Imaging bacterial 3D motion using digital in-line holographic microscopy and correlation-based de-noising algorithm.

Mehdi Molaei,Jian Sheng

doi:10.1364/oe.22.032119

Abstract

Better understanding of bacteria environment interactions in the context of biofilm formation requires accurate 3-dimentional measurements of bacteria motility. Digital Holographic Microscopy (DHM) has demonstrated its capability in resolving 3D distribution and mobility of particulates in a dense suspension. Due to their low scattering efficiency, bacteria are substantially difficult to be imaged by DHM. In this paper, we introduce a novel correlation-based de-noising algorithm to remove the background noise and enhance the quality of the hologram. Implemented in conjunction with DHM, we demonstrate that the method allows DHM to resolve 3-D E. coli bacteria locations of a dense suspension (>107 cells/ml) with submicron resolutions (<0.5 µm) over substantial depth and to obtain thousands of 3D cell trajectories.

Highlights

It has attracted significant interests recently in resolving key processes involved in biophysical interactions between bacteria and their constantly changing environment [1,2,3,4,5,6]
Differing from lens-less digital holographic microscopy, current Digital Holographic Microscopy (DHM) records the interference pattern via a microscope objective
This paper introduces a novel de-noising algorithm to be used with in-line digital holographic microscopy to measure the spatial distribution of sub-micron (~1 μm ) bacterial cells in a dense aqueous suspension (~107 cells/ml) over the depth of 0.1~1mm (100~1000 Db ), and

Summary

Introduction

It has attracted significant interests recently in resolving key processes involved in biophysical interactions between bacteria and their constantly changing environment [1,2,3,4,5,6] These processes include three-dimensional (3-D) bacterial locomotion, swarming behaviors and transport dynamics near complex boundaries such as surfaces or interfaces with heterogonous topology, roughness, and energy landscape, which often occur in a wide range of spatial and temporal scales, ranging from sub-microns to millimeters and from microseconds to hours. The technique has been further extended to confocal scanning microscopy [9], transmission electron microscopy [10], and later soft x-ray microscopy [11] as well as Electron Microscopy [12] Since these techniques require the sample to be stained or “frozen” in crystalline structures, they are not suitable for imaging live cells and quantifying their dynamic interactions

Methods

Results

Conclusion