Program In Molecular Medicine
Biochemistry, Biophysics, and Structural Biology | Bioimaging and Biomedical Optics | Bioinformatics | Laboratory and Basic Science Research | Research Methods in Life Sciences | Statistics and Probability | Theory and Algorithms
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 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)1–5, which coupled with the mathematical analysis of trajectories and with the interpretative modelling of motion modalities, is of key importance for the quantitative understanding of the heterogeneous intracellular dynamic behavior of fluorescently-labeled individual cellular structures, vesicles, virions and single-molecules. Despite substantial advances, the evaluation of analytical error propagation through SPT and motion analysis pipelines is absent from most available tools 6. 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.
bioinformatics, microscopy, image data, single-particle tracking
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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
DOI of Published Version
bioRxiv 379255; doi: https://doi.org/10.1101/379255. Link to preprint on bioRxiv.
Rigano A, Galli V, Gonciarz K, Sbalzarini IF, Strambio-De-Castillia C. (2018). An algorithm-centric Monte Carlo method to empirically quantify motion type estimation uncertainty in single-particle tracking [preprint]. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1101/379255. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/2088
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Biochemistry, Biophysics, and Structural Biology Commons, Bioimaging and Biomedical Optics Commons, Bioinformatics Commons, Laboratory and Basic Science Research Commons, Research Methods in Life Sciences Commons, Statistics and Probability Commons, Theory and Algorithms Commons