Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

CAT PETR: a graphical user interface for differential analysis of phosphorylation and expression data

Antibody microarray data provides a powerful and high-throughput tool to monitor global changes in cellular response to perturbation or genetic manipulation. However, while collecting such data has become increasingly accessible, a lack of specific computational tools has made their analysis limited. Here we present CAT PETR, a user friendly web application for the differential analysis of expression and phosphorylation data collected via antibody microarrays. Our application addresses the limitations of other GUI based tools by providing various data input options and visualizations. To illustrate its capabilities on real data, we show that CAT PETR both replicates previous findings, and reveals additional insights, using its advanced visualization and statistical options.

Protein Microarrays for Ocular Diseases

The eye is a multifaceted organ organized in several compartments with particular properties that reflect their diverse functions. The prevalence of ocular diseases is increasing, mainly because of its relationship with aging and of generalized lifestyle changes. However, the pathogenic molecular mechanisms of many common eye pathologies remain poorly understood. Considering the unquestionable importance of proteins in cellular processes and disease progression, proteomic techniques, such as protein microarrays, represent a valuable approach to analyze pathophysiological protein changes in the ocular environment. This technology enables to perform multiplex high-throughput protein expression profiling with minimal sample volume requirements broadening our knowledge of ocular proteome network in eye diseases.In this review, we present a brief summary of the main types of protein microarrays (antibody microarrays, reverse-phase protein microarrays, and protein microarrays) and their application for protein change detection in chronic ocular diseases such as dry eye, age-related macular degeneration, diabetic retinopathy, and glaucoma. The validation of these specific protein changes in eye pathologies may lead to the identification of new biomarkers, depiction of ocular disease pathways, and assistance in the diagnosis, prognosis, and development of new therapeutic options for eye pathologies.

Protein Microarrays: Valuable Tools for Ocular Diseases Research

The eye is a complex organ comprised of several compartments with exclusive and specialized properties that reflect their diverse functions. Although the prevalence of eye pathologies is increasing, mainly because of its correlation with aging and of generalized lifestyle changes, the pathogenic molecular mechanisms of many common ocular diseases remain poorly understood. Therefore, there is an unmet need to delve into the pathogenesis, diagnosis, and treatment of eye diseases to preserve ocular health and reduce the incidence of visual impairment or blindness. Proteomics analysis stands as a valuable tool for deciphering protein profiles related to specific ocular conditions. In turn, such profiles can lead to real breakthroughs in the fields of ocular science and ophthalmology. Among proteomics techniques, protein microarray technology stands out by providing expanded information using very small volumes of samples. In this review, we present a brief summary of the main types of protein microarrays and their application for the identification of protein changes in chronic ocular diseases such as dry eye, glaucoma, age-related macular degeneration, or diabetic retinopathy. The validation of these specific protein alterations could provide new biomarkers, disclose eye diseases pathways, and help in the diagnosis and development of novel therapies for eye pathologies.

Integrated Strategies to Gain a Systems-Level View of Dynamic Signaling Networks

In order to survive and function properly in the face of an ever changing environment, cells must be able to sense changes in their surroundings and respond accordingly. Cells process information about their environment through complex signaling networks composed of many discrete signaling molecules. Individual pathways within these networks are often tightly integrated and highly dynamic, allowing cells to respond to a given stimulus (or, as is typically the case under physiological conditions, a combination of stimuli) in a specific and appropriate manner. However, due to the size and complexity of many cellular signaling networks, it is often difficult to predict how cellular signaling networks will respond under a particular set of conditions. Indeed, crosstalk between individual signaling pathways may lead to responses that are nonintuitive (or even counterintuitive) based on examination of the individual pathways in isolation. Therefore, to gain a more comprehensive view of cell signaling processes, it is important to understand how signaling networks behave at the systems level. This requires integrated strategies that combine quantitative experimental data with computational models. In this chapter, we first examine some of the progress that has recently been made toward understanding the systems-level regulation of cellular signaling networks, with a particular emphasis on phosphorylation-dependent signaling networks. We then discuss how genetically targetable fluorescent biosensors are being used together with computational models to gain unique insights into the spatiotemporal regulation of signaling networks within single, living cells.