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In October 2018, Caterina became an associate member of the 4D Nucleome initiative with a project aimed at developing microscopy metadata and particle tracking standards to aide in the reproducibility of shared 4DN imaging datasets.

For more details about the aims of the 4DN project take a look here –>

From: Dekker et al. The 4D Nucleome project. Nature, 549: 219–226 (2017)



A manuscript describing the method we developed to quantify motion type estimation uncertainty was recently published on For more details see here:


Quantitative analysis of microscopy images is ideally suited for understanding the functional biological correlates of individual molecular species identified by one of the several available ‘omics’ techniques. Due to advances in fluorescent labeling, microscopy engineering, and image processing, it is now possible to routinely observe and quantitatively analyze at the high temporal and spatial resolution the real-time behavior of thousands of individual cellular structures as they perform their functional task inside living systems. Despite the central role of microscopic imaging in modern biology, unbiased inference, valid interpretation, scientific reproducibility and results dissemination are hampered by the still prevalent need for subjective interpretation of image data and by the limited attention given to the quantitative assessment and reporting of the error associated with each measurement or calculation, and on its effect on downstream analysis steps (i.e., error propagation). One of the mainstays of bioimage analysis is represented by single-particle tracking (SPT), which coupled with the mathematical analysis of trajectories and with the interpretative modeling of motion modalities, is of key importance for the quantitative understanding of the heterogeneous intracellular dynamic behavior of fluorescently labeled individual cellular structures, vesicles, viral particles and single-molecules. Despite substantial advances, the evaluation of analytical error propagation through SPT and motion analysis pipelines is absent from most available tools (Sbalzarini, 2016). This severely hinders the critical evaluation, comparison, reproducibility and integration of results emerging from different laboratories, at different times, under different experimental conditions and using different model systems. Here we describe a novel, algorithmic-centric, Monte Carlo method to assess the effect of experimental parameters such as signal to noise ratio (SNR), particle detection error, trajectory length, and the diffusivity characteristics of the moving particle on the uncertainty associated with motion type classification. The method is easily extensible to a wide variety of SPT algorithms, is made widely available via its implementation in our Open Microscopy Environment inteGrated Analysis (OMEGA) software tool for the management and analysis of tracking data, and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model.

A manuscript describing the OMEGA application was recently published on For more details see:


Open Microscopy Environment inteGrated Analysis (OMEGA) is a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides intuitive graphical interfaces to implement integrated particle tracking and motion analysis workflows while providing easy to use facilities to automatically keep track of error propagation, harvest data provenance and ensure the persistence of analysis results and metadata. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as the Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles movement; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty propagating through the motion analysis pipeline; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed complainant Minimum Information About Particle Tracking Experiments (MIAPTE; (Rigano and Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al., 2005). In so doing, OMEGA maintains a persistent link between raw image data, intermediate analysis steps, the overall analysis output, and all necessary metadata to repeat the analysis process and reproduce its results.

Screen shot of the main page of the OMEGA pilot GUI

Screen shot of the main page of the OMEGA pilot GUI

In September 2011, the OMEGA team has released the first version of the OMEGA pilot, with support from as part of the SyBIT project. The software development work was carried out at the of the Department of Innovative Technologies (DTI) of the University of Applied Sciences of Southern Switzerland (SUPSI) with support from the Information Systems and Networking Institute (ISIN) and the OpenBIS development team. This OMEGA pilot version reads images and image metadata stored within the OMERO image data repository, automatically performs the main steps of real time particle tracking utilizing a predefined work-flow and writes the results within a customized version of the openBIS metadata repository. In addition OMEGA allows data exploration and results visualization and produces publication quality graphs for data dissemination.

OMEGA is available in alpha release.

If you are interested in obtaining a copy of the software please contact Caterina Strambio De Castillia

Follow our progress on the OMEGA OMEGA-syBIT collaboration project wiki.