ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes

Abstract

The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).

SEGGA: a toolset for rapid automated analysis of epithelial cell polarity and dynamics

“…However, in the initial SEGGA output, 3-5% of cells could not be associated with a corresponding cell at the previous or following time points, an error rate comparable to the reported accuracy of other methods (Mosaliganti et al, 2012;Stegmaier et al, 2016). This frequency of errors is not compatible with long-term tracking, as many cell trajectories are interrupted by segmentation errors during the course of a typical movie.…”

“…Fully automated methods for image segmentation and analysis, which are optimized for speed, increase the throughput of data analysis by tolerating a non-negligible frequency of errors that would otherwise require substantial effort to correct. These methods are well suited for large tissues in which error correction is impractical, short-term behaviors during which time errors are less likely to accumulate, and tissues that do not undergo substantial rearrangement Aigouy et al, 2010;Fernandez et al, 2010;Bosveld et al, 2012;Mosaliganti et al, 2012;Khan et al, 2014;Guirao et al, 2015;Heller et al, 2016;Stegmaier et al, 2016). However, segmentation errors that lead to 1% untracked cells in each frame of a movie are predicted to interrupt more than half of all cell trajectories after 70 time points, making fully automated methods of limited use for long-term tracking.…”

Quantitative 4D analyses of epithelial folding during Drosophila gastrulation

“…Recently, true 3D computational methods have been successfully applied to somite formation in zebrafish to reconstruct cells that exhibit complex shapes. These cells, however, undergo only limited displacements (Mosaliganti et al, 2012). Here, we demonstrate computational methods that enable 3D quantitative analyses of cell shape change during an epithelial folding event in which cells undergo dramatic morphological changes and display large and rapid displacement.…”

Automated cell tracking identifies mechanically oriented cell divisions during Drosophila axis elongation

“…The watershed algorithm (Beucher, 1992), a region-growing method, is often used as the basis for cell segmentation from fluorescence microscopy images (Aigouy et al, 2010;Fernandez-Gonzalez and Zallen, 2011;Mashburn et al, 2012;Mosaliganti et al, 2012;Leung and Fernandez-Gonzalez, 2015). However, the success of watershedbased approaches critically depends on the identification of a single seed point within each cell to be segmented.…”

“…Alternatively, cell tracking can be made segmentation independent by matching image subregions instead of individual cells. Optic flow uses the cross-correlation of two images to calculate local similarities (Raffel et al, 1998) and can be used to track cells based on matching fluorescence patterns (Mosaliganti et al, 2012;Yu and Fernandez-Gonzalez, 2016). Segmentation-independent methods can be computationally expensive when fine spatial or temporal sampling is necessary, for instance in the case of rapidly moving cells.…”