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Clinical Microfluidic Chip Platform for the Isolation of Versatile Circulating Tumor Cells
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Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

Kuo Yang1, Jinjin Zhao1, Ying Huang1

  • 1Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China.

Talanta
|November 4, 2024
PubMed
Summary
This summary is machine-generated.

A novel SERS-based platform accurately classifies leukemia subtypes, acute lymphoblastic T-cell leukemia (T-ALL) and chronic myeloid leukemia (CML). This advancement aids early diagnosis and precision medicine through sensitive cell phenotyping.

Keywords:
LeukemiaMachine learningMicrofluidicsPhenotypingSurface-enhanced Raman spectroscopy (SERS)

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Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Oncology

Background:

  • Leukemia subtype classification is crucial for treatment, but early diagnosis is hindered by a lack of sensitive tools.
  • Accurate phenotyping of leukemia is essential due to varying disease progression and treatment responses.

Purpose of the Study:

  • To develop a highly sensitive and specific platform for classifying acute lymphoblastic T-cell leukemia (T-ALL) and chronic myeloid leukemia (CML).
  • To integrate microfluidic chips with Surface-Enhanced Raman Spectroscopy (SERS) and machine learning for automated leukemia diagnosis.

Main Methods:

  • Utilized microfluidic chips with ordered arrays for uniform and efficient tumor cell capture.
  • Employed spectrally orthogonal SERS aptamer nanoprobes for phenotypic analysis based on surface protein expression.
  • Applied machine learning algorithms for automated analysis of SERS spectral signatures.

Main Results:

  • Achieved 98.6% accuracy in classifying T-ALL and CML using 73 clinical blood samples.
  • Demonstrated effective phenotypic analysis of individual cells through composite spectral signatures.
  • Validated the platform's capability for sensitive and specific leukemia cell classification.

Conclusions:

  • The developed SERS-based platform shows significant potential for accurate clinical leukemia diagnosis.
  • This technology can advance precision medicine by enabling early and reliable subtype classification.
  • The integration of microfluidics, SERS, and machine learning offers a powerful approach for cancer diagnostics.