A Novel Computerized Cell Count Algorithm for Biofilm Analysis

<div><p>Biofilms are the preferred sessile and matrix-embedded life form of most microorganisms on surfaces. In the medical field, biofilms are a frequent cause of treatment failure because they protect the bacteria from antibiotics and immune cells. Antibiotics are selected according to the minimal inhibitory concentration (MIC) based on the planktonic form of bacteria. Determination of the minimal biofilm eradicating concentration (MBEC), which can be up to 1,000-fold greater than the MIC, is not currently conducted as routine diagnostic testing, primarily because of the methodical hurdles of available biofilm assessing protocols that are time- and cost-consuming. Comparative analysis of biofilms is also limited as most quantitative methods such as crystal violet staining are indirect and highly imprecise. In this paper, we present a novel algorithm for assessing biofilm resistance to antibiotics that overcomes several of the limitations of alternative methods. This algorithm aims for a computer-based analysis of confocal microscope 3D images of biofilms after live/dead stains providing various biofilm parameters such as numbers of viable and dead cells and their vertical distributions within the biofilm, or biofilm thickness. The performance of this algorithm was evaluated using computer-simulated 2D and 3D images of coccal and rodent cells varying different parameters such as cell density, shading or cell size. Finally, genuine biofilms that were untreated or treated with nitroxoline or colistin were analyzed and the results were compared with quantitative microbiological standard methods. This novel algorithm allows a direct, fast and reproducible analysis of biofilms after live/dead staining. It performed well in biofilms of moderate cell densities in a 2D set-up however the 3D analysis remains still imperfect and difficult to evaluate. Nevertheless, this is a first try to develop an easy but conclusive tool that eventually might be implemented into routine diagnostics to determine the MBEC and to improve outcomes of patients with biofilm-associated infections.</p></div

Comparison of simulated images with genuine biofilms.

<p>(A) <i>S</i>. <i>aureus</i> (cocci) biofilm and (B) <i>P</i>. <i>aeruginosa</i> (rods) biofilm. Simulations of 10,000 coccal (C) or rod (D) cells at a minimum and maximum declension = 1. All 2D-images of single layers are shown as section of similar resolution with an approximately edge length of 42 μm.</p

Histograms of viable and dead cells of <i>P</i>. <i>aeruginosa</i> biofilms treated by PBS or various concentrations of antibiotics.

<p>(A) PBS treatment; (B, D, F, H) Nitroxoline treatment and (C, E, G, I) colistin treatment (corresponding to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154937#pone.0154937.g006" target="_blank">Fig 6</a>). Viable cells are represented by green lines and dead cells in red lines. Error bars indicate the standard error of the mean (SEM) for three independent experiments. Concentrations of antibiotics are indicated above the corresponding histograms.</p

Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.

<p>Accuracy of the cell counting (<i>N</i>) and the calculation of the biomass (<i>A</i>) depending on coloration of the cells.</p

Principals of image processing and adaptive segmentation and binarization.

<p>(A) 2D CLSM image of one Z-layer; (B) Schematic example of a 3D grayscale histogram section (<i>g</i> = f[x, y]); (C) Schematic illustration of the Z-allocation of the cells; (D) 2D projection image of all Z-layers and the local grayscale maxima (indicated as red crosses); (E) Schematic illustration of the window adjustment (<i>w</i>, <i>w)</i> by prolongation and local intensity; (F) Segmented image by seeded region growing algorithm.</p

Concentration-response activities of nitroxoline and colistin against <i>P</i>. <i>aeruginosa</i> biofilms measured by different methods.

<p>(A-B) Direct count (<i>N</i> /cm²) of viable and dead bacteria by qBA; (C-D) Determination of viable cells on agar (CFU/mL); (E-F) Direct determination of the area (<i>A</i>) covered by green- and red-stained bacteria by qBA; (G-H) Crystal violet absorption.</p

Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.

<p>Accuracy of cell counting per Z-layer and of the total cell number of simulated 3D biofilms depending on <i>I</i> and <i>P</i> filters.</p

Comparison of a 2D and 3D analysis by qBA of an <i>E</i>. <i>coli</i> biofilm.

<p>(A) Analyzed biofilm layers scanned by CLSM (green and red channels overlapping). (B) Histogram of the viable (green) and dead (red) cells estimated in a 2D (dotted lines) and 3D (solid lines) setting. (C) Allocated (red crosses) local grayscale maxima in three neighboring layers (as indicated by the red dotted square in A). Biofilm images in A and B were processed by increasing the intensity and contrast of the signals for illustrative purpose.</p

Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.

<p>Accuracy of cell counts of a simulated 3D biofilm containing 995 dead cells.</p

A Novel Method for the Assessment of Targeted PEI-Based Nanoparticle Binding Based on a Static Surface Plasmon Resonance System

The delivery of nucleic acids is a major hurdle in gene therapy or therapeutic gene knockdown, and the development of intelligent and safe nanoparticles as carrier systems is thus under intense investigation. The introduction of ligands for their targeted delivery is of major interest. Here, we describe a novel approach for the analysis of the binding properties of antibody-functionalized nanoparticles, using surface plasmon resonance (SPR) in a static cuvette system. By chemical coupling of the Epidermal Growth Factor Receptor (EGFR)-specific antibody cetuximab to poly­(ethylene imine) (PEI) via a PEG-spacer and subsequent DNA or siRNA complexation, we generated targeted nanoplexes with low surface charge. Antibody-mediated uptake into EGFR overexpressing cells was observed. SPR measurements with use of a novel, protein A-based sandwich system for the immobilization of the target receptor in its correct steric orientation allowed the analysis of the specific PEI-PEG-cetuximab binding to EGFR and the determination of binding affinities. Importantly, our cuvette-based SPR assay system was also suitable for the monitoring of ligand-mediated nanoparticle binding, without convection or shear stress. We conclude that our SPR sandwich system allows the precise analysis of the binding of ligand-functionalized nanoparticles in real-time, and we thus establish SPR for the in vitro evaluation of ligand modifications for generating targeted nanoparticles