Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Abdelmoula, Walid M.; Škrášková, Karolina; Balluff, Benjamin; Carreira, Ricardo J.; Tolner, Else A.; Lelieveldt, Boudewijn P. F.; Maaten, Laurens van der; Morreau, Hans; Maagdenberg, Arn M. J. M. van den; Heeren, Ron M. A.; McDonnell, Liam A.; Dijkstra, Jouke

doi:10.1021/ac502170f

Cited by 66 publications

(78 citation statements)

References 28 publications

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“…Dedicated pathologic support and software tools are necessary for accurately correlating molecular and histological information and defining diagnostic patterns (67). Concomitantly, an effort should be made to assure the most appropriate statistical planning approaches and tools are used for interpreting and classifying mass spectra.…”

Section: Perspectives and Conclusionmentioning

confidence: 99%

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

2016

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BACKGROUND There is a clinical need for new technologies that would enable rapid disease diagnosis based on diagnostic molecular signatures. Ambient ionization mass spectrometry has revolutionized the means by which molecular information can be obtained from tissue samples in real time and with minimal sample pretreatment. New developments in ambient ionization techniques applied to clinical research suggest that ambient ionization mass spectrometry will soon become a routine medical tool for tissue diagnosis. CONTENT This review summarizes the main developments in ambient ionization techniques applied to tissue analysis, with focus on desorption electrospray ionization mass spectrometry, probe electrospray ionization, touch spray, and rapid evaporative ionization mass spectrometry. We describe their applications to human cancer research and surgical margin evaluation, highlighting integrated approaches tested for ex vivo and in vivo human cancer tissue analysis. We also discuss the challenges for clinical implementation of these tools and offer perspectives on the future of the field. SUMMARY A variety of studies have showcased the value of ambient ionization mass spectrometry for rapid and accurate cancer diagnosis. Small molecules have been identified as potential diagnostic biomarkers, including metabolites, fatty acids, and glycerophospholipids. Statistical analysis allows tissue discrimination with high accuracy rates (>95%) being common. This young field has challenges to overcome before it is ready to be broadly accepted as a medical tool for cancer diagnosis. Growing research in new, integrated ambient ionization mass spectrometry technologies and the ongoing improvements in the existing tools make this field very promising for future translation into the clinic.

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Section: Perspectives and Conclusionmentioning

confidence: 99%

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

2016

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“…However, optimization of the t‐SNE algorithm is ongoing (van der Maaten, ) and this is expected to improve in the future. Abdelmoula et al () have used this visualization in the generic spatial registration of IMS data to histological images. This same workflow was used for the registration of SIMS imaging data to histology in Škrášková et al ().…”

Section: Manifold Learningmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

187

190

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Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.

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“…by exploring protein and lipid markers, which differentiate tumour from non-tumour regions within one MALDI MSI study. Alternatively, an automated registration procedure can be performed, 145 which ultimately facilitates the correlation to anatomical atlases. In the case of head and neck cancer, the spatial molecular characterization is important, since half of the patients develop a locoregional recurrence although radical therapy was prescribed.…”

Section: Metabolomicsmentioning

confidence: 99%

MALDI mass spectrometric imaging meets “omics”: recent advances in the fruitful marriage

Crecelius

Schubert

Eggeling

2015

Analyst

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Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI MSI) is a method that allows the investigation of the molecular content of surfaces, in particular, tissues, within its morphological context. The applications of MALDI MSI in the field of large-scale mass spectrometric studies, which are typically denoted by the suffix "omics", are steadily increasing. This is because, on the one hand, technical advances regarding sample collection and preparation, matrix application, instrumentation, and data processing have enhanced the molecular specificity and sensitivity of MALDI MSI; on the other hand, the focus of the "omics" community has moved from establishing an inventory of certain compound classes to exploring their spatial distribution to gain novel insights. Thus, the aim of this mini-review is twofold, to display the state-of-the-art in terms of technical aspects in MALDI MSI and to highlight selected applications in the last two years, which either have significantly advanced a certain "omics" field or have introduced a new one through pioneering efforts.

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Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Cited by 66 publications

References 28 publications

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

Ambient Ionization Mass Spectrometry for Cancer Diagnosis and Surgical Margin Evaluation

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

MALDI mass spectrometric imaging meets “omics”: recent advances in the fruitful marriage

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