Single-cell measurement of plasmid copy number and promoter activity

Shao, Bin; Rammohan, Jayan; Anderson, Daniel A.; Alperovich, Nina; Ross, David; Voigt, Christopher A.

doi:10.1038/s41467-021-21734-y

Cited by 63 publications

(66 citation statements)

References 66 publications

(38 reference statements)

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“…This showcases insulators as a valuable genetic tool to decrease the influence of the genetic context. Ribozymes have been widely employed as standard genetic elements to control gene expression since then (Otto et al, 2019;Corrêa et al, 2020;Davey and Wilson, 2020;Kuo et al, 2020;Park et al, 2020;Shao et al, 2021) and have been proposed to become a part of a standard cassette design (Nielsen et al, 2013;Neves et al, 2020). The study by Lou et al (2012), however, also demonstrates that insulators must be verified first, as only half of the tested ribozymes worked reliably.…”

Section: Tackling Context Dependency: Insulating the Mrna From The Genetic Contextmentioning

confidence: 99%

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Tietze

Lale

2021

Microbial Biotechnology

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The field of synthetic biology is evolving at a fast pace. It is advancing beyond single-gene alterations in single hosts to the logical design of complex circuits and the development of integrated synthetic genomes. Recent breakthroughs in deep learning, which is increasingly used in de novo assembly of DNA components with predictable effects, are also aiding the discipline. Despite advances in computing, the field is still reliant on the availability of precharacterized DNA parts, whether natural or synthetic, to regulate gene expression in bacteria and make valuable compounds. In this review, we discuss the different bacterial synthetic biology methodologies employed in the creation of 5 0 regulatory regionspromoters, untranslated regions and 5 0 -end of coding sequences. We summarize methodologies and discuss their significance for each of the functional DNA components, and highlight the key advances made in bacterial engineering by concentrating on their flaws and strengths. We end the review by outlining the issues that the discipline may face in the near future.

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Section: Tackling Context Dependency: Insulating the Mrna From The Genetic Contextmentioning

confidence: 99%

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Tietze

Lale

2021

Microbial Biotechnology

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“…Generally, the distribution around the target copy number within each cell is a narrow Gaussian [43], but recent evidence showed that the standard deviation can be on the order of the mean copy number [44]. Replication control is based on feedback from the plasmid copy number in the cell (down-regulation at high copy numbers) [19].…”

Section: Plasmid Replication and Partitioningmentioning

confidence: 99%

“…This is especially important for low copy number plasmids, which are known to use partitioning systems for this purpose. However, non-random positioning has also been found for high copy number plasmids [45], which is beneficial if heterogeneity in copy number can indeed be large [44].…”

Section: Plasmid Replication and Partitioningmentioning

confidence: 99%

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

Igler

Huisman

Siedentop

et al. 2021

Preprint

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As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions, but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same host cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection--from systems to exclude infection by other plasmids to interactions in the regulation of plasmid copy number per cell--are well characterised at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics, and in turn, the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts eco-evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection.

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“…As a result, researchers can use statistical analysis to study the subtle and complex effects of cell-to-cell heterogeneity, gene expression dynamics, and cell-to-cell interactions in isogenic populations (1,11). This has led to fundamental discoveries related to antibiotic resistance (12,13), and has allowed for accurate characterization of genetic parts and circuits (14). Further, single-cell time-lapse analysis has revealed that cell division in E. coli is asymmetrical, where daughter cells receiving the 'old' pole grow more slowly than daughter cells receiving the 'new' pole (15).…”

Section: Introductionmentioning

confidence: 99%

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Alnahhas

Lugagne

Dunlop

2021

Preprint

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Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

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Single-cell measurement of plasmid copy number and promoter activity

Cited by 63 publications

References 66 publications

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Importance of the 5′ regulatory region to bacterial synthetic biology applications

Plasmid co-infection: linking biological mechanisms to ecological and evolutionary dynamics

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

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