1
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Scott H, Occhialini A, Lenaghan SC, Beal J. Simulations predict stronger CRISPRi transcriptional repression in plants for identical than heterogeneous gRNA target sites. Synth Biol (Oxf) 2025; 10:ysae020. [PMID: 40255684 PMCID: PMC12007490 DOI: 10.1093/synbio/ysae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 09/26/2024] [Accepted: 03/17/2025] [Indexed: 04/22/2025] Open
Abstract
Plant synthetic biologists have been working to adapt the CRISPRa and CRISPRi promoter regulation methods for applications such as improving crops or installing other valuable pathways. With other organisms, strong transcriptional control has typically required multiple gRNA target sites, which poses a critical engineering choice between heterogeneous sites, which allow each gRNA to target existing locations in a promoter, and identical sites, which typically require modification of the promoter. Here, we investigate the consequences of this choice for CRISPRi plant promoter regulation via simulation-based analysis, using model parameters based on single gRNA regulation and constitutive promoters in Nicotiana benthamiana and Arabidopsis thaliana. Using models of 2-6 gRNA target sites to compare heterogeneous versus identical sites for tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation, we find that identical gRNA target sites are predicted to yield far more effective transcriptional repression than heterogeneous sites.
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Affiliation(s)
- Helen Scott
- Intelligent Software and Systems, RTX BBN Technologies, 10 Moulton St., Cambridge, MA 02138, USA
| | - Alessandro Occhialini
- Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USA
- Center for Agricultural Synthetic Biology (CASB), University of Tennessee, Knoxville, TN 37996, USA
| | - Scott C Lenaghan
- Center for Agricultural Synthetic Biology (CASB), University of Tennessee, Knoxville, TN 37996, USA
- Department of Food Science, University of Tennessee, Knoxville, TN 37996, USA
| | - Jacob Beal
- Intelligent Software and Systems, RTX BBN Technologies, 10 Moulton St., Cambridge, MA 02138, USA
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2
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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.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA.
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3
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Pfotenhauer AC, Occhialini A, Nguyen MA, Scott H, Dice LT, Harbison SA, Li L, Reuter DN, Schimel TM, Stewart CN, Beal J, Lenaghan SC. Building the Plant SynBio Toolbox through Combinatorial Analysis of DNA Regulatory Elements. ACS Synth Biol 2022; 11:2741-2755. [PMID: 35901078 PMCID: PMC9396662 DOI: 10.1021/acssynbio.2c00147] [Citation(s) in RCA: 6] [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
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While the installation of complex genetic circuits in
microorganisms
is relatively routine, the synthetic biology toolbox is severely limited
in plants. Of particular concern is the absence of combinatorial analysis
of regulatory elements, the long design-build-test cycles associated
with transgenic plant analysis, and a lack of naming standardization
for cloning parts. Here, we use previously described plant regulatory
elements to design, build, and test 91 transgene cassettes for relative
expression strength. Constructs were transiently transfected into Nicotiana benthamiana leaves and expression of a
fluorescent reporter was measured from plant canopies, leaves, and
protoplasts isolated from transfected plants. As anticipated, a dynamic
level of expression was achieved from the library, ranging from near
undetectable for the weakest cassette to a ∼200-fold increase
for the strongest. Analysis of expression levels in plant canopies,
individual leaves, and protoplasts were correlated, indicating that
any of the methods could be used to evaluate regulatory elements in
plants. Through this effort, a well-curated 37-member part library
of plant regulatory elements was characterized, providing the necessary
data to standardize construct design for precision metabolic engineering
in plants.
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Affiliation(s)
- Alexander C Pfotenhauer
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Alessandro Occhialini
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Mary-Anne Nguyen
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Helen Scott
- Intelligent Software and Systems, Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Lezlee T Dice
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Stacee A Harbison
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Li Li
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - D Nikki Reuter
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - Tayler M Schimel
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
| | - C Neal Stewart
- Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States.,Department of Plant Sciences, University of Tennessee Knoxville, 2431 Joe Johnson Dr., Knoxville, Tennessee 37996, United States
| | - Jacob Beal
- Intelligent Software and Systems, Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Scott C Lenaghan
- Department of Food Science, University of Tennessee Knoxville, 102 Food Safety and Processing Building 2600 River Dr., Knoxville, Tennessee 37996, United States.,Center for Agricultural Synthetic Biology, University of Tennessee Institute of Agriculture, Knoxville, Tennessee 37996, United States
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4
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Beal J, Teague B, Sexton JT, Castillo-Hair S, DeLateur NA, Samineni M, Tabor JJ, Weiss R. Meeting Measurement Precision Requirements for Effective Engineering of Genetic Regulatory Networks. ACS Synth Biol 2022; 11:1196-1207. [PMID: 35156365 DOI: 10.1021/acssynbio.1c00488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Reliable, predictable engineering of cellular behavior is one of the key goals of synthetic biology. As the field matures, biological engineers will become increasingly reliant on computer models that allow for the rapid exploration of design space prior to the more costly construction and characterization of candidate designs. The efficacy of such models, however, depends on the accuracy of their predictions, the precision of the measurements used to parametrize the models, and the tolerance of biological devices for imperfections in modeling and measurement. To better understand this relationship, we have derived an Engineering Error Inequality that provides a quantitative mathematical bound on the relationship between predictability of results, model accuracy, measurement precision, and device characteristics. We apply this relation to estimate measurement precision requirements for engineering genetic regulatory networks given current model and device characteristics, recommending a target standard deviation of 1.5-fold. We then compare these requirements with the results of an interlaboratory study to validate that these requirements can be met via flow cytometry with matched instrument channels and an independent calibrant. On the basis of these results, we recommend a set of best practices for quality control of flow cytometry data and discuss how these might be extended to other measurement modalities and applied to support further development of genetic regulatory network engineering.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Brian Teague
- Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - John T. Sexton
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | | | - Nicholas A. DeLateur
- Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Meher Samineni
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Jeffrey J. Tabor
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
| | - Ron Weiss
- Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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5
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Bryce D, Goldman RP, DeHaven M, Beal J, Bartley B, Nguyen TT, Walczak N, Weston M, Zheng G, Nowak J, Lee P, Stubbs J, Gaffney N, Vaughn MW, Myers CJ, Moseley RC, Haase S, Deckard A, Cummins B, Leiby N. Round Trip: An Automated Pipeline for Experimental Design, Execution, and Analysis. ACS Synth Biol 2022; 11:608-622. [PMID: 35099189 DOI: 10.1021/acssynbio.1c00305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human-machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for high-throughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.
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Affiliation(s)
- Daniel Bryce
- SIFT, LLC., Minneapolis, Minnesota 55401, United States
| | | | | | - Jacob Beal
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Bryan Bartley
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Tramy T. Nguyen
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Nicholas Walczak
- Raytheon BBN Technologies, Cambridge, Massachusetts 02138, United States
| | - Mark Weston
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - George Zheng
- Netrias, Inc., Annapolis, Maryland 21409, United States
| | - Josh Nowak
- Strateos, Inc., Menlo Park, California 94025, United States
| | - Peter Lee
- Ginkgo Bioworks, Inc., Boston, Massachusetts 02210, United States
| | - Joe Stubbs
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | - Niall Gaffney
- Texas Advanced Computing Center, Austin, Texas 78758, United States
| | | | | | | | - Steven Haase
- Duke University, Durham, North Carolina 27708, United States
| | - Anastasia Deckard
- Geometric Data Analytics, Inc., Durham, North Carolina 27701, United States
| | - Bree Cummins
- Montana State University, Bozeman, Montana 59717, United States
| | - Nick Leiby
- Two Six Technologies, Inc., Arlington, Virginia 22203, United States
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6
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Gupta D, Sharma G, Saraswat P, Ranjan R. Synthetic Biology in Plants, a Boon for Coming Decades. Mol Biotechnol 2021; 63:1138-1154. [PMID: 34420149 DOI: 10.1007/s12033-021-00386-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 08/16/2021] [Indexed: 02/01/2023]
Abstract
Recently an enormous expansion of knowledge is seen in various disciplines of science. This surge of information has given rise to concept of interdisciplinary fields, which has resulted in emergence of newer research domains, one of them is 'Synthetic Biology' (SynBio). It captures basics from core biology and integrates it with concepts from the other areas of study such as chemical, electrical, and computational sciences. The essence of synthetic biology is to rewire, re-program, and re-create natural biological pathways, which are carried through genetic circuits. A genetic circuit is a functional assembly of basic biological entities (DNA, RNA, proteins), created using typical design, built, and test cycles. These circuits allow scientists to engineer nearly all biological systems for various useful purposes. The development of sophisticated molecular tools, techniques, genomic programs, and ease of nucleic acid synthesis have further fueled several innovative application of synthetic biology in areas like molecular medicines, pharmaceuticals, biofuels, drug discovery, metabolomics, developing plant biosensors, utilization of prokaryotic systems for metabolite production, and CRISPR/Cas9 in the crop improvement. These applications have largely been dominated by utilization of prokaryotic systems. However, newer researches have indicated positive growth of SynBio for the eukaryotic systems as well. This paper explores advances of synthetic biology in the plant field by elaborating on its core components and potential applications. Here, we have given a comprehensive idea of designing, development, and utilization of synthetic biology in the improvement of the present research state of plant system.
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Affiliation(s)
- Dipinte Gupta
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Gauri Sharma
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Pooja Saraswat
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India
| | - Rajiv Ranjan
- Plant Biotechnology Lab, Department of Botany, Faculty of Science, Dayalbagh Educational Institute (Deemed to be University), Dayalbagh, Agra, 282005, India.
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7
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Ross D. Automated analysis of bacterial flow cytometry data with FlowGateNIST. PLoS One 2021; 16:e0250753. [PMID: 34407072 PMCID: PMC8372958 DOI: 10.1371/journal.pone.0250753] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/21/2021] [Indexed: 11/18/2022] Open
Abstract
Flow cytometry is commonly used to evaluate the performance of engineered bacteria. With increasing use of high-throughput experimental methods, there is a need for automated analysis methods for flow cytometry data. Here, we describe FlowGateNIST, a Python package for automated analysis of bacterial flow cytometry data. The main components of FlowGateNIST perform automatic gating to differentiate between cells and background events and then between singlet and multiplet events. FlowGateNIST also includes a method for automatic calibration of fluorescence signals using fluorescence calibration beads. FlowGateNIST is open source and freely available with tutorials and example data to facilitate adoption by users with minimal programming experience.
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Affiliation(s)
- David Ross
- National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America
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8
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Comparative analysis of three studies measuring fluorescence from engineered bacterial genetic constructs. PLoS One 2021; 16:e0252263. [PMID: 34097703 PMCID: PMC8183995 DOI: 10.1371/journal.pone.0252263] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 05/11/2021] [Indexed: 11/19/2022] Open
Abstract
Reproducibility is a key challenge of synthetic biology, but the foundation of reproducibility is only as solid as the reference materials it is built upon. Here we focus on the reproducibility of fluorescence measurements from bacteria transformed with engineered genetic constructs. This comparative analysis comprises three large interlaboratory studies using flow cytometry and plate readers, identical genetic constructs, and compatible unit calibration protocols. Across all three studies, we find similarly high precision in the calibrants used for plate readers. We also find that fluorescence measurements agree closely across the flow cytometry results and two years of plate reader results, with an average standard deviation of 1.52-fold, while the third year of plate reader results are consistently shifted by more than an order of magnitude, with an average shift of 28.9-fold. Analyzing possible sources of error indicates this shift is due to incorrect preparation of the fluorescein calibrant. These findings suggest that measuring fluorescence from engineered constructs is highly reproducible, but also that there is a critical need for access to quality controlled fluorescent calibrants for plate readers.
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9
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Single-cell measurement of plasmid copy number and promoter activity. Nat Commun 2021; 12:1475. [PMID: 33674569 PMCID: PMC7935883 DOI: 10.1038/s41467-021-21734-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 02/08/2021] [Indexed: 01/31/2023] Open
Abstract
Accurate measurements of promoter activities are crucial for predictably building genetic systems. Here we report a method to simultaneously count plasmid DNA, RNA transcripts, and protein expression in single living bacteria. From these data, the activity of a promoter in units of RNAP/s can be inferred. This work facilitates the reporting of promoters in absolute units, the variability in their activity across a population, and their quantitative toll on cellular resources, all of which provide critical insights for cellular engineering.
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10
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TASBE Image Analytics: A Processing Pipeline for Quantifying Cell Organization from Fluorescent Microscopy. Methods Mol Biol 2020. [PMID: 33340350 DOI: 10.1007/978-1-0716-1174-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Laboratory automation now commonly allows high-throughput sample preparation, culturing, and acquisition of microscopy images, but quantitative image analysis is often still a painstaking and subjective process. This is a problem especially significant for work on programmed morphogenesis, where the spatial organization of cells and cell types is of paramount importance. To address the challenges of quantitative analysis for such experiments, we have developed TASBE Image Analytics, a software pipeline for automatically segmenting collections of cells using the fluorescence channels of microscopy images. With TASBE Image Analytics, collections of cells can be grouped into spatially disjoint segments, the movement or development of these segments tracked over time, and rich statistical data output in a standardized format for analysis. Processing is readily configurable, rapid, and produces results that closely match hand annotation by humans for all but the smallest and dimmest segments. TASBE Image Analytics can thus provide the analysis necessary to complete the design-build-test-learn cycle for high-throughput experiments in programmed morphogenesis, as validated by our application of this pipeline to process experiments on shape formation with engineered CHO and HEK293 cells.
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11
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Abstract
Engineering biological organisms is a complex, challenging, and often slow process. Other engineering domains have addressed such challenges with a combination of standardization and automation, enabling a divide‐and‐conquer approach to complexity and greatly increasing productivity. For example, standardization and automation allow rapid and predictable translation of prototypes into fielded applications (e.g., “design for manufacturability”), simplify sharing and reuse of work between groups, and enable reliable outsourcing and integration of specialized subsystems. Although this approach has also been part of the vision of synthetic biology, almost since its very inception (Knight & Sussman, 1998), this vision still remains largely unrealized (Carbonell et al, 2019). Despite significant progress over the last two decades, which have for example allowed obtaining and editing DNA sequences in easier and cheaper ways, the full process of organism engineering is still typically rather slow, manual, and artisanal.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA
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12
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Fedorec AJ, Robinson CM, Wen KY, Barnes CP. FlopR: An Open Source Software Package for Calibration and Normalization of Plate Reader and Flow Cytometry Data. ACS Synth Biol 2020; 9:2258-2266. [PMID: 32854500 PMCID: PMC7506944 DOI: 10.1021/acssynbio.0c00296] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Indexed: 01/03/2023]
Abstract
The measurement of gene expression using fluorescence markers has been a cornerstone of synthetic biology for the past two decades. However, the use of arbitrary units has limited the usefulness of these data for many quantitative purposes. Calibration of fluorescence measurements from flow cytometry and plate reader spectrophotometry has been implemented previously, but the tools are disjointed. Here we pull together, and in some cases improve, extant methods into a single software tool, written as a package in the R statistical framework. The workflow is validated using Escherichia coli engineered to express green fluorescent protein (GFP) from a set of commonly used constitutive promoters. We then demonstrate the package's power by identifying the time evolution of distinct subpopulations of bacteria from bulk plate reader data, a task previously reliant on laborious flow cytometry or colony counting experiments. Along with standardized parts and experimental methods, the development and dissemination of usable tools for quantitative measurement and data analysis will benefit the synthetic biology community by improving interoperability.
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Affiliation(s)
- Alex J.
H. Fedorec
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Clare M. Robinson
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Ke Yan Wen
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
| | - Chris P. Barnes
- Department
of Cell and Developmental Biology, University
College London, London WC1E 6BT, U.K.
- UCL
Genetics Institute, University College London, London WC1E 6BT, U.K.
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13
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Beal J, Farny NG, Haddock-Angelli T, Selvarajah V, Baldwin GS, Buckley-Taylor R, Gershater M, Kiga D, Marken J, Sanchania V, Sison A, Workman CT. Robust estimation of bacterial cell count from optical density. Commun Biol 2020; 3:512. [PMID: 32943734 PMCID: PMC7499192 DOI: 10.1038/s42003-020-01127-5] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 07/03/2020] [Indexed: 11/17/2022] Open
Abstract
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data. In an inter-laboratory study, the authors compare the accuracy and performance of three optical density calibration protocols (colloidal silica, serial dilution of silica microspheres, and colony-forming unit (CFU) assay). They demonstrate that serial dilution of silica microspheres is the best of these tested protocols, allowing precise and robust calibration that is easily assessed for quality control and can also evaluate the effective linear range of an instrument.
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Affiliation(s)
- Jacob Beal
- Raytheon BBN Technologies, Cambridge, MA, USA.
| | - Natalie G Farny
- Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA.
| | | | | | - Geoff S Baldwin
- Department of Life Sciences and IC-Centre for Synthetic Biology, Imperial College London, London, UK.
| | - Russell Buckley-Taylor
- Department of Life Sciences and IC-Centre for Synthetic Biology, Imperial College London, London, UK
| | | | - Daisuke Kiga
- Faculty of Science and Engineering, School of Advanced Science and Engineering, Waseda University, Tokyo, Japan
| | - John Marken
- Department of Bioengineering, California Institute of Technology, Pasadena, CA, USA
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14
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Wei X, Lu Y, Zhang X, Chen ML, Wang JH. Recent advances in single-cell ultra-trace analysis. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.115886] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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