MethodTaking advantage of the current rapid development in imaging systems and computer vision algorithms, we present HPGA, a high-throughput phenotyping platform for plant growth modeling and functional analysis, which produces better understanding of energy distribution in regards of the balance between growth and defense. HPGA has two components, PAE (Plant Area Estimation) and GMA (Growth Modeling and Analysis). In PAE, by taking the complex leaf overlap problem into consideration, the area of every plant is measured from top-view images in four steps. Given the abundant measurements obtained with PAE, in the second module GMA, a nonlinear growth model is applied to generate growth curves, followed by functional data analysis.ResultsExperimental results on model plant Arabidopsis thaliana show that, compared to an existing approach, HPGA reduces the error rate of measuring plant area by half. The application of HPGA on the cfq mutant plants under fluctuating light reveals the correlation between low photosynthetic rates and small plant area (compared to wild type), which raises a hypothesis that knocking out cfq changes the sensitivity of the energy distribution under fluctuating light conditions to repress leaf growth.AvailabilityHPGA is available at http://www.msu.edu/~jinchen/HPGA.

These authors contributed equally to this work. ‡ Shared senior authorship. SUMMARYLeaf chloroplast movement is thought to optimize light capture and to minimize photodamage. To better understand the impact of chloroplast movement on photosynthesis, we developed a technique based on the imaging of reflectance from leaf surfaces that enables continuous, high-sensitivity, non-invasive measurements of chloroplast movement in multiple intact plants under white actinic light. We validated the method by measuring photorelocation responses in Arabidopsis chloroplast division mutants with drastically enlarged chloroplasts, and in phototropin mutants with impaired photorelocation but normal chloroplast morphology, under different light regimes. Additionally, we expanded our platform to permit simultaneous image-based measurements of chlorophyll fluorescence and chloroplast movement. We show that chloroplast division mutants with enlarged, less-mobile chloroplasts exhibit greater photosystem II photodamage than is observed in the wild type, particularly under fluctuating high levels of light. Comparison between division mutants and the severe photorelocation mutant phot1-5 phot2-1 showed that these effects are not entirely attributable to diminished photorelocation responses, as previously hypothesized, implying that altered chloroplast morphology affects other photosynthetic processes. Our dual-imaging platform also allowed us to develop a straightforward approach to correct non-photochemical quenching (NPQ) calculations for interference from chloroplast movement. This correction method should be generally useful when fluorescence and reflectance are measured in the same experiments. The corrected data indicate that the energy-dependent (qE) and photoinhibitory (qI) components of NPQ contribute differentially to the NPQ phenotypes of the chloroplast division and photorelocation mutants. This imaging technology thus provides a platform for analyzing the contributions of chloroplast movement, chloroplast morphology and other phenotypic attributes to the overall photosynthetic performance of higher plants.

BackgroundCircadian rhythm pathways influence the expression patterns of as much as 31% of the Arabidopsis genome through complicated interaction pathways, and have been found to be significantly disrupted by biotic and abiotic stress treatments, complicating treatment-response gene discovery methods due to clock pattern mismatches in the fold change-based statistics. The PRIISM (Pattern Recomposition for the Isolation of Independent Signals in Microarray data) algorithm outlined in this paper is designed to separate pattern changes induced by different forces, including treatment-response pathways and circadian clock rhythm disruptions.ResultsUsing the Fourier transform, high-resolution time-series microarray data is projected to the frequency domain. By identifying the clock frequency range from the core circadian clock genes, we separate the frequency spectrum to different sections containing treatment-frequency (representing up- or down-regulation by an adaptive treatment response), clock-frequency (representing the circadian clock-disruption response) and noise-frequency components. Then, we project the components’ spectra back to the expression domain to reconstruct isolated, independent gene expression patterns representing the effects of the different influences.By applying PRIISM on a high-resolution time-series Arabidopsis microarray dataset under a cold treatment, we systematically evaluated our method using maximum fold change and principal component analyses. The results of this study showed that the ranked treatment-frequency fold change results produce fewer false positives than the original methodology, and the 26-hour timepoint in our dataset was the best statistic for distinguishing the most known cold-response genes. In addition, six novel cold-response genes were discovered. PRIISM also provides gene expression data which represents only circadian clock influences, and may be useful for circadian clock studies.ConclusionPRIISM is a novel approach for overcoming the problem of circadian disruptions from stress treatments on plants. PRIISM can be integrated with any existing analysis approach on gene expression data to separate circadian-influenced changes in gene expression, and it can be extended to apply to any organism with regular oscillations in gene expression patterns across a large portion of the genome.

Higher organisms possess many genes which cycle under normal conditions, to allow the organism to adapt to expected environmental conditions throughout the course of a day. However, treatment-induced disruption of regular cyclic gene expression patterns presents a significant challenge in novel gene discovery experiments because these disruptions can induce strong differential regulation events for genes that are not involved in an adaptive response to the treatment. To address this cycle disruption problem, we reviewed the state-of-art periodic pattern detection algorithms and a pattern decomposition algorithm (PRIISM), which is a knowledge-based Fourier analysis algorithm designed to distinguish the cyclic patterns from the rest gene expression patterns, and discussed potential future improvements.