Presenting on “Label-free optical microscopy of T cell and stem cell metabolism”

Melissa Skala, PhD, Professor of Biomedical Engineering, University of Wisconsin–Madison; Investigator, Morgridge Institute for Research, will speak on Thursday, November 3, 2022 at 10:00 am CST in Whitaker 218.

Abstract: T cell and stem cell manufacturing are two major components of the cell therapy market. However, cost-effective and time-efficient technologies are needed to monitor the quality of of cells during biomanufacturing. We have developed a non-invasive and label-free live cell imaging platform to monitor T cell function and predict the efficiency of stem cell differentiation at a single-cell level. Fluorescence lifetime imaging of the metabolic co-enzymes NAD(P)H and FAD was coupled with single cell segmentation and prospective classification algorithms. This approach identified T cell activation and function with high accuracy, and predicted stem cell differentiation efficiency early (day 1) with high accuracy. This non-invasive and label-free method could be used monitor cell manufacturing for T cell and stem cell therapies, reducing the cost and time to produce these therapies while improving potency and consistency.

Presenting on “Label-free optical microscopy of T cell and stem cell metabolism”

Melissa Skala, PhD, Professor of Biomedical Engineering, University of Wisconsin–Madison; Investigator, Morgridge Institute for Research, will speak on Thursday, November 3, 2022 at 10:00 am CST in Whitaker 218.

Abstract: T cell and stem cell manufacturing are two major components of the cell therapy market. However, cost-effective and time-efficient technologies are needed to monitor the quality of of cells during biomanufacturing. We have developed a non-invasive and label-free live cell imaging platform to monitor T cell function and predict the efficiency of stem cell differentiation at a single-cell level. Fluorescence lifetime imaging of the metabolic co-enzymes NAD(P)H and FAD was coupled with single cell segmentation and prospective classification algorithms. This approach identified T cell activation and function with high accuracy, and predicted stem cell differentiation efficiency early (day 1) with high accuracy. This non-invasive and label-free method could be used monitor cell manufacturing for T cell and stem cell therapies, reducing the cost and time to produce these therapies while improving potency and consistency.