Emerging Computational Methods in Mass Spectrometry Imaging

Abstract

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development

“…However, limitations such as the speed and sensitivity of MSI imaging, as well as the challenge of assigning measurement data to individual cells, may present barriers to the widespread application of MSI in cellular lipidomics. Nevertheless, these limitations also present opportunities for future development and improvement in the field, particularly in the development of computational methods [ 71 ]. The integration of multimodal imaging methods has the potential to bring a fresh perspective to cellular lipidomics and further expand our knowledge of lipid metabolism and its impact on cellular behavior.…”

Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution

“…Since A and B are discrete images, entropies can be expressed as sums instead of integrals. Thus, their individual entropies can be calculated as (4) where p X (x) is the probability distribution of pixels associated with image A or B. 79 information to accumarray().…”

“…Over the last years, mass spectrometry imaging (MSI) has emerged as a powerful tool for chemical imaging to increase understanding of spatial biochemical distribution dynamics in tissue that are associated with histopathological processes. − Moreover, acquisitions of multiple chemical imaging modalities contribute with complementary molecular information, specifically multimodal MSI or the integration of MSI with histological microscopy, vibrational spectroscopy, magnetic resonance imaging, as well as fluorescence microscopy. − The acquisition of imaging data in multiple modalities yields datasets that may be spatially misaligned. In order to combine such datasets, the imaging data need to be registered to one another, meaning precisely geometrically aligned and image distortion-corrected .…”

Mass Spectrometry-Based Glycoproteomic Workflows for Cancer Biomarker Discovery

“…Process convolution R packages are now available to assist with spatial information 122 ; additionally, other bioinformatics teams are beginning to investigate the use of machine learning to improve the initial raw data processing of these complex data sets. 123 , 124 …”