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High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global

Renzo Vanna1, Andrea Masella2, Manuela Bazzarelli2

  • 1Istituto di Fotonica e Nanotecnologie - Consiglio Nazionale delle Ricerche (IFN-CNR), c/o Politecnico di Milano, Milan 20133, Italy.

Analytical Chemistry
|May 31, 2024
PubMed
Summary

This study uses Raman hyperspectral imaging and global clustering to characterize leukemia subtypes. This automated approach identifies cellular components for improved leukemia diagnosis and research.

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Area of Science:

  • Biomedical Engineering
  • Chemical Biology
  • Hematology

Background:

  • Leukemia diagnosis relies on subjective smear analysis, which is time-consuming.
  • Accurate leukemia subtype identification is crucial for effective treatment.

Purpose of the Study:

  • To develop an objective method for characterizing leukemia subtypes using Raman hyperspectral imaging.
  • To explore the utility of global clustering for analyzing cellular components in leukemia.

Main Methods:

  • Analyzed bone marrow samples from 19 leukemia patients (9 subtypes).
  • Utilized high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra.
  • Applied an automated preprocessing pipeline and a single-step global clustering approach.

Main Results:

  • Identified key cellular components: cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB).
  • Enabled unsupervised creation of high-quality, single-cell pseudostained images.
  • Provided semiquantitative analysis of cellular component distribution.

Conclusions:

  • Raman hyperspectral imaging with global clustering shows potential for leukemia research.
  • This method offers an objective and detailed approach to cellular analysis in leukemia.
  • Highlights advantages and challenges of global clustering for leukemia subtype characterization.