Steroid receptors coordinate a diverse range of responses in higher eukaryotes and are involved in a wide range of human diseases. Steroid receptor response elements are present throughout the human genome and modulate chromatin remodeling and transcription in both a local and long-range fashion. As such, steroid receptor-mediated transcription is a paradigm of genetic control in the metazoan nucleus. Moreover, the ligand-dependent nature of these transcription factors makes them appealing targets for therapeutic intervention, necessitating a quantitative understanding of how receptors control output from target genes. The classic sigmoidal dose response of steroid-regulated gene products belies the complexity of a system which relies on an intricate, multi-step sequence of events to initiate transcription from a target gene. Depending on the particular steroid receptor, a partial list of events required to activate the target gene includes ligand-binding, receptor dimerization, nuclear translocation, eviction of co-repressors (i.e. histone deacetylases), recruitment of chromatin modifying enzymes (histone acetyltransferases, ATP-dependent remodeling enzymes, methyltransferases), and eventually recruitment of the basal transcription machinery. Despite this complexity, the dose-response is deceptively simple: it often takes the form of a simple Hill function with a Hill coefficient of unity, which has traditionally been interpreted to imply that ligand-binding is the only rate-limiting step in the activation pathway. This description models the dose response as a continuum where each cell transcribes RNA at rates proportional to the dosage level.However, single-cell studies of gene expression demonstrate that this description is incorrect. First, gene expression is not uniform over a population for a given dose, but shows variation from cell to cell, an observation which was first made for a steroid-responsive MMTV reporter gene. The authors demonstrated that the observed analog dose response was in fact a digital dose response (on or off) when viewed in single cells. Likewise, enhancers increase the probability of activation of a cell but not the strength of activation in the cell. Second, expression is only a snapshot of temporally evolving gene activity. Thus, a cell is counted as activated or not activated dependent on the moment it is observed. Third, since molecules involved in regulating transcription are usually present at low copy number, this leads to stochastic fluctuations (?noise?) and hence gene expression variation across the population. Finally, dynamic interactions between upstream regulators and chromatin add another level of complexity to the molecular events occurring during transcriptional activation. Under such conditions, the observed dose response does not result solely from ligand-binding but rather the composite result derived from many coupled reactions. In summary, population models of gene activation are too coarse to explain activation in single cells. Moreover, tools do not exist whereby the activity of single genes in single cells can be directly manipulated and measured.In FY12, we developed an approach for activating a steroid-receptor in order to achieve high temporal and spatial precision and directly measure the activity of a responsive gene in the same cell over time. In contrast to the ensemble thermodynamic approach derived from observations of cell populations, we have developed a single-molecule kinetic approach for interrogation of a single gene. This approach is based on photoactivation of a steroid receptorligand followed by observation of pre-mRNA synthesis at an active locus. The system consists of an exogenous reporter gene under control of the ecdysone receptor which is activated by the agonist ponasterone A. The real-time behavior of the gene is visualized using a bacteriophage capsid protein which binds MS2 RNA stem loops with high affinity to label nascent pre-mRNA in living cells. We demonstrate experimentally how the ensemble steroid dose response arises from the stochastic behavior of individual genes. These results suggests that the nuclear response element controls the frequency of gene activity but effects neither the duration of the active period nor the actual rate of transcripts produced during an active period. By using a caged ligand that could be uncaged by a light pulse we measured the impulse-response of the gene and determined that a single pulse of active ligand resulted in a corresponding burst of polymerase activity several hours later. Further, this photoactivatable ligand has the property of being an antagonist in the caged state and an agonist in the uncaged state, enabling a precise window for kinetic perturbation in single cells. Thus, we were able to propose and validate a stochastic model of steroid-receptor activity at the gene that provided a new framework for studying this ubiquitous mechanism of eukaryotic gene regulation and the temporal boundaries of chromatin remodeling.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
Application #
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
National Cancer Institute Division of Basic Sciences
Zip Code
Patange, Simona; Girvan, Michelle; Larson, Daniel R (2018) Single-cell systems biology: probing the basic unit of information flow. Curr Opin Syst Biol 8:7-15
Wan, Yihan; Larson, Daniel R (2018) Splicing heterogeneity: separating signal from noise. Genome Biol 19:86
Das, Satarupa; Parker, Joshua M; Guven, Can et al. (2017) Adenylyl cyclase mRNA localizes to the posterior of polarized DICTYOSTELIUM cells during chemotaxis. BMC Cell Biol 18:23
Hendy, Oliver; Campbell Jr, John; Weissman, Jocelyn D et al. (2017) Differential context-specific impact of individual core promoter elements on transcriptional dynamics. Mol Biol Cell 28:3360-3370
Ren, Gang; Jin, Wenfei; Cui, Kairong et al. (2017) CTCF-Mediated Enhancer-Promoter Interaction Is a Critical Regulator of Cell-to-Cell Variation of Gene Expression. Mol Cell 67:1049-1058.e6
Chen, Huimin; Larson, Daniel R (2016) What have single-molecule studies taught us about gene expression? Genes Dev 30:1796-810
Lenstra, Tineke L; Rodriguez, Joseph; Chen, Huimin et al. (2016) Transcription Dynamics in Living Cells. Annu Rev Biophys 45:25-47
Lenstra, Tineke L; Larson, Daniel R (2016) Single-Molecule mRNA Detection in Live Yeast. Curr Protoc Mol Biol 113:14.24.1-14.24.15
Coulon, A; Larson, D R (2016) Fluctuation Analysis: Dissecting Transcriptional Kinetics with Signal Theory. Methods Enzymol 572:159-91
Palangat, Murali; Larson, Daniel R (2016) Single-gene dual-color reporter cell line to analyze RNA synthesis in vivo. Methods 103:77-85

Showing the most recent 10 out of 22 publications