Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, Norman, OK, 73019, USA.
Anal Chim Acta. 2019 Dec 27;1092:42-48. doi: 10.1016/j.aca.2019.09.065. Epub 2019 Sep 25.
Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.
尽管存在评估化疗过程中耐药性的方法，但仍缺乏能够从其天然微环境中单细胞监测早期化疗阶段耐药程度的技术。在此，我们报告了一种分析方法，该方法将单细胞质谱（SCMS）基于代谢组学与机器学习（ML）模型相结合，以解决现有挑战。使用单探针 SCMS 技术测量了具有不同化疗诱导耐药程度（即无、低和高）的活癌细胞（HCT-116）的代谢组学谱。构建了一系列 ML 模型，包括随机森林（RF）、人工神经网络（ANN）和惩罚逻辑回归（LR），以预测单个细胞的耐药程度。在多个模型之间进行了性能的系统比较，并进行了实验验证。我们的结果表明，这些 ML 模型，尤其是在获得的 SCMS 数据集上构建的 RF 模型，可以快速准确地预测活单细胞的不同耐药程度。通过在单细胞水平上快速可靠地评估耐药性，我们的方法可潜在地用于评估临床化疗效果。
