The current research in the lab focuses on studying and predicting dynamic spatial-temporal processes in signal transduction and gene regulation by quantifying individual cells with single-molecule resolution. A current cellular biology topic of interest is how cells respond dynamically to changes in their environment utilizing their cellular gene, RNA and protein networks. We aim to approach this question by investigating endogenous, signal transduction and transcriptional regulatory networks of coding and non-coding RNA in yeast. Yeast is an ideal model organism for studying signal transduction and transcriptional regulatory networks because its components and genetics are well understood and can be easily manipulated. In addition many biophysical and biological principles are conserved in higher eukaryotic cells. We aim to investigate the architecture and functioning of these networks by measuring the dynamics of protein and RNA levels in single cells. Our research methods include a combination of single-cell techniques such as flow cytometry, live cell time-lapse microscopy, fluorescent in-situ hybridization with single-molecule resolution at the RNA level (single-molecule RNA-FISH) as well as single-molecule-based modeling. The main advantage to quantify single cells, is to distinguish between different regulatory mechanisms which can not be observed in population based experiments. Furthermore, this single cell data allows for developing better quantitative and predictive models as currently possible.
Dynamic signal transduction is a hallmark of life as signals outside and inside the cell are detected, processed and transduced into specific gene expression and cell phenotype outputs. The aim of this project is to investigate the dynamics in signal transduction and how it affects gene expression dynamics. Despite vast amounts of biochemical information, it remains difficult to understand and predict in-vivo responses of signal transduction and gene regulation pathways. We introduce a comprehensive approach, which integrates single-cell/single-molecule experiments with stochastic analysis, to identify predictive models of such processes. The identified model describes several novel dynamical features in transcriptional regulation in-vivo. Furthermore, we validate that the model accurately predicts the quantitative dynamics of cells in response to new environmental and genetic perturbations. Finally, since our approach is general, it can lead to similar predictive understanding for signal-activated transcription in any gene, pathway or organism ranging from yeast to human.
G. Neuert, et. al., "Systematic Identification of Signal-Activated Stochastic Gene Regulation", Science, 339 (6119), 584-587, 2013.
The sequencing of genomes from many different organisms has shown that a large fraction of the genome does not code for proteins alone but also does code for non-coding RNA molecules. Genome-wide studies have identified many long non-coding RNA molecules, yet very little is known about the function of these non-coding RNA molecules. We are investigating the transcriptional interference of long non-coding RNA strands in the regulation of development in single Saccharomyces cerevisiae yeast cells. Our single cell data demonstrate that the non-coding RNAs modulate the localization of key transcription factors, which influence the occurrence of downstream events that lead to active or silenced transcription. The combination of quantitative single-molecule RNA experiments in single cells with yeast genetics- and single-molecule-based modeling has enabled us to understand the molecular mechanism of transcriptional regulation of long non-coding RNA in greater detail.
van Werven, F.J., G. Neuert, et. al., "Transcription of two long non-coding RNAs mediates mating type control of gametogenesis in budding yeast", Cell, 150 (6), 1170–1181, 2012.
*Bumgarner, S. L., *G. Neuert, et. al. , “Single-cell analyses reveal that noncoding RNAs contribute to phenotypic heterogeneity in clonal populations by modulating transcription factor recruitment”, Molecular Cell, 45 (4), 1-13, 2012.
* Contributed equally
Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression ‘noise’, has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and mRNA levels, and have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive ‘fingerprint’ with which we explore fundamental questions on gene regulation.
*#Munsky, B., *#G. Neuert, and A. van Oudenaarden, "Using gene expression noise to understand gene regulation”, Science, 336 (6078), 183-187, 2012.
Neuert G, Munsky B, Tan RZ, Teytelman L, Khammash M, Van Oudenaarden A. Systematic identification of signal-activated stochastic gene regulation. Science (New York, N.Y.). 2013 Feb 1;339(6119). 584-7.
Munsky B, Fox Z, Neuert G. Integrating Single-Molecule Experiments and Discrete Stochastic Models to Understand Heterogeneous Gene Transcription Dynamics. Methods (San Diego, Calif.). 2015 Jun 12.
Munsky B, Neuert G. From Analog to Digital Gene Regulation Physical Biology. 2015 Jun 18;12(4). 045004 p.
Fox Z. Neuert G, Munsky B. Finite State Projection Based Bounds to Compare Chemical Master Equation Models Using Single-Cell Data Journal of Chemical Physics. 2016 Aug;7(145).
* Contributed equally
# Corresponding author