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Parisutham V, Chhabra S, Ali MZ, Brewster RC. Tunable transcription factor library for robust quantification of regulatory properties in Escherichia coli. Mol Syst Biol 2022; 18:e10843. [PMID: 35694815 PMCID: PMC9189660 DOI: 10.15252/msb.202110843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/12/2022] Open
Abstract
Predicting the quantitative regulatory function of transcription factors (TFs) based on factors such as binding sequence, binding location, and promoter type is not possible. The interconnected nature of gene networks and the difficulty in tuning individual TF concentrations make the isolated study of TF function challenging. Here, we present a library of Escherichia coli strains designed to allow for precise control of the concentration of individual TFs enabling the study of the role of TF concentration on physiology and regulation. We demonstrate the usefulness of this resource by measuring the regulatory function of the zinc-responsive TF, ZntR, and the paralogous TF pair, GalR/GalS. For ZntR, we find that zinc alters ZntR regulatory function in a way that enables activation of the regulated gene to be robust with respect to ZntR concentration. For GalR and GalS, we are able to demonstrate that these paralogous TFs have fundamentally distinct regulatory roles beyond differences in binding affinity.
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Affiliation(s)
- Vinuselvi Parisutham
- Department of Systems BiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
| | - Shivani Chhabra
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Md Zulfikar Ali
- Department of Systems BiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
| | - Robert C Brewster
- Department of Systems BiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
- Department of Microbiology and Physiological SystemsUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
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2
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Gelber I. Variance reducing and noise correction in protein quantification by measuring fluctuations in fluorescence due to photobleaching. Phys Biol 2022; 19. [PMID: 35290963 DOI: 10.1088/1478-3975/ac5e0f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 03/15/2022] [Indexed: 11/11/2022]
Abstract
Quantifying the absolute protein number using the ratio between the variance and the mean of the protein Fluorescence intensity is a straightforward method for microscopy imaging. Recently, this method has been expanded to fluorescence decaying processes due to photobleaching with binomial distribution. The article examines the method proposed and shows how it can be adapted to the case of variance in the initial number of proteins between the cells. The article shows that the method can be improved by the implementation of the information processing of each frame independently from other frames. By doing so, the variance in determining the protein number can be reduced. In addition, the article examines the management of unwanted noises in the measurement, offers a solution for the shot noise and background noise, examines the expected error caused by the decay constant inaccuracy, and analyzes the expected difficulties in conducting a practical experiment, which includes a non-exponential decay and variance in the photobleaching rate of the cells. The method can be applied to any superposition of n_0 discrete decaying processes. However, the evaluation of expected errors in quantification is essential for early planning of the experimental conditions and evaluation of the error.
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Affiliation(s)
- Itay Gelber
- Department of Physics, Ben-Gurion University of the Negev, beer sheva, Beer-Sheva, 84105, ISRAEL
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3
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Hummert J, Yserentant K, Fink T, Euchner J, Ho YX, Tashev SA, Herten DP. Photobleaching step analysis for robust determination of protein complex stoichiometries. Mol Biol Cell 2021; 32:ar35. [PMID: 34586828 PMCID: PMC8693960 DOI: 10.1091/mbc.e20-09-0568] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022] Open
Abstract
The counting of discrete photobleaching steps in fluorescence microscopy is ideally suited to study protein complex stoichiometry in situ. The counting range of photobleaching step analysis has been significantly improved with more-sophisticated algorithms for step detection, albeit at an increasing computational cost and with the necessity for high-quality data. Here, we address concerns regarding robustness, automation, and experimental validation, optimizing both data acquisition and analysis. To make full use of the potential of photobleaching step analysis, we evaluate various labeling strategies with respect to their molecular brightness, photostability, and photoblinking. The developed analysis algorithm focuses on automation and computational efficiency. Moreover, we validate the developed methods with experimental data acquired on DNA origami labeled with defined fluorophore numbers, demonstrating counting of up to 35 fluorophores. Finally, we show the power of the combination of optimized trace acquisition and automated data analysis by counting labeled nucleoporin 107 in nuclear pore complexes of intact U2OS cells. The successful in situ application promotes this framework as a new resource enabling cell biologists to robustly determine the stoichiometries of molecular assemblies at the single-molecule level in an automated manner.
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Affiliation(s)
- Johan Hummert
- Institute of Physical Chemistry, Heidelberg University, D-69120 Heidelberg, Germany
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
| | - Klaus Yserentant
- Institute of Physical Chemistry, Heidelberg University, D-69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, D-69120 Heidelberg, Germany
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
| | - Theresa Fink
- Institute of Physical Chemistry, Heidelberg University, D-69120 Heidelberg, Germany
| | - Jonas Euchner
- Institute of Physical Chemistry, Heidelberg University, D-69120 Heidelberg, Germany
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
| | - Yin Xin Ho
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
| | - Stanimir Asenov Tashev
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
| | - Dirk-Peter Herten
- Institute of Physical Chemistry, Heidelberg University, D-69120 Heidelberg, Germany
- Institute of Cardiovascular Sciences, College of Medical and Dental Sciences & School of Chemistry, University of Birmingham, Birmingham, B152TT UK
- Centre of Membrane Proteins and Receptors (COMPARE), The Universities of Birmingham and Nottingham, The Midlands, Birmingham, B15 2TT UK
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Liu J, Hansen D, Eck E, Kim YJ, Turner M, Alamos S, Garcia HG. Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage. PLoS Comput Biol 2021; 17:e1008999. [PMID: 34003867 PMCID: PMC8162642 DOI: 10.1371/journal.pcbi.1008999] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 05/28/2021] [Accepted: 04/23/2021] [Indexed: 12/23/2022] Open
Abstract
The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.
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Affiliation(s)
- Jonathan Liu
- Department of Physics, University of California at Berkeley, Berkeley, California, United States of America
| | - Donald Hansen
- Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
| | - Elizabeth Eck
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Yang Joon Kim
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Meghan Turner
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
| | - Simon Alamos
- Department of Plant and Microbial Biology, University of California at Berkeley, Berkeley, California, United States of America
| | - Hernan G. Garcia
- Department of Physics, University of California at Berkeley, Berkeley, California, United States of America
- Biophysics Graduate Group, University of California at Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, California, United States of America
- Institute for Quantitative Biosciences-QB3, University of California at Berkeley, Berkeley, California, United States of America
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Tonn MK, Thomas P, Barahona M, Oyarzún DA. Computation of Single-Cell Metabolite Distributions Using Mixture Models. Front Cell Dev Biol 2020; 8:614832. [PMID: 33415109 PMCID: PMC7783310 DOI: 10.3389/fcell.2020.614832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 11/26/2020] [Indexed: 12/30/2022] Open
Abstract
Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.
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Affiliation(s)
- Mona K. Tonn
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Philipp Thomas
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Diego A. Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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Scales N, Swain PS. Resolving fluorescent species by their brightness and diffusion using correlated photon-counting histograms. PLoS One 2019; 14:e0226063. [PMID: 31887113 PMCID: PMC6936799 DOI: 10.1371/journal.pone.0226063] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 11/19/2019] [Indexed: 12/27/2022] Open
Abstract
Fluorescence fluctuation spectroscopy (FFS) refers to techniques that analyze fluctuations in the fluorescence emitted by fluorophores diffusing in a small volume and can be used to distinguish between populations of molecules that exhibit differences in brightness or diffusion. For example, fluorescence correlation spectroscopy (FCS) resolves species through their diffusion by analyzing correlations in the fluorescence over time; photon counting histograms (PCH) and related methods based on moment analysis resolve species through their brightness by analyzing fluctuations in the photon counts. Here we introduce correlated photon counting histograms (cPCH), which uses both types of information to simultaneously resolve fluorescent species by their brightness and diffusion. We define the cPCH distribution by the probability to detect both a particular number of photons at the current time and another number at a later time. FCS and moment analysis are special cases of the moments of the cPCH distribution, and PCH is obtained by summing over the photon counts in either channel. cPCH is inherently a dual channel technique, and the expressions we develop apply to the dual colour case. Using simulations, we demonstrate that two species differing in both their diffusion and brightness can be better resolved with cPCH than with either FCS or PCH. Further, we show that cPCH can be extended both to longer dwell times to improve the signal-to-noise and to the analysis of images. By better exploiting the information available in fluorescence fluctuation spectroscopy, cPCH will be an enabling methodology for quantitative biology.
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Affiliation(s)
- Nathan Scales
- Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
| | - Peter S. Swain
- Department of Physiology, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec H3G 1Y6, Canada
- School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh EH9 3BF, United Kingdom
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