MicroMator: Open and Flexible Software for Reactive Microscopy

Fox, Zachary; Fletcher, Steven; Fraisse, Achille; Aditya, Chetan; Sosa-Carrillo, Sebastián; Gilles, Sébastien; Bertaux, François; Ruess, Jakob; Batt, Grégory

doi:10.1101/2021.03.12.435206

Cited by 14 publications

(24 citation statements)

References 27 publications

(37 reference statements)

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“…To further establish that our system remains functional in different experimental contexts, we cultured cells in a microfluidic chamber and stimulated them periodically via short pulses of light on our microscopy platform 30 . Through regular imaging, cellular fluorescence was used to classify cells as differentiated ( Figure 1d ).…”

Section: Resultsmentioning

confidence: 99%

“…We used an in-house software, called MicroMator, for the automated acquisition and cell tracking. Cell segmentation was achieved via SegMator, an in-house neural net based segmentation algorithm 30 . Python was used for data analysis and visualization (Supplementary text II).…”

Section: Methodsmentioning

confidence: 99%

“…the use of light to trigger cellular processes, has contributed significantly to control applications by increasing the spatiotemporal resolution of the control signal 23,24,[28][29][30][31][32][33][34][35][36][37][38] . Control of protein expression using light has been demonstrated both at the population level 23,28,31 and in single cells 30,33,37,39 . Optogenetics has been used to control cellular processes in other contexts, for instance, signalling dynamics 24 , morphogenesis 36 , neuroscience 38 , bioproduction and metabolic engineering 29,35 .…”

Section: Introductionmentioning

confidence: 99%

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A light tunable differentiation system for the creation and control of consortia in yeast

Aditya

Bertaux

Batt

et al. 2021

Preprint

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Artificial microbial consortia seek to leverage division-of-labour to optimize function and possess immense potential for bioproduction. Co-culturing approaches, the preferred mode of generating a consortium, remain limited in their ability to give rise to stable consortia having finely tuned compositions. Here, we present an artificial differentiation system in budding yeast capable of generating stable microbial consortia with custom functionalities from a single strain at user-defined composition in space and in time based on optogenetically-driven genetic rewiring. Owing to fast, reproducible, and light-tunable dynamics, our system enables dynamic control of consortia composition in continuous cultures for extended periods. We further demonstrate that our system can be extended in a straightforward manner to give rise to consortia with multiple subpopulations. Our artificial differentiation strategy establishes a novel paradigm for the creation of complex microbial consortia that are simple to implement, precisely controllable, and versatile to use.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A light tunable differentiation system for the creation and control of consortia in yeast

Aditya

Bertaux

Batt

et al. 2021

Preprint

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“…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). 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.…”

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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“…While the complexity of biological systems has not changed, the availability and reliability of data certainly has. For instance, experimental techniques and platforms nowadays allow us to observe the onset of transcription at single molecule precision, to fully automatically measure the expression levels of genes every couple of minutes, to perturb and drive gene expression in populations or individual cells [20, 17, 29, 6, 1], and even to let computational models interact with single cell gene expression processes in real time [2]. To which degree these new capacities will eventually allow us to resolve the ill-posedness of reverse engineering models of biological systems from experimental data remains to be clarified, but in any case the availability of new types of data calls for new methods that are capable of exploiting the data in its entirety.…”

Section: Introductionmentioning

confidence: 99%

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level

Davidović

Chait

Batt

et al. 2021

Preprint

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Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.

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MicroMator: Open and Flexible Software for Reactive Microscopy

Cited by 14 publications

References 27 publications

A light tunable differentiation system for the creation and control of consortia in yeast

A light tunable differentiation system for the creation and control of consortia in yeast

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

Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level

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