Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights

Buchberger, Amanda Rae; DeLaney, Kellen; Johnson, Jillian; Li, Lingjun

doi:10.1021/acs.analchem.7b04733

Cited by 763 publications

(692 citation statements)

References 272 publications

(767 reference statements)

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“…Capturing spatial distributions of molecules in tissue sections is a prerequisite for any hypothesis-driven or discovery-oriented investigation of biology and medicine on the levels of tissues and the organism. In the past two decades, a window of opportunity has been opened by the development and further maturation of imaging mass spectrometry (imaging MS), a powerful and versatile technology for spatial molecular analysis (Doerr, 2018;Dreisewerd and Yew, 2017;Buchberger et al , 2018) with a particular interest in clinical (Vaysse et al , 2017) and pharmaceutical applications (Schulz et al , 2018) . For a tissue section, imaging MS generates a hyperspectral image encompassing thousands to millions of ion images, each image representing the distribution of a particular molecule or several molecules in the section.…”

Section: Introductionmentioning

confidence: 99%

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

Ovchinnikova

Rakhlin²,

Stuart

et al. 2019

Preprint

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Motivation: Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. Results: We present ColocAI, an artificial intelligence approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets. Availability and Implementation: https://github.com/metaspace2020/coloc Contact: theodore.alexandrov@embl.de

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Section: Introductionmentioning

confidence: 99%

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

Ovchinnikova

Rakhlin²,

Stuart

et al. 2019

Preprint

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“…On the other hand, proteins were also reported to discriminate between normal tissues and tumors using nanoDESI MSI 11 . However, one of the challenges for clinical study by DESI and nanoDESI MSI is its spatial resolving power [28][29][30][31] .…”

Section: Introductionmentioning

confidence: 99%

High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins

Lin

Chen

Huang

et al. 2019

Preprint

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Mass spectrometry imaging (MSI) using ambient ionization technique enables a direct chemical investigation of biological samples with minimal sample pretreatment. However, detailed morphological information of the sample is often lost due to its limited spatial resolution. We demonstrated that the fusion of ambient ionization MSI with optical microscopy of routine hematoxylin and eosin (H&E) staining produces predictive, high-resolution molecular imaging. In this study, desorption electrospray ionization (DESI) and nanospray desorption electrospray ionization (nanoDESI) mass spectrometry were emplo yed to visualize lipid and protein species on mouse tissue sections. The resulting molecular distributions obtained by ambient ionization MSI-microscopy fusion were verified with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MSI and routine immunohistochemistry (IHC) staining on the adjacent sections. Label-free molecular imaging with 5-µm spatial resolution can be achieved using DESI and nanoDESI, whereas the typical spatial resolution of ambient ionization MSI is ~100 µm. In this regard, sharpened molecular histology of tissue sections was achieved, providing complementary references to the pathology. Such multi-modality integration enabled the discovery of potential tumor biomarkers. After image fusion, we identified about 70% more potential biomarkers that could be used to determine the tumor margins on a lung tissue section with metastatic tumors. spectrometry (SIMS) MSI, were reported to achieve sharpened molecular imaging with spatial resolution at cellular level on tissue sections 36,37 . approach for image fusion was reported to be broadly applicable to different tissue type. The distribution of ions was clear at the sub-cellular level. Sharped ion images of MALDI mass spectrometry fused with microscopy of mouse tissue sections were shown in their study 37 . In addition, fusion of SIMS and electron microscopy images were also reported 38 . Although high resolution molecular distribution were revealed, these mass spectrometry methods operated under high vacuum, which limits the compatibility with conventional imaging methods for pathological examinations.In our study, an optical microscope was applied to obtain images containing meticulous details of H&E stained tissue sections. Using multivariate regression algorithm, we fused MSI with optical microscopy and generated predictive fine-grained MSI. This method was applied in different tissue types and demonstrated as a tool for cancer diagnosis. By combining optical microscopy with ambient ionization methods, DESI, and nanoDESI, for tissue mapping with image fusion, both the image resolution and image quality were greatly improved. The workflow of our study is shown in Fig. 1. ResultsElevating spatial resolution of lipid species mapping in DESI MSI. The results for fusion of DESI MSI for mouse brain and cerebellum coronal sections were shown in Fig. 2, whereas the results for mouse kidney sections were shown in Fig. 3. In Fig. 2, the dis...

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“…Imaging approaches, such as state-of-the-art microscopy and mass spectrometry imaging, are powerful technologies to gain spatial molecular information about leukocyte localization in tissues. Matrix-assisted laser desorption / ionization mass spectrometry imaging (MALDI-MSI) is widely used as a label-free imaging technique, which provides important information on the proteomic landscape (Buchberger et al, 2018;Holzlechner et al, 2017), having the potential to unravel molecular mechanisms that regulates the localization of leukocytes (Caprioli et al, 1997;Steurer et al, 2014). Technological and methodological advancements in the past 10-15 years have led to the refinement of this method with a broad range of applications (Oetjen et al, 2016;Seeley and Caprioli, 2008;Urbanek et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In order to determine the identity of a specific m/z value, tandem MS (MS/MS), i.e. LC-MS/MS, in combination with computational methods, existing libraries and coregistration algorithms is able to identify the spatial distribution of proteins (Buchberger et al, 2018;Schey et al, 2013;Vogeser and Parhofer, 2007). Protein Inference Algorithm (PIA) has recently been established to combine peptide spectrum matches from different search engines, increasing consistency of the results (Uszkoreit et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Relocation of macrophages maintains the barrier function of the urothelium and protects against persistent infection

Bottek

Soun

Volke

et al. 2019

Preprint

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Macrophages perform essential functions during bacterial infections, such as phagocytosis of pathogens and elimination of neutrophils to reduce spreading of infection, inflammation and tissue damage. The spatial distribution of macrophages is critical to respond to tissue specific adaptations upon infections. Using a novel algorithm for correlative mass spectrometry imaging and state-of-the-art multiplex microscopy, we report here that macrophages within the urinary bladder are positioned in the connective tissue underneath the urothelium. Invading uropathogenic E.coli induced an IL-6-dependent CX 3 CL1 expression by urothelial cells, facilitating relocation of macrophages from the connective tissue into the urothelium. These cells phagocytosed UPECs and eliminated neutrophils to maintain barrier function of the urothelium, preventing persistent and recurrent urinary tract infection. Bottek et al., 2019 Macrophages maintain barrier function GRAPHICAL ABSTRACT

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Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights

Cited by 763 publications

References 272 publications

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

ColocAI: artificial intelligence approach to quantify co-localization between mass spectrometry images

High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins

Relocation of macrophages maintains the barrier function of the urothelium and protects against persistent infection

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