Separation and single-cell analysis for free gastric cancer cells in ascites and peritoneal lavages based on microfluidic chips

BACKGROUNDS: Detecting free cancer cells from ascites and peritoneal lavages is crucial for diagnosing gastric cancer (GC). However, traditional methods are limited for early-stage diagnosis due to their low sensitivity. METHODS: A label-free, rapid, and high-throughput technique was developed for separating cancer cells from ascites and peritoneal lavages using an integrated microfluidic device, taking advantage of dean flow fractionation and deterministic lateral displacement. Afterward, separated cells were analyzed using a microfluidic single-cell trapping array chip (SCTA-chip). In situ immunofluorescence for EpCAM, YAP-1, HER-2, CD45 molecular expressions, and Wright-Giemsa staining were performed for cells in SCTA-chips. At last, YAP1 and HER-2 expression in tissues was analyzed by immunohistochemistry. FINDINGS: Through integrated microfluidic device, cancer cells were successfully separated from simulated peritoneal lavages containing 1/10,000 cancer cells with recovery rate of 84.8% and purity of 72.4%. Afterward, cancer cells were isolated from 12 patients’ ascites samples. Cytological examinations showed cancer cells were efficiently enriched with background cells excluded. Afterwards, separated cells from ascites were analyzed by SCTA-chips, and recognized as cancer cells through EpCAM(+)/CD45(−) expression and Wright-Giemsa staining. Interestingly, 8 out of 12 ascites samples showed HER-2(+) cancer cells. At last, the results through a serial expression analysis showed that YAP1 and HER-2 have discordant expression during metastasis. INTERPRETATION: Microfluidic Chips developed in our study could not only rapidly detect label-free free GC cells in ascites and peritoneal lavages with high-throughput, they could also analyze ascites cancer cells at the single-cell level, improving peritoneal metastasis diagnosis and investigation of therapeutic targets. FUNDING: This research was supported by 10.13039/501100001809National Natural Science Foundation of China (22134004, U1908207, 91859111); 10.13039/501100007129Natural Science Foundation of Shandong Province of China (ZR2019JQ06); Taishan Scholars Program of Shandong Province tsqn (201909077); Local Science and Technology Development Fund Guided by the Central Government (YDZX20203700002568); Applied Basic Research Program of Liaoning Province (2022020284-JH2/1013).