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

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)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.

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

“…All OMEGA algorithmic components were tested on artificial image and trajectory data as described in Supplemental Information 1 and elsewhere (Rigano et al, 2018). As proof of concept, OMEGA supported motion analysis workflows were utilized to analyzed both standard SPT benchmarking datasets (Figure 7;Chenouard et al, 2014), as well as real-life datasets depicting retroviral particle trafficking within living human cells (Clark et al, 2013;Pereira et al, 2012).…”

“…In subsequent releases, we plan to develop batch processing of entire image datasets. Extensive validation of OMEGA components was conducted as described either here (Supplemental Information 1) or elsewhere (Rigano et al, 2018). Related error estimation and data provenance aspects are presented in parallel manuscripts (Rigano et al, 2018;Rigano and Strambio-De-Castillia, 2017).…”

“…The OMEGA SNR Estimation module is extended by plugins that estimate the SNR associated with each tracked particle allowing the identification of images whose quality does not support reliable particle detection and tracking. At time of writing, OMEGA ships with a single local SNR Estimation plugin consisting of a custom Java implementation of the MOSAICsuite's local SNR Estimation algorithm originally developed by the MOSAIC group in Matlab (Xiao et al, 2016;Gong and Sbalzarini, 2016;Rigano et al, 2018;Rigano and Strambio-De-Castillia, 2018a). This routine, which was benchmarked as described (Supplemental Information 1), uses the particle coordinates obtained from the Particle Detection plugin to extract intensity values from each associated image plane and estimate background, noise and SNR pertaining to the area immediately surrounding each particle.…”

“…The global minimum local SNR each given trajectory is utilized to estimate the confidence associated with both ODC and SMSS estimations and with motion type classification as described (Rigano et al, 2018). In addition, image averages, minimum and maximum local SNR values can be utilized by the user to evaluate the overall image quality and general performance of the particle detector as specified by the algorithm developer (see below).…”

“…To address this issue, OMEGA incorporates both theoretical and empirical methods to estimate uncertainties associated with motion analysis results. Details of our error estimation procedure are described in an associated manuscript (Rigano et al, 2018), here we provide a short description of the method.…”