ProtocolNavigator: emulation-based software for the design, documentation and reproduction biological experiments

Khan, Imtiaz; Fraser, Adam; Bray, Mark-Anthony; Smith, Paul J.; White, Nick; Carpenter, Anne E.; Errington, Rachel J.

doi:10.1093/bioinformatics/btu554

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…The most important output of MYOCYTER is the numerical data in form of two files (“Results [date and time].txt” and “Amplitudes only [date and time].txt”) including all the results and applied settings from an evaluation, allowing complete retracement of the whole experiment 10–12 . “Results.txt” contains a large tabulator-separated data table for every single cell of every single video evaluated in the according file folder, listing 43 different parameters overall, additional statistics and the applied settings (Online Supplement, user guide).…”

Section: Resultsmentioning

confidence: 99%

The “MYOCYTER” – Convert cellular and cardiac contractions into numbers with ImageJ

Grune

Ott

Häseli

et al. 2019

Sci Rep

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Measurement and quantification of cardiomyocyte or cardiac contractions as important (patho) physiologic parameters require highly specialized and expensive setups of fully integrated hard- and software that may be very difficult to use and may also depend on highly sophisticated methods of further data evaluation. With MYOCYTER (MC) we present a complete and highly customizable open-source macro for ImageJ, enabling fast, reliable user-friendly large scale analysis extracting an extensive amount of parameters from (even multiple) video recorded contracting cells or whole hearts, gained from a very competitive experimental setup. The extracted parameters enable extensive further (statistical) analysis to identify and quantify the effects of pathologic changes or drugs. Using videos following known mathematical functions, we were able to demonstrate the accuracy of MYOCYTER’s data extraction, also successfully applied the software to both cellular and animal models, introducing innovations like dynamic thresholding, automatic multi-cell recognition, “masked” evaluation and change of applied parameters even after evaluation.

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Section: Resultsmentioning

confidence: 99%

The “MYOCYTER” – Convert cellular and cardiac contractions into numbers with ImageJ

Grune

Ott

Häseli

et al. 2019

Sci Rep

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show abstract

“…PLOS has entered into a collaboration with Protocols.io [ 29 ] to facilitate reporting, sharing, and improving protocols. Another effort, ProtocolNavigator [ 30 ], enables collection of highly detailed experimental information and storage of provenance information; there are also supporting links to stored data and explanatory videos [ 31 ]. Challenges associated with data and digital resources are being considered by the Research Data Alliance (RDA) [ 32 ].…”

Section: What Are the Hurdles That Keep Ideas Such As These From Beinmentioning

confidence: 99%

How measurement science can improve confidence in research results

et al. 2018

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The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.

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“…A final, but critical aspect of the inputs for cell imaging experiments is documenting metadata about how cells, reagents and instrumentation were used [ 23 ]. Storing and accessing metadata describing a cellular imaging experiment has been the focus of several research efforts including, ProtocolNavigator [ 24 ] and the Open Microscopy Environment project [ 25 , 26 ]. This metadata serves as evidence for measurement reproducibility in the cell image experiments.…”

Section: Resultsmentioning

confidence: 99%

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

et al. 2015

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BackgroundThe goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.MethodsWe define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.ResultsThe survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.ConclusionsThe novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

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ProtocolNavigator: emulation-based software for the design, documentation and reproduction biological experiments

Abstract: ProtocolNavigator was implemented in Python 2.7, using the wx module to build the graphical user interface. It is a platform-independent software and freely available from http://protocolnavigator.org/index.html under the GPL v2 license.

Cited by 9 publications

References 9 publications

The “MYOCYTER” – Convert cellular and cardiac contractions into numbers with ImageJ

The “MYOCYTER” – Convert cellular and cardiac contractions into numbers with ImageJ

How measurement science can improve confidence in research results

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

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