Dataset Information

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

ABSTRACT: Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.

SUBMITTER: Sanchez-Corrales YE

PROVIDER: S-EPMC5897594 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

Sánchez-Corrales Yara E YE Hartley Matthew M van Rooij Jop J Marée Athanasius F M AFM Grieneisen Verônica A VA

Development (Cambridge, England) 20180320 6

Quantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors ...[more]

PMID: 29444894

Similar Datasets

Project description:BACKGROUND:Triatoma dimidiata (Reduviidae: Triatominae) is an important vector of Chagas disease in various countries in the Americas. Phylogenetic studies have defined three lineages in Mexico and part of Central America. While there is a marked genetic differentiation, methods for identifying them using morphometric analyses with landmarks have not yet been fully resolutive. Elliptical Fourier descriptors (EFDs), which mathematically describe the shape of any closed two-dimensional contours, could be a potentially useful alternative method. Our objective was to validate the use of EFDs for the identification of three lineages of this species complex. METHOD:A total of 84 dorsal view images of individuals of the three lineages were used. Body contours were described with EFDs using between 5 and 30 harmonics. The number of obtained coefficients was reduced by a principal components analysis and the first axis scores were used as shape variables. A linear discriminant function analysis and an ordination plot of the discriminant analysis were performed using the shape variables. A confusion matrix of the ordination plot of the discriminant analysis was obtained to estimate the classification errors, the first five PC scores were statistically compared, and a neural network were then performed using the shape variables. RESULTS:The first principal component explained 50% of the variability, regardless the number of harmonics used. The results of discriminant analysis get improved by increasing the number of harmonics and components considered. With 25 harmonics and 30 components, the identification of haplogroups was achieved with an overall efficiency greater than 97%. The ordering diagram showed the correct discrimination of haplogroups, with only one error of discrimination corroborated by the confusion matrix. When comparing the first five PC scores, significant differences were found among at least two haplogroups. The 30 multilayer perceptron neural networks were also efficient in identification, reaching 91% efficiency with the validation data. CONCLUSIONS:The use of EFD is a simple and useful method for the identification of the main lineages of Triatoma dimidiata, with high values of correct identification.

Dataset Information

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

Publications

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets