OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control

Abstract

MOTIVATIONParticle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al., 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards.RESULTSHere, we present Open Microscopy Environment inteGrated Analysis (OMEGA), 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 FAIRsharing.org 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.Availability and implementationOMEGA is a cross-platform, open-source software developed in Java. Source code and cross-platform binaries are freely available on GitHub at https://github.com/OmegaProject/Omega (doi: 10.5281/zenodo.2535523), under the GNU General Public License v.3.Contactcaterina.strambio@umassmed.edu and alex.rigano@umassmed.eduSupplementary informationSupplementary Material is available at BioRxiv.org

Community Standards for Open Cell Migration Data

“…In the data package, the data (objects, links, and optionally tracks, Figure 4B) are stored in tabular form as comma-separated-values (CSV) files, while metadata and schema information are stored as a JavaScript Object Notation (JSON) file ( Figure 4C). The development of the biotracks format is complementary to the OMEGA system for particle tracking data, which has particular emphasis on results from viral and vesicular trafficking experiments 19 .…”

An algorithm-centric Monte Carlo method to empirically quantify motion type estimation uncertainty in single-particle tracking

“…Because the trueness of a given spot detector algorithm cannot be estimated unless the correct position of each particle under observation is completely known, the performance of particle detection/localization algorithms is generally assessed using artificial images ( Figure 2). Specifically, in order to empirically assess and record the localization accuracy and precision of the OMEGA MOSAICsuite FPD plugin 7,49,55,56 , individual simulated single point fluorescent sources were placed at random known sub-pixel positions (i.e., ground truth) within the central pixel of 20 x 20 pixel synthetic planes as described (Materials and Methods;…”

“…Supplemental Figure 1) 7,49 . To simulate different image qualities, images were generated to reflect twelve different input peak intensity and corresponding SNR values ( Supplemental Table I) and the stationary feature points present in each synthetic plane were then localized using the OMEGA plugin mentioned above (Supplemental Figure 2).…”

“…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 7 , and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model 8 . 3 Background Time series of optical images acquired from living cells are the starting point for the analysis of the intracellular dynamics and interactions of myriads of heterogeneous cellular features [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] .…”

“…To obviate this difficulty, the algorithm-centric empirical method described here utilizes Monte Carlo simulation to generate sets of ideal trajectories, utilizes artificial images of point emitters in the presence of known amount of noise to empirically derive distributions of observed localization errors for each algorithm under study, simulates the effect of localization noise on ideal trajectories by offsetting the position of each detection with randomly sampled error values from such pre-computed distributions, and estimates the probability density of observed results to predict the error associated with potential results. As a proof of concept the approach concentrates on the estimation of error associated with the MOSAICsuite Feature Point Tracking algorithm 49,55,56 and on the computation of ODC and SMSS as implemented in the recently released Open Microscopy Environment inteGrated Analysis (OMEGA) software platform ( Figure 1) 7 . However the method is designed to be easily extendable to other particle detectors and to be used for other global trajectory measures.…”