LinTIMaT, a Novel Statistical Learning Approach For Tracing Cell Lineage – Researh by a young Prof from Indian Institute of Technology, Kanpur – Mr. Hamim Zafar
Our body consists of numerous cells that are functioning continuously to keep us alive. Even though the cells in different organs contain almost similar DNA, they can perform different functions. While the cells perform different functions, their journey actually starts in a single cell. Over time cells divide, and ancestor cells give rise to descendant cells forming a lineage tree that elucidates the genealogical relationship between cells. During this process, descendant cells can also acquire new specialized functions that are different from their ancestors. When a group of cells perform similar functions, they are said to compose a cell type. The process in which a cell transforms from one cell type to another is known as cellular differentiation. Understanding this cellular differentiation process and these cell lineage trees that delineate the ancestral relationship between cells is crucial for understanding the normal development process in an organism as well as what goes wrong in pathologies such as cancer.
To address this important research problem in developmental biology, Dr. Hamim Zafar from IIT Kanpur, in collaboration with researchers from Carnegie Mellon University, has developed a statistical learning method called ‘LinTIMaT’ that can reconstruct cell lineages for an individual organism or at the species-level. The method and its applications on whole-organism lineage reconstruction have been reported in an article published in the journal Nature Communications. The research team consists of Dr. Hamim Zafar who is a joint faculty in the CSE and BSBE departments at IIT Kanpur, Chieh Lin (co-first author in the study) and Prof. Ziv Bar-Joseph from Carnegie Mellon University.
Inference of cell lineages in a multicellular organism is a challenging task. The best way of describing how a mother cell divides into daughter cells is through live tracking which is not possible for most organisms. To circumvent this problem, scientists use imaging-based or genetic markers to tag the cells. The challenge lies in scaling the method for a whole organism, as thousands of cells need to be tagged even to recover a part of the cell lineage. Even more challenging is to simultaneously profile the lineage and function information from the cells as needed to understand the connection between cell lineage and differentiation.
Thanks to the development of high-throughput sequencing technologies, experimental biologists can now combine a genetic engineering tool named CRISPR-Cas9 with another technology named single-cell RNA sequencing to generate high-throughput data suitable for reconstructing cell lineages. For the same cell, CRISPR-Cas9 system gives a set of mutation that encodes the lineage information whereas single-cell RNA sequencing provides gene expression values that indicate cellular functions. While this combination provided valuable insights regarding development, “the studies also have several limitations” mentioned Zafar, “for reconstructing the lineage, these studies resorted to using a classical, off-the-shelf, method for phylogenetic tree building that uses only mutation information, and fails to recover branches and cannot resolve branchings in later stages of development.” Also, the mutations are random and make it impossible to reconstruct species-level cell lineage.
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