DeepScratch: Single-cell based topological metrics of scratch wound assays

Javer, Avelino; Rittscher, Jens; Sailem, Heba

doi:10.1016/j.csbj.2020.08.018

Cited by 12 publications

(15 citation statements)

References 40 publications

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“…2B-D). The selected genes were shown to affect cell migration 15 . We also visualised cell proliferation because proliferation rate can also affect wound closure (Methods).…”

Section: Resultsmentioning

confidence: 99%

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ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

Y²,

Zourob³

et al. 2021

Preprint

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Intuitive visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns in complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by a user-friendly web-based application for creating interactive quantitative pictorial representations of phenotypic data and facilitating the understanding and analysis of image datasets (www.shapography.com). ShapoGraphy enables the user to create a structure of interest as a set of shapes. Each shape can encode different variables that are mapped to the shape dimensions, colours, symbols, and stroke features. We illustrate the utility of ShapoGraphy using various image data, including high dimensional multiplexed data. Our results show that ShapoGraphy allows a better understanding of cellular phenotypes and relationships between variables. In conclusion, ShopoGraphy supports scientific discovery and communication by providing a wide range of users with a rich vocabulary to create engaging and intuitive representations of diverse data types.

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“…2B-D). The selected genes were shown to affect cell migration 15 . We also visualised cell proliferation because proliferation rate can also affect wound closure (Methods).…”

Section: Resultsmentioning

confidence: 99%

“…Cells were imaged at 0h and 24h following wounding at 4x objective. Cells were detected and the wound area was segmented using DeepScratch 15 . Measurements of wound size and cell numbers at 24h were normalised to timepoint 0h and represented using ShapoGraphy.…”

Section: Wound Scratch Datamentioning

confidence: 99%

ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

Y²,

Zourob³

et al. 2021

Preprint

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“…For conventional edge detection, we applied the Canny method [ 38 ]. Customized image processing software based on the Canny method was implemented by using MATLAB’s image processing toolbox [ edge ( Image , ‘canny’ ]).…”

Section: Methodsmentioning

confidence: 99%

Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

Oldenburg

Maletzki

Strohbach

et al. 2021

BMC Mol and Cell Biol

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Background Endothelial healing after deployment of cardiovascular devices is particularly important in the context of clinical outcome. It is therefore of great interest to develop tools for a precise prediction of endothelial growth after injury in the process of implant deployment. For experimental investigation of re-endothelialization in vitro cell migration assays are routinely used. However, semi-automatic analyses of live cell images are often based on gray value distributions and are as such limited by image quality and user dependence. The rise of deep learning algorithms offers promising opportunities for application in medical image analysis. Here, we present an intelligent cell detection (iCD) approach for comprehensive assay analysis to obtain essential characteristics on cell and population scale. Results In an in vitro wound healing assay, we compared conventional analysis methods with our iCD approach. Therefore we determined cell density and cell velocity on cell scale and the movement of the cell layer as well as the gap closure between two cell monolayers on population scale. Our data demonstrate that cell density analysis based on deep learning algorithms is superior to an adaptive threshold method regarding robustness against image distortion. In addition, results on cell scale obtained with iCD are in agreement with manually velocity detection, while conventional methods, such as Cell Image Velocimetry (CIV), underestimate cell velocity by a factor of 0.5. Further, we found that iCD analysis of the monolayer movement gave results just as well as manual freehand detection, while conventional methods again shows more frayed leading edge detection compared to manual detection. Analysis of monolayer edge protrusion by ICD also produced results, which are close to manual estimation with an relative error of 11.7%. In comparison, the conventional Canny method gave a relative error of 76.4%. Conclusion The results of our experiments indicate that deep learning algorithms such as our iCD have the ability to outperform conventional methods in the field of wound healing analysis. The combined analysis on cell and population scale using iCD is very well suited for timesaving and high quality wound healing analysis enabling the research community to gain detailed understanding of endothelial movement.

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“…Images of cells were acquired at 4x objective, which allowed the entire experimental well to be captured in two images that were stitched together, resulting in 512 × 1392-pixel images, where each pixel corresponds to 2.97µm [18]. We used DeepScratch [20] to analyse image data from various gene knockdowns. DeepScratch is a neural network with U-NET architecture.…”

Section: Data From a Gene Knockdown Screenmentioning

confidence: 99%

“…Changes in cell counts are a simple summary statistic to extract from scratch assay data [16,19,20] and provide an indication of the average net proliferation rate in a large population of cells, such as the cell monolayer used for scratch assays. However, computing an average net 4).…”

Section: Density-dependent Patterns Of Proliferation Reproduce Experi...mentioning

confidence: 99%

Efficient Bayesian inference for mechanistic modelling with high-throughput data

Martina-Perez

Sailem

Baker

2022

Preprint

Self Cite

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Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a \textit{minibatch} approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.

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DeepScratch: Single-cell based topological metrics of scratch wound assays

Cited by 12 publications

References 40 publications

ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

ShapoGraphy: a glyph-oriented visualisation approach for creating pictorial representations of bioimaging data

Methodology for comprehensive cell-level analysis of wound healing experiments using deep learning in MATLAB

Efficient Bayesian inference for mechanistic modelling with high-throughput data

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