Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described.qPCR Technique: The Perception and Reality qPCR (see Glossary) is generally viewed by researchers as a powerful technique that can provide precise and quantitative data reflecting the biology of the tested experimental parameters. However, without following strict guidelines, validation and data analysis procedures, the results can be far from valid [1,2]. Unfortunately, the adoption and transfer of inadequate and varied protocols between individual laboratory members and laboratories throughout the scientific community have led to frustration in reproducing data [3][4][5]. This has driven the production of the minimum information for publication of quantitative real-time PCR experiments (MIQE) guidelines and related methodology articles to help the scientific community in augmenting experimental rigor and uniformity to produce more reliable and consistent data [6][7][8]. Nevertheless, there remain concerns regarding the quality of qPCR results in the published literature [1,2].When designing experiments for qPCR, all protocols, such as sample handling, harvesting, nucleic acid extraction, reverse transcription, and qPCR should be described and vetted in detail. Mistakes or assumptions can be made in the planning process, resulting in a flawed experimental design with results and conclusions based on artefacts of pre and/or post sample handling procedures as opposed to the true effect of the tested experimental parameters [7]. Poorly optimized reactions can result in data that are consequent to a combination of sample contaminants and/or poor annealing temperature, leading to misinterpreted results and conclusions that are difficult or even impossible to reproduce [9,10].Despite the MIQE guidelines and other methodology articles, the variability and reproducibility pitfalls associated with qPCR remain elusive for many laboratories [7,11]. This review article describes the major sources of error associated with a qPCR experiment and strategies for their Highlights qPCR is more complex than perceived by many scientists.

BackgroundNeonatal lung injury, a leading cause of morbidity in prematurely born infants, has been associated with arrested alveolar development and is often accompanied by goblet cell hyperplasia. Genes that regulate alveolarization and inflammation are likely to contribute to susceptibility to neonatal lung injury. We previously cloned Lgl1, a developmentally regulated secreted glycoprotein in the lung. In rat, O2 toxicity caused reduced levels of Lgl1, which normalized during recovery. We report here on the generation of an Lgl1 knockout mouse in order to determine whether deficiency of Lgl1 is associated with arrested alveolarization and contributes to neonatal lung injury.MethodsAn Lgl1 knockout mouse was generated by introduction of a neomycin cassette in exon 2 of the Lgl1 gene. To evaluate the pulmonary phenotype of Lgl1+/- mice, we assessed lung morphology, Lgl1 RNA and protein, elastin fibers and lung function. We also analyzed tracheal goblet cells, and expression of mucin, interleukin (IL)-4 and IL-13 as markers of inflammation.ResultsAbsence of Lgl1 was lethal prior to lung formation. Postnatal Lgl1+/- lungs displayed delayed histological maturation, goblet cell hyperplasia, fragmented elastin fibers, and elevated expression of TH2 cytokines (IL-4 and IL-13). At one month of age, reduced expression of Lgl1 was associated with elevated tropoelastin expression and altered pulmonary mechanics.ConclusionOur findings confirm that Lgl1 is essential for viability and is required for developmental processes that precede lung formation. Lgl1+/- mice display a complex phenotype characterized by delayed histological maturation, features of inflammation in the post-natal period and altered lung mechanics at maturity. Lgl1 haploinsufficiency may contribute to lung disease in prematurity and to increased risk for late-onset respiratory disease.

Bronchopulmonary dysplasia (BPD), a major cause of morbidity in premature infants, is characterized by arrest of lung growth and inhibited alveologenesis. We had earlier cloned lategestation lung 1 (LGL1), a glucocorticoid (GC)-induced, developmentally regulated gene in lung mesenchyme, and showed that reduced levels of late-gestation lung 1 protein (lgl1) inhibit lung branching. Maximal fetal expression of LGL1 is concordant with the onset of alveolar septation, suggesting an additional role for lgl1 in alveologenesis. At postnatal d 7, during the period of maximal septation in postnatal rat lung, lgl1 concentrates at the tips of budding secondary alveolar septa. We studied two models of impaired postnatal alveologenesis generated by exposure of newborn rats to 60% O 2 for 2 wk or 95% O 2 for 1 wk. A profound decrease of lgl1 expression with oxygen exposure was observed in both animal models. Animals exposed to 95% O 2 for 1 wk recovered in air over a 3-wk period, associated with normalization of lgl1 levels. Changes in lung levels of ␣-actin (a marker of myofibroblast differentiation associated with alveologenesis) and the mesenchymal marker vimentin were significant but less marked. Our findings support a role for lgl1 in postnatal lung development. We speculate that deficiency of lgl1 contributes to the arrested alveolar partitioning observed in BPD and that recovery is associated with normalization of lgl1 levels.