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Synchrotron X-ray Microdiffraction and Fluorescence Imaging of Mineral and Rock Samples
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ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy.

M A Z Chowdhury1, K Ok2, Y Luo3

  • 1Data Science and Learning Division, Argonne National Laboratory, Lemont, IL 60439, USA.

Journal of Synchrotron Radiation
|November 8, 2022
PubMed
Summary

Synchrotron X-ray fluorescence microscopy maps elements in E. coli. Machine learning distinguishes treated cells from background, enabling automated identification and analysis of biological samples.

Keywords:
E. coliX-ray fluorescencefuzzy clusteringlive cell imagingmicroscopyprincipal components analysisregion-of-interest detection

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Area of Science:

  • Bionanotechnology
  • Biophysics
  • Analytical Chemistry

Background:

  • Synchrotron X-ray fluorescence (XRF) microscopy is used to map elemental distributions in biological samples.
  • Cryogenic sample preparation and XRF mapping provide insights into cellular functions.
  • Label-free elemental and morphological data can serve as biological fingerprints for cell identification.

Purpose of the Study:

  • To develop automated methods for extracting and identifying bacterial cells from XRF data.
  • To implement machine learning models for distinguishing treated E. coli cells from background noise.
  • To enable label-free identification of cellular responses to different chemical treatments.

Main Methods:

  • Applying synchrotron XRF microscopy at Argonne National Laboratory's Bionanoprobe.
  • Automating cell extraction from raw XRF measurements using binary conversion and feature definition.
  • Utilizing machine learning, including principal component analysis and fuzzy clustering, for cell identification and background separation.
  • Ranking cells via fuzzy clustering to identify regions of interest for automated experimentation.

Main Results:

  • Successful automated extraction and identification of E. coli cells from XRF measurements.
  • Machine learning models effectively distinguished between healthy and chemically treated cells without manual annotation.
  • Fuzzy clustering provided a ranking of cells, guiding automated experimental analysis.
  • The study discussed the impact of dwell time and data volume on software usability.

Conclusions:

  • Automated XRF data analysis with machine learning can overcome limitations in cell identification.
  • Label-free elemental mapping provides a powerful tool for distinguishing cellular states.
  • The developed methods facilitate high-throughput analysis of biological samples in response to various treatments.