News

In this exciting paper from UCLA Health, researchers combine high-speed Quantitative Phase Imaging (QPI) from PHASICS and machine learning to enable rapid and label-free classification of tumor-reactive T cell killing. Phasics QPI camera provides numerous quantitative morphological data of cells within a population in real-time.

This helps building rich features descriptors that leads to very accurate and robust classification.

This study paves the way towards an efficient label-free method for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy.

• Read the full article here
• Learn more about Phasics QPI cameras and life science applications here

Image legend: Progression of the quantitative phase density map during tumor-reactive T cell mediated killing and top ten extractable QPM features. (a) Representative LCI images of a single F5 TCR-transduced CD8+ T cell killing a MART1 + M202 melanoma cell over time. Phase density and mass distribution is shown in color scale ranging from 0 (background) to 2 pg/nm². (b) Heat map of the top ten extracted QPM features of target cells for alive cell events versus T cell killed events. Each row represents an individual cell, and each major column represents a tumor cell feature. Each sub-column is a QPM image collection time point, here represented by 3 sub-columns for each imaging feature spanning 30 m. Tumor cell features in T cell killing events have more pronounced differences between imaging frames than alive tumor cell features, which are represented by changes in color intensity.