Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics

蛋白质组学 蛋白质组 细胞 肺表面活性物质 化学 循环肿瘤细胞 单细胞分析 定量蛋白质组学 样品制备 色谱法 转移 生物 生物化学 癌症 遗传学 基因
作者
Chia‐Feng Tsai,Pengfei Zhang,David Scholten,Kendall Martin,Yi-Ting Wang,Rui Zhao,William Chrisler,Dhwani Patel,Maowei Dou,Yuzhi Jia,Carolina Reduzzi,Xia Liu,Ronald J. Moore,Kristin Burnum-Johnson,Miao‐Hsia Lin,Chuan‐Chih Hsu,Jon Jacobs,Jacob Kagan,Sudhir Srivastava,Karin Rodland
出处
期刊:Communications biology [Nature Portfolio]
卷期号:4 (1) 被引量:88
标识
DOI:10.1038/s42003-021-01797-9
摘要

Abstract Large numbers of cells are generally required for quantitative global proteome profiling due to surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) method termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses for effective processing of single cells, thus improving detection sensitivity for single-cell proteomics. This method allows convenient label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.
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