Enhancement of Ambient Mass Spectrometry Imaging Data by Image Restoration

Xiang, Yuchen; Martin, Metodiev,; Wang, Meiqi; Cao, Boxuan; Bunch, Josephine; Takáts, Zoltán

doi:10.3390/metabo13050669

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…Spectral differences between MALDI data and REIMS data are yet to be assessed, however, the recent interest in the field of MSI to characterize and compare various modalities is noteworthy. This could potentially lead to a scenario where data from one modality can provide useful information for optimization of another modality 36 , 37 .…”

Section: Discussionmentioning

confidence: 99%

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Brorsen,

McKenzie,

Tullin

et al. 2024

Sci Rep

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Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.

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Section: Discussionmentioning

confidence: 99%

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Brorsen,

McKenzie,

Tullin

et al. 2024

Sci Rep

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“…The power of deep learning extends beyond this, particularly in the field of image super-resolution (SR) . Super-resolution deep learning has been effectively applied in mass spectrometry imaging (MSI) and illustrated unparalleled ability in enhancing the resolution of MSI . Such success suggests the possibility that the technique might be applicable to resolution enhancement of complex NMR spectra.…”

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confidence: 99%

Resolution Enhancement of Metabolomic J-Res NMR Spectra Using Deep Learning

Yan,

Judge,

Athersuch

et al. 2024

Anal. Chem.

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J-Resolved (J-Res) nuclear magnetic resonance (NMR) spectroscopy is pivotal in NMR-based metabolomics, but practitioners face a choice between time-consuming high-resolution (HR) experiments or shorter low-resolution (LR) experiments which exhibit significant peak overlap. Deep learning neural networks have been successfully used in many fields to enhance quality of natural images, especially with regard to resolution, and therefore offer the prospect of improving two-dimensional (2D) NMR data. Here, we introduce the J-RESRGAN, an adapted and modified generative adversarial network (GAN) for image super-resolution (SR), which we trained specifically for metabolomic J-Res spectra to enhance peak resolution. A novel symmetric loss function was introduced, exploiting the inherent vertical symmetry of J-Res NMR spectra. Model training used simulated high-resolution J-Res spectra of complex mixtures, with corresponding low-resolution spectra generated via blurring and down-sampling. Evaluation of peak pair resolvability on J-RESRGAN demonstrated remarkable improvement in resolution across a variety of samples. In simulated plasma data, 100% of peak pairs exhibited enhanced resolution in super-resolution spectra compared to their low-resolution counterparts. Similarly, enhanced resolution was observed in 80.8−100% of peak pairs in experimental plasma, 85.0−96.7% in urine, 94.4−98.9% in full fat milk, and 82.6−91.7% in orange juice. J-RESRGAN is not sample type, spectrometer or field strength dependent and improvements on previously acquired data can be seen in seconds on a standard desktop computer. We believe this demonstrates the promise of deep learning methods to enhance NMR metabolomic data, and in particular, the power of J-RESRGAN to elucidate overlapping peaks, advancing precision in a wide variety of NMR-based metabolomics studies. The model, J-RESRGAN, is openly accessible for download on GitHub at https://github.com/yanyan5420/J-RESRGAN.

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Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry

Ozturk,

Venkataraman,

Rivera Moctezuma

et al. 2024

Preprint

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Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creating high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.

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Enhancement of Ambient Mass Spectrometry Imaging Data by Image Restoration

Cited by 3 publications

References 42 publications

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Resolution Enhancement of Metabolomic J-Res NMR Spectra Using Deep Learning

Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry

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