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First published online January 12, 2007; 10.1104/pp.106.093054 Plant Physiology 143:1314-1326 (2007) © 2007 American Society of Plant Biologists OPEN ACCESS ARTICLE
Neural Network Analyses of Infrared Spectra for Classifying Cell Wall Architectures1,[W],[OA]Department of Biological Sciences (M.C.M., J.C.T., T.L.) and Department of Botany and Plant Pathology (B.R.U., A.O., N.C.C.), Purdue University, West Lafayette, Indiana 47907; Department of Food Material Sciences, Institute of Food Research, Colney, Norwich NR4 7UA, United Kingdom (M.D., R.H.W.); and Department of Cell and Developmental Biology, John Innes Centre, Colney, Norwich NR4 7UH, United Kingdom (B.W.)
About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1  3),(1 4)- -D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 3),(1 4)--D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification can be broadened to phenotypes resulting from mutations in genes encoding proteins for which a function is yet to be described.
As our knowledge of the transcriptome and proteome increases, we are still deficient in our understanding of how genes and their products contribute to downstream phenotypes. For cell wall polysaccharides, the plant's principal biomass, the problem of attempting to connect genotype with phenotype is especially difficult. The cell wall comprises several structurally complex polymers, most of which are synthesized in the Golgi, secreted to the plasma membrane, and assembled with cellulose microfibrils extracellularly (McCann and Roberts, 1991  ; Carpita and Gibeaut, 1993). Although over 1,000 cell wall-related genes have been annotated, in many instances, neither the biochemical function nor the expression patterns of these genes is established (McCann and Carpita, 2005). Most importantly, plants are highly responsive to alterations in their cell walls, such as those induced by mutation or environmental perturbation, and gene expression or enzyme activities are adjusted to compensate for deficiencies in unanticipated ways (Iraki et al., 1989; Shedletzky et al., 1992; His et al., 2001; Caño-Delgado et al., 2003; Madson et al., 2003).
Somerville et al. (2004)
As cell walls comprise secondary gene products, it is especially important that multivariate data representative of these structures are included in a systems approach. In one example from yeast, the correlation coefficient between changes in transcript and protein levels upon perturbations in carbon metabolism is 0.3 (Ideker et al., 2001
The range of cell wall mutants in grass species is very limited (e.g. Li et al., 2002
Grass species and related commelinoid monocot cell walls display considerable and well-characterized variation in polymer synthesis, assembly, and hydrolysis during coleoptile growth (Darvill et al., 1978
Changes in Cell Wall Composition during Elongation of Hybrid and W22 Coleoptiles Etiolated hybrid coleoptiles begin to elongate about 1 d after imbibition and reach a maximum elongation rate between 2 and 3 d (Fig. 1A ). Final length is achieved between 5 and 6 d, when the etiolated leaf blades emerge and split the coleoptile open. After leaf emergence, coleoptiles begin to senesce, and subsequent loss of water causes a slight shrinkage. Germination and elongation of W22 coleoptiles is delayed about one half day with respect to hybrid coleoptiles, but cessation of growth and senescence are coincident with the hybrid. Figure 1A shows physiological growth stages of hybrid and inbred coleoptiles normalized to illustrate the equivalent transition stages. However, the hybrid coleoptile achieves a maximal length of about 4.3 cm, whereas that of W22 is 3.5 cm.
In hybrid coleoptiles, cellulose content is initially 10% of the dry mass of the wall and increases to 20% at the onset of elongation. The proportion of cellulose remains at 20% during elongation but increases to nearly 40% during senescence (Fig. 1B). The (1 3),(1 4)--D-glucan is nearly absent from embryonic coleoptiles but increases rapidly during elongation, reaching a maximum content coincident with the highest rates of elongation, and then decreases markedly as growth ceases (Fig. 1B).
Epitopes of (1
Noncellulosic monosaccharide compositions were obtained from populations of hybrid and W22 coleoptiles by gas chromatography-mass spectrometry of alditol acetates. Xyl, Glc, Ara, and Gal are the four most abundant monosaccharides (Fig. 1, G and H). Ara content decreases during growth, and Xyl later increases toward the end of growth in both hybrid and W22 coleoptiles. Gal remains constant at about 10 mol %. Changes in Glc correlate to some extent with the changes observed in (1
At least 36 FTIR spectra were obtained from cell walls of populations of coleoptiles from each one-half-d interval; the averaged spectra for each interval reflect the compositional changes that occur during the course of cell elongation (Fig. 2A
). Multivariate partial least squares predicted with a correlation coefficient of between 0.75 and 0.95 the mol % of the four most abundant monosaccharides (Supplemental Fig. S1). Peak assignments are taken from Carpita et al. (2001)
Spectra are constituted by a discrete series of measurements at defined intervals; by nature, they are multivariate. Statistical algorithms for treating multivariate data such as PCA (Kemsley, 1998 ) can be applied to reveal molecular features that are the basis for discriminating among populations (Chen et al., 1998). PCA reduces the high dimensionality of spectral data to a smaller set of computer-derived variables, termed principal components (PCs), that together account for all of the variance in a set of (sometimes apparently similar) spectra (Kemsley, 1998). Each spectrum has an associated value for each new variable, the PC score, representing its relative distance from the mean of the population. Exploratory PCA shows that cell walls representing individual embryonic coleoptiles (days 1 and 1.5) are generally well resolved from elongating (days 3.5 and 4) and senescent (days 5.5 and 6) coleoptiles (Fig. 2E). Cell walls from embryonic coleoptiles are resolved from elongating and senescing coleoptiles by PC 1, the latter having higher scores on this component. Walls from both embryonic and elongating stages have generally higher PC 2 scores than walls of the senescent coleoptiles. The loadings reveal some of the spectral features that are associated with PC 1 and PC 2 as sources of variance (Fig. 2, F and G). For PC 1, which accounts for 61% of the variance, the loading is positive for peaks associated with cellulose and other -linked cross-linking glycans (1157, 1103, 1053, and 898 cm–1), as well as absorbances at 1018 and 991 (not assigned) and negative for peaks associated with proteins (1651, 1628, and 1535 cm–1). Walls of elongating coleoptiles were enriched in compounds contributing to positive PC 1 loadings relative to the embryonic walls. In this instance, the loading for PC 1 resembles features of the digital subtraction spectrum (Fig. 2, C and F). In contrast, PC 2 loadings, accounting for 22% of variance, were independent of protein content but suggested enrichment in embryonic and elongating coleoptile walls of compounds with absorbances at 1168, 1111, 1083, and 1041 cm–1 of the carbohydrate fingerprint region compared to those of senescent coleoptiles.
For the three groups of spectra derived from embryonic, elongating, and senescent cell walls (Fig. 2E), the percentages of correct classification of individual spectra to each group by cross-validation tests were 69% using five PCs. However, if spectra representing one-half-d and 1-d intervals of growth are included for analysis by exploratory PCA, many of 10 subpopulations of spectra are overlapped in the scores plot and not resolved from each other (Fig. 3 ). As expected, the loadings for PC 1 and PC 2 (data not shown) closely resemble those for the scores plot of Figure 2, F and G. A linear discriminant analysis (LDA) by cross validation after PCA correctly classified an average of only 32% of all spectra using five PCs, ranging from 3% (day 4) up to 61% (day 5.5) for each growth interval (Table I ). Thus, PCA is not robust in classification when many classes are included and when only slight differences in wall composition or architecture may distinguish each class. As our ultimate goal is to be able to compare cell wall phenotypes on a genome-wide scale, we need a classification tool that is robust for many classes of data.
Analysis of the Spectral Data for Coleoptile Cell Walls by ANN As exploratory PCA was not able to discriminate 10 growth stages (Fig. 3), we applied ANN analyses to our spectroscopic data. ANNs are algorithms with the capacity to analyze multivariate data (each spectrum is 250 variates) from large numbers of observations (spectra derived from individuals within populations) and large numbers of potential classes of observations within a data set (all 12 growth stages). The neural network reports both on class assignment (percent correct classification) and also on the probability of membership of each class, for each spectrum. As for the LDA described above, we have used cross validation to measure the ability of the classification tool in prediction. The spectra used to test the predictive ability of the network are not included in the set of spectra used to train the network. Our results are from six different networks, each of which was trained with five-sixths of the data, reserving a different one-sixth in each case. Then we summed the predictions of those test sets for the six networks. It should be noted that the success rate by cross-validation analysis for both PCA and ANNs is lower than classification success if all data are included and success is measured simply as classification to the correct class. We applied two kinds of neural network to the spectroscopic data collected from the time course of hybrid coleoptile growth.
First, we used a supervised approach provided by a genetic algorithm (Neuroshell 2, Ward Systems Group, proprietary). A genetic network comprises three layers: an input layer that comprises the absorbance values of infrared spectra at 250 discrete wavenumbers, a hidden layer of neurons, each of which is connected to all of the inputs and all of the outputs, and an output layer, which are the classes to which each individual might belong, defined by growth interval (Goldberg, 1989
Second, we used an unsupervised Kohonen network (Lavine et al., 2004 ), in which the output classes are not defined for each spectrum. Instead of optimizing classification to predefined classes, the network simply assigns spectra to a number of classes based on structure that it detects within the entire data set. Thus, we can test whether or not our predicted class structure (classification by one-half-d growth interval) is valid. A Kohonen network comprises input and output layers only; the output layer has one neuron for each possible output category. The number of output classes can be varied; we experimentally increased the number of classes from two to 20, with the best classification observed at 12 classes. Above 12, new classes were left empty (data not shown). In contrast to the relatively poor classification by PCA (3%–61%), the Kohonen network correctly classified an average of 77% of all spectra, ranging from 50% (day 4) to 97% (day 4.5) for each growth interval and from 72% (day 4) to 100% (days 1.5, 2.5, and 4.5) for growth intervals plus or minus one half d (Fig. 5A
; Table I). The cluster plot of the correlation analysis (Fig. 5B) is similar to that of the genetic algorithm (Fig. 4B). In the cluster plots (Figs. 4B and 5B), only the first two sources of variance from the correspondence analysis are plotted, but values for all degrees of freedom (for n groups comprising probability scores of class membership, n – 1 F values) are obtained.
Testing the Model with the W22 Population
The cell walls of hybrid maize coleoptiles have been well characterized with respect to the dynamics of composition and architecture during growth (Carpita et al., 2001 The spectra from the time course of hybrid coleoptile elongation were used to train a genetic network, with output classes specified as each of the one-half-d growth intervals. In this case, the genetic, rather than Kohonen, network is appropriate, because we know that the output classes are different from each other on the basis of their chemical compositions. We have prior knowledge of what the classification structure should be, and this knowledge can be used to optimize the predictive ability of a genetic network. The network, trained with spectra from the hybrid time course, was then tested for its ability to classify populations of W22 walls sampled at one-half-d intervals. The confusion and probability matrices show that incorrect assignments and probability values were made generally to neighboring growth intervals (Fig. 6A ). The lag of one half d before the onset of rapid cell elongation is reflected by assignment of W22 embryonic walls to the next earlier one-half-d hybrid class instead of the elongating wall cluster (Fig. 6B). After the 3.5-d interval, W22 and the hybrid maize were clustered generally at the equivalent one-half-d age representative of physiological age; the difference between hybrid and W22 in final coleoptile length before senescence was not a criterion in assignment.
However, the model described above assumes that the test set of data from W22 coleoptiles can be classified into the same 12 classes as the training set of data from hybrid coleoptiles. We can establish whether this assumption is justified using a Kohonen network in which 24 classes are specified as the number of possible classes into which individual spectra may be assigned. The confusion and probability matrices show that spectra for some growth intervals were assigned almost equally as hybrid time points or the lagging W22 time points (Fig. 7A ). Only coleoptiles at maximal elongation rate could be clearly resolved into distinct classes. In this case, the cluster plot shows a tight mapping of the W22 time course with the hybrid time course, showing that the clustering in Figure 7A was not the result of forcing the data into only 12 possible classes (Fig. 7B). As the cluster plot is two dimensional, we can take account of all dimensions by constructing a dendrogram based on the average probability values for each of the 24 possible classes (Fig. 8 ). For each set of 36 spectra that actually belong to a single class, for example, W22 1-d-old coleoptile walls, each spectrum has a set of 24 probability values of belonging to the 24 classes, most of which are zero. We can average the probabilities for each class for the 36 spectra and then use these average probability values to construct a dendrogram. The dendrogram shows tight relationships between walls of similar developmental stage and composition, whether W22 or hybrid, independent of age.
In this article, we establish ANNs as a suitable classification tool for altered wall phenotypes. We used a model system, the growth of the maize coleoptile, as a system in which there are defined and well-characterized changes in wall composition and architecture (Carpita, 1996 ; Carpita et al., 2001).
We can monitor monosaccharides, their linkages, oligomers or epitopes of particular polysaccharides, and cellulose content by means of labor-intensive assays that are each indicative but not comprehensive of complex architectural changes. FTIR spectroscopy has advantages of providing a single and rapid assay that is sensitive to a large range of compositional and architectural features (polymer conformations, hydration state, and extent and nature of cross-linking, features that affect frequencies of molecular vibrations in the infrared spectrum) that most chemical assays cannot detect. In many instances, we cannot interpret spectral peaks in terms of specific wall modifications, but we can at least detect that changes have occurred. Our measurements of monosaccharide, cellulose, and (1
Fully connected feed-forward ANNs have been widely used for multivariate data, including IR spectra (Kell et al., 2001
A drawback of ANNs is that the equations that describe the weightings for each neuron, even in a Kohonen network, do not provide a means to visualize individual spectral features that are used for classification in the way that PC loadings can. Therefore, the ANNs reveal class structure, but the nature of the class structure must either be inferred from prior knowledge of classes or by more limited comparisons in PCA or by digital subtractions. Nevertheless, the probability relationships between spectra can be visualized by correspondence analysis by comparing sources of variance in probability measurements, calculated as F values. The first two F values in the cluster plot in Figure 6 together represent 31.5% of the total variance in the population. The hysteresis in the cluster plot suggests that at least the F2 axis may be dominated by (1 By establishing spectrotypes for known cell wall alterations, as confirmed by chemical analyses, we aim to interpret the spectrotypes of unknowns by comparison. As we did not have prior knowledge of the chemical composition of W22 cell walls, then these samples constitute a set of unknowns that can be mapped on to the model built from the spectra derived from hybrid coleoptile walls and their compositions predicted. These experiments provide us with a means of inferring the sensitivity of the ANNs to compositional changes as reflected in the IR spectra. In screening a collection of mutants for altered wall phenotypes, it is useful to have some well-characterized samples that can be used to train ANNs and that can be used as standards to infer the composition of unknowns. Arabidopsis mutants that affect most of the major cell wall components have been identified and characterized. However, there are comparatively few maize mutants in cell wall-related genes. Until the extended cell wall phenotypes of these mutants have been characterized, a mutant screen could simply measure divergence from the range of developmentally regulated changes in maize wall compositions using unsupervised Kohonen networks.
The application of ANNs to spectroscopic data, and to other multivariate measurements of phenotype, provides a framework for the systematic classification of cell wall phenotypes in response to numerous perturbations. We predict that ANNs will have a widespread application to plant cell wall biology. For example, one may apply these algorithms at the species level for evolutionary-developmental cell wall taxonomies, for tracking genetic variation in recombinant inbred lines for cell wall-related quality traits, and for phenotyping the cell wall changes that occur downstream of signal transduction pathways. A systems approach to cell wall biology is now required to integrate our existing knowledge base of the molecular machinery of the wall and to predict the missing elements of its complex and dynamic architecture.
Plant Material Maize (Zea mays) hybrid caryopses (Mo17 x B73 foundation; Asgrow Seeds) and W22 caryopses (from Don McCarty and Karen Koch, University of Florida) were soaked overnight in darkness in water bubbled with air at 29°C, sown in moist vermiculite, and incubated in darkness at 29°C for an additional 24 to 144 h. Coleoptiles were harvested during the 1- to 7-d incubation at 0.5- to 1-d intervals, frozen in liquid nitrogen, and stored at –80°C until all samples could be processed. The tips and central portions of some coleoptiles were fixed for low-temperature embedding for transmission electron microscopy.
Isolation of cell walls from coleoptiles is essential to detect subtle changes in wall composition at 0.5-d intervals by IR spectroscopy. Cell walls were prepared from frozen maize coleoptiles by homogenization in 1% (w/v) SDS in 50 mM Tris-HCl, pH 7.2, at ambient temperature in a glass-glass motorized grinder (Kontes-Duall, Thomas Scientific). Cell walls were collected on nylon mesh filters (45 µm2; Nitex), and washed sequentially with water, 50% (v/v) ethanol, acetone, and then resuspended in water.
For microspectroscopy, materials were mounted in the wells of IR-reflective, gold-plated microscope slides (Thermo-Electron). The windows and slides with cell wall preparations were supported on the stage of a Nicolet Continuum series microscope accessory to a 670 IR spectrophotometer with a liquid nitrogen-cooled mercury-cadmium telluride detector (Thermo-Electron). An area of wall (up to 125 x 125 µm), excluding vascular walls, was selected for spectral collection in transflectance mode. In transflectance, the beam is transmitted through the wall sample, reflected off the gold-plated slide, and then transmitted through the sample a second time. One hundred and twenty eight interferograms were collected with 8 cm–1 resolution and coadded to improve the signal-to-noise ratio for each sample. Three spectra were collected from different areas of each sample and then area averaged and baseline corrected. The triplicate-averaged spectra from 36 to 60 hybrid or inbred coleoptiles were then averaged and used for digital subtraction.
Baseline-corrected and area-normalized data sets of spectra are then used in the chemometric analyses. Most of the PCA was carried out with WIN-DAS software (Kemsley, 1998
FTIR spectra were analyzed by genetic and Kohonen algorithms using the combination of NeuroShell2 and Classifier software (Ward Systems Group). The algorithms are proprietary. Spectra were truncated to the range 800 to 1800 cm–1, baseline corrected and area normalized, and input as PAT (pattern) files in wavenumber versus absorbance format with 250 variates. In each case, the generalization capabilities of the network were validated using k-fold cross validation; the data set was divided into k subsets, and k networks are trained and tested. Each time, one of the k subsets is used as the test set and the other k-1 subsets are pooled to form a training set. The average errors across all k trials were calculated. For the genetic networks, 432 spectra for hybrid maize belonging to 12 classes (each representing one-half-d growth interval) were trained through 129 generations. Class membership is specified in the final column for each spectrum of the data set matrix. The genetic network is an acyclic feed-forward network using 250 spectral variates as input and 50 hidden layer neurons. For the Kohonen network, the class membership was not specified, and spectra were input to the network in random order. The learning rate for the Kohonen network was set at 0.5, with 0.5 initial weights, a neighborhood size of 11 neurons, and the network was trained for 50 epochs. The number of epochs is selected as the minimum number of epochs to achieve optimal classification success without overfitting. Euclidean distances were used to measure the distance between the classes of spectra. After obtaining the neuron values, probabilistic algorithms were used to obtain the probability and confusion matrices (Neuroshell2, Ward Systems Group, proprietary). The ANNs produce two kinds of classification tables. First, a confusion matrix is a table of numbers of spectra assigned by the network to each class compared to their actual class identities. Second, the ANN calculates the probability of class membership for each spectrum. We have represented these two outputs in tables showing actual versus predicted class assignments that are color-coded across ranges of average class probability values overlaid with the numbers of spectra from the confusion matrix. We also used the original values for each spectrum in the ANN probability matrix as a contingency data matrix to visualize the relationships between classes by correspondence analysis using XLSTAT v. 7.5.2 (Kovach Computing Services). The calculated eigenvalues were ranked in order of percent variance as F1 to F(n – 1) for n classes, and cluster plots were generated by plotting values of F1 against F2.
Portions of the walls were hydrolyzed with 2 M trifluoroacetic acid (TFA) containing 400 nmol of myo-inositol for 90 min at 120°C in 1-mL conical Reacti-vials (Pierce Chemical). After hydrolysis, insoluble material was pelleted by centrifugation, and the supernatant TFA was collected and evaporated under a stream of filtered air. The insoluble material (mostly cellulose) was washed several times with water and collected by centrifugation. The pellet was suspended in 0.8 mL of water, and 100 µL was assayed for Glc equivalents by the phenol-sulfuric method (Dubois et al., 1956
Monosaccharides in the TFA-soluble fraction were converted to alditol acetates (Gibeaut and Carpita, 1991
Maize cell walls were incubated for 3 h at 37°C with a Bacillus subtilis (1
Tissue blocks of about 2 mm3 were taken from the midsection of 2- to 5-d-old maize coleoptiles and fixed overnight in 2% glutaraldehyde in 0.1 M sodium cacodylate, pH 7.2, then low-temperature embedded, as described previously (Carpita et al., 2001
The following materials are available in the online version of this article.
We thank Dr. Chris Clifton, Department of Computer Sciences, Purdue University, for valuable discussions and his review of the manuscript. Received November 15, 2006; accepted December 11, 2006; published January 12, 2007.
1 This work was supported by the National Science Foundation Genome Research Program (to N.C.C. and M.C.M.), and by the Biotechnology and Biological Sciences Research Council (to M.C.M., M.D., and R.H.W.). Journal paper number 17,933 of the Purdue University Agricultural Experiment Station. 
2 These authors contributed equally to the paper.
3 Present address: Department of Plant Biology, Cornell University, Ithaca, NY 14853.
4 Present address: Department of Food Science, Cornell University, Ithaca, NY 14853. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Nicholas C. Carpita (carpita{at}purdue.edu).
[W] The online version of this article contains Web-only data.
[OA] Open Access articles can be viewed online without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.106.093054 * Corresponding author; e-mail carpita{at}purdue.edu; fax 765–494–0363.
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