The characterization of column heating effect in nanoflow liquid chromatography mass spectrometry (nanoLC‐MS)–based proteomics

Guo, Fang; Hu, Hao; Liu, Ping; Tan, Minjia; Pan, Jianyi; Zhai, Linhui

doi:10.1002/jms.4441

Cited by 12 publications

(6 citation statements)

References 42 publications

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“…The sample was analyzed by using an EASY-nLC 1200 HPLC tandem with the Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, United States). The peptide was resolved in buffer A (2% ACN in water and 0.1% formic acid) and separated by using a home-made C 18 capillary column (25 cm × 75 μm, 1.9 µm particle size, and 100 Å pore size) ( Li et al, 2020b ), A column oven was used and the heating temperature was set at 60°C ( Kyte and Doolittle, 1982 ).…”

Section: Methodsmentioning

confidence: 99%

PBC, an easy and efficient strategy for high-throughput protein C-terminome profiling

Zhai

Wang

et al. 2022

Front. Cell Dev. Biol.

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High-throughput profiling of protein C-termini is still a challenging task. Proteomics provides a powerful technology for systematic and high-throughput study of protein C-termini. Various C-terminal peptide enrichment strategies based on chemical derivatization and chromatography separation have been reported. However, they are still costly and time-consuming, with low enrichment efficiency for C-terminal peptides. In this study, by taking advantage of the high reaction selectivity of 2-pyridinecarboxaldehyde (2-PCA) with an α-amino group on peptide N-terminus and high affinity between biotin and streptavidin, we developed a 2-PCA- and biotin labeling–based C-terminomic (PBC) strategy for a high-efficiency and high-throughput analysis of protein C-terminome. Triplicates of PBC experiments identified a total of 1,975 C-terminal peptides corresponding to 1,190 proteins from 293 T cell line, which is 180% higher than the highest reported number of C-terminal peptides identified from mammalian cells by chemical derivatization–based C-terminomics study. The enrichment efficiency (68%) is the highest among the C-terminomics methods currently reported. In addition, we not only uncovered 50 proteins with truncated C-termini which were significantly enriched in extracellular exosome, vesicle, and ribosome by a bioinformatic analysis but also systematically characterized the whole PTMs on C-terminal in 293 T cells, suggesting PBC as a powerful tool for protein C-terminal degradomics and PTMs investigation. In conclusion, the PBC strategy would benefit high-efficiency and high-throughput profiling of protein C-terminome.

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Section: Methodsmentioning

confidence: 99%

PBC, an easy and efficient strategy for high-throughput protein C-terminome profiling

Zhai

Wang

et al. 2022

Front. Cell Dev. Biol.

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“…The peptide samples were resuspended in buffer C (0.1% formic acid (FA) in 2 % ACN) and loaded onto a home‐made 20 cm capillary column (75 μm internal diameter) with packing C 18 resins (1.8 μm particle size, 100 Å pole size, Dikma Technologies, USA). The column was heated with the temperature kept at 60°C [31]. A 65 min gradient with a constant flow rate of 300 nL/min was used to separate peptides.…”

Section: Methodsmentioning

confidence: 99%

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

Zhai

et al. 2021

Proteomics Clinical Apps

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Purpose: Mass spectrometry-based proteomics performs well in high throughput detection of urinary proteins. Nonetheless, protein identification depth and reproducibility remain the challenges in diabetic urinary proteome with high complexity and broad dynamic range, especially for low-abundant proteins. As a new data acquisition strategy, the BoxCar method was reported to benefit for low-abundant protein identification. Whether it is propitious to diabetic samples with high dynamic range proteomes has not been discussed yet. We aimed to apply BoxCar method to diabetic urine sample analysis, and to compare it with standard data dependent acquisition (DDA) method on protein identification in detail. Experimental Design:We performed seven technical replicates analysis on two urine samples from healthy individuals and diabetic patients to evaluate protein detection of BoxCar and standard DDA methods on single sample. Further comparison of two methods was made on multiple diabetic urine samples.Results: BoxCar could increase over 20% of identified proteins and performed better quantitative reproducibility than standard DDA method either in single or multiple diabetic urinary samples. BoxCar also improved the detection of low-abundant proteins.Functional enrichment analysis of normal albuminuria or microalbuminuria samples indicated that BoxCar acquired more diabetes-related biological information. Conclusions and Clinical Relevance:The study demonstrates that BoxCar could enhance the depth and reproducibility in diabetic urinary proteome analysis, which provides reference for mass spectrometry approach selection in clinical urinary proteomic research.

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“…Twenty percent of data was withheld for model evaluation. Chronologer was evaluated against competing tools (DeepLC, Prosit, SSRCalc, DeepPhospho) using tryptic HeLa peptides by Searle et al, 6 a multi-protease panel by Richard et al, 43 tryptic peptides separated at different column temperatures by Li et al, 44 and phosphopeptides from HeLa lysate by Searle et al. 45 Prosit predictions were made using the 2019 iRT model through the available ProteomicsDB web server; DeepLC predictions were performed locally with v.1.1.2 and the pre-trained model based on Bruderer et al; 28 DeepPhospho predictions were performed locally using the 20211212 build with the included pre-trained RT model.…”

Section: Chronologer Architecture Model Training and Evaluationmentioning

confidence: 99%

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

Wilburn

Shannon

Spicer

et al. 2023

Preprint

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In proteomics experiments, peptide retention time (RT) is an orthogonal property to fragmentation when assessing detection confidence. Advances in deep learning enable accurate RT prediction for any peptide from sequence alone, including those yet to be experimentally observed. Here we present Chronologer, an open-source software tool for rapid and accurate peptide RT prediction. Using new approaches to harmonize and false-discovery correct across independently collected datasets, Chronologer is built on a massive database with >2.2 million peptides including 10 common post-translational modification (PTM) types. By linking knowledge learned across diverse peptide chemistries, Chronologer predicts RTs with less than two-thirds the error of other deep learning tools. We show how RT for rare PTMs, such as OGlcNAc, can be learned with high accuracy using as few as 10-100 example peptides in newly harmonized datasets. This iteratively updatable workflow enables Chronologer to comprehensively predict RTs for PTM-marked peptides across entire proteomes.

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The characterization of column heating effect in nanoflow liquid chromatography mass spectrometry (nanoLC‐MS)–based proteomics

Cited by 12 publications

References 42 publications

PBC, an easy and efficient strategy for high-throughput protein C-terminome profiling

PBC, an easy and efficient strategy for high-throughput protein C-terminome profiling

BoxCar increases the depth and reproducibility of diabetic urinary proteome analysis

Deep learning from harmonized peptide libraries enables retention time prediction of diverse post translational modifications

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