1
|
Beal J. Flow Cytometry Quantification of Transient Transfections in Mammalian Cells. Methods Mol Biol 2024; 2774:153-176. [PMID: 38441764 DOI: 10.1007/978-1-0716-3718-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Flow cytometry is a powerful quantitative assay supporting high-throughput collection of single-cell data with a high dynamic range. For flow cytometry to yield reproducible data with a quantitative relationship to the underlying biology, however, requires that (1) appropriate process controls are collected along with experimental samples, (2) these process controls are used for unit calibration and quality control, and (3) data are analyzed using appropriate statistics. To this end, this chapter describes methods for quantitative flow cytometry through the addition of process controls and analyses, thereby enabling better development, modeling, and debugging of engineered biological organisms. The methods described here have specifically been developed in the context of transient transfections in mammalian cells but may in many cases be adaptable to other categories of transfection and other types of cells.
Collapse
Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA.
| |
Collapse
|
2
|
Mager M, Pineda Hernandez H, Brandenburg F, López-Maury L, McCormick AJ, Nürnberg DJ, Orthwein T, Russo DA, Victoria AJ, Wang X, Zedler JAZ, Branco dos Santos F, Schmelling NM. Interlaboratory Reproducibility in Growth and Reporter Expression in the Cyanobacterium Synechocystis sp. PCC 6803. ACS Synth Biol 2023; 12:1823-1835. [PMID: 37246820 PMCID: PMC10278186 DOI: 10.1021/acssynbio.3c00150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Indexed: 05/30/2023]
Abstract
In recent years, a plethora of new synthetic biology tools for use in cyanobacteria have been published; however, their reported characterizations often cannot be reproduced, greatly limiting the comparability of results and hindering their applicability. In this interlaboratory study, the reproducibility of a standard microbiological experiment for the cyanobacterial model organism Synechocystis sp. PCC 6803 was assessed. Participants from eight different laboratories quantified the fluorescence intensity of mVENUS as a proxy for the transcription activity of the three promoters PJ23100, PrhaBAD, and PpetE over time. In addition, growth rates were measured to compare growth conditions between laboratories. By establishing strict and standardized laboratory protocols, reflecting frequently reported methods, we aimed to identify issues with state-of-the-art procedures and assess their effect on reproducibility. Significant differences in spectrophotometer measurements across laboratories from identical samples were found, suggesting that commonly used reporting practices of optical density values need to be supplemented by cell count or biomass measurements. Further, despite standardized light intensity in the incubators, significantly different growth rates between incubators used in this study were observed, highlighting the need for additional reporting requirements of growth conditions for phototrophic organisms beyond the light intensity and CO2 supply. Despite the use of a regulatory system orthogonal to Synechocystis sp. PCC 6803, PrhaBAD, and a high level of protocol standardization, ∼32% variation in promoter activity under induced conditions was found across laboratories, suggesting that the reproducibility of other data in the field of cyanobacteria might be affected similarly.
Collapse
Affiliation(s)
- Maurice Mager
- Institute
for Synthetic Microbiology, Heinrich Heine
University Duesseldorf, Universitaetsstrasse 1, 40225 Duesseldorf, Germany
| | - Hugo Pineda Hernandez
- Molecular
Microbial Physiology Group, Swammerdam Institute for Life Sciences,
Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Fabian Brandenburg
- Helmholtz
Centre for Environmental Research (UFZ), Permoserstrasse 15, 04318 Leipzig, Germany
| | - Luis López-Maury
- Instituto
de Bioquímica Vegetal y Fotosíntesis, University of Seville − CSIC, Américo Vespucio 49, 41092 Sevilla, Spain
- Departamento
de Bioquímica Vegetal y Biología Molecular, Facultad
de Biología, University of Seville, Avenida Reina Mercedes, 41012 Sevilla, Spain
| | - Alistair J. McCormick
- Institute
of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, 1.04 Daniel Rutherford Building, King’s
Buildings, EH9 3BF Edinburgh, U.K.
| | - Dennis J. Nürnberg
- Department
of Physics, Experimental Biophysics, Freie
University Berlin, Arnimallee
14, 14195 Berlin, Germany
- Dahlem
Centre of Plant Sciences, Freie Universität
Berlin, Albrecht-Thaer-Weg 6, 14195 Berlin, Germany
| | - Tim Orthwein
- Interfaculty
Institute of Microbiology and Infection Medicine, University of Tuebingen, Auf der Morgenstelle 28, 72076 Tübingen, Germany
| | - David A. Russo
- Institute
for Inorganic and Analytical Chemistry, Bioorganic Analytics, Friedrich Schiller University Jena, Lessingstrasse 8, 07743 Jena, Germany
| | - Angelo Joshua Victoria
- Institute
of Molecular Plant Sciences, School of Biological Sciences, University of Edinburgh, 1.04 Daniel Rutherford Building, King’s
Buildings, EH9 3BF Edinburgh, U.K.
| | - Xiaoran Wang
- Department
of Physics, Experimental Biophysics, Freie
University Berlin, Arnimallee
14, 14195 Berlin, Germany
| | - Julie A. Z. Zedler
- Matthias
Schleiden Institute for Genetics, Bioinformatics and Molecular Botany,
Synthetic Biology of Photosynthetic Organisms, Friedrich Schiller University Jena, Dornburgerstrasse 159, 07743 Jena, Germany
| | - Filipe Branco dos Santos
- Molecular
Microbial Physiology Group, Swammerdam Institute for Life Sciences,
Faculty of Science, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Nicolas M. Schmelling
- Institute
for Synthetic Microbiology, Heinrich Heine
University Duesseldorf, Universitaetsstrasse 1, 40225 Duesseldorf, Germany
| |
Collapse
|
3
|
Ba F, Ji X, Huang S, Zhang Y, Liu WQ, Liu Y, Ling S, Li J. Engineering Escherichia coli to Utilize Erythritol as Sole Carbon Source. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207008. [PMID: 36938858 DOI: 10.1002/advs.202207008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/16/2023] [Indexed: 05/18/2023]
Abstract
Erythritol, one of the natural sugar alcohols, is widely used as a sugar substitute sweetener in food industries. Humans themselves are not able to catabolize erythritol and their gut microbes lack related catabolic pathways either to metabolize erythritol. Here, Escherichia coli (E. coli) is engineered to utilize erythritol as sole carbon source aiming for defined applications. First, the erythritol metabolic gene cluster is isolated and the erythritol-binding transcriptional repressor and its DNA-binding site are experimentally characterized. Transcriptome analysis suggests that carbohydrate metabolism-related genes in the engineered E. coli are overall upregulated. In particular, the enzymes of transaldolase (talA and talB) and transketolase (tktA and tktB) are notably overexpressed (e.g., the expression of tktB is improved by nearly sixfold). By overexpression of the four genes, cell growth can be increased as high as three times compared to the cell cultivation without overexpression. Finally, engineered E. coli strains can be used as a living detector to distinguish erythritol-containing soda soft drinks and can grow in the simulated intestinal fluid supplemented with erythritol. This work is expected to inspire the engineering of more hosts to respond and utilize erythritol for broad applications in metabolic engineering, synthetic biology, and biomedical engineering.
Collapse
Affiliation(s)
- Fang Ba
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Xiangyang Ji
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Shuhui Huang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Yufei Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Wan-Qiu Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Yifan Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Shengjie Ling
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| | - Jian Li
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, P. R. China
| |
Collapse
|
4
|
González-Cebrián A, Borràs-Ferrís J, Boada Y, Vignoni A, Ferrer A, Picó J. PLATERO: A calibration protocol for plate reader green fluorescence measurements. Front Bioeng Biotechnol 2023; 11:1104445. [PMID: 36741754 PMCID: PMC9895789 DOI: 10.3389/fbioe.2023.1104445] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/09/2023] [Indexed: 01/22/2023] Open
Abstract
One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.
Collapse
Affiliation(s)
- Alba González-Cebrián
- Multivariate Statistical Engineering Group, Department of Applied Statistics and O.R. and Quality, Universitat Politècnica de València, València, Spain
| | - Joan Borràs-Ferrís
- Multivariate Statistical Engineering Group, Department of Applied Statistics and O.R. and Quality, Universitat Politècnica de València, València, Spain
| | - Yadira Boada
- Synthetic Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Alberto Ferrer
- Multivariate Statistical Engineering Group, Department of Applied Statistics and O.R. and Quality, Universitat Politècnica de València, València, Spain
| | - Jesús Picó
- Synthetic Biology and Biosystems Control Lab, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| |
Collapse
|
5
|
Beal J, Telmer CA, Vignoni A, Boada Y, Baldwin GS, Hallett L, Lee T, Selvarajah V, Billerbeck S, Brown B, Cai GN, Cai L, Eisenstein E, Kiga D, Ross D, Alperovich N, Sprent N, Thompson J, Young EM, Endy D, Haddock-Angelli T. Multicolor Plate Reader Fluorescence Calibration. Synth Biol (Oxf) 2022; 7:ysac010. [PMID: 35949424 PMCID: PMC9357555 DOI: 10.1093/synbio/ysac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 06/15/2022] [Accepted: 07/20/2022] [Indexed: 11/14/2022] Open
Abstract
Plate readers are commonly used to measure cell growth and fluorescence, yet the utility and reproducibility of plate reader data is limited by the fact that it is typically reported in arbitrary or relative units. We have previously established a robust serial dilution protocol for calibration of plate reader measurements of absorbance to estimated bacterial cell count and for green fluorescence from proteins expressed in bacterial cells to molecules of equivalent fluorescein. We now extend these protocols to calibration of red fluorescence to the sulforhodamine-101 fluorescent dye and blue fluorescence to Cascade Blue. Evaluating calibration efficacy via an interlaboratory study, we find that these calibrants do indeed provide comparable precision to the prior calibrants and that they enable effective cross-laboratory comparison of measurements of red and blue fluorescence from proteins expressed in bacterial cells.
Collapse
Affiliation(s)
- Jacob Beal
- Intelligent Software and Systems, Raytheon BBN Technologies , 10 Moulton Street, Cambridge 02138, MA, USA
| | - Cheryl A Telmer
- Department of Biological Sciences, Carnegie Mellon University , 4400 Fifth Avenue, Pittsburgh 15213, PA, USA
| | - Alejandro Vignoni
- Synthetic Biology and Biosystems Control Group, Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia , Camino de Vera s/n, Valencia 46022, Spain
| | - Yadira Boada
- Synthetic Biology and Biosystems Control Group, Instituto de Automatica e Informatica Industrial, Universitat Politecnica de Valencia , Camino de Vera s/n, Valencia 46022, Spain
| | - Geoff S Baldwin
- Department of Life Sciences, Imperial College London , South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Liam Hallett
- Department of Life Sciences, Imperial College London , South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Taeyang Lee
- Department of Life Sciences, Imperial College London , South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | | | - Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen , Nijenborgh 7, Groningen 9747 AG, The Netherlands
| | - Bradley Brown
- School of Engineering, Newcastle University , Devonshire Building, Devonshire Terrace, NE1 7RU Newcastle Upon Tyne, UK
| | - Guo-nan Cai
- School of Life Sciences, Fudan University , 220 Handan Road, Shanghai 200433, China
| | - Liang Cai
- School of Life Sciences, Fudan University , 220 Handan Road, Shanghai 200433, China
| | - Edward Eisenstein
- Fischell Department of Bioengineering, University of Maryland Institute of Bioscience and Biotechnology Research, , 9600 Gudelsky Drive, Rockville 20850, MD, USA
| | - Daisuke Kiga
- School of Advanced Science and Engineering, Waseda University , 2-2 Wakamatsu Cho, Totsukamachi, Shinjuku City 169-8050, Tokyo, Japan
| | - David Ross
- Material Measurement Laboratory, National Institute of Standards and Technology , 100 Bureau Dr., Gaithersburg 20899, MD, USA
| | - Nina Alperovich
- Material Measurement Laboratory, National Institute of Standards and Technology , 100 Bureau Dr., Gaithersburg 20899, MD, USA
| | - Noah Sprent
- Department of Chemical Engineering, Imperial College London , South Kensington Campus, Exhibition Road, London SW7 2AZ, UK
| | - Jaclyn Thompson
- Chemical Engineering, Worcester Polytechnic Institute , 100 Institute Road, Worcester 01609-2280, MA, USA
| | - Eric M Young
- Chemical Engineering, Worcester Polytechnic Institute , 100 Institute Road, Worcester 01609-2280, MA, USA
| | - Drew Endy
- Bioengineering, Stanford University , 443 Via Ortega, Stanford 94305, CA, USA
| | | |
Collapse
|
6
|
Zhang M, Holowko MB, Hayman Zumpe H, Ong CS. Machine Learning Guided Batched Design of a Bacterial Ribosome Binding Site. ACS Synth Biol 2022; 11:2314-2326. [PMID: 35704784 PMCID: PMC9295160 DOI: 10.1021/acssynbio.2c00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Optimization of gene expression levels is an essential part of the organism design process. Fine control of this process can be achieved by engineering transcription and translation control elements, including the ribosome binding site (RBS). Unfortunately, the design of specific genetic parts remains challenging because of the lack of reliable design methods. To address this problem, we have created a machine learning guided Design-Build-Test-Learn (DBTL) cycle for the experimental design of bacterial RBSs to demonstrate how small genetic parts can be reliably designed using relatively small, high-quality data sets. We used Gaussian Process Regression for the Learn phase of the cycle and the Upper Confidence Bound multiarmed bandit algorithm for the Design of genetic variants to be tested in vivo. We have integrated these machine learning algorithms with laboratory automation and high-throughput processes for reliable data generation. Notably, by Testing a total of 450 RBS variants in four DBTL cycles, we have experimentally validated RBSs with high translation initiation rates equaling or exceeding our benchmark RBS by up to 34%. Overall, our results show that machine learning is a powerful tool for designing RBSs, and they pave the way toward more complicated genetic devices.
Collapse
Affiliation(s)
- Mengyan Zhang
- Machine Learning and Artificial Intelligence Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.,Department of Computer Science, Australian National University, Canberra, ACT 2601, Australia.,Data61, CSIRO, Canberra, ACT 2601, Australia
| | - Maciej Bartosz Holowko
- Synthetic Biology Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.,Land and Water, CSIRO, Canberra, ACT 2601, Australia
| | - Huw Hayman Zumpe
- Synthetic Biology Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.,Land and Water, CSIRO, Canberra, ACT 2601, Australia
| | - Cheng Soon Ong
- Machine Learning and Artificial Intelligence Future Science Platform, CSIRO, Canberra, ACT 2601, Australia.,Department of Computer Science, Australian National University, Canberra, ACT 2601, Australia.,Data61, CSIRO, Canberra, ACT 2601, Australia
| |
Collapse
|
7
|
A FAIR-compliant parts catalogue for genome engineering and expression control in Saccharomyces cerevisiae. Synth Syst Biotechnol 2022; 7:657-663. [PMID: 35224233 PMCID: PMC8857431 DOI: 10.1016/j.synbio.2022.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/21/2022] [Accepted: 02/04/2022] [Indexed: 11/22/2022] Open
Abstract
The synthetic biology toolkit for baker's yeast, Saccharomyces cerevisiae, includes extensive genome engineering toolkits and parts repositories. However, with the increasing complexity of engineering tasks and versatile applications of this model eukaryote, there is a continued interest to expand and diversify the rational engineering capabilities in this chassis by FAIR (findable, accessible, interoperable, and reproducible) compliance. In this study, we designed and characterised 41 synthetic guide RNA sequences to expand the CRISPR-based genome engineering capabilities for easy and efficient replacement of genomically encoded elements. Moreover, we characterize in high temporal resolution 20 native promoters and 18 terminators using fluorescein and LUDOX CL-X as references for GFP expression and OD600 measurements, respectively. Additionally, all data and reported analysis is provided in a publicly accessible jupyter notebook providing a tool for researchers with low-coding skills to further explore the generated data as well as a template for researchers to write their own scripts. We expect the data, parts, and databases associated with this study to support a FAIR-compliant resource for further advancing the engineering of yeasts.
Collapse
|
8
|
|