Abstract

BackgroundCellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.ResultsWe generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the fluorescent levels in the cell walls. Micrographs are composed of pixels, which have information on position and intensity. From the pixel information we calculated three types of features (spatial, intensity and Haralick) in ER bodies corresponding to segmented cells. The spatial features include basic information on shape, e.g., surface area and perimeter. The intensity features include information on mean, standard deviation and quantile of fluorescence intensities within an ER body. Haralick features describe the texture features, which can be calculated mathematically from the interrelationship between the pixel information. Together these parameters were subjected to multivariate analysis to estimate the morphological diversity. Additionally, we calculated the displacement of the ER bodies using the positional information in time-lapse images. We captured similar morphological diversity and movement within ER body phenotypes in several microscopy experiments performed in different settings and scanned under different objectives. We then described differences in morphology and movement of ER bodies between A. thaliana wild type and mutants deficient in ER body-related genes.ConclusionsThe findings unexpectedly revealed multiple genetic factors that are involved in the shape and size of ER bodies in A. thaliana. This is the first report showing morphological characteristics in addition to the movement of cellular components and it quantitatively summarises plant phenotypic differences even in plants that show similar cellular components. The estimation of morphological diversity was independent of the cell staining method and the objective lens used in the microscopy. Hence, our study enables a robust estimation of plant phenotypes by recognizing small differences in complex cell organelle shapes and their movement, which is beneficial in a comprehensive analysis of the molecular mechanism for cell organelle formation that is independent of technical variations.

Highlights

  • Cellular components are controlled by genetic and physiological factors that define their shape and size

  • Cell segmentation based on the red fluorescence of cell walls provided 12,408 cell images in the wild type, 17,205 cell images in the nai1-1 mutants, 9109 cell images in the leb-1 bglu21-1 mutants, 10,664 cell images in the meb1-1 mutants, 6862 cell images in the meb2-1 mutants and 10,357 cell images in the meb1-1 meb2-1 mutants (Table 2)

  • Image‐wise and segmented cell‐wise analysis The z-scores of 40 features (6 spatial, 8 intensity and 26 Haralick features) were calculated from the merged micrograph images from the wild type and mutants based on green fluorescent protein (GFP) fluorescence of endoplasmic reticulum (ER) and ER bodies (Additional file 5A)

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Summary

Introduction

Cellular components are controlled by genetic and physiological factors that define their shape and size. Micrograph-based image profiling frequently uses Haralick features as texture features to understand morphological differences. In the field of medical research, image data from positron emission tomography (PET) and magnetic resonance imaging (MRI) are used for profiling with this feature to detect anomalies [2, 3]. This feature set is exploited and considered important in the diagnosis of tumour cells. These medical studies suggest that Haralick features could be useful in the quantitative analysis of plant cell imaging

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