Theodore Alexandrov, Michael Becker, Sören Deininger, Günther Ernst, Liane Wehder, Markus Grasmair, Ferdinand von Eggeling, Herbert Thiele, and Peter Maass

 Background on MS Imaging and goals of paper  Methods  Results  Conclusions and Criticism

 Background on MS Imaging and goals of paper  Methods  Results  Conclusions and Criticism

 In the words of All-Mighty Wikipedia:  Mass spectrometry imaging is a technique used in mass spectrometry to visualize the spatial distribution of e.g. compounds, biomarker, metabolites, peptides or proteins by their molecular masses.  Or in images:

 To propose a new procedure for spatial segmentation of MALDI-imaging datasets.  This procedure clusters all spectra into different groups based on their similarity.  This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample.

(it is MS Imaging after all)

 Current multivariate algorithm (PCA) are not meant for MS data and cannot be used to directly interpret the data.  Current clustering algorithm do not take in account spatial information.  Here, we assume that spectra close to each other should be similar.

 Background on MS Imaging and goals of paper  Methods  Results  Conclusions and Criticism

 Rat brain coronal section ◦ 80 µm raster ◦ 200 laser shots per position; spectra ◦ Data acquired: 2.5 kDa-25 kDa ◦ Data considered: 2.5 kDa-10 kDa; 3045 points  Section of neuroendocrine tumor (NET) invading the small intestine ◦ 50 µm raster ◦ 300 laser shots per position; spectra ◦ Data acquired:1 kDa-30 kDa ◦ Data considered: 3.2 kDa-18kDa; 5027 points

 Baseline correction ◦ TopHat algorithm, minimal baseline width set to 10%, default in ClinProTools  No normalization  No binning  ASCII -> Matlab

 Part1: conventional peak picking applied to each 10 th spectrum. Select 10 peaks. ◦ Orthogonal Matching Pursuit (OMP) because it is fast and simple ◦ Gaussian kernel deconvolution  Part 2: keep consensus peaks: ◦ Only keep peaks that appear in at least 1% of the considered spectra ◦ Omit spurious peaks

 Imaging dataset is a reduced datacube with 3 coordinates: x, y, m/z (reduced in m/z dimension by peak picking)  MALDI-imaging data is noisy  Must be able to keep fine anatomical or histological details  Grasmair modification of Total Variation minimizing Chambolle algorithm ◦ Parameter θ between 0.5 and 1: smoothness of resulting image

 Total variation (TV) ~ sum of absolute differences between neighboring pixels  Chambolle algorithm searches for an approximation of the image with small TV  Chambolle algorithm => smoothness adjusted globally by manually choosing a parameter  Grasmair locally adapts denoising parameter of Chambolle

 Specify number of cluster a-priori  High Dimensional Discriminant Clustering (HDDC) ◦ Available in Matlab tool box ◦ Each cluster is modeled by a Gaussian distribution of its own covariance structure. ◦ HDDC developed for high-dimensional data (d > 10) ◦ Note: In Matlab HDDC = high-dimensional data clustering

 Background on MS Imaging and goals of paper  Methods  Results  Conclusions and Criticism

 used 2019 spectra out of (10%)  potential peaks: 373 peaks (red triangles)  consensus peaks: 110 peaks (green triangles) ◦ Present in at least 20 spectra out of the 2019 (1%)  Discarded peaks mostly in low m/z regions  Hypothesize they are noise peaks because MALDI imaging spectra have high baseline in low m/z region.

 OMP successfully detects major peaks  Gaussian function provides reasonable approxima tion of peak shape

 Strong noise  Noise variance changes within m/z image and between m/z images  Noise variance is linearly proportional to peak intensity

 Apply Grasmair method to selected 110 consensus peaks  Efficiently removes the noise while not smoothing out edges

 Shows anatomical features  Restricted to spatial resolution of MALDI- imaging dataset

 No denoising: borders do not match as well  3x3 median smoothing: bad edge preservation  5x5 median smoothing: lose many regions

 Find mass values expressed in region

 3 main parameters in addition to peak width ◦ Portion of spectra considered for peak picking (each 10 th spectrum) ◦ Number of peaks selected for each spectrum (10 peaks) ◦ Percentage of spectra where peak is found for consensus peak list (1%)

 Robust to changes of second and third parameter peaks 0.1% 1% 5%

 Increase of parameter 1 can be compensated by higher value for parameter 2 Each 5 th spectrum Each 20 th spectrum

 Segmentation maps for ◦ 3 levels of denoising (0.6, 0.7, 0.8) ◦ 3 number of clusters (6, 8, 10)  Decrease in number of clusters merge features  Too much denoising causes loss of structure details

 Background on MS Imaging and goals of paper  Methods  Results  Conclusions and Criticism

 Peak picking: usually done on mean spectrum ◦ 1% consensus better for peaks in small spatial area  Edge-preserving denoising ◦ One study with average moving window and one study posthoc to improve classification  Clustering methods ◦ HDDC better results than k-means but significantly slower ◦ Currently, mostly hierarchical clustering = memory intensive  Importance to cancer studies ◦ Represents a proteomic functional topographic map

 Didn’t explain why they got rid of part of the range for which the data was acquired  Dataset reduction by peak picking ◦ done initially on per spectrum basis, it may get rid of lower abundance peaks which still show interesting image ◦ Also, because the peak must be present in 1% of the 10% selected spectra, can miss smaller regions of interest if bad selection of 10%  Highly parameterized + slow running time would make it hard to run many trials