Dataset Information

Robust quantitative scratch assay.

ABSTRACT:

Unlabelled

The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images. This Robust Quantitative Scratch Assay algorithm introduced statistical outputs where migration rates are estimated, cellular behaviour is distinguished and outliers are identified among groups of unique experimental conditions. Furthermore, the RQSA decreased measurement errors and increased accuracy in the wound boundary at comparable processing times compared to previously developed method (TScratch).

Availability and implementation

The RQSA is freely available at: http://ophid.utoronto.ca/RQSA/RQSA_Scripts.zip The image sets used for training and validation and results are available at: (http://ophid.utoronto.ca/RQSA/trainingSet.zip, http://ophid.utoronto.ca/RQSA/validationSet.zip, http://ophid.utoronto.ca/RQSA/ValidationSetResults.zip, http://ophid.utoronto.ca/RQSA/ValidationSet_H1975.zip, http://ophid.utoronto.ca/RQSA/ValidationSet_H1975Results.zip, http://ophid.utoronto.ca/RQSA/RobustnessSet.zip, http://ophid.utoronto.ca/RQSA/RobustnessSet.zip). Supplementary Material is provided for detailed description of the development of the RQSA.

Contact

juris@ai.utoronto.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Vargas A

PROVIDER: S-EPMC4848397 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robust quantitative scratch assay.

Vargas Andrea A Angeli Marc M Pastrello Chiara C McQuaid Rosanne R Li Han H Jurisicova Andrea A Jurisica Igor I

Bioinformatics (Oxford, England) 20151231 9

<h4>Unlabelled</h4>The wound healing assay (or scratch assay) is a technique frequently used to quantify the dependence of cell motility-a central process in tissue repair and evolution of disease-subject to various treatments conditions. However processing the resulting data is a laborious task due its high throughput and variability across images. This Robust Quantitative Scratch Assay algorithm introduced statistical outputs where migration rates are estimated, cellular behaviour is distingui ...[more]

PMID: 26722119

Similar Datasets

Project description:Aggressive cellular phenotypes such as uncontrolled proliferation and increased migration capacity engender cellular transformation, malignancy and metastasis. While genetic mutations are undisputed drivers of cancer initiation and progression, it is increasingly accepted that external factors are also playing a major role. Two recently studied modulators of breast cancer are changes in the cellular mechanical microenvironment and alterations in calcium homeostasis. While many studies investigate these factors separately in breast cancer cells, very few do so in combination. This current work sets a foundation to explore mechano-calcium relationships driving malignant progression in breast cancer. Utilizing real-time imaging of an in vitro scratch assay, we were able to resolve mechanically-sensitive calcium signaling in human breast cancer cells. We observed rapid initiation of intracellular calcium elevations within seconds in cells at the immediate wound edge, followed by a time-dependent increase in calcium in cells at distances up to 500μm from the scratch wound. Calcium signaling to neighboring cells away from the wound edge returned to baseline within seconds. Calcium elevations at the wound edge however, persisted for up to 50 minutes. Rigorous quantification showed that extracellular calcium was necessary for persistent calcium elevation at the wound edge, but intercellular signal propagation was dependent on internal calcium stores. In addition, intercellular signaling required extracellular ATP and activation of P2Y2 receptors. Through comparison of scratch-induced signaling from multiple cell lines, we report drastic reductions in response from aggressively tumorigenic and metastatic cells. The real-time scratch assay established here provides quantitative data on the molecular mechanisms that support rapid scratch-induced calcium signaling in breast cancer cells. These mechanisms now provide a clear framework for investigating which short-term calcium signals promote long-term changes in cancer cell biology.

Project description:Moving cell fronts are an essential feature of wound healing, development and disease. The rate at which a cell front moves is driven, in part, by the cell motility, quantified in terms of the cell diffusivity D, and the cell proliferation rate λ. Scratch assays are a commonly reported procedure used to investigate the motion of cell fronts where an initial cell monolayer is scratched, and the motion of the front is monitored over a short period of time, often less than 24 h. The simplest way of quantifying a scratch assay is to monitor the progression of the leading edge. Use of leading edge data is very convenient because, unlike other methods, it is non-destructive and does not require labelling, tracking or counting individual cells among the population. In this work, we study short-time leading edge data in a scratch assay using a discrete mathematical model and automated image analysis with the aim of investigating whether such data allow us to reliably identify D and λ. Using a naive calibration approach where we simply scan the relevant region of the (D, λ) parameter space, we show that there are many choices of D and λ for which our model produces indistinguishable short-time leading edge data. Therefore, without due care, it is impossible to estimate D and λ from this kind of data. To address this, we present a modified approach accounting for the fact that cell motility occurs over a much shorter time scale than proliferation. Using this information, we divide the duration of the experiment into two periods, and we estimate D using data from the first period, whereas we estimate λ using data from the second period. We confirm the accuracy of our approach using in silico data and a new set of in vitro data, which shows that our method recovers estimates of D and λ that are consistent with previously reported values except that that our approach is fast, inexpensive, non-destructive and avoids the need for cell labelling and cell counting.

Project description:Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

Project description:Quantitative Susceptibility Mapping (QSM), best known as a surrogate for tissue iron content, is becoming a highly relevant MRI contrast for monitoring cellular and vascular status in aging, addiction, traumatic brain injury and, in general, a wide range of neurological disorders. In this study we present a new Bayesian QSM algorithm, named Multi-Scale Dipole Inversion (MSDI), which builds on the nonlinear Morphology-Enabled Dipole Inversion (nMEDI) framework, incorporating three additional features: (i) improved implementation of Laplace's equation to reduce the influence of background fields through variable harmonic filtering and subsequent deconvolution, (ii) improved error control through dynamic phase-reliability compensation across spatial scales, and (iii) scalewise use of the morphological prior. More generally, this new pre-conditioned QSM formalism aims to reduce the impact of dipole-incompatible fields and measurement errors such as flow effects, poor signal-to-noise ratio or other data inconsistencies that can lead to streaking and shadowing artefacts. In terms of performance, MSDI is the first algorithm to rank in the top-10 for all metrics evaluated in the 2016 QSM Reconstruction Challenge. It also demonstrated lower variance than nMEDI and more stable behaviour in scan-rescan reproducibility experiments for different MRI acquisitions at 3 and 7 Tesla. In the present work, we also explored new forms of susceptibility MRI contrast making explicit use of the differential information across spatial scales. Specifically, we show MSDI-derived examples of: (i) enhanced anatomical detail with susceptibility inversions from short-range dipole fields (hereby referred to as High-Pass Susceptibility Mapping or HPSM), (ii) high specificity to venous-blood susceptibilities for highly regularised HPSM (making a case for MSDI-based Venography or VenoMSDI), (iii) improved tissue specificity (and possibly statistical conditioning) for Macroscopic-Vessel Suppressed Susceptibility Mapping (MVSSM), and (iv) high spatial specificity and definition for HPSM-based Susceptibility-Weighted Imaging (HPSM-SWI) and related intensity projections.

Dataset Information

Robust quantitative scratch assay.

Unlabelled

Availability and implementation

Contact

Supplementary information

Publications

Robust quantitative scratch assay.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets