SpotitPy: a semi-automated tool for object-based co-localization of fluorescent labels in microscopy images

Abstract

Background In fluorescence microscopy, co-localization refers to the spatial overlap between different fluorescent labels in cells. The degree of overlap between two or more channels in a microscope may reveal a physical interaction or topological functional interconnection between molecules. Recent advances in the imaging field require the development of specialized computational analysis software for the unbiased assessment of fluorescently labelled microscopy images. Results Here we present SpotitPy, a semi-automated image analysis tool for 2D object-based co-localization. SpotitPy allows the user to select fluorescent labels and perform a semi-automated and robust segmentation of the region of interest in distinct cell types. The workflow integrates advanced pre-processing manipulations for de-noising and in-depth semi-automated quantification of the co-localized fluorescent labels in two different channels. We validated SpotitPy by quantitatively assessing the presence of cytoplasmic ribonucleoprotein granules, e.g. processing (P) bodies, under conditions that challenge mRNA translation, thus highlighting SpotitPy benefits for semi-automatic, accurate analysis of large image datasets in eukaryotic cells. SpotitPy comes in a command line interface or a simple graphical user interphase and can be used as a standalone application. Conclusions Overall, we present a novel and user-friendly tool that performs a semi-automated image analysis for 2D object-based co-localization. SpotitPy can provide reproducible and robust quantifications for large datasets within a limited timeframe. The software is open-source and can be found in the GitHub project repository: (https://github.com/alexiaales/SpotitPy).

Imaging-Based Quantitative Assessment of Biomolecular Condensatesin vitroand in Cells

“…Whilst the pipeline shares overlapping features with several other publicly-available image analysis plugins (69–72), the main distinction is that PhaseMetrics was specifically developed with the aim in mind to report on features that are particularly relevant in the assessment of biomolecular condensate properties. The analysis parameters, including for example the measurements reporting on particle shape (circularity and perimeter) and spatial heterogeneity of the signal distribution within the particles (skewness), were specifically selected to aid in the detection of changing particle properties in response to environmental cues.…”

“…One important concern that has been raised is that many imaging studies rely on descriptive, phenotypic features (13), stressing the need for more detailed, quantitative visual investigations of morphological and physicochemical properties that could assist in the critical assessment of changing material properties of condensates. Several tools have been developed for the quantitative analysis of imaging data (69)(70)(71)(72). However, most of these tools were designed for specific applications, and thus are not specifically suited for the analysis of biomolecular condensate properties.…”