Reduction of Confinement Error in Single-Molecule Tracking in Live Bacterial Cells Using SPICER

Abstract

Single-molecule tracking can extract quantitative kinetic information and identify possible state transitions of diffusing molecules (such as switching between binding and unbinding) in the in vivo environment of living cells. Confined diffusion, caused by the encompassing membrane boundary of the cell, results in increased uncertainties in identifying state-associated diffusion coefficients and transition probabilities. This problem is particularly acute in bacterial cells because of their small sizes. A standard approach to eliminating confinement errors in bacterial cells is to analyze molecule displacements only along the long axis of the cell, where molecules experience the least confinement, and hence turn three-dimensional tracking into a one-dimensional problem. However, this approach dramatically decreases the amount of data usable for statistical analysis and leads to increased uncertainties in identifying different states. Here, we present a simple algorithm, termed single-particle tracking improvement with confinement error reduction (SPICER), which significantly decreases confinement errors by selectively incorporating data not only from the long axis but also from the short axes of the cell. We validate SPICER using both reaction-diffusion simulations and experimental single-molecule tracking (SMT) data of RNA polymerase in live Escherichia coli cells. SPICER is easy to implement with existing SMT analysis routines and should find broad applications in SMT analysis.