Towards Unbiased Fluorophore Counting in Superresolution Fluorescence Microscopy.

Area of Science:

• Nanoscale imaging
• Biophysics
• Analytical chemistry

Background:

• Fluorescence superresolution microscopy offers unprecedented accuracy for imaging nano-sized structures.
• Current methods lack absolute quantification of fluorophores, limiting precise analysis.
• There is a need for calibration-free methods to determine fluorophore numbers in superresolution images.

Purpose of the Study:

• To develop a novel method for absolute fluorophore quantification in superresolution microscopy.
• To extract quantitative information on fluorophore numbers directly from raw imaging data.
• To overcome the limitation of lacking an absolute scale in current superresolution images.

Main Methods:

• Utilized a detailed statistical model of the temporal imaging process.
• Employed a hidden Markov model operating on two timescales.
• Applied the model to experimental data from single Alexa 647 molecules and DNA origami structures.

Main Results:

• Successfully extracted absolute fluorophore numbers from raw superresolution microscopy data.
• Demonstrated the model's efficacy without requiring additional calibration measurements.
• Validated the method on well-characterized samples, including single molecules and DNA nanostructures.

Conclusions:

• The developed statistical model enables accurate, calibration-free quantification of fluorophores in superresolution microscopy.
• This breakthrough enhances the analytical power of superresolution techniques in life sciences and beyond.
• The method provides an absolute scale for imaging, paving the way for more precise nanoscale investigations.