"Current landscape of plasma proteomics from technical innovations to biological insights and biomarker discovery"
Kirsher DY, Chand S, Phong A, Nguyen B, Szoke BG, Ahadi S — Communications Chemistry (2025)
PMC12462477
A self-contained Jupyter notebook that replicates all major figures and analyses from a landmark benchmarking study comparing 8 plasma proteomics platforms on 78 individuals covering 13,011 unique proteins.
| Platform | Type | Proteins | Median CV |
|---|---|---|---|
| SomaScan 11K | Affinity / aptamer | 9,852 | 5.3% |
| SomaScan 7K | Affinity / aptamer | 6,467 | 5.8% |
| Olink 5K | Affinity / PEA | 5,416 | 26.8% |
| Olink 3K | Affinity / PEA | 2,925 | 11.4% |
| NULISA | Affinity / antibody | 377 | ~8% |
| MS-Nanoparticle | MS / Seer Proteograph | 5,943 | 26.4% |
| MS-HAP Depletion | MS / Biognosys | 3,575 | 29.8% |
| MS-IS Targeted | MS / SureQuant | 551 | 8.3% |
# 1. Clone / enter the repo
cd plasma-proteomics-analysis
# 2. Install dependencies
pip install -r requirements.txt
# 3. Launch Jupyter
jupyter lab plasma_proteomics_analysis.ipynb| Section | Figures replicated | Description |
|---|---|---|
| 1 | — | Setup, imports, CheckpointManager |
| 2 | Fig 1 | 78-person cohort demographics |
| 3 | — | Simulate platform data (realistic CVs, missingness) |
| 4 | Fig 2 A–D | CV, completeness, linearity, FDA biomarker coverage |
| 5 | Fig 3 A | Protein coverage, pairwise Jaccard overlap |
| 6 | Fig 3 B | Cross-platform Spearman correlation heatmap |
| 7 | Fig 4 | ApoE isoform analysis |
| 8 | Fig 6 | Age biomarker discovery — linear models, volcano plots |
| 9 | Fig 5 | Variance decomposition |
| 10 | Fig 6 | Pathway enrichment (GO / Reactome) |
| 11 | — | Templates to load real data from PRIDE / supplementary |
Every section ends with ckpt.save("section_name", data). After a kernel restart:
data = ckpt.load("section_name") # instant restore from checkpoints/*.pkl
ckpt.list_checkpoints() # see all saved stateCheckpoints are stored in checkpoints/ and persisted across sessions. All figures are
saved to figures/ automatically.
| Platform | PRIDE accession |
|---|---|
| MS-Nanoparticle | PXD067119 |
| MS-HAP Depletion | PXD067064 |
| MS-IS Targeted | PXD067061 |
Available upon request from the corresponding author:
sahadi@alkahest.com
Download from the PMC article page; contains all protein lists, UniProt IDs, and cross-platform correlation matrices. Load with:
supp = pd.read_excel("supplementary_data.xlsx", sheet_name=None)Once you have real data, replace the platform_data dict entries in Section 3 and
re-run Sections 4–10 — all code is data-agnostic.
# Create a new repo at github.com then:
git remote add origin https://github.com/<your-username>/plasma-proteomics-analysis.git
git push -u origin mainKirsher DY, Chand S, Phong A, Nguyen B, Szoke BG, Ahadi S.
Current landscape of plasma proteomics from technical innovations
to biological insights and biomarker discovery.
Commun Chem. 2025. PMC12462477. DOI:10.1038/s42004-025-01456-5