Recent advances in analyzing data at the single-cell level have helped biologists make great strides in uncovering new information about cells and their behaviors. One commonly used approach, known as clustering, allows scientists to group cells based on characteristics such as the unique patterns of active or inactive genes or by the progeny of duplicating cells, known as clones, over several generations.
Although single-cell clustering has led to many significant findings, for example, new cancer cell subsets or the way immature stem cells mature into “specialized” cells, researchers to this point had not been able to marry what they knew about gene-activation patterns with what they knew about clone lineages.
Now, research published in Cell Genomics led by University of Pennsylvania professor of bioengineering Arjun Raj has resulted in the development of ClonoCluster, an open-source tool that combines unique patterns of gene activation with clonal information. This produces hybrid cluster data that can quickly identify new cellular traits; that can then be used to better understand resistance to some cancer therapies.
“Before, these were independent modalities, where you would cluster the cells that express the same genes in one lot and cluster the others that share a common ancestor in another,” says Lee Richman, first paper author and a former postdoc in the Raj Lab who is now at Brigham and Women’s Hospital in Boston. “What’s exciting is that this tool allows you to draw new lines around your clusters and explore their properties, which could help us identify new cell types, functions, and molecular pathways.”
Researchers in the Raj Lab use a technique known as barcoding to assign labels to cells they are interested in studying, particularly useful for tracking cells, clustering data based on cells’ offspring, and following lineages over time. Believing they could parse more valuable information out of this data by incorporating the cell’s unique patterns of gene activation, the researchers applied ClonoCluster to six experimental datasets that used barcoding to track dividing cells’ offspring. Specifically, they looked at the development of chemotherapy resistance and of stem cells into specialized tissue types.
This story is by Nathi Magubane. Read more about ‘New Single Cell Analysis Tool’ at Penn Today.