Quantitative NMR-Based Metabolomics on Tissue Biomarkers and Its Translation into In Vivo Magnetic Resonance Spectroscopy.

Nuclear magnetic resonance (NMR) spectroscopy is an established analytical platform for analyzing metabolic profiles of cells, tissues, and body fluids. There are several advantages in introducing an NMR-based study design into metabolomics studies, including a fast and comprehensive detection, characterization, and quantification of dozens of endogenous metabolites in a single NMR spectrum. Quantitative proton 1H-NMR is the most useful NMR-based platform for metabolomics. The frozen tissues can be analyzed noninvasively using a high-resolution magic angle spinning (HR-MAS) 1H-NMR spectroscopy; or several extraction techniques can be applied to detect additional metabolites using a conventional liquid-based NMR technique. In this chapter, we report on tissue collection, handling, extraction methods, and 1H-NMR acquisition protocols developed in the past decades for a precise and quantitative NMR-metabolomics approach. The NMR acquisition protocols (both HR-MAS and conventional 1H-NMR spectroscopy) and spectral analysis steps are also presented. Since NMR can be applied "in vivo" using horizontal bore MRI scanners, several in vivo sequences for localized 1H-MRS (magnetic resonance spectroscopy) are presented which can be directly applied for noninvasive detection of brain metabolites.

Computational analysis of signaling patterns in single cells.

Signaling proteins are flexible in both form and function. They can bind to multiple molecular partners and integrate diverse types of cellular information. When imaged by time-lapse microscopy, many signaling proteins show complex patterns of activity or localization that vary from cell to cell. This heterogeneity is so prevalent that it has spurred the development of new computational strategies to analyze single-cell signaling patterns. A collective observation from these analyses is that cells appear less heterogeneous when their responses are normalized to, or synchronized with, other single-cell measurements. In many cases, these transformed signaling patterns show distinct dynamical trends that correspond with predictable phenotypic outcomes. When signaling mechanisms are unclear, computational models can suggest putative molecular interactions that are experimentally testable. Thus, computational analysis of single-cell signaling has not only provided new ways to quantify the responses of individual cells, but has helped resolve longstanding questions surrounding many well-studied human signaling proteins including NF-κB, p53, ERK1/2, and CDK2. A number of specific challenges lie ahead for single-cell analysis such as quantifying the contribution of non-cell autonomous signaling as well as the characterization of protein signaling dynamics in vivo.

Retinal progenitor cells, differentiation, and barriers to cell cycle reentry.

Neurogenesis in the retina occurs via the coordination of proliferation, cell cycle exit and differentiation of retinal progenitor cells. Until recently, it was widely assumed that once a retinal progenitor cell produced a postmitotic neuron, there was no possibility for cell-cycle re-entry. However, recent studies have shown that mature differentiated horizontal neurons with reduced Rb pathway function can re-enter the cell cycle and proliferate while maintaining their differentiated features. This chapter will explore the molecular and cellular mechanisms that help to keep differentiated retinal neurons and glia postmitotic. We propose that there are cell-type specific barriers to cell-cycle re-entry by differentiated neurons and these may include apoptosis, chromatin/epigenetics mechanisms, cellular morphology and/or metabolic demands that are distinct across cell populations. Our data suggest that differentiated neurons span a continuum of cellular properties related to their ability to re-enter the cell cycle and undergo cytokinesis while maintaining their differentiated features. A deeper understanding of these processes may allow us to begin to explain the cell type specificity of neuronal cell death and tumor susceptibility. For example, neurons that have more barriers to cell-cycle re-entry may be less likely to form tumors but more likely to undergo degeneration. Conversely, neurons that have fewer barriers to cell-cycle re-entry may be more likely to form tumors but less likely to undergo degeneration.

Parallel processing across neural systems: implications for a multiple memory system hypothesis.

A common conceptualization of the organization of memory systems in brain is that different types of memory are mediated by distinct neural systems. Strong support for this view comes from studies that show double (or triple) dissociations between spatial, response, and emotional memories following selective lesions of hippocampus, striatum, and the amygdala. Here, we examine the extent to which hippocampal and striatal neural activity patterns support the multiple memory systems view. A comparison is made between hippocampal and striatal neural correlates with behavior during asymptotic performance of spatial and response maze tasks. Location- (or place), movement, and reward-specific firing patterns were found in both structures regardless of the task demands. Many, but not all, place fields of hippocampal and striatal neurons were similarly affected by changes in the visual and reward context regardless of the cognitive demands. Also, many, but not all, hippocampal and striatal movement-sensitive neurons showed significant changes in their behavioral correlates after a change in visual context, irrespective of cognitive strategy. Similar partial reorganization was observed following manipulations of the reward condition for cells recorded from both structures, again regardless of task. Assuming that representations that persist across context changes reflect learned information, we make the following conclusions. First, the consistent pattern of partial reorganization supports a view that the analysis of spatial, response, and reinforcement information is accomplished via an error-driven, or match-mismatch, algorithm across neural systems. Second, task-relevant processing occurs continuously within hippocampus and striatum regardless of the cognitive demands of the task. Third, given the high degree of parallel processing across allegedly different memory systems, we propose that different neural systems may effectively compete for control of a behavioral expression system. The strength of the influence of any one neural system on behavioral output is likely modulated by factors such as motivation, experience, or hormone status.