Exploring Three-Dimensional Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry Data: Three-Dimensional Spatial Segmentation of Mouse Kidney

Abstract

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting k-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 μm thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 μm. Altogether, 512 495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases.

Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition

“…With the SCiLS Lab software (SCiLS, Bremen, Germany), version 2015b for the entire data set, we applied the spatial segmentation method as proposed in [14] which has been generalized for 3D MALDI imaging in [34,35]. In short, we performed peak picking, applied edge-preserving de-noising to the m/z-images of detected peaks, and clustered the reduced and processed spectra by their similarity.…”

“…In short, we performed peak picking, applied edge-preserving de-noising to the m/z-images of detected peaks, and clustered the reduced and processed spectra by their similarity. As clustering method, we have used the bisecting k-means algorithm for faster processing which is a hierarchical top-down clustering method that iteratively divides a set of spectra into two sets that are maximally different, see [14,34]. The segmentation results are presented as a segmentation map and a corresponding hierarchical dendrogram, with pseudo-colors assigned to pixels.…”

Quantification of Small Molecule Drugs in Biological Tissue Sections by Imaging Mass Spectrometry Using Surrogate Tissue-Based Calibration Standards

“…To overcome this, the 3D IMS technique might be useful. 30,31) MALDI-IMS analysis o en requires a normalization method for precise imaging and quanti cation because it is necessary to control for inhomogeneous sample-matrix co-crystallization within a single tissue section and for tissue-speci c ion suppression. We previously reported a normalization method that used the signal intensity of a matrix compound, uniformly coated on tissue sections, as an internal standard.…”

Toponome imaging system: multiplex biomarkers in oncology

“…IMS technology is capable of 3D reconstruction at 50 mm lateral resolution [43], but this level of resolution does not enable direct decoding of two-dimensional and three-dimensional large protein complexes within all sites within a cell or tissue. However, the high subcellular resolution of TIS enables direct decoding of protein complexes [2,44,45].…”