IMAGE ANALYSIS AS A RAPID PATHOGEN DETECTION METHOD FOR USE IN POULTRY INDUSTRY

An image analysis system was developed and evaluated as a method for rapid detection of Salmonella typhimurium in pure culture and in chicken washes. A direct immunomagnetic separation and immunofluorescent staining technique was developed to capture and identify target cells. Digital images were acquired and segmented into background and bacteria. Bacteria were enumerated using a custom designed image analysis software. The image analyses results were compared with manual enumeration. A correlation coefficient of 0.78 was established between manual and image analysis counts. In addition, the difference between the manual and the image analysis bacterial counts in individual images was low. Image analysis took an average of 15 s to analyze an image. The results indicate that the proposed system has the potential to be used as a rapid screening procedure for bacterial detection in the food industry.     

临床常见革兰氏阳性球菌蛋白指纹库的构建

为建立临床常见革兰氏阳性球菌的蛋白指纹库,为快速鉴定这些细菌奠定基础,收集了从临床中分离获得的185株革兰氏阳性球菌,包括金黄色葡萄球菌、表皮葡萄球菌、溶血性葡萄球菌、粪肠球菌和屎肠球菌。将这些菌株分成建模组和验证组,利用表面增强激光解析电离飞行时间质谱检测细菌蛋白,用ProteinChip和Biomarker Wizard软件对建模组细菌数据进行分析,筛选出每种细菌各自稳定表达的蛋白峰,并将数据导入自建的Fingerwave软件建立了临床常见革兰氏阳性球菌的蛋白指纹库。随后,将验证组细菌的蛋白峰数据与蛋白指纹库中蛋白峰数据进行相似度分析,以评价其鉴定符合率。建立了包含5种临床常见革兰氏阳性球菌的蛋白指纹库,利用其对验证组菌株进行鉴定,与应用传统微生物学鉴定及分子生物学方法获得的鉴定结果的符合率为100%。结果表明,进一步扩大并完善革兰氏阳性球菌的蛋白指纹库,将为临床病原菌的快速鉴定提供可能。     

Fabrication of titanium based MALDI bacterial chips for rapid, sensitive and direct analysis of pathogenic bacteria

For the first time, we report the fabrication of a titanium bacterial chip for MALDI-MS produced from a simple, cost effective and rapid heat treatment process. This bacterial chip can be reused many times and is highly versatile. These bacterial chips serve dual roles: (1) They can be applied as MALDI-MS target plates for direct and highly sensitive bacterial analysis. (2) They can be used as bacterial sensors for direct analysis of the captured bacteria using MALDI-MS. The sensitivity of these chips when used as bacterial sensors is <10(3)cfu/mL. The lowest detectable concentration for direct MALDI-MS analysis was found to be 10(4)cfu/mL. The results were further justified by using standard plate counting method combined with Tukey-Kramer statistical analysis and fluorescence imaging followed by image processing for fluorescence quantification using ImageJ software to substantiate the MALDI-MS results.     

Characterization of bacterial growth on solid medium with image analysis.

Alkaliphilic bacterial strains producing the enzyme cyclodextrin glucanotransferase were cultivated on solid agar medium containing an indicator system detecting the enzyme. The growth of the colony and the surrounding diffusion zone, due to the enzyme, were measured by the image analysis during the cultivation. It was possible to differentiate between relatively similar clones by observing quantitatively the changes at and around the colony. Optimal experimental conditions for such measurements are discussed. The image analysis technique provides a potential tool for characterizing microbes grown on solid media.     

Differential expression analysis of Escherichia coli proteins using a novel software for relative quantitation of LC-MS/MS data

The study of changes in protein levels between samples derived from cells representing different biological conditions is a key to the understanding of cellular function. There are two main methods available that allow both for global scanning for significantly varying proteins and targeted profiling of proteins of interest. One method is based on 2-D gel electrophoresis and image analysis of labelled proteins. The other method is based on LC-MS/MS analysis of either unlabelled peptides or peptides derived from isotopically labelled proteins or peptides. In this study, the non-labelling approach was used involving a new software, DeCyder MS Differential Analysis Software (DeCyder MS) intended for automated detection and relative quantitation of unlabelled peptides in LC-MS/MS data.Total protein extracts of E. coli strains expressing varying levels of dihydrofolate reductase and integron integrase were digested with trypsin and analyzed using a nanoscale liquid chromatography system, Ettan MDLC, online connected to an LTQTM linear ion-trap mass spectrometer fitted with a nanospray interface. Acquired MS data were subjected to DeCyder MS analysis where 2-D representations of the peptide patterns from individual LC-MS/MS analyses were matched and compared.This approach to unlabelled quantitative analysis of the E. coli proteome resulted in relative protein abundances that were in good agreement with results obtained from traditional methods for measuring protein levels.     

A novel approach to spot detection for two-dimensional gel electrophoresis images using pixel value collection

Separation of complex mixtures of proteins by two-dimensional gel electrophoresis (2-DE) is a fundamental component of current proteomic technology. Quantitative analysis of the images generated by digitization of such gels is critical for the identification of alterations in protein expression within a given biological system. Despite the availability of several commercially available software packages designed for this purpose, image analysis is extremely resource intensive, subjective and remains a major bottleneck. In addition to reducing throughput, the requirement for manual intervention results in the introduction of operator subjectivity, which can limit the statistical significance of the numerical data generated. A key requirement of image analysis is the accurate definition of protein spot boundaries using a suitable method of image segmentation. We describe a method of spot detection applicable to 2-DE image files using a segmentation method involving pixel value collection via serial analysis of the image through its range of density levels. This algorithm is reproducible, sensitive, accurate and primarily designed to be automatic, removing operator subjectivity. Furthermore, it is believed that this method may offer the potential for improved spot detection over currently available software.     

Classification and identification of bacteria by mass spectrometry and computational analysis

Background

In general, the definite determination of bacterial species is a tedious process and requires extensive manual labour. Novel technologies for bacterial detection and analysis can therefore help microbiologists in minimising their efforts in developing a number of microbiological applications.

Methodology

We present a robust, standardized procedure for automated bacterial analysis that is based on the detection of patterns of protein masses by MALDI mass spectrometry. We particularly applied the approach for classifying and identifying strains in species of the genus Erwinia. Many species of this genus are associated with disastrous plant diseases such as fire blight. Using our experimental procedure, we created a general bacterial mass spectra database that currently contains 2800 entries of bacteria of different genera. This database will be steadily expanded. To support users with a feasible analytical method, we developed and tested comprehensive software tools that are demonstrated herein. Furthermore, to gain additional analytical accuracy and reliability in the analysis we used genotyping of single nucleotide polymorphisms by mass spectrometry to unambiguously determine closely related strains that are difficult to distinguish by only relying on protein mass pattern detection.

Conclusions

With the method for bacterial analysis, we could identify fire blight pathogens from a variety of biological sources. The method can be used for a number of additional bacterial genera. Moreover, the mass spectrometry approach presented allows the integration of data from different biological levels such as the genome and the proteome.     

PSICIC: Noise and Asymmetry in Bacterial Division Revealed by Computational Image Analysis at Sub-Pixel Resolution

Live-cell imaging by light microscopy has demonstrated that all cells are spatially and temporally organized. Quantitative, computational image analysis is an important part of cellular imaging, providing both enriched information about individual cell properties and the ability to analyze large datasets. However, such studies are often limited by the small size and variable shape of objects of interest. Here, we address two outstanding problems in bacterial cell division by developing a generally applicable, standardized, and modular software suite termed Projected System of Internal Coordinates from Interpolated Contours (PSICIC) that solves common problems in image quantitation. PSICIC implements interpolated-contour analysis for accurate and precise determination of cell borders and automatically generates internal coordinate systems that are superimposable regardless of cell geometry. We have used PSICIC to establish that the cell-fate determinant, SpoIIE, is asymmetrically localized during Bacillus subtilis sporulation, thereby demonstrating the ability of PSICIC to discern protein localization features at sub-pixel scales. We also used PSICIC to examine the accuracy of cell division in Esherichia coli and found a new role for the Min system in regulating division-site placement throughout the cell length, but only prior to the initiation of cell constriction. These results extend our understanding of the regulation of both asymmetry and accuracy in bacterial division while demonstrating the general applicability of PSICIC as a computational approach for quantitative, high-throughput analysis of cellular images.     

Classification of genus Pseudomonas by MALDI-TOF MS based on ribosomal protein coding in S10-spc-alpha operon at strain level

We have proposed a rapid phylogenetic classification at the strain level by MALDI-TOF MS using ribosomal protein matching profiling. In this study, the S10-spc-alpha operon, encoding half of the ribosomal subunit proteins and highly conserved in eubacterial genomes, was selected for construction of the ribosomal protein database as biomarkers for bacterial identification by MALDI-TOF MS analysis to establish a more reliable phylogenetic classification. Our method revealed that the 14 reliable and reproducible ribosomal subunit proteins with less than m/z 15,000, except for L14, coded in the S10-spc-alpha operon were significantly useful biomarkers for bacterial classification at species and strain levels by MALDI-TOF MS analysis of genus Pseudomonas strains. The obtained phylogenetic tree was consisted with that based on genetic sequence (gyrB). Since S10-spc-alpha operons of genus Pseudomonas strains were sequenced using specific primers designed based on nucleotide sequences of genome-sequenced strains, the ribosomal subunit proteins encoded in S10-spc-alpha operon were suitable biomarkers for construction and correction of the database. MALDI-TOF MS analysis using these 14 selected ribosomal proteins is a rapid, efficient, and versatile bacterial identification method with the validation procedure for the obtained results.     

Bedaquiline susceptibility test for totally drug-resistant tuberculosis <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis>

This study aimed to provide information that bedaquilline is significantly effective for treatment of totally drug resistant (TDR) Mycobacterium tuberculosis that shows resistant to all first- and second-line drugs-using an innovative disc agarose channel (DAC) system. Time-lapse images of single bacterial cells under culture conditions with different concentrations of bedaquiline were analysed by image processing software to determine minimum inhibitory concentrations (MICs). Bedaquiline inhibited the growth of TDR M. tuberculosis strains, with MIC values ranging from 0.125 to 0.5 mg/L. The results of the present study demonstrate that bedaquiline, newly approved by the United States Food and Drug Administration (FDA), may offer therapeutic solutions for TDR-TB.     

A 11.5 A single particle reconstruction of GroEL using EMAN.

Single-particle analysis has become an increasingly important method for structural determination of large macromolecular assemblies. GroEL is an 800 kDa molecular chaperone, which, along with its co-chaperonin GroES, promotes protein folding both in vitro and in the bacterial cell. EMAN is a single-particle analysis software package, which was first publicly distributed in 2000. We present a three-dimensional reconstruction of native naked GroEL to approximately 11.5 A performed entirely with EMAN. We demonstrate that the single-particle reconstruction, X-ray scattering data and X-ray crystal structure all agree well at this resolution. These results validate the specific methods of image restoration, reconstruction and evaluation techniques implemented in EMAN. It also demonstrates that the single-particle reconstruction technique and X-ray crystallography will yield consistent structure factors, even at low resolution, when image restoration is performed correctly. A detailed comparison of the single-particle and X-ray structures exhibits some small variations in the equatorial domain of the molecule, likely due to the absence of crystal packing forces in the single-particle reconstruction.     

Enumeration and characterization of bacteria in mineral water by improved direct viable count method

Fifteen strains from two emergent mineral waters were isolated and tentatively identified with API 20NE and BIOLOG GN systems. These strains were screened for their sensitivities to seven replication-inhibiting antibiotics of the (fluoro)quinolone group (nalidixic and pipemidic acid, flumequine, norfloxacin, ofloxacin, pefloxacin and ciprofloxacin). It was shown that the direct viable count (DVC) procedure could be improved by using certain antibiotic cocktails, which were active against the isolates. Geometric bacterial features were successfully determined with image analysis and adapted software (ICONIX, Perfect Image). Elongations were significant and allowed rapid discrimination of antibiotic inhibited and non-inhibited strains. Particular isolates in a mixed culture were characterized and enumerated after only 14 h exposure with the appropriate antibiotic cocktail. This method can also be applied to other communities, such as mixed cultures in bio-fermentors or in food with known microflora.     

Classification and counting of fluorescent pollen using an image analysis system

Pollen viability is commonly assessed by fluorochromatic reaction (FCR) because of the high correlation between positive fluorescence of the pollen grains and their ability to germinate. One of the advantages of this method is its simplicity. An experiment to test FCR analysis for reproducibility, however, showed that results are affected by subjectivity. There is little consistency between analysts, and assessment by the same analyst may differ for the same pollen sample image examined at different times. These problems were solved by a computerized image analysis system that provides a method for classifying positive and negative fluorescent pollen and automatic counting of the grains in each class. The computerized image analysis system does not change the biochemistry of the FCR test, but avoids some experimental errors owing to the subjectivity of the analyst. Microscope images of the pollen after FCR were digitized and later analyzed by specially designed software, "Plant Meter." This software deletes the dark background of the image to isolate the grains, and subsequently counts positive and negative fluorescent pollen grains. An experiment was carried out to validate software output and it showed reliable results. Moreover, the software is user friendly and very little training is necessary for analysts to achieve reliable results.     

影像分析在生化研究中的应用概况

影像分析是一项重要的微观物质形态测量和浓度测量技术,已经在众多领域中得到应用。本主要介绍影像分析系统的基本结构及其在生化领域中的应用情况,包括细胞形态观察与测量、突变菌株的区分与筛选、蛋白质与ＤＮＡ分子浓度的测量。     

Protein spot detection and quantification in 2-DE gel images using machine-learning methods

Two-dimensional gel electrophoresis (2-DE) is the most established protein separation method used in expression proteomics. Despite the existence of sophisticated software tools, 2-DE gel image analysis still remains a serious bottleneck. The low accuracies of commercial software packages and the extensive manual calibration that they often require for acceptable results show that we are far from achieving the goal of a fully automated and reliable, high-throughput gel processing system. We present a novel spot detection and quantification methodology which draws heavily from unsupervised machine-learning methods. Using the proposed hierarchical machine learning-based segmentation methodology reduces both the number of faint spots missed (improves sensitivity) and the number of extraneous spots introduced (improves precision). The detection and quantification performance has been thoroughly evaluated and is shown to compare favorably (higher F-measure) to a commercially available software package (PDQuest). The whole image analysis pipeline that we have developed is fully automated and can be used for high-throughput proteomics analysis since it does not require any manual intervention for recalibration every time a new 2-DE gel image is to be analyzed. Furthermore, it can be easily parallelized for high performance and also applied without any modification to prealigned group average gels.     

Evaluation of a scanner-assisted colorimetric MIC method for susceptibility testing of gram-negative fermentative bacteria

We describe the ScanMIC method, a colorimetric MIC method for susceptibility testing of gram-negative fermentative bacteria. The method is a slight modification of the National Committee for Clinical Laboratory Standards (NCCLS) recommended broth microdilution method that uses a redox indicator 2,3,5-triphenyltetrazolium chloride (TTC) to enhance the estimate of bacterial growth inhibition in a microplate and a flatbed scanner to capture the microplate image. In-house software was developed to transform the microplate image into numerical values based on the amount of bacterial growth and to generate the MICs automatically. The choice of indicator was based on its low toxicity and ease of reading by scanner. We compared the ScanMIC method to the NCCLS recommended broth microdilution method with 197 coliform strains against seven antibacterial agents. The interpretative categorical agreement was obtained in 92.4% of the assays, and the agreement for MIC differences (within +/-1 log(2) dilution) was obtained in 96% for ScanMIC versus broth microdilution and 97% for a two-step incubation colorimetric broth microdilution versus the broth microdilution method. The method was found to be labor-saving, not to require any initial investment, and to show reliable results. Thus, the ScanMIC method could be useful for epidemiological surveys that include susceptibility testing of bacteria.     

Software for quantification of labeled bacteria from digital microscope images by automated image analysis

Automated image analysis software, CellC, was developed and validated for quantification of bacterial cells from digital microscope images. CellC enables automated enumeration of bacterial cells, comparison of total count and specific count images [e.g., 4',6-diamino-2-phenylindole (DAPI) and fluorescence in situ hybridization (FISH) images], and provides quantitative estimates of cell morphology. The software includes an intuitive graphical user interface that enables easy usage as well as sequential analysis of multiple images without user intervention. Validation of enumeration reveals correlation to be better than 0.98 when total bacterial counts by CellC are compared with manual enumeration, with all validated image types. The software is freely available and modifiable: the executable files and MATLAB source codes can be obtained at www. cs. tut.fi/sgn/csb/cellc.     

A rapid, semi-automated SDS-PAGE identification system for oral anaerobic bacteria

SDS-polyacrylamide gel electrophoresis is a useful technique in bacterial differentiation and identification. A rapid, semi-automated SDS-PAGE system (Phast System) was assessed for identification of formate-fumarate-requiring, asaccharolytic, Gram-negative oral anaerobes. The system permitted loading, separation and staining of gels within 2 h. Percentage similarities between strains were determined using correlation coefficients and cluster analysis. The protein profiles were sufficiently reproducible provided that distorted profiles were disregarded. Strains were successfully separated into their species, with the exception of Bacteroides ureolyticus NCTC 10939, which appeared to be distinct from other strains of that species. Twenty-nine unidentified formate-fumarate-requiring, sub-gingival plaque strains were suitably clustered with the standard strains as verified by a series of physiological and biochemical tests.