Real-time visualization of mutations and their fitness effects in single bacteria

Elez, Marina

doi:10.1038/s41596-019-0215-x

Cited by 14 publications

(15 citation statements)

References 32 publications

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“…µMA (for microfluidic Mutation Accumulation) also based on on time-lapse imaging and mother machine allows following fitness evolution during mutation accumulation and characterizing the distribution of fitness effects of spontaneous mutations. Detailed description of these two approaches and instructions on how perform them can be found in an accompanying protocol [15] BACMMAN was designed to also work on datasets other than MV and µMA. In order to facilitate the adaptation of BACMMAN algorithms to data from other labs, we developed specific test procedures allowing easy parameter optimization for all the different steps of preprocessing, segmentation and tracking.…”

Section: Development Of Bacmannmentioning

confidence: 99%

High-throughput detection and tracking of cells and intracellular spots in mother machine experiments

2019

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The analysis of bacteria at the single cell level is essential to characterize processes in which cellular heterogeneity plays an important role. BACMMAN (BACteria Mother Machine ANalysis) is a software allowing fast and reliable automated image analysis of high-throughput 2D or 3D time-series images from experiments using the "mother machine", a very popular microfluidic device allowing biological processes in bacteria to be investigated at the singlecell level. Here we describe how to use some of the BACMMAN features including i) segmentation and tracking of bacteria and intracellular fluorescent spots, ii) visualization and editing of the results iii) configuration of the image processing pipeline for different datasets, and iv) BACMMAN coupling to data analysis software for visualization and analysis of data subsets with specific properties. Among software specifically dedicated to the analysis of mother machine data, only BACMMAN allows segmentation and tracking of both bacteria and intracellular spots. For a single position, single channel with 1000 frames (2GB dataset) image processing takes about 6 min on a regular computer. Numerous implemented algorithms, easy configuration and high modularity ensure wide applicability of the BACMMAN software.

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Section: Development Of Bacmannmentioning

confidence: 99%

High-throughput detection and tracking of cells and intracellular spots in mother machine experiments

2019

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“…Using their microfluidic platform, heterogenous changes in the secretion rates of ADAM and MMPs were observed in response to PMA stimulation, which may be used to predict HepG2 cell fates. In another recent study, a microfluidic platform that combined mutation visualization (MV) and microfluidic mutation accumulation (µMA) enabled real-time tracing of mutations of single bacteria [153].…”

Section: Techniques On the Horizonmentioning

confidence: 99%

The Role of Single-Cell Technology in the Study and Control of Infectious Diseases

Lin

Tay

Lu³

et al. 2020

Cells

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The advent of single-cell research in the recent decade has allowed biological studies at an unprecedented resolution and scale. In particular, single-cell analysis techniques such as Next-Generation Sequencing (NGS) and Fluorescence-Activated Cell Sorting (FACS) have helped show substantial links between cellular heterogeneity and infectious disease progression. The extensive characterization of genomic and phenotypic biomarkers, in addition to host–pathogen interactions at the single-cell level, has resulted in the discovery of previously unknown infection mechanisms as well as potential treatment options. In this article, we review the various single-cell technologies and their applications in the ongoing fight against infectious diseases, as well as discuss the potential opportunities for future development.

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“…Cells can grow and divide inside the microchannels for hundreds of generations, allowing the imaging of 10 5 − 10 6 individual cells per experiment. Mother machine devices are being increasingly used for single-cell studies on bacteria to investigate various subjects, such as gene expression and regulation [17,3,8], mutagenesis and evolution [21,20] or single cell response to antibiotics [1,2]. The massive amount of data generated by long-term imaging of cells growing in the mother machine (typically several hundred gigabytes worth of images per experiment) needs to be processed automatically, in particular methods for automatic segmentation and tracking of cells with very low error rate are needed.…”

Section: Contextmentioning

confidence: 99%

DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine

Ollion¹

2020

Preprint

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The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for single-cell level quantitative analysis and characterization of many cellular processes such as gene expression and regulation, mutagenesis or response to antibiotics. The automated and quantitative analysis of the massive amount of data generated by such experiments is now the limiting step. In particular the segmentation and tracking of bacteria cells imaged in phase-contrast microscopy-with error rates compatible with high-throughput data-is a challenging problem. In this work, we describe a novel formulation of the multi-object tracking problem, in which tracking is performed by a regression of the bacteria's displacement, allowing simultaneous tracking of multiple bacteria, despite their growth and division over time. Our method performs jointly segmentation and tracking, leveraging sequential information to increase segmentation accuracy. We introduce a Deep Neural Network (DNN) architecture taking advantage of a self-attention mechanism which yields less than 0.005% tracking error rate and less than 0.03% segmentation error rate. We demonstrate superior performance and speed compared to state-of-the-art methods. While this method is particularly well suited for mother machine microscopy data, its general joint tracking and segmentation formulation could be applied to many other problems with different geometries.

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Real-time visualization of mutations and their fitness effects in single bacteria

Cited by 14 publications

References 32 publications

High-throughput detection and tracking of cells and intracellular spots in mother machine experiments

High-throughput detection and tracking of cells and intracellular spots in mother machine experiments

The Role of Single-Cell Technology in the Study and Control of Infectious Diseases

DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine

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