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Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model
Suissa MV, Koutures A, Harker S, Vaughan B, Kfir J, Bhat A, Shihabi R, Dewal MB et al.. bioRxiv, 2026-09-16
Topic: Disease mechanisms in lab and animal studies
Alzheimer's disease (AD) is characterized by the deposition of amyloid-beta (Aβ) and microtubule-associated protein tau (MAPT) neurofibrillary tangles in the brain; however, the molecular mechanisms underlying associated synaptic dysfunction remain unclear. Eratos' AI for Spatial Computing and Embedded Neurotherapeutic Discovery (ASCENDTM) engine was employed to analyze multiplexed expansion revealing (multiExR) data from the 5xFAD and wild-type mouse somatosensory cortex, enabling quantitative mapping of Aβ, RIM1, and GluA2 nanodomains. ASCENDTM identified significant and previously unreported protein associations, including nanoscale colocalization of Aβ with postsynaptic AMPA-receptor subunit GluA2 and amyloid-bridged Aβ-GluA2-RIM1 ternary assemblies. These spatial signatures indicate complex synaptic disruption, defined by distinct morphological and density profiles associated with neurobiological and neuroinflammatory pathology. Integrating high-dimensional spatial computing with cross-modal literature synthesis advances traditional microscopy analysis toward autonomous, AI-driven scientific discovery. Identifying novel, druggable interfaces within native tissue expedites the discovery of precision neurotherapeutics for AD and other complex central nervous system disorders.
Key findings
- ASCENDTM identified significant and previously unreported protein associations, including nanoscale colocalization of Aβ with postsynaptic AMPA-receptor subunit GluA2 and amyloid-bridged Aβ-GluA2-RIM1 ternary assemblies.
- These spatial signatures indicate complex synaptic disruption, defined by distinct morphological and density profiles associated with neurobiological and neuroinflammatory pathology.
Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Laboratory or animal study (title names “5xfad”) — scores zero |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Suissa, M. V., Koutures, A., Harker, S., Vaughan, B., Kfir, J., Bhat, A., et al. (2026). Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model. bioRxiv. https://doi.org/10.64898/2026.09.09.750448
Vancouver
Suissa MV, Koutures A, Harker S, Vaughan B, Kfir J, Bhat A, et al. Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model. bioRxiv. 2026. doi:10.64898/2026.09.09.750448.
BibTeX
@article{suissa2026-2f103e54,
author = {Suissa, M. V. and Koutures, A. and Harker, S. and Vaughan, B. and Kfir, J. and Bhat, A. and Shihabi, R. and Dewal, M. B. and others},
title = {{Autonomous AI-Driven Nanoscale Spatial Mapping Reveals Novel Targets and Ternary Architectures in 5xFAD Alzheimer's Disease Model}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.09.09.750448},
url = {https://doi.org/10.64898/2026.09.09.750448}
}
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