Barcoding small extracellular vesicles with new CRISPR-based system

Cell-to-cell communication through nanosized particles, working as messengers and carriers, can now be analyzed in a whole new way, thanks to a new method involving CRISPR gene-editing technology. The particles, known as small extracellular vesicles (sEVs), play an important role in the spread of disease and as potential drug carriers. The newly developed system, named CIBER, enables thousands of genes to be studied at once, by labeling sEVs with a kind of RNA "barcode." With this, researchers hope to find what factors are involved in sEV release from host cells. This will help advance our understanding of basic sEV biology and may aid in the development of new treatments for diseases, such as cancer.

Small EVs have been found to play a key role in cell-to-cell communication. Depending on what "cargo" they carry from their host cell (which can include RNA, proteins and lipids), sEVs can help maintain normal tissue functions or can further the spread of diseases. Because of this, researchers are interested in how sEVs form and are released. However, separating sEVs from other molecules and identifying the factors which lead to their release is both difficult and time-consuming with conventional methods. So, a team in Japan has developed a new technique.

The CIBER system works by using CRISPR-guide RNA (gRNA) to knock out (incapacitate) a specific gene in each cell, which in turn is barcoded into the sEVs the cell releases. This enables the researchers to track and estimate the amount of sEVs released by the host cell. Current methods to study factors involved in sEV release involve separating cells into wells, disturbing the expression and activity of a gene in the cells in each well, and then measuring how that impacts the amount of sEVs released. However, with the CIBER system, thousands of cells with different genes knocked out can be studied together in one pool. Thanks to this, researchers can study the various complex factors involved in sEV release simultaneously, as well as estimate the amount of sEVs and different types (subpopulations) released from each cell.

Source:

University of Tokyo

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