16 research outputs found
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 MS<sup>3</sup> scanning under collision-induced
dissociation (CID) by ion trap mass spectrometry. However, due to
the limited speed and low resolution of the MS<sup>3</sup> 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 MS<sup>2</sup> 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