Citations

Abstract A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.

Pancreatic ductal adenocarcinoma is the most common and aggressive type of pancreatic cancer, with a 5-year survival rate that is less than 10%. New biomarkers to aid in predicting the prognosis of pancreatic ductal adenocarcinoma patients are needed. Previous proteomic studies have to a great extent focused on finding proteins of value for the diagnosis of pancreatic ductal adenocarcinoma. There is a lack of studies that have profiled the serum or plasma proteome in order to discover candidates for new prognostic biomarkers. In this study, we have used ultra-performance liquid chromatography–ultra-definition mass spectrometry to analyze the serum samples of 21 pancreatic ductal adenocarcinoma patients with short or long survival. Statistical analysis discovered 31 proteins whose expression differed significantly between pancreatic ductal adenocarcinoma patients with short or long survival. Pathway analysis discovered multiple canonical pathways enriched in this data set, with several pathways having roles in inflammation and lipid metabolism. The serum proteins identified here, which include complement components and several enzymes, could be of value as candidates for new noninvasive prognostic markers.

AbstractColorectal cancer (CRC) includes tumors in the right colon, left colon, and rectum, although they differ significantly from each other in aspects such as prognosis and treatment. Few previous mass spectrometry‐based studies have analyzed differences in protein expression depending on the tumor location. In this study, we have used mass spectrometry‐based proteomics to analyze plasma samples from 83 CRC patients to study if differences in plasma protein expression can be seen depending on primary tumor location (right colon, left colon, or rectum). Differences were studied between the groups both regardless of and according to tumor stage (II or III). Large differences in plasma protein expression were seen, and we found that plasma samples from patients with rectal cancer separated from samples from patients with colon cancer when analyzed by principal component analysis and hierarchical clustering. Samples from patients with cancer in the right and left colon also tended to separate from each other. Pathway analysis discovered canonical pathways involved in lipid metabolism and inflammation to be enriched. This study will help to further define CRC as distinct entities depending on tumor location, as shown by the widespread differences in plasma protein profile and dysregulated pathways.

Abstract Colorectal cancer (CRC) stands for 10% of the worldwide cancer burden and has recently become the second most common cause of cancer death. The 5‐year survival rate depends mainly on stage at diagnosis. Mass spectrometric proteomic analysis is widely used to study the plasma proteome, which is complex and contains multitudes of proteins. In this study, we have used Ultra Performance Liquid Chromatography‐Ultra Definition Mass Spectrometry (UPLC‐UDMS E )‐based proteomics to analyze plasma samples from 76 CRC patients. We identified several plasma proteins, such as CP, TVP23C, FETUB, and IGFBP3, of which altered levels led to significant differences in survival, as seen by Cox regression and Kaplan‐Meier analysis. Additionally, during Cox regression analysis, samples were adjusted for age and/or tumor stage, enabling stringent analysis. These proteins, although in need of further validation, could be of use during patient follow‐up, as their levels can non‐invasively be measured from blood samples, and could be of use in predicting patient outcome. Several of these proteins additionally have roles in metabolism and inflammation, two processes central to the development and progression of cancer, further indicating their importance in cancer.