Characterization of Drug Resistance in Chronic Myeloid Leukemia Cells Based on Laser Tweezers Raman Spectroscopy.
Qian ZhangMinlu YeLingyan WangDongmei JiangShuting YaoDonghong LinYang ChenShangyuan FengTing YangJianda HuPublished in: Applied spectroscopy (2021)
Multidrug resistance is highly associated with poor prognosis of chronic myeloid leukemia. This work aims to explore whether the laser tweezers Raman spectroscopy (LTRS) could be practical in separating adriamycin-resistant chronic myeloid leukemia cells K562/adriamycin from its parental cells K562, and to explore the potential mechanisms. Detection of LTRS initially reflected the spectral differences caused by chemoresistance including bands assigned to carbohydrates, amino acid, protein, lipids, and nucleic acid. In addition, principal components analysis as well as the classification and regression trees algorithms showed that the specificity and sensitivity were above 90%. Moreover, the band data-based classification and regression tree model and receiver operating characteristic curve further determined some important bands and band intensity ratios to be reliable indexes in discriminating K562 chemoresistance status. Finally, we highlighted three metabolism pathways correlated with chemoresistance. This work demonstrates that the label-free LTRS analysis combined with multivariate statistical analyses have great potential to be a novel analytical strategy at the single-cell level for rapid evaluation of the chemoresistance status of K562 cells.
Keyphrases
- chronic myeloid leukemia
- induced apoptosis
- raman spectroscopy
- poor prognosis
- cell cycle arrest
- machine learning
- label free
- deep learning
- single cell
- amino acid
- signaling pathway
- cell death
- magnetic resonance imaging
- oxidative stress
- computed tomography
- risk assessment
- magnetic resonance
- artificial intelligence
- high intensity
- liquid chromatography
- mass spectrometry