Back

Patient cerebral organoids capture Alzheimers disease proteomic biomarkers and drug targets

Thomson, S.; Li, X. C.; Walker, S.; Tang, T. C. Y.; Graham, M. E.; Olechnowicz, S. W. Z.; Bowden, R.; Global Neurodegeneration Proteomics Consortium, ; Waugh, K. A.; Slawson, C.; Burns, J. M.; Swerdlow, R. H.; Wilkins, H. M.; Shvetcov, A.; Finney, C. A.

2026-06-26 neuroscience
10.64898/2026.06.22.733874 bioRxiv
Show abstract

Patient iPSC-derived cerebral organoids are a leading human model of Alzheimers disease, yet their proteome has never been benchmarked against human disease. Clinical cohorts now nominate thousands of biomarkers and drug targets across three proteomic platforms, and whether patient organoids capture these candidates is unknown. Here, we profile AD and control cerebral organoids containing neurons, astrocytes, and microglia on the three platforms driving clinical discovery, mass spectrometry, SomaScan, and Olink, in both conditioned media and lysate. Benchmarked against 121 studies and clinical cohorts of over 17,000 plasma, CSF, and cortex samples, patient organoids detect almost every nominated candidate and reproduce the disease-associated change in roughly one in four of the most reproducible. This convergence spans plasma, CSF, and cortex, and extends to synaptic, mitochondrial, and proteostatic biology. We provide the first multi-platform reference proteome of a patient-derived AD model, establishing it as a translationally relevant system for studying AD.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 6%
22.2%
Similar papers in this journal
2
Nature Neuroscience
○ 252 papers in training set
Top 0.5%
9.9%
Similar papers in this journal
3
Alzheimer's & Dementia
177 papers in training set
Top 0.7%
8.1%
4
Nature Aging
○ 60 papers in training set
Top 0.3%
5.6%
Similar papers in this journal
5
Molecular Neurodegeneration
○ 55 papers in training set
Top 0.5%
4.1%
Similar papers in this journal
6
Cell Reports
○ 1498 papers in training set
Top 11%
3.3%
50% of probability mass above
7
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 16%
3.2%
8
Science Advances
● 1243 papers in training set
Top 14%
2.5%
9
Neuron
○ 337 papers in training set
Top 3%
2.5%
10
Cell
○ 431 papers in training set
Top 5%
1.9%
11
eLife
● 5828 papers in training set
Top 47%
1.9%
12
Communications Medicine
○ 113 papers in training set
Top 2%
1.7%
13
eBioMedicine
○ 183 papers in training set
Top 3%
1.5%
14
Molecular Psychiatry
○ 282 papers in training set
Top 4%
1.5%
15
Scientific Reports
○ 3612 papers in training set
Top 59%
1.4%
16
npj Parkinson's Disease
○ 105 papers in training set
Top 0.9%
1.4%
17
iScience
○ 1154 papers in training set
Top 23%
1.4%
18
Acta Neuropathologica
○ 58 papers in training set
Top 1%
1.1%
19
Cell Stem Cell
○ 62 papers in training set
Top 1%
1.1%
20
Cell Reports Medicine
○ 153 papers in training set
Top 3%
1.1%
21
Nature Metabolism
○ 69 papers in training set
Top 1%
1.1%
22
Translational Neurodegeneration
○ 10 papers in training set
Top 0.2%
1.1%
23
Nature
○ 645 papers in training set
Top 8%
1.1%
24
Communications Biology
○ 993 papers in training set
Top 24%
1.1%
25
Science
● 477 papers in training set
Top 8%
0.9%
26
Science Translational Medicine
● 127 papers in training set
Top 3%
0.9%
27
Nature Medicine
○ 125 papers in training set
Top 4%
0.6%
28
Brain
◐ 168 papers in training set
Top 3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.