Automatic Registration of the Mass Spectrometry Imaging Data of Sagittal Brain Slices to the Reference Atlas

Abstract

The registration of the mass spectrometry imaging (MSI) data with mouse brain tissue slices from the atlases could perform automatic anatomical interpretation, and the comparison of MSI data in particular brain regions from different mice could be accelerated. However, the current registration of MSI data with mouse brain tissue slices is mainly focused on the coronal. Although the sagittal plane is able to provide more information about brain regions on a single histological slice than the coronal, it is difficult to directly register the complete sagittal brain slices of a mouse as a result of the more significant individualized differences and more positional shifts of brain regions. Herein, by adding the auxiliary line on the two brain regions of central canal (CC) and cerebral peduncle (CP), the registration accuracy of the MSI data with sagittal brain slices has been improved (∼2–5-folds for different brain regions). Moreover, the histological sections with different degrees deformation and different dyeing effects have been used to verify that this pipeline has a certain universality. Our method facilitates the rapid comparison of sagittal plane MSI data from different animals and accelerates the application in the discovery of disease markers.

Emerging Computational Methods in Mass Spectrometry Imaging

“…Data from other modalities provide complementary molecular or spatial information, which may be used to enhance the quality of MSI data or provide additional insights using more targeted analytical tools. MSI data have been coupled with histology, [ 91 , 133 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 ] fluorescence microscopy, [ 188 , 189 , 190 , 191 ] Allen brain atlas, [ 107 , 192 ] topology, [ 193 , 194 , 195 ] electron microscopy, [ 196 , 197 ] Raman spectroscopy, [ 198 , 199 , 200 , 201 ] infrared spectroscopy, [ 202 ] magnetic resonance imaging, [ 93 , 203 ] and microsampling LC‐MS/MS analysis of proteins, transcripts, and genes. [ 164 , 204 , 205 , 206 , 207 , 208 , 209 , 210 ] In these studies, computational approaches have been used for registration of the multimodal data.…”

“…For example, the profiles of hippocampus, central canal, and cerebral peduncle in brain samples have been used to aid image registration of MSI data to the Allen brain atlas and histology images. [ 107 , 185 ] In the absence of fiducial markers, the geometrical information from the whole image may be used to facilitate registration. In conventional metrics for evaluating image similarity, such as mutual information, input images are required to have the same dimensionality.…”

Advances in multimodal mass spectrometry for single‐cell analysis and imaging enhancement

“…Tian et al . [80] described a pipeline for automatic registration of mouse sagittal DESI‐MSI data to the Allen Brain Atlas, providing annotation of detected molecular species by brain region without the need for a histology expert. Recent developments also include the use of nonlinear registration methods to correct local geometric alterations.…”

Mass Spectrometry in China

“…Computational methods are also critical in MS imaging due to the high complexity and dimensionality of the data. Huang et al developed a method for automatic registration of the MSI data for sagittal brain sections to a reference atlas by adding auxiliary lines between brain regions . Dong, Zhao, Cai et al developed a flexible unsupervised deep learning model for analyzing metabolic heterogeneity based on MSI data without prior knowledge of histology .…”

“…Huang et al developed a method for automatic registration of the MSI data for sagittal brain sections to a reference atlas by adding auxiliary lines between brain regions. 28 Dong, Zhao, Cai et al developed a flexible unsupervised deep learning model for analyzing metabolic heterogeneity based on MSI data without prior knowledge of histology. 29 For tackling a similar challenge, Huang, Zeng, Wu et al reported an information entropy-based strategy for dimension reduction and better data visualization.…”