Name: measuring the shape and size of activated sludge particles immobilized in agar with an open source software pipeline
Uploaded: 2019-01-30T14:45:00
Description: Watch this Scientific Journal Video about Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline at JoVE.com

This work was supported by a grant from the National Science Foundation CBET&#160;1336544.
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1. Collect samples for particle analysis
<ol>
	<li>Determine the sample volume for specific reactors that will produce sufficient particles for statistical analysis10 (&#62;500) while avoiding particle overlap.
	<ol>
		<li>Assume that a range of 0.5 to 2 mL per sample of mixed liquor is sufficient for activated sludge samples with a mixed liquor suspended solids (MLSS) between 250 and 5,000 mg/L.</li>
		<li>Otherwise, prepare three test agar plates using 0.5, 2, and 5 mL of sample (steps 1.2 thr...

<ol>
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Effect of Hydrodynamics on Aerobic Granulation Available from: <a target="_blank" href="https://repository.lib.ncsu.edu/handle/1840.16/8761">https://repository.lib.ncsu.edu/handle/1840.16/8761</a> (2012)</li><li>Tay, J. H., Liu, Q. S., Liu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microscopic+observation+of+aerobic+granulation+in+sequential+aerobic+sludge+blanket+reactor.">Microscopic observation of aerobic granulation in sequential aerobic sludge blanket reactor.</a> Journal of Applied Microbiology. 91 (1), 168-175 (2001).</li><li>Kelly, R. 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W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Avoiding+twisted+pixels:+ethical+guidelines+for+the+appropriate+use+and+manipulation+of+scientific+digital+images.">Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images.</a> Science and Engineering Ethics. 16 (4), 639-667 (2010).</li><li>Wickham, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tidy+Data.">Tidy Data.</a> Journal of Statistical Software. 59 (10), (2014).</li><li>van Rossum, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Python tutorial. , (1995).</li><li>Schindelin, J., Arganda-Carreras, I., Frise, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FIJI:+an+open-source+platform+for+biological-image+analysis.">FIJI: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li><li>. SParMorIA: Sludge Particle Morphological ImageAnalysis Available from: <a target="_blank" href="https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis">https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis</a> (2018)</li><li>Ferreira, T., Rasband, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> ImageJ User Guide: IJ 1.42 r. , (2012).</li><li>Sezgin, M., Sankur, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survey+over+image+thresholding+techniques+and+quantitative+performance+evaluation.">Survey over image thresholding techniques and quantitative performance evaluation.</a> Journal of Electronic Imaging. 13 (1), 146-166 (2004).</li><li>Kr&#246;ner, S., Dom&#233;nech Carb&#243;, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determination+of+minimum+pixel+resolution+for+shape+analysis:+Proposal+of+a+new+data+validation+method+for+computerized+images.">Determination of minimum pixel resolution for shape analysis: Proposal of a new data validation method for computerized images.</a> Powder Technology. 245, 297-313 (2013).</li><li>McKinney, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Data+Structures+for+Statistical+Computing+in+Python.">Data Structures for Statistical Computing in Python.</a> Proceedings of the 9th Python in Science Conference. , 51-56 (2010).</li><li>Waskom, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> seaborn: v0.5.0 (November 2014). , (2014).</li><li>. dplyr: A Grammar of Data Manipulation Available from: <a target="_blank" href="https://cran.r-project.org/web/packages/dplyr/index.html">https://cran.r-project.org/web/packages/dplyr/index.html</a> (2016)</li><li>Riley, R. S., Ben-Ezra, J. M., Massey, D., Slyter, R. L., Romagnoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+Photography:+A+Primer+for+Pathologists.">Digital Photography: A Primer for Pathologists.</a> Journal of Clinical Laboratory Analysis. 18 (2), 91-128 (2004).</li><li>Singh, S., Bray, M. A., Jones, T. R., Carpenter, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pipeline+for+illumination+correction+of+images+for+high-throughput+microscopy.">Pipeline for illumination correction of images for high-throughput microscopy.</a> Journal of Microscopy. 256 (3), 231-236 (2014).</li><li>Eastwood, B. S., Childs, E. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+Alignment+for+Multiple+Camera+High+Dynamic+Range+Microscopy.">Image Alignment for Multiple Camera High Dynamic Range Microscopy.</a> Proceedings. IEEE Workshop on Applications of Computer Vision. , 225-232 (2012).</li><li>High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis. IEEE Journal of Selected Topics in Signal Processing (Special Issue on: Digital Image Processing Techniques for Oncology) Available from: <a target="_blank" href="https://www.lfb.rwth-aachen.de/en/">https://www.lfb.rwth-aachen.de/en/</a> (2009)</li><li>Jain, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Supervised+Learning+of+Image+Restoration+with+Convolutional+Networks.">Supervised Learning of Image Restoration with Convolutional Networks.</a> 2007 IEEE 11th International Conference on Computer Vision. , 1-8 (2007).</li></ol>

Although the image analysis system is fairly robust and QC steps are taken to ensure poor images are removed, proper attention to specific issues in sampling, plate preparation, and image acquisition can improve both the accuracy of the data and the proportion of images passing QC.
Sampling concentration 
Assuming a representative sample has been taken, the most important step is to ensure that sufficient particles are present for representative9 a...

The purpose of this method is to provide a well-defined, repeatable, and partially-automated method for determining size and shape distributions of particles in bioreactors, particularly those containing activated sludge flocs and aerobic granules1,2. The rationale behind this method were to enhance the affordability, simplicity, throughput, and repeatability of our existing in-house protocols3,4, ease particle measurement for others, and facilitate sharing and comparison of data.
There are two broad categories of particle measurement analysis - direct imaging and inferential methods using such qualities as light scattering5. Although inferential methods can be automated and have large throughput, the equipment is expensive. In addition, while inferential methods can accurately determine the equivalent size of a particle6, they do not provide detailed shape information7.
Because of the need for shape data, we have based our method on direct imaging. While some high-throughput imaging methods exist, they have traditionally required either expensive commercial hardware or custom built solutions8,9. Our method has been developed to employ common, affordable hardware and software that, although suffering from a reduction in throughput, produces far more particle images than the minimum needed for many analyses10.
Existing protocols may not specify important sampling and image acquisition steps. Other protocols may specify manual steps that introduce subjective bias (such as ad hoc thresholding11). A well-defined method that specifies sampling, immobilization and image acquisition steps combined with freely available analysis software will enhance both within-lab image analysis and comparisons between labs. A major goal of this protocol is to provide a workflow and tools that should lead to reproducible results from different labs for the same sample.
Apart from normalizing the image analysis process, the data produced by this pipeline is recorded in a well-defined, well-formatted file12 suitable for use by popular data analysis packages13,14, easing experiment specific analyses (such as custom figure generation) and facilitating data sharing between labs.
This protocol is especially suggested for researchers who require particle shape data, do not have access to inferential methods, do not wish to develop their own image analysis pipeline, and wish to share their data easily with others

<table>
	<tbody>
		<tr>
			<td>10% Bleach solution</td>
			<td>Chlorox</td>
			<td>31009</td>
			<td>For workspace disinfection.</td>
		</tr>
		<tr>
			<td>15 mL centrifuge tube with cap</td>
			<td>Corning</td>
			<td>430790</td>
			<td>Per sample.</td>
		</tr>
		<tr>
			<td>50 mL Erlenmeyer flask</td>
			<td>Corning</td>
			<td>4980-50</td>
			<td>Other vessels are suitable so long as they can contain &#62; 40 mL of sample and allow mixing</td>
		</tr>
		<tr>
			<td>500 mL Kimax Bottle</td>
			<td>Kimble-Chase</td>
			<td>14395-50</td>
			<td>Or otherwise sufficient for agar handling</td>
		</tr>
		<tr>
			<td>Agar</td>
			<td>BD</td>
			<td>214010</td>
			<td>Solid, to prepare 7.5% gel. 7 mL per sample.</td>
		</tr>
		<tr>
			<td>Data analysis software</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>R or Python are suggested</td>
		</tr>
		<tr>
			<td>Deionized water</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Sufficient to prepare stain and agar. If unavailable, tap should be fine.</td>
		</tr>
		<tr>
			<td>Desktop computer</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Image analysis is not CPU intensive, any &#39;ordinary&#39; desktop computer circa 2017 should be sufficient.</td>
		</tr>
		<tr>
			<td>External hard drive</td>
			<td>Seagate</td>
			<td>STEB5000100</td>
			<td>Not fully required, but extremely useful given the number an size of images. 2 or more TB of storage suggested.</td>
		</tr>
		<tr>
			<td>FIJI</td>
			<td>NIH</td>
			<td>version 1.51d</td>
			<td>Version is ImageJ core. Plugins are updated as of writing. Available at: https://imagej.net/Fiji/Downloads</td>
		</tr>
		<tr>
			<td>GIT</td>
			<td>Open Source</td>
			<td>version 2.19.1 or later</td>
			<td>Available at: https://git-scm.com/</td>
		</tr>
		<tr>
			<td>Image capture software</td>
			<td>ToupView</td>
			<td>version 3.7.5177</td>
			<td>Any compatible with camera, may come with camera. Should allow saving TIFF images with spatial calibration data.</td>
		</tr>
		<tr>
			<td>Mechanical (X/Y) Stage</td>
			<td>OMAX</td>
			<td>A512</td>
			<td>Not fully required, but greatly aids image acquisition.</td>
		</tr>
		<tr>
			<td>Methylene blue</td>
			<td>Fisher</td>
			<td>M291-100</td>
			<td>Solid, to prepare 1% w/v solution. 5 uL solution per sample.</td>
		</tr>
		<tr>
			<td>Microscope camera</td>
			<td>OMAX</td>
			<td>A35140U</td>
			<td>Any digitial camera compatible with microscope. Resolution providing at least 5 um per pixel at 10x magnification and a dynamic range of at least 8 bits per pixel per color channel is suggested.</td>
		</tr>
		<tr>
			<td>Optical Stage Micrometer</td>
			<td>OMAX</td>
			<td>A36CALM1</td>
			<td>Or otherwise sufficient for spatial calibration.</td>
		</tr>
		<tr>
			<td>Petri dish, 100 mm</td>
			<td>Fisher</td>
			<td>FB0875712</td>
			<td>1 per sample.</td>
		</tr>
		<tr>
			<td>PPE</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Standard lab coat, gloves, and eyewear.</td>
		</tr>
		<tr>
			<td>Sparmoria macro</td>
			<td>NCSU</td>
			<td>version 0.2.1</td>
			<td>Available at github repository : https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis</td>
		</tr>
		<tr>
			<td>Stereo/dissecting microscope</td>
			<td>Nikon</td>
			<td>SMZ-2T</td>
			<td>Should provide 10 to 20x magnficiation and allow digital photos either with a buit-in camera or profide a mounting point for a CCD.</td>
		</tr>
	</tbody>
</table>

measuring the shape and size of activated sludge particles immobilized in agar with an open source software pipeline

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and shape of activated sludge flocs can indicate the conditions at the microscale, and also directly affect how well the sludge settles in a clarifier.
Particle size and shape are both misleadingly &#39;simple&#39; measurements. Many subtle issues, often unaddressed in informal protocols, can arise when sampling, imaging, and analyzing particles. Sampling methods may be biased or not provide enough statistical power. The samples themselves may be poorly preserved or undergo alteration during immobilization. Images may not be of sufficient quality; overlapping particles, depth of field, magnification level, and various noise can all produce poor results. Poorly specified analysis can introduce bias, such as that produced by manual image thresholding and segmentation.
Affordability and throughput are desirable alongside reproducibility. An affordable, high throughput method can enable more frequent particle measurement, producing many images containing thousands of particles. A method that uses inexpensive reagents, a common dissecting microscope, and freely-available open source analysis software allows repeatable, accessible, reproducible, and partially-automated experimental results. Further, the product of such a method can be well-formatted, well-defined, and easily understood by data analysis software, easing both within-lab analyses and data sharing between labs.
We present a protocol that details the steps needed to produce such a product, including: sampling, sample preparation and immobilization in agar, digital image acquisition, digital image analysis, and examples of experiment-specific figure generation from the analysis results. We have also included an open-source data analysis pipeline to support this protocol.

Files Generated 
The process illustrated in Figure 1 will produce two files per image analyzed. The first file is a comma separated value (CSV) text file where each row corresponds to an individual particle and the columns describe various particle metrics such as area, circularity, and solidity and defined in the ImageJ manual17. Example CSV files are included as supplemental information and in the examples/data ...

Watch this Scientific Journal Video about Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline at JoVE.com

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

The size and shape of particles in activated sludge are important parameters that are measured using varying methods. Inaccuracies arise from non-representative sampling, suboptimal images, and subjective analysis parameters. To minimize these errors and ease measurement, we present a protocol specifying every step, including an open source software pipeline.

Experimental bioreactors, such as those treating wastewater, contain particles whose size and shape are important parameters. For example, the size and ...

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