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Rapid Classification of Pantoea spp. via Raman Flow Cytometry.

Daoshun Zhang1, Shuhua Tian2, Bing Feng2

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A new platform combines dielectrophoresis-activated Raman sorting and deep learning for rapid microbial identification. This method accurately classifies bacteria like Pantoea in diverse samples, overcoming limitations of traditional techniques.

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Area of Science:

  • Microbiology and Spectroscopy
  • Computational Biology and Machine Learning

Background:

  • Accurate microbial taxonomic identification is crucial for understanding ecosystem functions.
  • Conventional methods like pure-culture techniques are slow, costly, and have limited resolution.
  • There is a need for high-throughput, high-resolution microbial identification methods.

Purpose of the Study:

  • To develop an integrated platform for rapid and accurate microbial classification.
  • To establish a deep learning model for species- and strain-level identification.
  • To apply the platform to the taxonomically challenging genus *Pantoea*.

Main Methods:

  • Integrated platform combining positive dielectrophoresis-activated Raman-activated cell sorting (pDEP-RACS) and deep ResNet (ResNet-18).
  • Construction of a reference Ramanome database with 180,000 single-cell Raman spectra (SCRS) from 12 *Pantoea* species.
  • Development and validation of a ResNet-18 classification model using SCRS data.

Main Results:

  • The classification model achieved high accuracy (96.9% mean accuracy, 97.3% recall) for *Pantoea* isolates.
  • The platform demonstrated high reproducibility, especially in nutrient-starved samples (87.9% accuracy).
  • Accurate species identification in synthetic communities (≤3.21% absolute abundance error) and consistency with 16S rRNA sequencing in rice seed microbiomes (34.8% vs 45%).

Conclusions:

  • The pDEP-RACS and ResNet platform enables rapid, species-, and strain-level classification of *Pantoea*.
  • This integrated approach significantly enhances microbial identification throughput (>7,200 SCRS/hour).
  • The platform is effective for analyzing both cultured microbes and complex environmental samples like microbiomes.