Host: Jellert Gaublomme
Title: Multimodal analysis of single cell trajectories
Abstract: Single-cell RNA-sequencing has emerged as a popular technique for dissecting temporal processes such as tumor development or cell differentiation from snapshots of asynchronous ensembles of cells. Current efforts for studying cellular trajectories are now exploring the benefit afforded by measuring additional properties from every cell, in addition to its transcriptome. In this talk, I will survey our efforts for developing and applying computational tools for leveraging two such types of coupled measurements. Our first case is CITE-Seq - a method for estimating the abundance of dozens of proteins on a cell’s membrane in addition to its transcriptome. We designed a computational method, Total-VI, that uses CITE-seq data to learn a representation of a cell’s state that reflects both its RNA and protein expression, while capturing uncertainties and propagating them to a range of tasks (e.g., batch correction, sub- population identification, differential expression). I will demonstrate the utility of Total-VI in the context of an ongoing study of T cell lineage specification in the thymus. In the second part of my talk I will focus on lineage tracing of single cells using heritable mutations that are introduced by CRISPR/Cas9. I will describe a suite of computational tools (Cassiopeia) we designed for leveraging the editing outcomes in single cells in order to infer and study their clonal relationships. I will demonstrate the utility of this approach in studying the sub-clonal dynamics of metastasis in a lung cancer xenograft mouse model.
Please email [email protected] for Zoom information to attend this seminar.