Abstract
The rapid development of new imaging approaches is generating larger and more complex datasets revealing the time evolution of individual cells and biomolecules. Single-molecule techniques, in particular, provide access to rare intermediates in complex, multistage molecular pathways. However, few standards exist for processing these information-rich datasets, posing challenges for wider dissemination. Here, we present Mars, an open-source platform for storing and processing image-derived properties of biomolecules. Mars provides Fiji/ImageJ2 commands written in Java for common single-molecule analysis tasks using a Molecule Archive architecture that is easily adapted to complex, multistep analysis workflows. Three diverse workflows involving molecule tracking, multichannel fluorescence imaging, and force spectroscopy, demonstrate the range of analysis applications. A comprehensive graphical user interface written in JavaFX enhances biomolecule feature exploration by providing charting, tagging, region highlighting, scriptable dashboards, and interactive image views. The interoperability of ImageJ2 ensures Molecule Archives can easily be opened in multiple environments, including those written in Python using PyImageJ, for interactive scripting and visualization. Mars provides a flexible solution for reproducible analysis of image-derived properties, facilitating the discovery and quantitative classification of new biological phenomena with an open data format accessible to everyone.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
This version of the manuscript has been revised in response to reviewer feedback. A new dynamic smFRET dataset was collected and is presented in figure 5. FRET validation measurements have been added in figure 5 - figure supplement 1. The main text has been revised to include a new first results section that introduces the single-molecule analysis challenges Mars was designed to solve. Workflow 2, the single-molecule FRET section has been expended to include the dynamic FRET data now presented in figure 5 and the online examples have been greatly streamlined and improved. The Mars commands section has been revised to include a basic guide to using Mars for those not yet ready to try it directly. The methods section has been expanded in support of the revisions. The Mars documentation site and tutorial repositories on GitHub have been substantially updated and are linked throughout the new manuscript.
