Identifying developmental phases in the Arabidopsis thaliana rosette using integrative segmentation models

Abstract

SummaryThe change in leaf size and shape during ontogeny associated with heteroblastic development is a composite trait for which extensive spatiotemporal data can be acquired using phenotyping platforms. However, only part of the information contained in such data is exploited, and developmental phases are usually defined using a selected organ trait. We here introduce new methods for identifying developmental phases in the Arabidopsis rosette using various traits and minimum a priori assumptions.A pipeline of analysis was developed combining image analysis and statistical models to integrate morphological, shape, dimensional and expansion dynamics traits for the successive leaves of the Arabidopsis rosette. Dedicated segmentation models called semi-Markov switching models were built for selected genotypes in order to identify rosette developmental phases.Four successive developmental phases referred to as seedling, juvenile, transition and adult were identified for the different genotypes. We show that the degree of covering of the leaf abaxial surface with trichomes is insufficient to define these developmental phases.Using our pipeline of analysis, we were able to identify the supplementary seedling phase and to uncover the structuring role of various leaf traits. This enabled us to compare on a more objective basis the vegetative development of Arabidopsis mutants.

Identification of successive flowering phases highlights a new genetic control of the flowering pattern in strawberry

“…Various segmentation models have been previously applied to identify growth or developmental phases at different scales in plants (see e.g. Dambreville et al , 2015 ; Lièvre et al , 2016 ; Guédon et al , 2007 ). By identifying successive synchronous phases in flowering and by showing that the split into two perpetual flowering phases can be genetically explained, this study illustrates the relevance of statistical models for better characterizing dynamic developmental traits using longitudinal analyses of plant phenotyping data.…”

“…Until now, the dynamics of developmental traits have not explicitly been considered in genetic studies, whereas recent studies have shown an interest in longitudinal data analyses for deciphering such complex traits (see Dambreville et al (2015) concerning growth and developmental stages in mango growth units and Lièvre et al (2016) concerning developmental phases in Arabidospsis rosette). In this setting, the successive measurements of the developmental traits of interest are directly analysed with appropriated statistical models (see Diggle et al (2002) for a general introduction to longitudinal data analysis).…”

A New Phenotyping Pipeline Reveals Three Types of Lateral Roots and a Random Branching Pattern in Two Cereals

“…In order to capture such behavior, the proposed modeling framework can be extended directly by adding states in series for modeling successive growth phases for type A lateral roots. Such an extension of semi-Markov switching models with states in series was developed recently for modeling successive developmental phase in Arabidopsis rosettes by Lièvre et al (2016). We may expect a single state with decreasing growth rate following the current increasing growth rate state A or an intermediate roughly stationary growth state between the increasing and decreasing growth rate states.…”

“…Most of these platforms generate traits that give a global view of the root architecture. By contrast, the development of individual roots during long periods of time is rarely studied, whereas temporal analyses are more developed for the aerial parts (Lièvre et al, 2016). These studies have been hampered by the difficulty of collecting individual root growth data.…”

“…We used it to characterize the early developmental patterns of root systems in two cereals, pearl millet and maize. In a first step, lateral root growth rate profiles were analyzed using semi-Markov switching linear models (SMS-LMs; for another application of similar statistical models, see Lièvre et al, 2016). This model-based clustering of growth rate profiles led us to identify three types of lateral root.…”