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

Rigano, Alessandro and Galli, Vanni and Gonciarz, Krzysztof and Sbalzarini, Ivo F and Strambio-De-Castillia, Caterina (2018) An algorithm-centric Monte Carlo method to empirically quantify motion type estimation uncertainty in single-particle tracking. UNSPECIFIED. bioRxiv. p. 379255.

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Abstract

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), 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).

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