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Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments
Brown BRP, Li X, Grasty MR, Miranker AD, Gürsoy G.. bioRxiv, 2026-10-02
Topic: Genetics and APOE
Cerebrospinal fluid (CSF) contains brain derived proteins that can be leveraged as biomarkers for Alzheimer’s disease (AD) and other neurodegenerative diseases. However, converting protein intensities to a disease signal remains a challenge because physiology driven changes to the localization of proteins across CSF subcompartments are obscured when CSF is analyzed alone. Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal. Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EV ps ). In a ROS/MAP cohort of 48 participants, integrating CSF and EV ps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls. Notably, the joint signal identified as many brain-related biomarker candidates as studies using substantially larger cohorts. By demonstrating that integration of CSF and EV ps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.
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APA 7th
Brown, B. R. P., Li, X., Grasty, M. R., Miranker, A. D., & Gürsoy, G. (2026). Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments. bioRxiv. https://doi.org/10.64898/2026.09.30.754912
Vancouver
Brown BRP, Li X, Grasty MR, Miranker AD, Gürsoy G. Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments. bioRxiv. 2026. doi:10.64898/2026.09.30.754912.
BibTeX
@article{brown2026-33c99656,
author = {Brown, B. R. P. and Li, X. and Grasty, M. R. and Miranker, A. D. and Gürsoy, G.},
title = {{Integrating CSF and EV Proteomes Enhances Biomarker Discovery by Capturing Shared Variation Across Compartments}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.09.30.754912},
url = {https://doi.org/10.64898/2026.09.30.754912}
}
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Key findings
- Where subcompartment data, such as that from extracellular vesicles (EVs), are also measured, separate but parallel analyses of EVs and CSF can obscure their shared disease signal.
- Here, we present an integrated matrix factorization approach that combines proteomic signals from matched CSF and phosphatidylserine exposed EVs and nanoparticles (EV ps ).
- In a ROS/MAP cohort of 48 participants, integrating CSF and EV ps signals improved biological concordance across compartments and increased the number of candidate proteins distinguishing participants with AD from controls.
- Notably, the joint signal identified as many brain-related biomarker candidates as studies using substantially larger cohorts.
- By demonstrating that integration of CSF and EV ps proteomes maximizes information from limited samples, this framework offers a path toward more efficient biomarker discovery across two compartments increasingly leveraged in neurodegenerative disease research.
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Study details
- Participants48
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 31 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, 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 19 of the 62 points we could judge it on, which is 31 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 | 3 / 5 | Rated “moderate” 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 | 1 / 4 | 48 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| 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 | 19 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 19 of the 62 points we could judge it on is 31 out of 100. |
| Total | 31 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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