Rare osteosarcoma cell subpopulation protein array and profiling using imaging mass cytometry and bioinformatics...

Area of Science:

• Oncology
• Biotechnology
• Bioinformatics

Background:

• Single rare cell characterization is crucial for advancing personalized cancer therapy.
• Imaging mass cytometry (IMC) offers a promising approach by integrating mass spectrometry-based cytometry by time-of-flight (MS-CyTOF) and microscopy.
• IMC has the potential to address key challenges in rare cell analysis.

Purpose of the Study:

• To investigate the utility of IMC combined with t-Distributed Stochastic Neighbor Embedding (t-SNE) for rare cell characterization.
• To evaluate the method's ability to identify heterogeneity and discriminate between cell populations.
• To explore its application in analyzing patient-derived samples.

Main Methods:

• Utilized IMC on human sarcoma cell lines (osteosarcoma TC71, OHS) and patient-derived xenograft (PDX) cell lines (M31, M36, M60).
• Applied bioinformatics-based t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis to highly multiplexed IMC imaging data.
• Validated the approach using sarcoma patient-derived circulating tumor cells (CTCs).

Main Results:

• Successfully identified cellular heterogeneity within tumor cell lines, PDX cells, and patient CTCs.
• Detected multiple protein targets and protein localization to distinguish rare cells.
• Demonstrated that the t-SNE-based approach can identify rare cells and discriminate between varied cell groups, revealing similarities and differences.

Conclusions:

• The developed IMC and t-SNE method facilitates rare cell identification and characterization.
• This approach advances patient-specific CTC fingerprinting.
• Enables accurate tumor status assessment through minimally-invasive liquid biopsies.