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>
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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.

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