Tracking bacterial lineages in complex and dynamic environments with applications for growth control and persistence

Bakshi, Somenath; Leoncini, Emanuele; Baker, Charles J.; Cañas-Duarte, Silvia J.; Okumuş, Burak; Paulsson, Johan

doi:10.1038/s41564-021-00900-4

Cited by 87 publications

(131 citation statements)

References 53 publications

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“…The automation of hardware and software for microscopy has resulted in researchers' ability to generate massive datasets containing images of cells over time. For example, in a recent high throughput experiment Bakshi et al imaged 10 8 Escherichia coli over days by acquiring 705 field of views every few minutes [1]. Additionally, recent studies have used closed-loop microscopy and optogenetic platforms to control gene expression in single cells in real time [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…These improvements in microscopy have motivated the need for automated image analysis, as traditional approaches that require manual error correction cannot keep pace with the size of these new datasets or the rate at which they can be acquired. More generally, segmentation and tracking have historically required intensive user input as well as custom image processing code or experimental modifications such as the use of dedicated fluorophores [1,[5][6][7][8]. These requirements limit throughput and can introduce burdensome experimental constraints.…”

Section: Introductionmentioning

confidence: 99%

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

O’Connor

Alnahhas²,

Lugagne³

et al. 2022

PLoS Comput Biol

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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 images of 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. 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 as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. 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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

O’Connor

Alnahhas²,

Lugagne³

et al. 2022

PLoS Comput Biol

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show abstract

“…Traditional approaches studying single microbial isolates in solid or liquid culture systems have yielded deep insights into bacterial genetics and functions, but are not well-suited for studying complex ecosystems. More recent higher-throughput in vitro approaches include multi-stage fermenters 23 , microfluidic devices 24 and microbial-host cell co-culture systems 25 . Even with these advances, culturing native microbiomes in vitro remains very challenging, especially for longer durations of time, due to the extremely varied chemical, physical and nutritional requirements of the diverse micro-organisms present.…”

Section: Introductionmentioning

confidence: 99%

Microbial dynamics inference at ecosystem-scale

Gibson

Kim

Acharya

et al. 2021

Preprint

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Despite the importance of microbial dysbiosis in human disease, the phenomenon remains poorly understood. We provide the first comprehensive and predictive model of dysbiosis at ecosystem-scale, leveraging our new machine learning method for efficiently inferring compact and interpretable dynamical systems models. Coupling this approach with the most densely temporally sampled interventional study of the microbiome to date, using microbiota from healthy and dysbiotic human donors that we transplanted into mice subjected to antibiotic and dietary interventions, we demonstrate superior predictive performance of our method over state-of-the-art techniques. Moreover, we demonstrate that our approach uncovers intrinsic dynamical properties of dysbiosis driven by destabilizing competitive cycles, in contrast to stabilizing interaction chains in the healthy microbiome, which have implications for restoration of the microbiome to treat disease.

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“…The automation of hardware and software for microscopy has resulted in researchers' ability to generate massive datasets containing images of cells over time. For example, in a recent high throughput experiment Bakshi et al imaged 8 Escherichia coli over days by acquiring 705 field of views every few minutes (1). Additionally, recent studies have used closed-loop microscopy and optogenetic platforms to control gene expression in single cells in real time (2)(3)(4).…”

Section: Introductionmentioning

confidence: 99%

“…With rapid cell cycle times, small cell sizes, and high throughput microfluidic devices, it is possible for researchers to generate large datasets containing thousands of single cells over periods of hours or days. 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).…”

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.

show abstract

Tracking bacterial lineages in complex and dynamic environments with applications for growth control and persistence

Cited by 87 publications

References 53 publications

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

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

Microbial dynamics inference at ecosystem-scale

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

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