Name: automated 3d optical coherence tomography to elucidate biofilm morphogenesis over large spatial scales
Uploaded: 2019-08-21T13:00:00
Description: Watch this Scientific Journal Video about Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales at JoVE.com

We thank Mauricio Aguirre Morales for his contribution to the development of this system. &#160;Financial support came from the Swiss National Science Foundation to T.J.B.

1. Setup of the Positioning Device
<ol>
	<li>Wire the positioning device to a microcontroller board, following the instruction in https://github.com/grbl/grbl/wiki/Connecting-Grbl.</li>
	<li>Connect the microcontroller to a single-board computer with internet connection via a USB cable and install the GRBL server as described in https://gitlab.com/FlumeAutomation/GRBL_Server.git. Now the positioning device should be navigable from a webpage hosted at http://IP:5020/. Alternatively, the positioning device can be navigat...

<ol>
	<li>Flemming, H. C., Wingender, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biofilm+matrix.">The biofilm matrix.</a> Nature reviews. Microbiology. 8, 623-633 (2010).</li><li>Flemming, H. -. C., 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=Biofilms:+an+emergent+form+of+bacterial+life.">Biofilms: an emergent form of bacterial life.</a> Nature reviews. Microbiology. 14, 563 (2016).</li><li>Stoodley, P., Lewandowski, Z., Boyle, J. D., Lappin-Scott, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oscillation+characteristics+of+biofilm+streamers+in+turbulent+flowing+water+as+related+to+drag+and+pressure+drop.">Oscillation characteristics of biofilm streamers in turbulent flowing water as related to drag and pressure drop.</a> Biotechnology and Bioengineering. 57, 536-544 (1998).</li><li>Stoodley, P., Lewandowski, Z., Boyle, J. D., Lappin-Scott, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+formation+of+migratory+ripples+in+a+mixed+species+bacterial+biofilm+growing+in+turbulent+flow.">The formation of migratory ripples in a mixed species bacterial biofilm growing in turbulent flow.</a> Environmental microbiology. 1, 447-455 (1999).</li><li>Banin, E., Vasil, M. L., Greenberg, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Iron+and+Pseudomonas+aeruginosa+biofilm+formation.">Iron and Pseudomonas aeruginosa biofilm formation.</a> Proceedings of the Natural Academy of Sciences U.S.A. 102, 11076-11081 (2005).</li><li>Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M., Packmann, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ecology+and+biogeochemistry+of+stream+biofilms.">The ecology and biogeochemistry of stream biofilms.</a> Microbiology. 14, 251-263 (2016).</li><li>Battin, T. J., 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=Microbial+landscapes:+new+paths+to+biofilm+research.">Microbial landscapes: new paths to biofilm research.</a> Nature Reviews. Microbiology. 5, 76-81 (2007).</li><li>Neu, T. R., Lawrence, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Innovative+techniques,+sensors,+and+approaches+for+imaging+biofilms+at+different+scales.">Innovative techniques, sensors, and approaches for imaging biofilms at different scales.</a> Trends in Microbiology. 23, 233-242 (2015).</li><li>Meleppat, R. K., Shearwood, C., Seah, L. K., Matham, M. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+optical+coherence+microscopy+for+the+in+situ+investigation+of+the+biofilm.">Quantitative optical coherence microscopy for the in situ investigation of the biofilm.</a> J. of Biomedical Optics. 21 (12), 127002 (2016).</li><li>Wagner, M., Horn, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+coherence+tomography+in+biofilm+research:+A+comprehensive+review.">Optical coherence tomography in biofilm research: A comprehensive review.</a> Biotechnology and Bioengineering. 114, 1386-1402 (2017).</li><li>Huang, D., 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=Optical+coherence+tomography.">Optical coherence tomography.</a> Science. 254, 1178-1181 (1991).</li><li>Haisch, C., Niessner, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualisation+of+transient+processes+in+biofilms+by+optical+coherence+tomography.">Visualisation of transient processes in biofilms by optical coherence tomography.</a> Water Resources. 41, 2467-2472 (2007).</li><li>Drexler, W., Fujimoto, J. 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> Optical Coherence Tomography: Technology and Applications. , (2008).</li><li>Fercher, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+coherence+tomography+&#8211;+development,+principles,+applications.">Optical coherence tomography &#8211; development, principles, applications.</a> Zeitschrift f&#252;r Medizinische Physik. 20, 251-276 (2010).</li><li>Lee, H. -. C., Liu, J. J., Sheikine, Y., Aguirre, A. D., Connolly, J. L., Fujimoto, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ultrahigh+speed+spectral-domain+optical+coherence+microscopy.">Ultrahigh speed spectral-domain optical coherence microscopy.</a> Biomedical Optics Express. , 41236-41254 (2013).</li><li>Fortunato, L., Leiknes, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-situ+biofouling+assessment+in+spacer+filled+channels+using+optical+coherence+tomography+(OCT):+3D+biofilm+thickness+mapping.">In-situ biofouling assessment in spacer filled channels using optical coherence tomography (OCT): 3D biofilm thickness mapping.</a> Bioresource Technology. 229, 231-235 (2017).</li><li>Niederdorfer, R., Peter, H., Battin, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attached+biofilms+and+suspended+aggregates+are+distinct+microbial+lifestyles+emanating+from+differing+hydraulics.">Attached biofilms and suspended aggregates are distinct microbial lifestyles emanating from differing hydraulics.</a> Nature Microbiology. 1, 16178 (2016).</li><li>Roche, K. R., 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=Benthic+biofilm+controls+on+fine+particle+dynamics+in+streams.">Benthic biofilm controls on fine particle dynamics in streams.</a> Water Resources. 53, 222-236 (2016).</li><li>Fortunato, L., Jeong, S., Wang, Y., Behzad, A. R., Leiknes, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+approach+to+characterize+fouling+on+a+flat+sheet+membrane+gravity+driven+submerged+membrane+bioreactor.">Integrated approach to characterize fouling on a flat sheet membrane gravity driven submerged membrane bioreactor.</a> Bioresource Technology. 222, 335-343 (2016).</li><li>Morgenroth, E., Milferstedt, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biofilm+engineering:+linking+biofilm+development+at+different+length+and+time+scales.">Biofilm engineering: linking biofilm development at different length and time scales.</a> Reviews in Environmental Science and Bio/Technology. 8, 203-208 (2009).</li></ol>

OCT imaging is well suited to resolve structures in the micrometer range with a FOV of several square millimeters. It is thus a powerful tool for biofilm research10,18. However, OCT is currently limited to a maximum scan area of 100 - 256 mm2, while biofilm structural patterns often exceed this spatial scale19, especially when morphological differentiation is driven by large scale environmental gradients20</sup...

Biofilms are a highly successful microbial lifestyle adaptation and these interphase-associated and matrix-enclosed communities of microorganisms dominate microbial life in natural and industrial settings1,2. There, biofilms form complex architectures, such as elongated streamers3, ripples4 or mushroom-like caps5 with important consequences for biofilm growth, structural stability and resistance to stress6. While much about biofilm structural differentiation has been learned from work on mono-species cultures grown in miniature flow chambers, most biofilms are highly complex communities often including members of all domains of life6. Appreciating these complex biofilms as microbial landscapes7 and understanding how biofilm structure and function interact in complex communities is thus at the forefront of biofilm research.
A mechanistic understanding of the morphogenesis of complex biofilms in response to environmental cues requires carefully designed experiments in conjunction with spatially and temporally resolved observations of biofilm physical structure across relevant scales8. However, the non-destructive observation of biofilm growth in experimental systems has been severely limited by logistic constraints such as the need to move samples (e.g., to a microscope) often damaging the delicate biofilm structure.
The protocol presented here introduces a fully automated system based on optical coherence tomography (OCT), which allows the in situ, non-invasive monitoring of biofilm morphogenesis at the mesoscale (mm range). OCT is an emerging imaging technique in biofilm research with applications in water treatment and biofouling research, medicine9 and stream ecology10. In OCT, a low coherence light source is split into a sample and reference arm; the interference of the light reflected and scattered by the biofilm (sample arm) and the light of the reference arm is analyzed. A series of axial intensity profiles (A-scans) which contains depth-resolved structural information is acquired and merged into a B-scan (a cross section). A series of adjacent B-scans composes the final 3D volume scan10. OCT provides a lateral optical resolution in the range of approximately 10 &#181;m and is therefore well suited to study mesoscopic structural differentiation of biofilms10,12. For a more detailed description of OCT, refer to Drexler and Fujimoto13and Fercher and colleagues14. Although the field-of-view of a single OCT xy-scan reaches up to hundreds of square micrometers, larger-scale patterns cannot be quantified by means of OCT in a single scan. With respect to biofilms in natural habitats such as streams and rivers, this currently limits our ability to assess biofilm morphogenesis at scales matching the physical and hydraulic template of the habitat.
In order to surpass these spatial limits and to acquire OCT scans automatically, a spectral-domain OCT imaging probe was mounted on a 3-axis positioning system. The installation permits the acquisition of several OCT scans in an overlapping mosaic pattern (tile scan), effectively achieving the tomographic imaging of surface areas up to 100 cm2. Furthermore, the high positioning precision of this system enables to reliably monitor the growth and development of biofilm features in specific sites during long-term experiments. The system is modular and individual components (i.e., positioning device and OCT) of the installation can be used as standalone solutions or flexibly combined. Figure 1 provides an overview of the hard- and software components of the installation.
The system was tested with a commercially available GRBL-controlled CNC positioning device (Table of Materials). The operating distances of this specific positioning platform are 600&#215;840&#215;140 mm, with a manufacturer-indicated accuracy of +/- 0.05 mm and a programmable resolution of 0.005 mm. GRBL is an open-source (GPLv3 License), high-performance motion control for CNC devices. Therefore, every GRBL-based (version &#62; 1.1) positioning device should be compatible with the guidelines and software packages presented here. Moreover, the software could be adapted to other stepmotor controllers with STEP-DIR input type with few modifications.
The OCT device used to assess the performance of the system (Table of Materials) features a low coherence light source with a center wavelength of 930 nm (bandwidth = 160 nm) and adjustable reference arm length and intensity. In the example presented here, an immersion adapter for dipping the OCT probe into flowing water was also used (Table of Materials). The software package developed here for automated OCT scan acquisition critically depends on the SDK provided together with the specific OCT system, however, OCT systems from the same manufacturer with different scan lenses and central wavelengths should be readily compatible.
The GRBL device is controlled by a web server installed on a single-board computer (Figure 1). This grants remote control of the device from any computer with local network or internet access. The OCT device is controlled by a separate computer, allowing the operation of the OCT system aside the automated experimental setup. Finally, the software packages include libraries to synchronize OCT probe positioning and OCT scan acquisition (i.e., to automatically acquire 3D imaging datasets in a mosaic pattern or in a set of defined positions). Defining the position of the OCT probe in 3D effectively allows to adjust the focal plane specifically for (regional) sets of scans. Specifically, on uneven surfaces, different focal planes (i.e., different positions in z direction) can be specified for each OCT scan.
A set of software packages was developed to process raw OCT scans (Table 1). Navigation of the positioning device, OCT scan acquisition and dataset processing are performed with Python-coded Jupyter notebooks, which allow remarkable flexibility in the development and optimization of the software. Two worked and annotated examples of such notebooks (for image acquisition and processing, respectively) are available from https://gitlab.com/FlumeAutomation/automated-oct-scans-acquisition.git They are intended as starting points for customization of the method. A Jupyter notebook is a web browser based application which contain cells with annotated Python code. Each step is contained in a cell of the notebook, which can be executed separately. Due to the different length of the light path through the scan lens (spherical aberration)15, the raw OCT scans appear distorted (Figure 2A). We developed an algorithm to automatically correct for this distortion in acquired OCT scans (contained in ImageProcessing.ipynb, Supplementary File 1). Furthermore, biofilm morphology can be visualized as a 2D elevation map, as was previously used in membrane systems16, and we illustrate how elevation maps obtained from scans taken in a tiling array can be stitched.
Finally, the functionality of the described laboratory installation is illustrated using a flume experiment in which phototrophic stream biofilm is exposed to a gradient of flow velocity.

<table border="0" cellpadding="0" cellspacing="0" style="width:631px;" width="631">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">OCT Probe</td>
			<td style="width:129px;">Thorlabs</td>
			<td style="width:119px;">GAN210C1</td>
			<td style="width:167px;">OCT imaging device</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">OCT scan lens</td>
			<td style="width:129px;">Thorlabs&#160;</td>
			<td style="width:119px;">OCT-LK3-BB</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Immersion adapter</td>
			<td style="width:129px;">Thorlabs&#160;</td>
			<td style="width:119px;">OCT-IMM3-SP1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Stepcraft 840 CK</td>
			<td style="width:129px;">STEPCRAFT</td>
			<td style="width:119px;">NA</td>
			<td>positioning device</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">microcontroller</td>
			<td style="width:129px;">Arduino Uno R3</td>
			<td style="width:119px;">NA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Single-board computer</td>
			<td style="width:129px;">Raspberry PI</td>
			<td style="width:119px;">NA</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">camera</td>
			<td style="width:129px;">Canon EOS 7D Mark II</td>
			<td style="width:119px;">NA</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">camera lens</td>
			<td style="width:129px;">Canon MACRO EFS 35 mm</td>
			<td style="width:119px;">NA</td>
			<td></td>
		</tr>
	</tbody>
</table>

automated 3d optical coherence tomography to elucidate biofilm morphogenesis over large spatial scales

Biofilms are a most successful microbial lifestyle and prevail in a multitude of environmental and engineered settings. Understanding biofilm morphogenesis, that is the structural diversification of biofilms during community assembly, represents a remarkable challenge across spatial and temporal scales. Here, we present an automated biofilm imaging system based on optical coherence tomography (OCT). OCT is an emerging imaging technique in biofilm research. However, the amount of data that currently can be acquired and processed hampers the statistical inference of large scale patterns in biofilm morphology. The automated OCT imaging system allows covering large spatial and extended temporal scales of biofilm growth. It combines a commercially available OCT system with a robotic positioning platform and a suite of software solutions to control the positioning of the OCT scanning probe, as well as the acquisition and processing of 3D biofilm imaging datasets. This setup allows the in situ and non-invasive automated&#160;monitoring of biofilm development and may be further developed to couple OCT imaging with macrophotography and microsensor profiling.

We demonstrate the functionality of the automated OCT imaging system using a flume experiment designed to study the spatio-temporal morphogenesis of phototrophic stream biofilms. A gradually narrowing geometry of the flumes induced gradients in flow velocity along the center of the flume (see reference17).&#160; The temporal development and structural differentiation of biofilm was monitored over 18 days&#160;with the aim to better understand the effects&#160;of hydrodynamic conditions on biofilm mo...

Watch this Scientific Journal Video about Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales at JoVE.com

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Microbial biofilms form complex architectures at interphases and develop into highly scale-dependent spatial patterns. Here, we introduce an experimental system (hard- and software) for the automated acquisition of 3D optical coherence tomography (OCT) datasets. This toolset allows the non-invasive and multi-scale characterization of biofilm morphogenesis in space and time.

Biofilms are a most successful microbial lifestyle and prevail in a multitude of environmental and engineered settings. Understanding biofilm ...

Here we present an automated system based on optical coherence tomography or OCT. This allows us to monitor biofilms'structure over large spatial scales or extended periods of time. OCT imaging is well suited to resolve structures in the micrometer range, however it's currently limited to a maximum area of about 250 square millimeters.Biofilms often exceed this scale, especially when the differentiation is driven by large-scale environmental gradients. The experimental setup allow us to monitor the three-dimensional morphogenesis of biofilms over large spatial scales and extended periods of time. The system is fast, precise, and it works autonomously.We studied the morphogenesis of biofilms in streams where they drive important ecosystem processes. However, the system may be used to study biofilms in other natural system or engineered environments. The software for positioning, image acquisition, and analysis is written in Python.They are available through Jupyter Notebooks. These are user-friendly, freely available, and flexible solutions. We believe that the visual representation of the setup helps other users to reproduce the installation and to better understand the software.We hope this inspires other researchers to adopt similar approaches. Here is an overview of the installation. The system is composed of a precision positioning device, the OCT probe, and it is assembled around a plexiglass flume.Begin by wiring the positioning device by following along with instructions posted on GitHub. Once connected, install the GRBL server as described in a separate GitLab page. Positioning system can now be controlled through this web page;alternatively it can be controlled through a Python script as shown in the worked example.Position the computer and the OCT base unit on a bench next to the experimental setup containing microfluidic devices, flow chambers, flumes, or filtration systems. If not already installed, install the OCT system together with the available software as described by the manufacturer. Then install the software packages for automated OCT scan acquisition as described in the GitLab documents linked to here.To begin image acquisition, mount the optical coherence tomography probe to the positioning device using a compatible dovetail holder. If required, install an immersion adapter on the objective lens, then power on the OCT system and the positioning device. Open the commercial OCT software, locate a site of interest, focus on the sample, and adjust the reference arm and light source intensity for optimal image quality.Note the coordinates and repeat this procedure for a number of positions while maintaining the same reference arm length and intensity. Open the ImageAcquisition. ipynb file found in this article's Supplementary File 2 in Jupyter Notebook.Each cell contains code to perform specific tasks and can be run separately via pressing Cell and then Run or Control and Enter or Shift and Enter. Follow the worked example to set the path to the required libraries to connect the positioning device to calibrate the positioning device to initialize the OCT scanner. Then adjust the acquisition parameters, including the refractive index, the size of the field of view, and the number of A scans per B scan.Further, set the signal boundaries of the OCT scan based on intensity histograms of preliminary scans and the destination folder for acquired data and metadata. Depending on the field of view and resolution, the file size may reach up to 1.5 gigabytes per OCT scan. These two parameters determine the size of the voxels in the final data set and the size of the output file.They should match the optical resolution of the OCT probe. As highlighted in the worked example, you may acquire a single OCT scan with default parameters or acquire a single scan specifying a different set of parameters. You may also provide specific coordinates to move the positioning device and acquire a single OCT scan.This feature allows you to repeatedly return to the exact same position in the experiment with high spatial accuracy. Data is saved in 8bit. raw format to save storage space.Metadata, including the OCT settings and coordinates, are saved in the same folder in a json file with the same naming convention. Alternatively, specify a list of positions of interest and acquire the respective OCT scans automatically. In order to characterize biofilm morphological structures across large environmental gradients, acquire scans in a mosaic pattern.For this, specify the number of neighboring tiles with a default overlap of 30%The raw OCT scans appear distorted. This is due to differences in path length through the optical system. We developed an algorithm which correct this distortion as shown in the worked examples.To begin image correction, open the Jupyter notebook ImageProcessing.ipynb. Following this example, first crop the OCT scans in order to exclude spurious signals and reorient the data set so that the biofilm appears above the substratum. Next correct for spherical aberration.To accomplish this, the algorithm localizes a highly reflective flat surface in the OCT scan and uses this as reference to flatten the scans. Across a 20 by 20 grid, the algorithm then identifies local maxima in signal intensity to localize the reference surface. Then a second order polynomial surface is fitted across these points and used to shift each pixel of the OCT scan in Z direction.The parameters of this algorithm should be adjusted to the characteristics of the OCT scan. This correction enables a homogeneous reference surface across multiple images and thus facilitates stitching of large-scale images. Once the image has been flattened, the images are corrected for background noise by identifying an empty area of the image above the biofilm and subtracting average background intensity.Next compute an elevation map from the 3D OCT data set. In order to do so, define a reference surface such as the substratum and choose an appropriate intensity threshold. Then an elevation map is rendered with the height of the biofilm reported as grayscale value.If images were acquired in a mosaic pattern, stitch the respective elevation maps by applying the stitching algorithm. Using automated OCT imaging, the spatiotemporal morphogenesis of phototrophic stream biofilms was examined using flume experiments. The flumes are made of plexiglass and gradually widen from the in to the outflow.This results in a gradient in flow velocity. Here is an elevation map of a biofilm growing along the entire flow velocity gradient. Importantly, the automated OCT imaging system allows a continuous measurement of structural parameters such as biofilm thickness, roughness, and biovolume under differing flow conditions ranging from low-flow velocity to high-flow velocity conditions.Along with morphological changes, average biovolume significantly decreased as a function of distance from the inlet in the flume. The quality of the OCT scans critically depends on the reference arm length and the focus distance. You may need to readjust this parameter during the experiments.To ensure the accuracy of the positioning device, remember to perform regularly homing operations. This automated imaging device can be readily coupled with microsensor profiling for a functional characterization of biofilms. OCT is an emerging imaging technique and we anticipate that the system presented here stimulates research on biofilm structure.This may be relevant for technologies such as drinking water treatment or bioprocessing.

automated-3d-optical-coherence-tomography-to-elucidate-biofilm

Research

JoVE Journal

Environment

Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

Next-generation sequencing of environmental samples can be challenging because of the variable DNA quantity and quality in these samples. High quality DNA libraries are needed for optimal results from next-generation sequencing. Environmental samples such as water may have low quality and quantities of DNA as well as contaminants that co-precipitate with DNA. The mechanical and enzymatic processes involved in extraction and library preparation may further damage the DNA. Gel size selection enables purification and recovery of DNA fragments of a defined size for sequencing applications. Nevertheless, this task is one of the most time-consuming steps in the DNA library preparation workflow. The protocol described here enables complete automation of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments. 
In this study, we describe a high-throughput approach to prepare high quality DNA libraries from freshwater samples that can be applied also to other environmental samples. We used an indirect approach to concentrate bacterial cells from environmental freshwater samples; DNA was extracted using a commercially available DNA extraction kit, and DNA libraries were prepared using a commercial transposon-based protocol. DNA fragments of 500 to 800 bp were gel size selected using Ranger Technology, an automated electrophoresis workstation. Sequencing of the size-selected DNA libraries demonstrated significant improvements to read length and quality of the sequencing reads.

This manuscript describes an automated gel size selection approach for purifying DNA fragments for next-generation sequencing. The Ranger Technology provides complete automation of the entire process of agarose gel loading, electrophoretic analysis, and recovery of targeted DNA fragments allowing for high-throughput and high quality next-generation sequencing libraries.

Next-generation sequencing of environmental samples can be challenging because of the variable DNA quantity and quality in these samples. High quality DNA ...

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during storage. These processes are the major sources of greenhouse gas (GHG) emissions from animal production systems. Techniques for measuring enteric CH4 vary from direct measurements (respiration chambers, which are highly accurate, but with limited applicability) to various indirect methods (sniffers, laser technology, which are practical, but with variable accuracy). The sulfur hexafluoride (SF6)&#160;tracer gas method is commonly used to measure enteric CH4 production by animal scientists and more recently, application of an Automated Head-Chamber System (AHCS) (GreenFeed, C-Lock, Inc., Rapid City, SD), which is the focus of this experiment, has been growing. AHCS is an automated system to monitor CH4 and carbon dioxide (CO2) mass fluxes from the breath of ruminant animals. In a typical AHCS operation, small quantities of baiting feed are dispensed to individual animals to lure them to AHCS multiple times daily. As the animal visits AHCS, a fan system pulls air past the animal&#8217;s muzzle into an intake manifold, and through an air collection pipe where continuous airflow rates are measured. A sub-sample of air is pumped out of the pipe into non-dispersive infra-red sensors for continuous measurement of CH4 and CO2 concentrations. Field comparisons of AHCS to respiration chambers or SF6 have demonstrated that AHCS produces repeatable and accurate CH4 emission results, provided that animal visits to AHCS are sufficient so emission estimates are representative of the diurnal rhythm of rumen gas production. Here, we demonstrate the use of AHCS to measure CO2 and CH4 fluxes from dairy cows given a control diet or a diet supplemented with technical-grade cashew nut shell liquid.

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Accuracy and precision of the techniques used to measure methane emissions from ruminant animals are critically important for the success of greenhouse gas mitigation efforts. This manuscript describes the principles and operation of an automated system to monitor methane and carbon dioxide mass fluxes from the breath of ruminant animals.

Ruminant animals (domesticated or wild) emit methane (CH4) through enteric fermentation in their digestive tract and from decomposition of manure during ...

Using Pharmacological Manipulation and High-precision Radio Telemetry to Study the Spatial Cognition in Free-ranging Animals

An animal&#39;s ability to perceive and learn about its environment plays a key role in many behavioral processes, including navigation, migration, dispersal and foraging. However, the understanding of the role of cognition in the development of navigation strategies and the mechanisms underlying these strategies is limited by the methodological difficulties involved in monitoring, manipulating the cognition of, and tracking wild animals. This study describes a protocol for addressing the role of cognition in navigation that combines pharmacological manipulation of behavior with high-precision radio telemetry. The approach uses scopolamine, a muscarinic acetylcholine receptor antagonist, to manipulate cognitive spatial abilities. Treated animals are then monitored with high frequency and high spatial resolution via remote triangulation. This protocol was applied within a population of Eastern painted turtles (Chrysemys picta) that has inhabited seasonally ephemeral water sources for ~100 years, moving between far-off sources using precise (&#177; 3.5 m), complex (i.e., non-linear with high tortuosity that traverse multiple habitats), and predictable routes learned before 4 years of age. This study showed that the processes used by these turtles are consistent with spatial memory formation and recall. Together, these results are consistent with a role of spatial cognition in complex navigation and highlight the integration of ecological and pharmacological techniques in the study of cognition and navigation.

This paper describes a novel protocol that combines the pharmacological manipulation of memory and radio telemetry to document and quantify the role of cognition in navigation.

An animal&#39;s ability to perceive and learn about its environment plays a key role in many behavioral processes, including navigation, migration, ...

Biofilm Removal Using Carbon Dioxide Aerosols without Nitrogen Purge

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. Therefore, it is highly desirable to remove biofilms from surfaces. Many studies on CO2 aerosol cleaning have employed nitrogen purges to increase biofilm removal efficiency by reducing the moisture condensation generated during the cleaning. However, in this study, periodic jets of CO2 aerosols without nitrogen purges were used to remove Pseudomonas putida biofilms from polished stainless steel surfaces. CO2 aerosols are mixtures of solid and gaseous CO2 and are generated when high-pressure CO2 gas is adiabatically expanded through a nozzle. These high-speed aerosols were applied to a biofilm that had been grown for 24 hr. The removal efficiency ranged from 90.36% to 98.29% and was evaluated by measuring the fluorescence intensity of the biofilm as the treatment time was varied from 16 sec to 88 sec. We also performed experiments to compare the removal efficiencies with and without nitrogen purges; the measured biofilm removal efficiencies were not significantly different from each other (t-test, p &gt; 0.55). Therefore, this technique can be used to clean various bio-contaminated surfaces within one minute.

Biofilms on surfaces can be effectively and rapidly removed by using a periodic jet of carbon dioxide aerosols without a nitrogen purge.

Biofilms can cause serious concerns in many applications. Not only can they cause economic losses, but they can also present a public health hazard. ...

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration experiment conducted at a test site near the Tottori Sand Dune, Japan. The antenna array used in this study consists of 10 transmitting antennas (Tx) and 11 receiving antennas (Rx). For this experiment, the system was configured to use all possible Tx-Rx pairings, resulting in a Multi-Offset Gather (MOG) consisting of 110 Tx-Rx combinations. The array was left stationary at a position directly above the infiltration area and data were collected every 1.5 seconds using a time-based trigger. Common-Offset Gather (COG) and Common Mid-Point (CMP) data cubes were reconstructed from the MOG data during post-processing. There have been few studies that used time-lapse CMP data to estimate changes in velocity of propagation. In this study, electromagnetic (EM) wave velocity was estimated heuristically at 1-minute intervals from the reconstructed CMP data through curve fitting, using the hyperbola equation. We then proceeded to calculate the depth of the wetting front. The evolution of the wetting front over time obtain through this method is consistent with the observations from a soil moisture sensor which was placed at a depth below 20 cm. The results obtained in this study demonstrate the ability of such array GPR system to monitor a subsurface dynamic process like water infiltration accurately and quantitatively.

Here we present a Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array for monitoring the dynamic process of subsurface water infiltration. A time-lapse radar image of the infiltration process allowed estimating the depth of the wetting front during the course of the infiltration process.

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration ...

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, we described a protocol for multiple analyses of the biological compositions in environmental PM. Five experiments are presented: (1) PM number monitoring by using a laser particle counter; (2) PM collection by using a cyclonic aerosol sampler; (3) PM collection by using a high-volume air sampler with filters; (4) culturable microbes collection by the Andersen six-stage sampler; and (5) detection of biological composition of environmental PM by bacterial 16SrDNA and fungal ITS region sequencing. We selected hazy days and a livestock farm as two typical examples of the application in this protocol. In this study, these two sampling methods, cyclonic aerosol sampler and filter sampler, showed different sampling efficiency. The cyclonic aerosol sampler performed much better in terms of collecting bacteria, while these two methods showed the same efficiency in collecting fungi. Filter samplers can work under low temperature conditions while cyclonic aerosol samplers have a sampling limitation for temperature. A solid impacting sampler, such as an Andersen six-stage sampler, can be used to sample bioaerosols directly into the culture medium, which increases the survival rate of culturable microorganisms. However, this method mainly relies on culture while more than 99% of microbes cannot be cultured. DNA extracted from the culturable bacteria collected by the Andersen six-stage sampler and samples collected by cyclonic aerosol sampler and filter sampler were detected by bacterial 16S rDNA and fungal ITS region sequencing.All the methods above may have wide application in many fields of study, such as environmental monitoring and airborne pathogen detection. From these results, we can conclude that these methods can be used under different conditions and may help other researchers further explore the health impacts of environmental bioaerosols.

Understanding the biological composition of environmental particulate matter is important for the study of its significant impacts on human health and disease spread. Here, we used three types of bioaerosol sampling methods and a biological analysis of airborne microbes to better explore airborne microbial communities under different environmental conditions.

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, ...

Atom Probe Tomography Analysis of Exsolved Mineral Phases

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in lamellae exsolved from host mineral phases. Thus, the analysis of the orientation, size, morphology, composition and spacing of exsolution lamellae is an area of active research in the geosciences. The conventional study of these lamellae has been conducted by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and more recently with focused ion beam (FIB)-based nanotomography, yet with limited chemical information. Here, we explore the use of atom probe tomography (APT) for the nanoscale analysis of ilmenite exsolution lamellae in igneous titanomagnetite from ash deposits erupted from the active Soufri&#232;re Hills Volcano (Montserrat, British West Indies). APT allows the precise calculation of interlamellar spacings (14&#8211;29 &#177; 2 nm) and reveals smooth diffusion profiles with no sharp phase boundaries during the exchange of Fe and Ti/O between the exsolved lamellae and the host crystal. Our results suggest that this novel approach permits nanoscale measurements of lamellae composition and interlamellar spacing that may provide a means to estimate the lava dome temperatures necessary to model extrusion rates and lava dome failure, both of which play a key role in volcanic hazard mitigation efforts.

Analysis of the morphology, composition, and spacing of exsolution lamellae can provide essential information to understand geological processes related to volcanism and metamorphism. We present a novel application of APT for the characterization of such lamellae and compare this approach to the conventional use of electron microscopy and FIB-based nanotomography.

Element diffusion rates and temperature/pressure control a range of fundamental volcanic and metamorphic processes. Such processes are often recorded in ...

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

High-Throughput Tree Ring Analysis and Density Estimation

Tree Core Analysis with X-ray Computed Tomography

An X-ray computed tomography (CT) toolchain is presented to obtain tree-ring width (TRW), maximum latewood density (MXD), other density parameters, and quantitative wood anatomy (QWA) data without the need for labor-intensive surface treatment or any physical sample preparation. The focus here is on increment cores and scanning procedures at resolutions ranging from 60 &#181;m down to 4 &#181;m. Three scales are defined at which wood should be looked at: (i) inter-ring scale, (ii) ring scale, i.e., tree-ring analysis and densitometry scale, as well as (iii) anatomical scale, the latter approaching the conventional thin-section quality. Custom-designed sample holders for each of these scales enable high-throughput scanning of multiple increment cores. A series of software routines were specifically developed to efficiently treat three-dimensional X-ray CT images of the tree cores for TRW and densitometry. This work briefly explains the basic principles of CT, which are needed for a proper understanding of the protocol. The protocol is presented for some known species that are commonly used in dendrochronology. The combination of rough density estimates, TRW and MXD data, as well as quantitative anatomy data, allows us to broaden and deepen current analyses for climate reconstructions or tree response, as well as further develop the field of dendroecology/climatology and archeology.

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Here we show how to process tree cores with an X-ray computed tomography toolchain. Except for chemical extraction for some purposes, no further physical lab treatment is needed. The toolchain can be used for biomass estimations, for obtaining MXD/tree-ring width data as well as for obtaining quantitative wood anatomy data.

An X-ray computed tomography (CT) toolchain is presented to obtain tree-ring width (TRW), maximum latewood density (MXD), other density parameters, and ...

Rapid and Non-Destructive Characterization of Biofilm Elastic Properties

Quantifying Elastic Properties of Environmental Biofilms using Optical Coherence Elastography

Biofilms are complex biomaterials comprising a well-organized network of microbial cells encased in self-produced extracellular polymeric substances (EPS). This paper presents a detailed account of the implementation of optical coherence elastography (OCE) measurements tailored for the elastic characterization of biofilms. OCE is a non-destructive optical technique that enables the local mapping of the microstructure, morphology, and viscoelastic properties of partially transparent soft materials with high spatial and temporal resolution. We provide a comprehensive guide detailing the essential procedures for the correct implementation of this technique, along with a methodology to estimate the bulk Young's modulus of granular biofilms from the collected measurements. These consist of the system setup, data acquisition, and postprocessing. In the discussion, we delve into the underlying physics of the sensors used in OCE and explore the fundamental limitations regarding the spatial and temporal scales of OCE measurements. We conclude with potential future directions for advancing the OCE technique to facilitate elastic measurements of environmental biofilms.

Author Spotlight: Characterizing Environmental Biofilm Mechanics Using Optical Coherence Elastography and its Applications in Wastewater Treatment

This paper highlights the optical coherence elastography (OCE) technique's efficacy in rapidly and non-destructively characterizing biofilm elastic properties. We elucidate critical OCE implementation procedures for accurate measurements and present Young's modulus values for two granular biofilms.

Biofilms are complex biomaterials comprising a well-organized network of microbial cells encased in self-produced extracellular polymeric substances ...