Quantification of Mitochondrial Network Characteristics in Health and Disease.

The term 'mitochondrial dynamics' is commonly used to refer to ongoing fusion and fission of mitochondrial structures within a living cell. A growing number of diseases, from Charcot Marie Tooth Type 2a neuropathies to cancer, is known to be associated with the dysregulation of mitochondrial dynamics, leading to irregularities of mitochondrial network morphology that are associated with aberrant metabolism and cellular dysfunction. Studying these phenomena, and potential pharmacological interventions to correct them, in cultured cells is a powerful approach to developing treatments or cures. Appropriately designed experiments and quantitative approaches for characterizing mitochondrial morphology and function are essential for furthering our understanding. In this chapter, we discuss the importance of cell incubation conditions, choices around imaging modalities, and data analysis tools with respect to experimental outcomes and the interpretation of results from studies of mitochondrial dynamics. We focus primarily on the quantitative analysis of mitochondrial morphology, providing an overview of the available tools and approaches currently being used and discussing some of the strengths and weaknesses associated with each. Finally, we discuss how the ongoing development of imaging and analysis tools continues to improve our ability to study normal and aberrant mitochondrial physiology in vitro and in vivo.

Data mining crystallization databases: knowledge-based approaches to optimize protein crystal screens.

Protein crystallization is a major bottleneck in protein X-ray crystallography, the workhorse of most structural proteomics projects. Because the principles that govern protein crystallization are too poorly understood to allow them to be used in a strongly predictive sense, the most common crystallization strategy entails screening a wide variety of solution conditions to identify the small subset that will support crystal nucleation and growth. We tested the hypothesis that more efficient crystallization strategies could be formulated by extracting useful patterns and correlations from the large data sets of crystallization trials created in structural proteomics projects. A database of crystallization conditions was constructed for 755 different proteins purified and crystallized under uniform conditions. Forty-five percent of the proteins formed crystals. Data mining identified the conditions that crystallize the most proteins, revealed that many conditions are highly correlated in their behavior, and showed that the crystallization success rate is markedly dependent on the organism from which proteins derive. Of the proteins that crystallized in a 48-condition experiment, 60% could be crystallized in as few as 6 conditions and 94% in 24 conditions. Consideration of the full range of information coming from crystal screening trials allows one to design screens that are maximally productive while consuming minimal resources, and also suggests further useful conditions for extending existing screens.