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Updated: Jul 4, 2026
09:26
Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis
Published on: May 23, 2021
Negative-Ion Mode MALDI-TOF MS Combined with Machine Learning for the Rapid Identification of Colistin-Resistant E.
Yulu Shi1,2, Xuemei Gou1, Qingfeng Li3
1Department of Microbiology, The Affiliated Yongchuan Hospital of Chongqing Medical University & General Practice School of Chongqing Medical University, Chongqing 402160, China.
ACS Omega
|July 3, 2026
Summary
A new workflow uses mass spectrometry and machine learning to rapidly identify colistin-resistant Enterobacter cloacae complex (ECC) isolates. This method significantly speeds up detection, aiding earlier treatment and infection control for these major hospital pathogens.
Area of Science:
- Clinical microbiology
- Mass spectrometry
- Machine learning
Background:
- The Enterobacter cloacae complex (ECC) consists of significant nosocomial pathogens.
- Increasing resistance to colistin, a last-line antibiotic, poses a major clinical challenge.
Purpose of the Study:
- To develop a rapid workflow for identifying colistin-resistant ECC (COL-R-ECC).
- To integrate negative-ion mode MALDI-TOF mass spectrometry with machine learning for enhanced diagnostic capabilities.
Main Methods:
- Analysis of 267 clinical ECC isolates using MALDI-TOF MS and whole-genome sequencing for species identification.
- Training a 1D-CNN with SE module on spectra from 217 isolates, focusing on lipid A-enriched signals (m/z 1500-3000) after lipid extraction.
- Evaluating the model on an independent cohort of 50 isolates, employing spectral binning and smoothing techniques.
Main Results:
- The optimized model achieved 95.5% accuracy internally and 96.0% accuracy in external validation.
- The workflow demonstrated superior performance compared to conventional machine-learning baselines (F1 scores of 0.943 and 0.960).
- SHAP analysis identified 30 key lipid-associated features, offering insights into resistance mechanisms.
Conclusions:
- This workflow enables rapid identification of COL-R-ECC within approximately 1 hour.
- The method is substantially faster than standard susceptibility testing, facilitating earlier targeted therapy.
- The approach supports improved infection control strategies for ECC in healthcare settings.
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