_df, ints_df
  115. if all_wells == False:
  116. histos_pd, norhistos_pd, wells_pd = process_folder(path, woi, intensity_range, bin_size, intensity_cutoff)
  117. else:
  118. histos_pd, norhistos_pd, wells_pd = process_folder(path, generate_allwells(path), intensity_range, bin_size, intensity_cutoff)
  119. histos_pd.to_csv(saveto+'\\Histogram.csv', index = None)
  120. norhistos_pd.to_csv(saveto+'\\Normalised_Histogram_CO_'+str(intensity_cutoff)+'.csv', index = None)
  121. wells_pd.to_csv(saveto+'\\Num_wells_CO_'+str(intensity_cutoff)+'.csv', index = None)
  122. if all_wells == False:
  123. aebs_df, ints_df = output_aeb_int(path, saveto, woi, intensity_CO_start, intensity_CO_end, intensity_CO_step)
  124. else:
  125. aebs_df, ints_df = output_aeb_int(path, saveto, generate_allwells(path), intensity_CO_start, intensity_CO_end, intensity_CO_step)
  126. aebs_df.to_csv(saveto+'\\New_AEB.csv', index = None)
  127. ints_df.to_csv(saveto+'\\Integrated_intensity.csv', index = None)

Simoa_brightness_analysis.py, under MIT · at the source

Overview

Authors: Dorothea Böken1,2, Yunzhao Wu1,2, Jianli Zhang3, Zengjie Xia4,5,6, Paula Beltran-Lobo7, Cara L. Croft8, Savinu Weerasekera1, Amanda Heslegrave4,5, Henrik Zetterberg4,5,6,9,10,11,12, Ashvini Keshavan5,13, Jonathan M. Schott5,13, Maria Jimenez-Sanchez7, David C. Duffy3, David Klenerman1,2
ORCID iDs: Dorothea Böken, Cara L. Croft, Savinu Weerasekera, Henrik Zetterberg, Maria Jimenez-Sanchez
13 affiliations
  1. Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
  2. UK Dementia Research Institute, University of Cambridge, Cambridge CB2 0AH, U.K
  3. Quanterix Corporation, 900 Middlesex Turnpike, Billerica, Massachusetts 01821, United States
  4. UCL Queen Square Institute of Neurology, London WC1N 3BG, U.K
  5. UK Dementia Research Institute at University College London, London WC1N 3BG, U.K
  6. Hong Kong Center for Neurodegenerative Diseases, Hong Kong 999077, China
  7. Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King’s College London, London SE5 9RX, U.K
  8. Centre for Neuroscience, Surgery and Trauma, The Blizard Institute, Queen Mary University of London, London E1 2AT, U.K
  9. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal 43180, Sweden
  10. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 43180, Sweden
  11. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin–Madison, Madison, Wisconsin 53792, United States
  12. Department of Pathology and Laboratory Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53792, United States
  13. Dementia Reseach Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London WC1N 3BG, U.K
Institutions: University of Cambridge (United Kingdom); UK Dementia Research Institute (United Kingdom); Quanterix (United States) (United States); UCL Queen Square Institute of Neurology (United Kingdom); University College London (United Kingdom); King's College London (United Kingdom); Queen Mary University of London (United Kingdom); Blizard Institute (United Kingdom); Sahlgrenska University Hospital (Sweden); University of Gothenburg (Sweden)
Journal: Analytical chemistry, volume 98, issue 26, pages 19882-19893
Dates: received 14 May 2026; accepted 23 June 2026; published online 25 June 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1021/acs.analchem.6c03315 · PMID 42348734 · PMCID PMC13347706 · OpenAlex W7165913955
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
MeSH: Protein Aggregates*, Single Molecule Imaging*, tau Proteins*, Alzheimer Disease, Humans (* major topic)
Topic: Advanced Biosensing Techniques and Applications (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Alzheimer’s Association (NA); LifeArc (NA); Weston Brain Institute (NA); Brain Research UK (NA); UK Medical Research Council (MR/V036947/1); British Heart Foundation (NA); Christ's College, University of Cambridge (NA); Wolfson Foundation (NA); Alzheimer’s Research UK (ARUK-RADF2019A-003); University College London Biomedical Research Centre (NA); UK Dementia Research Institute (UKDRI-1003); International Alliance for Cancer Early Detection, Cancer Research UK (EDDAPA-2024/100006)
Citations: cited by 1 paper (Europe PMC); 30 references in the paper

Abstract

Protein aggregation is a central feature of many neurodegenerative diseases, yet methods to characterize aggregate size in complex biological samples remain limited. Here, we show that fluorescence intensity from individual single-molecule array (Simoa) microwells encodes size-dependent information beyond conventional digital quantification. Using defined synthetic tau assemblies, we establish that increasing aggregate size produces higher microwell brightness. Applying this technique to human brain homogenate reveals a shift toward larger tau aggregates in Alzheimer’s disease compared to age-matched controls, in agreement with orthogonal measurements by single-molecule super-resolution microscopy. Brightness profiling further captures time-dependent aggregate size increase in a neuronal cell model, demonstrating sensitivity to dynamic changes in aggregation. Although resolution is limited between similarly sized small species, Simoa brightness robustly reports population-level shifts in aggregate size distributions. These findings repurpose a widely used ultrasensitive detection platform to provide high-throughput structural as well as quantitative insight into protein aggregation in biological systems.

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

Repositories

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

Zenodo 20140389

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the availability statement
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: doi.org/10.5281/zenodo.20140389

yingzhang1122/simoabrightnessanalysis

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: db6d9c568259f20a451f1439ac0187201c56af43, 5 May 2026
Languages: Python (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: license file
Not found: README, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/Yingzhang1122/SimoaBrightnessAnalysis/tree/Simoa_brightness

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;
  • 2 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.

Data availability

All original brightness analysis code has been deposited at Zenodo at DOI: 10.5281/zenodo.20140389 (https://doi.org/10.5281/zenodo.20140389) and is publicly available as of the date of publication. All data are available in the manuscript or the Supporting Information (https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.6c03315/suppl_file/ac6c03315_si_001.pdf). Raw data available on written request to D.K.

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

  • Publisher: n/a → American Chemical Society

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 5 MeSH terms, 12 funders, 30 references.

Cite

This paper

Böken, D., Wu, Y., Zhang, J., Xia, Z., Beltran-Lobo, P., Croft, C. L., Weerasekera, S., Heslegrave, A., Zetterberg, H., Keshavan, A., Schott, J. M., Jimenez-Sanchez, M., Duffy, D. C., & Klenerman, D. (2026). Profiling Protein Aggregate Size Using Single-Molecule Array Technology. Analytical chemistry, 98(26), 19882-19893. https://doi.org/10.1021/acs.analchem.6c03315

BibTeX

@article{boken2026profiling,
author = {Böken, Dorothea and Wu, Yunzhao and Zhang, Jianli and Xia, Zengjie and Beltran-Lobo, Paula and Croft, Cara L. and Weerasekera, Savinu and Heslegrave, Amanda and Zetterberg, Henrik and Keshavan, Ashvini and Schott, Jonathan M. and Jimenez-Sanchez, Maria and Duffy, David C. and Klenerman, David},
title = {{Profiling Protein Aggregate Size Using Single-Molecule Array Technology}},
journal = {Analytical chemistry},
year = {2026},
month = jun,
volume = {98},
number = {26},
pages = {19882--19893},
publisher = {American Chemical Society},
issn = {0003-2700},
doi = {10.1021/acs.analchem.6c03315},
url = {https://doi.org/10.1021/acs.analchem.6c03315},
pmid = {42348734},
pmcid = {PMC13347706}
}

RIS

TY - JOUR
AU - Böken, Dorothea
AU - Wu, Yunzhao
AU - Zhang, Jianli
AU - Xia, Zengjie
AU - Beltran-Lobo, Paula
AU - Croft, Cara L.
AU - Weerasekera, Savinu
AU - Heslegrave, Amanda
AU - Zetterberg, Henrik
AU - Keshavan, Ashvini
AU - Schott, Jonathan M.
AU - Jimenez-Sanchez, Maria
AU - Duffy, David C.
AU - Klenerman, David
TI - Profiling Protein Aggregate Size Using Single-Molecule Array Technology
T2 - Analytical chemistry
J2 - Anal Chem
PY - 2026
DA - 2026/06/25
VL - 98
IS - 26
SP - 19882
EP - 19893
SN - 0003-2700
PB - American Chemical Society
DO - 10.1021/acs.analchem.6c03315
UR - https://doi.org/10.1021/acs.analchem.6c03315
LA - en
ER -

CSL-JSON

{
"id": "10.1021/acs.analchem.6c03315",
"type": "article-journal",
"title": "Profiling Protein Aggregate Size Using Single-Molecule Array Technology",
"container-title": "Analytical chemistry",
"author": [
{
"family": "Böken",
"given": "Dorothea"
},
{
"family": "Wu",
"given": "Yunzhao"
},
{
"family": "Zhang",
"given": "Jianli"
},
{
"family": "Xia",
"given": "Zengjie"
},
{
"family": "Beltran-Lobo",
"given": "Paula"
},
{
"family": "Croft",
"given": "Cara L."
},
{
"family": "Weerasekera",
"given": "Savinu"
},
{
"family": "Heslegrave",
"given": "Amanda"
},
{
"family": "Zetterberg",
"given": "Henrik"
},
{
"family": "Keshavan",
"given": "Ashvini"
},
{
"family": "Schott",
"given": "Jonathan M."
},
{
"family": "Jimenez-Sanchez",
"given": "Maria"
},
{
"family": "Duffy",
"given": "David C."
},
{
"family": "Klenerman",
"given": "David"
}
],
"container-title-short": "Anal Chem",
"volume": "98",
"issue": "26",
"page": "19882-19893",
"DOI": "10.1021/acs.analchem.6c03315",
"PMID": "42348734",
"PMCID": "PMC13347706",
"ISSN": "0003-2700",
"publisher": "American Chemical Society",
"URL": "https://doi.org/10.1021/acs.analchem.6c03315",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
25
]
]
}
}

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