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Proteomic analysis is intrinsically an iterative, incremental process. Information is usually acquired gradually by researchers, and in different projects. At the same time, there are relatively few examples of biological data management systems which take into account this reality, most of them usually treat the experiment generated data as static and unchangeable: data are never reconsidered, or seldom, whereas technology becomes more powerful or that other researchers have brought information on data correction. And yet, post-planned analysis [21] which involves multiple iterations and subsequent re-investigations of previously prepared data might bring tremendous benefits.

We describe LineageD—a hybrid web‐based system to predict, visualize, and interactively adjust plant embryo cell lineages. Currently, plant biologists explore the development of an embryo and its hierarchical cell lineage manually, based on a 3D dataset that represents the embryo status at one point in time. This human decision‐making process, however, is time‐consuming, tedious, and error‐prone due to the lack of integrated graphical support for specifying the cell lineage. To fill this gap, we developed a new system to support the biologists in their tasks using an interactive combination of 3D visualization, abstract data visualization, and correctable machine learning to modify the proposed cell lineage. We use existing manually established cell lineages to obtain a neural network model. We then allow biologists to use this model to repeatedly predict assignments of a single cell division stage. After each hierarchy level prediction, we allow them to interactively adjust the machine learning based assignment, which we then integrate into the pool of verified assignments for further predictions. In addition to building the hierarchy this way in a bottom‐up fashion, we also offer users to divide the whole embryo and create the hierarchy tree in a top‐down fashion for a few steps, improving the ML‐based assignments by reducing the potential for wrong predictions. We visualize the continuously updated embryo and its hierarchical development using both 3D spatial and abstract tree representations, together with information about the model's confidence and spatial properties. We conducted case study validations with five expert biologists to explore the utility of our approach and to assess the potential for LineageD to be used in their daily workflow. We found that the visualizations of both 3D representations and abstract representations help with decision making and the hierarchy tree top‐down building approach can reduce assignments errors in real practice. [ABSTRACT FROM AUTHOR]

One essential assumption used in object detection and labeling by imaging is that the photometric properties of object are homogeneous. This homogeneousness requirement is often violated in microscopy imaging. Classical methods are usually of high computational cost and fail to give stable solution. This paper presents a low computational complexity and robust method for three-dimensional (3-D) biological object detection and labeling. The developed approach is based on a statistical, nonparametric framework. Image is first divided into regular nonoverlapped regions and each region is evaluated according to a general photometric variability model. The regions not consistent with this model are considered as aberration in the data and excluded from the analysis procedure. Simultaneously, the interior parts of the object are detected, they correspond to regions where the supposed model is valid. In the second stage, the valid regions from a same object are merged together depending on a set of hypotheses. These hypotheses are generated by taking into account photometric and geometric properties of objects of interest and the merging is achieved according to an iterative algorithm. The approach has been applied in investigations of spatial distribution of nuclei within colonic glands of rats observed with the help of confocal fluorescence microscopy. Index Terms--Cytometry, confocal microscopy, image analysis, intestinal crypts, multivariate statistics, model validation.

Investigation of dynamic processes in cell biology very often relies on the observation in two dimensions of three-dimensional (3D) biological processes. Consequently, the data are partial and statistical methods and models are required to recover the parameters describing the dynamical processes. In the case of molecules moving over the 3D surface, such as proteins on walls of bacteria cell, a large portion of the 3D surface is not observed in two-dimensional (2D)-time microscopy. It follows that biomolecules may disappear for a period of time in a region of interest, and then reappear later. Assuming Brownian motion with drift, we address the mathematical problem of the reconstruction of biomolecules trajectories on a cylindrical surface. A subregion of the cylinder is typically recorded during the observation period, and biomolecules may appear or disappear in any place of the 3D surface. The performance of the method is demonstrated on simulated particle trajectories that mimic MreB protein dynamics observed in 2D time-lapse fluorescence microscopy in rod-shaped bacteria. [ABSTRACT FROM AUTHOR]

A study is conducted to characterize the proteomic profile of Lactococcus lactis during its development in milk. The proteomic analysis provides the role of glutamine synthetase and further genetic experiments highlights the key role of this enzyme in the development of Lactococcus lactis in dairy environments.

Plant tissue architecture and organ morphogenesis rely on the proper orientation of cell divisions. Previous attempts to predict division planes from cell geometry in plants mostly focused on 2D symmetric divisions. Using the stereotyped division patterns of Arabidopsis thaliana early embryogenesis, we investigated geometrical principles underlying plane selection in symmetric and in asymmetric divisions within complex 3D cell shapes. Introducing a 3D computational model of cell division, we show that area minimization constrained on passing through the cell centroid predicts observed divisions. Our results suggest that the positioning of division planes ensues from cell geometry and gives rise to spatially organized cell types with stereotyped shapes, thus underlining the role of self-organization in the developing architecture of the embryo. Our data further suggested the rule could be interpreted as surface minimization constrained by the nucleus position, which was validated using live imaging of cell divisions in the stomatal cell lineage. [ABSTRACT FROM AUTHOR]

Arabidopsis thalianaprovides a scientifically attractive and simple model for studying root growth and architecture and, subsequently, for discovering new genes involved in the control of these characters in plants. We have used the natural variation available in Arabidopsis accessions and mapped quantitative trait loci (QTLs) for primary root length (PRL), lateral root number (LRN) and density (LRD) and for total length of the lateral root system (LRL) in the Bay-0 × Shahdara population. Total phenotypic variation was very large, and despite the importance of the environmental component we were able to map 13 QTLs and one epistatic interaction between QTLs. Our results highlight the biological relevance and genetic control of lateral root density in this material. We were also able to show that variation in the extent of the lateral root system depends mainly on the growth of the existing lateral roots rather than in a change in LRN. Factors controlling lateral root growth seemed to have no major effect on primary root growth. Moreover, Shahdara QTL alleles always increased the length of the lateral roots, which may be taken as an adaptation to its very dry natural environment in Tadjikistan. A QTL for PRL was confirmed using a type of near-isogenic line called a heterogeneous inbred family (HIF), and this QTL is a candidate for further fine-mapping and cloning. [ABSTRACT FROM AUTHOR]

Presents a low computational complexity and robust method for three-dimensional biological object detection and labeling. Assumption used in object detection and labeling by imaging; Area where the analysis of three-dimensional structure of tissues, cellular constituents is important; Processes needed in confocal fluorescence microscopy imaging.