Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics

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.