Stochastic gene expression - Within the confines of individual cells, minute changes in the concentration or spatial arrangement of molecular species can produce substantial effects. For example, a transcription factor equally prevalent in two isogenic cells might be bound to a promoter in one and unbound in another, subject to the dictates of statistical mechanics. Protein production would consequently begin in one cell and not the other, amplifying the fluctuation, and propelling each cell to a different fate. Identical genotype and an identical growth environment are thus insufficient to ensure that two cells will develop the same phenotypes. A major goal of our research has been to identify and differentiate between the myriad possible origins of this variability, understand which are biologically important, which are not, and to put firm numbers on each of them. In our lab we use budding yeast, C. elegans, and mammalian cells as experimental model systems.

Some papers from our lab on this topic:

Ni Ji, Teije Middelkoop, Remco Mentink, Marco Betist, Satto Tonegawa, Dylan Mooijman, Hendrik Korswagen, and Alexander van Oudenaarden. Feedback control of gene expression variability in the Caenorhabditis elegans Wnt pathway. Cell 155, 869880 (2013) [pdf].

Miaoqing Fang, Huangming Xie, Stephanie Dougan, Hidde Ploegh, and Alexander van Oudenaarden. Stochastic cytokine expression induces mixed T helper cell states. PLoS Biology 11: e1001618. doi:10.1371/journal.pbio.1001618 (2013) [pdf].

Gregor Neuert, Brian Munsky, Rui Zhen Tan, Leonid Teytelman, Mustafa Khammash, and Alexander van Oudenaarden. Systematic identification of signal-activated stochastic gene regulation. Science 339, 584 – 587 (2013) [pdf].

Brian Munsky, Gregor Neuert, and Alexander van Oudenaarden. Using gene expression noise to understand gene regulation. Science 336, 183 – 187 (2012) [pdf].

Arjun Raj, Scott Rifkin, Erik Andersen, and Alexander van Oudenaarden. Variability in gene expression underlies incomplete penetrance. Nature 135, 913 – 918 (2010) [pdf].

Arjun Raj and Alexander van Oudenaarden. Nature, nurture, or chance: Stochastic gene expression and its consequences. Cell 135, 216 – 226 (2008) [pdf].

Murat Acar, Jay Mettetal, and Alexander van Oudenaarden. Stochastic switching as a survival strategy in fluctuating environments. Nature Genetics 40, 471 – 475 (2008) [pdf].

Jeff Chabot, Juan Pedraza, Prashant Luitel, and Alexander van Oudenaarden. Stochastic gene expression out-of-steady-state in the cyanobacterial circadian clock. Nature 450, 1249 – 1252 (2007) [pdf].

Developing novel tools to quantify gene expression in single cells - As it has become increasingly apparent that gene expression in individual cells deviates significantly from the average behavior of cell populations, new methods that provide accurate integer counts of mRNA copy numbers in individual cells are needed.  Ideally, such methods should also reveal the intracellular locations of the mRNAs, as mRNA localization is often used by cells to spatially restrict the activity genes. We recently developed a method for imaging individual mRNA molecules in fixed cells by probing each mRNA species with 48 or more short, singly labeled oligonucleotide probes. This makes each mRNA molecule visible as a computationally identifiable fluorescent spot by fluorescence microscopy. We demonstrated simultaneous detection of three mRNA species in single cells and mRNA detection in yeast, nematodes, fruit fly wing discs, and mammalian cell lines and neurons.

Some papers from our lab on this topic:

Anna Lyubimova, Shalev Itzkovitz, Philipp Junker, Zi Fan, Xuebing Wu, and Alexander van Oudenaarden. Single-molecule mRNA detection and counting in mammalian tissue. Nature Protocols 8, 1743 – 1758 (2013) [pdf].

Clinton Hansen and Alexander van Oudenaarden. Allele-specific detection of single mRNA molecules in situ. Nature Methods 10, 869 – 871 (2013) [pdf].

Magda Bienko, Nicola Crosetto, Lenny Teytelman, Sandy Klemm, Shalev Itzkovitz, and Alexander van Oudenaarden. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH. Nature Methods 10, 122 – 124 (2013) [pdf].

Shalev Itzkovitz, Anna Lyubimova, Irene Blat, Mindy Maynard, Johan van Es, Jaqueline Lees, Tyler Jacks, Hans Clevers, and Alexander van Oudenaarden. Single-molecule transcript counting of stem-cell markers in the mouse intestine. Nature Cell Biology 14, 106 – 114 (2012) [pdf].

Shalev Itzkovitz and Alexander van Oudenaarden. Validating transcripts with probes and imaging technology. Nature Methods 8, S12 – S19 (2011) [pdf].

Arjun Raj, Scott Rifkin, Erik Andersen, and Alexander van Oudenaarden. Variability in gene expression underlies incomplete penetrance. Nature 135, 913 – 918 (2010) [pdf].

Arjun Raj and Alexander van Oudenaarden. Single-molecule approaches to stochastic gene expression. Annual Review of Biophysics 38, 255 – 270 (2009) [pdf].

Arjun Raj, Patrick van den Bogaard, Scott Rifkin, Alexander van Oudenaarden, and Sanjay Tyagi. Imaging individual mRNA molecules using multiple singly labeled probes. Nature Methods 5, 877 – 879 (2008) [pdf].

MicroRNAs - MicroRNAs are short, highly conserved non-coding RNA molecules that repress gene expression in a sequence-dependent manner. MicroRNAs regulate protein synthesis in the cell cytoplasm by promoting target mRNAs’ degradation and/or inhibiting their translation. Their importance is suggested by the predictions that each miRNA targets hundreds of genes and that the majority of protein-coding genes are miRNA targets; by their abundance, with some miRNAs expressed as high as 50,000 copies per cell; and by their sequence conservation, with some miRNAs conserved from sea urchins to humans. miRNAs can regulate a large variety of cellular processes, from differentiation and proliferation to apoptosis. miRNAs also confer robustness to systems by stabilizing gene expression during stress and in developmental transitions. In our lab we use a combination of quantitative single cell experiments and models to better understand microRNA regulation. We are particularly interested in how microRNAs can generate thresholds in target gene expression and mediate feedforward and feedback loops in gene networks.

Some papers from our lab on this topic:

Dong hyun Kim, Dominic Grün, and Alexander van Oudenaarden. Dampening of expression oscillations by synchronous regulation of a microRNA and its target. Nature Genetics 45, 1337 – 1344 (2013) [pdf].

Shankar Mukherji, Margaret Ebert, Grace Zheng, John Tsang, Phil Sharp, and Alexander van Oudenaarden. MicroRNAs can generate thresholds in target gene expression. Nature Genetics 43, 854 – 859 (2011) [pdf].

John Tsang, Margaret Ebert, and Alexander van Oudenaarden. Genome-wide dissection of microRNA functions and cotargeting networks using gene set signatures. Molecular Cell 38, 140 – 153 (2010) [pdf].

John Tsang, Jun Zhu, and Alexander van Oudenaarden. MicroRNA-mediated feedback and feedforward Molecular Cell 26, 753 – 767 (2007) [pdf].

Quantitative approaches to signal transduction in single cells - The mechanisms cells use to sense and respond to environmental changes include complicated systems of biochemical reactions that occur with rates spanning a wide dynamic range. Reactions can be fast, such as association and dissociation between a ligand and its receptor (<1 s), or slow, such as protein synthesis (>1000 s). Although a system may comprise hundreds of reactions, often only a few of them dictate the system dynamics. Unfortunately, identification of the dominant processes is often difficult, and many models instead incorporate knowledge of all reactions in the system. In our lab we take a complementary approach by using tools from systems engineering and control theory to determine the core network that underlies the observed dynamics. For example we have been exploring how oscillatory signals propagate through a signal transduction cascade, which allowed us to identify and to model concisely the interactions that dominate system dynamics. Furthermore we are interested in how feedback regulation in these signal transduction networks can provide perfect adaptation. For a perfectly adapting system the steady-state output of these networks is independent of steady-state input.

Some papers from our lab on this topic:

Gregor Neuert, Brian Munsky, Rui Zhen Tan, Leonid Teytelman, Mustafa Khammash, and Alexander van Oudenaarden. Systematic identification of signal-activated stochastic gene regulation. Science 339, 584 – 587 (2013) [pdf].

Shalev Itzkovitz, Irene Blat, Tyler Jacks, Hans Clevers, and Alexander van Oudenaarden. Optimality in the development of intestinal crypts. Cell 148, 608 – 619 (2012) [pdf].

Murat Acar, Bernardo Pando, Frances Arnold, Michael Elowitz, and Alexander van Oudenaarden. A general mechanism for network-dosage compensation in gene circuits. Science 329, 1656 – 1660 (2010) [pdf].

Hyun Youk and Alexander van Oudenaarden. Growth landscape formed by perception and import of glucose in yeast. Nature 462, 875 – 880 (2009) [pdf].

Dale Muzzey, Carlos Gomez-Uribe, Jerome Mettetal, and Alexander van Oudenaarden. A systems-level analysis of perfect adaptation in yeast osmoregulation. Cell 138, 160 – 171 (2009) [pdf].

Jerome Mettetal, Dale Muzzey, Carlos Gómez-Uribe, and Alexander van Oudenaarden. The frequency dependence of osmo-adaptation in Saccharomyces cerevisiae. Science 319, 482 – 484 (2008) [pdf].

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