TITLE: LC/MS analysis of plasma samples from PPMI
AUTHORS: John Chen, Romeo Maciuca, Paul Benton, Jung Suh, Sonnet Davis, Sarah Huntwork-Rodriguez, Ning Xia, Michael A. Schwarzschild

[DESCRIPTION]
Plasma samples from PPMI were analyzed by liquid chromatography with mass spectrometry (LC/MS) for a variety of metabolites (including piperine) and lipids as interrelated markers of Parkinson's disease and its pathophysiology. 

[STEPS]
SECTION: Plasma sample preparation for LC/MS analysis
1. Thawed plasma samples were spun down at 3.5k*g for 10 min at 4°C. 
SECTION: Plasma sample preparation for LC/MS analysis
2. Plasma (10 µL) samples were transferred to an Agilent Captiva collection plate and processed via the Agilent Bravo Metabolomics Sample Prep Platform (Agilent Technologies, Santa Clara, CA).
SECTION: Plasma sample preparation for LC/MS analysis
3. Bravos Low Volume Plasma Metabolite/Lipid protocols were applied.  1:1 methanol/ethanol (v/v) (112.5 µL) containing internal standards were added into each. 
SECTION: Plasma sample preparation for LC/MS analysis
4. Plates were shaken for 1 min at 1000 rpm and incubated for 10 min at room temperature. 
SECTION: Plasma sample preparation for LC/MS analysis
5. Water (82.5 µL) was added to each well, shaken for 1 min at 500 rpm, and incubated for 10 min at room temperature. 
SECTION: Plasma sample preparation for LC/MS analysis
6. The sample (200 µL) was transferred to a Captiva EMR-Lipid plate to collect metabolite filtrate and remove protein and lipids.
SECTION: Plasma sample preparation for LC/MS analysis
7. The Captiva EMR-Lipid plate was washed twice with 250 µL of 1:1:1 water/ethanol/methanol (v/v/v). 
SECTION: Plasma sample preparation for LC/MS analysis
8. The metabolite filtrate was dried under N2 gas for 4 h then reconstituted with 200 µL methanol.
SECTION: Plasma sample preparation for LC/MS analysis
9. To collect the lipids retained on the Captiva EMR-Lipid plate, 1:2 dichloromethane/methanol (v/v) (1.8 mL) was added to the Captiva EMR-Lipid plate for elution. 
SECTION: Plasma sample preparation for LC/MS analysis
10. Lipid filtrate was collected in a 2mL glass coated microplate, dried under N2 gas for 4 h, and reconstituted with 200 µL methanol. 
SECTION: Plasma sample preparation for LC/MS analysis
11. For analysis of the GlcCer/GalCer panel, a 25 µL aliquot from the methanol lipid fraction was dried under N2 gas for 4 h and resuspended in 50 μL of 92.5/5/2.5 acetonitrile/isopropanol/water) with 5mM ammonium formate and 0.5% formic Acid.
SECTION: Data reporting: Targeted LC-MS/MS data analysis
12. Both targeted and untargeted metabolomics and lipidomics analysis were analyzed in 8 different batches. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
13. Samples included in each batch were selected such that independent factors and covariates of interests were randomized and evenly distributed between batches. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
14. Additionally, 3 pooled control and 3 pooled PD case plasma samples all samples were created by PPMI and these were distributed evenly within and between batches. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
15. Each of the pooled samples were injected 3-6 times within each batch and values from replicate extractions and injections were used to calculate assay CV and inter-batch deviations. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
16. Missing peak areas results were identified and visualized using the naniar r package (version 0.6.1).  
SECTION: Data reporting: Targeted LC-MS/MS data analysis
17. Missing values were imputed using k- nearest neighbors imputation method using aggregated information generated from 5 closest donor values.
SECTION: Data reporting: Targeted LC-MS/MS data analysis
18. Imputations were performed using the VIM r package (version 6.1.1). 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
19. Following data imputation, areas for endogenous metabolites and lipids were divided by areas
of analyte specific spiked surrogate stable isotope internal standards (See method supplement table) and converted into area ratios.  
SECTION: Data reporting: Targeted LC-MS/MS data analysis
20. Subsequently, inter-batch variances of area ratios were corrected by using the Combat function provided in the sva r package (version 3.38).
SECTION: Data reporting: Targeted LC-MS/MS data analysis
21. Relative log expression plots of area ratios were plotted to visualize variations within and across
batches. Following correction for between batch effects, imputed values were removed. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
22. For each non-imputed quantified analyte, un-normalized peak area (UNITS = area), ratios of endogenous metabolite area to surrogate internal standards (UNITS = area_ratio) and batch adjusted area ratios (UNITS = adjusted_area_ratio) were reported. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
23. Batch adjusted area ratios are recommended for downstream analysis. However, unnormalized areas and area ratios provided should allow for different normalization schemes to be adopted by end data consumers. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
24. Note that analytes with missing values in >70% of the samples were not reported. 
SECTION: Data reporting: Targeted LC-MS/MS data analysis
25. Data analysis and reports were generated using R version 4.0.2.
SECTION: Untargeted LC-MS data analysis
26.  Data files were extracted from Thermo ‘raw’ format to mzML using proteowizard msconvert
(version-3.0.21334). 
SECTION: Untargeted LC-MS data analysis
27. A table of files and batch number was produced using the file name nomenclature.
SECTION: Untargeted LC-MS data analysis
28.  This was feed into the apLCMS algorithm using the latest update (version 6.6.8) for multi-batch
processing (two.step.hybrid), as described in Liu et al.(1)
SECTION: Untargeted LC-MS data analysis
29. Briefly, this method performs peak detection on each file within a batch, while also
gain accuracy and power by using knowledge from other samples within the batch.

SECTION: Untargeted LC-MS data analysis
30. Once peak detection is finished retention time alignment is performed. 
SECTION: Untargeted LC-MS data analysis
31. The algorithm finds a medium retention time deviation sample within the batch and
uses this sample to align the other samples to this medium sample. 
SECTION: Untargeted LC-MS data analysis
32. When all the batches have been processed a pairwise retention time alignment (similar to
the above method) and weak-signal recovery is performed across the batches. 
SECTION: Untargeted LC-MS data analysis
33. The weak-signal recovery adds accuracy to downstream data analysis while also
adding alignment across batches.