Application Note # MT-111 Concise Interpretation of MALDI Imaging Data by Probabilistic Latent Semantic Analysis (plsa)

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1 Application Note # MT-111 Concise Interpretation of MALDI Imaging Data by Probabilistic Latent Semantic Analysis (plsa) MALDI imaging datasets can be very information-rich, containing hundreds of mass signals. If the underlying tissue sample is also complex as is typically the case with cancer samples the analysis of individual mass signals in their histological context can become very timeconsuming. Mathematical ways of representing the data in a concise fashion therefore become very important. We describe here the use of probabilistic latent semantic analysis (plsa) for this purpose. The main advantage of this technique is the fact that the components computed by plsa can be interpreted as real tissue components, e.g cell types, even if the spatial resolution is not sufficient to resolve these cells and their respective mass spectrometric profiles. Introduction MALDI imaging data are high-dimensional data, with two spatial dimensions and hundreds of molecular dimensions. This leads to enormous data complexity. The task of analyzing every individual mass in its histological context can become prohibitively time-consuming, especially if a large number of samples are to be analyzed. A number of multivariate methods for reducing the complexity of the data, such as principal component analysis (PCA) and hierarchical clustering [1], have been described. Each of these techniques has particular advantages. For example, PCA can be used to find the highest variance in the data and is a good tool for checking the quality of the sample preparation. Hierarchical clustering allows user interaction and therefore a semi-supervised annotation of the data, and it often finds minute differences between similar tissue states. Probabilistic latent semantic analysis (plsa) is a statistical method that was originally introduced for the automatic recognition of topics in written documents [2]. In 2008, the use of plsa for analysis of imaging mass spectrometry data was reported by Hanselmann et al. [3]. Here we describe the use of plsa for the analysis of MALDI imaging data and the advantages of this technique under certain circumstances, especially if different cell types are present in tissue in varying proportions. Under these circumstances, plsa allows conclusions to be drawn regarding individual cell types and their respective mass spectra, even if the spatial resolution of the MALDI imaging experiment does not resolve single cells. This can be of particular interest in complex tumor samples, where, for example, immune cells are infiltrating a heterogeneous population of tumor cells. To provide an example that is simple enough to assess the results in an understandable fashion, but at the same time is complex enough to demonstrate the power of this analysis, the

2 technique is first explained using a simulated tumor dataset and then applied to a real tumor dataset. Experimental The simulated dataset The simulation is based on a simplified tumor dataset that contains only five different cell types: stromal cells, epithelial cells, tumor cells, lymphocytes and stromal cells associated with an extracellular microenvironment in a specific part of the tumor. The characteristic of real tumor samples in which cells are not homogenously distributed but found in varying proportions and densities is reflected in the simulation. An optical image of the simulated tumor section is shown in Figure 1. MALDI imaging does not usually resolve single cells and a MALDI image of this section would therefore be more blurred, as depicted in Figure 2. For the simulation it is assumed that each cell type has a characteristic MALDI Optical image of the simulated tumor section Stroma Epithelial cells Tumor cells Lymphoctyes Special microenvironment Figure 1: Simulated cell distribution in a tumor section. The dataset contains stroma (white), epithelial cells (green), tumor cells (red), lymphocytes (blue) and cells associated with an extracellular tumor microenvironment (yellow). As in real tumors, these cells are present in varying densities and mixed in the section. For the simulation, each cell type contributes a specific mass spectrum to the imaging data. MALDI image of same section as fig. 1. Figure 2: Since MALDI imaging does not usually resolve single cells, in this simulation nine cells contribute to one mass spectrometric pixel, corresponding to a MALDI image resolution of ~30 µm. This figure shows the optical image at the same downsampled resolution. A characteristic feature of this dataset is that there is not a single pixel where only one cell type contributes to the mass spectrum. spectrum, with tumor and epithelial cells showing a high similarity. In the simulation, almost all peaks are present in more than one cell type, and it assumed that nine cells always result in one mass spectrometric pixel (a realistic assumption for the spatial resolution of 20 µm that can be achieved on Bruker MALDI-TOF instruments with smartbeam II lasers for protein measurements [4]). The spectra show a realistic random variability in the peak intensities between the spectra. Figure 3 shows a panel of different mass images. Spectra acquisition was performed in the flexcontrol simulation mode using fleximaging software, the mass spectrometric profiles are inspired by real data. The plsa calculation was performed using ClinProTools 3.0, and results were imported into fleximaging 3.0. Real tumor dataset The real dataset is a part of breast cancer section. The sample was cryosectioned, prepared using the ImagePrep using sinapinic acid matrix and measured in linear mode with a lateral resolution of 70 µm. After acquisition, the MALDI matrix was removed and H&E staining was performed. Results Simulation The numbers of components that are calculated by the plsa must be estimated in advance. Since the number of cell types in the calculation is known, we used five as the number of components to be calculated. Estimating the number of components for real-life datasets is discussed below. Figure 4 shows the distribution of plsa scores for the simulated tumor dataset. The five components can be unambiguously assigned to the five different cell types used for the simulation, even though there was no single cell resolution in the mass spectrometric dataset. The result indicates a very low density of normal epithelial cells in the tumorous area, although no such cells were present in the simulation. This is because the spectra of epithelial cells are very similar to those of tumor cells and there may be more than one possibility to calculate the decomposition of the spectra. Nevertheless, for practical purposes the decomposition gives a very good approximation of the original dataset. A particular advantage of the plsa method over the PCA method is that the loadings of the plsa can be interpreted as real spectra, while the loadings of the PCA show negative contributions of masses, which do not have any analytical meaning. Figure 5 shows a representation of the plsa loadings as mass spectra. The results of the plsa can therefore be interpreted as mass spectra specific for real tissue components (e.g. different cell types) and their

3 Panel of different mass images Figure 3: Representative single mass images from the simulated dataset. As in real datasets, most masses appear in more than one cell type. Distribution of plsa scores MALDI imaging dataset plsa Epithelium Tumor Stroma Lymphocytes Microenviron. Figure 4: Images generated from the plsa scores: The plsa scores correspond directly to the density of the different cell types in the simulated tumor. The plsa scores can therefore be directly interpreted as real tissue components with a unique identifying protein peak pattern.

4 plsa loadings as mass spectra Figure 5: The plsa loadings can be interpreted as mass spectra specific for the different cell types. respective distribution in the tissue even if there is no single cell resolution achieved in the MALDI imaging experiment. For plsa calculation the number of components must be estimated by the user in advance. A reasonable knowledge of the tissue (as can be gained by H&E staining and educated tissue examination) can be used for estimation. However, there may be more information than expected in the MALDI imaging dataset, because some features may only be visible in the molecular image. On the other hand, if different cell types show very similar spectra, the correct number of components for the calculation may be lower than expected. For this reason we recommend repeating the calculation for different numbers of components and comparing the results to the histology. The correct number of components can be judged from this comparison. If an automated estimation of the correct number of components is desired, then the Akaike information criterion (AIC) can be used [3, 5]. This is a calculation that weighs up how much of the dataset is explained against the number of parameters used. The AIC curve shows a minimum which is expected to be the correct number of components. However, in real-life datasets the minimum can be quite broad. In any case, the AIC calculation can be very helpful if only limited information about the sample is available. To calculate the plsa based on the AIC, 1 is specified as number of components in ClinProTools. Real data The real tumor dataset was calculated with an automatic assignment of the number of components based on the AIC. The AIC curve is shown in Figure 6. It shows a broad minimum around 6 components, indicating an initial automatic calculation using 6 components. Figure 7 shows the results for the decomposition of the tumor data set. Some of the components can be clearly interpreted: A as stroma; B as infiltrating lymphocytes; C and D as invasive tumor

5 cells, with D showing the highest intensity where the highest density of tumor cells is found; E might be related to some specific environment of tumor cells or a different type of tumor cells; F is related to precursor lesions. For comparison, the calculation was repeated using five and seven components (data not shown). In this case the results looked essentially similar: when calculated using five components, components C and E co-located, which would be consistent with the histology. If seven components are calculated, component D is separated into two components that show a Akaike information criterion similar spatial distribution, indicating over-fitting. The real tumor dataset also illustrates one limitation of plsa: The tumor contains one ductal carcinoma in situ (marked by an arrow in the H&E stain in Figure 7) that is also picked up by some mass signals (data not shown). This feature does not show up in the plsa calculation. This is consistent with the fact that a dataset cannot be concise and comprehensive at the same time, so some detail will not be seen. While plsa allows inferences to be made about specific cell types, these are based on the statistical analysis of many similar cells (or tissue features). If specific claims on individual features (or cells) in the dataset are to be made, these individual features must still be resolved. Conclusion plsa, which is integrated into the Bruker Imaging software suite (ClinProTools 3.0), is a very powerful method for interpreting MALDI imaging data. Provided the dataset shows enough heterogeneity, it can deliver a concise representation of the MALDI imaging dataset in which individual cell types and their respective spectra can be computed, even if the MALDI imaging experiment only reaches near-cellular resolution (~20-50 µm). Acknowledgements Figure 6: The AIC curve for the real tumor dataset shows a minumum at 6 components. However, the minimum is broad, so 5 or 7 components might also be reasonable values. The authors thank Fred Hamprecht and Michael Hanselmann for helpful discussions and the matlab source code for the plsa. Decomposition of a real tumor data O A B C D E F Figure 7: O: Optical image of an H&E stained breast cancer section and corresponding plsa components from the respective imaging dataset. Arrow indicates a ductal carcinoma in situ. Components can be interpreted as A: stroma, B: lymphocyte infiltration, C and D: invasive tumor, E: invasive tumor/microenvironment, F: Tumor precursor lesions

6 Bruker Daltonics is continually improving its products and reserves the right to change specifications without notice. Bruker Daltonics , MT-111, # References [1] Deininger SO, Becker M, Suckau D Tutorial: multivariate statistical treatment of imaging data for clinical biomarker discovery Methods Mol Biol 2010(656): [2] Hofmann T, Probabilistic Latent Semantic Indexing Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval (SIGIR-99), 1999 [3] Hanselmann M, Kirchner M, Renard BY, Amstalden ER, Glunde K, Heeren RM, Hamprecht FA Concise representation of mass spectrometry images by probabilistic latent semantic analysis Anal Chem. 2008; 80(24): [4] Lagarrigue M, Becker M, Lavigne R, Deininger SO, Walch A, Aubry F, Suckau D, Pineau C. Revisiting rat spermatogenesis with MALDI imaging at 20-microm resolution. Mol Cell Proteomics ;10(3) :M [5] Akaike H, A new look at the statistical model identification IEEE Transactions on Automatic Control 1974; 19 (6): Keywords MALDI Imaging Clinical plsa AIC data interpretation Instrumentation & Software ultraflex TOF/TOF fleximaging Authors Sören-Oliver Deininger, Klaus Meyer, Axel Walch; Bruker Daltonik and Institute of Pathology, Helmholtz-Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany. For research use only. Not for use in diagnostic procedures. Bruker Daltonik GmbH Bremen Germany Phone +49 (0) Fax +49 (0) sales@bdal.de Bruker Daltonics Inc. Billerica, MA USA Phone +1 (978) Fax +1 (978) ms-sales@bdal.com Bruker Daltonics Inc. Fremont, CA USA Phone +1 (510) Fax +1 (510) ms-sales@bdal.com