Picture free recall performance linked to the brain's structural connectome

Memory functions are highly variable between healthy humans. The neural correlates of this variability remain largely unknown.; Here, we investigated how differences in free recall performance are associated with DTI-based properties of the brain's structural connectome and with grey matter volumes in 664 healthy young individuals tested in the same MR scanner.; Global structural connectivity, but not overall or regional grey matter volumes, positively correlated with recall performance. Moreover, a set of 22 inter-regional connections, including some with no previously reported relation to human memory, such as the connection between the temporal pole and the nucleus accumbens, explained 7.8% of phenotypic variance.; In conclusion, this large-scale study indicates that individual memory performance is associated with the level of structural brain connectivity

Defining motility in the Staphylococci

The ability of bacteria to move is critical for their survival in diverse environments and multiple ways have evolved to achieve this. Two forms of motility have recently been described for Staphylococcus aureus, an organism previously considered to be non-motile. One form is called spreading, which is a type of sliding motility and the second form involves comet formation, which has many observable characteristics associated with gliding motility. Darting motility has also been observed in Staphylococcus epidermidis. This review describes how motility is defined and how we distinguish between passive and active motility. We discuss the characteristics of the various forms of Staphylococci motility, the molecular mechanisms involved and the potential future research directions

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism. To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography–high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine

Metabolite Profiling and Stable Isotope Tracing in Sorted Subpopulations of Mammalian Cells

Biological samples such as tissues, blood, or tumors are often complex and harbor heterogeneous populations of cells. Separating out specific cell types or subpopulations from such complex mixtures to study their metabolic phenotypes is challenging because experimental procedures for separation may disturb the metabolic state of cells. To address this issue, we developed a method for analysis of cell subpopulations using stable isotope tracing and fluorescence-activated cell sorting followed by liquid chromatography–high-resolution mass spectrometry. To ensure a faithful representation of cellular metabolism after cell sorting, we benchmarked sorted extraction against direct extraction. While peak areas differed markedly with lower signal for amino acids but higher signal for nucleotides, mass isotopomer distributions from sorted cells were generally in good agreement with those obtained from direct extractions, indicating that they reflect the true metabolic state of cells prior to sorting. In proof-of-principle studies, our method revealed metabolic phenotypes specific to T cell subtypes, and also metabolic features of cells in the committed phase of the cell division cycle. Our approach enables studies of a wide range of adherent and suspension cell subpopulations, which we anticipate will be of broad importance in cell biology and biomedicine