We present data on cell-to-cell variability (“‘noise”') of gene expression in human cells, obtained through a combination of single-molecule mRNA FISH and single-cell volume measurements. We find that noise in terms of mRNA numbers exceeds the noise in terms of mRNA concentration. This study provides an improved method to determine gene expression noise.

Tumor evolution is shaped by many variables, potentially involving external selective pressures induced by therapies1. After surgery, estrogen receptor (ERα) positive breast cancer (BCa) patients are treated with adjuvant endocrine therapy2 including selective estrogen receptor modulators (SERMs) and/or aromatase inhibitors (AIs)3. However, over 20% of patients relapse within 10 years and eventually progress to incurable metastatic disease4. Here we demonstrate that the choice of therapy has a fundamental influence on the genetic landscape of relapsed diseases: in this study, 21.5% of AI-treated, relapsed patients had acquired CYP19A1 gene (aromatase) amplification (CYP19A1amp). Relapsed patients also developed numerous mutations targeting key breast cancer genes including ESR1 and CYP19A1. Strikingly, CYP19A1amp cells also emerge in vitro but only in AI resistant models. CYP19A1 amplification causes increased aromatase activity and estrogen-independent ERα binding to target genes resulting in CYP19A1amp cells displaying decreased sensitivity to AI treatment. Collectively these data suggest that AI treatment itself selects for acquired CYP19A1 amplification and promotes local autocrine estrogen signalling in AI resistant metastatic patients.

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.

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. STEM CELLS 2013;31:838-848 Disclosure of potential conflicts of interest is found at the end of this article.