RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 μg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1–2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research

A Highly Efficient and Visualized Method for Glycan Enrichment by Self-Assembling Pyrene Derivative Functionalized Free Graphene Oxide

Protein glycosylation plays key roles in many biological processes, such as cell growth, differentiation, and cell–cell recognition. Therefore, global structure profiling of glycans is very important for investigating the biological significance and roles of glycans in disease occurrence and development. Mass spectrometry (MS) is currently the most powerful technique for structure analysis of oligosaccharides, but the limited availability of glycan/glycoproteins from natural sources restricts the wide adoption of this technique in large-scale glycan profiling. Though various enrichment methods have been developed, most methods relay on the weak physical affinity between glycans and adsorbents that yields insufficient enrichment efficiency. Furthermore, the lack of monitoring the extent/completeness of enrichment may lead to incomplete enrichment unless repeated sample loading and prolonged incubation are adopted, which limits sample handling throughput. Here, we report a rapid, highly efficient, and visualized approach for glycan enrichment using 1-pyrenebutyryl chloride functionalized free graphene oxide (PCGO). In this approach, glycan capturing is achieved by reversible covalent bond formation between the hydroxyl groups of glycans and the acyl chloride groups on graphene oxide (GO) introduced by π–π stacking of 1-pyrenebutyryl chloride on the GO surface. The multiple hydroxyl groups of glycans lead to cross-linking and self-assembly of free PCGO sheets into visible aggregation within 30 s, therefore achieving simple visual monitoring of the enrichment process. Improved enrichment efficiency is achieved by the large specific surface area of free PCGO and heavy functionalization of highly active 1-pyrenebutyryl chloride. Application of this method in enrichment of standard oligosaccharides or <i>N</i>-glycans released from glycoproteins results in remarkably increased MS signal intensity (approximately 50 times), S/N, and number of glycoform identified

Strategy Integrating Stepped Fragmentation and Glycan Diagnostic Ion-Based Spectrum Refinement for the Identification of Core Fucosylated Glycoproteome Using Mass Spectrometry

Core fucosylation (CF) is a special glycosylation pattern of proteins that has a strong relationship with cancer. The Food and Drug Administration (FDA) has approved the core fucosylated α-fetoprotein as a biomarker for the early diagnosis of hepatocellular carcinoma (HCC). The technology for identifying core fucosylated proteins has significant practical value. The major method for core fucosylated glycoprotein/glycopeptide analysis is neutral loss-based MS³ scanning under collision-induced dissociation (CID) by ion trap mass spectrometry. However, due to the limited speed and low resolution of the MS³ scan mode, it is difficult to achieve high-throughput, with only dozens of core fucosylated proteins identified in a single run. In this work, we developed a novel strategy for the identification of CF glycopeptides at a large scale, integrating the stepped fragmentation function, one novel feature of quadrupole-orbitrap mass spectrometry, with “glycan diagnostic ion”-based spectrum optimization. By using stepped fragmentation, we were able to obtain both highly accurate glycan and peptide information of a simplified CF glycopeptide in one spectrum. Moreover, the spectrum could be recorded with the same high speed as the conventional MS² scan. By using the “glycan diagnostic ion”-based spectrum refinement method, the efficiency of the CF glycopeptide discovery was significantly improved. We demonstrated the feasibility and reproducibility of our method by analyzing CF glycoproteomes of mouse liver tissue and HeLa cell samples spiked with standard CF glycoprotein. In total, 1364 and 856 CF glycopeptides belonging to 702 and 449 CF glycoproteins were identified, respectively, within a 78-min gradient analysis, which was approximately a 7-fold increase in the identification efficiency of CF glycopeptides compared to the currently used method. In this work, we took core fucosylated glycopeptides as a practical example to demonstrate the great potential of our novel method for use in glycoproteome analysis, and we also anticipate using the flexible novel method in other research fields

Bioinformatics analysis of identified N-glycoproteins.

<p>A) Cellular component annotation of identified N-glycoproteins. B) Biological functions of differentially expressed N-glycoproteins.</p

Summary of identified N-glycosites.

<p>A) The number of unique N-glycosites identified and the percentage of N-glycopeptides from all of the identified peptides in each cell line. B) Overlap of N-glycosites between the different enrichment methods. C) Overlap of the N-glycosites and proteins between the different cell lines. D) Number of N-glycosites identified per protein.</p

Overview of the experimental workflow.

<p>A) The secretome was collected from the conditioned medium. B) N-glycosylated peptides were enriched using hydrazide chemistry and zic-HILIC methods. First, proteins were digested using FASP, and then the N-glycosylated peptides were captured using two methods, followed by de-glycosylation using PNGase F and LC-MS-MS analysis. C) Label-free quantitative analysis.</p

Network view of the up-regulated N-glycoproteins in HCCLM3 cells.

<p>A) The networks of the top 3 liver-related diseases. B) The number of related genes and the <i>p</i>-value of the top 3 liver-related diseases that are indicated in A). C) Cellular motility network. (The proteins with higher expression in HCCLM3 cells are in red (Ratio > 2), whereas the other proteins that were generated from the IPA database are not colored.).</p

Validation of the differential expression of two selected N-glycoproteins.

<p>A) Ten micrograms secretome protein samples were separated on SDS-PAGE gels, transferred to PVDF membranes, and probed with anti-FN1 or FAT1 antibodies. B) The de-glycosylation of the same amount of secreted proteins from MHCC97L and HCCLM3 cells was performed with PNGase F cleavage for 12 h. Proteins were separated on SDS-PAGE and analyzed by western blotting.</p

Proteome-wide correlation of proteins' abundance with their functional categorization across six species.

<p>Abundance distribution of proteins in the mass and the information categories was compared by cumulative curves in <i>H. sapiens</i> (<b>A</b>), <i>M. musculus</i> (<b>B</b>), <i>D. melanogaster</i> (<b>C</b>), <i>C. elegans</i> (<b>D</b>), <i>S. cerevisiae</i> (<b>E</b>), and <i>E. coli</i> (<b>F</b>). Stratified comparison: mass <i>vs.</i> information processing activities in <i>H. sapiens</i> (<b>G</b>) and <i>S. cerevisiae</i> (<b>H</b>); metabolism subclasses in <i>H. sapiens</i> (<b>I</b>) and <i>S. cerevisiae</i> (<b>J</b>). Comparison among biogenesis machines of three bio-molecules in <i>H. sapiens</i> (<b>K</b>), <i>M. musculus</i> (<b>L</b>), <i>D. melanogaster</i> (M), <i>C. elegans</i> (<b>N</b>), <i>S. cerevisiae</i> (<b>O</b>), and <i>E. coli</i> (<b>P</b>).</p

Proteome-wide correlation of proteins' abundance with their origin time across six species.

<p>The relationship between origin time and abundance of proteins in <i>H. sapiens</i> (<b>A</b>), <i>M. musculus</i> (<b>B</b>), <i>D. melanogaster</i> (<b>C</b>), <i>C. elegans</i> (<b>D</b>), <i>S. cerevisiae</i> (<b>E</b>), and <i>E. coli</i> (<b>F</b>) were analyzed by Spearman rank correlation method. Protein origin time are categorized according to the data in OrthoMCL database. For (A)–(E), I, <1 Gya; II, 1–1.58 Gya; III, 1.58–1.84 Gya; IV, 1.84–2.23 Gya; V, 2.23–4 Gya; VI, >4 Gya. For (F), I, <2.6 Gya; II, 2.6–4 Gya; III, >4 Gya. <i>R</i> represents Spearman rank correlation coefficient and <i>P</i> represents its <i>P</i>-value. The values of upper and lower quartile are indicated as upper and lower edges of the box, and the values of median are indicated as a red bar in the box. The maximum whisker length is set as 1.5, which means points are drawn as outliers (dotted individually outside the bars) if they are larger than q3+1.5×(q3−q1) (shown as the upper bar) or smaller than q1−1.5×(q3−q1) (shown as the lower bar), where q1 and q3 are the 25th and 75th percentiles respectively. (<b><i>SCIN</i></b>: Spectral Count Index Normalized (11); <b><i>SCI</i></b>: Spectral Count Index (11); <b><i>NSAF</i></b>: Normalized Spectral Abundance Factor (12)).</p