= Emerging. More than 5 years before clinical availability. (19.79%, 2022)
= Expected to be clinically available in 1 to 4 years. (37.97%, 2022)
= Clinically available now. (42.25%, 2022)
MSACL 2022 : Plubell

MSACL 2022 Abstract

Self-Classified Topic Area(s): Proteomics > Logistics for Bringing Basic Science to the Clinic

Podium Presentation in De Anza 3 on Wednesday at 17:10 (Chair: Patrick Vanderboom)

Using Data Independent Acquisition to Inform the Development of Cerebrospinal Fluid Triple Quadrupole Assays

Deanna L. Plubell (1), Eric Huang (1), Tom Montine (2), Michael J. MacCoss (1)
(1)University of Washington, Department of Genome Sciences, Seattle, WA, USA; (2) Stanford University, Department of Pathology, Stanford, CA, USA

Deanna Plubell, MS (Presenter)
University of Washington

Presenter Bio: As an undergraduate and Master’s student researcher with Dr. David Lindsey (Walla Walla University, College Place, WA) I obtained a strong base in molecular biology techniques through the investigation of the molecular basis for initiation of cell state transition. After completion of my Master’s thesis research I moved into lipoprotein and adipose tissue translational research with Drs. Nathalie Pamir and Sergio Fazio (Oregon Health & Science University, Portland, OR). With Dr. Pamir, I developed and implemented a method for analysis of adipose tissue proteomes, and investigated high-density lipoprotein function and composition in several model organisms and human disease, such as ischemic stroke. With Dr. Fazio, I contributed to the investigation of the molecular mechanisms of lipoprotein function and regulation in atherosclerosis. My time at OHSU provided me with experience directly interfacing between disease models and patients, and impressed upon me the importance of clinically motivated investigation.
While at OHSU I became interested in mass spectrometry proteomics, which led me to pursuing a PhD at the University of Washington with Dr. Michael MacCoss as my mentor. When I first joined the MacCoss lab, I began to test using data-independent acquisition mass spec results to inform and schedule target peptides for selected reaction monitoring assays. This work led to an oral presentation at the American Society of Mass Spectrometry annual conference in 2018. I have continued to develop this approach into a streamlined workflow, which is the subject of my abstract submitted for presentation at this year's MSACL. In addition to developing targeted assays, I am using data-independent acquisition mass spec for peptide quantitation in patient cohort studies. Although there are challenges in scaling DIA analysis to large cohorts, I believe that it will prove to be important in clinical research, and I am interested in continuing to improve it’s efficacy.


INTRODUCTION: Targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted assay, we must determine which peptides are good proxies for a protein or proteoform in a biological matrix. This typically requires relying on selecting peptides based on predetermined biochemical properties, selecting based on analysis by semi-random sampling, or selecting after empirical measurements on recombinant proteins. These methods usually require additional refinement and testing due to poor prediction of performance in the biological matrix.

OBJECTIVES: We show DIA gas-phase fractionated narrow window libraries aid in the development of quantitative and reliable selected reaction monitoring (SRM) by predicting well performing peptides.

METHODS: Using a human spectral library to search our on-column chromatogram library we detect 6193 peptides mapping to 2172 proteins. Of the peptides detected, 3889 have 3+ co-varying, interference free transitions. Following curation based on reproducibility, intensity, and quality of detection by DIA, we select optimal peptides for each protein. To demonstrate the suitability of this workflow, we developed a targeted assay for 100 CSF proteins from a previously published assay.

RESULTS: Approximately 75% of the peptides we selected were unique to our assay, with 25% overlapping with the previously published assay. Performance of the peptides we selected (median CV 3.6%) are comparable with the previous assay (median CV 3.2%). Our workflow simplifies adding targets to the existing assay, and enables the development of new assays for other detected proteins without additional up-front acquisition. To demonstrate this capability, we built an additional targeted assay for two peptides for 62 proteins previously associated with chronic pain in CSF, were selected based on their performance in our DIA. The resulting assay shows low variability between multi-day replicates (median CV 3%).

DIA-MS also aids in determining if additional peptides should be analyzed to account for the biomedically relevant differences being studied. In DIA-MS analysis of CSF from Parkinson’s, probable Alzheimer’s, and healthy patients, we find some proteins to have a common abundance profile across all detectable peptides. One of these cases is GAPDH protein, which shows consistently higher abundance in Parkinson’s patients. In other cases we find peptides within a protein to have differential abundance profiles across experimental sample groups. One such protein is SCG2, which has previously been proposed as a biomarker of Alzheimer’s disease. We find some SCG2 peptides are increased in Parkinson’s compared to healthy and Alzheimer’s, while other peptides are decreased.

CONCLUSION: Observing peptide chromatographic performance in the biological matrix through narrow window data-independent acquisition (DIA) improves our selection of peptides for targeted analysis. Additionally, DIA-MS provides us with quantitative data for comparisons in discovery cohorts, which can further inform our selection of targets.

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