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Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data.

Guangyu Li1, Zijian Wang2,3, Chieh Wu4

  • 1School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14850, United States.

ISME Communications
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

Single-cell Raman Spectroscopy (SCRS) effectively identifies microbial taxonomy and growth stages. This method accurately classifies microbes from environmental samples, advancing ecological studies.

Keywords:
algorithm benchmarkenvironmental microbiomemachine learningmicrobial ecologysingle-cell Raman spectroscopysingle-cell taxonomy identification

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Area of Science:

  • Environmental microbiology
  • Biotechnology
  • Spectroscopy

Background:

  • Single-cell Raman Spectroscopy (SCRS) is a powerful technique for non-intrusive, high-resolution, high-throughput single-cell analysis.
  • Its application in environmental ecology for microbial phenotyping requires optimized analysis techniques and robust classification models.

Purpose of the Study:

  • To establish and optimize analysis techniques and classifiers for SCRS-based microbial taxonomy identification.
  • To assess the impact of cellular growth status on SCRS data and classification accuracy.
  • To validate the application of SCRS models for taxonomic identification in environmental samples.

Main Methods:

  • Benchmarking five dimensionality reduction methods and 10 classifiers using a comprehensive SCRS dataset.
  • Evaluating classification accuracy with and without considering cell growth stage.
  • Developing and applying a two-step classifier model for simultaneous growth and taxonomy classification.
  • Validating models trained on pure cultures against environmental microbial samples using SCRS and Raman-FISH.

Main Results:

  • Unsupervised dimensionality reduction methods and non-neural network classifiers achieved up to 96.1% taxonomy classification accuracy, balancing efficiency and performance.
  • Cellular growth variance had a minimal impact (<2.9%) on accuracy compared to model selection (up to 41.4%).
  • A two-step classifier model achieved high accuracy for both growth stage (93.3%) and taxonomy (94%) classification.
  • Models trained on pure cultures successfully identified microbes in environmental samples with 79% accuracy, confirmed by Raman-FISH.

Conclusions:

  • Optimized SCRS analysis techniques and classifiers provide accurate microbial taxonomy identification.
  • SCRS is a viable tool for analyzing microbial communities in environmental samples, extending beyond laboratory conditions.
  • This work lays the foundation for standardizing SCRS in microbial ecology for understanding organismal functions and environmental interactions.