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Three-dimensional imaging mass spectrometry (3D imaging MS) is a spatially resolved
analytical technique for three-dimensional molecular analysis of a tissue specimen,
entire organ, or agar plate. 3D imaging MS can image the spatial distribution of thousands
of molecules such as proteins, peptides, lipids, and small molecules 1]. Usually, 3D imaging MS is performed by serial sectioning of a sample followed by
two-dimensional (2D) imaging MS analysis of each section. 2D imaging MS is an established
technique of analytical chemistry for surface molecular analysis with various applications
in biology and medicine 2]. 2D imaging MS collects mass spectra pixel by pixel over the sample surface. For
each pixel, the mass spectrum represents the intensities of thousands to millions
of mass-to-charge (m/z) values, which depends on the sampling rate of the detector and the mass resolving
power of the instrument. The intensity at an m/z-value is proportional to the number of ions with this m/z-value that are desorbed from the area of the sample surface corresponding to the
respective pixel.

Various ionization sources and mass spectrometric techniques have been coupled and
developed for imaging MS and, consequently, for serial sectioning-based 3D imaging
MS; see 3],4] for a review. Two different ionization techniques have been used to acquire the data
provided by us: matrix-assisted laser desorption/ionization (MALDI) and desorption
electro spray ionization (DESI). In MALDI imaging MS, a small organic compound, the
so-called matrix, is applied to the surface of a section, usually in a solution with
an organic solvent. The matrix has two functions: first, the organic solvent helps
to extract analytes from the sample, which then cocrystallize with the matrix compound;
second, the matrix helps to softly dissipate the energy from high-frequency laser
pulses to the sample to desorb and ionize intact analytes from the sample surface
5]-7].

DESI-imaging MS uses another principle for producing ions and runs under atmospheric
pressure 8]. A pneumatically assisted electrospray is directed onto the sample surface where
it generates a liquid film that desorbs analytes from the sample surface. Upon impact
of further primary droplets, secondary droplets containing analyte molecules are ejected
from the liquid film and subsequently sampled by an extended mass spectrometer inlet
capillary (a so-called sniffer).

In both ionization techniques, ions are formed from a small area of the sample surface,
and these are directed into the mass spectrometer. A movable stage translates the
sample under the ionization probe to acquire mass spectra from the different raster
positions (pixels) across the sample.

An imaging MS dataset can be considered as a datacube or hyperspectral image with
spectra assigned with spatial x– and y-coordinates, or molecular ion images, each representing relative intensities of ions
with a specific m/z value 9]. Imaging MS enables one either to visualize the spatial distribution of a particular
ion within the section or to evaluate the molecular composition at a particular pixel.
Analysis and interpretation of high-dimensional imaging MS data require automated
computational methods 10]-13], and 3D imaging MS leads to additional computational challenges as one dataset encompasses
10–100 imaging MS datasets of serial sections.

In this data note, a total of five 3D imaging MS datasets in the imzML format (an
open and standard file format for imaging MS data 14]) are provided and available for download in the MetaboLights repository [MTBLS176],
as well as the GigaScience GigaDB respository 15]. The imzML file structure consists of an XML-like file containing metadata (*.imzML)
and a binary data file containing spectra (*.ibd); both are unequivocally linked by
a universally unique identifier. In the imzML files provided here, the relative position
of each voxel in the 3D space is stored in the “userParam” field.

The 3D DESI-imaging MS dataset is provided both in multiple imzML files each containing
an 2D imaging MS dataset of an individual section and in a single HDF5 16] file containing the metadata, coregistered imaging MS data, and optical [haematoxylin
and eosin (HE)-stained] images.

The data-acquisition parameters are briefly described in the following section. General
information about each dataset can be found in Additional file 1. An overview showing intensity distributions for exemplary m/z-values together with the mean spectrum for each dataset is provided in Additional
file 2.

3D MALDI imaging MS dataset of a mouse kidney

The dataset comprises 75 sections from the central part of a mouse kidney that was
PAXgene® fixed and paraffin embedded. As such, it is a part of the kidney dataset
that was presented in a previous publication to demonstrate the experimental and computational
pipeline for 3D imaging MS 17]. However, the dataset itself was never published. Microtome sections with a thickness
of 3.5 ?m were covered with 10 mg/ml of sinapinic acid (SA) in 60% acetonitrile and
0.2% trifluoroacetic acid as matrix after paraffin removal and washing as described
previously 17]. The matrix was applied using a vaporization sprayer (ImagePrepâ„¢, Bruker Daltonics,
Bremen, Germany). Spectra were acquired using a Bruker Daltonics Autoflex speedâ„¢ MALDI
mass spectrometer in linear positive mode in the mass range of 2,000-20,000 m/z and a deflection of 1,500 m/z. In total, the dataset comprised 1,362,830 spectra, each containing 7,680 data points.
Each spectrum was acquired with 200 laser shots, and the random walk option was set
to 20 shots per position. A medium-size laser focus was chosen, so as to be suitable
for the selected lateral resolution of 50 ?m pixel size. During the data acquisition,
the spectra preprocessing included a Gaussian spectral smoothing with a width of 2
within 4 cycles as well as baseline reduction using the Top Hat algorithm. The data
for all 75 sections were imported into the software SCiLS Lab (SCiLS, Bremen, Germany)
version 2014b. The registration of individual sections was performed with the aim
to reconstruct the original relations between the sections. For this purpose, the
so-called user-guided rigid registration was used, and this was performed interactively
as follows. First, the first of the consecutive sections was placed in the center
of the software view. Then, each of the following sections was positioned over the
previous image and moved in the x– and y-directions and rotated with the help of the interactive software (keyboard, mouse);
the half-transparent overlap with the previous image helps evaluate the positioning.
The method allows for compensation of rotations and translations. Finally, the dataset
containing spectra with adjusted spatial coordinates x and y and newly assigned coordinate z was exported into the imzML format with files named 3DMouseKidney.ibd and 3DMouseKidney.imzML.
These files are described in the corresponding Readme (Additional file 3). A visualization of the 3D mouse kidney dataset performed in the software SCiLS
Lab, version 2014b is shown in Additional file 2: Figure S1.

3D MALDI imaging MS dataset of a mouse pancreas

The 3D mouse pancreas dataset was created in a similar fashion to the mouse kidney
dataset. A C57BL/6 mouse was sacrificed, and the pancreas was immediately isolated,
fixed in PAXgene® Tissue Containers according to the manufacturer’s instructions (Qiagen,
Hilden, Germany), dehydrated, and embedded in low-melting-point paraffin as described
previously 17]. Sections (5 ?m in thickness) were cut on a microtome and mounted on indium-tin-coated
conductive glass slides (Bruker Daltonics). After paraffin removal and washing, 2,5-dihydroxybenzoic
acid (DHB), dissolved at 30 mg/ml in 50% methanol with 0.2% TFA as a matrix, was used.
Spectra from 29 consecutive sections were acquired using a Bruker Daltonics Autoflex
speedâ„¢ mass spectrometer in linear positive mode in the mass range 1,600-15,000 m/z. A medium-size laser diameter was used, with a lateral resolution of 60 ?m and 500
laser shots per pixel were accumulated with the random walk option set to 100 shots
per position. The complete dataset with 29 sections comprised 497,225 spectra with
13,312 data points per spectrum. The unprocessed raw data were imported into the software
SCiLS Lab, version 2014b. For 3D image registration in SCiLS Lab, a section thickness
of 5 ?m was selected. The image registration was performed as described earlier for
the 3D mouse kidney. Data conversion into the imzML format was performed as described
for the mouse kidney above, and the files which are described in Additional file 4 were named 3D_Mouse_Pancreas.ibd and 3D_Mouse_Pancreas.imzML. A visualization of
the 3D mouse pancreas dataset is shown in Additional file 2: Figure S2.

3D MALDI imaging MS dataset of a human oral squamous cell carcinoma

A tissue specimen from a patient with an oral squamous cell carcinoma (OSCC) was obtained
from the Department of Otorhinolaryngology, University Hospital Jena. The necessary
approval was obtained from the local Ethics Committee, approval No. 3008-12/10.

3D MALDI imaging MS analysis was applied to 58 cryosections, each with a thickness
of 10 ?m. The sections were mounted on indium-tin-oxide-coated conductive glass slides
(Bruker Daltonics) and stored at ?80°C until use. After drying under vacuum for 15
min, the slides were washed twice for 2 min in 70% ethanol and thereafter for 2 min
in 99% ethanol. The SA used as a matrix was applied using the Bruker ImagePrepâ„¢ device.
MALDI imaging MS was performed on an Autoflex speedâ„¢ mass spectrometer (Bruker Daltonics)
in linear positive mode. Spectra were acquired in the mass range 2,000-20,000 m/z with a deflection set to 1,500 m/z. Each spectrum was a sum of 200 laser shots, and the random walk option was set to
25 shots per position. A medium-size laser diameter was selected for the chosen lateral
resolution of 60 ?m. In total, the dataset comprised 828,558 spectra with 7,680 data
points per spectrum. The spectra were preprocessed during acquisition applying Gaussian
spectral smoothing with a width of 2 within 4 cycles as well as baseline reduction
using the Top Hat algorithm. The data for all sections were imported into the software
SCiLS Lab, version 2014b, and rigid image registration was performed by user-guided
stacking of the optical images as described earlier for the 3D mouse kidney dataset.
A slice thickness, or z-distance, of 60 ?m was selected to produce voxels of 60 ?m³. Finally, the dataset was exported to the imzML format producing files 3D_OSCC.ibd
and 3D_OSCC.imzML as described in Additional file 5. A visualization of the 3D human OSCC dataset is shown in Additional file 2: Figure S3.

3D MALDI imaging MS datasets of cultured microbial colonies in a time course experiment

3D MALDI imaging MS is very suitable for studying the metabolic exchange between interacting
microbes 18],19]. For this dataset, metabolic exchange of the interacting microbes Streptomyces coelicolor A3(2) and Bacillus subtilis PY79 was followed in a time-course experiment on the first, fourth, and eighth days
after co-inoculation in a Petri dish. Culturing of the microbes and sample preparation
for 3D MALDI imaging MS were performed as described elsewhere 19]. Briefly, equally sized agar slices were sectioned and mounted on a MALDI-TOF steel
target. A universal matrix (a mixture of alpha-cyano-4-hydroxycinnamic acid and 2,5-dihydroxybenzoic
acid) was applied with a 50 ?m pore size sieve, and the samples were allowed to dry
completely. Spectra were acquired on an Autoflexâ„¢ MALDI-TOF mass spectrometer (Bruker)
in linear positive mode in the mass range of 0–4,000 m/z using a large laser diameter and 300 shots per spectrum. A lateral resolution of
400 ?m was selected. All individual sections were imported into the software SCiLS
Lab, version 2014b, for 3D volume generation. In total, the dataset comprised 17,672
spectra, and the bin size was reduced to 40,299 data points per spectrum during import.
To construct a 3D volume that resembled the length, width, and height of the original
agar block, a thickness of 1,500 ?m per section producing voxels of 400?×?400?×?1,500
?m was chosen. The 3D volume was built up, starting with the first section from the
day 1 post-inoculation dataset. After completion of image registration from the first
time point, a spacing of 10.5 mm was introduced, starting with the block from the
time point day 4. The same steps were repeated for the block from time point day 8
after inoculation. Besides these additional steps, the image registration was performed
as described earlier for the 3D mouse kidney dataset. The complete dataset was then
exported into the imzML format to produce the files Microbe_Interaction_3D_Timecourse_LP.ibd
and Microbe_Interaction_3D_Timecourse_LP.imzML which are described in the corresponding
Readme file (Additional file 6). A visualization of the 3D dataset of the microbial colonies in a time-course experiment
is shown in Additional file 2: Figure S4.

3D DESI-imaging MS dataset of a human colorectal adenocarcinoma

Sections from a single colorectal adenocarcinoma (n?=?26) were analyzed by DESI-imaging
MS. The tissue specimen was snap-frozen in liquid nitrogen and stored in a freezer
at ?80°C prior to cryosectioning at 10 ?m thickness using a Microm HM550 Cryostat
(Thermo Fisher Scientific, Runcorn, UK) set at ?16°C, and thaw mounted onto SuperFrost®
Glass slides (Thermo Fisher Scientific). Distilled water was used to mount the sample
to the sample holder, and the cryosectioning was performed without embedding medium.
The built-in vacutome function of the cryostat was used to facilitate sectioning.
The slides were stored in closed containers at ?80°C prior to analysis and allowed
to thaw at room temperature under nitrogen flow prior to DESI-imaging MS acquisition.

Sections were cut to a step size of 10 ?m, and every tenth section was imaged. Four
sequential sections were deposited on each slide. The instrumental spatial resolution
was set to 100 ?m, and analysis of every tenth 10 ?m section resulted in 100 ?m³ voxels.

Imaging MS data were acquired in the negative-ion mode over an m/z range of 200–1,050 using a Thermo Exactive instrument (Thermo Scientific GmbH, Bremen,
Germany) coupled to a home-built automated DESI-imaging source as described previously
20]. The solvent used for DESI analysis was methanol/water (95/5 v/v) at a flow rate
of 1.5 ml/min. Nitrogen was used as a nebulizing gas at a pressure of 7 bar. The distance
between the DESI spray tip and the sample surface was set to 1.5 mm; the distance
between the DESI spray tip and the mass spectrometer was set to 14 mm; and the distance
between the inlet capillary and the sample surface was 0.1 mm. The spray angle was
80°, whereas the collection angle was fixed at 10°. The spray voltage used for analysis
was 4.5 kV. Each row of pixels was acquired as a continuous line scan over the sample
surface and saved in a separate raw file. All Thermo raw files of one imaging experiment
were then converted to imzML format using the imzML converter v1.1.4.5i 21]. The imzML files were named with reference to the section number and location of
the section on the slide. For example, in the file named “120TopL, 90TopR, 110BottomL,
100BottomR-centroid.imzML”, the top-right section was the 90th section cut from the
sample at a depth of 900 ?m. A more detailed description can be found in Additional
file 7.

Following imaging, the sections were stained with HE. A consultant histopathologist
assessed the samples for histological tissue types (independently of the results of
DESI-imaging). The sample was found to consist mainly of two tissue types: tumor and
connective tissue. HE scanned sections were digitalized using a Nanozoomer 2.0-HT
C9600 slide scanning instrument (Hamamatsu Photonics, Hamamatsu City, Japan).

In addition to providing imzML files, each storing imaging MS data of an individual
serial section, the full dataset was provided after several processing steps (see
below) in an HDF5 file. A description of the HDF5 file can be found in Additional
file 8. HDF5 is a flexible and platform independent format for storing large datasets; for
more information on HDF5, see 16] along with example code for a range of programming languages. The GitHub repository
(see 22]) contains a MATLAB function (import3dh5.m) that can be used to import the data and
provide some context to the MATLAB functions used for reading HDF5 files (for example,
h5readatt, h5read, h5info). Data within the HDF5 file are arranged as follows: the
m/z vector is stored at “/mz” and data from the nth slice can be found in the “/data/sn”
group. Each of these groups contains the optical image (“/data/sn/op”), MS image (“/data/sn/x”)
and the section number (“/data/sn/zPosition”). Sample metadata are stored in the root
directory (“/”).

The compilation of 3D DESI-imaging MS dataset into the HDF5 file included the following
preprocessing stages: (a) matching of peak lists within and between all tissue sections;
(b) separation of neighboring tissue sections into separate imaging MS datasets; (c)
automated co-registration of histological and MS images for 3D dataset compilation;
and (d) spectral normalization to account for overall intensity bias between spectral
profiles. The resulting workflow for 3D DESI-imaging MS dataset compilation was devised
based on image alignment and peak matching algorithms published previously 23].

(a) Owing to inherent variability in mass detection, molecular ion species within
an m/z range smaller than the native accuracy of the mass spectrometer (5 ppm in our case)
were assigned to the same molecular ion species uniformly for all pixels across tissue
sections.

(b) In order to be able to divide the slides properly into separate sections, the
optical and MS images were aligned by means of overlap between tissue object pixels
in MS and optical images. The aligned optical image was thus a warped form of the
original (the MS image remains static) by means of affine transformation as previously
described 23]. Four polygons were drawn over the newly aligned optical image, and these regions
were exported to individual files.

(c) The individual MS imaging datasets were aligned to each other. By default, the
procedure was started with the first slice (that is, slice number 10), which was used
as the template image and was the only image that remained unchanged. The procedure
was for the optical image of the subsequent section to be co-registered with the optical
image of the preceding slice (fixed), and the required transformation was applied
to both MS and optical images. These newly transformed images thus formed the template
for the subsequent slice. The process was continued until the last slice was reached.
As a consequence of the alignment, all of the optical images had the same dimensions,
as did the MS images. For more information on the co-registration and transformation
used for this dataset, please refer to 23].

Median fold change normalization was finally applied to reduce any variation in overall
signal intensity between spectral profiles within and between tissue samples. An illustration
of the 3D DESI-imaging MS dataset of a colorectal adenocarcinoma visualizing the distributions
of two exemplary m/z-values is shown in Additional file 2: Figure S5.

Instructions for loading the imzML files

Currently, there is no 3D-oriented data format for storing 3D imaging MS data and
no free software for loading and visualizing 3D imaging MS data. Data were provided
in the imzML format, an open and community-accepted format for exchange of imaging
MS data, and for each spectrum the user-defined parameters of its location in 3D space
were introduced. For more information on the imzML format, including instructions
on how to read it, please refer to 21]. Several freely available software packages are available for reading 2D imzML files,
including BioMap 24], Datacube Explorer 25], and MSiReader 26]. However, these software packages do not allow one to open datasets that are as large
as those provided here and are for 2D data only. The Volume Explorer software was
developed at FOM Institute AMOLF for 3D imaging MS data analysis and visualization;
it is not available for download but was reported to be available on request 25].

The datasets are available for download in the MetaboLights repository [MTBLS176],
as well as the GigaScience GigaDB respository 15]. For loading data from the provided datasets, a script that can load individual spectra
or images is provided. The script uses a Java based imzML data parser freely available
at 27] as a part of the imzMLConverter Java package 28]. The script for each MALDI imaging MS dataset (3D kidney, 3D pancreas, 3D OSCC, 3D
time course) was adapted, and this was provided as Additional files 9, 10, 11, and 12.

Data quality

For 3D imaging MS, the reproducibility of the measurements for the individual section
is of high importance. Currently, there are no quality-control standards either for
2D or for 3D imaging MS data. In our experiments, the quality control began with a
visual evaluation of the integrity of each serial section. Where applicable, controlled
conditions for matrix application for the MALDI imaging MS datasets were used to guarantee
equal amounts of matrix and a homogenous matrix layer, a prerequisite for reproducible
spectra quality. The instrument acquisition parameters and experimental conditions
for DESI-imaging MS were kept consistent across all adjacent tissue sections to minimize
any unwanted variation. The spectra quality was ascertained by manual acquisition
of test spectra from each section before starting the automatic acquisition, and calibration
standards were used to reduce sectionwide peak shifts. Selected spectra and images
from all datasets were visually inspected, and it was checked whether known anatomical
structures were detectable based on m/z values or cluster map analysis.

Potential use

The main aim of this data note is to stimulate bioinformatic developments in the new,
promising, and challenging field of 3D imaging MS by providing the bioinformatics
community with several high-quality 3D imaging MS datasets representing different
samples and types of mass spectrometry. We encourage bioinformaticians to develop
algorithms for efficient spectral processing specifically for 3D imaging MS.

Analyzing 3D imaging MS data is challenging because of the complexity, 3D-dimensionality
and size. The size of a 3D imaging MS dataset can be as high as 100 GB, depending
on the instrument’s resolving power. The size will only increase with the introduction
into 3D imaging MS of ultrahigh-resolution mass spectrometry, such as Fourier transform-ion
cyclotron resonance or Orbitrap. This large dataset requires efficient algorithms
potentially integrated with data-compression methods to aid data storage and to facilitate
data querying, analysis, and visualization, to be performed in the cloud, on a server,
or on a personal workstation.

Note that 3D imaging MS data are prone to considerable variability, because the sectionwide
analysis and long acquisition time span several days, or sometimes weeks. The development
of methods compensating for these effects would increase the reproducibility of the
experiments. This includes normalization, baseline correction, noise reduction, and,
in particular, peak alignment that needs to be performed on a large number of spectra
with the peaks between sections expected to be misaligned to a higher degree than
within one section.

As for 2D imaging MS data analysis, there is still a need for open-access software
tools for the analysis of 3D imaging MS data, including dimensionally reduction algorithms
and methods for unsupervised and supervised data analysis.

By making our datasets available for the community, we aim to stimulate the development,
evaluation, and comparison of novel and efficient algorithms for analysis and interpretation
of large 3D imaging MS datasets.

Another aim for sharing the datasets is to facilitate inter-laboratory comparisons
of 3D imaging MS datasets, essential for raising the level of the technology and paving
the way to open-access science.