Cell-to-Cell Transcription Variability as Measured by Single-Molecule RNA FISH to Detect Epigenetic State Switching.

Single-molecule RNA fluorescent in situ hybridization (smRNA FISH) allows for the visualization, localization, and quantification of RNA transcripts within individual cells and tissues using custom-designed fluorescently labeled oligonucleotide probes. Here we describe a protocol for the preparation, imaging, and analysis of a smRNA FISH experiment that can be applied to any RNA of choice. We also provide insights as to how this powerful tool can be used to study epigenetic regulation, for example, following the epigenetic editing of genes.

Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli.

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

Epigenetic imprinting during assisted reproductive technologies: The effect of temporal and cumulative fluctuations in methionine cycling on the DNA methylation state.

Assisted reproductive technology (ART) exposes gametes and embryos to an artificial environment that does not resemble the conditions of natural conception, and therefore might change epigenetic regulation of genes that are imprinted during development. In the present review, we discuss the relationship between susceptibility of specific genes to receive an altered epigenetic composition during ART processes, possibly via alterations in the biochemical folate and methionine cycle. We provide a comprehensive view of the current state of epigenetic patterning in ART-conceived healthy children and in Angelman syndrome (AS) and Beckwith-Wiedemann syndrome (BWS) patients. We illustrate that similar genes--that is, MEST, KCNQ1OT1, and IGF2--possess an altered DNA methylation profile in animal models, ART-conceived healthy children, and AS and BWS patients. The developmental stage at which these genes receive their epigenetic imprint appears to coincide with the specific moment that ART takes place. We highlight that ART procedures affect physiological levels of enzymes and substrates involved in the folate and methionine cycle thereby altering the DNA methylation state. Moreover, although the DNA methylation rate appears to be robust: (i) temporal imbalances coinciding with defined moments of epigenetic imprinting of specific genes affect the eventual DNA methylation state of those genes and (ii) cumulative ART effects on methionine and folate cycling can alter DNA methylation rates. These observations underscore the necessity to further investigate consequences of ART treatments on the epigenetic profile.

The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise.

Transcriptional stochasticity can be measured by counting the number of mRNA molecules per cell. Cell-to-cell variability is best captured in terms of concentration rather than molecule counts, because reaction rates depend on concentrations. We combined single-molecule mRNA counting with single-cell volume measurements to quantify the statistics of both transcript numbers and concentrations in human cells. We compared three cell clones that differ only in the genomic integration site of an identical constitutively expressed reporter gene. The transcript number per cell varied proportionally with cell volume in all three clones, indicating concentration homeostasis. We found that the cell-to-cell variability in the mRNA concentration is almost exclusively due to cell-to-cell variation in gene expression activity, whereas the cell-to-cell variation in mRNA number is larger, due to a significant contribution of cell volume variability. We concluded that the precise relationship between transcript number and cell volume sets the biological stochasticity of living cells. This study highlights the importance of the quantitative measurement of transcript concentrations in studies of cell-to-cell variability in biology.

Concise review: the dynamics of induced pluripotency and its behavior captured in gene network motifs.

The flexibility of cellular identity is clearly demonstrated by the possibility to reprogram fully differentiated somatic cells to induced pluripotent stem (iPS) cells through forced expression of a set of transcription factors. The generation of iPS cells is of great interest as they provide a tremendous potential for regenerative medicine and an attractive platform to investigate pluripotency. Despite having gathered much attention, the molecular details and responsible gene regulatory networks during the reprogramming process are largely unresolved. In this review, we analyze the sequence and dynamics of reprogramming to construct a timeline of the molecular events taking place during induced pluripotency. We use this timeline as a road map to explore the distinct phases of the reprogramming process and to suggest gene network motifs that are able to describe its systems behavior. We conclude that the gene networks involved in reprogramming comprise several feedforward loops combined with autoregulatory behavior and one or more AND gate motifs that can explain the observed dynamics of induced pluripotency. Our proposed timeline and derived gene network motif behavior could serve as a tool to understand the systems behavior of reprogramming and identify key transitions and/or transcription factors that influence somatic cell reprogramming. Such a systems biology strategy could provide ways to define and explore the use of additional regulatory factors acting at defined gene network motifs to potentially overcome the current challenges of inefficient, slow, and partial somatic cell reprogramming and hence set ground of using iPS cells for clinical and therapeutic use.