Robust RT-qPCR Data Normalization: Validation and Selection of Internal Reference Genes during Post-Experimental Data Analysis

Reverse transcription and real-time PCR (RT-qPCR) has been widely used for rapid quantification of relative gene expression. To offset technical confounding variations, stably-expressed internal reference genes are measured simultaneously along with target genes for data normalization. Statistic methods have been developed for reference validation; however normalization of RT-qPCR data still remains arbitrary due to pre-experimental determination of particular reference genes. To establish a method for determination of the most stable normalizing factor (NF) across samples for robust data normalization, we measured the expression of 20 candidate reference genes and 7 target genes in 15 Drosophila head cDNA samples using RT-qPCR. The 20 reference genes exhibit sample-specific variation in their expression stability. Unexpectedly the NF variation across samples does not exhibit a continuous decrease with pairwise inclusion of more reference genes, suggesting that either too few or too many reference genes may detriment the robustness of data normalization. The optimal number of reference genes predicted by the minimal and most stable NF variation differs greatly from 1 to more than 10 based on particular sample sets. We also found that GstD1, InR and Hsp70 expression exhibits an age-dependent increase in fly heads; however their relative expression levels are significantly affected by NF using different numbers of reference genes. Due to highly dependent on actual data, RT-qPCR reference genes thus have to be validated and selected at post-experimental data analysis stage rather than by pre-experimental determination

Abeta42-Induced Neurodegeneration via an Age-Dependent Autophagic-Lysosomal Injury in Drosophila

The mechanism of widespread neuronal death occurring in Alzheimer's disease (AD) remains enigmatic even after extensive investigation during the last two decades. Amyloid beta 42 peptide (Aβ1–42) is believed to play a causative role in the development of AD. Here we expressed human Aβ1–42 and amyloid beta 40 (Aβ1–40) in Drosophila neurons. Aβ1–42 but not Aβ1–40 causes an extensive accumulation of autophagic vesicles that become increasingly dysfunctional with age. Aβ1–42-induced impairment of the degradative function, as well as the structural integrity, of post-lysosomal autophagic vesicles triggers a neurodegenerative cascade that can be enhanced by autophagy activation or partially rescued by autophagy inhibition. Compromise and leakage from post-lysosomal vesicles result in cytosolic acidification, additional damage to membranes and organelles, and erosive destruction of cytoplasm leading to eventual neuron death. Neuronal autophagy initially appears to play a pro-survival role that changes in an age-dependent way to a pro-death role in the context of Aβ1–42 expression. Our in vivo observations provide a mechanistic understanding for the differential neurotoxicity of Aβ1–42 and Aβ1–40, and reveal an Aβ1–42-induced death execution pathway mediated by an age-dependent autophagic-lysosomal injury

SASqPCR: Robust and Rapid Analysis of RT-qPCR Data in SAS

Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list

Macro variables of SASqPCR.

<p>Macro variables of SASqPCR.</p

The analytic procedure of the example RT-qPCR data using SASqPCR.

<p>*The folder “X:\qPCR” in code #1, #2 and #3 needs to be changed to the appropriate path and filename so that SAS software can successfully access it. Input names of genes and samples must exactly match those in the original dataset. Please note that it is possible but not necessary to use the same Excel file to save the raw Ct data and exported results.</p

The workflow of SASqPCR.

<p>Raw Ct data is imported as a temporary SAS dataset. It is recommended, but not required, to check data integrity and variable consistency using proper SAS procedures. SASqPCR sequentially calculates PCR efficiency, expression stability of candidate reference genes, optimal reference genes, normalized relative expression of target genes, and makes statistical testing. Results are automatically exported and saved. The exported results from each analytical step may serve as a reference for assigning input macro variables for the next step in the workflow. Users can also customize their analyses by arbitrarily excluding particular genes and/or samples. The interface of different computational components allows users to get optimal results. The “user-defined operation” refers to any user-developed programs to extend the analytical function of SASqPCR. Exp., expression; Ref., reference.</p