Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Abstract

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

Untargeted metabolomic, and proteomic analysis identifies metabolic biomarkers and pathway alterations in individuals with 22q11.2 deletion syndrome

“…Some limitations of this study include the chosen analytical method and non-causal study design. Here we employ an MS1-based label-free quantification using match between runs (LFQ-MBR) to transfer identifications from a library built using separate data-dependent MS2 acquisitions, an approach well established in proteomics, which, may, therefore, suffer from increased rates of false quantifications (Almeida et al, 2021 ; Geyer et al, 2016 ; Lim et al, 2019 ; Yu et al, 2021 ). However, in contrast to labeled (e.g., TMT) and LFQ-DIA approaches, LFQ-MBR is biomolecule-agnostic and is readily applied to not only peptides, but also lipids, metabolites, and other analytes, and an important part of this analysis.…”

Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics

“…For example, proteins secreted from tissues that regulate cell adhesion, signaling, and developmental functions as well as cytokines associated with immunity are ranked >1000 in concentration. [39][40][41] We performed gene ontology (GO) analysis using all the protein accession numbers identified in each condition. The results are displayed on the right-hand side of each waterfall plot in Figure 3.…”

Biomarkers Found in the Tumor Interstitial Fluid may Help Explain the Differential Behavior Among Keratinocyte Carcinomas

“…However, there is little data regarding TIF directly from melanoma or KC skin biopsies. Alternatively, in melanoma, the role of the tumor microenvironment has been extensively studied from plasma components ( 6 , 7 , 8 , 9 , 10 , 11 , 12 ), providing evidence of the secretome and exosomes affecting the course of tumorigenesis, metastasis, and responsiveness to therapy ( 10 ). In contrast, the tumor microenvironment from KC has been poorly studied.…”