Uncovering Proton Transmembrane Dynamics in Single Bacteria with Array Enhanced Autocorrelation Spectroscopy

The analysis of high-throughput yet noisy data presents a fundamental challenge across multiple scientific disciplines, particularly in extracting precise kinetic parameters from stochastic, weak signals generated by microfluidic and array-based platforms. To address this limitation, we have developed a computational-analytical framework based on array reconstruction for autocorrelation enhancement analysis. This method effectively overcomes the signal-to-noise barrier by reconstructing massive parallel signals into organized ensembles, converting disordered temporal fluctuations into physically interpretable autocorrelation spectra. When applied to single-bacterium proton transmembrane transport, this approach generates array enhanced autocorrelation spectroscopy that enables quantitative extraction of key parameters, including event number and characteristic time constant, revealing distinctive kinetic patterns across different environmental conditions and bacterial species. This methodology provides a robust and generalizable approach for standardized quantification of stochastic microscopic dynamics, advancing the development of analytical methodologies for single-cell and single-molecule dynamic measurements.