Yang Zhicheng, Jin Kai, Chen Yimin, Liu Qian, Chen Hongxu, Hu Siyi, Wang Yuqiu, Pan Zilu, Feng Fang, Shi Mude, Xie Hua, Ma Hanbin, Zhou Hu
Department of Analytical Chemistry, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
University of the Chinese Academy of Sciences, Beijing 100049, China.
JACS Au. 2024 Mar 26;4(5):1811-1823. doi: 10.1021/jacsau.4c00027. eCollection 2024 May 27.
Single-cell proteomics offers unparalleled insights into cellular diversity and molecular mechanisms, enabling a deeper understanding of complex biological processes at the individual cell level. Here, we develop an integrated sample processing on an active-matrix digital microfluidic chip for single-cell proteomics (AM-DMF-SCP). Employing the AM-DMF-SCP approach and data-independent acquisition (DIA), we identify an average of 2258 protein groups in single HeLa cells within 15 min of the liquid chromatography gradient. We performed comparative analyses of three tumor cell lines: HeLa, A549, and HepG2, and machine learning was utilized to identify the unique features of these cell lines. Applying the AM-DMF-SCP to characterize the proteomes of a third-generation EGFR inhibitor, ASK120067-resistant cells (67R) and their parental NCI-H1975 cells, we observed a potential correlation between elevated VIM expression and 67R resistance, which is consistent with the findings from bulk sample analyses. These results suggest that AM-DMF-SCP is an automated, robust, and sensitive platform for single-cell proteomics and demonstrate the potential for providing valuable insights into cellular mechanisms.
单细胞蛋白质组学为细胞多样性和分子机制提供了无与伦比的见解，能够在单个细胞水平上更深入地理解复杂的生物过程。在此，我们开发了一种用于单细胞蛋白质组学的主动矩阵数字微流控芯片上的集成样品处理方法（AM-DMF-SCP）。采用AM-DMF-SCP方法和数据非依赖采集（DIA），我们在液相色谱梯度的15分钟内，平均在单个HeLa细胞中鉴定出2258个蛋白质组。我们对三种肿瘤细胞系：HeLa、A549和HepG2进行了比较分析，并利用机器学习来识别这些细胞系的独特特征。将AM-DMF-SCP应用于表征第三代EGFR抑制剂ASK120067耐药细胞（67R）及其亲本NCI-H1975细胞的蛋白质组，我们观察到波形蛋白（VIM）表达升高与67R耐药之间存在潜在相关性，这与大量样品分析的结果一致。这些结果表明，AM-DMF-SCP是一个用于单细胞蛋白质组学的自动化、稳健且灵敏的平台，并证明了其在提供细胞机制有价值见解方面的潜力。
