Serial 3D Imaging Mass Spectrometry at Its Tipping Point

Abstract

Since biology is by and large a 3-dimensional phenomenon, it is hardly surprising that 3D imaging has had a significant impact on many challenges in the life sciences. Imaging mass spectrometry (MS) is a spatially resolved label-free analytical technique that recently maturated into a powerful tool for in situ localization of hundreds of molecular species. Serial 3D imaging MS reconstructs 3D molecular images from serial sections imaged with mass spectrometry. As such, it provides a novel 3D imaging modality inheriting the advantages of imaging MS. Serial 3D imaging MS has been steadily developing over the past decade, and many of the technical challenges have been met. Essential tools and protocols were developed, in particular to improve the reproducibility of sample preparation, speed up data acquisition, and enable computationally intensive analysis of the big data generated. As a result, experimental data is starting to emerge that takes advantage of the extra spatial dimension that 3D imaging MS offers. Most studies still focus on method development rather than on exploring specific biological problems. The future success of 3D imaging MS requires it to find its own niche alongside existing 3D imaging modalities through finding applications that benefit from 3D imaging and at the same time utilize the unique chemical sensitivity of imaging mass spectrometry. This perspective critically reviews the challenges encountered during the development of serial-sectioning 3D imaging MS and discusses the steps needed to tip it from being an academic curiosity into a tool of choice for answering biological and medical questions.

3D Mass Spectrometry Imaging Reveals a Very Heterogeneous Drug Distribution in Tumors

“…Normalization of the PTX ion signal (m/z 284.2) was obtained computationally dividing its intensity by the intensity of the IS (m/z 289.2) pixel by pixel, to compensate ion suppression effects due to different tissue histology27.…”

“…During the past decade it has been seen that, like for other 3D imaging techniques, serial computational merging of two dimensional mass spectrometric images makes possible to assemble a three-dimensional model of a whole organ, giving a more accurate description of the molecule distribution2627. MSI can generate 3D images of different compounds like proteins, peptides, lipids and endogenous or exogenous metabolites28.…”

Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

“…2–5 Determining the regions of tumour similarity and heterogeneity is not only crucial to investigate the nature of the diversity of tumours and to classify those into sub-groups, but can provide, through a topological mapping of the heterogeneity, an invaluable tool to understand the possible interactions between those different cell clusters. 6 The study of biological interactions in three dimensions is essential, 7–9 since biochemical mechanisms occur in a 3-dimensional environment whose complexity and richness may not be captured by the analysis of only a 2-dimensional sample of the tissue. From the point of view of statistical modelling, the lack of the standard state (the ‘normal’ cell type for this tissue) and a comprehensive compendium of the possible tumour cell types represents the biggest obstacle in the identification of tumour sub-types, requiring the employment of unsupervised learning techniques.…”

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

“…To analyze several consecutive tissue sections at a time we generated a 3D MALDI imaging and a 3D high resolution histological image model respectively [16,17]. The simultaneous analysis should give us a more complex insight into cancer tissue.…”