Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry.

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages.

PURPOSE: To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules. METHODS: The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples. RESULTS: Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning. CONCLUSIONS: This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

A computational approach to studying ageing at the individual level.

The ageing process is actively regulated throughout an organism's life, but studying the rate of ageing in individuals is difficult with conventional methods. Consequently, ageing studies typically make biological inference based on population mortality rates, which often do not accurately reflect the probabilities of death at the individual level. To study the relationship between individual and population mortality rates, we integrated in vivo switch experiments with in silico stochastic simulations to elucidate how carefully designed experiments allow key aspects of individual ageing to be deduced from group mortality measurements. As our case study, we used the recent report demonstrating that pheromones of the opposite sex decrease lifespan in Drosophila melanogaster by reversibly increasing population mortality rates. We showed that the population mortality reversal following pheromone removal was almost surely occurring in individuals, albeit more slowly than suggested by population measures. Furthermore, heterogeneity among individuals due to the inherent stochasticity of behavioural interactions skewed population mortality rates in middle-age away from the individual-level trajectories of which they are comprised. This article exemplifies how computational models function as important predictive tools for designing wet-laboratory experiments to use population mortality rates to understand how genetic and environmental manipulations affect ageing in the individual.

Connecting the dots: the effects of macromolecular crowding on cell physiology.

The physicochemical properties of cellular environments with a high macromolecular content have been systematically characterized to explain differences observed in the diffusion coefficients, kinetics parameters, and thermodynamic properties of proteins inside and outside of cells. However, much less attention has been given to the effects of macromolecular crowding on cell physiology. Here, we review recent findings that shed some light on the role of crowding in various cellular processes, such as reduction of biochemical activities, structural reorganization of the cytoplasm, cytoplasm fluidity, and cellular dormancy. We conclude by presenting some unresolved problems that require the attention of biophysicists, biochemists, and cell physiologists. Although it is still underappreciated, macromolecular crowding plays a critical role in life as we know it.

Circadian rhythms regulate amelogenesis.

Ameloblasts, the cells responsible for making enamel, modify their morphological features in response to specialized functions necessary for synchronized ameloblast differentiation and enamel formation. Secretory and maturation ameloblasts are characterized by the expression of stage-specific genes which follows strictly controlled repetitive patterns. Circadian rhythms are recognized as key regulators of the development and diseases of many tissues including bone. Our aim was to gain novel insights on the role of clock genes in enamel formation and to explore the potential links between circadian rhythms and amelogenesis. Our data shows definitive evidence that the main clock genes (Bmal1, Clock, Per1 and Per2) oscillate in ameloblasts at regular circadian (24 h) intervals both at RNA and protein levels. This study also reveals that the two markers of ameloblast differentiation i.e. amelogenin (Amelx; a marker of secretory stage ameloblasts) and kallikrein-related peptidase 4 (Klk4, a marker of maturation stage ameloblasts) are downstream targets of clock genes. Both, Amelx and Klk4 show 24h oscillatory expression patterns and their expression levels are up-regulated after Bmal1 over-expression in HAT-7 ameloblast cells. Taken together, these data suggest that both the secretory and the maturation stages of amelogenesis might be under circadian control. Changes in clock gene expression patterns might result in significant alterations of enamel apposition and mineralization.

A graphical user interface for a method to infer kinetics and network architecture (MIKANA).

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

Enzyme catalyzed reactions: from experiment to computational mechanism reconstruction.

The traditional experimental practice in enzyme kinetics involves the measurement of substrate or product concentrations as a function of time. Advances in computing have produced novel approaches for modeling enzyme catalyzed reactions from time course data. One example of such an approach is the selection of appropriate chemical reactions that best fit the data. A common limitation of this approach resides in the number of chemical species considered. The number of possible chemical reactions grows exponentially with the number of chemical species, which makes difficult to select reactions that uniquely describe the data and diminishes the efficiency of the methods. In addition, a method's performance is also dependent on several quantitative and qualitative properties of the time course data, of which we know very little. This information is important to experimentalists as it could allow them to setup their experiments in ways that optimize the network reconstruction. We have previously described a method for inferring reaction mechanisms and kinetic rate parameters from time course data. Here, we address the limitations in the number of chemical reactions by allowing the introduction of information about chemical interactions. We also address the unknown properties of the input data by determining experimental data properties that maximize our method's performance. We investigate the following properties: initial substrate-enzyme concentration ratios; initial substrate-enzyme concentration variation ranges; number of data points; number of different experiments (time courses); and noise. We test the method using data generated in silico from the Michaelis-Menten and the Hartley-Kilby reaction mechanisms. Our results demonstrate the importance of experimental design for time course assays that has not been considered in experimental protocols. These considerations can have far reaching implications for the computational mechanism reconstruction process.