Thursday, November 9, 2023 | 1:30 PM - 2:30 PM
Preston M. Green Hall, Rodin Auditorium, L0120
135 N Skinker Blvd, St. Louis, MO 63112, USA
Comprehensively Profiling and Exploring Multiplexed Immunofluorescence Images of Brain Tissue for Driving Therapeutics Development
Abstract: Brain tissue is multiscale-complex, intricate, and importantly, delicate. A brain injury can unleash a complex web of pathological alterations in all types of brain cells at multiple scales, ranging from individual cells to multi-cellular niches to the layered brain cytoarchitecture. Additional alterations result from secondary injuries, regenerative processes, inflammation, tissue remodeling, drug treatments, and drug side effects. Many of these alterations can be subtle and/or latent, only discernible by sensing changes in cell morphology, cyto- or myelo-architecture, or the expression patterns of molecular markers. Some alterations can be in brain regions that are distant from the injury/damage site. For rational therapeutics development, we need the ability to reveal these changes in a comprehensive manner.
In this talk, I will describe a comprehensive approach to pathological brain tissue mapping for. The idea is to replace the many low information content assays with a single comprehensive assay based on highly multiplexed imaging of brain sections employing 10 – 100 molecular markers, sufficient to capture all the major brain cell types, and their functional states, over extended brain regions. Analyzing these multiplex images is challenging due to their complexity, variability, and size. Training deep neural networks for conducting these tasks ordinarily requires significant human labor. I will describe a combination of fully automated signal reconstruction, scalable cell detection and phenotyping, and high-dimensional data analysis approaches to generate quantitative readouts of cellular alterations at multiple scales ranging from individual cells to multi-cellular units, large cellular ensembles (e.g., cortical layers), and atlas-defined brain regions. I will conclude with early results from a visual search engine that can learn the characteristics of one or more multiplex image datasets, and provide the ability to profile them driven by visual prompts.