QA/QC

NB Nadia Boufaied
PC Paolo Chetta
TH Tarek Hallal
SC Stefano Cacciatore
DL Daniela Lalli
CL Carole Luthold
KH Kevin Homsy
EI Eddie L. Imada
SS Sudeepa Syamala
CP Cornelia Photopoulos
AM Anna Di Matteo
AP Anna de Polo
AS Alessandra Maria Storaci
YH Ying Huang
FG Francesca Giunchi
PS Patricia A. Sheridan
GM Gregory Michelotti
QN Quang-De Nguyen
XZ Xin Zhao
YL Yang Liu
ED Elai Davicioni
DS Daniel E. Spratt
SS Simone Sabbioneda
GM Giovanni Maga
LM Lorelei A. Mucci
CG Claudia Ghigna
LM Luigi Marchionni
LB Lisa M. Butler
LE Leigh Ellis
FB François Bordeleau
ML Massimo Loda
VV Valentina Vaira
DL David P. Labbé
GZ Giorgia Zadra
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Several controls were analyzed together with experimental samples: (i) a pooled matrix sample, generated by combining 20 μl of each experimental sample (i.e., prostate or serum), was injected six times for each dataset and served as a technical replicate to assess process variability; (ii) five water aliquots were extracted and analyzed to serve as blank for artifact determination; (iii) a cocktail of internal standards, carefully chosen to not interfere with the measurement of endogenous compounds, was spiked into every analyzed sample to monitor instrument performance and was also used as a retention marker for chromatographic alignment (LC Neg: D7-glucose, d3-methionine, d3-leucine, d8-phenylalanine, d5-tryptophan, bromophenylalanine, d15-octanoic acid, d19-decanoic acid, d27-tretadecanoic acid, d35-octadecanoic acid, d2-eicosanoic acid; LC HILIC: D35-octadecanoic acid, d5-indole acetic acid, bromophenylalanine, d5-tryptophan, d4-tryrosine, d3-serine, d3-aspartic acid, d7-ornithine, d4-lysine; LC Pos: D7-glucose, d3-methionine, d3-leucine, d8-phenylalanine, d5-tryptophan, bromophenylalanine, d4-tryrosine, d5-indole acetic acid, d5-hippuric acid, amitriptyline, d9-progesterone, d4-dioctylphthalate).

Instrument variability was determined by calculating the median relative standard deviation (RSD) for the standards that were added to each sample before injection into the mass spectrometer. The QCed data were automatically organized into curated metabolic pathways and piped into a secure, cloud-based digital environment for further analysis using custom-built bioinformatics tools, providing detailed pathway annotation based on integration of literature and institutional knowledge. For QA/QC, pooled QC plasma replicates from study samples was used to determine endogenous biochemical variability by calculating the median RSD for all endogenous metabolites (i.e., non-instrument standards) present in 100% of the pooled matrix samples, with representative RSD = 10% across all biochemicals. Experimental samples were randomized across the platform run with QC samples spaced to avoid batch effects.

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