Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping

The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific analytical tools. Herein, we propose a SERS-based platform for the classification of acute lymphoblastic T-cell leukemia (T-ALL) and chronic myeloid leukemia (CML) through the combination of machine learning and microfluidic chips. The ordered arrays in microfluidic channels reshape the microscopic flow field and contacting interfaces, facilitating the uniform and efficient capture of tumor cells. To enable phenotypic analysis, spectrally orthogonal SERS aptamer nanoprobes were applied, providing composite spectral signatures of individual cells in accordance with surface protein expression. Further, machine learning algorithms were employed to analyze the SERS signatures automatically, resulting in an accuracy of 98.6 % for 73 clinical blood samples. The results demonstrate that this platform holds promising potential for clinical leukemia diagnosis and precision medicine.