Using mass cytometry, imaging, and machine learning to understand malignant brain tumor biology
Presented by Rebecca Ihrie, PhD
Associate Professor of Cell and Developmental Biology
A central goal in single-cell cancer research is to identify phenotypically distinct cells that have prognostic significance for patients. This need is acute in brain tumor glioblastoma, where changes in gene sequence, copy number or transcript expression can define patient or cell subgroups, but these subgroups are not associated with large differences in clinical outcomes.
Rebecca Ihrie, PhD, will present recent work using suspension mass cytometry (used in CyTOF® instruments) and Imaging Mass Cytometry™ to
• study glioblastoma patient samples in suspension and tissue sections;
• identify prognostic subpopulations of cancer and immune cells;
• locate these cells spatially within tumors and the brain.
She will also discuss ongoing work using these approaches to understand how tumor contact with a brain stem cell niche may alter the properties of cancer and immune cells in these tumors and impact patient survival.
In this webinar, sponsored by Fluidigm, attendees will learn about
• the unique value that high-dimensional mass cytometry brings to Ihrie’s brain tumor research;
• Ihrie’s workflow from tissue processing through data analysis;
• Risk Assessment Population IDentification (RAPID), an unsupervised machine learning algorithm used in her research;
• development and validation of a brain-centric imaging panel.
Leelatian, N. et al. “Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells.” eLife (2020): 9:e56879.
Mistry, A.M. et al. “Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumours.” FEBS Journal (2019): 1,523–1,539.
Leelatian, N. et al. “Preparing viable single cells from human tissue and tumors for cytomic analysis.” Current Protocols in Molecular Biology (2017): 25C.1.1–25C.1.23.
For Research Use Only. Not for use in diagnostic procedures.