|Lindsay Pino is a fourth year Ph.D. candidate in the UW Genome Sciences program. Under the joint advisorship of Drs. Michael J. MacCoss and William Stafford Noble, she is investigating how genetic and environmental perturbations alter the yeast proteome by incorporating analytical chemistry, molecular biology, bioinformatics, and machine learning. Her research interest is in high dimensional mass spectrometry proteomics, which combines her formal undergraduate training in biochemistry and molecular biology with her high-throughput ‘omics post-bacc experience and her current computational training. Her doctoral thesis work explores potential molecular signatures of longevity in yeast. Before coming to the University of Washington for graduate school, she spent two years in South Korea as a Fulbright scholar and then three years working as a research associate at the Broad Institute of MIT and Harvard.
For an analytical measurement to be quantitative, there must be a change in signal that reflects the change in quantity. Here we present a simple and inexpensive strategy for single point intensity calibration using a common external reference sample. We show that this calibration, minimizes quantitative variance between day, between instrument platforms, and between laboratories. As long as labs perform their measurements relative to the same reference, quantitative measurements are calibrated on the same scale. A strategy for defining the performance limits of each measured analyte using a novel strategy for creating a “matched matrix calibration curve” will be presented for assessment of the LOD and LOQ. These analyses are can be facilitated using Skyline’s calibration features.