An integrated flow cytometry-based platform for isolation and molecular characterization of circulating tumor single cells and clusters

• Published in: Scientific reports Vol. 8; no. 1; pp. 5035 - 14
• Main Authors: Bhagwat, Neha, Dulmage, Keely, Pletcher, Charles H., Wang, Ling, DeMuth, William, Sen, Moen, Balli, David, Yee, Stephanie S., Sa, Silin, Tong, Frances, Yu, Liping, Moore, Jonni S., Stanger, Ben Z., Dixon, Eric P., Carpenter, Erica L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.03.2018; Nature Publishing Group
• Subjects: 631/61; 631/67/1857; Blood cells; Cell viability; Flow cytometry; Gene expression; Genetic engineering; Humanities and Social Sciences; Mesenchyme; multidisciplinary; Pancreatic cancer; Phenotypes; Ribonucleic acid; RNA; Science; Single-cell protein; Transcription; Transcriptomes; Tumor cells; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-23217-5

Comprehensive molecular analysis of rare circulating tumor cells (CTCs) and cell clusters is often hampered by low throughput and purity, as well as cell loss. To address this, we developed a fully integrated platform for flow cytometry-based isolation of CTCs and clusters from blood that can be combined with whole transcriptome analysis or targeted RNA transcript quantification. Downstream molecular signature can be linked to cell phenotype through index sorting. This newly developed platform utilizes in-line magnetic particle-based leukocyte depletion, and acoustic cell focusing and washing to achieve >98% reduction of blood cells and non-cellular debris, along with >1.5 log-fold enrichment of spiked tumor cells. We could also detect 1 spiked-in tumor cell in 1 million WBCs in 4/7 replicates. Importantly, the use of a large 200μm nozzle and low sheath pressure (3.5 psi) minimized shear forces, thereby maintaining cell viability and integrity while allowing for simultaneous recovery of single cells and clusters from blood. As proof of principle, we isolated and transcriptionally characterized 63 single CTCs from a genetically engineered pancreatic cancer mouse model (n = 12 mice) and, using index sorting, were able to identify distinct epithelial and mesenchymal sub-populations based on linked single cell protein and gene expression.