To improve therapies for cancer and other diseases, researchers strive to identify tissue-specific therapeutic targets and diagnostic biomarkers in every patient. Identifying specific targets and biomarkers can be achieved by analyzing the cellular composition of tumors at the single-cell level. Although tissue profiling technologies such as single-cell RNA and single-nuclei RNA sequencing provide cell-type-specific information at unprecedented resolution, their implementation has technical and financial challenges that prevent their widespread adoption in clinical settings.
"Cancer cells in each tumor can vary in presentation, genetics and therapeutic responses. Some cells may be sensitive while others are resistant to chemotherapy. Chemoresistant cells are often responsible for the tumor's persistence and negative outcomes for the patients. We want to be able to identify chemoresistant cells before therapy begins, which will allow us to select therapies that target both chemosensitive and chemoresistant cancer cells.
For the last 20 years, people have analyzed the RNA of tumors in bulk. This approach provides an average of the RNAs present in all those cells."For instance, if 97% of the cells are chemosensitive and 3% chemoresistant, most of what we detect belongs to the chemosensitive cells—the cells that predominate. Consequently, the tumor's profiling is nearly blind to its chemoresistant component and its translation into the clinic is challenging," Sumazin said.
Furthermore, if there are chemoresistant cells in the tumor sample, scRNA-seq can reveal the difference between chemoresistant and chemosensitive cancer cells, valuable information when planning cancer treatment. "Here, we wanted to improve this situation by providing a way to make single-cell RNA analyses accessible to more people," Sumazin said."We devised SQUID, a computational approach to predict the single-cell RNA composition of a tumor sample using only the data of the bulk analysis of the sample. SQUID can reveal if chemoresistant cells are in the tumor sample and the chemotherapy they are sensitive to."also designed to predict single-cell RNA information from bulk data.
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