ARTICLE pubs.acs.org/IECR
Characterization of Surface Coats of Bacterial Spores with Atomic Force Microscopy and Wavelets Wei Sun,† Jose A. Romagnoli,‡ Ahmet Palazoglu,§ and Pieter Stroeve*,§ †
College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China Cain Department of Chemical Engineering, Louisiana State University, Baton Rouge, Louisiana 70803-7303, United States § Department of Chemical Engineering and Materials Science, University of California, Davis, California 95616, United States ‡
ABSTRACT: Atomic force microscopy images of the surface coatings of bacterial spores can be analyzed by wavelet analysis to rapidly determine the characteristic morphology of the spore coat. The identification of bacterial spores in the environment is an important problem for preventing disease, identifying bacterial contamination of air, water, and soil, and evaluating the effects of chemicals on bacteria. In this work, we analyze AFM data of the native surface topography and ultra structure of spore coats of native Bacillus atrophaeus and Bacillus thuringiensis. We show that if the analysis of the images is done as a function of the rotation angle of the image observation, the morphology and the characteristic sizes of the bacterial coats can be accurately analyzed. The technique has potential to rapidly identify bacterial spores and viruses if a library of images of various bacteria are obtained.
1. INTRODUCTION The rapid identification and characterization of bacteria, bacterial spores, and viruses in the environment is an important problem for preventing disease, identifying bacterial and viral contamination of water, air, and soils, understanding the behavior of microbial cells at interfaces, and determining the effect of biochemicals on bacteria. The study of bacteria spores and viruses on the micro- and nanoscale, including the shape of bacteria, spores, and viruses, surface topology, supramolecular structures, chemical composition, and physicochemical properties of the surfaces of bacteria, spores, and viruses, is an emerging field for obtaining information that cannot only be used to identify species, but also to provide information on their growth, biology, and differentiation.1-3 The detection of biological warfare agents has led to growing interest in the rapid and sensitive detection of pathogenic organisms, particularly bacterial spores4 whose surface topology may have distinct signatures that could be used to enable their identification. The surface of bacteria, bacterial spores, and viruses can be probed by many techniques,1 including transmission electron microscopy (TEM),5-10 photoemission (PE) near edge X-ray absorption fine structure (NEXAFS) spectroscopy,11 microelectrophoresis,12 and scanning probe microscopy (SPM) such as atomic force microscopy (AFM).13-20 AFM is particularly useful because it can provide information about surface architecture of bacteria and viruses on the nanometer scale. For the rapid measurement and characterization of samples, such as chemicals, toxins, bacteria, viruses, and physical properties of samples, there have been a number of reports on the development and use of portable instruments that can be easily moved from one to another location to make measurements in the field.21-31 There is interest in developing portable detection devices, such as AFM, for field-usable detection of bacteria and viruses.23 Coupled to use of sensitive detection using a portable AFM in the field for bacteria and virus identification, there would be an urgent need to r 2011 American Chemical Society
conduct rapid image analysis of the obtained data in the field, which could then be used to rapidly identify bacteria and viruses. Scanning probe microscopy, such as AFM, has established its place in material and biological characterization.32,33 It has been applied to various inorganic and organic materials, and to various interfaces (wet and dry). For example, in the semiconductor industry, SPM has been used to examine wafer cleaning methods, mask overlay registration, etching, planarization, in situ deposition, surface profiles of bare wafers and deposited films, and, additionally, for defect detection and failure analysis, and for measuring soft samples like unbaked photo resist. In biological applications, AFM can provide 3-D images of surface topography of biological specimens in ambient liquid or gas environments over a range of temperatures. Unlike electron microscopes, samples do not need to be stained, coated, or frozen. Previously, one of us has shown that AFM is able to visualize supported lipid bilayers (SLB) under water and allows measurement of the interactions of SLB with other molecules, such as antibacterial peptides.32,33 Using wavelet analysis, we analyzed the interaction of an antibacterial peptide, protegrin, and a variation of protegrin with SLB.34,35 In addition to high-resolution profiling of surface morphology and nanostructure, AFM allows the determination of local materials properties and surface compositional mapping in heterogeneous samples. We have shown that these techniques allow examination not only of the topmost surface features, but also of the underlying nearsurface sample structure. High-resolution AFM studies have been made on the visualization of the native surface topography and ultra structure of spore coats of native Bacillus atrophaeus, Bacillus cereus, and Bacillus thuringiensis.16-18 Received: May 25, 2010 Accepted: December 23, 2010 Revised: December 2, 2010 Published: February 2, 2011 2876
dx.doi.org/10.1021/ie101153y | Ind. Eng. Chem. Res. 2011, 50, 2876–2882
Industrial & Engineering Chemistry Research The present work is an approach to achieving rapid analysis of AFM images of pathogens using common software platforms such as MATLAB. We apply wavelet theory and power spectral density (PSD) toward pattern recognition of bacterial spore coats. Wavelets are mathematical functions with certain properties that can facilitate the representation of other functions or data signals.36 Without compromising information on relevant features, wavelet decomposition makes it possible to observe a given signal (image) at different resolutions or frequencies that correspond to different topographical features, such as underlying peaks and valleys, or detailed variations on a surface. Wavelet transformation has been a key technique in signal processing applications due to its capability of time-frequency localization.37 As opposed to Fourier transformation that identifies key frequency components in a signal, wavelet transformation can also provide a spatial (or temporal) localization of the frequency information. This makes it an ideal tool to detect trends and discontinuities, as well as to denoise signals.38 In image analysis, wavelet transformation can capture patterns at all relevant frequency scales, thus providing a level of detail that may not be possible otherwise.37 We have shown previously that wavelet decomposition can help in extracting specific image features from AFM images and allow them to be studied in isolation.35 Wavelet decomposition has been also used in scanning electron microscopy (SEM) image processing and in the context of fault detection in the etching process.39,40 Our ultimate aim is toward automatic analysis of AFM images of pathogens using common software-based methods. In this Article, we apply wavelet theory and PSD toward pattern recognition of bacterial spores. In particular, we analyze AFM data of the native surface topography and ultra structure of spore coats of native Bacillus atrophaeus and Bacillus thuringiensis.16,17 First, a necessary mathematical background on wavelet theory is briefly introduced. The emphasis is given toward shifting wavelet concepts from time-frequency to space-frequency framework. Specifically, we emphasize the applicability of wavelet tools for 2-D AFM image analysis. All calculations are performed in the MATLAB environment using our custom codes. Two case studies are presented to demonstrate the potential of wavelet analysis and power spectral density toward automated pattern recognition on bacterial spores.
2. MATERIALS AND METHODS 2.1. Bacillus atrophaeus and Bacillus thuringiensis. AFM images of the spore coats were generously provided to us by Dr. M. Plomp and Dr. A. J. Malkin of the Lawrence Livermore National Laboratory (LLNL). The B. thuringiensis israelensis (ATC 35646) was obtained from the Bacillus Genetic Stock Center and the B. atrophaeus (ATCC 9372) was obtained by the procedures described by Malkin and colleagues. Spore preparation and purification was performed as previously described.16-18 Droplets of spores were deposited directly onto a substrate and allowed ∼10 min to settle, after which the substrate was rinsed with double-distilled water and analyzed by AFM. For the imaging of hydrated spore samples, freshly cleaved mica and polycoated vinyl plastic were utilized as substrates. The AFM images were collected using Digital Instruments Multimode Nanoscope IIIa and IV AFM (Veeco, Santa Barbara, CA), which was operated in tapping mode. Tapping amplitude, phase, and height images were collected simultaneously. Height images were used for quantitative measurements. Oxide-sharpened
ARTICLE
silicon nitride tips (force constant ∼0.1 N/m and resonance frequency of ∼9 kHz) were purchased from Veeco and employed for imaging in water. During experiments, feedback imaging parameters (integral and proportional grains) were adjusted to the highest possible level that did not result in feedback oscillations. Details of the AFM procedure have been published previously.16-18 2.2. Wavelets and Spectral Analysis. For a signal X(t) that belongs to the class of square-integrable functions, one can define the continuous wavelet transform (CWT) as ! Z þ¥ ∧ 1 t k XðtÞψ dt ð1Þ W ðj, kÞ ¼ 1=2 j j -¥ The wavelet function ψ(t) is referred to as the “mother wavelet” and defined by the translation and scale parameters, k and j, respectively: ! 1 t-k ψj, k ¼ 1=2 ψ ð2Þ j j When a signal is decomposed using a specific wavelet function, the result is a picture of the energy contained in the signal as a function of both the spatial dimension (and time) and the wavelet scale (or frequency). This provides an effective analysis method that allows the study of multiscale, nonstationary processes occurring over a finite spatial (or temporal) domain. One can further construct an orthogonal basis of functions for CWT by choosing the scale to be the power of 2 and the time to be an integer multiple of the scale. Using integers m and n to redefine the translation and scale parameters, k and j, the mother wavelet in eq 2 is rephrased as ψm, n ðtÞ ¼ 2n=2 ψð2n t - mÞ Thus, the wavelet transform in eq 1 is now given as Z þ¥ ∧ W ðm=2n , 1=2n Þ ¼ 2n=2 XðtÞψð2n t - mÞ dt -¥
ð3Þ
ð4Þ
When the wavelet function is orthonormal, the resulting analysis formula is the discrete wavelet transform (DWT): Z þ¥ Cm , n ¼ XðtÞψm, n ðtÞ dt ð5Þ -¥
Here, Cm,n is the discrete wavelet coefficient. The reconstruction of the original signal through the synthesis formula is called inverse wavelet transform (IDWT) and is defined as XX Cm, n ψm, n ð6Þ XðtÞ ¼ n
m
If one uses an intermediate scale, eq 6 can be expressed as two sums: XX X Cm, n ψm, n þ Cm, n ψm, n ð7Þ Xl ðtÞ ¼ ngn0 m
n
