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Smart-Seq Aims to Improve on Methods for Single-Cell Transcriptomics

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A new mRNA-seq method developed by researchers at the Karolinksa Institute in Stockholm and the Ludwig Cancer Institute, called Smart-Seq, can be used to study single-cell transcriptomics. Working in collaboration with Illumina, the researchers used the method to analyze gene expression in circulating tumor cells, though the Karolinska's Rickard Sandberg says it could be used to study a number of rare cell types.

"This method [makes it] possible for basically anyone to do single-cell transcriptome studies," Sandberg says. "That's to say, both medical or biological questions."

Current methods to examine single-cell transcriptomics suffer from limitations — one gives better measurements of the 3' end of transcripts while another better detects the 5' end. "The feature that we liked about Smart-Seq is that it has better full-length coverage across transcripts that can inform you, not only about the gene expression, but which variant of a gene is expressed," Sandberg says.

While many steps of the Smart-Seq protocol are similar to those of other approaches, it relies on template-switching technology, which Sandberg says allows it to generate full-length transcripts that can then be sequenced.

Using a series of RNA dilutions, Sandberg and his team determined that Smart-Seq generates results from 10-nanogram samples that are similar to results from standard mRNA-seq, and, with 1 nanogram of RNA, Smart-Seq results showed a small increase in variability as compared to the 10-nanogram samples. The team published its results in Nature Biotechnology in July.

As a further example, the researchers applied Smart-Seq to the analysis of gene expression of circulating tumor cells isolated from the blood of a melanoma patient. They report finding hundreds of differentially expressed genes from comparing a few cells per cell type. "We were hoping that the levels of variation would be low enough, that yes, you don't need to sequence 50 cells of each type in order to draw biological conclusions," Sandberg says. "In retrospect, we are happy that with a few cells we can see many, many significant differences — several hundreds of them."

This approach, he adds, could also be used to study cell heterogeneity in tumors or differences between normal cell types.

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