Abstract: Single-cell RNA-sequencing has emerged as a popular technique for dissecting temporal processes such as tumor development and cell differentiation from snapshots of asynchronous ensembles of cells. Ongoing efforts in
Abstract: Single-cell RNA-sequencing has emerged as a popular technique for dissecting temporal processes such as tumor development and cell differentiation from snapshots of asynchronous ensembles of cells. Ongoing efforts in this area are now exploring the benefit of measuring a variety of molecular features from every cell, in addition to RNA expression. In this talk, I will present Total Variational Inference (Total-VI) a method for analyzing joint measurements of surface proteins (for dozens of proteins) and gene expression (transcriptome wide) from the same cells (using CITE-seq). Total-VI learns a probabilistic representation of a cell’s state that reflects both its RNA and protein expression, while capturing uncertainties and propagating them to a variety of tasks (e.g., sub- population identification, differential expression). I will describe an application of Total-VI for studying T cell development in the thymus, which enabled us to finely map the changes that occur in transcript and surface protein abundance during the different phases of this process, and helped identify early regulators of divergence between the two primary (CD4+ and CD8+) lineages. While in the latter analysis the relatedness between cells (thus their time ordering) was inferred based on similarities in protein and RNA expression, new developments in Cas9- based lineage tracing now open the way to map their clonal relationships (i.e., single cell phylogenies). I will end this talk with a brief overview of our efforts in this budding area along with an outlook for future opportunities in studying how cellular populations evolve over time.
Speaker: Nir Yossef
Bio: Nir Yosef received his Ph.D. in computer science from Tel Aviv University and then proceeded to postdoctoral training at the Broad Institute, where he developed and applied methods in computational genomics for studying a variety of topics such as the regulation of telomere length and the differentiation of T helper cells. Nir joined the faculty at UC Berkeley in 2014, where he is currently an associate professor of computer science and a core member at the center of computational biology. He is also an associate member of the Ragon Institute of MGH, MIT and Harvard and a Chan Zuckerberg Biohub investigator. The Yosef lab is developing data- driven methods for studying how changes in transcription are associated with various phenotypes in the immune system. In that capacity, the lab is developing and building on techniques from algorithms and statistical machine learning to leverage single cell genomics data, with the goal of better understanding the factors that contribute to variability between cells, (e.g, metabolism, chromatin structure) and their effects on human health (e.g., in autoimmunity). A second area of research is method development for studying regulatory regions in the genome, based on chromatin profiles and massively parallel reporter assays.
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(Thursday) 3:00 pm - 4:00 pm
CDSLNCI CCR Cancer Data Science Lab