Ariana Shannon   Ariana Shannon is a fourth-year pre-doctoral graduate student within the Ohio State Biochemistry Program. In 2019 she joined the lab of Dr. Amanda Hummon, while using DIA to study tumor-stromal interactions within 3D cell-based tumor cocultures models. In 2022, she began to additionally work with Dr. Brian Searle developing methods to characterize immunological assays with targeted and global mass-spectrometry based proteomics.

Generating fit-for-purpose targeted assays from a catalog of pre-screened peptides using data-independent acquisition (DIA) based figures of merit

Introduction: The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) classifies proteomics studies in three tiers, where targeted assays are typically Tier-1 (clinical) or Tier-2 (non-clinical), and global studies are Tier-3. Tier-2 targeted proteomics assays using parallel reaction monitoring (PRM) are developed by assessing figures-of-merit (such as limit of quantification) with stable isotope labeled (SIL) peptides and refining or re-selecting those targets through iterative experiments. This process is expensive and time consuming as SIL peptides must be purchased prior to validation. Read More
We propose a method of developing Tier-2 PRM assays using global DIA measurements to simultaneously estimate figures-of-merit for thousands of peptides before SIL peptides are introduced, letting us rapidly build “du jour” assays from a catalog of pre-screened peptides. The Skyline software provides the necessary infrastructure for us to visualize, and refine the results of both DIA and PRM data. We intend to pre-process data using EncyclopeDIA and Skyline batch in tandem with a customized output report to calculate figures-of-merit using a streamlined, reproducible process. Methods: Global data was acquired for CD8+ T cells isolated from in vitro expanded PMEL-transgenic mice using gas-phase fractionation (GPF) DIA on an Exploris 480 mass spectrometer. A .sky template document was generated for Skyline batch to import EncyclopeDIA searched wide-window DIA data. Using a custom analysis and report script, we determined figures-of-merit for all peptides, including limits of detection (LoD) and quantification (LoQ) using the matrix-matched approach. For response curves, dimethyl-labeled human Jurkat T cells are used as a suitable background matrix. Humans and mice share 85% homology within protein-coding exons; labeling Jurkat peptides mitigates this issue by shifting precursor and y-type ions out of DIA windows. Peptide stability was assessed through storage in 7℃ for up to 7 days with up to 5 freeze-thaw cycles. Digestion repeatability will be determined with a 5 digestion by 5 replicate experiment. Preliminary data: We demonstrate this rapid PRM development approach by monitoring CD8+ T cell exhaustion in mice. First we determined the components of CD8+ T cell proteome using five GPF-DIA datasets with 2 m/z isolation windows derived from multiple mouse in vivo tumor and in vitro exhaustion models. We flow-activated cell sorted the T cells into 3 exhaustion states; progenitor, or stem-like, acutely and chronically-exhausted cells. Cumulatively, 6,801 proteins were identified, of which Reactome mapped 38.2% proteins to immune-related functions. Of these, we observed 283 immune-related proteins, 130 homeostasis proteins, 357 proteins involved in signal transduction, and 47 involved in autophagy. In particular, we observed 151 established exhaustion-related protein markers (over 500 total peptides). T cell exhaustion in cancer is difficult to study because they are both small (approximately 1/10th of a HeLa cell) and are rare in the tumor microenvironment. Transcriptomic data, which requires lower sample amount compared to proteomics, is typically used to study exhaustion due to the low numbers of naïve or differentiated cells present in mouse models. To determine the minimum number of cells required for proteomics sample preparation and measurement, effector T cell peptides were prepared in decreasing numbers (100k, 50k, 10k, 1k, 500). We detected several well-characterized peptides such as DAALMVTNDGATLIK from Cct2, which was regularly measured in 500 cell samples, and had an LoQ of approximately 1500 cells. However, the majority of exhaustion-related peptides have LoQs between 10k and 50k cells. We will discuss estimations of additional figures-of-merit using DIA data and a new computational tool to construct PRM assays from selected proteins of interest. We will demonstrate a computer scheduled exhaustion-specific assay to quantify protein fingerprints in progenitor, acute and chronically exhausted T cells, and contrast those results with single-cell flow cytometry data. Novel aspect: The DIA-to-PRM method will enable streamlined analysis of on-the-fly or new peptide targets routinely used model systems.