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NIH Virtual Seminar: Spatial Transcriptomic Analysis and Cell Type Characterization by RNAscope™ HiPlex Assay

Thursday, November 5, 2020 @ 10am PST | 1pm EST | 7pm CEST

Characterizing the transcriptomic profiles of individual cells by single-cell RNA sequencing (scRNA-seq) has become a universal tool to identify both known and novel cell populations, ushering science in a new era of single cell biology.

However, scRNA-seq utilizes dissociated cells with results in the loss of spatial organization of the cell population being analyzed. It is therefore essential to complement scRNA-seq analysis with RNAscope™ in situ hybridization (ISH) in order to obtain visual confirmation of both single cell and spatial gene expression.

In this webinar, Dr. Ariel Levine from NIH NINDS will share her latest publication using RNAscope HiPlex assay, to reveal spinal cord cell type organization, validate a combinatorial set of markers for in-tissue spatial gene expression analysis, and optimize the computational classification based on transcriptomic data.



SPEAKER


Kim Leitzel, Msc

Ariel Levine, M.D., Ph.D.
Earl Stadtman Investigator
National Institute of Neurological Disorders and Stroke
National Institute of Health


Dr. Levine received an undergraduate degree in biology from Brandeis University in 2000, a Ph.D. from The Rockefeller University in 2008, and an M.D. from Cornell University in 2009. During her graduate research with Dr. Ali Brivanlou, she studied the role of TGF-β signaling during embryonic development. Dr. Levine did postdoctoral research with Dr. Samuel Pfaff at The Salk Institute, where she identified a novel population of spinal neurons that encode “motor synergies” – modular neural programs for simple movements that are thought to underlie a wide variety of common behaviors. She was an Associate Member of the Reeve Foundation Consortium and a Fellow of the George Hewitt Foundation. She joined NINDS in 2015 where her lab studies how the molecules, neurons, and circuits of the spinal cord mediate normal behavior and learn.

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