Cell-type-specific transcriptomics in chimeric models using transcriptome-based masks

Regulatory networks involving different cell types control inflammation, morphogenesis and tissue homeostasis. Cell-type-specific transcriptional profiling offers a powerful tool for analyzing such cross-talk but is often hampered by mingling of cells within a tissue. Here, we present a novel method that performs cell-type-specific expression measurements without prior cell separation. This involves inter-species transplantation or chimeric co-culture models among which the human mouse system is frequently used. Here, we exploit the sufficiently divergent transcriptomes of human and mouse in conjunction with high-density oligonucleotide arrays. This required a masking procedure based on transcriptome databases and exhaustive fuzzy mapping of oligonucleotide probes onto these data. The approach was tested in a human-mouse experiment, demonstrating that we can efficiently measure species-specific transcriptional profiles in chimeric RNA samples without physically separating cells. Our results stress the importance of transcriptome databases with accurate 3' mRNA termination for computational prediction of accurate probe masks. We find that most human and mouse T-untranslated region contain unique stretches to allow for an effective control of cross-hybridization between the two species. This approach can be applied to xenograft models studying tumorhost interactions, morphogenesis or immune responses.