Do binucleate cardiomyocytes have a role in myocardial repair? Insights using isolated rodent myocytes and cell culture.

Neonatal and adult cardiomyocytes were isolated from rat hearts. Some of the adult myocytes were cultured to allow for cell dedifferentiation, a phenomenon thought to mimic cell changes that occur in stressed myocardium, with myocytes regressing to a fetal pattern of metabolism and stellate neonatal shape.Using fluorescence deconvolution microscopy, cells were probed with fluorescent markers and scanned for a number of proteins associated with ion control, calcium movements and cardiac function. Image analysis of deconvoluted image stacks and sequential real-time image recordings of calcium transients of cells were made.All three myocyte groups were predominantly comprised of binucleate cells. Clustering of proteins to a single nucleus was a common observation, suggesting that one nucleus is active in protein synthesis pathways, while the other nucleus assumes a 'dormant' or different role and that cardiomyocytes might be mitotically active even in late development, or specific protein syntheses could be targeted and regulated for reintroduction into the cell cycle.Such possibilities would extend cardiac disease associated stem cell research and therapy options, while producing valuable insights into developmental and death pathways of binucleate cardiomyocytes (word count 183).

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^