Automated multi-target super-resolution microscopy with trust regions.

Area of Science:

• Biophysics
• Optical Microscopy
• Cell Biology

Background:

• Sequential localization microscopy enables multi-target imaging without multiple fluorophores.
• Automating this process is crucial for high-throughput biological research.
• Precise stage control and data acquisition are key challenges.

Purpose of the Study:

• To introduce the Sequential Super-resolution Microscope (SeqSRM) for automated multi-target imaging.
• To demonstrate the impact of cell morphology changes on super-resolution reconstruction fidelity.
• To develop a method for correcting reconstruction errors using brightfield imaging.

Main Methods:

• Development of a dedicated microscope (SeqSRM) with nanometer-scale stage stabilization.
• Automated data acquisition for user-selected cells on a coverslip.
• Periodic brightfield imaging to detect and mask morphological shifts.

Main Results:

• SeqSRM automates sequential relabeling and data acquisition with minimal user intervention.
• Nanometer-scale cell morphology changes were shown to affect super-resolution overlay fidelity.
• Brightfield imaging effectively identified and masked regions affected by morphological shifts.
• Automated multi-target imaging was achieved without distinct fluorophores or emission channels.

Conclusions:

• The SeqSRM system enables robust, automated multi-target super-resolution microscopy.
• It ensures accurate relative organization of targets by correcting for morphological variations.
• This technology advances high-throughput biological imaging and analysis.