期刊
ANALYTICAL CHEMISTRY
卷 94, 期 15, 页码 6026-6035出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.analchem.2c00679
关键词
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资金
- National Science Foundation of China [22077079, 81871448]
- Shanghai Municipal Education Commission Project [ZXWF082101]
- Shanghai Municipal Science and Technology Project [2017SHZDZX01, 18430760500]
Label-free proteomics with trace clinical samples provides valuable insights for personalized medicine. This study presents a sickle-like inertial microfluidic system for rare cell separation and label-free proteomics analysis. The system successfully identified different protein groups and demonstrated its capability in clinical samples, supporting personalized treatment decision making.
Label-free proteomics with trace clinical samples provides a wealth of actionableinsights for personalized medicine. Clinically acquired primary cells, such as circulating tumorcells (CTCs), are usually with low abundance that is prohibitive for conventional label-freeproteomics analysis. Here, we present a sickle-like inertial microfluidic system for online rarecell separation and tandem label-free proteomics (namely, Orcs-proteomics). Orcs-proteomicsadopts a buffer system with 0.1%N-dodecyl beta-D-maltoside (DDM), 1 mM Tris (2-carboxyethyl) phosphine (TCEP), and 2 mM 2-chloroacetamide (CAA) for cell lysis andreductive alkylation. We demonstrate the application of Orcs-proteomics with 293T cells andmanage to identify 913, 1563, 2271, and 2770 protein groups with 4, 13, 68, and 119 cells,respectively. We then spike MCF7 cells with white blood cells (WBCs) to simulate the patient'sblood sample. Orcs-proteomics identifies more than 2000 protein groups with an average of 61MCF7 cells. We further recruit two advanced breast cancer patients and collect 5 and 7 CTCsfrom each patient through minimally invasive blood drawing. Orcs-proteomics manages toidentify 973 and 1135 protein groups for each patient. Therefore, Orcs-proteomics empowers rare cells simultaneously to beseparated and counted for proteomics and provides technical support for personalized treatment decision making with rare primarypatient samples.
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