Dozent: Prof. Dr. David Suter, Associate Prof., Institute of Bioengineering, Ecole Polytechnique Federale de Lausanne (EPFL)

Abstract:
Assigning single cell transcriptomes to cellular lineage trees has transformed our
understanding of differentiation during development, regeneration, and disease.
However, lineage identification is technically demanding and most single cell (sc) RNAseq
datasets are devoid of lineage information. In this talk I will describe Gene Expression
Memory-based Lineage Inference (GEMLI), a computational tool allowing to robustly
determine cell lineages solely from scRNA-seq datasets. GEMLI allows to study heritable
gene expression, to discriminate symmetric and asymmetric cell fate decisions and to
reconstruct individual multicellular structures from pooled scRNA-seq datasets. In human
breast cancer biopsies, GEMLI revealed previously unknown gene expression changes at
the onset of cancer invasiveness. The universal applicability of GEMLI allows studying the
role of cell lineage trees in a wide range of physiological and pathological contexts. GEMLI
is available as an R package on GitHub (https://github.com/UPSUTER/GEMLI).