Imaging methodologies for systems biology

Structural plasticity of the living kinetochore

The kinetochore is a large, evolutionarily conserved protein structure that connects chromosomes with microtubules. During chromosome segregation, outer kinetochore components track depolymerizing ends of microtubules to facilitate the separation of chromosomes into two cells. In budding yeast, each chromosome has a point centromere upon which a single kinetochore is built, which attaches to a single microtubule. This defined architecture facilitates quantitative examination of kinetochores during the cell cycle. Using three independent measures-calibrated imaging, FRAP, and photoconversion-we find that the Dam1 submodule is unchanged during anaphase, whereas MIND and Ndc80 submodules add copies to form an "anaphase configuration" kinetochore. Microtubule depolymerization and kinesin-related motors contribute to copy addition. Mathematical simulations indicate that the addition of microtubule attachments could facilitate tracking during rapid microtubule depolymerization. We speculate that the minimal kinetochore configuration, which exists from G1 through metaphase, allows for correction of misattachments. Our study provides insight into dynamics and plasticity of the kinetochore structure during chromosome segregation in living cells.

An Object-Oriented Regression for Building Disease Predictive Models with Multiallelic HLA Genes

Recent genome-wide association studies confirm that human leukocyte antigen (HLA) genes have the strongest associations with several autoimmune diseases, including type 1 diabetes (T1D), providing an impetus to reduce this genetic association to practice through an HLA-based disease predictive model. However, conventional model-building methods tend to be suboptimal when predictors are highly polymorphic with many rare alleles combined with complex patterns of sequence homology within and between genes. To circumvent this challenge, we describe an alternative methodology; treating complex genotypes of HLA genes as "objects" or "exemplars," one focuses on systemic associations of disease phenotype with "objects" via similarity measurements. Conceptually, this approach assigns disease risks base on complex genotype profiles instead of specific disease-associated genotypes or alleles. Effectively, it transforms large, discrete, and sparse HLA genotypes into a matrix of similarity-based covariates. By the Kernel representative theorem and machine learning techniques, it uses a penalized likelihood method to select disease-associated exemplars in building predictive models. To illustrate this methodology, we apply it to a T1D study with eight HLA genes (HLA-DRB1, HLA-DRB3, HLA-DRB4, HLA-DRB5, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1) to build a predictive model. The resulted predictive model has an area under curve of 0.92 in the training set, and 0.89 in the validating set, indicating that this methodology is useful to build predictive models with complex HLA genotypes.

Quantitative dynamics of VE-cadherin at endothelial cell junctions at a glance: basic requirements and current concepts

Intercellular junctions of the vascular endothelium are dynamic structures that display a high degree of plasticity, which is required to contribute to their regulation of many physiological and pathological processes including monolayer integrity, barrier function, wound healing and angiogenesis. Vascular endothelial cadherin (VE-cadherin) is connected via catenins to the actin cytoskeleton, both of which are key structures in endothelial junction regulation, and thus are the focus of much investigation. Fluorescence-based live cell imaging is the method of choice to study dynamic remodeling in living cells. Although these methods have been successfully applied to many cell types, investigations of endothelial junction dynamics were for a long time limited as they are largely resistant to transfection using many classical protocols. Application of virus-based gene transduction techniques, together with advanced microscopy, now allows both sufficient expression of fluorescence tagged junction-localized proteins in the endothelium and time-lapse recording over long periods. Using highly spatiotemporally resolved fluorescence microscopy it turned out that endothelial junctions display extensive junction heterogeneity at the subcellular level; a fact that largely limits automated quantification by available software. Recent work describes open software tools to quantitatively analyze large amounts of fluorescence-based image data in either single or confluent epithelial and endothelial cells. Based on quantitative VE-cadherin and actin dynamics novel key players, mechanisms and concepts have been suggested that control endothelial junction dynamics. Here we aim to summarize the recent developments in the field.