peptide fractionation
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2020 ◽  
pp. mcp.RA120.002411
Author(s):  
Weixian Deng ◽  
Jihui Sha ◽  
Kathrin Plath ◽  
James A Wohlschlegel

Deep proteome coverage in bottom-up proteomics requires peptide-level fractionation to simplify the complex peptide mixture before analysis by tandem mass spectrometry. By decreasing the number of co-eluting precursor peptide ions, fractionation effectively reduces the complexity of the sample leading to higher sample coverage and reduced bias towards high abundance precursors that are preferentially identified in data-dependent acquisition strategies. To achieve this goal, we report a bead-based off-line peptide fractionation method termed CIF or Carboxylate modified magnetic bead-based isopropanol gradient peptide fractionation. CIF is an extension of the SP3 (single-pot solid-phase-enhanced sample preparation) strategy and provides an effective but complementary approach to other commonly used fractionation methods including strong cation exchange (SCX) and reversed phase (RP)-based chromatography. We demonstrate that CIF is an effective offline separation strategy capable of increasing the depth of peptide analyte coverage both when used alone or as a second dimension of peptide fractionation in conjunction with high pH RP. These features make it ideally suited for a wide range of proteomic applications including the affinity purification of low abundance bait proteins.


Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2383
Author(s):  
Misol Do ◽  
Hongbeom Kim ◽  
Dongyoon Shin ◽  
Joonho Park ◽  
Haeryoung Kim ◽  
...  

The incidence of patients with pancreatic cystic lesions, particularly intraductal papillary mucinous neoplasm (IPMN), is increasing. Current guidelines, which primarily consider radiological features and laboratory data, have had limited success in predicting malignant IPMN. The lack of a definitive diagnostic method has led to low-risk IPMN patients undergoing unnecessary surgeries. To address this issue, we discovered IPMN marker candidates by analyzing pancreatic cystic fluid by mass spectrometry. A total of 30 cyst fluid samples, comprising IPMN dysplasia and other cystic lesions, were evaluated. Mucus was removed by brief sonication, and the resulting supernatant was subjected to filter-aided sample preparation and high-pH peptide fractionation. Subsequently, the samples were analyzed by LC-MS/MS. Using several bioinformatics tools, such as gene ontology and ingenuity pathway analysis, we detailed IPMNs at the molecular level. Among the 5834 proteins identified in our dataset, 364 proteins were differentially expressed between IPMN dysplasia. The 19 final candidates consistently increased or decreased with greater IPMN malignancy. CD55 was validated in an independent cohort by ELISA, Western blot, and IHC, and the results were consistent with the MS data. In summary, we have determined the characteristics of pancreatic cyst fluid proteins and discovered potential biomarkers for IPMN dysplasia.


2020 ◽  
Vol 92 (13) ◽  
pp. 8893-8900
Author(s):  
Xue Lu ◽  
Zhikun Wang ◽  
Yan Gao ◽  
Wendong Chen ◽  
Lingjue Wang ◽  
...  

2019 ◽  
Vol 42 (24) ◽  
pp. 3712-3717
Author(s):  
Yassel Ramos ◽  
Annia González ◽  
Patricia Sosa‐Acosta ◽  
Yasset Perez‐Riverol ◽  
Yairet García ◽  
...  

2019 ◽  
Vol 5 (2) ◽  
pp. eaau7220 ◽  
Author(s):  
Nicholas J. Ashton ◽  
Alejo J. Nevado-Holgado ◽  
Imelda S. Barber ◽  
Steven Lynham ◽  
Veer Gupta ◽  
...  

A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.


2019 ◽  
Vol 11 (36) ◽  
pp. 4693-4698 ◽  
Author(s):  
Hyeyoon Kim ◽  
Kisoon Dan ◽  
Hyunsuk Shin ◽  
Junghun Lee ◽  
Joseph Injae Wang ◽  
...  

Development of an efficient method for tip-based high-pH peptide fractionation suitable for proteomic analysis using small amounts of protein.


2018 ◽  
Vol 411 (2) ◽  
pp. 459-469 ◽  
Author(s):  
Zeyu Sun ◽  
Feiyang Ji ◽  
Zhengyi Jiang ◽  
Lanjuan Li

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