Image: SPLiT-seq (Georg Seelig)
Adapted from a UW Today release
UW Bioengineering Ph.D. student Charles Roco is co-lead author on a paper published Mar. 15 in Science that reports on SPLiT-seq — or Split Pool Ligation-based Transcriptome sequencing — a new method to classify and track different types of cells in tissue samples. This method reliably tracks gene activity in a tissue sample to the individual cell level.
SPLiT-seq offers an alternative to current RNA sequencing techniques. Standard RNA sequencing methods profile RNA across a whole tissue, but this approach does not tell researchers how the cells in a tissue sample differ from each other. Single-cell RNA sequencing allows RNA from isolated cells to be sequenced, but this approach is costly and does not scale well to experiments that might involve large numbers of different cells.
This technique allows researchers to perform single-cell RNA sequencing without isolating individual cells. “Using SPLiT-seq, it becomes possible to measure gene activity in individual cells, even if there are hundreds of thousands of different cells in a tissue sample,” explains the paper’s senior author Georg Seelig, UW associate professor in UW’s Department of Electrical Engineering and the Paul G. Allen School of Computer Science & Engineering, in a UW News & Information article.
SPLiT-seq splits cells in four rounds of shuffling into different pools. At each step, the RNA in each pool is labeled with its own unique DNA “barcode.” At the end of this process, RNA from each cell contains its own unique combination of barcode. Using this method, the researchers were able to measure the gene activity of over 156,000 cells from mouse brain and spinal cord tissue samples, and identify that over over 100 different types of cells were present in the samples.
SPLiT-seq delivers detailed biological information at the cost of “just a penny per cell,” said Seelig, which is a significantly lower cost than other single-cell RNA sequencing methods. It could help researchers gain insight into how tissues develop, and identify gene expression changes associated with the onset of complex diseases like Parkinson’s disease or cancer.