Mother machine image analysis with MM3

Jt, Sauls; Jw, Schroeder; Brown, Steven W.; Gl, Treut; F, Si; D, Li; Wang, Jue D.; Jun, Suckjoon

doi:10.1101/810036

Cited by 20 publications

(29 citation statements)

References 24 publications

(20 reference statements)

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“…A typical experiment produced data for around 2,500 cells (see Table 2 for experimental conditions). We used custom software to extract single-cell data from raw images (20) (Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

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Control of Bacillus subtilis Replication Initiation during Physiological Transitions and Perturbations

Sauls

Cox

et al. 2019

mBio

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High-throughput, quantitative approaches have enabled the discovery of fundamental principles describing bacterial physiology. These principles provide a foundation for predicting the behavior of biological systems, a widely held aspiration. However, these approaches are often exclusively applied to the best-known model organism, E. coli. In this report, we investigate to what extent quantitative principles discovered in Gram-negative E. coli are applicable to Gram-positive B. subtilis. We found that these two extremely divergent bacterial species employ deeply similar strategies in order to coordinate growth, cell size, and the cell cycle. These similarities mean that the quantitative physiological principles described here can likely provide a beachhead for others who wish to understand additional, less-studied prokaryotes.

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Section: Resultsmentioning

confidence: 99%

“…Mother machine images were processed with custom Python software (20). The pipeline employs raw images to produce objects which represent a cell and contain all measured parameters.…”

Section: Methodsmentioning

confidence: 99%

Control of Bacillus subtilis Replication Initiation during Physiological Transitions and Perturbations

Sauls

Cox

et al. 2019

mBio

Self Cite

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“…While a plethora of software suites have been developed for single-cell segmentation and tracking [12][13][14][15], including code specific to analysis of mother machine data [11,[16][17][18][19], the vast majority require manual inputs from the experimenter and are designed for a posteriori processing. The relatively recent breakthrough in biomedical image analysis brought by deep convolutional neural networks, and the U-Net [20] architecture in particular, has introduced an era of fast-paced developments in the field [21].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight 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 intervention. 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.

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“…Though image segmentation has been studied for long it still presents with new challenges in analysis and often times a predetermined set of operations produces a poor quality of segmentation due to the noise and variability in biological images. Many studies have developed image analysis methods for bacterial growth analysis from mother machine data [2][3][4][5][6][7][8][9][10][11] using conventional and machine learning methods. Here we present a set of modular programs which can take a stack of images of rod shaped bacteria from a mother machine experiment and segment them to produce an easy to read data structure with the information of cell divisions.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and analysis of mother machine data: SAM

Banerjee

Stephenson

Das

2020

Preprint

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Time-lapse imaging of bacteria growing in micro-channels in a controlled environment has been instrumental in studying the single cell dynamics of bacterial growth. This kind of a microfluidic setup with growth chambers is popularly known as mother machine [1]. In a typical experiment with such a set-up, bacterial growth can be studied for numerous generations with high resolution and temporal precision using image processing. However, as in any other experiment involving imaging, the image data from a typical mother machine experiment has considerable intensity fluctuations, cell intrusion, cell overlapping, filamentation etc. The large amount of data produced in such experiments makes it hard for manual analysis and correction of such unwanted aberrations. We have developed a modular code for segmentation and analysis of mother machine data (SAM) for rod shaped bacteria where we can detect such aberrations and correctly treat them without manual supervision. We track cumulative cell size and use an adaptive segmentation method to avoid faulty detection of cell division. SAM is currently written and compiled using MATLAB. It is fast (∼ 15 min/GB of image) and can be efficiently coupled with shell scripting to process large amount of data with systematic creation of output file structures and graphical results. It has been tested for many different experimental data and is publicly available in Github.

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Mother machine image analysis with MM3

Cited by 20 publications

References 24 publications

Control of Bacillus subtilis Replication Initiation during Physiological Transitions and Perturbations

Control of Bacillus subtilis Replication Initiation during Physiological Transitions and Perturbations

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

Segmentation and analysis of mother machine data: SAM

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