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PICquant‐ an automated platform for biomarker discovery in complex patient materials
Author(s) -
Templeton Dennis J,
Bachmann Lorin Henrich,
Cross Janet,
Murgai Meera,
Moshnikov Sergey,
Lyons Charles E
Publication year - 2007
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.21.5.a181-b
Subject(s) - biomarker discovery , computational biology , biomarker , identification (biology) , proteomics , peptide , mass spectrometry , urine , bioinformatics , chemistry , biology , medicine , chromatography , biochemistry , gene , botany
The ability to quantify protein abundance in complex biological materials is a major challenge to biomarker discovery. Stable isotope mass tagging is emerging as a useful means for quantitative mass spectroscopy. We have developed new stable mass tagging reagent, 13C phenylisocyanate, (PIC) and written software for automated peptide quantification, that we call PICquant. Together, this platform offers significant advantages: unbiased labeling of all peptides, using inexpensive reagents; discernment of the rare unlabeled peptides; positive identification of ion charge and label status; automated quantification of peptide abundance at high throughput; enhanced peptide ion sequencing; direct comparison of experiments run days or months apart. PICquant routinely quantifies over two thousand peptides in human urine samples, and uses clustering and marker identification analyses essentially similar to the analysis of gene array data. Machine learning protocols enable statistical identification of peptide markers that correlate best with clinical state. Current applications of the PICquant technology include urine proteomics for markers of cancer and gestational diabetes, identification of protein markers in formalin‐fixed paraffin embedded tumor tissue, identification of diagnostic markers in pancreatic cyst fluid, and analysis of proteins in CSF from Alzheimer's patients. The authors are actively seeking collaboration fromclinical investigators who have biological materials that could prove sources of clinically significant biomarkers. This work is supported by NCI grant CA126101.