Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics.
Anal Chem
; 91(9): 5768-5776, 2019 05 07.
Article
em En
| MEDLINE
| ID: mdl-30929422
ABSTRACT
Recent developments in high-resolution mass spectrometry (HRMS) technology enabled ultrasensitive detection of proteins, peptides, and metabolites in limited amounts of samples, even single cells. However, extraction of trace-abundance signals from complex data sets ( m/ z value, separation time, signal abundance) that result from ultrasensitive studies requires improved data processing algorithms. To bridge this gap, we here developed "Trace", a software framework that incorporates machine learning (ML) to automate feature selection and optimization for the extraction of trace-level signals from HRMS data. The method was validated using primary (raw) and manually curated data sets from single-cell metabolomic studies of the South African clawed frog ( Xenopus laevis) embryo using capillary electrophoresis electrospray ionization HRMS. We demonstrated that Trace combines sensitivity, accuracy, and robustness with high data processing throughput to recognize signals, including those previously identified as metabolites in single-cell capillary electrophoresis HRMS measurements that we conducted over several months. These performance metrics combined with a compatibility with MS data in open-source (mzML) format make Trace an attractive software resource to facilitate data analysis for studies employing ultrasensitive high-resolution MS.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Xenopus laevis
/
Software
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Espectrometria de Massas por Ionização por Electrospray
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Embrião não Mamífero
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Metaboloma
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Análise de Célula Única
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Aprendizado de Máquina
Tipo de estudo:
Diagnostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article