Mass spectrometry (MS) based proteomic technologies enable the identification and quantification of membrane proteins as well as their post-translational modifications. A prerequisite for their quantitative and reliable MS-based bottom-up analysis is the efficient digestion into peptides by proteases, though digestion of membrane proteins is typically challenging due to their inherent properties such as hydrophobicity. Here, we investigated the effect of eight commercially available MS-compatible surfactants, two organic solvents, and two chaotropes on the enzymatic digestion efficiency of membrane protein-enriched complex mixtures in a multiphase study using a gelfree approach. Multiple parameters, including the number of peptides and proteins identified, total protein sequence coverage, and digestion specificity were used to evaluate transmembrane protein digestion performance. A new open-source software tool was developed to allow for the specific assessment of transmembrane domain sequence coverage. Results demonstrate that while Progenta anionic surfactants outperform other surfactants when tested alone, combinations of guanidine and acetonitrile improve performance of all surfactants to near similar levels as well as enhance trypsin specificity to >90%, which has critical implications for future quantitative and qualitative proteomic studies.

Peptide cleanup is essential for the removal of contaminating substances that may be introduced during sample preparation steps in bottom-up proteomic workflows. Recent studies have described benefits of carboxylate-modified paramagnetic particles over traditional reversed-phase methods for detergent and polymer removal, but challenges with reproducibility have limited the widespread implementation of this approach among laboratories. To overcome these challenges, the current study systematically evaluated key experimental parameters regarding the use of carboxylate-modified paramagnetic particles and determined those that are critical for maximum performance and peptide recovery and those for which the protocol is tolerant to deviation. These results supported the development of a detailed, easy-to-use standard operating protocol, termed SP2, which can be applied to remove detergents and polymers from peptide samples while concentrating the sample in solvent that is directly compatible with typical LC-MS workflows. We demonstrate that SP2 can be applied to phosphopeptides and glycopeptides, and that the approach is compatible with robotic liquid handling for automated sample processing. Altogether, the results of this study and accompanying detailed operating protocols for both manual and automated processing are expected to facilitate reproducible implementation of SP2 for various proteomics applications and will especially benefit core or shared resource facilities where unknown or unexpected contaminants may be particularly problematic.

Motivation Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing ‘omic’ discovery datasets is the selection of candidate markers that are most applicable for downstream applications. Results Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. Availability and implementation Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie. Contact Rebekah.gundry@unmc.edu Supplementary information Supplementary data are available at Bioinformatics online.

High-grade serous carcinoma (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. The large degree of clinical heterogeneity within HGSC has justified deviations from the traditional one-size-fits-all clinical management approach. However, the majority of HGSC patients still relapse with chemo-resistant cancer and eventually succumb to their disease, evidence that further work is needed to improve patient outcomes. Advancements in high-throughput technologies have enabled novel insights into biological complexity, offering a large potential for informing precision medicine efforts. Here, we review the current landscape of clinical management for HGSC and highlight applications of high-throughput biological approaches for molecular subtyping and the discovery of putative blood-based biomarkers and novel therapeutic targets. Additionally, we present recent improvements in model systems and discuss how their intersection with high-throughput platforms and technological advancements is positioned to accelerate the realization of precision medicine in HGSC.