基于单细胞质谱分析的膀胱癌细胞分型研究  

Typing of Bladder Cancer Cells Based on Single-cell Mass Spectrometry

作　　者：孙佳琪 陈安琪 闫明月 傅广候 李刚强 金百冶[4] 陈腊 闻路红 SUN Jia-qi;CHEN An-qi;YAN Ming-yue;FU Guang-hou;LI Gang-qiang;JIN Bai-ye;CHEN La;WEN Lu-hong(The Research Institute of Advanced Technology,Ningbo University,Ningbo 315211,China;China Innovation Instrument Co.Ltd.,Ningbo 315100,China;Hua Yue Enterprise Holdings Ltd,Guangzhou 511400,China;The First Affiliated Hospital of Zhejiang University School of Medicine,Hangzhou 310009,China)

机构地区：[1]宁波大学高等技术研究院,浙江宁波315211 [2]宁波华仪宁创智能科技有限公司,浙江宁波315100 [3]广州市华粤行仪器有限公司,广东广州511400 [4]浙江大学医学院附属第一医院,浙江杭州310009

出　　处：《分析测试学报》2023年第5期621-627,共7页Journal of Instrumental Analysis

基　　金：国家重点研发计划资助项目(2022YFF0705002);国家自然科学基金资助项目(81902604);浙江省重点研发计划项目(2020C03026,2020C02023);宁波市3315创新团队项目(2017A-17-C);宁波市重点研发计划项目(2022Z130);广州市番禺区创新创业领军团队资助项目(2017-R01-5);宁波大学王宽诚幸福基金项目。

摘　　要：单细胞质谱分析能够获得单个细胞的代谢图谱,揭示细胞之间的异质性,在肿瘤学研究中具有重要价值。该文采用单细胞质谱和机器学习技术,建立了膀胱癌细胞亚型的鉴别方法。基于所采集的单细胞代谢数据,分别使用线性判别分析、随机森林、支持向量机、逻辑回归建立了机器学习分类模型,并进行了模型的性能评估。结果表明,各机器学习模型均具有良好的膀胱癌细胞分型能力,分类准确率≥94.9%,灵敏度≥88.6%,特异度≥93.3%。其中,随机森林算法的分类准确率达100%,模型的受试者工作特征曲线下面积达1。该方法实现了膀胱癌单细胞的代谢物检测及细胞亚型区分,也为更广泛的单细胞代谢组学研究提供了参考。Single-cell mass spectrometry analysis enables metabolic profiling of individual cells,helps to reveal the heterogeneity among cells,which is of great significance in oncology research.Bladder cancer is the most common malignant tumor in the urinary system at present.Accurate iden⁃tification on the types of bladder cancer cells has an important value in life science and clinical appli⁃cation in the selection of treatment plan,prognosis judgment and drug resistance evaluation of pa⁃tients.In this paper,single-cell mass spectrometry combined with machine learning was used to identify bladder cancer cells.The metabolic profiles for different bladder cancer cell subtypes were investigated by single-cell mass spectrometry analysis system,and classification algorithms were studied.Based on the collected single cell metabolic data,t-distributed stochastic neighbor embed⁃ding(t-SNE)clustering algorithm was used for dimensionality reduction analysis on the data,and the difference between the single cell metabolic profile was visualized in the two-dimensional space.In order to accurately identify different types of bladder cancer cells,linear discriminant analysis,ran⁃dom forest,support vector machine and logistic regression were respectively used to establish ma⁃chine learning classification models,and grid search method and 5-fold cross-validation were used to optimize the model parameters.Then,five repeats of 10-fold cross-validation were performed on all data sets,and the averaged statistical result was taken as the final result.Accuracy,sensitivity,specificity,receiver operating characteristic(ROC)analysis and other indicators were used to comprehensively evaluate the performance of the model.The results showed that the metabolites of a sin⁃gle bladder cancer cell,such as ADP,ATP,glutamic acid,pyroglutamic acid,glutathione,etc,were successfully detected by the single-cell mass spectrometry system.There were significant differ⁃ences among different types of bladder cancer cells,as well as large differen

关 键 词：单细胞质谱分析 膀胱癌 代谢物检测 细胞分型 

分 类 号：O657.63[理学—分析化学] Q251[理学—化学]