Three-dimensional biofilm structure quantification

Quantitative parameters describing biofilm physical structure have been extracted from three-dimensional confocal laser scanning microscopy images and used to compare biofilm structures, monitor biofilm development, and quantify environmental factors affecting biofilm structure. Researchers have previously used biovolume, volume to surface ratio, roughness coefficient, and mean and maximum thicknesses to compare biofilm structures. The selection of these parameters is dependent on the availability of software to perform calculations. We believe it is necessary to develop more comprehensive parameters to describe heterogeneous biofilm morphology in three dimensions. This research presents parameters describing three-dimensional biofilm heterogeneity, size, and morphology of biomass calculated from confocal laser scanning microscopy images. This study extends previous work which extracted quantitative parameters regarding morphological features from two-dimensional biofilm images to three-dimensional biofilm images. We describe two types of parameters: (1) textural parameters showing microscale heterogeneity of biofilms and (2) volumetric parameters describing size and morphology of biomass. The three-dimensional features presented are average (ADD) and maximum diffusion distances (MDD), fractal dimension, average run lengths (in X, Y and Z directions), aspect ratio, textural entropy, energy and homogeneity. We discuss the meaning of each parameter and present the calculations in detail. The developed algorithms, including automatic thresholding, are implemented in software as MATLAB programs which will be available at site prior to publication of the paper.     

Quantifying biofilm structure: facts and fiction

There is no doubt among biofilm researchers that biofilm structure is important to many biofilm processes, such as the transport of nutrients to deeper layers of the biofilm. However, biofilm structure is an elusive term understood only qualitatively, and as such it cannot be directly correlated with any measurable parameters characterizing biofilm performance. To correlate biofilm structure with the parameters characterizing biofilm performance, such as the rate of nutrient transport within the space occupied by the biofilms, biofilm structure must first be quantified and expressed numerically on an appropriate scale. The task of extracting numerical parameters quantifying biofilm structure relies on using biofilm imaging and image analysis. Although defining parameters characterizing biofilm structure is relatively straightforward, and multiple parameters have been described in the computer science literature, interpreting the results of such analyses is not trivial. Existing computer software developed by several research groups, including ours, for the sole purpose of analyzing biofilm images helps quantify parameters from biofilm images but does nothing to help interpret the results of such analyses. Although computing structural parameters from biofilm images permits correlating biofilm structure with other biofilm processes, the meaning of the results is not obvious. The first step to understanding the quantification of biofilm structure, developing image analysis, methods to quantify information from biofilm images, has been made by several research groups. The next step is to explain the meaning of these analyses. This presentation explains the meaning of several parameters commonly used to characterize biofilm structure. It also reviews the authors' research and experience in quantifying biofilm structure and their attempts to quantitatively relate biofilm structure to fundamental biofilm processes.     

Role of correlated noise in textural features extraction

Predictive models of tumor response based on heterogeneity metrics in medical images, such as textural features, are highly suggestive. However, the demonstrated sensitivity of these features to noise does affect the model being developed. An in-depth analysis of the noise influence on the extraction of texture features was performed based on the assumption that an improvement in information quality can also enhance the predictive model. A heuristic approach was used that recognizes from the beginning that the noise has its own texture and it was analysed how it affects the quantitative signal data. A simple procedure to obtain noise image estimation is shown; one which makes it possible to extract the noise-texture features at each observation. The distance measured between the textural features in signal and estimated noise images allows us to determine the features affected in each observation by the noise and, for example, to exclude some of them from the model. A demonstration was carried out using synthetic images applying realistic noise models found in medical images. Drawn conclusions were applied to a public cohort of clinical images obtained using FDG-PET to show how the predictive model could be improved. A gain in the area under the receiver operating characteristic curve between 10 and 20% when noise texture information is used was shown. An improvement between 20 and 30% can be appreciated in the estimated model quality.     

Effects of biofilm heterogeneity on the apparent mechanical properties obtained by shear rheometry

Rheometry is an experimental technique widely used to determine the mechanical properties of biofilms. However, it characterizes the bulk mechanical behavior of the whole biofilm. The effects of biofilm mechanical heterogeneity on rheometry measurements are not known. We used laboratory experiments and computer modeling to explore the effects of biofilm mechanical heterogeneity on the results obtained by rheometry. A synthetic biofilm with layered mechanical properties was studied, and a viscoelastic biofilm theory was employed using the Kelvin–Voigt model. Agar gels with different concentrations were used to prepare the layered, heterogeneous biofilm, which was characterized for mechanical properties in shear mode with a rheometer. Both experiments and simulations indicated that the biofilm properties from rheometry were strongly biased by the weakest portion of the biofilm. The simulation results using linearly stratified mechanical properties from a previous study also showed that the weaker portions of the biofilm dominated the mechanical properties in creep tests. We note that the model can be used as a predictive tool to explore the mechanical behavior of complex biofilm structures beyond those accessible to experiments. Since most biofilms display some degree of mechanical heterogeneity, our results suggest caution should be used in the interpretation of rheometry data. It does not necessarily provide the “average” mechanical properties of the entire biofilm if the mechanical properties are stratified.     

Three-dimensional biofilm image reconstruction for assessing structural parameters

Parameters representing three-dimensional (3D) biofilm structure are quantified from confocal laser-scanning microscope (CLSM) images. These 3D parameters describe the distribution of biomass pixels within the space occupied by a biofilm; however, they lack a direct connection to biofilm activity. As a result, researchers choose a handful of parameters without there being a consensus on a standard set of parameters. We hypothesized that a select 3D parameter set could be used to reconstruct a biofilm image and that the reconstructed and original biofilm images would have similar activities. To test this hypothesis, an algorithm was developed to reconstruct a biofilm image with parameters identical to those of the original CLSM image. We introduced an objective method to assess the reconstruction algorithm by comparing the activities of the original and reconstructed biofilm images. We found that biofilm images with identical structural parameters showed nearly identical activities and substrate concentration profiles. This implies that the set containing all common structural parameters can successfully describe biofilm structure. This finding is significant, as it opens the door to the next step, of finding a smaller standard set of biofilm structural parameters that can be used to compare biofilm structure.     

Automatic annotation of spatial expression patterns via sparse Bayesian factor models

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.     

Dental microwear texture analysis: technical considerations

Dental microwear analysis is commonly used to infer aspects of diet in extinct primates. Conventional methods of microwear analysis have usually been limited to two-dimensional imaging studies using a scanning electron microscope and the identification of apparent individual features. These methods have proved time-consuming and prone to subjectivity and observer error. Here we describe a new methodological approach to microwear: dental microwear texture analysis, based on three-dimensional surface measurements taken using white-light confocal microscopy and scale-sensitive fractal analysis. Surface parameters for complexity, scale of maximum complexity, anisotropy, heterogeneity, and textural fill volume offer repeatable, quantitative characterizations of three-dimensional surfaces, free of observer measurement error. Some results are presented to illustrate how these parameters distinguish extant primates with different diets. In this case, microwear surfaces of Cebus apella and Lophocebus albigena, which consume some harder food items, have higher average values for complexity than do folivores or soft fruit eaters.     

Biofilm image reconstruction for assessing structural parameters

The structure of biofilms can be numerically quantified from microscopy images using structural parameters. These parameters are used in biofilm image analysis to compare biofilms, to monitor temporal variation in biofilm structure, to quantify the effects of antibiotics on biofilm structure and to determine the effects of environmental conditions on biofilm structure. It is often hypothesized that biofilms with similar structural parameter values will have similar structures; however, this hypothesis has never been tested. The main goal was to test the hypothesis that the commonly used structural parameters can characterize the differences or similarities between biofilm structures. To achieve this goal (1) biofilm image reconstruction was developed as a new tool for assessing structural parameters, (2) independent reconstructions using the same starting structural parameters were tested to see how they differed from each other, (3) the effect of the original image parameter values on reconstruction success was evaluated, and (4) the effect of the number and type of the parameters on reconstruction success was evaluated. It was found that two biofilms characterized by identical commonly used structural parameter values may look different, that the number and size of clusters in the original biofilm image affect image reconstruction success and that, in general, a small set of arbitrarily selected parameters may not reveal relevant differences between biofilm structures.     

Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.     

The progress of multimodal imaging combination and subregion based radiomics research of cancers

In recent years, with the standardization of radiomics methods; development of tools; and popularization of the concept, radiomics has been widely used in all aspects of tumor diagnosis; treatment; and prognosis. As the study of radiomics in cancer has become more advanced, the currently used methods have revealed their shortcomings. The performance of cancer radiomics based on single-modality medical images, which based on their imaging principles, only partially reflects tumor information, has been necessarily compromised. Using the whole tumor as a region of interest to extract radiomic features inevitably leads to the loss of intra-tumoral heterogeneity of, which also affects the performance of radiomics. Radiomics of multimodal images extracts various aspects of information from images of each modality and then integrates them together for model construction; thus, avoiding missing information. Subregional segmentation based on multimodal medical image combinations allows radiomics features acquired from subregions to retain tumor heterogeneity, further improving the performance of radiomics. In this review, we provide a detailed summary of the current research on the radiomics of multimodal images of cancer and tumor subregion-based radiomics, and then raised some of the research problems and also provide a thorough discussion on these issues.     

Gene expression analysis of monospecies Salmonella typhimurium biofilms using differential fluorescence induction

Bacterial biofilm formation is an important cause of environmental persistence of food-borne pathogens, such as Salmonella Typhimurium. As the ensemble of bacterial cells within a biofilm represents different physiological states, even for monospecies biofilms, gene expression patterns in these multicellular assemblages show a high degree of heterogeneity. This heterogeneity might mask differential gene expression that occurs only in subpopulations of the entire biofilm population when using methods that average expression output. In an attempt to address this problem and to refine expression analysis in biofilm studies, we used the Differential Fluorescence Induction (DFI) technique to gain more insight in S. Typhimurium biofilm gene expression. Using this single cell approach, we were able to identify 26 genetic loci showing biofilm specific increased expression. For a selected number of identified genes, we confirmed the DFI results by the construction of defined promoter fusions, measurement of relative gene expression levels and construction of mutants. Overall, we have shown for the first time that the DFI technique can be used in biofilm research. The fact that this analysis revealed genes that have not been linked with Salmonella biofilm formation in previous studies using different approaches illustrates that no single technique, in casu biofilm formation, is able to identify all genes related to a given phenotype.     

Contourlet Textual Features: Improving the Diagnosis of Solitary Pulmonary Nodules in Two Dimensional CT Images

Objective

To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer.

Materials and Methods

A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.

Results

Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.

Conclusion

Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.     

Prediction of chemotherapy response in breast cancer patients at pre-treatment using second derivative texture of CT images and machine learning

Although neoadjuvant chemotherapy (NAC) is a crucial component of treatment for locally advanced breast cancer (LABC), only about 70% of patients respond to it. Effective adjustment of NAC for individual patients can significantly improve survival rates of those resistant to standard regimens. Thus, the early prediction of NAC outcome is of great importance in facilitating a personalized paradigm for breast cancer therapeutics. In this study, quantitative computed tomography (qCT) parametric imaging in conjunction with machine learning techniques were investigated to predict LABC tumor response to NAC. Textural and second derivative textural (SDT) features of CT images of 72 patients diagnosed with LABC were analysed before the initiation of NAC to quantify intra-tumor heterogeneity. These quantitative features were processed through a correlation-based feature reduction followed by a sequential feature selection with a bootstrap 0.632+ area under the receiver operating characteristic (ROC) curve (AUC0.632+) criterion. The best feature subset consisted of a combination of one textural and three SDT features. Using these features, an AdaBoost decision tree could predict the patient response with a cross-validated AUC0.632+ accuracy, sensitivity and specificity of 0.88, 85%, 88% and 75%, respectively. This study demonstrates, for the first time, that a combination of textural and SDT features of CT images can be used to predict breast cancer response NAC prior to the start of treatment which can potentially facilitate early therapy adjustments.     

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Biofilm formation is a general attribute to almost all bacteria ^1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly constituted by proteins and exopolysaccharides, among other factors ^7-10. The microbial community encased within the biofilm often shows the differentiation of distinct subpopulation of specialized cells ^11-17. These subpopulations coexist and often show spatial and temporal organization within the biofilm ^18-21.Biofilm formation in the model organism Bacillus subtilis requires the differentiation of distinct subpopulations of specialized cells. Among them, the subpopulation of matrix producers, responsible to produce and secrete the extracellular matrix of the biofilm is essential for biofilm formation ^11,19. Hence, differentiation of matrix producers is a hallmark of biofilm formation in B. subtilis.We have used fluorescent reporters to visualize and quantify the subpopulation of matrix producers in biofilms of B. subtilis^15,19,22-24. Concretely, we have observed that the subpopulation of matrix producers differentiates in response to the presence of self-produced extracellular signal surfactin ²⁵. Interestingly, surfactin is produced by a subpopulation of specialized cells different from the subpopulation of matrix producers ¹⁵.We have detailed in this report the technical approach necessary to visualize and quantify the subpopulation of matrix producers and surfactin producers within the biofilms of B.subtilis. To do this, fluorescent reporters of genes required for matrix production and surfactin production are inserted into the chromosome of B. subtilis. Reporters are expressed only in a subpopulation of specialized cells. Then, the subpopulations can be monitored using fluorescence microscopy and flow cytometry (See Fig 1).The fact that different subpopulations of specialized cells coexist within multicellular communities of bacteria gives us a different perspective about the regulation of gene expression in prokaryotes. This protocol addresses this phenomenon experimentally and it can be easily adapted to any other working model, to elucidate the molecular mechanisms underlying phenotypic heterogeneity within a microbial community.     

A quantitative proteomic analysis of biofilm adaptation by the periodontal pathogen Tannerella forsythia

Tannerella forsythia is a Gram‐negative anaerobe that is one of the most prominent inhabitants of the sub‐gingival plaque biofilm, which is crucial for causing periodontitis. We have used iTRAQ proteomics to identify and quantify alterations in global protein expression of T. forsythia during growth in a biofilm. This is the first proteomic study concentrating on biofilm growth in this key periodontal pathogen, and this study has identified several changes in protein expression. Moreover, we introduce a rigorous statistical method utilising peptide‐level intensities of iTRAQ reporters to determine which proteins are significantly regulated. In total, 348 proteins were identified and quantified with the expression of 44 proteins being significantly altered between biofilm and planktonic cells. We identified proteins from all cell compartments, and highlighted a marked upregulation in the relative abundances of predicted outer membrane proteins in biofilm cells. These included putative transport systems and the T. forsythia S‐layer proteins. These data and our finding that the butyrate production pathway is markedly downregulated in biofilms indicate possible alterations in host interaction capability. We also identified upregulation of putative oxidative stress response proteins, and showed that biofilm cells are 10 to 20 fold more resistant to oxidative stress. This may represent an important adaptation of this organism to prolonged persistence and immune evasion in the oral cavity.     

Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting

We present an analytical method using correlation functions to quantify clustering in super-resolution fluorescence localization images and electron microscopy images of static surfaces in two dimensions. We use this method to quantify how over-counting of labeled molecules contributes to apparent self-clustering and to calculate the effective lateral resolution of an image. This treatment applies to distributions of proteins and lipids in cell membranes, where there is significant interest in using electron microscopy and super-resolution fluorescence localization techniques to probe membrane heterogeneity. When images are quantified using pair auto-correlation functions, the magnitude of apparent clustering arising from over-counting varies inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. In contrast, we demonstrate that over-counting does not give rise to apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (FcεRI) on the plasma membranes of chemically fixed RBL-2H3 mast cells from images acquired using stochastic optical reconstruction microscopy (STORM/dSTORM) and scanning electron microscopy (SEM). We find that apparent clustering of FcεRI-bound IgE is dominated by over-counting labels on individual complexes when IgE is directly conjugated to organic fluorophores. We verify this observation by measuring pair cross-correlation functions between two distinguishably labeled pools of IgE-FcεRI on the cell surface using both imaging methods. After correcting for over-counting, we observe weak but significant self-clustering of IgE-FcεRI in fluorescence localization measurements, and no residual self-clustering as detected with SEM. We also apply this method to quantify IgE-FcεRI redistribution after deliberate clustering by crosslinking with two distinct trivalent ligands of defined architectures, and we evaluate contributions from both over-counting of labels and redistribution of proteins.