Balance between dopamine and adenosine signals regulates the PKA/Rap1 pathway in striatal medium spiny neurons.

Medium spiny neurons (MSNs) expressing dopamine D1 receptor (D1R) or D2 receptor (D2R) are major components of the striatum. Stimulation of D1R activates protein kinase A (PKA) through Golf to increase neuronal activity, while D2R stimulation inhibits PKA through Gi. Adenosine A2A receptor (A2AR) coupled to Golf is highly expressed in D2R-MSNs within the striatum. However, how dopamine and adenosine co-operatively regulate PKA activity remains largely unknown. Here, we measured Rap1gap serine 563 phosphorylation to monitor PKA activity and examined dopamine and adenosine signals in MSNs. We found that a D1R agonist increased Rap1gap phosphorylation in striatal slices and in D1R-MSNs in vivo. A2AR agonist CGS21680 increased Rap1gap phosphorylation, and pretreatment with the D2R agonist quinpirole blocked this effect in striatal slices. D2R antagonist eticlopride increased Rap1gap phosphorylation in D2R-MSNs in vivo, and the effect of eticlopride was blocked by the pretreatment with the A2AR antagonist SCH58261. These results suggest that adenosine positively regulates PKA in D2R-MSNs through A2AR, while this effect is blocked by basal dopamine in vivo. Incorporating computational model analysis, we propose that the shift from D1R-MSNs to D2R-MSNs or vice versa appears to depend predominantly on a change in dopamine concentration.

Two New FRET Imaging Measures: Linearly Proportional to and Highly Contrasting the Fraction of Active Molecules.

We developed two new FRET imaging measures for intramolecular FRET biosensors, called linearly proportional (LP) and highly contrasting (HC) measures, which can be easily calculated by the fluorescence intensities of donor and acceptor as a ratio between their weighted sums. As an alternative to the conventional ratiometric measure, which non-linearly depends on the fraction of active molecule, we first developed the LP measure, which is linearly proportional to the fraction of active molecules. The LP measure inherently unmixes bleed-through signals and is robust against fluorescence noise. By extending the LP measure, we furthermore designed the HC measure, which provides highly contrasting images of the molecular activity, more than the ratiometric measure. In addition to their advantages, these measures are insensitive to the biosensor expression level, which is a fundamental property of the ratiometric measure. Using artificial data and FRET imaging data, we showed that the LP measure effectively represents the fraction of active molecules and that the HC measure improves visual interpretability by providing high contrast images of molecular activity. Therefore, the LP and HC measures allow us to gain more quantitative and qualitative insights from FRET imaging than the ratiometric measure.

High-speed multineuron calcium imaging using Nipkow-type confocal microscopy.

Conventional confocal and two-photon microscopy scan the field of view sequentially with single-point laser illumination. This raster-scanning method constrains video speeds to tens of frames per second, which are too slow to capture the temporal patterns of fast electrical events initiated by neurons. Nipkow-type spinning-disk confocal microscopy resolves this problem by the use of multiple laser beams. We describe experimental procedures for functional multineuron calcium imaging (fMCI) based on Nipkow-disk confocal microscopy, which enables us to monitor the activities of hundreds of neurons en masse at a cellular resolution at up to 2000 fps.

Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles.

Multiclass classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. There have been many studies of aggregating binary classifiers to construct a multiclass classifier based on one-versus-the-rest (1R), one-versus-one (11), or other coding strategies, as well as some comparison studies between them. However, the studies found that the best coding depends on each situation. Therefore, a new problem, which we call the "optimal coding problem," has arisen: how can we determine which coding is the optimal one in each situation? To approach this optimal coding problem, we propose a novel framework for constructing a multiclass classifier, in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. Although there is no a priori answer to the optimal coding problem, our weight tuning method can be a consistent answer to the problem. We apply this method to various classification problems including a synthesized data set and some cancer diagnosis data sets from gene expression profiling. The results demonstrate that, in most situations, our method can improve classification accuracy over simple voting heuristics and is better than or comparable to state-of-the-art multiclass predictors.

A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors.

BACKGROUND: Although microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected. RESULTS: With four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set included 119 samples, and the predictors were evaluated by strict leave-one-out cross validation. Trials included classical combinations, i.e., one-to-one, one-to-the-rest, but the predictor using more combination exhibited the better prediction accuracy. This characteristic was consistent with other gene expression data sets. The performance of the selected predictor was then tested with an independent set consisting of 49 samples. The resulting test prediction accuracy was 85.7%. CONCLUSION: Molecular diagnosis of thyroid tissues is feasible by gene expression profiling, and the current level is promising towards the automatic diagnostic tool to complement the present medical procedures. A multi-class predictor with an exhaustive combination of binary classifiers could achieve a higher prediction accuracy than those with classical combinations and other predictors such as multi-class SVM. The probabilistic outputs of the predictor offer more detailed information for each sample, which enables visualization of each sample in low-dimensional classification spaces. These new concepts should help to improve the multi-class classification including that of cancer tissues.

Adaptor-tagged competitive polymerase chain reaction: amplification bias and quantified gene expression levels.

Adaptor-tagged competitive polymerase chain reaction (ATAC-PCR) is an advanced version of quantitative competitive PCR characterized by the addition of unique adaptors to different cDNA samples. It is currently the only quantitative PCR technique that enables large-scale gene expression analysis. Multiplex application of ATAC-PCR employs seven adaptors, two or three of which are used as controls to generate a calibration curve. The characteristics of the ATAC-PCR method for large-scale data production, including any adaptor- and gene-dependent amplification biases, were evaluated by using this method to analyze the expression of 384 mouse brain genes. Short adaptors tended to amplify at higher efficiency than did long adaptors. The population of genes with a high amplification bias increased with the use of short adaptors. Subtracting the median value of all adaptor-dependent biases could reduce this bias; the majority of genes displayed a small gene-dependent bias, which facilitated reliable quantification. We modified ATAC-PCR to estimate molecular numbers of transcripts by introducing synthetic standards. This modification demonstrated that gene expression levels in mammalian cells are varied over seven orders of magnitude.