A three-stage search strategy combining database reduction and retention time filtering to improve the sensitivity of low-input and single-cell proteomic analysis.

When performing proteome profiling of low-input and single-cell samples, achieving deep protein coverage is very challenging due to the sensitivity limitation of current proteomic methods. Herein, we introduce a three-stage search strategy that combines the advantages of database reduction and Δ retention time (ΔRT) filtering. The strategy improves peptide/protein identification and reproducibility by retaining more correct identifications and filtering out incorrect identifications.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis.

A new integrated method for tissue extracellular vesicle enrichment and proteome profiling.

Extracellular vesicles (EVs) are membranous vesicles released by cells that carry a number of biologically important components such as lipids, proteins, and mRNAs. EVs can mediate cancer cell migration, invasion, angiogenesis, and cell survival, greatly contributing to cell-to-cell communication in the tumor microenvironment. Additionally, EVs have been found to have diagnostic and prognostic significance in various cancers.

Synthesis of Surface-Functionalized Molybdenum Disulfide Nanomaterials for Efficient Adsorption and Deep Profiling of the Human Plasma Proteome by Data-Independent Acquisition.

Blood is one of the most important clinical samples for protein biomarker discovery, as it provides rich physiological and pathological information and is easy to obtain with low invasiveness. However, the discovery of protein biomarkers in the blood by mass spectrometry (MS)-based proteomic strategies has been shown to be highly challenging due to the particularly large concentration range of proteins and the strong interference by the high-abundant proteins in the blood. Therefore, developing sensitive methods for low-abundant biomarker protein identification is a key issue that has received great attention.

A New Strategy for High-Efficient Tandem Enrichment and Simultaneous Profiling of N-Glycopeptides and Phosphopeptides in Lung Cancer Tissue.

N-glycosylation and phosphorylation, two common posttranslational modifications, play important roles in various biological processes and are extensively studied for biomarker and drug target screening. Because of their low abundance, enrichment of N-glycopeptides and phosphopeptides prior to LC-MS/MS analysis is essential. However, simultaneous characterization of these two types of posttranslational modifications in complex biological samples is still challenging, especially for tiny amount of samples obtained in tissue biopsy.

[Research progress and application of retention time prediction method based on deep learning].

In "shotgun" proteomics strategy, the proteome is explained by analyzing tryptic digested peptides using liquid chromatography-mass spectrometry. In this strategy, the retention time of peptides in liquid chromatography separation can be predicted based on the peptide sequence. This is a useful feature for peptide identification.