Multisensor Imaging—From Sample Preparation to Integrated Multimodal Interpretation of LA-ICPMS and MALDI MS Imaging Data

Abstract

Laterally resolved chemical analysis (chemical imaging) has increasingly attracted attention in the Life Sciences during the past years. While some developments have provided improvements in lateral resolution and speed of analysis, there is a trend toward the combination of two or more analysis techniques, so-called multisensor imaging, for providing deeper information into the biochemical processes within one sample. In this work, a human malignant pleural mesothelioma sample from a patient treated with cisplatin as a cytostatic agent has been analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). While LA-ICPMS was able to provide quantitative information on the platinum distribution along with the distribution of other elemental analytes in the tissue sample, MALDI MS could reveal full information on lipid distributions, as both modes of polarity, negative and positive, were used for measurements. Tandem MS experiments verified the occurrence of distinct lipid classes. All imaging analyses were performed using a lateral resolution of 40 μm, providing information with excellent depth of details. By analyzing the very same tissue section, it was possible to perfectly correlate the obtained analyte distribution information in an evaluation approach comprising LA-ICPMS and MALDI MS data. Correlations between platinum, phosphorus, and lipid distributions were found by the use of advanced statistics. The present proof-of-principle study demonstrates the benefit of data combination for outcomes beyond one method imaging modality and highlights the value of advanced chemical imaging in the Life Sciences.

Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon

“…Besides, an increasing interest has arisen in research focused on elemental and molecular information that play crucial roles in both physiological and pathological metabolic processes. MALDI MSI was combined with laser-ablation inductively coupled plasma MS (LAICP MS) to study lipid changes colocalized with platinum, sulfur or phosphor distributions [74], and SIMS data were combined with topographical information from AFM to record accurate chemical 3D maps [75]. Advanced PHI includes R&D setups and pipelines that showcase combinations of in-vivo OI, OCT/PAI, US, MRI, CT, or PET [8,76].…”

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

“…Huang et al developed a graphical data processing pipeline for MSI based spatially resolved metabolomics ( Huang et al, 2019 ), which could achieve multivariate statistical results in an intuitive and simple way as well as discovery low-abundant but reliable biomarkers in heterogeneous tumors. MSI has been employed to different cancers including brain ( Jarmusch et al, 2016 ; Clark et al, 2018 ), breast ( Guenther et al, 2015 ; Abdelmoula et al, 2016 ; Angerer et al, 2016 ; Wang S. et al, 2016 ; Torata et al, 2018 ; Vidavsky et al, 2019 ), lung ( Calligaris et al, 2015 ; Li T. et al, 2015 ; Carter et al, 2017 ; Holzlechner et al, 2018 ), ovarian ( Dória et al, 2016 ; Briggs et al, 2019 ), prostrate ( Wang et al, 2017 ), esophageal ( Guo et al, 2014 ; Abbassi-Ghadi et al, 2016 ; Sun et al, 2019a ), colon ( Hiraide et al, 2016 ; Inglese et al, 2017 ), oral ( Uchiyama et al, 2014 ; Bednarczyk et al, 2019 ), skin ( Xu et al, 2017 ; Margulis et al, 2018 ), adrenal gland ( Sun et al, 2019b ) and gastrointestinal stromal tumors ( Abu Sammour et al, 2019 ) for spatial metabolomics analysis. MSI is also used to determine the metabolite changes in the 3D osteosarcoma cell culture model upon drug treatments ( Palubeckaitė et al, 2020 ).…”

Discovering new lipidomic features using cell type specific fluorophore expression to provide spatial and biological specificity in a multimodal workflow with MALDI IMS

“…Traditionally, multimodal imaging has relied on manual interpretation of co-registered ion images 13 , which is prone to human bias. Other supervised and unsupervised approaches have been used to improve data analysis [14][15][16][17][18][19][20] . Each of these approaches still requires an independent benchmark to define cells or structures.…”