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

Summary

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.

Abstract

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 monitoring of biofilm development and may be further developed to couple OCT imaging with macrophotography and microsensor profiling.

Introduction

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 settings^1,2. 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 ....

Protocol

1. Setup of the Positioning Device

1. Wire the positioning device to a microcontroller board, following the instruction in https://github.com/grbl/grbl/wiki/Connecting-Grbl.
2. 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 navigated with a Python script, as demonstrated in the first part of the worked example ImagesAcquisition.ipynb (Supplementary File....

Results

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).  The temporal development and structural differentiation of biofilm was monitored over 18 days with the aim to better understand the effects of hydrodynamic conditions on biofilm morphogenesis.

Discussion

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 research^10,18. 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

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
OCT Probe	Thorlabs	GAN210C1	OCT imaging device
OCT scan lens	Thorlabs	OCT-LK3-BB
Immersion adapter	Thorlabs	OCT-IMM3-SP1
Stepcraft 840 CK	STEPCRAFT	NA	positioning device
microcontroller	Arduino Uno R3	NA
Single-board computer	Raspberry PI	NA
camera	Canon EOS 7D Mark II	NA
camera lens	Canon MACRO EFS 35 mm	NA