Scientists developed new shuffling trick, to measure gene activity in single cells

A new method to classify and track the multitude of cells in a tissue sample.

Image: University of Washington
Image: University of Washington

A single cell can frame an agreeable piece of a tissue, or denounce any kind of authority and go up against an infected state, similar to the disease. Be that as it may, scientists have since a long time ago attempted to distinguish and track the various kinds of cells covering up inside tissues.

Scientists the University of Washington in collaboration with Allen Institute for Brain Science have developed a new shuffling trick to classify and track the multitude of cells in a tissue sample. This new approach is 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. 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.

The technique enables scientists to sequence RNA without ever isolating individual cells. The researchers put the cells through four rounds of “shuffling” — 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 “barcode.” At the end of four rounds of shuffling and labeling, RNA from each cell essentially contained its own unique combination of barcodes — and that barcode combination is included in the bulk sequencing of all the RNA in the tissue.

Senior author Georg Seelig said, “Cells differ from each other based on the activity of their genes — which genes are switched off or switched on. 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.”

“With these ‘split-pool barcoding steps,’ we solve a big problem in measuring gene expression: reliably identifying which RNA molecules came from which cell in the original tissue sample.”

Co-author Bosiljka Tasic said, “With that problem addressed, we can begin to ask biological questions about the different types of cells we define in the tissue.”

The group performed SPLiT-seq on the brain and spinal string tissue tests from research facility mice. Utilizing SPLiT-seq, they could gauge the quality movement of more than 156,000 cells. In light of examples of quality action, they assessed that in excess of 100 unique kinds of cells were available in those tissue tests – including neurons and glial cells at different phases of advancement and separation.

SPLiT-seq can convey this rich exhibit of natural information at a cost of “only a penny for every cell,” said Seelig in a story by the Allen Institute for Brain Science. This is an essentially bring down cost than other single-cell RNA sequencing approaches, as indicated by the specialists.

The analysts say that SPLiT-seq could answer critical inquiries regarding how tissues create and distinguish minute changes in quality articulation that go before the beginning of complex maladies like Parkinson’s disease or cancer.

Co-lead authors on the paper are UW electrical engineering postdoctoral researcher Alexander Rosenberg and Charles Roco, a UW doctoral student in the Department of Bioengineering. Additional UW 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; Suzie Pun, a professor of bioengineering; and Drew Sellers, a research assistant professor of bioengineering and scientist with the UW 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.

The paper is published March 15 in the journal Science.

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