This work was supported by a grant from the National Science Foundation CBET&#160;1336544.
The FIJI, R, and Python logos are used with the in accordance with the following trademark policies: 
Python: <a href="https://www.python.org/psf/trademarks/">https://www.python.org/psf/trademarks/</a> 
R: <a href="https://www.r-project.org/logo/">https://www.r-project.org/Logo/</a> , as per the CC-BY-SA 4.0 license listed at: <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/Licenses/by-sa/4.0/</a> 
Fiji: <a href="https://imagej.net/Licensing">https://imagej.net/Licensing</a>

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 through 2.7).</li>
		<li>Visually estimate which (if any) sample volumes best meet the criteria listed in step 1.1.</li>
		<li>If particles still overlap for the 0.5 mL sample, repeat steps 1.1.2 and 1.1.3 with three 0.5 mL samples diluted with an added 0.5, 1, and 2 mL of phosphate buffered saline to determine the degree to which a 0.5 mL sample must be diluted prior to step 2.1. 
		NOTE: Steps 1.1 &#8722; 1.1.4 need only be performed once per experiment, or if the reactor contents change such that subsequent measurements no longer meet the criteria listed in step 1.1.</li>
	</ol>
	</li>
	<li>Acquire a representative sample from a well-mixed portion of the reactor by grabbing ~40 mL in a beaker or 50 mL centrifuge tube, gently mixing, and immediately pouring the determined sample volume of the well-mixed grab into a 15 mL centrifuge tube. Dilute the sample, necessary, as determined by step 1.1.4. 
	NOTE: The protocol can be paused here and the sample may be stored under refrigeration (4 &#176;C) for up to 48 hours. Do not freeze the sample. 
	CAUTION: Common preservation media (e.g., formaldehyde/formamide) are not suitable. The large surface area of the plate, in combination with the open container, heat from the light source, and potentially poorly ventilated microscopy setup produce unnecessarily hazardous conditions for little gain in image quality.</li>
</ol>
2. Prepare agar plates of stained, immobilized particles
<ol>
	<li>Add 5 &#181;L of 1% (w/v) methylene blue to each sample, then cap and gently invert at 3 least times to mix. Allow samples to stain for at least 5 but no more than 30 minutes at room temperature.</li>
	<li>Prepare approximately 10 mL per sample of 7.5% (w/v) agar in deionized water. 
	NOTE: Agar may be produced ahead of time and stored indefinitely if sterilized. Agarose may be substituted, but does not substantially improve images.</li>
	<li>Melt agar using a microwave or water bath and allow to cool slightly before use. Ensure the agar is completely melted and pours easily. Solid globules of agar will stain differently, producing poor quality images.</li>
	<li>Transfer sufficient melted 7.5% (w/v) agar to the centrifuge tube to bring the total tube volume to between 6.5 and 9 mL.</li>
	<li>Recap centrifuge tubes and gently invert at least 3 times to mix.</li>
	<li>While pointing the cap away from oneself or in a hood, open the cap. Pour the tube contents into a 100 mm plastic Petri dish while gently rocking the dish to achieve a full, smooth coating and a visually uniform distribution of particles. 
	CAUTION: The heat from the agar may produce a slight overpressure in the tube. This often produces an audible hiss and has the potential to expel small droplets of hot agar.</li>
	<li>Allow the plates to cool at room temperature for at least 5 minutes, until the agar solidifies. 
	NOTE: The protocol may be paused here. Store plated inverted and sealed (e.g., in a sealing plastic bag or with paraffin film) for up to 48 hours under refrigeration (4 &#176;C).</li>
</ol>
3. Acquire particle images using a stereomicroscope and digital camera
<ol>
	<li>Place the uncovered plate face up on the microscope stage of a stereomicroscope capable of 10x to 20x magnification. Illuminate the sample from below with even, diffuse light using equipment such as an LED illuminator stand or light plate.</li>
	<li>Open the image capture software, ensure the microscope light path is set to Photo, and click on the appropriate camera from the camera list.</li>
	<li>Adjust the microscope so that multiple particles appear in the software in the focal plane with large, well-defined edges. Use a magnification of 10-20x to measure particles while maintaining a relatively deep focal plane.
	<ol>
		<li>Temporarily remove the agar plate and place the micrometer on the stage. Adjust the fine focus until the graduations on the micrometer appear sharply focused in the image capture software.</li>
		<li>If not previously calibrated, record the pixel to micro ratio for the current magnification.
		<ol>
			<li>Set the zoom to 100% by clicking Zoom &#62; Actual Size and select Options &#62; Calibrate then align the red calibration bar in the main viewport along the long axis of the micrometer, with the vertical bars centered on the 0 and 200 &#181;m graduations. In the Calibrate dialog box, enter the current magnification level and actual length of 200 &#181;m.</li>
		</ol>
		</li>
		<li>If already calibrated select Magnification from the menu bar, and the select the current magnification level and confirm the calibration.
		<ol>
			<li>Select Measurements &#62; Line &#62; Arbitrary Line. Click on the intersection of the 0 graduation and long axis of the micrometer. Click again on the intersection at 200 and the long the axis. A correct calibration should display approximately 200 &#181;m. Delete the line by clicking on it, pressing delete, and pressing Yes on the confirmation box. 
			NOTE: The instructions given for selecting the camera and calibration are specific to the software used for this hardware. Similar functions should be available in other imaging software. The goal is to determine the pixel to micron ratio of the image for accurate particle size measurement.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Replace the agar plate and adjust the fine focus to achieve maximum detail in the imaging software.</li>
	<li>Adjust the imaging software so that maximum image quality is achieved.
	<ol>
		<li>Increase the bit depth to the maximum value allowed, by selecting the radio button in the Bit Depth panel of the Camera sidebar. Set the software to acquire grayscale images by selecting the appropriate radio button in the Color/Gray panel of the Camera sidebar.</li>
		<li>Collapse any open sidebar panels between exposure and histogram. Reduce the gain to 1.0 and increase the exposure until a clear image appears in the viewport and until the histogram appears as a distribution that is not clipped by either end of the histogram box.</li>
		<li>Adjust the histogram to avoid over and underexposure. In the histogram panel of the camera side bar, slide the left boundary of the histogram to just outside the lowest values and the right boundary to just outside the highest values.</li>
	</ol>
	</li>
	<li>Save the image as an uncompressed TIFF, including magnification information in the image metadata, using the File &#62; Save as dialog box, selecting the TIFF format, and ensuring that the Save with calibration information box is checked. 
	NOTE: Saving image metadata, including spatial calibration, may vary between acquisition programs. FIJI15, the underlying software used by the pipeline, understands most common variants. The important information to record is the pixel height, width, and associated unit(s).</li>
	<li>Using either a mobile stage or manually moving the plate itself, select another area, which does not overlap previous images, following a path which alternates between left-to-right and right-to-left as one moves down the plate; also known as a &#8216;lawnmower search pattern&#8217;. Repeat step 3.6 until sufficient images are produced to capture at least 500 visually estimated particles, more are better. 
	NOTE: Alternatives patterns (e.g., circular, random) are acceptable but should be reported. Acquiring multiple overlapping images for combination into a mosaic via digital stitching produces a resulting file size which greatly hinders downstream processing and artifacts from stitching may be introduced and is not currently recommended.</li>
	<li>Retain plates until after image analysis for potential follow-up imaging. After final imaging, discard as appropriate for biological waste.</li>
</ol>
4. Measure and analyze particle silhouettes
<ol>
	<li>Install the required image analysis software packages
	<ol>
		<li>Install FIJI (an enhanced version of the National Institutes of Health&#8217;s ImageJ v1.52e following the instructions at: https://imagej.net/Fiji/Downloads</li>
		<li>Install git, if not already present, by following the instructions at: https://git-scm.com/downloads</li>
		<li>Acquire the particle analysis code from by cloning the git repository16.
		<ol>
			<li>At the command line, retrieve the latest version of the code by typing: 
			git clone https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis.git
			<ol>
				<li>Install the analysis code following the instructions in the README.md text file found in the top-level directory of the cloned repository. 
				NOTE: Using git is preferred, as it will automatically retrieve the latest version of the code. If git is not available, it is also possible to download the code as a zip file on the release page at: https://github.com/joeweaver/SParMorIA-Sludge-Particle-Morphological-Image-Analysis/releases</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Edit a text file listing the directories to be processed, along with optional parameters. Refer to the examples/analysis subdirectory for a list of parameters and examples.</li>
	</ol>
	</li>
	<li>Run the analysis on the command line by typing: 
	&#60;FIJI-PATH&#62;\ImageJ-win64.exe --console -macro SParMorIA-SludgeParticle_Morphological_Image_Analysis &#60;paramsfile&#62; 
	where &#60;FIJI-path&#62; is the directory in which ImageJ-win64.exe is located and &#60;paramsfile&#62; the location of the text file describing the analysis setup 
	NOTE: The name of the executable may differ, depending on which operating system FIJI is installed on. Depending on the number and size of images, the analysis may take a few minutes to hours and will run automatically.</li>
	<li>Perform a quality control check
	<ol>
		<li>Examine the quality control files located in the overlay subdirectory of the specified output directory. Note images with spurious, missed, and poorly captured particles, all apparent as shaded outlines which do not match the background. Refer to Figure 3 for examples of such. The particle data are now ready for experiment-specific analysis figure generation.</li>
	</ol>
	</li>
	<li>Reject either whole plates or individual particles by specifying in the analysis code the noted files and/or particle IDs to be ignored. Refer to examples/censoring in the repository for relevant R and Python code.</li>
	<li>Generate experiment specific figures using the image analysis results for each image are stored in a TIDY12 comma separated text file in the results subdirectory of the specified output directory. Refer to the examples/figures/R and examples/figures/Python subdirectories for examples of how to read the results files.</li>
</ol>

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The authors have nothing to disclose.

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 &gt; 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 &#039;ordinary&#039; 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 ...

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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 ...

measuring-shape-size-activated-sludge-particles-immobilized-agar-with

Research

JoVE Journal

Environment

7.1K Views.  North Carolina State University. De grootte en vorm van actieve slibdeeltjes is van cruciaal belang voor de efficiëntie van de afvalwaterbehandeling. Toch is hun meting vaak ad hoc. Dit protocol biedt een herhaalbare, goed gedefinieerde en semi-automatiseerbare meting voor hun meting.Het belangrijkste voordeel van dit protocol is dat het een grote populatie van deeltjes kan meten over een breed scala van morfologieën, terwijl het verminderen van subjectieve en systematische bias. Hoewel deze techniek is gericht op a...

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

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

There are few high-throughput in vitro systems which facilitate the development of multi-species biofilms that contain numerous species commonly detected within in vivo oral biofilms. Furthermore, a system that uses natural human saliva as the nutrient source, instead of artificial media, is particularly desirable in order to support the expression of cellular and biofilm-specific properties that mimic the in vivo communities. We describe a method for the development of multi-species oral biofilms that are comparable, with respect to species composition, to supragingival dental plaque, under conditions similar to the human oral cavity. Specifically, this methods article will describe how a commercially available microfluidic system can be adapted to facilitate the development of multi-species oral biofilms derived from and grown within pooled saliva. Furthermore, a description of how the system can be used in conjunction with a confocal laser scanning microscope to generate 3-D biofilm reconstructions for architectural and viability analyses will be presented. Given the broad diversity of microorganisms that grow within biofilms in the microfluidic system (including Streptococcus, Neisseria, Veillonella, Gemella, and Porphyromonas), a protocol will also be presented describing how to harvest the biofilm cells for further subculture&#160;or DNA extraction and analysis. The limits of both the microfluidic biofilm system and the current state-of-the-art data analyses will be addressed. Ultimately, it is envisioned that this article will provide a baseline technique that will improve the study of oral biofilms and aid in the development of additional technologies that can be integrated with the microfluidic platform.

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

The goal of this methods paper is to describe the use of a microfluidic system for the development of multi-species biofilms that contain species typically identified in human supragingival dental plaque.&#160;Methods to describe biofilm architecture, biofilm viability, and an approach to harvest biofilm for culture-dependent or culture-independent analyses are highlighted.

There are few high-throughput in vitro systems which facilitate the development of multi-species biofilms that contain numerous species commonly detected ...

Bioengineering

Modeling Methanotroph Niches – The Gradient Syringe Model Ecosystem

Agarose-Based Model Ecosystem for Cultivating Methanotrophs in a Methane-Oxygen Counter Gradient

Aerobic methane-oxidizing bacteria, known as methanotrophs, serve important roles in biogeochemical cycling. Methanotrophs occupy a specific environmental niche within methane-oxygen counter gradients found in soils and sediments, which influences their behavior on an individual and community level. However, conventional methods to study the physiology of these greenhouse gas-mitigating microorganisms often use homogeneous planktonic cultures, which do not accurately represent the spatial and chemical gradients found in the environment. This hinders scientists' understanding of how these bacteria behave in situ. Here, a simple, inexpensive model ecosystem called the gradient syringe is described, which uses semi-solid agarose to recreate the steep methane-oxygen counter gradients characteristic of methanotrophs' natural habitats. The gradient syringe allows for the cultivation of methanotrophic strains and the enrichment of mixed methane-oxidizing consortia from environmental samples, revealing phenotypes only visible in this spatially resolved context. This protocol also reports various biochemical assays that have been modified to be compatible with the semi-solid agarose matrix, which may be valuable to researchers culturing microorganisms within other agarose-based systems.

Author Spotlight: Designing Simple and Inexpensive Techniques to Grow Methane-Oxidizing Bacteria in the Laboratory

A protocol is described for preparing a simple model ecosystem that recreates the methane-oxygen counter gradient found in the natural habitat of aerobic methane-oxidizing bacteria, enabling the study of their physiology in a spatially resolved context. Modifications to common biochemical assays for use with the agarose-based model ecosystem are also described.

Aerobic methane-oxidizing bacteria, known as methanotrophs, serve important roles in biogeochemical cycling. Methanotrophs occupy a specific environmental ...

Separation and Identification of Conventional Microplastics from Farmland Soils

Microplastics (MPs) pollution in the terrestrial environment has received increasing attention over the last decade, with increasing studies describing the numbers and types of MPs in different soil systems and their impacts on soil and crop health. However, different MPs extraction and analytical methods are used, limiting opportunities to compare results and generate reliable evidence for industry advice and policymakers. Here, we present a protocol that describes the methodology for sampling, separation, and chemical identification of conventional MPs&#160;from soil. The method is low-cost, and the materials are readily available. This enhances operational ease and may help with widespread adoption. The protocol provides detailed information on sample collection from the top 0-30 cm of soil using plastic-free utensils; simulation of different soil types through the use of various solid media (such as bentonite clay, silicon dioxide, and non-contaminated soil), with the addition of the same mass&#160;of polyethylene(PE)-MPs for subsequent quantification; density separation of plastic particles utilizing saturated sodium chloride (NaCl) solution and digestion of organic impurities in the supernatant using 4 M sodium hydroxide (NaOH) solution; quantification of particles using fluorescent microscopy after Nile Red staining; and polymer identification using micro&#160;Fourier-Transform Infrared Spectroscopy (&#956;-FTIR) or Laser-Direct Infrared (LDIR) spectroscopy. The MPs recovery rate ranged from 83% - 90% for the abovementioned media. This protocol presents an efficient method for soil MPs analysis that is optimized for feasibility, applicability, and cost-effectiveness. Moreover, the video accompanied can guide the process of analyzing the soil MPs step-by-step virtually. This study is dedicated to standardizing the methods for soil MPs analysis, enhancing the connectivity and comparability of measurements, and establishing a foundation for more standardized and scientific research.

Presented here is a method for extracting microplastics from soil and identifying their polymer types. The method has been optimized for execution, applicability, and cost-effectiveness. It lays a scientific foundation for standardizing the analytical method to identify microplastics in soils.

Microplastics (MPs) pollution in the terrestrial environment has received increasing attention over the last decade, with increasing studies describing ...

Quantifying Bacterial Surface Swarming Motility on Inducer Gradient Plates

Bacterial swarming motility is a common microbiological phenotype that bacterial communities use to migrate over semisolid surfaces. In investigations of induced swarming motility, specific concentration&#160;of an inducer may not be able to report events occurring within the optimal concentration range to elicit the desired responses from a species. Semisolid plates containing multiple concentrations are commonly used to investigate the response within an inducer concentration range. However, separate semisolid plates increase variations in medium viscosity and moisture content within each plate due to nonuniform solidification time.
This paper describes a one-step method to simultaneously test surface swarming motility on a single gradient plate, where the isometrically arranged test wells allow the simultaneous acquisition of multiconcentration responses. In the present work, the surface swarming of Escherichia coli K12 and Pseudomonas aeruginosa PAO1 were evaluated in response to a concentration gradient of inducers such as resveratrol and arabinose. Periodically, the swarm morphologies were imaged using an imaging system to capture the entire surface swarming process.
The quantitative measurement of the swarm morphologies was acquired using ImageJ software, providing analyzable information of the swarm area. This paper presents a simple gradient swarm plate method that provides qualitative and quantitative information about the inducers&#39; effects on surface swarming, which can be extended to study the effects of other inducers on a broader range of motile bacterial species.

Here, we describe the use of inducer gradient plates to evaluate bacterial swarming motility while simultaneously obtaining multiple concentration responses.

Bacterial swarming motility is a common microbiological phenotype that bacterial communities use to migrate over semisolid surfaces. In investigations of ...

Biochemistry

Semi-Automated Phenotypic Analysis of Functional 3D Spheroid Cell Cultures

We present a protocol that describes the properties and advantages of using a standalone clinostat incubator for growing, treating, and monitoring 3D cell cultures. The clinostat mimics an environment where cells can assemble as highly reproducible spheroids with low shear forces and active nutrient diffusion. We demonstrate that both cancer and non-cancer hepatocytes (HepG2/C3A and THLE-3 cell lines) require 3 weeks of growth prior to achieving functionalities comparable to liver cells. This protocol highlights the convenience of utilizing incubators for 3D cells with cameras monitoring the cell growth, as snapshots can be taken to count and measure spheroids upon treatment. We describe the comparison of THLE-3 and HepG2/C3A cell lines, showing how non-cancerous cell lines can be grown as well as immortalized cancer cells. We demonstrate and illustrate how proteomics experiments can be conducted from a few spheroids, which can be collected without perturbing cell signaling, i.e., no trypsinization required. We show that proteomics analysis can be used to monitor the typical liver phenotype of respiratory chain metabolism and the production of proteins involved in metal detoxification and describe a semi-automated system to count and measure the spheroid's area. Altogether, the protocol presents a toolbox that comprises a phenotypic characterization via image capture and a proteomics pipeline to experiment on 3D cell culture models.

We present a protocol for growing high-reproducible spheroids and their phenotypic characterization using image capture and proteomics.

We present a protocol that describes the properties and advantages of using a standalone clinostat incubator for growing, treating, and monitoring 3D cell ...

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved oxygen concentration). Nevertheless, the morphology of cells can be a suitable indicator for optimal conditions, since it changes with dependence on the growth state, product accumulation and cell stress. Furthermore, the single-cell size distribution provides not only information about the cultivation conditions, but also about the population heterogeneity. To gain such information, a photo-optical in situ microscopy device1 was developed to enable the monitoring of the single-cell size distribution directly in the cell suspension in bioreactors. An automated image analysis is coupled to the microscopy based on a neural network model, which is trained with user-annotated images. Several parameters, which are gained from the captures of the microscope, are correlated to process relevant features of the cells, like their metabolic activity. Until now, the presented in situ microscopy probe series was applied to measure the pellet size in filamentous fungi suspensions. It was used to distinguish the single-cell size in microalgae cultivation and relate it to lipid accumulation. The shape of cellular particles was related to budding in yeast cultures. The microscopy analysis can be generally split into three steps: (i) image acquisition, (ii) particle identification, and (iii) data analysis, respectively. All steps have to be adapted to the organism, and therefore specific annotated information is required in order to achieve reliable results. The ability to monitor changes in cell morphology directly in line or on line (in a by-pass) enables real-time values for monitoring and control, in process development as well as in production scale. If the off line data correlates with the real-time data, the current tedious off line measurements with unknown influences on the cell size become needless.

In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

A photo-optical in situ microscopy device was developed to monitor the size of single cells directly in the cell suspension. The real-time measurement is conducted by coupling the photo-optical sterilizable probe to an automated image analysis. Morphological changes appear with dependence on the growth state and cultivation conditions.

In situ monitoring in microbial bioprocesses is mostly restricted to chemical and physical properties of the medium (e.g.,pH value and the dissolved ...

Real-time Imaging and Quantification of Fungal Biofilm Development Using a Two-Phase Recirculating Flow System

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. While models for the growth under flow have been developed, many of these systems are expensive, or do not allow imaging while the cells are under flow. We have developed a novel apparatus that allows us to image the growth and development of Candida albicans cells under flow and in real-time. Here, we detail the protocol for the assembly and use of this flow apparatus, as well as the quantification of data that are generated. We are able to quantify the rates that the cells attach to and detach from the slide, as well as to determine a measure of the biomass on the slide over time. This system is both economical and versatile, working with many types of light microscopes, including inexpensive benchtop microscopes, and is capable of extended imaging times compared to other flow systems. Overall, this is a low-throughput system that can provide highly detailed real-time information on the biofilm growth of fungal species under flow.

We describe the assembly, operation, and cleaning of a flow apparatus designed to image fungal biofilm formation in real time while under flow. We also provide and discuss quantitative algorithms to be used on the acquired images.

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. ...

Immunology and Infection

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Pseudomonas aeruginosa (Pa) is one of the most common opportunistic pathogens associated with cystic fibrosis (CF). Once Pa colonization is established, a large proportion of the infecting bacteria form biofilms within airway sputum. Pa biofilms isolated from CF sputum have been shown to grow in small, dense aggregates of ~10-1,000 cells that are spatially organized and exhibit clinically relevant phenotypes such as antimicrobial tolerance. One of the biggest challenges to studying how Pa aggregates respond to the changing sputum environment is the lack of nutritionally relevant and robust systems that promote aggregate formation. Using a synthetic CF sputum medium (SCFM2), the life history of Pa aggregates can be observed using confocal laser scanning microscopy (CLSM) and image analysis at the resolution of a single cell. This in vitro system allows the observation of thousands of aggregates of varying size in real time, three dimensions, and at the micron scale. At the individual and population levels, having the ability to group aggregates by phenotype and position facilitates the observation of aggregates at different developmental stages and their response to changes in the microenvironment, such as antibiotic treatment, to be differentiated with precision.

Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

Synthetic cystic fibrosis sputum medium (SCFM2) can be utilized in combination with both confocal laser scanning microscopy and fluorescence-activated cell sorting to observe bacterial aggregates at high resolution. This paper details methods to assess aggregate populations during antimicrobial treatment as a platform for future studies.

Pseudomonas aeruginosa (Pa) is one of the most common opportunistic pathogens associated with cystic fibrosis (CF). Once Pa colonization is established, a ...

Plasmid Stability Analysis with Open-Source Droplet Microfluidics

Plasmids play a vital role in synthetic biology by enabling the introduction and expression of foreign genes in various organisms, thereby facilitating the construction of biological circuits and pathways within and between cell populations. For many applications, maintaining functional plasmids without antibiotic selection is critical. This study introduces an open-hardware-based microfluidic workflow for analyzing plasmid retention by culturing single cells in gel microdroplets and quantifying microcolonies using fluorescence microscopy. This approach allows for the parallel analysis of numerous droplets and microcolonies, providing greater statistical power compared to traditional plate counting and enabling the integration of the assay into other droplet microfluidic workflows. By using plasmids expressing fluorescent proteins alongside a non-specific fluorescent DNA stain, single colonies can be identified and differentiated based on plasmid loss or fluorescent marker expression. Notably, this advanced workflow, implemented with open-source hardware, offers precise flow control and temperature management of both the sample and the microfluidic chip. These features enhance the workflow&#39;s ease of use, robustness, and accessibility. While the study focuses on Escherichia coli as the experimental model, the method&#39;s true potential lies in its versatility. It can be adapted for various studies requiring fluorescence signal quantification from plasmids or stains, as well as for other applications. The adoption of open-source hardware broadens the potential for conducting high-throughput bioanalyses using accessible technology in diverse research settings.

An accessible, open-source microfluidic workflow is presented for the parallelized analysis of plasmid retention in bacteria. By employing fluorescence microscopy to quantify plasmid presence in single-cell microcolonies within gel microdroplets, this method provides a precise, accessible, and scalable alternative to traditional plate counting.

Plasmids play a vital role in synthetic biology by enabling the introduction and expression of foreign genes in various organisms, thereby facilitating ...

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

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