Shaping bacterial population behavior through computer-interfaced control of individual cells

Chait, Remy; Ruess, Jakob; Bergmiller, Tobias; Tkačik, Gašper; Guet, Călin C.

doi:10.1038/s41467-017-01683-1

Cited by 103 publications

(152 citation statements)

References 53 publications

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“…CcaSR v 2.0 exhibits 17 times less leakiness than v 1.0, and 117‐fold activation . We recently combined CcaSR v 2.0 with an engineered red light sensor to program two gene expression signals simultaneously and independently, whereas Guet and co‐workers used it to extend in silico control of gene expression to the single‐cell level …”

Section: Figurementioning

confidence: 99%

A Miniaturized Escherichia coli Green Light Sensor with High Dynamic Range

Ong

Tabor

2018

ChemBioChem

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Genetically engineered photoreceptors enable unrivaled control over gene expression. Previously, we ported the Synechocystis PCC 6803 CcaSR two-component system, which is activated by green light and deactivated by red, into Escherichia coli, resulting in a sensor with a sixfold dynamic range. Later, we optimized pathway protein expression levels and the output promoter sequence to decrease transcriptional leakiness and to increase the dynamic range to approximately 120-fold. These CcaSR v 1.0 and v 2.0 systems have been used for precise quantitative, temporal, and spatial control of gene expression for a variety of applications. Recently, other workers deleted two PAS domains of unknown function from the CcaS sensor histidine kinase in a system similar to CcaSR v 1.0. Here we apply these deletions to CcaSR v 2.0, resulting in a v 3.0 light sensor with an output four times less leaky and a dynamic range of nearly 600-fold. We demonstrate that the PAS domain deletions have no deleterious effect on CcaSR green light sensitivity or response dynamics. CcaSR v 3.0 is the best-performing engineered bacterial green light sensor available, and should have broad applications in fundamental and synthetic biology studies.

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Section: Figurementioning

confidence: 99%

A Miniaturized Escherichia coli Green Light Sensor with High Dynamic Range

Ong

Tabor

2018

ChemBioChem

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“…1A). This experimental set up has been widely adopted as a popular standard for long-term, single-cell time-lapse imaging of bacteria such as E. coli [5][6][7][8], Bacillus subtilis [6,9], and Corynebacterium glutamicum [10]. Unfortunately, analysis of raw single-cell microscopy images remains a major bottleneck despite major efforts in this area.…”

Section: Introductionmentioning

confidence: 99%

“…As an example, a small number of recent studies have highlighted the potential of computerbased feedback control of gene expression in single cells as a new experimental paradigm [8,20,21]. This approach automatically adjusts chemical or optogenetic inputs based on real-time quantification of gene expression levels in cells to precisely and dynamically perturb regulatory networks.…”

Section: Introductionmentioning

confidence: 99%

“…However, to be able to perform feedback control at the single-cell level, image analysis must be performed on-the-fly, without any human supervision. To circumvent this problem, researchers have exploited constrained experimental geometries to localize cells [8], developed customized interfaces with flow cytometry [23,24], or restricted studies to experimental durations that avoid too many cell division events or conditions where the cells escape from the field of view [20]. By providing rapid access to a suite of quantitative, dynamic measurements about single cells, unsupervised approaches to image processing offer the potential to expand the scope of experiments that can exploit dynamic, real-time cell tracking.…”

Section: Introductionmentioning

confidence: 99%

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DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Lugagne¹,

Lin²,

Dunlop³

2019

Preprint

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AbstractMicroscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human supervision and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a “mother machine” microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human supervision. Further, the algorithm is fast, with complete analysis of a typical frame containing ∼150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.Author SummaryAutomated microscopy experiments can generate massive data sets, allowing for detailed analysis of cell physiology and properties such as gene expression. In particular, dynamic measurements of gene expression with time-lapse microscopy have proved invaluable for understanding how gene regulatory networks operate. However, image analysis remains a key bottleneck in the analysis pipeline, typically requiring human supervision and a posteriori processing. Recently, machine learning-based approaches have ushered in a new era of rapid, unsupervised image analysis. In this work, we use and repurpose the U-Net deep learning algorithm to develop an image processing pipeline that can not only accurately identify the location of cells in an image, but also track them over time as they grow and divide. As an application, we focus on multi-hour time-lapse movies of bacteria growing in a microfluidic device. Our algorithm is accurate and fast, with error rates near 1% and requiring less than a second to analyze a typical movie frame. This increase in speed and fidelity has the potential to open new experimental avenues, e.g. where images are analyzed on-the-fly so that experimental conditions can be updated in real time.

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“…We implemented cell-in-the-loop using optogenetics, which have been shown to afford excellent spatiotemporal precision in applications including feedback control [33] [34] [35] [36] and oscillatory synchronization [37]. We engineered Saccharomyces cerevisiae to respond to blue light [38] by increasing gene expression as measured by a fast-acting fluorescent reporter [39].…”

Section: Introductionmentioning

confidence: 99%

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

Perkins¹,

Benzinger

Arcak³

et al. 2019

Preprint

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Designing and implementing synthetic biological pattern formation remains a challenge due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a "cell-in-the-loop" approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present a theory that offers an easy-to-use test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrated spontaneous checkerboard patterning in an optogenetic setup where cell-to-cell signaling was emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produced spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

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Shaping bacterial population behavior through computer-interfaced control of individual cells

Cited by 103 publications

References 53 publications

A Miniaturized Escherichia coli Green Light Sensor with High Dynamic Range

A Miniaturized Escherichia coli Green Light Sensor with High Dynamic Range

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

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