Prediction-Based Algorithms for Long-Range High-Speed Force Spectroscopy

Area of Science:

• Biophysics
• Materials Science
• Microscopy

Background:

• Force spectroscopy is crucial for understanding biological sample mechanics.
• Traditional methods are limited by slow acquisition speeds and data density.
• High-speed, long-range measurements are needed for complex biological systems.

Purpose of the Study:

• To develop a high-speed force microscopy platform for rapid mechanical mapping.
• To enable long-range, high-speed force spectroscopy on biological samples.
• To reduce data acquisition time and density for complex samples.

Main Methods:

• Integration of a confocal microscope with a z-stage for long-range capabilities.
• Development of FPGA-based control software with a custom GUI.
• Implementation of probe-engagement prediction algorithms for accelerated approach and controlled deceleration.
• Utilizing ultrashort cantilevers functionalized with adhesion molecules for specific force measurements.

Main Results:

• Demonstrated proof-of-concept mechanical maps of cell clusters, extracting topography and viscoelastic parameters.
• Successfully probed membrane tether extraction forces from monocytic cells at velocities up to 6000 μm/s.
• Showcased the system's ability to handle samples with large topography variations and long-distance measurements.
• Validated the reduction in force curve acquisition time and data density.

Conclusions:

• The developed platform enables rapid, quantitative mechanical mapping of heterogeneous biological samples.
• Coupling extended z-displacement with prediction-based algorithms enhances measurement efficiency.
• The system supports high-velocity measurements crucial for exploring dynamic biological processes like tether formation.