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

Decoding transcriptional effects of experimental tissue-tissue or cell-cell interactions is important; for example, to better understand tumor-stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression.

To circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting.

Under stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology-referred to as S(3) ("S-cube")-to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct.

In conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions.