A data treatment method for detecting fluorescence anisotropy peaks in capillary electropherograms.

Area of Science:

• Analytical Chemistry
• Biophysics

Background:

• Capillary electrophoresis (CE) is a powerful separation technique.
• Detecting subtle signals like fluorescence anisotropy (FA) in CE can be challenging due to background noise.
• Amyloid-beta (Aβ) aggregates are implicated in neurodegenerative diseases and their detection is of significant interest.

Purpose of the Study:

• To present a new data treatment method for enhancing the detection of fluorescence anisotropy peaks.
• To demonstrate the method's effectiveness in identifying previously undetectable FA signals from individual Aβ (1-42) aggregates.

Main Methods:

• Development of a data treatment algorithm to process fluorescence anisotropy versus time data.
• Application of the method to laser-induced fluorescence anisotropy data from Aβ (1-42) aggregates separated by CE.
• Comparison of electropherograms before and after data treatment.

Main Results:

• The data treatment method successfully converted noisy FA plots into those with distinct, detectable peaks.
• Previously unobserved FA peaks from individual Aβ aggregates were identified after applying the method.
• The method significantly improved the sensitivity for detecting FA signals in CE.

Conclusions:

• The developed data treatment method is effective for detecting weak fluorescence anisotropy signals in CE.
• This approach facilitates the analysis of individual Aβ aggregates and potentially other analytes.
• The method's applicability extends beyond Aβ analysis and CE to other separation techniques with FA detection.