Abstract
Background: Single rare cell characterization represents a new scientific front in personalized therapy. Imaging mass cytometry (IMC) may be able to address all these questions by combining the power of MS-CyTOF and microscopy. Methods: We have investigated this IMC method using < 100 to up to 1000 cells from human sarcoma tumor cell lines by incorporating bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis of highly multiplexed IMC imaging data. We tested this process on osteosarcoma cell lines TC71, OHS as well as osteosarcoma patient-derived xenograft (PDX) cell lines M31, M36, and M60. We also validated our analysis using sarcoma patient-derived CTCs. Results: We successfully identified heterogeneity within individual tumor cell lines, the same PDX cells, and the CTCs from the same patient by detecting multiple protein targets and protein localization. Overall, these data reveal that our t-SNE-based approach can not only identify rare cells within the same cell line or cell population, but also discriminate amongst varied groups to detect similarities and differences. Conclusions: This method helps us make greater inroads towards generating patient-specific CTC fingerprinting that could provide an accurate tumor status from a minimally-invasive liquid biopsy.
Original language | English (US) |
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Article number | 715 |
Journal | BMC cancer |
Volume | 20 |
Issue number | 1 |
DOIs | |
State | Published - Jul 31 2020 |
Keywords
- Cell surface vimentin (CSV)
- Circulating tumor cells (CTCs)
- Copy number variations (CNV)
- Cytometry time-of-flight (CyTOF)
- Fine needle aspirates (FNA)
- Fluorescence associated cell-sorting (FACS)
- Imaging mass cytometry (IMC)
- Patient-derived xenograft (PDX)
- Smooth muscle actin (SMA)
- T-distributed stochastic neighbor embedding (t-SNE)
ASJC Scopus subject areas
- Genetics
- Oncology
- Cancer Research
MD Anderson CCSG core facilities
- Bioinformatics Shared Resource
- Flow Cytometry and Cellular Imaging Facility