Abigail Burrows Franco, Ph.D., obtained her PhD from the University of Nebraska – Lincoln in 2020 and completed her post-doctoral studies at the University of Kentucky under the guidance of Dr. Scott Stanley. During her post-doctoral training, Dr. Burrows Franco was tasked with identifying peptide and protein biomarkers in equine athletes in response to drug administration. This work was in support of the Equine Biological Passport research program. Currently, she is a staff scientist continuing to develop the Equine Biological Passport within Dr. Stanley’s research lab and also serves as the services director of the Research Mass Spec core facility at the University of Kentucky.
Data Independent Acquisition (DIA) methods have become an attractive alternative to traditional Data Dependent Acquisition (DDA) methods for quantitative proteomics and biomarker identification. Here, we describe a novel DIA methodology for evaluating the equine proteome. Detectable peptides can serve as biomarkers for equine medicine to diagnose health conditions, monitor disease progression, and detect performance-enhancing drugs in anti-doping efforts. Typically, DDA-based spectrum libraries are required prior to DIA data analysis. However, we generated a chromatogram library using narrow-window gas phase fractionation (GPF) DIA without previously generating a spectral library. Our primary aim was to develop a routine DIA method for identification of peptide biomarkers in healthy horses. Ultimately, this method will be implemented to establish baseline measurements of equine biomarker peptides. Serum (n=20) were collected from a population of healthy horses. Sera were pooled and tryptically digested using the Thermo Scientific EasyPep Maxi MS Sample Prep Kit. Prepared digests were analyzed on a high-resolution accurate mass coupled to a nano-liquid chromatography system. Six narrow-window GPFs with staggered windows were analyzed from 400-1000 m/z by 100 Da windows and 4 Da scan ranges (Pino et al., 2020). Digested peptides (500 ng) were separated over a 65-minute gradient. Collected DIA data were analyzed against a predicted peptide library (Prosit DB, EncyclopeDIA). Data were analyzed in Skyline (22.2.0.351) and peptides were filtered for suitability as routine biomarker peptides. Peptides were confirmed in a wide-window DIA method (400-1000 m/z; 12 Da).
Read More Each pooled serum sample was injected six times in order to cover the entire mass range of the narrow-window isolation scheme. The use of pooled serum reduces the time for sample preparation, and preserves the integrity of the sample matrix. Soy lectin protein was added prior to digestion to monitor digestion efficiency, while PRTC was added following digestion prior to injection to evaluate instrument performance. Narrow-window data were analyzed using Skyline against the predicted chromatogram library (Prosit DB, EncyclopeDIA). In this work, an ELIB (DIA-based chromatogram library) was used to select biomarker peptides for routine monitoring. Peptides were detected across 51 overlapping windows at 4 m/z width. Detected peptides represent all the potential peptide detections within the wide-window DIA methods. Data and peptides were filtered in Skyline to remove any non-detectable peptides. Following peptide filtering we identified ~2000 unique peptides, representing approximately 300 proteins from the narrow-window acquisitions. The relative abundances of detected peptides were determined. Peptides detectable in the wide-window acquisitions were filtered for suitability as candidate biomarkers for screening methods. Due to the size of the equine database (Uniprot, Reviewed) selected, these observations are expected for a non-depleted serum. By using HRAM mass spectrometry to generate an equine chromatogram library, we can monitor thousands of peptides in the wide-window DIA method. Additionally, this workflow will be transferred to a targeted peptide method using SRM transitions. A major advantage of performing DIA-GPF is the depth of proteome coverage achieved in a non-depleted serum digest. Furthermore, this work has been a collaboration between Thermo Fisher Scientific, Mike MacCoss of the University of Washington, and the University of Kentucky. Panorama was additionally utilized between collaborators to facilitate data and information sharing.
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