Multicellular organisms, including humans, can be incredibly complex even though they start from one fertilized cell. The many cells that compose the tissues and organs in a body are derived from parent cells that have divided to produce daughter cells. So the lineage of a cell can be traced back just like people can trace their ancestry through generations. Scientists have now developed a new tool to improve and simplify the study of lineage tracing. The method, which can be used to study the journey of cells through development, human disease, or other processes, has been reported in Nature Communications.
Lineage tracing can be very challenging and time consuming. For example, a fluorescent dye can be injected into a zebrafish embryo, which is transparent, at an early stage of development. Images can then be taken as the embryo develops. But this takes a lot of effort. So this new technique aims to overcome those kinds of technical hurdles. The tool has been called Gene Expression Memory-based Lineage Inference (GEMLI), and it takes advantage of single-cell RNA sequencing (scRNA-seq) data. This scRNA-seq data reveals what genes are active in a specific cell at a certain moment in time, and once generated, can be used in diferent analyses.
In this study, the researchers capitalized on a kind of memory of gene expression in cells. There are some genes that are active at about the same level through several generations of cell division, and this 'memory' can be utilized to identify genes that have the same lineage. The ancestry of a gene can be traced by with GEMLI based on the patterns of gene expression in cells.
GEMLI was tested in different types of cells, under various conditions; this included in vitro and in vivo blood, intestinal, fibroblast, and embryonic stem cells, as well as several kinds of cancer cells. GEMLI could be applied in any of these scenarios.
For example, GEMLI was successfully applied to primary human breast cancer cells, which have not been successfully subjected to lineage tracing before, noted the researchers.
GEMLI is best applied to the reconstruction of lineages of about 30 to 50 cells, which is a relatively small to medium sized group. This way, the branching points that emerge as cancer progresses can be observed, noted study co-author David Suter of EPFL. "By identifying cells at the transition point from an in situ to an invasive phenotype, one can recover genes that potentially drive cancer progression."
GEMLI also does not require specialized equipment, although sc-RNA seq data is sometimes expensive to obtain. GEMLI itself is freely accessible on GitHub, and can be used with almost any scRNA-seq data.
"We are excited about GEMLI's potential in leveraging the large number of publicly available human cancer scRNA-seq datasets to dissect how other types of cancers switch to an invasive phenotype," said Suter.
Sources: EPFL, Nature Communications
