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; 20185 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; 27360 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 10th 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 20185 (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 MALDIimaging
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
10th 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
5
0.1%
1%
5%
10
20
peaks

Increase of parameter 1 can be compensated
by higher value for parameter 2
Each 20th spectrum
Each 5th 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