Event-driven Microscopy for Smart Microfluidic Single-cell Analysis

Decorative image, HI EMSIG
Image: Johannes Seiffarth, FZ Jülich

Microfluidic live-cell imaging (MLCI) unlocks spatio-temporal insights into population heterogeneity emerging from a single cell. Deep-learning powered image analysis, developed in the HI project SATOMI, facilitates offline analysis of high-quality data. For becoming a versatile screening tool, MLCI must master the challenge of robust real-time analysis, capable to detect rare events in hundreds of parallel experiments and resolve their temporal evolution.

Towards this, EMSIG brings smart live-event detection capabilities to MLCI to facilitate the adaptive optimization of biological event resolution and autonomously counteracting deteriorating image qualities, as examples. The distinct high-risk factor is in the robust and accurate real-time classification of events at the interface between imaging, image analysis and biology. If successful, to date unavailable insights into biological processes are unlocked and the technology readiness level of microfluidic live-cell analysis boosted.

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