As highly sensitive camera pixel sensor arrays have grown both larger and faster and optical microscopy techniques become ever more refined, there has been an explosion in the quantity of data acquired during routine light microscopy. At the single-molecule level, this analysis involves multiple steps and can quickly become computationally expensive , and in some cases intractable on an ordinary office workstation . Moreover, complex bespoke software can present a high activation barrier to entry for new users. In this work , we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike. We demonstrate that the performance of this code is on a par with our previous MATLAB implementation but runs at a fraction of the computational cost. We show the code is capable of extracting fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules. It can also evaluate diffusion coefficients for the relatively short single-particle tracking data that is characteristic of time-resolved image stacks. To facilitate benchmarking against other codes, we also include data simulation routines which may trivially be used to compare different analysis programs. Finally, we show that PySTACHIO works also with two-color data and can perform colocalization analysis based on overlap integrals, to infer interactions between differently labelled biomolecules. We hope that by making this freely available for use and modification we can make complex single-molecule analysis of light microscopy data more accessible.
As camera pixel sensors grow larger and faster and optical microscopy techniques become ever more refined, there has been explosions in the quantity of data acquired during routine light microscopy. At the single-molecule level, this analysis involves multiple steps and can quickly become computationally expensive and intractable on ordinary office workstations . Moreover, complex bespoke software can present high activation barriers for new users. Here , we present our recent efforts to redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with both GUI and command-line implementations to facilitate its use on both local machines and remote clusters, and by beginners and advanced users alike. We demonstrate the performance of this code matches our previous MATLAB implementation but at a fraction of the computational cost. We show the code can extract fluorescence intensity values corresponding to single reporter dye molecules and, using these, to estimate molecular stoichiometries and single cell copy numbers of fluorescently labeled biomolecules. It can also evaluate diffusion coefficients for the relatively short single-particle tracking data that is characteristic of time-resolved image stacks. To facilitate benchmarking against other codes, we also include data simulation routines which may trivially be used to compare different analysis programs. Finally, we show that PySTACHIO works also with two-color data and can perform colocalization analysis based on overlap integrals, to infer interactions between differently labelled biomolecules. We hope that by making this freely available for use and modification we can make complex single-molecule analysis of light microscopy data more accessible.
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