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Uncovering Proton Transmembrane Dynamics in Single Bacteria with Array Enhanced Autocorrelation Spectroscopy.
Yaoyao Zhang1, Jia Gao1, Yaohua Li2
1State Key Laboratory of Analytical Chemistry for Life Science, Department of Laboratory Medicine, Nanjing Drum Tower Hospital, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
Analytical Chemistry
|March 28, 2026
Summary
We developed a new computational method to improve signal analysis for noisy biological data. This technique enhances autocorrelation spectroscopy, enabling precise kinetic measurements from single cells and molecules.
Area of Science:
- Biophysics
- Analytical Chemistry
- Microfluidics
Background:
- High-throughput data analysis is challenged by noise, hindering precise kinetic parameter extraction from weak signals.
- Stochastic signals from microfluidic and array-based platforms require advanced analytical methods for accurate interpretation.
Purpose of the Study:
- To develop a computational-analytical framework to overcome the signal-to-noise barrier in high-throughput data.
- To enable quantitative extraction of kinetic parameters from stochastic microscopic dynamics.
Main Methods:
- Developed an array reconstruction method for autocorrelation enhancement analysis.
- Reconstructed massive parallel signals into organized ensembles to convert temporal fluctuations into autocorrelation spectra.
- Applied the method to single-bacterium proton transmembrane transport.
Main Results:
- Achieved array enhanced autocorrelation spectroscopy (AEACS) for quantitative analysis.
- Successfully extracted key kinetic parameters like event number and time constant.
- Revealed distinct kinetic patterns in single-bacterium proton transport under varying conditions.
Conclusions:
- The developed framework offers a robust and generalizable approach for quantifying stochastic microscopic dynamics.
- This methodology advances analytical techniques for single-cell and single-molecule dynamic measurements.
- Provides standardized quantification for weak signal analysis across scientific disciplines.
