Longitudinal systemic transcriptomic profiling and neuropathological deposition of neutrophil extracellular traps in Parkinson’s Disease

Authors

Huu Dat Nguyen^1,2,3*, Seungmin Lee⁴, Hyeo Il Ma^1,2,3, Yun Joong Kim⁵, Han-Joon Kim⁴, Young Eun Kim^1,2,3,†

^†Corresponding author
^*First author, Lead contact

Affiliations

¹ Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University, Anyang, Gyeonggi, Republic of Korea
² Laboratory of Parkinson’s Disease and Neurodegenerative disease, Hallym Institute for Translational Medicine, Anyang, Gyeonggi, Republic of Korea
³ Hallym Neurological Institute, Hallym University, Anyang, Gyeonggi, Republic of Korea
⁴ Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
⁵ Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi, Republic of Korea

Contacts

Professor Young Eun Kim, MD., PhD.                 – | ✉️ Email
Huu Dat Nguyen, Engr., MMSc., PhD.                 – | ✉️ Email

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Repository structure

Analyses are organised by data domain. Statistics are in R; confocal image processing and the post-mortem brain-ELISA / Western-blot quantification are in Python.

NETs-PD/
├── PPMI-Baseline/            # PPMI baseline cohort (Figure 1, Suppl. S1–S3)
│   ├── Fig1bc_S1_S3a.r                       # baseline differential expression
│   ├── PPMI-BL-visit_Volcano.ipynb           # baseline volcano
│   ├── Baseline_Neutrophil_Deconvolution.R   # CIBERSORTx + per-cell PADI4 (Suppl. S2)
│   ├── Baseline_ROC_Biomarkers.R             # diagnostic ROC panel (Suppl. S1)
│   └── Baseline_Clinical_Correlations.R      # DaT / UPDRS correlations (Suppl. S3)
├── PPMI-Longitudinal/        # PPMI longitudinal cohort (Figure 2, Suppl. S5)
│   ├── Fig2_S3.r                             # longitudinal mixed-model trajectories
│   ├── PPMI-all-visit_NETs.ipynb             # all-visit preparation
│   ├── Longitudinal_Trajectories.R           # random-slope LMM + Bayesian (Figure 2)
│   └── Longitudinal_Stability.R              # ICC / variance partition / Bayesian ICC (Suppl. S5)
├── Regional-Cohort/          # serum + post-mortem brain cohorts (Figures 3–4, Suppl. WB)
│   ├── Serum_MPO-DNA_ELISA.R                 # serum MPO-DNA (Figure 3a)
│   ├── Serum_CitH3-DNA_ELISA.R               # serum CitH3-DNA (Figure 3b)
│   ├── Serum_Biomarker_ROC.R                 # serum biomarker ROC (Figure 3)
│   ├── Brain_ELISA_3Markers.py               # brain MPO/NE/CitH3-DNA + composite (Figure 4)
│   └── Western_Blot_MPO_60kDa.py             # mature MPO ~60 kDa re-quantification (Suppl. WB)
└── Confocal-Image-Pipeline/  # post-mortem confocal NETs in cortex + substantia nigra (Figure 5)
    └── (see Confocal-Image-Pipeline/README.md for the ordered pipeline)

Data

Scripts read their inputs from a local data/ directory (or a path passed as the first command-line argument) and write to results/. Source data are not distributed here. PPMI transcriptomic and clinical data are controlled-access and available from the PPMI upon application; human serum and post-mortem brain data are available from the corresponding author on reasonable request.

Software

• R ≥ 4.4 — tidyverse, lme4/lmerTest, emmeans, sandwich, boot, brms, performance, pROC, glmnet, limma/edgeR, WRS2.
• Python ≥ 3.11 — numpy, pandas, scipy, statsmodels, scikit-image, cellpose, napari, aicsimageio/Bio-Formats, tifffile.

Statistical framework (applied throughout): covariate-adjusted (mixed-effects) models with HC3 robust standard errors, estimated marginal means with Holm adjustment, stratified/cluster bootstrap confidence intervals, and Bayesian sensitivity analyses with weakly informative priors (primary inference for the small post-mortem cohorts).