High-Definition Biotechnology

Precision Enabled: Discovery of Gene Networks and New Drug Targets in Metastatic Melanoma with Single Cell Sequencing

Wednesday, February 7
11:00 AM - 11:30 AM
Location: 6E

Despite the buzz regarding cloud facilitated data integration, notification, and tracking, LIMS and electronic laboratory notebooks have failed to deliver for multi-omics. Instead, current solutions are open source or have the user-friendliness of an electronic medical records system -- raising the activation energy and time required to install, to collaborate, and ultimately to produce insight. Herein, we describe a workflow-based collaboration and communication approach and its use in a coordinating the analysis of single cell gene expression from 19 melanoma metastases. We show that each metastatic tissue is unique to each patient, but identify a rare subpopulation present in each tumor which shares the same gene expression pattern. Thus, we identify two new drug targets shared between patients, and uncover the gene expression pattern of the immune response, particularly exhausted CD8+ T cells within each metastatic tissue could be reversed. Thus, we show how single cell phenotyping and gene expression experiment execution may be coordinated and executed to reveal both tumor and immune response drug targets and gene expression networks. We extend this work to compare the tumor gene expression patterns to the published literature and drug trials, and show that drug repurposing may be an effective strategy for melanoma.

Michael D. Stadnisky

CEO
FlowJo, LLC

Michael Stadnisky, Ph.D. is the CEO of FlowJo, LLC, the leading single cell flow cytometry and sequencing informatics company. Mike recently led his team to bring a new gene expression analysis platform to scientists in under 9 months in collaboration with Illumina. He is an author of 4 patents, was a finalist for the 2014 International Society for the Advancement of Cytometry President’s Award.

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Precision Enabled: Discovery of Gene Networks and New Drug Targets in Metastatic Melanoma with Single Cell Sequencing



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