Quantitative Analysis of Live-Cell Growth at the Shoot Apex of Arabidopsis thaliana: Algorithms for Feature Measurement and Temporal Alignment

Abstract

Study of the molecular control of organ growth requires establishment of the causal relationship between gene expression and cell behaviors. We seek to understand this relationship at the shoot apical meristem (SAM) of model plant Arabidopsis thaliana. This requires the spatial mapping and temporal alignment of different functional domains into a single template. Live-cell imaging techniques allow us to observe real-time organ primordia growth and gene expression dynamics at cellular resolution. In this paper, we propose a framework for the measurement of growth features at the 3D reconstructed surface of organ primordia, as well as algorithms for robust time alignment of primordia. We computed areas and deformation values from reconstructed 3D surfaces of individual primordia from live-cell imaging data. Based on these growth measurements, we applied a multiple feature landscape matching (LAM-M) algorithm to ensure a reliable temporal alignment of multiple primordia. Although the original landscape matching (LAM) algorithm motivated our alignment approach, it sometimes fails to properly align growth curves in the presence of high noise/distortion. To overcome this shortcoming, we modified the cost function to consider the landscape of the corresponding growth features. We also present an alternate parameter-free growth alignment algorithm which performs as well as LAM-M for high-quality data, but is more robust to the presence of outliers or noise. Results on primordia and guppy evolutionary growth data show that the proposed alignment framework performs at least as well as the LAM algorithm in the general case, and significantly better in the case of increased noise.

Plant chemical biology: are we meeting the promise?

“…Another area that we have noted previously is the need for continued enhancement in plants of automated screening utilizing image and video analysis (Hicks and Raikhel, 2009). This includes the tracking of plant development at macro and micro levels [for several recent examples, see (Tisne et al, 2013; Sozzani et al, 2014)], vesicle movement related to basic mechanisms, plant immunity and development within tissues such as the meristem (Salomon et al, 2010; Tataw et al, 2013; Ung et al, 2013; Beck et al, 2014). In quantifying aspects of vesicle or organelle diameter, velocity, and directionality we can learn about the regulation of dynamic subcellular compartments in real time.…”

Time series classification based on triadic time series motifs

“…Quantifying the similarity of time series has always been a very useful primitives for time series analysis, with applications to many fields (Hu et al, 2016;Silva et al, 2015;Gomes and Batista, 2015;Mueen et al, 2009;Mcgovern et al, 2011;Chiu et al, 2003;Mueen and Keogh, 2010;Tataw et al, 2013). The key point of measuring similarity is to define a suitable and effective distance between two time series (Hu et al, 2015;Tarango et al, 2014;Miśkiewicz and Ausloos, 2008).…”