Skip to main content

Transcription on the single-cell and single-molecule level – understanding and controlling it

Principal Supervisor: Dr. Daniel Hebenstreit, School of Life Sciences

Co-supervisor: Dr. Louise Dyson, Warwick Mathematics Institute

PhD project title: Transcription on the single-cell and single-molecule level – understanding and controlling it

University of Registration: University of Warwick

Project outline:


Biology is characterized by fluctuations and variability on many levels, both over time and across stationary ensembles. Many quantities such as the cell size or the numbers of mRNAs and proteins differ widely among the cells of otherwise identical populations.

The amount of this stochastic variation is frequently quantified as ‘noise’ by the coefficient of variation (CV) or similar statistical properties of the single-cell distributions [1, 2]. Biological noise has received much attention recently and was found to greatly impact many systems. Several works demonstrate functional roles for stochastic variation [3, 4] while control theoretical considerations show that it is difficult to completely suppress fluctuations [5].

Due to its central role in biology, transcription is of particular interest as a source of noise and also as being affected by it. Single-cell mRNA distributions have been studied in various different systems (e.g. [6-8]). Attempts have been made to mathematically explain the shapes of these distributions based on the biological mechanisms and processes associated with transcription (e.g. [9, 10]). The noise resulting from transcription is inherited by the translation machinery and can be buffered or further amplified under various conditions [11-13]. A central finding has been the distinction of intrinsic and extrinsic contributions to noise [11, 13, 14].

Intrinsic factors are usually understood as those affecting the noise in single genes only, exhibiting limited correlations with other processes in the cell. A classical intrinsic noise source is the dependence of gene regulatory mechanisms on low molecule numbers and the stochastic nature of biochemical reactions under such conditions [15, 16]. Another important intrinsic factor concerns the dynamics of transcription, which have been found to be ‘burst’-like in most studied species, from bacteria [10, 17] to mammalian cells [16, 18-20]. Transcriptional bursting reflects stochastic switching of a gene’s state between ‘off’ and ‘on’, with transcription occurring as short, intense bursts during the ‘on’ phase only [21]. The mechanistic background of the irregular bursts is still somewhat unclear and proposed explanations include changing chromatin status [9], DNA supercoiling [22], and others [23].

Extrinsic noise sources usually correspond to more general factors that affect the state of a cell and thus produce correlations among different genes or other processes. A major factor is cell division, as proteins are randomly split among daughter cells, as the cell volume changes, as a varying growth rate dynamically changes the number of proteins such as RNA polymerase that are available for transcription, and as DNA replication during S-phase potentially alters the chromatin state and thus the number of active genes [11, 24-26]. Fluctuations in these factors also occur independently from the cell cycle and further complicate the picture.

It is currently an unsolved problem to understand how all these factors act jointly to produce certain distributions of mRNA numbers for different genes.


The central goal of the project will be to analyse and quantify all known major contributors to transcriptional single cell variation in a mammalian cell line, including intrinsic and extrinsic factors. A second, synthetic biology-focused objective will be to use this knowledge and specifically engineer changes in transcriptional dynamics and mRNA distributions by targeting the identified factors in bespoke ways.


The project will be developed in three major phases:

1. Data collection

We will employ a combination of next generation sequencing (Dropseq [27]/single-cell RNA-seq, ChIP-seq) and flow cytometry to measure transcript numbers at single cell resolution, along with chromatin status and extrinsic factors (cell size, cell cycle phase).

2. Analysis

We will use bioinformatics approaches to integrate the datasets obtained in the first phase, and to quantify and visualize dependencies among the different factors.

3. Verification/ Engineering

We will employ genome-editing by CRISPR/Cas9 to alter cellular pathways in line with the findings from phases 1 & 2. This will verify the results and convert these into engineering approaches for synthetic biology undertakings.

BBSRC Strategic Research Priority: Molecules, Cells and Systems

Techniques that will be undertaken during the project:

  • Flow cytometry
  • Dropseq (single cell RNA-sequencing)
  • ChIP-seq
  • Bioinformatics
  • Possibly model fitting
  • Genome-editing (CRISPR/Cas9)
  • Cell culture
  • Standard molecular biology (clonging, qPCR)


  • Paulsson, J., Nature, 2004. 427(6973): p. 415.
  • Wilkinson, D.J., Nat Rev Genet, 2009. 10(2): p. 122.
  • Eldar, A., et al., Nature, 2010. 467(7312): p. 167.
  • Balazsi, G., et al., Cell, 2011. 144(6): p. 910.
  • Lestas, I., et al., Nature, 2010. 467(7312): p. 174.
  • Zenklusen, D., et al., Nat Struct Mol Biol, 2008. 15(12): p. 1263.
  • Yunger, S., et al., Nat Methods, 2010. 7(8): p. 631.
  • Dar, R.D., et al., Proc Natl Acad Sci U S A, 2012. 109(43): p. 17454.
  • Raser, J.M., et al., Science, 2004. 304(5678): p. 1811.
  • So, L.H., et al., Nat Genet, 2011. 43(6): p. 554.
  • Paulsson, J., Physics of Life Reviews, 2005. 2(2): p. 157.
  • Pedraza, J.M., et al., Science, 2008. 319(5861): p. 339.
  • Newman, J.R., et al., Nature, 2006. 441(7095): p. 840.
  • Swain, P.S., et al., Proc Natl Acad Sci U S A, 2002. 99(20): p. 12795.
  • Kepler, T.B., et al., Biophys J, 2001. 81(6): p. 3116.
  • Raj, A., et al., PLoS Biol, 2006. 4(10): p. e309.
  • Golding, I., et al., Cell, 2005. 123(6): p. 1025.
  • Suter, D.M., et al., Science, 2011. 332(6028): p. 472.
  • Hebenstreit, D., et al., Mol Syst Biol, 2011. 7: p. 497.
  • Singh, A., et al., Biophys J, 2010. 98(8): p. L32.
  • Munsky, B., et al., Science, 2012. 336(6078): p. 183.
  • Chong, S., et al., Cell, 2014. 158(2): p. 314.
  • Suter, D.M., et al., Curr Opin Cell Biol, 2011. 23(6): p. 657.
  • Zopf, C.J., et al., PLoS Comput Biol, 2013. 9(7): p. e1003161.
  • Huh, D., et al., Proc Natl Acad Sci U S A, 2011. 108(36): p. 15004.
  • Ramaswamy, R., et al., Nat Commun, 2012. 3: p. 779.
  • Klein, A.M., et al., Cell, 2015. 161(5): p. 1187.

Contact: Dr. Daniel Hebenstreit, School of Life Sciences