Spherical Harmonics based extraction and annotation of cell shape in 3D time-lapse microscopy sequences

Abstract

Understanding the mechanisms involved in cell deformation and motility is of major interest in numerous areas of life sciences. Precise quantification of cell shape requires robust shape description tools to be amenable to subsequent analysis and classification. The main difficulty lies in the great variability of cell shapes within a given homogeneous population. In this work, we propose a framework for cell shape extraction and classification for 3D time-lapse sequences of living cells, based on the SPherical HARMonics transform (SPHARM). Starting from an initial segmentation of the cell surface over time, this mathematical representation enables us to represent each extracted surface by a unique set of coefficients, while taking into account invariance properties such as translation or orientation. Then, unsupervised classification is conducted using a multi-class K-Means approach, so as to extract the most pertinent number of classes representing the different phases of the cell deformation. Experimental results on several sequences give encouraging results, and show that the proposed approach can be used to perform automated sequence annotation, and can be further applied to compare shape characteristics across different cell populations.

Aspergillus fumigatus, One Uninucleate Species with Disparate Offspring

“…The germination process was followed over a 24-h time period under a Nikon TI microscope equipped with a 37 °C incubator and a 40× objective, with 1 picture captured every 4 min. Data were analyzed using ICY software [ 21 , 22 , 23 ]. We set up a specific program to automatically detect the conidia and measure their size over time.…”

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

“…The SPHARM transform considers any closed surface as a function of the unit sphere, and simplifies this function into a unique set of coefficients, facilitating subsequent shape characterization and classification. This technique offers interesting properties such as position and orientation invariance [266], and is thus well suited for shape sets with high variability such as living cells [263], [264]. They have been complemented by the spherical wavelets [277] that are constructed by analogy to wavelets in the plane via appropriate spherical projections, and are particularly well adapted to localize features along surfaces.…”

“…In protrusive structures, multiple actin filaments are dynamically arranged in cross-linked webs (as in lamellipodia) or parallel bundles (as in filopodia). Among the several types of cell migration that have been identified, amoeboid motion [263] is characterized by a crawling-like displacement, and more precisely by the movement induced by the production of blebs, that are local bulges in the cell membrane [264], [265], [262]. These protrusions appear in a very fast and sudden manner, and are thought to be produced by the rupture of the links that attach the cell cytoskeleton to the membrane [266].…”

Characterization of cell shape and deformation in 3D using Spherical Harmonics

“…In [13], we used SPHARM analysis to detect major deformation phases based on the evolution of all coefficients over time. Here we push this idea further by detecting temporal events of interest, such as the formation of blebs as well as patterns of deformation.…”