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 .
As camera pixel arrays have grown larger and faster , and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microcopy . At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive , in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data . To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration , to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized .
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