Dataset Information

Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.

ABSTRACT: Correct cell/cell interactions and motion dynamics are fundamental in tissue homeostasis, and defects in these cellular processes cause diseases. Therefore, there is strong interest in identifying factors, including drug candidates that affect cell/cell interactions and motion dynamics. However, existing quantitative tools for systematically interrogating complex motion phenotypes in timelapse datasets are limited. We present Motion Sensing Superpixels (MOSES), a computational framework that measures and characterises biological motion with a unique superpixel 'mesh' formulation. Using published datasets, MOSES demonstrates single-cell tracking capability and more advanced population quantification than Particle Image Velocimetry approaches. From > 190 co-culture videos, MOSES motion-mapped the interactions between human esophageal squamous epithelial and columnar cells mimicking the esophageal squamous-columnar junction, a site where Barrett's esophagus and esophageal adenocarcinoma often arise clinically. MOSES is a powerful tool that will facilitate unbiased, systematic analysis of cellular dynamics from high-content time-lapse imaging screens with little prior knowledge and few assumptions.

SUBMITTER: Zhou FY

PROVIDER: S-EPMC6391079 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.

Zhou Felix Y FY Ruiz-Puig Carlos C Owen Richard P RP White Michael J MJ Rittscher Jens J Lu Xin X

eLife 20190226

Correct cell/cell interactions and motion dynamics are fundamental in tissue homeostasis, and defects in these cellular processes cause diseases. Therefore, there is strong interest in identifying factors, including drug candidates that affect cell/cell interactions and motion dynamics. However, existing quantitative tools for systematically interrogating complex motion phenotypes in timelapse datasets are limited. We present Motion Sensing Superpixels (MOSES), a computational framework that mea ...[more]

PMID: 30803483

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Project description:PurposeMagnetic resonance imaging (MRI) is increasingly used in radiation oncology for target delineation and radiotherapy treatment planning, for example, in patients with gynecological cancers. As a consequence of pelvic radiotherapy, a part of the bowel is irradiated, yielding risk of bowel toxicity. Existing dose-effect models predicting bowel toxicity are inconclusive and bowel motion might be an important confounding factor. The exact motion of the bowel and dosimetric effects of its motion are yet uncharted territories in radiotherapy. In diagnostic radiology methods on the acquisition of dynamic MRI sequences were developed for bowel motility visualization and quantification. Our study aim was to develop an imaging technique based on three-dimensional (3D) cine-MRI to visualize and quantify bowel motion and demonstrate it in a cohort of gynecological cancer patients.MethodsWe developed an MRI acquisition suitable for 3D bowel motion quantification, namely a balanced turbo field echo sequence (TE = 1.39 ms, TR = 2.8 ms), acquiring images in 3.7 s (dynamic) with a 1.25 × 1.25 × 2.5 mm3 resolution, yielding a field of view of 200 × 200 × 125 mm3 . These MRI bowel motion sequences were acquired in 22 gynecological patients. During a 10-min scan, 160 dynamics were acquired. Subsequent dynamics were deformably registered using a B-spline transformation model, resulting in 159 3D deformation vector fields (DVFs) per MRI set. From the 159 DVFs, the average vector length was calculated per voxel to generate bowel motion maps. Quality assurance was performed on all 159 DVFs per MRI, using the Jacobian Determinant and the Harmonic Energy as deformable image registration error metrics. In order to quantify bowel motion, we introduced the concept of cumulative motion-volume histogram (MVH) of the bowel bag volume. Finally, interpatient variation of bowel motion was analyzed using the MVH parameters M10%, M50%, and M90%. The M10%/M50%/M90% represents the minimum bowel motion per frame of 10%/50%/90% of the bowel bag volume.ResultsThe motion maps resulted in a visualization of areas with small and large movements within the bowel bag. After applying quality assurance, the M10%, M50%, and M90% were 4.4 (range 2.2-7.6) mm, 2.2 (range 0.9-4.1) mm, and 0.5 (range 0.2-1.4) mm per frame, on average over all patients, respectively.ConclusionWe have developed a method to visualize and quantify 3D bowel motion with the use of bowel motion specific MRI sequences in 22 gynecological cancer patients. This 3D cine-MRI-based quantification tool and the concept of MVHs can be used in further studies to determine the effect of radiotherapy on bowel motion and to find the relation with dose effects to the small bowel. In addition, the developed technique can be a very interesting application for bowel motility assessment in diagnostic radiology.

Project description:Precise spatiotemporal regulation is the foundation for the healthy development and maintenance of living organisms. All cells must correctly execute their function in the right place at the right time. Cellular motion is thus an important dynamic readout of signaling in key disease-relevant molecular pathways. However despite the rapid advancement of imaging technology, a comprehensive quantitative description of motion imaged under different imaging modalities at all spatiotemporal scales; molecular, cellular and tissue-level is still lacking. Generally, cells move either 'individually' or 'collectively' as a group with nearby cells. Current computational tools specifically focus on one or the other regime, limiting their general applicability. To address this, we recently developed and reported a new computational framework, Motion Sensing Superpixels (MOSES). Incorporating the individual advantages of single cell trackers for individual cell and particle image velocimetry (PIV) for collective cell motion analyses, MOSES enables 'mesoscale' analysis of both single-cell and collective motion over arbitrarily long times. At the same time, MOSES readily complements existing single-cell tracking workflows with additional characterization of global motion patterns and interaction analysis between cells and also operates directly on PIV extracted motion fields to yield rich motion trajectories analogous for single-cell tracks suitable for high-throughput motion phenotyping. This protocol provides a step-by-step practical guide for those interested in applying MOSES to their own datasets. The protocol highlights the salient features of a MOSES analysis and demonstrates the ease-of-use and wide applicability of MOSES to biological imaging through demo experimental analyses with ready-to-use code snippets of four datasets from different microscope modalities; phase-contrast, fluorescent, lightsheet and intra-vital microscopy. In addition we discuss critical points of consideration in the analysis.

Dataset Information

Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.

Publications

Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.

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