Faculty Opinions recommendation of A single-molecule view of transcription reveals convoys of RNA polymerases and multi-scale bursting.

This book presents a comprehensive overview of the foundations of single-molecule studies, based on manipulation of the molecules and observation of these with fluorescent probes. It first discusses the forces present at the single-molecule scale, the methods to manipulate them, and their pros and cons. It goes on to present an introduction to single-molecule fluorescent studies based on a quantum description of absorption and emission of radiation due to Einstein. Various considerations in the study of single molecules are introduced (including signal to noise, non-radiative decay, triplet states, etc.) and some novel super-resolution methods are sketched. The elastic and dynamic properties of polymers, their relation to experiments on DNA and RNA, and the structural transitions observed in those molecules upon stretching, twisting, and unzipping are presented. The use of these single-molecule approaches for the investigation of DNA–protein interactions is highlighted via the study of DNA and RNA polymerases, helicases, and topoisomerases. Beyond the confirmation of expected mechanisms (e.g., the relaxation of DNA torsion by topoisomerases in quantized steps) and the discovery of unexpected ones (e.g., strand-switching by helicases, DNA scrunching by RNA polymerases, and chiral discrimination by bacterial topoII), these approaches have also fostered novel (third generation) sequencing technologies.

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A Novel Complex: A Quantum Dot Conjugated to an Active T7RNA Polymerase

Journal of Nanomaterials ◽

10.1155/2013/468105 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Mette Eriksen ◽

Peter Horvath ◽

Michael A. Sørensen ◽

Szabolcs Semsey ◽

Lene B. Oddershede ◽

...

Keyword(s):

Quantum Dot ◽

Rna Polymerase ◽

Single Molecule ◽

Rna Polymerases ◽

Fluorescent Signal ◽

Single Molecule Level ◽

Single Molecule Studies ◽

Novel Complex ◽

Luminescent Nanoparticle

To perform single-molecule studies of the T7RNA polymerase, it is crucial to visualize an individual T7RNA polymerase, for example, through a fluorescent signal. We present a novel complex combining two different molecular functions, an active T7RNA polymerase and a highly luminescent nanoparticle, a quantum dot. The complex has the advantage of both constituents: the complex can traffic along DNA and simultaneously be visualized, both at the ensemble and at the single-molecule level. The labeling was mediated through anin vivobiotinylation of a His-tagged T7RNA polymerase and subsequent binding of a streptavidin-coated quantum dot. Our technique allows for easy purification of the quantum dot labeled T7RNA polymerases from the reactants. Also, the conjugation does not alter the functionality of the polymerase; it retains the ability to bind and transcribe.

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Combining confocal and single molecule localisation microscopy: A correlative approach to multi-scale tissue imaging

Methods ◽

10.1016/j.ymeth.2015.03.011 ◽

2015 ◽

Vol 88 ◽

pp. 98-108 ◽

Cited By ~ 21

Author(s):

David J. Crossman ◽

Yufeng Hou ◽

Isuru Jayasinghe ◽

David Baddeley ◽

Christian Soeller

Keyword(s):

Single Molecule ◽

Tissue Imaging ◽

Multi Scale ◽

Localisation Microscopy

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Bayesian inference and comparison of stochastic transcription elongation models

10.1101/499277 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jordan Douglas ◽

Richard Kingston ◽

Alexei J. Drummond

Keyword(s):

Bayesian Inference ◽

Markov Process ◽

Single Molecule ◽

Continuous Time ◽

Transcription Elongation ◽

Rna Polymerases ◽

Partial Equilibrium ◽

Single Molecule Level ◽

Bayesian Techniques ◽

E Coli

AbstractTranscription elongation can be modelled as a three step process, involving polymerase translocation, NTP binding, and nucleotide incorporation into the nascent mRNA. This cycle of events can be simulated at the single-molecule level as a continuous-time Markov process using parameters derived from single-molecule experiments. Previously developed models differ in the way they are parameterised, and in their incorporation of partial equilibrium approximations.We have formulated a hierarchical network comprised of 12 sequence-dependent transcription elongation models. The simplest model has two parameters and assumes that both translocation and NTP binding can be modelled as equilibrium processes. The most complex model has six parameters makes no partial equilibrium assumptions. We systematically compared the ability of these models to explain published force-velocity data, using approximate Bayesian computation. This analysis was performed using data for the RNA polymerase complexes ofE. coli, S. cerevisiaeand Bacteriophage T7.Our analysis indicates that the polymerases differ significantly in their translocation rates, with the rates in T7 pol being fast compared toE. coliRNAP andS. cerevisiaepol II. Different models are applicable in different cases. We also show that all three RNA polymerases have an energetic preference for the posttranslocated state over the pretranslocated state. A Bayesian inference and model selection framework, like the one presented in this publication, should be routinely applicable to the interrogation of single-molecule datasets.Author summaryTranscription is a critical biological process which occurs in all living organisms. It involves copying the organism’s genetic material into messenger RNA (mRNA) which directs protein synthesis on the ribosome. Transcription is performed by RNA polymerases which have been extensively studied using both ensemble and single-molecule techniques (see reviews: [1, 2]). Single-molecule data provides unique insights into the molecular behaviour of RNA polymerases. Transcription at the single-molecule level can be computationally simulated as a continuous-time Markov process and the model outputs compared with experimental data. In this study we use Bayesian techniques to perform a systematic comparison of 12 stochastic models of transcriptional elongation. We demonstrate how equilibrium approximations can strengthen or weaken the model, and show how Bayesian techniques can identify necessary or unnecessary model parameters. We describe a framework to a) simulate, b) perform inference on, and c) compare models of transcription elongation.

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Cell scientist to watch – Verena Ruprecht

Journal of Cell Science ◽

10.1242/jcs.259094 ◽

2021 ◽

Vol 134 (14) ◽

Keyword(s):

Single Molecule ◽

Super Resolution ◽

Young Investigator ◽

Collaborative Project ◽

Johannes Kepler ◽

Multi Scale ◽

Amoeboid Cell ◽

Independent Laboratory ◽

Resolution Imaging ◽

Genomic Regulation

ABSTRACT Verena Ruprecht received her PhD in Biophysics from the Johannes Kepler University in 2010 for her work on developing single-molecule super-resolution imaging tools in the lab of Gerhard Schütz. Following a research visit in Didier Marguet's lab at the Centre d'Immunologie de Marseille-Luminy (CIML) in France, she moved to the Institute of Science and Technology (IST) in Austria for a postdoc, working jointly with Carl-Philipp Heisenberg and Michael Sixt. There, she discovered a unique amoeboid cell migration mode in early zebrafish embryos, termed stable-bleb migration. Verena started her independent laboratory at the Centre for Genomic Regulation (CRG) in Barcelona, Spain, in September 2016. Her group combines genetic and biophysical methods with multi-scale imaging and mathematical modelling to study cellular dynamics in embryo development. In 2020, Verena was selected as an EMBO Young Investigator and in the same year awarded an HFSP Young Investigator Grant for a collaborative project to study the biophysics of zebrafish fertilization.

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Mechanisms of backtrack recovery by RNA polymerases I and II

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1517011113 ◽

2016 ◽

Vol 113 (11) ◽

pp. 2946-2951 ◽

Cited By ~ 58

Author(s):

Ana Lisica ◽

Christoph Engel ◽

Marcus Jahnel ◽

Édgar Roldán ◽

Eric A. Galburt ◽

...

Keyword(s):

Optical Tweezers ◽

Single Molecule ◽

Stochastic Theory ◽

Rna Polymerases ◽

Rna Cleavage ◽

Cellular Functions ◽

Pol Ii ◽

Pol I ◽

Transcript Cleavage ◽

Polymerase I

During DNA transcription, RNA polymerases often adopt inactive backtracked states. Recovery from backtracks can occur by 1D diffusion or cleavage of backtracked RNA, but how polymerases make this choice is unknown. Here, we use single-molecule optical tweezers experiments and stochastic theory to show that the choice of a backtrack recovery mechanism is determined by a kinetic competition between 1D diffusion and RNA cleavage. Notably, RNA polymerase I (Pol I) and Pol II recover from shallow backtracks by 1D diffusion, use RNA cleavage to recover from intermediary depths, and are unable to recover from extensive backtracks. Furthermore, Pol I and Pol II use distinct mechanisms to avoid nonrecoverable backtracking. Pol I is protected by its subunit A12.2, which decreases the rate of 1D diffusion and enables transcript cleavage up to 20 nt. In contrast, Pol II is fully protected through association with the cleavage stimulatory factor TFIIS, which enables rapid recovery from any depth by RNA cleavage. Taken together, we identify distinct backtrack recovery strategies of Pol I and Pol II, shedding light on the evolution of cellular functions of these key enzymes.

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