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Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

Mayer C, Metscher BD, Müller GB, Mitteroecker P - PLoS ONE (2014)

Bottom Line: The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry.We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout.Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.

View Article: PubMed Central - PubMed

Affiliation: Department of Theoretical Biology, Faculty of Life Sciences, University of Vienna, Althanstraße 14, A-1090, Vienna, Austria.

ABSTRACT
The ways in which embryo development can vary across individuals of a population determine how genetic variation translates into adult phenotypic variation. The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry. By drawing from the strength of geometric morphometrics and pixel/voxel-based image analysis, we present a new approach for the biometric analysis of two-dimensional and three-dimensional embryonic images. Well-differentiated structures are described in terms of their shape, whereas structures with diffuse boundaries, such as emerging cell condensations or molecular gradients, are described as spatial patterns of intensities. We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout. Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.

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Principal component analyses.Principal component analyses of (A) fin shape and (B) cell density, as well as (C) a joint ordination of both fin shape and cell density based on a scaled partial least squares analysis [37]. Each symbol in the scatter plots corresponds to one individual, and the distance between individuals approximates the overall amount of shape difference or difference in cell density. The axes of these plots correspond to patterns of shape change, to patterns of change in cell density, and to a combination of both, respectively. These patterns are visualized by reconstructed shapes and cell density patterns along the axes that correspond to the limits of variation occurring in the sample (approximately 3 standard deviations from the mean).
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pone-0115076-g005: Principal component analyses.Principal component analyses of (A) fin shape and (B) cell density, as well as (C) a joint ordination of both fin shape and cell density based on a scaled partial least squares analysis [37]. Each symbol in the scatter plots corresponds to one individual, and the distance between individuals approximates the overall amount of shape difference or difference in cell density. The axes of these plots correspond to patterns of shape change, to patterns of change in cell density, and to a combination of both, respectively. These patterns are visualized by reconstructed shapes and cell density patterns along the axes that correspond to the limits of variation occurring in the sample (approximately 3 standard deviations from the mean).

Mentions: We used a between-group principal component analysis [32] to ordinate the multivariate shape differences among the specimens (Fig. 5a). In the scatter plot of the first two between-group principal components (PCs), each symbol represents one individual, and the distance between two symbols approximates the magnitude of overall shape difference between the respective individuals. The first two PCs represent the shape distances among the three group means exactly and account for 91% of total variation between the 20 individual shapes. The shape features corresponding to the two axes are visualized by reconstructed shapes along the corresponding axis locations. The scatter plot shows that – despite considerable individual variation – the three age groups differed in average fin shape and were even separated without individual overlap. The groups differ along PC 1, mainly representing the relative size increase of the fin fold, associated with a shape change from a rounded to a triangular shape. More of these changes had occurred between 21 dpf and 40 dpf as compared to the second period. The second principal component, representing the shape of the fin fold independent of its size, appeared considerably more variable at 21 dpf as compared with the two later stages.


Studying developmental variation with Geometric Morphometric Image Analysis (GMIA).

Mayer C, Metscher BD, Müller GB, Mitteroecker P - PLoS ONE (2014)

Principal component analyses.Principal component analyses of (A) fin shape and (B) cell density, as well as (C) a joint ordination of both fin shape and cell density based on a scaled partial least squares analysis [37]. Each symbol in the scatter plots corresponds to one individual, and the distance between individuals approximates the overall amount of shape difference or difference in cell density. The axes of these plots correspond to patterns of shape change, to patterns of change in cell density, and to a combination of both, respectively. These patterns are visualized by reconstructed shapes and cell density patterns along the axes that correspond to the limits of variation occurring in the sample (approximately 3 standard deviations from the mean).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264869&req=5

pone-0115076-g005: Principal component analyses.Principal component analyses of (A) fin shape and (B) cell density, as well as (C) a joint ordination of both fin shape and cell density based on a scaled partial least squares analysis [37]. Each symbol in the scatter plots corresponds to one individual, and the distance between individuals approximates the overall amount of shape difference or difference in cell density. The axes of these plots correspond to patterns of shape change, to patterns of change in cell density, and to a combination of both, respectively. These patterns are visualized by reconstructed shapes and cell density patterns along the axes that correspond to the limits of variation occurring in the sample (approximately 3 standard deviations from the mean).
Mentions: We used a between-group principal component analysis [32] to ordinate the multivariate shape differences among the specimens (Fig. 5a). In the scatter plot of the first two between-group principal components (PCs), each symbol represents one individual, and the distance between two symbols approximates the magnitude of overall shape difference between the respective individuals. The first two PCs represent the shape distances among the three group means exactly and account for 91% of total variation between the 20 individual shapes. The shape features corresponding to the two axes are visualized by reconstructed shapes along the corresponding axis locations. The scatter plot shows that – despite considerable individual variation – the three age groups differed in average fin shape and were even separated without individual overlap. The groups differ along PC 1, mainly representing the relative size increase of the fin fold, associated with a shape change from a rounded to a triangular shape. More of these changes had occurred between 21 dpf and 40 dpf as compared to the second period. The second principal component, representing the shape of the fin fold independent of its size, appeared considerably more variable at 21 dpf as compared with the two later stages.

Bottom Line: The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry.We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout.Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.

View Article: PubMed Central - PubMed

Affiliation: Department of Theoretical Biology, Faculty of Life Sciences, University of Vienna, Althanstraße 14, A-1090, Vienna, Austria.

ABSTRACT
The ways in which embryo development can vary across individuals of a population determine how genetic variation translates into adult phenotypic variation. The study of developmental variation has been hampered by the lack of quantitative methods for the joint analysis of embryo shape and the spatial distribution of cellular activity within the developing embryo geometry. By drawing from the strength of geometric morphometrics and pixel/voxel-based image analysis, we present a new approach for the biometric analysis of two-dimensional and three-dimensional embryonic images. Well-differentiated structures are described in terms of their shape, whereas structures with diffuse boundaries, such as emerging cell condensations or molecular gradients, are described as spatial patterns of intensities. We applied this approach to microscopic images of the tail fins of larval and juvenile rainbow trout. Inter-individual variation of shape and cell density was found highly spatially structured across the tail fin and temporally dynamic throughout the investigated period.

Show MeSH
Related in: MedlinePlus