Accurate and statistically verified quantification of relative mRNA abundances using SYBR Green I and real-time RT-PCR.

Among the many methods currently available for quantifying mRNA transcript abundance, reverse transcription-polymerase chain reaction (RT-PCR) has proved to be the most sensitive. Recently, several protocols for real-time relative RT-PCR using the reporter dye SYBR Green I have appeared in the literature. In these methods, sample and control mRNA abundance is quantified relative to an internal reference RNA whose abundance is known not to change under the differing experimental conditions. We have developed new data analysis procedures for the two most promising of these methodologies and generated data appropriate to assess both the accuracy and precision of the two protocols. We demonstrate that while both methods produce results that are precise when 18S rRNA is used as an internal reference, only one of these methods produces consistently accurate results. We have used this latter system to show that mRNA abundances can be accurately measured and strongly correlate with cell surface protein and carbohydrate expression as assessed by flow cytometry under different conditions of B cell activation.

SAS programs for real-time RT-PCR having multiple independent samples.

Relative real-time reverse transcription PCR (RT-PCR) has become an important tool for quantifying changes in messenger RNA (mRNA) populations following differential development or stimulation of tissues or cells. However, the best methods for conducting such experiments and analyzing the resultant data remain an issue of discussion. In this report we describe an appropriate experimental methodology and the computer programs necessary to generate a meaningful statistical analysis of the combined biological and experimental variability in such experiments. Specifically, logarithmic transformations of raw fluorescence data from the log-linear portion of real-time PCR growth curves for both target and reference genes are analyzed using a SAS/STAT Mixed Procedure program specifically designed to give a point estimate of the relative expression ratio of the target gene with associated 95% confidence interval. The program code is open-source and is printed in the text.

Nectar quality perception by honey bees (Apis mellifera ligustica).

In exploring how foragers perceive rewards, we often find that well-motivated individuals are not too choosy and unmotivated individuals are unreliable and inconsistent. Nevertheless, when given a choice we see that individuals can clearly distinguish between rewards. Here we develop the logic of using responses to two-choice problems as a derivative function of perceived reward, and utilize this model to examine honey bee perception of nectar quality. Measuring the derivative allows us to deduce the perceived reward function. The derivative function of the perceived reward equation gives the rate of change of the reward perceived for each reward value. This approach depends on presenting free-flying foragers with a series of two different rewards presented simultaneously (i.e., two-choice, binomial tests). We also examine how honey bees integrate information from a range of reward qualities to formulate a functional response. Results suggest that honey bees overestimate higher quality rewards and that direct comparison is an important step in the integration of information from a range of rewards.