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For biologists, a single cell is a world of its own: It can form a harmonious part of a tissue, or go rogue and take on a diseased state, like cancer. But biologists have long struggled to identify and track the many different types of cells hiding within tissues. Researchers have developed a new method to classify and track the multitude of cells in a tissue sample. In a paper published March 15, the team reports that this new approach — known as SPLiT-seq — reliably tracks gene activity in a tissue down to the level of single cells. SPLiT-seq — which stands for Split Pool Ligation-based Transcriptome sequencing — combines a traditional approach to measuring gene expression with a new twist. For more than a decade, scientists have measured gene expression in tissues by sequencing the genetic “letters” of RNA, the DNA-like molecule that is the first step in gene expression. This standard approach — known as RNA-sequencing — profiles RNA across the whole tissue. But this approach does not tell researchers how cells within the tissue differ from one another. Single-cell RNA-sequencing addresses this by sequencing RNA from isolated cells, but existing methods are costly and do not scale well. SPLiT-seq makes it possible to perform single-cell RNA-sequencing without ever isolating individual cells. SPLiT-seq can deliver this rich array of biological data at a cost of “just a penny per cell.” This is a significantly lower cost than other single-cell RNA sequencing approaches, according to the researchers.

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