Yang Kuo, Zhao Jinjin, Huang Ying, Sheng Hai, Wang Zhuyuan
Advanced Photonics Center, School of Electronic Science and Engineering, Southeast University, Nanjing, 210096, China.
Nanjing Foreign Language School, Nanjing, 210008, China.
Talanta. 2025 Feb 1;283:127148. doi: 10.1016/j.talanta.2024.127148. Epub 2024 Nov 2.
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.
白血病不同亚型之间的疾病进展和治疗方案差异很大，这凸显了表型分析的重要性。然而，由于缺乏高度灵敏且特异的分析工具，白血病的早期分型仍然具有挑战性。在此，我们提出了一种基于表面增强拉曼光谱（SERS）的平台，通过机器学习和微流控芯片的结合对急性淋巴细胞性T细胞白血病（T-ALL）和慢性髓性白血病（CML）进行分类。微流控通道中的有序阵列重塑了微观流场和接触界面，有助于均匀且高效地捕获肿瘤细胞。为了实现表型分析，应用了光谱正交的SERS适配体纳米探针，根据表面蛋白表达提供单个细胞的复合光谱特征。此外，采用机器学习算法自动分析SERS特征，对73份临床血样的分析准确率达98.6%。结果表明，该平台在临床白血病诊断和精准医学方面具有广阔的应用前景。
