Qualitative and quantitative top-down proteomics of human colorectal cancer cell lines identified 23000 proteoforms and revealed drastic proteoform-level differences between metastatic and non-metastatic cancer cells

McCool, Elijah N.; Xu, Tian; Chen, Wen-rong; Beller, Nicole C; Nolan, Scott M.; Hummon, Amanda B.; Liu, Xiaowen; Sun, Liangliang

doi:10.1101/2021.10.27.466093

Cited by 2 publications

(4 citation statements)

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“…Two top-down MS/MS data sets were used in this study: one public RPLC−MS/MS data 42 (MASSIVE: MSV000080257) and one CZE-MS/MS data. 6 The RPLC−MS/MS data set was generated from ovarian tumor samples. A solid-phase extraction column (360 μm o.d.…”

Section: Top-down Ms Data Setsmentioning

confidence: 99%

“…The CZE-MS/MS data sets were obtained from SW480 and SW620 colon cancer cells. 6 Sample proteins were first separated by an SEC column into six fractions, and then each fraction was injected into a linear polyacrylamide-coated fused silica capillary (1 m, 50 μm i.d., 360 μm o.d.) with 5% acetic acid as the background electrolyte.…”

Section: Top-down Ms Data Setsmentioning

confidence: 99%

“…Many proteoform separation techniques have been used to increase proteoform coverage in top-down MS, , which is desirable in proteoform-wide studies for proteoform function analysis and proteoform biomarker discovery . Liquid chromatography (LC) and capillary zone electrophoresis (CZE) are two main techniques for proteoform separation in top-down proteomics. , In an LC experiment, proteoforms are separated based on their hydrophobicity, size, or other properties using an LC column.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

et al. 2022

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Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two primary proteoform separation methods in mass spectrometry (MS)-based topdown proteomics. Proteoform retention time (RT) prediction in RPLC and migration time (MT) prediction in CZE provide additional information for accurate proteoform identification and quantification. While existing methods are mainly focused on peptide RT and MT prediction in bottom-up MS, there is still a lack of methods for proteoform RT and MT prediction in topdown MS. We systematically evaluated eight machine learning models and a transfer learning method for proteoform RT prediction and five models and the transfer learning method for proteoform MT prediction. Experimental results showed that a gated recurrent unit (GRU)based model with transfer learning achieved a high accuracy (R = 0.978) for proteoform RT prediction and that the GRU-based model and a fully connected neural network model obtained a high accuracy of R = 0.982 and 0.981 for proteoform MT prediction, respectively.

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Section: Top-down Ms Data Setsmentioning

confidence: 99%

Section: Top-down Ms Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

et al. 2022

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“…Advances in high resolution and high accuracy MS instruments significantly increased proteoform identifications and amino acid sequence converge in proteome-wide top-down proteomics analysis [4]. Recent top-down MS studies identified more than 23,000 proteoforms from colorectal cancer cells [5] and about 30,000 proteoforms from human blood and bone marrow cells [6]. Top-down MS-based proteoform profiling has successfully identified differentially expressed proteoforms associated with diseases [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

TopFD - A Proteoform Feature Detection Tool for Top-Down Proteomics

Basharat

Zang

Sun

et al. 2022

Preprint

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Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, Promex, FlashDeconv, and Xtract using five top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.

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Qualitative and quantitative top-down proteomics of human colorectal cancer cell lines identified 23000 proteoforms and revealed drastic proteoform-level differences between metastatic and non-metastatic cancer cells

Cited by 2 publications

References 54 publications

Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry

TopFD - A Proteoform Feature Detection Tool for Top-Down Proteomics

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