Single-cell genomics is an alluring area that holds the potential to change the way we understand cell populations. Due to the small amount of DNA within a single cell, whole-genome amplification becomes a mandatory step in many single-cell applications. Unfortunately, single-cell whole-genome amplification (scWGA) strategies suffer from several technical biases that complicate the posterior interpretation of the data. Here we compared the performance of six different scWGA methods (GenomiPhi, REPLIg, TruePrime, Ampli1, MALBAC, and PicoPLEX) after amplifying and low-pass sequencing the complete genome of 230 healthy/tumoral human cells. Overall, REPLIg outperformed competing methods regarding DNA yield, amplicon size, amplification breadth, amplification uniformity –being the only method with a random amplification bias–, and false single-nucleotide variant calls. On the other hand, non-MDA methods, and in particular Ampli1, showed less allelic imbalance and ADO, more reliable copy-number profiles and less chimeric amplicons. While no single scWGA method showed optimal performance for every aspect, they clearly have distinct advantages. Our results provide a convenient guide for selecting a scWGA method depending on the question of interest while revealing relevant weaknesses that should be considered during the analysis and interpretation of single-cell sequencing data.
PartitionTest test suite
Abstract We report an assay of egg-to-adult viability in full-sibling mutation accumulation (MA) lines derived from a completely homozygous population of Drosophila melanogaster and maintained for 210 generations. A simultaneous evaluation was also made of a large population derived from the same origin and maintained as a control for the same period. We also present computer simulations to explore the possible decline in viability of the control population due to mutation accumulation and the possible effect of selection within and between MA lines. For this purpose, we used two mutational models independent from the data analyzed and based on radically different assumptions. The first model implies a large number of mutations of small effect, whereas the second implies a much smaller number of mutations with much larger effects. The observed rate of decline in mean viability was very small but significant (0.077%). The rate of increase in among line variance (0.189 × 10−3) was similar to those obtained previously in the same lines. The simulation results indicated that a model of many mutations of small effect is incompatible with the evolution of the mean viability of the control and MA lines over generations, the distribution of line means after 210 generations of mutation accumulation, and the pattern of line extinction over generations. Basically, this model predicted a large drop in viability, both in the control and particularly the MA lines, that is not observed empirically. It also predicted a rate of line extinction too low in the early generations and too high in the later ones. In contrast, the model based on few mutations of large effect was generally consistent with all the observations.
With the use of ambulatory monitoring, a circadian blood pressure pattern has been shown to characterize normotensive as well as hypertensive pregnant women. However, the potential differences between healthy and complicated pregnancies in pulse pressure, an independent marker of cardiovascular risk in the general population, have not yet been investigated. We analyzed 2523 blood pressure series sampled for 48 hours once every 4 weeks from the first obstetric visit until delivery in 245 women with uncomplicated pregnancies, 140 with gestational hypertension, and 49 who developed preeclampsia. Compared with uncomplicated pregnancies, a statistically significant elevation in the 24-hour mean of pulse pressure is found in complicated pregnancies in all trimesters ( P <0.001). Results further indicate similar 24-hour mean of pulse pressure between gestational hypertension and preeclampsia in the first trimester of pregnancy ( P =0.158). The increase in pulse pressure among women who developed preeclampsia compared with women with gestational hypertension, although small, was statistically significant in the second trimester (1.4 mm Hg; P =0.010) and, to a larger extent, in the third trimester of pregnancy (1.8 mm Hg; P <0.001). The differences in pulse pressure between healthy and complicated pregnancies, observed already in the first trimester of gestation, are found when systolic and diastolic blood pressure for women with a later diagnosis of gestational hypertension or preeclampsia are within the accepted range of normotension. Moreover, ambulatory pulse pressure provides higher sensitivity than clinic measurements for the diagnosis of hypertension in pregnancy.
Hermida, R C; Calvo, C; Ayala, D E; Lopez, J E; Dominguez, M J; Covelo, M; Fontao, M J; Soler, R; Mojon, A; Fernandez, J R Author Information
Chaetominine is a cytotoxic alkaloid that has been demonstrated to promote apoptotic cell death in human leukemia K562 cells. In the present study, chaetominine inhibited K562 (IC<sub>50</sub> 34 nM) and SW1116 (IC<sub>50</sub> 46 nM) cell growth. However, it remains unclear whether the inhibition of cell growth is associated with the cell cycle. To assess this potential relationship, the effect of chaetominine on the cell cycle of K562 and SW1116 cells was examined. Chaetominine treatment caused cell apoptosis and G<sub>1</sub>-phase arrest in SW1116 cells. Conversely, K562 cells underwent S-phase arrest according to flow cytometric analysis. The present study also aimed to elucidate the molecular mechanisms underpinning cell cycle regulation following the incubation of the associated cells with chaetominine. Western blot and reverse transcription-quantitative polymerase chain reaction analyses suggested that chaetominine treatment facilitated the expression of p53, p21, checkpoint kinase 2 (Chk2) and phosphorylated ataxia telangiectasia mutated (p-ATM) and caused a reduction in the mRNA levels of cyclin E and cyclin-dependent kinases (CDKs) 2 and 4. These results suggest that chaetominine may be involved in the regulation of p53/p21 and ATM and Rad3-related (ATM)/Chk2 signaling in SW1116 cells. Previous studies have demonstrated that these signaling pathways are responsible for G<sub>1</sub>-phase arrest. Results of the present study demonstrated that the expression of p-ATR and Chk1 were increased in K562 cells. Additionally, cdc25A levels were decreased, while protein and gene expression levels of cyclin A and CDK2 were repressed. These results elucidated the role of chaetominine in in the regulation of ATR/cdc25A/Chk1 expression in K562 cells. These proteins are thus important determinants in the initiation of S-phase arrest. These data support the hypothesis that chaetominine is a potential anti-cancer therapeutic agent that targets the cell cycle.
In molecular evolution, computer simulations have been commonly used to understand the interactions among evolutionary processes, for hypothesis testing, to evaluate and compare different analytical methods, or to estimate evolutionary parameters. This chapter describes simulation algorithms for the evolution of coding sequences under different evolutionary scenarios, potential uses of these simulations, and current simulation software. In particular, it discusses forward-time simulations, simulation of coalescent histories, and simulation of codon substitutions.
We report an assay of egg‐to‐adult viability in full‐sibling mutation accumulation (MA) lines derived from a completely homozygous population of Drosophila melanogaster and maintained for 210 generations. A simultaneous evaluation was also made of a large population derived from the same origin and maintained as a control for the same period. We also present computer simulations to explore the possible decline in viability of the control population due to mutation accumulation and the possible effect of selection within and between MA lines. For this purpose, we used two mutational models independent from the data analyzed and based on radically different assumptions. The first model implies a large number of mutations of small effect, whereas the second implies a much smaller number of mutations with much larger effects. The observed rate of decline in mean viability was very small but significant (0.077%). The rate of increase in among line variance (0.189 X 10∼3) was similar to those obtained previously in the same lines. The simulation results indicated that a model of many mutations of small effect is incompatible with the evolution of the mean viability of the control and MA lines over generations, the distribution of line means after 210 generations of mutation accumulation, and the pattern of line extinction over generations. Basically, this model predicted a large drop in viability, both in the control and particularly the MA lines, that is not observed empirically. It also predicted a rate of line extinction too low in the early generations and too high in the later ones. In contrast, the model based on few mutations of large effect was generally consistent with all the observations.
