1
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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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2
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Cheng H, Miller D, Southwell N, Fischer JL, Taylor I, Salbaum JM, Kappen C, Hu F, Yang C, Gross SS, D'Aurelio M, Chen Q. Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575105. [PMID: 38370710 PMCID: PMC10871215 DOI: 10.1101/2024.01.10.575105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
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3
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Vermeulen I, Rodriguez-Alvarez N, François L, Viot D, Poosti F, Aronica E, Dedeurwaerdere S, Barton P, Cillero-Pastor B, Heeren RMA. Spatial omics reveals molecular changes in focal cortical dysplasia type II. Neurobiol Dis 2024; 195:106491. [PMID: 38575092 DOI: 10.1016/j.nbd.2024.106491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/14/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024] Open
Abstract
Focal cortical dysplasia (FCD) represents a group of diverse localized cortical lesions that are highly epileptogenic and occur due to abnormal brain development caused by genetic mutations, involving the mammalian target of rapamycin (mTOR). These somatic mutations lead to mosaicism in the affected brain, posing challenges to unravel the direct and indirect functional consequences of these mutations. To comprehensively characterize the impact of mTOR mutations on the brain, we employed here a multimodal approach in a preclinical mouse model of FCD type II (Rheb), focusing on spatial omics techniques to define the proteomic and lipidomic changes. Mass Spectrometry Imaging (MSI) combined with fluorescence imaging and label free proteomics, revealed insight into the brain's lipidome and proteome within the FCD type II affected region in the mouse model. MSI visualized disrupted neuronal migration and differential lipid distribution including a reduction in sulfatides in the FCD type II-affected region, which play a role in brain myelination. MSI-guided laser capture microdissection (LMD) was conducted on FCD type II and control regions, followed by label free proteomics, revealing changes in myelination pathways by oligodendrocytes. Surgical resections of FCD type IIb and postmortem human cortex were analyzed by bulk transcriptomics to unravel the interplay between genetic mutations and molecular changes in FCD type II. Our comparative analysis of protein pathways and enriched Gene Ontology pathways related to myelination in the FCD type II-affected mouse model and human FCD type IIb transcriptomics highlights the animal model's translational value. This dual approach, including mouse model proteomics and human transcriptomics strengthens our understanding of the functional consequences arising from somatic mutations in FCD type II, as well as the identification of pathways that may be used as therapeutic strategies in the future.
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Affiliation(s)
- Isabeau Vermeulen
- Maastricht MultiModal Molecular Imaging Institute (M4i), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands
| | | | - Liesbeth François
- UCB Pharma, Chemin du Foriest 1, 1420 Braine-l'Alleud, Walloon Region, Belgium
| | - Delphine Viot
- UCB Pharma, Chemin du Foriest 1, 1420 Braine-l'Alleud, Walloon Region, Belgium
| | - Fariba Poosti
- UCB Pharma, Chemin du Foriest 1, 1420 Braine-l'Alleud, Walloon Region, Belgium
| | - Eleonora Aronica
- Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Department of (Neuro)Pathology, De Boelelaan 1108, 1081 HV Amsterdam, the Netherlands; Stichting Epilepsie Instellingen Nederland (SEIN), Achterweg 3, 2103 SW Heemstede, the Netherlands
| | | | - Patrick Barton
- UCB Pharma, 216 Bath Rd, Slough, SL1 3WE Berkshire, United Kingdom
| | - Berta Cillero-Pastor
- Maastricht MultiModal Molecular Imaging Institute (M4i), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands; Cell Biology-Inspired Tissue Engineering (cBITE), MERLN, Maastricht University, Universiteitssingel 40, 6229 ET Maastricht, Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging Institute (M4i), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, the Netherlands.
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4
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Zhang W, Patterson NH, Verbeeck N, Moore JL, Ly A, Caprioli RM, De Moor B, Norris JL, Claesen M. Multimodal MALDI imaging mass spectrometry for improved diagnosis of melanoma. PLoS One 2024; 19:e0304709. [PMID: 38820337 PMCID: PMC11142536 DOI: 10.1371/journal.pone.0304709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 05/17/2024] [Indexed: 06/02/2024] Open
Abstract
Imaging mass spectrometry (IMS) provides promising avenues to augment histopathological investigation with rich spatio-molecular information. We have previously developed a classification model to differentiate melanoma from nevi lesions based on IMS protein data, a task that is challenging solely by histopathologic evaluation. Most IMS-focused studies collect microscopy in tandem with IMS data, but this microscopy data is generally omitted in downstream data analysis. Microscopy, nevertheless, forms the basis for traditional histopathology and thus contains invaluable morphological information. In this work, we developed a multimodal classification pipeline that uses deep learning, in the form of a pre-trained artificial neural network, to extract the meaningful morphological features from histopathological images, and combine it with the IMS data. To test whether this deep learning-based classification strategy can improve on our previous results in classification of melanocytic neoplasia, we utilized MALDI IMS data with collected serial H&E stained sections for 331 patients, and compared this multimodal classification pipeline to classifiers using either exclusively microscopy or IMS data. The multimodal pipeline achieved the best performance, with ROC-AUCs of 0.968 vs. 0.938 vs. 0.931 for the multimodal, unimodal microscopy and unimodal IMS pipelines respectively. Due to the use of a pre-trained network to perform the morphological feature extraction, this pipeline does not require any training on large amounts of microscopy data. As such, this framework can be readily applied to improve classification performance in other experimental settings where microscopy data is acquired in tandem with IMS experiments.
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Affiliation(s)
- Wanqiu Zhang
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- Aspect Analytics NV, Genk, Belgium
| | - Nathan Heath Patterson
- Frontier Diagnostics, LLC, Nashville, Tennessee, United States of America
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | | | - Jessica L. Moore
- Frontier Diagnostics, LLC, Nashville, Tennessee, United States of America
| | - Alice Ly
- Aspect Analytics NV, Genk, Belgium
| | - Richard M. Caprioli
- Frontier Diagnostics, LLC, Nashville, Tennessee, United States of America
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Bart De Moor
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Jeremy L. Norris
- Frontier Diagnostics, LLC, Nashville, Tennessee, United States of America
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, United States of America
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5
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Lu Y, Cao Y, Tang X, Hu N, Wang Z, Xu P, Hua Z, Wang Y, Su Y, Guo Y. Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances. Talanta 2024; 272:125757. [PMID: 38368831 DOI: 10.1016/j.talanta.2024.125757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/28/2024] [Accepted: 02/05/2024] [Indexed: 02/20/2024]
Abstract
Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.
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Affiliation(s)
- Yingjie Lu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China; Department of Pharmacognosy, School of Pharmacy, Naval Medical University, Shanghai, 200433, China
| | - Yuqi Cao
- Technical Centre, Shanghai Tobacco (Group) Corp., Shanghai, 200082, China
| | - Xiaohang Tang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Na Hu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Zhengyong Wang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China
| | - Peng Xu
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, Beijing, 100193, China
| | - Zhendong Hua
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, Beijing, 100193, China
| | - Youmei Wang
- Key Laboratory of Drug Monitoring and Control, Drug Intelligence and Forensic Center, Ministry of Public Security, Beijing, 100193, China.
| | - Yue Su
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, China.
| | - Yinlong Guo
- State Key Laboratory of Organometallic Chemistry and National Center for Organic Mass Spectrometry in Shanghai, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 345 Lingling Road, Shanghai, 200032, China.
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6
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Shah M, Guo L, Xu X, Deng L, Lu K, Dong J, Zhao C, Xu J. eLIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity. J Proteome Res 2024. [PMID: 38690713 DOI: 10.1021/acs.jproteome.3c00764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.
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Affiliation(s)
- Mudassir Shah
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Lei Guo
- Interdisciplinary Institute of Medical Engineering, Fuzhou University, Fuzhou 350108, China
| | - Xiangnan Xu
- School of Business and Economics, Humboldt-Universität zu Berlin, Berlin 10099, Germany
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, Nanchang 330013, China
| | - Keyi Lu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Jiyang Dong
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Jingjing Xu
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
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7
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Prentice BM. Imaging with mass spectrometry: Which ionization technique is best? JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e5016. [PMID: 38625003 DOI: 10.1002/jms.5016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/07/2024] [Accepted: 02/21/2024] [Indexed: 04/17/2024]
Abstract
The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.
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Affiliation(s)
- Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, Florida, USA
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8
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Ellin NR, Guo Y, Miranda-Quintana RA, Prentice BM. Extended similarity methods for efficient data mining in imaging mass spectrometry. DIGITAL DISCOVERY 2024; 3:805-817. [PMID: 38638647 PMCID: PMC11022984 DOI: 10.1039/d3dd00165b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Yingchan Guo
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
- Quantum Theory Project, University of Florida Gainesville FL 32611-7200 USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
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9
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Pekov SI, Bormotov DS, Bocharova SI, Sorokin AA, Derkach MM, Popov IA. Mass spectrometry for neurosurgery: Intraoperative support in decision-making. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38571445 DOI: 10.1002/mas.21883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/29/2024] [Accepted: 03/23/2024] [Indexed: 04/05/2024]
Abstract
Ambient ionization mass spectrometry was proved to be a powerful tool for oncological surgery. Still, it remains a translational technique on the way from laboratory to clinic. Brain surgery is the most sensitive to resection accuracy field since the balance between completeness of resection and minimization of nerve fiber damage determines patient outcome and quality of life. In this review, we summarize efforts made to develop various intraoperative support techniques for oncological neurosurgery and discuss difficulties arising on the way to clinical implementation of mass spectrometry-guided brain surgery.
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Affiliation(s)
- Stanislav I Pekov
- Skolkovo Institute of Science and Technology, Moscow, Russian Federation
- Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
- Siberian State Medical University, Tomsk, Russian Federation
| | - Denis S Bormotov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | | | - Anatoly A Sorokin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | - Maria M Derkach
- Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
| | - Igor A Popov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
- Siberian State Medical University, Tomsk, Russian Federation
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10
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Kuderha A, Adingo W, Chikere B, Kulimushi M, Jules K. A Framework for Unsupervised Profiling of Malaria Vectors' Insecticide Resistance Using Machine Learning Technique. Vector Borne Zoonotic Dis 2024. [PMID: 38573213 DOI: 10.1089/vbz.2023.0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024] Open
Abstract
Background: There is a need to identify different insecticide resistance profiles that represent circumscription-encapsulation of knowledge about malaria vectors' insecticide resistance to increase our understanding of malaria vectors' insecticide resistance dynamics. Methods: Data used in this study are part of the aggregation of over 20,000 mosquito collections done between 1957 and 2018. We applied two data preprocessing steps. We developed three clustering machine learning models based on the K-means algorithm with three selected datasets. The elbow method was used to fine-tune the hyperparameters. We used the silhouette score to assess the clustering results produced by each of the three models. The proposed framework incorporates continuous learning, allowing the machine learning models to learn continuously. Results: For the first model, the optimal number of clusters (profiles) k was 17. For the second model, we found four profiles. For the third model, the optimal number of profiles was 7. Discussion: We found that the insecticide resistance profiles have dynamic resistance levels with respect to the insecticide component, species component, location component, and time component. This profiling task provided knowledge about the evolution of malaria vectors' insecticide resistance in the African continent by encapsulating the information on the complex interaction between the different dimensions of malaria vectors' insecticide resistance into different profiles. Policy makers can use the knowledge about the different profiles found from the analysis of available insecticide resistance monitoring data (through profiling) by using our proposed approach to set up malaria vector control strategies that consider the locations, species present in those locations, and potentially efficient insecticides.
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Affiliation(s)
- Ashuza Kuderha
- Département de Sciences de l'Informatique, Faculté de Sciences, Université Catholique de Bukavu, Bukavu, DR Congo
- Covenant Applied Informatics and Communication-Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Nigeria
- Department of Computer and Information Sciences, College of Science and Technology, Covenant University, Ota, Nigeria
- Département de Planification Régionale, Institut Supérieur de Développement Rural de Bukavu, Bukavu, DR Congo
| | - Wisdom Adingo
- Covenant Applied Informatics and Communication-Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Nigeria
- Department of Computer and Information Sciences, College of Science and Technology, Covenant University, Ota, Nigeria
- Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
| | - Bruno Chikere
- Covenant Applied Informatics and Communication-Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Nigeria
- Department of Biochemistry, College of Science and Technology, Covenant University, Ota, Nigeria
| | - Mugisho Kulimushi
- Centre de Recherche en Environnement et Géoressources, Université Catholique de Bukavu, Bukavu, DR Congo
- Département de Sciences de l'Environnement, Faculté de Sciences, Université Catholique de Bukavu, Bukavu, DR Congo
| | - Kala Jules
- Department of Data Science, School of STEM, International University of Grand Bassam, Grand Bassam, Ivory Coast
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11
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Kibbe RR, Muddiman DC. Quantitative mass spectrometry imaging (qMSI): A tutorial. JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e5009. [PMID: 38488849 DOI: 10.1002/jms.5009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 01/29/2024] [Indexed: 03/19/2024]
Abstract
Mass spectrometry imaging (MSI) is an analytical technique that enables the simultaneous detection of hundreds to thousands of chemical species while retaining their spatial information; usually, MSI is applied to biological tissues. Combining these elements can create ion images, which allows for the identification and localization of multiple chemical species within the sample. Being able to produce molecular images of biological tissues has already impacted the study of health and disease; however, the next logical step is being able to combine MSI with quantitative mass spectrometry methods to both quantify and determine the localization of disease progression or drug action. In this tutorial, we will detail the main factors to consider when designing a qMSI experiment and highlight the methods that have been developed to overcome these added complexities, specifically for those newer to the field of MSI.
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Affiliation(s)
- Russell R Kibbe
- Department of Chemistry, FTMS Laboratory for Human Health Research, North Carolina State University, Raleigh, North Carolina, USA
| | - David C Muddiman
- Department of Chemistry, FTMS Laboratory for Human Health Research, North Carolina State University, Raleigh, North Carolina, USA
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12
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Ding Y, Sun Q, Lin Y, Ping Q, Peng N, Wang L, Li Y. Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction. WATER RESEARCH 2024; 253:121267. [PMID: 38350192 DOI: 10.1016/j.watres.2024.121267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.
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Affiliation(s)
- Yizhe Ding
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qiya Sun
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Yuqian Lin
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qian Ping
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Nuo Peng
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Lin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Yongmei Li
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
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13
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Horn PJ, Chapman KD. Imaging plant metabolism in situ. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1654-1670. [PMID: 37889862 PMCID: PMC10938046 DOI: 10.1093/jxb/erad423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/25/2023] [Indexed: 10/29/2023]
Abstract
Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.
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Affiliation(s)
- Patrick J Horn
- BioDiscovery Institute and Department of Biological Sciences, University of North Texas, Denton TX 76203, USA
| | - Kent D Chapman
- BioDiscovery Institute and Department of Biological Sciences, University of North Texas, Denton TX 76203, USA
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14
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Sarretto T, Gardner W, Brungs D, Napaki S, Pigram PJ, Ellis SR. A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:466-475. [PMID: 38407924 DOI: 10.1021/jasms.3c00357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.
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Affiliation(s)
- Tassiani Sarretto
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia, 2522
| | - Wil Gardner
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Australia, 3086
| | - Daniel Brungs
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia, 2522
| | - Sarbar Napaki
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia, 2522
| | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Australia, 3086
| | - Shane R Ellis
- Molecular Horizons and School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, Australia, 2522
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15
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Guo L, Xie C, Miao R, Xu J, Xu X, Fang J, Wang X, Liu W, Liao X, Wang J, Dong J, Cai Z. DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging. Anal Chem 2024; 96:3829-3836. [PMID: 38377545 PMCID: PMC10918617 DOI: 10.1021/acs.analchem.3c05002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/27/2024] [Accepted: 02/03/2024] [Indexed: 02/22/2024]
Abstract
Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.
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Affiliation(s)
- Lei Guo
- Interdisciplinary
Institute of Medical Engineering, Fuzhou
University, Fuzhou 350108, China
| | - Chengyi Xie
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
- Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Rui Miao
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Jingjing Xu
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Xiangnan Xu
- School
of Business and Economics, Humboldt-Universitat
zu Berlin, Berlin 10099, Germany
| | - Jiacheng Fang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Xiaoxiao Wang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Wuping Liu
- International
Joint Research Center for Medical Metabolomics, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China
| | - Xiangwen Liao
- Interdisciplinary
Institute of Medical Engineering, Fuzhou
University, Fuzhou 350108, China
| | - Jianing Wang
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute for Data Science in Health
and Medicine, Xiamen University, Xiamen 361005, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong SAR 999077, China
- Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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16
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Calvo I, Montilla A, Huergo C, Martín-Saiz L, Martín-Allende J, Tepavcevic V, Domercq M, Fernández JA. Combining imaging mass spectrometry and immunohistochemistry to analyse the lipidome of spinal cord inflammation. Anal Bioanal Chem 2024; 416:1923-1933. [PMID: 38326664 PMCID: PMC10902057 DOI: 10.1007/s00216-024-05190-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
Inflammation is a complex process that accompanies many pathologies. Actually, dysregulation of the inflammatory process is behind many autoimmune diseases. Thus, treatment of such pathologies may benefit from in-depth knowledge of the metabolic changes associated with inflammation. Here, we developed a strategy to characterize the lipid fingerprint of inflammation in a mouse model of spinal cord injury. Using lipid imaging mass spectrometry (LIMS), we scanned spinal cord sections from nine animals injected with lysophosphatidylcholine, a chemical model of demyelination. The lesions were demonstrated to be highly heterogeneous, and therefore, comparison with immunofluorescence experiments carried out in the same section scanned by LIMS was required to accurately identify the morphology of the lesion. Following this protocol, three main areas were defined: the lesion core, the peri-lesion, which is the front of the lesion and is rich in infiltrating cells, and the uninvolved tissue. Segmentation of the LIMS experiments allowed us to isolate the lipid fingerprint of each area in a precise way, as demonstrated by the analysis using classification models. A clear difference in lipid signature was observed between the lesion front and the epicentre, where the damage was maximized. This study is a first step to unravel the changes in the lipidome associated with inflammation in the context of diverse pathologies, such as multiple sclerosis.
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Affiliation(s)
- Ibai Calvo
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain
| | - Alejandro Montilla
- Achucarro Basque Center for Neurosciencie, Bº Sarriena s/n, 48940, Leioa, Spain
- Department Neuroscience, Faculty of Medicine, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain
| | - Cristina Huergo
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain
| | - Lucía Martín-Saiz
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain
| | - Javier Martín-Allende
- Department of Languages and Computer Systems, School of Engineering, University of the Basque Country (UPV/EHU), Paseo Rafael Moreno "Pitxitxi", n. 2/3, 48013, Bilbao, Spain
| | - Vanja Tepavcevic
- Achucarro Basque Center for Neurosciencie, Bº Sarriena s/n, 48940, Leioa, Spain
| | - María Domercq
- Achucarro Basque Center for Neurosciencie, Bº Sarriena s/n, 48940, Leioa, Spain.
- Department Neuroscience, Faculty of Medicine, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain.
| | - José A Fernández
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bº Sarriena s/n, 48940, Leioa, Spain.
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17
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Zoabi A, Bentov-Arava E, Sultan A, Elia A, Shalev O, Orevi M, Gofrit ON, Margulis K. Adipose tissue composition determines its computed tomography radiodensity. Eur Radiol 2024; 34:1635-1644. [PMID: 37656176 DOI: 10.1007/s00330-023-09911-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 09/02/2023]
Abstract
OBJECTIVES Adipose tissue radiodensity in computed tomography (CT) performed before surgeries can predict surgical difficulty. Despite its clinical importance, little is known about what influences radiodensity. This study combines desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and electrospray ionization (ESI) with machine learning to unveil how chemical composition of adipose tissue determines its radiodensity. METHODS Patients in the study underwent abdominal surgeries. Before surgery, CT radiodensity of fat near operated sites was measured. Fifty-three fat samples were collected and analyzed by DESI-MSI, ESI, and histology, and then sorted by radiodensity, demographic parameters, and adipocyte size. A non-negative matrix factorization (NMF) algorithm was developed to differentiate between high and low radiodensities. RESULTS No associations between radiodensity and patient age, gender, weight, height, or fat origin were found. Body mass index showed negative correlation with radiodensity. A substantial difference in chemical composition between adipose tissues of high and low radiodensities was observed. More radiodense tissues exhibited greater abundance of high molecular weight species, such as phospholipids of various types, ceramides, cholesterol esters and diglycerides, and about 70% smaller adipocyte size. Less radiodense tissue showed high abundance of short acyl-tail fatty acids. CONCLUSIONS This study unveils the connection between abdominal adipose tissue radiodensity and its chemical composition. Because the radiodensity of the fat around the surgical site is associated with surgical difficulty, it is important to understand how adipose tissue composition affects this parameter. We conclude that fat tissue with a higher content of various phospholipids and waxy lipids is more CT radiodense. CLINICAL RELEVANCE STATEMENT This study establishes the connection between the CT radiodensity of adipose tissue and its chemical composition. Clinicians may use this information for preoperative planning of surgical procedures, potentially modifying their surgical approach (for example, performing partial nephrectomy openly rather than laparoscopically). KEY POINTS • Adipose tissue radiodensity values in computed tomography images taken prior to the surgery can potentially predict surgery difficulty. • Fifty-three human specimens were analyzed by advanced mass spectrometry, molecular imaging, and machine learning to establish the key features that determine Hounsfield units' values of adipose tissue. • The findings of this research will enable clinicians to better prepare for surgical procedures and select operative strategies.
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Affiliation(s)
- Amani Zoabi
- The Institute for Drug Research, the School of Pharmacy, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Einav Bentov-Arava
- The Institute for Drug Research, the School of Pharmacy, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Adan Sultan
- The Institute for Drug Research, the School of Pharmacy, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anna Elia
- Department of Pathology, Hadassah Medical Center, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ori Shalev
- Metabolomics Center, Core Research Facility, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Marina Orevi
- Nuclear Medicine, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Ofer N Gofrit
- Department of Urology, Hadassah Medical Center the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Katherine Margulis
- The Institute for Drug Research, the School of Pharmacy, the Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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18
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Kumar BS. Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview. Mass Spectrom (Tokyo) 2024; 13:A0142. [PMID: 38435075 PMCID: PMC10904931 DOI: 10.5702/massspectrometry.a0142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/06/2024] [Indexed: 03/05/2024] Open
Abstract
Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.
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19
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Slijkhuis N, Razzi F, Korteland SA, Heijs B, van Gaalen K, Duncker DJ, van der Steen AFW, van Steijn V, van Beusekom HMM, van Soest G. Spatial lipidomics of coronary atherosclerotic plaque development in a familial hypercholesterolemia swine model. J Lipid Res 2024; 65:100504. [PMID: 38246237 PMCID: PMC10879031 DOI: 10.1016/j.jlr.2024.100504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Coronary atherosclerosis is caused by plaque build-up, with lipids playing a pivotal role in its progression. However, lipid composition and distribution within coronary atherosclerosis remain unknown. This study aims to characterize lipids and investigate differences in lipid composition across disease stages to aid in the understanding of disease progression. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to visualize lipid distributions in coronary artery sections (n = 17) from hypercholesterolemic swine. We performed histology on consecutive sections to classify the artery segments and to investigate colocalization between lipids and histological regions of interest in advanced plaque, including necrotic core and inflammatory cells. Segments were classified as healthy (n = 6), mild (n = 6), and advanced disease (n = 5) artery segments. Multivariate data analysis was employed to find differences in lipid composition between the segment types, and the lipids' spatial distribution was investigated using non-negative matrix factorization (NMF). Through this process, MALDI-MSI detected 473 lipid-related features. NMF clustering described three components in positive ionization mode: triacylglycerides (TAG), phosphatidylcholines (PC), and cholesterol species. In negative ionization mode, two components were identified: one driven by phosphatidylinositol(PI)(38:4), and one driven by ceramide-phosphoethanolamine(36:1). Multivariate data analysis showed the association between advanced disease and specific lipid signatures like PC(O-40:5) and cholesterylester(CE)(18:2). Ether-linked phospholipids and LysoPC species were found to colocalize with necrotic core, and mostly CE, ceramide, and PI species colocalized with inflammatory cells. This study, therefore, uncovers distinct lipid signatures correlated with plaque development and their colocalization with necrotic core and inflammatory cells, enhancing our understanding of coronary atherosclerosis progression.
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Affiliation(s)
- Nuria Slijkhuis
- Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Francesca Razzi
- Department of Experimental Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands; Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Suze-Anne Korteland
- Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Bram Heijs
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - Kim van Gaalen
- Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Dirk J Duncker
- Department of Experimental Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Antonius F W van der Steen
- Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Department of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands
| | - Volkert van Steijn
- Department of Chemical Engineering, Delft University of Technology, Delft, The Netherlands
| | - Heleen M M van Beusekom
- Department of Experimental Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands
| | - Gijs van Soest
- Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands; Department of Precision and Microsystems Engineering, Delft University of Technology, Delft, The Netherlands; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
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20
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LI F, LUO Q. [Application advances of mass spectrometry imaging technology in environmental pollutants analysis and their toxicity research]. Se Pu 2024; 42:150-158. [PMID: 38374595 PMCID: PMC10877477 DOI: 10.3724/sp.j.1123.2023.11005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Indexed: 02/21/2024] Open
Abstract
Environmental exposures have significant impacts on human health and can contribute to the occurrence and development of diseases. Pollutants can enter the body through ingestion, inhalation, dermal absorption, or mother-to-child transmission, and can metabolize and/or accumulate in different tissues and organs. These pollutants can recognize and interact with various biomolecules, including DNA, RNA, proteins, and metabolites, disrupting biological processes and leading to adverse effects in living organisms. Thus, it is crucial to analysis the exogenous pollutants in the body, identify potential biomarkers and investigate their toxic effects. Numerous studies have shown that the metabolism rate of environmental pollutants greatly differs in various tissues and organs, their accumulation is also heterogeneous and dynamically changing. Moreover, the synthesis and accumulation of endogenous metabolites exhibit precise spatial distributions in tissues and cells. Mapping the spatial distributions of both pollutants and endogenous metabolites can discover relevant exposure biomarkers and provide a better understanding of their toxic effects and molecular mechanisms. Mass spectrometry is currently the preferred method for the qualitative and quantitative analysis of various compounds, and has been extensively utilized in pollutant and metabolomics analyses. Mass spectrometry imaging (MSI) is an emerging technology for molecular imaging that combines the information obtained by mass spectrometry with the visualization of the two- and three-dimensional spatial distributions of various molecular species in thin sample sections. Unlike other molecular imaging techniques, MSI can perform the label-free and untargeted analysis of thousands of molecules, such as elements, metabolites, lipids, peptides, proteins, pollutants, and drugs, in a single experiment with high sensitivity and throughput. Different MSI technologies, such as matrix-assisted laser desorption ionization mass spectrometry imaging, secondary ion mass spectrometry imaging, desorption electrospray ionization mass spectrometry imaging, and laser ablation inductively coupled plasma mass spectrometry imaging, have been introduced for the mapping of compounds and elements in biological, medical, and clinical research. MSI technologies have recently been utilized to characterize the spatial distribution of pollutants in the whole body and specific tissues of organisms, assess the toxic effects of pollutants at the molecular level, and identify exposure biomarkers. Such developments have brought new perspectives to investigate the toxicity of environmental pollutants. In this review, we provide an overview of the principles, characteristics, mass analyzers, and workflows of different MSI techniques and introduce their latest application advances in the analysis of environmental pollutants and their toxic effects.
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21
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Feitosa-Junior OR, Lubbe A, Kosina SM, Martins-Junior J, Barbosa D, Baccari C, Zaini PA, Bowen BP, Northen TR, Lindow SE, da Silva AM. The Exometabolome of Xylella fastidiosa in Contact with Paraburkholderia phytofirmans Supernatant Reveals Changes in Nicotinamide, Amino Acids, Biotin, and Plant Hormones. Metabolites 2024; 14:82. [PMID: 38392974 PMCID: PMC10890622 DOI: 10.3390/metabo14020082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 02/25/2024] Open
Abstract
Microbial competition within plant tissues affects invading pathogens' fitness. Metabolomics is a great tool for studying their biochemical interactions by identifying accumulated metabolites. Xylella fastidiosa, a Gram-negative bacterium causing Pierce's disease (PD) in grapevines, secretes various virulence factors including cell wall-degrading enzymes, adhesion proteins, and quorum-sensing molecules. These factors, along with outer membrane vesicles, contribute to its pathogenicity. Previous studies demonstrated that co-inoculating X. fastidiosa with the Paraburkholderia phytofirmans strain PsJN suppressed PD symptoms. Here, we further investigated the interaction between the phytopathogen and the endophyte by analyzing the exometabolome of wild-type X. fastidiosa and a diffusible signaling factor (DSF) mutant lacking quorum sensing, cultivated with 20% P. phytofirmans spent media. Liquid chromatography-mass spectrometry (LC-MS) and the Method for Metabolite Annotation and Gene Integration (MAGI) were used to detect and map metabolites to genomes, revealing a total of 121 metabolites, of which 25 were further investigated. These metabolites potentially relate to host adaptation, virulence, and pathogenicity. Notably, this study presents the first comprehensive profile of X. fastidiosa in the presence of a P. phytofirmans spent media. The results highlight that P. phytofirmans and the absence of functional quorum sensing affect the ratios of glutamine to glutamate (Gln:Glu) in X. fastidiosa. Additionally, two compounds with plant metabolism and growth properties, 2-aminoisobutyric acid and gibberellic acid, were downregulated when X. fastidiosa interacted with P. phytofirmans. These findings suggest that P. phytofirmans-mediated disease suppression involves modulation of the exometabolome of X. fastidiosa, impacting plant immunity.
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Affiliation(s)
- Oseias R Feitosa-Junior
- Department of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo 05508-900, SP, Brazil
- The DOE Joint Genome Institute, Berkeley, CA 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Andrea Lubbe
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Suzanne M Kosina
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Joaquim Martins-Junior
- Department of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo 05508-900, SP, Brazil
| | - Deibs Barbosa
- Department of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo 05508-900, SP, Brazil
| | - Clelia Baccari
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Paulo A Zaini
- Department of Plant Sciences, University of California, Davis, CA 95616, USA
| | - Benjamin P Bowen
- The DOE Joint Genome Institute, Berkeley, CA 94720, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Trent R Northen
- The DOE Joint Genome Institute, Berkeley, CA 94720, USA
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Steven E Lindow
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Aline M da Silva
- Department of Biochemistry, Institute of Chemistry, University of Sao Paulo, Sao Paulo 05508-900, SP, Brazil
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22
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Gardner W, Winkler DA, Bamford SE, Muir BW, Pigram PJ. Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning. SMALL METHODS 2024:e2301230. [PMID: 38204217 DOI: 10.1002/smtd.202301230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/03/2023] [Indexed: 01/12/2024]
Abstract
Supervised and unsupervised machine learning algorithms are routinely applied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.
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Affiliation(s)
- Wil Gardner
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - David A Winkler
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, Victoria, 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
- Advanced Materials and Healthcare Technologies, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Sarah E Bamford
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
| | | | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
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23
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Rajbhandari P, Neelakantan TV, Hosny N, Stockwell BR. Spatial pharmacology using mass spectrometry imaging. Trends Pharmacol Sci 2024; 45:67-80. [PMID: 38103980 PMCID: PMC10842749 DOI: 10.1016/j.tips.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/07/2023] [Accepted: 11/11/2023] [Indexed: 12/19/2023]
Abstract
The emerging and powerful field of spatial pharmacology can map the spatial distribution of drugs and their metabolites, as well as their effects on endogenous biomolecules including metabolites, lipids, proteins, peptides, and glycans, without the need for labeling. This is enabled by mass spectrometry imaging (MSI) that provides previously inaccessible information in diverse phases of drug discovery and development. We provide a perspective on how MSI technologies and computational tools can be implemented to reveal quantitative spatial drug pharmacokinetics and toxicology, tissue subtyping, and associated biomarkers. We also highlight the emerging potential of comprehensive spatial pharmacology through integration of multimodal MSI data with other spatial technologies. Finally, we describe how to overcome challenges including improving reproducibility and compound annotation to generate robust conclusions that will improve drug discovery and development processes.
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Affiliation(s)
- Presha Rajbhandari
- Department of Biological Sciences, Columbia University, New York, NY, USA
| | | | - Noreen Hosny
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Brent R Stockwell
- Department of Biological Sciences, Columbia University, New York, NY, USA; Department of Chemistry, Columbia University, New York, NY, USA; Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA; Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA.
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24
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Krestensen KK, Heeren RMA, Balluff B. State-of-the-art mass spectrometry imaging applications in biomedical research. Analyst 2023; 148:6161-6187. [PMID: 37947390 DOI: 10.1039/d3an01495a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.
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Affiliation(s)
- Kasper K Krestensen
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Ron M A Heeren
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
| | - Benjamin Balluff
- The Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, 6229 ER Maastricht, The Netherlands.
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25
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Djambazova KV, van Ardenne JM, Spraggins JM. Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research. Trends Analyt Chem 2023; 169:117344. [PMID: 38045023 PMCID: PMC10688507 DOI: 10.1016/j.trac.2023.117344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.
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Affiliation(s)
- Katerina V. Djambazova
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
| | - Jacqueline M. van Ardenne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Jeffrey M. Spraggins
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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26
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Moore JL, Charkoftaki G. A Guide to MALDI Imaging Mass Spectrometry for Tissues. J Proteome Res 2023; 22:3401-3417. [PMID: 37877579 DOI: 10.1021/acs.jproteome.3c00167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.
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Affiliation(s)
- Jessica L Moore
- Department of Proteomics, Discovery Life Sciences, Huntsville, Alabama 35806, United States
| | - Georgia Charkoftaki
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, Connecticut 06520, United States
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27
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Zafeiropoulos N, Bitilis P, Tsekouras GE, Kotis K. Graph Neural Networks for Parkinson's Disease Monitoring and Alerting. SENSORS (BASEL, SWITZERLAND) 2023; 23:8936. [PMID: 37960634 PMCID: PMC10648881 DOI: 10.3390/s23218936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson's disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.
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Affiliation(s)
| | | | | | - Konstantinos Kotis
- Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece; (N.Z.); (P.B.); (G.E.T.)
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28
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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29
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Parker GD, Hanley L, Yu XY. Mass Spectral Imaging to Map Plant-Microbe Interactions. Microorganisms 2023; 11:2045. [PMID: 37630605 PMCID: PMC10459445 DOI: 10.3390/microorganisms11082045] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 07/23/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Plant-microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.
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Affiliation(s)
- Gabriel D. Parker
- Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607, USA
| | - Luke Hanley
- Department of Chemistry, University of Illinois Chicago, Chicago, IL 60607, USA
| | - Xiao-Ying Yu
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
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30
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Liu CC, Greenwald NF, Kong A, McCaffrey EF, Leow KX, Mrdjen D, Cannon BJ, Rumberger JL, Varra SR, Angelo M. Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nat Commun 2023; 14:4618. [PMID: 37528072 PMCID: PMC10393943 DOI: 10.1038/s41467-023-40068-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.
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Affiliation(s)
- Candace C Liu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Alex Kong
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Ke Xuan Leow
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Dunja Mrdjen
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bryan J Cannon
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Josef Lorenz Rumberger
- Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany
- Charité University Medicine, Berlin, Germany
| | | | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA.
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31
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Ellin NR, Miranda-Quintana RA, Prentice BM. Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550838. [PMID: 37546817 PMCID: PMC10402165 DOI: 10.1101/2023.07.27.550838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x , y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O ( N ) , of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
- Quantum Theory Project, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
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32
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Marshall CR, Farrow MA, Djambazova KV, Spraggins JM. Untangling Alzheimer's disease with spatial multi-omics: a brief review. Front Aging Neurosci 2023; 15:1150512. [PMID: 37533766 PMCID: PMC10390637 DOI: 10.3389/fnagi.2023.1150512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/13/2023] [Indexed: 08/04/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.
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Affiliation(s)
- Cody R. Marshall
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Melissa A. Farrow
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Katerina V. Djambazova
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Chemical and Physical Biology Program, Vanderbilt University School of Medicine, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
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33
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Esselman AB, Patterson NH, Migas LG, Dufresne M, Djambazova KV, Colley ME, Van de Plas R, Spraggins JM. Microscopy-Directed Imaging Mass Spectrometry for Rapid High Spatial Resolution Molecular Imaging of Glomeruli. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37319264 DOI: 10.1021/jasms.3c00033] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The glomerulus is a multicellular functional tissue unit (FTU) of the nephron that is responsible for blood filtration. Each glomerulus contains multiple substructures and cell types that are crucial for their function. To understand normal aging and disease in kidneys, methods for high spatial resolution molecular imaging within these FTUs across whole slide images is required. Here we demonstrate a workflow using microscopy-driven selected sampling to enable 5 μm pixel size matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) of all glomeruli within whole slide human kidney tissues. Such high spatial resolution imaging entails large numbers of pixels, increasing the data acquisition times. Automating FTU-specific tissue sampling enables high-resolution analysis of critical tissue structures, while concurrently maintaining throughput. Glomeruli were automatically segmented using coregistered autofluorescence microscopy data, and these segmentations were translated into MALDI IMS measurement regions. This allowed high-throughput acquisition of 268 glomeruli from a single whole slide human kidney tissue section. Unsupervised machine learning methods were used to discover molecular profiles of glomerular subregions and differentiate between healthy and diseased glomeruli. Average spectra for each glomerulus were analyzed using Uniform Manifold Approximation and Projection (UMAP) and k-means clustering, yielding 7 distinct groups of differentiated healthy and diseased glomeruli. Pixel-wise k-means clustering was applied to all glomeruli, showing unique molecular profiles localized to subregions within each glomerulus. Automated microscopy-driven, FTU-targeted acquisition for high spatial resolution molecular imaging maintains high-throughput and enables rapid assessment of whole slide images at cellular resolution and identification of tissue features associated with normal aging and disease.
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Affiliation(s)
- Allison B Esselman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Lukasz G Migas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands
| | - Martin Dufresne
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Katerina V Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Madeline E Colley
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 Delft, The Netherlands
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
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Guo L, Zhu J, Wang K, Cheng KK, Xu J, Dong L, Xu X, Chen C, Shah M, Peng Z, Wang J, Cai Z, Dong J. Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging. Anal Chem 2023. [PMID: 37296503 DOI: 10.1021/acs.analchem.3c02002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.
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Affiliation(s)
- Lei Guo
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Jinyu Zhu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Keqi Wang
- Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China
| | - Kian-Kai Cheng
- Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor 81310, Malaysia
| | - Jingjing Xu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Liheng Dong
- School of Computing and Data Science, Xiamen University Malaysia, Sepang 43600, Malaysia
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia
| | - Can Chen
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Mudassir Shah
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
| | - Zhangxiao Peng
- Department of Molecular Oncology, Eastern Hepatobiliary Surgery Hospital & National Centre for Liver Cancer, Navy Military Medical University, Shanghai 200438, China
| | - Jianing Wang
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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35
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Yin C, Yan B. Machine learning in basic scientific research on oral diseases. DIGITAL MEDICINE 2023; 9. [DOI: 10.1097/dm-2023-00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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36
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Payton A, Roell KR, Rebuli ME, Valdar W, Jaspers I, Rager JE. Navigating the bridge between wet and dry lab toxicology research to address current challenges with high-dimensional data. FRONTIERS IN TOXICOLOGY 2023; 5:1171175. [PMID: 37304253 PMCID: PMC10250703 DOI: 10.3389/ftox.2023.1171175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/19/2023] [Indexed: 06/13/2023] Open
Abstract
Toxicology research has rapidly evolved, leveraging increasingly advanced technologies in high-throughput approaches to yield important information on toxicological mechanisms and health outcomes. Data produced through toxicology studies are consequently becoming larger, often producing high-dimensional data. These types of data hold promise for imparting new knowledge, yet inherently have complexities causing them to be a rate-limiting element for researchers, particularly those that are housed in "wet lab" settings (i.e., researchers that use liquids to analyze various chemicals and biomarkers as opposed to more computationally focused, "dry lab" researchers). These types of challenges represent topics of ongoing conversation amongst our team and researchers in the field. The aim of this perspective is to i) summarize hurdles in analyzing high-dimensional data in toxicology that require improved training and translation for wet lab researchers, ii) highlight example methods that have aided in translating data analysis techniques to wet lab researchers; and iii) describe challenges that remain to be effectively addressed, to date, in toxicology research. Specific aspects include methodologies that could be introduced to wet lab researchers, including data pre-processing, machine learning, and data reduction. Current challenges discussed include model interpretability, study biases, and data analysis training. Example efforts implemented to translate these data analysis techniques are also mentioned, including online data analysis resources and hands-on workshops. Questions are also posed to continue conversation in the toxicology community. Contents of this perspective represent timely issues broadly occurring in the fields of bioinformatics and toxicology that require ongoing dialogue between wet and dry lab researchers.
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Affiliation(s)
- Alexis Payton
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Kyle R. Roell
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Meghan E. Rebuli
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, NC, United States
| | - Ilona Jaspers
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Pediatrics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia E. Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Environmental Medicine, Asthma and Lung Biology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- The Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Curriculum in Toxicology and Environmental Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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37
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Bamford SE, Yalcin D, Gardner W, Winkler DA, Kohl TM, Muir BW, Howard S, Bruton EA, Pigram PJ. Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data. Anal Chem 2023; 95:7968-7976. [PMID: 37172328 DOI: 10.1021/acs.analchem.3c00769] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios.
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Affiliation(s)
- Sarah E Bamford
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - Dilek Yalcin
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
- CSIRO Manufacturing, Clayton, Victoria 3168, Australia
| | - Wil Gardner
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
| | - David A Winkler
- La Trobe Institute for Molecular Science, La Trobe University, Bundoora, Victoria 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia
- School of Pharmacy, University of Nottingham, Nottingham NG7 2RD, U.K
| | - Thomas M Kohl
- CSIRO Manufacturing, Clayton, Victoria 3168, Australia
| | | | - Shaun Howard
- CSIRO Manufacturing, Clayton, Victoria 3168, Australia
| | - Eric A Bruton
- Boeing Defense, Space and Security, P.O. Box 516, St. Louis, Missouri 63166, United States
| | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria 3086, Australia
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Sharman K, Patterson NH, Weiss A, Neumann EK, Guiberson ER, Ryan DJ, Gutierrez DB, Spraggins JM, Van de Plas R, Skaar EP, Caprioli RM. Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue. J Proteome Res 2023; 22:1394-1405. [PMID: 35849531 PMCID: PMC9845430 DOI: 10.1021/acs.jproteome.2c00206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.
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Affiliation(s)
- Kavya Sharman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Program in Chemical & Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Andy Weiss
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Elizabeth K Neumann
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Emma R Guiberson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Daniel J Ryan
- Pfizer Inc., Chesterfield, Missouri 63017, United States
| | - Danielle B Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Eric P Skaar
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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Sharman K, Patterson NH, Migas LG, Neumann EK, Allen J, Gibson-Corley KN, Spraggins JM, Van de Plas R, Skaar EP, Caprioli RM. MALDI IMS-Derived Molecular Contour Maps: Augmenting Histology Whole-Slide Images. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:905-912. [PMID: 37061946 PMCID: PMC10787559 DOI: 10.1021/jasms.2c00370] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Imaging mass spectrometry (IMS) provides untargeted, highly multiplexed maps of molecular distributions in tissue. Ion images are routinely presented as heatmaps and can be overlaid onto complementary microscopy images that provide greater context. However, heatmaps use transparency blending to visualize both images, obscuring subtle quantitative differences and distribution gradients. Here, we developed a contour mapping approach that combines information from IMS ion intensity distributions with that of stained microscopy. As a case study, we applied this approach to imaging data from Staphylococcus aureus-infected murine kidney. In a univariate, or single molecular species, use-case of the contour map representation of IMS data, certain lipids colocalizing with regions of infection were selected using Pearson's correlation coefficient. Contour maps of these lipids overlaid with stained microscopy showed enhanced visualization of lipid distributions and spatial gradients in and around the bacterial abscess as compared to traditional heatmaps. The full IMS data set comprising hundreds of individual ion images was then grouped into a smaller subset of representative patterns using non-negative matrix factorization (NMF). Contour maps of these multivariate NMF images revealed distinct molecular profiles of the major abscesses and surrounding immune response. This contour mapping workflow also enabled a molecular visualization of the transition zone at the host-pathogen interface, providing potential clues about the spatial molecular dynamics beyond what histological staining alone provides. In summary, we developed a new IMS-based contour mapping approach to augment classical stained microscopy images, providing an enhanced and more interpretable visualization of IMS-microscopy multimodal molecular imaging data sets.
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Affiliation(s)
- Kavya Sharman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Program in Chemical & Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Lukasz G Migas
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Elizabeth K Neumann
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Jamie Allen
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Katherine N Gibson-Corley
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Eric P Skaar
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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40
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Mogilenko DA, Sergushichev A, Artyomov MN. Systems Immunology Approaches to Metabolism. Annu Rev Immunol 2023; 41:317-342. [PMID: 37126419 DOI: 10.1146/annurev-immunol-101220-031513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.
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Affiliation(s)
- Denis A Mogilenko
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Current affiliation: Department of Medicine, Department of Pathology, Microbiology, and Immunology, and Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Alexey Sergushichev
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
- Computer Technologies Laboratory, ITMO University, Saint Petersburg, Russia
| | - Maxim N Artyomov
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA; ,
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41
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Lin BJ, Kuo TC, Chung HH, Huang YC, Wang MY, Hsu CC, Yao PY, Tseng YJ. MSIr: Automatic Registration Service for Mass Spectrometry Imaging and Histology. Anal Chem 2023; 95:3317-3324. [PMID: 36724516 PMCID: PMC9933042 DOI: 10.1021/acs.analchem.2c04360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool that can be used to simultaneously investigate the spatial distribution of different molecules in samples. However, it is difficult to comprehensively analyze complex biological systems with only a single analytical technique due to different analytical properties and application limitations. Therefore, many analytical methods have been combined to extend data interpretation, evaluate data credibility, and facilitate data mining to explore important temporal and spatial relationships in biological systems. Image registration is an initial and critical step for multimodal imaging data fusion. However, the image registration of multimodal images is not a simple task. The property difference between each data modality may include spatial resolution, image characteristics, or both. The image registrations between MSI and different imaging techniques are often achieved indirectly through histology. Many methods exist for image registration between MSI data and histological images. However, most of them are manual or semiautomatic and have their prerequisites. Here, we built MSI Registrar (MSIr), a web service for automatic registration between MSI and histology. It can help to reduce subjectivity and processing time efficiently. MSIr provides an interface for manually selecting region of interests from histological images; the user selects regions of interest to extract the corresponding spectrum indices in MSI data. In the performance evaluation, MSIr can quickly map MSI data to histological images and help pinpoint molecular components at specific locations in tissues. Most registrations were adequate and were without excessive shifts. MSIr is freely available at https://msir.cmdm.tw and https://github.com/CMDM-Lab/MSIr.
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Affiliation(s)
- Bo-Jhang Lin
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Tien-Chueh Kuo
- The
Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10617, Taiwan
| | - Hsin-Hsiang Chung
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Ying-Chen Huang
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Ming-Yang Wang
- Department
of Surgery, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Cheng-Chih Hsu
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Po-Yang Yao
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
| | - Yufeng Jane Tseng
- Graduate
Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan,The
Metabolomics Core Laboratory, Centers of Genomic and Precision Medicine, National Taiwan University, Taipei 10617, Taiwan,Department
of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan,School of
Pharmacy, College of Medicine, National
Taiwan University, Taipei 10002, Taiwan,. Phone: +886.2.3366.4888#529. Fax: +886.2.23628167
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42
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Spatial segmentation of mass spectrometry imaging data featuring selected principal components. Talanta 2023; 253:123958. [PMID: 36179560 DOI: 10.1016/j.talanta.2022.123958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 12/13/2022]
Abstract
Spatial segmentation aims to find homogeneous/heterogeneous subgroups of spectra or ion images in mass spectrometry imaging (MSI) data. The maps it generated inform researchers of vital characteristics of the data and thus provide the basis for strategizing further biological analysis. Dimensional reduction and clustering are two basic steps of segmentation. Due to the variations in the quality, resolution, density of spectral information, and sizes, not all datasets could be segmented ideally with combinations of different dimensional reduction and clustering algorithms. Here, we proposed a segmentation pipeline that utilized pattern compression by principal component analysis (PCA) and represented by principal components. Instead of preprocessed or raw MSI data, normalized principal components were used for the segmentation process. Multiple datasets of rat brains and mouse kidneys were tested, and the proposed segmentation pipeline presented the obvious advantage of easy-to-use and can be readily intergraded with other existing innovative pipelines.
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43
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Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
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Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
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Guo L, Dong J, Xu X, Wu Z, Zhang Y, Wang Y, Li P, Tang Z, Zhao C, Cai Z. Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data. Anal Chem 2023; 95:1924-1932. [PMID: 36633187 PMCID: PMC9878502 DOI: 10.1021/acs.analchem.2c04045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.
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Affiliation(s)
- Lei Guo
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Xiangnan Xu
- School
of Mathematics and Statistics, The University
of Sydney, Sydney, NSW 2006, Australia
| | - Zhichao Wu
- School
of Artificial Intelligence, Beijing Normal
University, Beijing 100875, China
| | - Yinbin Zhang
- Department
of Oncology, The Second Affiliated Hospital
of Medical College, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China
| | - Yongwei Wang
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Pengfei Li
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Zhi Tang
- School
of Public Health, Dongguan Key Laboratory of Environmental Medicine, Institute of Environmental Health, Guangdong Medical
University, Dongguan, Guangdong 523808, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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Chen X, Shu W, Zhao L, Wan J. Advanced mass spectrometric and spectroscopic methods coupled with machine learning for in vitro diagnosis. VIEW 2022. [DOI: 10.1002/viw.20220038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Xiaonan Chen
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Weikang Shu
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Liang Zhao
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
| | - Jingjing Wan
- School of Chemistry and Molecular Engineering East China Normal University Shanghai China
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Guo A, Chen Z, Li F, Luo Q. Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation. Gigascience 2022; 12:giad021. [PMID: 37039115 PMCID: PMC10087011 DOI: 10.1093/gigascience/giad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/17/2023] [Accepted: 03/13/2023] [Indexed: 04/12/2023] Open
Abstract
Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning-based algorithm is proposed to extract "histomorphological feature spectra" across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
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Affiliation(s)
- Ang Guo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Zhiyu Chen
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fang Li
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Qian Luo
- Institute of Biomedicine and Biotechnology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Wang HY, Kuo CH, Chung CR, Lin WY, Wang YC, Lin TW, Yu JR, Lu JJ, Wu TS. Rapid and Accurate Discrimination of Mycobacterium abscessus Subspecies Based on Matrix-Assisted Laser Desorption Ionization-Time of Flight Spectrum and Machine Learning Algorithms. Biomedicines 2022; 11:biomedicines11010045. [PMID: 36672552 PMCID: PMC9856018 DOI: 10.3390/biomedicines11010045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
Mycobacterium abscessus complex (MABC) has been reported to cause complicated infections. Subspecies identification of MABC is crucial for adequate treatment due to different antimicrobial resistance properties amid subspecies. However, long incubation days are needed for the traditional antibiotic susceptibility testing (AST). Delayed effective antibiotics administration often causes unfavorable outcomes. Thus, we proposed a novel approach to identify subspecies and potential antibiotic resistance, guiding early and accurate treatment. Subspecies of MABC isolates were determined by secA1, rpoB, and hsp65. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) spectra were analyzed, and informative peaks were detected by random forest (RF) importance. Machine learning (ML) algorithms were used to build models for classifying MABC subspecies based on spectrum. The models were validated by repeated five-fold cross-validation to avoid over-fitting. In total, 102 MABC isolates (52 subspecies abscessus and 50 subspecies massiliense) were analyzed. Top informative peaks including m/z 6715, 4739, etc. were identified. RF model attained AUROC of 0.9166 (95% CI: 0.9072-0.9196) and outperformed other algorithms in discriminating abscessus from massiliense. We developed a MALDI-TOF based ML model for rapid and accurate MABC subspecies identification. Due to the significant correlation between subspecies and corresponding antibiotics resistance, this diagnostic tool guides a more precise and timelier MABC subspecies-specific treatment.
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Affiliation(s)
- Hsin-Yao Wang
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chi-Heng Kuo
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | | | - Yu-Chiang Wang
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ting-Wei Lin
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jia-Ruei Yu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
| | - Jang-Jih Lu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital at Linkou, Taoyuan City 333423, Taiwan
- School of Medicine, Chang Gung University, Taoyuan City 333323, Taiwan
- Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan City 333323, Taiwan
| | - Ting-Shu Wu
- Division of Infectious Diseases, Departments of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Taoyuan City 333423, Taiwan
- Correspondence: ; Tel.: +886-3-3281200-7955
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Mrukwa G, Polanska J. DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data. BMC Bioinformatics 2022; 23:538. [PMID: 36503372 PMCID: PMC9743550 DOI: 10.1186/s12859-022-05093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 12/01/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Investigating molecular heterogeneity provides insights into tumour origin and metabolomics. The increasing amount of data gathered makes manual analyses infeasible-therefore, automated unsupervised learning approaches are utilised for discovering tissue heterogeneity. However, automated analyses require experience setting the algorithms' hyperparameters and expert knowledge about the analysed biological processes. Moreover, feature engineering is needed to obtain valuable results because of the numerous features measured. RESULTS We propose DiviK: a scalable stepwise algorithm with local data-driven feature space adaptation for segmenting high-dimensional datasets. The algorithm is compared to the optional solutions (regular k-means, spatial and spectral approaches) combined with different feature engineering techniques (None, PCA, EXIMS, UMAP, Neural Ions). Three quality indices: Dice Index, Rand Index and EXIMS score, focusing on the overall composition of the clustering, coverage of the tumour region and spatial cluster consistency, are used to assess the quality of unsupervised analyses. Algorithms were validated on mass spectrometry imaging (MSI) datasets-2D human cancer tissue samples and 3D mouse kidney images. DiviK algorithm performed the best among the four clustering algorithms compared (overall quality score 1.24, 0.58 and 162 for d(0, 0, 0), d(1, 1, 1) and the sum of ranks, respectively), with spectral clustering being mostly second. Feature engineering techniques impact the overall clustering results less than the algorithms themselves (partial [Formula: see text] effect size: 0.141 versus 0.345, Kendall's concordance index: 0.424 versus 0.138 for d(0, 0, 0)). CONCLUSIONS DiviK could be the default choice in the exploration of MSI data. Thanks to its unique, GMM-based local optimisation of the feature space and deglomerative schema, DiviK results do not strongly depend on the feature engineering technique applied and can reveal the hidden structure in a tissue sample. Additionally, DiviK shows high scalability, and it can process at once the big omics data with more than 1.5 mln instances and a few thousand features. Finally, due to its simplicity, DiviK is easily generalisable to an even more flexible framework. Therefore, it is helpful for other -omics data (as single cell spatial transcriptomic) or tabular data in general (including medical images after appropriate embedding). A generic implementation is freely available under Apache 2.0 license at https://github.com/gmrukwa/divik .
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Affiliation(s)
- Grzegorz Mrukwa
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland ,Netguru, Małe Garbary 9, 61-756 Poznań, Poland
| | - Joanna Polanska
- grid.6979.10000 0001 2335 3149Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
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.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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50
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Forbes TP, Gillen JG, Souna AJ, Lawrence J. Unsupervised Pharmaceutical Polymorph Identification and Multicomponent Particle Mapping of ToF-SIMS Data by Non-Negative Matrix Factorization. Anal Chem 2022; 94:16443-16450. [DOI: 10.1021/acs.analchem.2c03913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Thomas P. Forbes
- Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - John Greg Gillen
- Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Amanda J. Souna
- Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
- Department of Chemistry and Biochemistry, University of Maryland, College Park, Maryland 20742, United States
| | - Jeffrey Lawrence
- Materials Measurement Science Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
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