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 at the 91探花 and the have developed a new method to classify and track the multitude of cells in a tissue sample. In a published March 15 in the journal , the team reports that this new approach 鈥� known as SPLiT-seq 鈥� reliably tracks gene activity in a tissue down to the level of single cells.
鈥淐ells differ from each other based on the activity of their genes 鈥� which genes are switched off or switched on,鈥� said senior author , a 91探花associate professor in both the Department of Electrical Engineering and the Paul G. Allen School of Computer Science & Engineering. 鈥淯sing 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.鈥�
SPLiT-seq 鈥� which stands for Split Pool Ligation-based Transcriptome sequencing 鈥� 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 鈥渓etters鈥� of RNA, the DNA-like molecule that is the first step in gene expression. This standard approach 鈥� known as RNA-sequencing 鈥� 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. The researchers put the cells through four rounds of 鈥渟huffling鈥� 鈥� splitting them into separate pools and mixing them back together. At each shuffling step, they labeled the RNA in each pool with its own unique DNA 鈥渂arcode.鈥� At the end of four rounds of shuffling and labeling, RNA from each cell essentially contained its own unique combination of barcodes 鈥� and that barcode combination is included in the bulk sequencing of all the RNA in the tissue.
鈥淲ith these 鈥榮plit-pool barcoding steps,鈥� we solve a big problem in measuring gene expression: reliably identifying which RNA molecules came from which cell in the original tissue sample,鈥� said , who is also a researcher in the 91探花Molecular Engineering & Sciences Institute.
鈥淲ith that problem addressed, we can begin to ask biological questions about the different types of cells we define in the tissue,鈥� said co-author , Associate Director of Molecular Genetics at the Allen Institute for Brain Science.
The team performed SPLiT-seq on brain and spinal cord tissue samples from laboratory mice. Using SPLiT-seq, they could measure the gene activity of over 156,000 cells. Based on patterns of gene activity, they estimated that more than 100 different types of cells were present in those tissue samples 鈥� including neurons and glial cells at various stages of development and differentiation.
SPLiT-seq can deliver this rich array of biological data at a cost of 鈥渏ust a penny per cell,鈥� said Seelig in a 聽by the Allen Institute for Brain Science. This is a significantly lower cost than other single-cell RNA sequencing approaches, according to the researchers.
The researchers say that SPLiT-seq could answer important questions about how tissues develop, and identify minute changes in gene expression that precede the onset of complex diseases like Parkinson鈥檚 disease or cancer.
Co-lead authors on the paper are 91探花electrical engineering postdoctoral researcher and , a 91探花doctoral student in the Department of Bioengineering. Additional 91探花co-authors are Richard Muscat, Anna Kuchina, Paul Sample and Sumit Mukherjee in the Department of Electrical Engineering; David Peeler in the Department of Bioengineering; Wei Chen in the Molecular Engineering & Sciences Institute; , a professor of bioengineering; and Drew Sellers, a research assistant professor of bioengineering and scientist with the 91探花Institute for Stem Cell and Regenerative Medicine. Additional co-authors from Allen Institute for Brain Science are Zizhen Yao and Lucas Gray. The research was funded by the National Institutes of Health, the National Science Foundation and the Allen Institute for Brain Science.
###
For more information, contact Rosenberg at alex.b.rosenberg@gmail.com or 773-294-4109 and Seelig at gseelig@uw.edu or 206-294-8180.