Decoding breast cancer tissue-stroma interactions using species-specific sequencing.

Area of Science:

• Transcriptomics
• Bioinformatics
• Cancer Research

Background:

• Understanding cell-cell interactions, like tumor-stroma dynamics in xenografts, is crucial.
• Analyzing mixed human and mouse cells often requires pre-sorting, which can alter gene expression.

Purpose of the Study:

• To develop a bioinformatics technique for analyzing mixed human and mouse transcriptomes without cell separation.
• To enable accurate genome-wide transcriptome analysis of intermixed cells in experimental models.

Main Methods:

• A novel bioinformatics approach to separate mouse and human transcriptomes from mixed-species sequencing data.
• Utilizing species-specific sequencing technology, termed S(3) (S-cube).

Main Results:

• The S(3) technique successfully assigns 99% of genes to their species of origin with high accuracy (0.2% misassignment).
• Application in xenograft models revealed distinct transcriptional responses of MCF7 and MDA-MB-231 breast cancer cells and their stromal interactions.
• Insights into Notch signaling pathways were also obtained.

Conclusions:

• S(3) technology provides a faithful method for recording transcriptomic changes in mixed human-mouse cell populations.
• This technology is versatile and applicable to diverse research questions in xenograft and cell-interaction studies.