Detection of a Gravitropism Phenotype inglutamate receptor-like 3.3Mutants ofArabidopsis thalianaUsing Machine Vision and Computation

Abstract

Gene disruption frequently produces no phenotype in the model plant Arabidopsis thaliana, complicating studies of gene function. Functional redundancy between gene family members is one common explanation but inadequate detection methods could also be responsible. Here, newly developed methods for automated capture and processing of time series of images, followed by computational analysis employing modified linear discriminant analysis (LDA) and wavelet-based differentiation, were employed in a study of mutants lacking the Glutamate Receptor-Like 3.3 gene. Root gravitropism was selected as the process to study with high spatiotemporal resolution because the ligand-gated Ca 21 -permeable channel encoded by GLR3.3 may contribute to the ion fluxes associated with gravity signal transduction in roots. Time series of root tip angles were collected from wild type and two different glr3.3 mutants across a grid of seed-size and seedling-age conditions previously found to be important to gravitropism. Statistical tests of average responses detected no significant difference between populations, but LDA separated both mutant alleles from the wild type. After projecting the data onto LDA solution vectors, glr3.3 mutants displayed greater population variance than the wild type in all four conditions. In three conditions the projection means also differed significantly between mutant and wild type. Wavelet analysis of the raw response curves showed that the LDA-detected phenotypes related to an early deceleration and subsequent slower-bending phase in glr3.3 mutants. These statistically significant, heritable, computationbased phenotypes generated insight into functions of GLR3.3 in gravitropism. The methods could be generally applicable to the study of phenotypes and therefore gene function.

3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture

“…We found it notable that during this period the solidity and WDRs remained nearly constant in the parental line Bala but dropped dramatically in the parental line Azucena. Genes controlling such architectural trends could be mapped using functions derived from time-course phenotyping data (33,34). We envision the combination of high-throughput 3D root imaging and multitrait analysis with modern sequencing as a powerful approach to closing the phenotype-genotype gap.…”

“…These patterns highlight the juxtaposition between an even, compact form of growth versus one that is rangy and deep. Whether these trends are emergent properties of local growth patterns or in part are controlled globally remains an important open question that we can begin to answer by mapping RSA over time and by developing traits that describe specific growth behavior (33,34 QTLs ranging between 6.9% and 14.2% of genetic variation explained and typically colocalized (Fig. 5).…”

Using Flatbed Scanners to Collect High-resolution Time-lapsed Images of the Arabidopsis Root Gravitropic Response

“…For example, CCD cameras have been used to capture individual seedling responses to light and gravity at high spatial and temporal resolution 1,8,12 . Other systems have been developed allowing measurement of root tip orientation of multiple roots from a single image (e.g.…”

“…Collection of high-resolution phenotypic data is useful in studies that aim to understand the interplay of genetics and environment in mediating organismal function 1,2 . Studies of this nature are also inherently large in scale, making it additionally necessary that methods employed for measuring phenotypes in this context be high in throughput 3,4 .…”

“…In general, an image is relatively easy to capture, facilitating throughput, and when taken at sufficient resolution, subtle phenotypes can be detected 1,2,7 . Imaging technologies tend to be modifiable to fit a system or process of interest and are generally scalable [10][11][12] .…”