Dataset Information

A Rapid and Sensitive Single-cell Proteomic Method Based on Fast LC Separation, Deep-learning RT Prediction and MS1-only Acquisition

ABSTRACT: We report an integrate strategy that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for sensitive and rapid single-cell proteome analysis. Finally, SCP-MS1 was applied to sorafenib resistant HepG2 cells.

ORGANISM(S): Homo Sapiens

SUBMITTER: Weijie Qin

PROVIDER: PXD036211 | iProX | Fri Aug 26 00:00:00 BST 2022

REPOSITORIES: iProX

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Publications

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.

Fang Wei W Du Zhuokun Z Kong Linlin L Fu Bin B Wang Guibin G Zhang Yangjun Y Qin Weijie W

Analytica chimica acta 20230302

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and M ...[more]

PMID: 36925302

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Project description:In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Dataset Information

A Rapid and Sensitive Single-cell Proteomic Method Based on Fast LC Separation, Deep-learning RT Prediction and MS1-only Acquisition

Publications

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition.

Similar Datasets

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