Webinar: A Single-Cell Multiomics Workflow: Impact of Human Disease Variants on Relevant Cell States

Advances in genetics and sequencing have identified a plethora of disease-associated genetic alterations. To determine causality between genetics and disease, accurate models for molecular dissection are required; however, the rapid expansion of transcriptional populations identified through single-cell analyses presents a major challenge for accurate mutant vs. wild-type comparisons.

To address this, a team at Cincinnati Children’s Hospital Medical Center explored a single-cell multiomics workflow to analyze newly generated mouse models of human severe congenital neutropenia (SCN) bearing patient-derived mutations in the GFI1 transcription factor. In this webinar, David Muench, post-doctoral research scientist, describes the work the team did to generate single-cell references for granulopoietic genomic states using CITE-Seq before aligning mutant cells to their wild-type equivalents to ultimately identify differentially expressed genes and epigenetic loci.

You will learn about:

• How a multiomics approach greatly improves the precision of molecular analyses.
• Why a single mutation affects each cell state differently.
• Why single-cell analyses are critical to accurately understand the impact of mutations or therapy.