BACKGROUND: Single-molecule localization microscopy is a super-resolution microscopy technique that allows for nanoscale determination of the localization and organization of proteins in biological samples. For biological interpretation of the data it is essential to extract quantitative information from the super-resolution data sets. Due to the complexity and size of these data sets flexible and user-friendly software is required. RESULTS: We developed SMoLR (Single Molecule Localization in R): a flexible framework that enables exploration and analysis of single-molecule localization data within the R programming environment. SMoLR is a package aimed at extracting, visualizing and analyzing quantitative information from localization data obtained by single-molecule microscopy. SMoLR is a platform not only to visualize nanoscale subcellular structures but additionally provides means to obtain statistical information about the distribution and localization of molecules within them. This can be done for individual images or SMoLR can be used to analyze a large set of super-resolution images at once. Additionally, we describe a method using SMoLR for image feature-based particle averaging, resulting in identification of common features among nanoscale structures. CONCLUSIONS: Embedded in the extensive R programming environment, SMoLR allows scientists to study the nanoscale organization of biomolecules in cells by extracting and visualizing quantitative information and hence provides insight in a wide-variety of different biological processes at the single-molecule level.

MOTIVATION: Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. RESULTS: Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. AVAILABILITY AND IMPLEMENTATION: The described system was implemented in MATLAB (The MathWorks, Inc., USA) and compiled to run within the MATLAB environment. The routines together with four sample datasets are available at http://celmia.bigr.nl/. The software is planned for public release, free of charge for non-commercial use, after publication of this article.

Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.

In the above titled paper (ibid., vol. 29, no. 3, pp. 852-867, Mar. 10), several figure citations were incorrect. The correct figure citations are provided here.