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Light-emitting diodes (LEDs) can and are currently integrated into light microscopes. They have numerous advantages as illumination sources. Most notably, they provide intensity (brightness) and spectral control during bio-imaging. For transmitted light imaging, LEDs can replace the traditional tungsten filament bulb, while offering longer life, little-to-no color temperature shift resulting from an intensity change, reduced emission in the infrared region, (a property important for live cell imaging), and reduced cost of ownership. For fluorescent imaging, in which the typical illumination sources are mercury or xenon lamps, LEDs offer the advantages of a longer lifespan, greater spatial and temporal stability, elimination of the need for mechanical shutters and neutral density filters, significantly lower cost of ownership, and reduction of photon dose at the specimen. Additionally, LEDs permit vibration-free, high-speed spectral and temporal modulation. This modulation allows more information to be obtained for a given photon dose.

InVivo^™ Analyzer Suite 3.0 Software Offers Fast Acquisition and Complete Analysis for Live Cell Imaging.

We propose a novel approach to 3D image registration of intracellular volumes. The approach extends a standard image registration framework to the curved cell geometry. An intracellular volume is mapped onto another intracellular domain by using two pairs of point set surfaces approximating their nuclear and plasma membranes. The mapping function consists of the affine transformation, tetrahedral barycentric interpolation, and least-squares formulation of radial basis functions for extracted cell geometry features. An interactive volume registration system is also developed based on our approach. We demonstrate that our approach is capable of creating cell models containing multiple organelles from observed data of living cells.

Genetic perturbation of T cell receptor (TCR) T cells is a promising method to unlock better TCR T cell performance to create more powerful cancer immunotherapies, but understanding the changes to T cell behavior induced by genetic perturbations remains a challenge. Prior studies have evaluated the effect of different genetic modifications with cytokine production and metabolic activity assays. Live-cell imaging is an inexpensive and robust approach to capture TCR T cell responses to cancer. Most methods to quantify T cell responses in live-cell imaging data use simple approaches to count T cells and cancer cells across time, effectively quantifying how much space in the 2D well each cell type covers, leaving actionable information unexplored. In this study, we characterize changes in TCR T cell’s interactions with cancer cells from live-cell imaging data using explainable artificial intelligence (AI). We train convolutional neural networks to distinguish behaviors in TCR T cell with CRISPR knock outs of CUL5, RASA2, and a safe harbor control knockout. We use explainable AI to identify specific interaction types that define different knock-out conditions. We find that T cell and cancer cell coverage is a strong marker of TCR T cell modification when comparing similar experimental time points, but differences in cell aggregation characterize CUL5KO and RASA2KO behavior across all time points. Our pipeline for discovery in live-cell imaging data can be used for characterizing complex behaviors in arbitrary live-cell imaging datasets, and we describe best practices for this goal.