Targeted protein quantification using sparse reference labeling.
Targeted proteomics is a method of choice for accurate and high-throughput quantification of predefined sets of proteins. Many workflows use isotope-labeled reference peptides for every target protein, which is time consuming and costly. We report a statistical approach for quantifying full protein panels with a reduced set of reference peptides. This label-sparse approach achieves accurate quantification while reducing experimental cost and time. It is implemented in the software tool SparseQuant.
1] Department of Statistics, Purdue University, West Lafayette, Indiana, USA. [2].
Ching-Yun
CY
Chang
1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2].
Eduard
E
Sabidó
1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. [2] Competence Center for Systems Physiology and Metabolic Diseases, ETH Zurich, Zurich, Switzerland. [3] Faculty of Science, University of Zurich, Zurich, Switzerland.
Ruedi
R
Aebersold
1] Department of Statistics, Purdue University, West Lafayette, Indiana, USA. [2] Department of Computer Science, Purdue University, West Lafayette, Indiana, USA.
Olga
O
Vitek
D000818:Animals; D045744:Cell Line, Tumor; D002623:Chemistry Techniques, Analytical / Q000379:methods*; D006801:Humans; D007553:Isotope Labeling; D008099:Liver / Q000737:chemistry / Q000378:metabolism; D051379:Mice; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D040901:Proteomics; D012015:Reference Standards; D013997:Time Factors
D011506:Proteins