1
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Good CJ, Butrico CE, Colley ME, Emmerson LN, Gibson-Corley KN, Cassat JE, Spraggins JM, Caprioli RM. Uncovering lipid dynamics in Staphylococcus aureus osteomyelitis using multimodal imaging mass spectrometry. Cell Chem Biol 2024; 31:1852-1868.e5. [PMID: 39389064 DOI: 10.1016/j.chembiol.2024.09.005] [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: 01/19/2024] [Revised: 06/20/2024] [Accepted: 09/18/2024] [Indexed: 10/12/2024]
Abstract
Osteomyelitis occurs when Staphylococcus aureus invades the bone microenvironment, resulting in a bone marrow abscess with a spatially defined architecture of cells and biomolecules. Imaging mass spectrometry and microscopy are tools that can be employed to interrogate the lipidome of S. aureus-infected murine femurs and reveal metabolic and signaling consequences of infection. Here, nearly 250 lipids were spatially mapped to healthy and infection-associated morphological features throughout the femur, establishing composition profiles for tissue types. Ether lipids and arachidonoyl lipids were altered between cells and tissue structures in abscesses, suggesting their roles in abscess formation and inflammatory signaling. Sterols, triglycerides, bis(monoacylglycero)phosphates, and gangliosides possessed ring-like distributions throughout the abscess, suggesting a hypothesized dysregulation of lipid metabolism in a population of cells that cannot be discerned with traditional microscopy. These data provide insight into the signaling function and metabolism of cells in the fibrotic border of abscesses, likely characteristic of lipid-laden macrophages.
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Affiliation(s)
- Christopher J Good
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Casey E Butrico
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Madeline E Colley
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Lauren N Emmerson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA; Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN 37235, USA
| | - Katherine N Gibson-Corley
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - James E Cassat
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA; Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA.
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA; Department of Medicine, Vanderbilt University, Nashville, TN 37235, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA
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2
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Zhu E, Xie Q, Huang X, Zhang Z. Application of spatial omics in gastric cancer. Pathol Res Pract 2024; 262:155503. [PMID: 39128411 DOI: 10.1016/j.prp.2024.155503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/13/2024]
Abstract
Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.
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Affiliation(s)
- Erran Zhu
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Qi Xie
- Department of Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Xinqi Huang
- Excellent Class, Clinical Medicine, Grade 20, Hengyang Medical College, University of South China, Hengyang, Hunan, 421001, China
| | - Zhiwei Zhang
- Cancer Research Institute of Hengyang Medical College, University of South China; Key Laboratory of Cancer Cellular and Molecular Pathology of Hunan; Department of Pathology, Department of Pathology of Hengyang Medical College, University of South China; The First Affiliated Hospital of University of South China, China.
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3
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Liang Z, Guo Y, Diao X, Prentice BM. Enhancing Spatial Resolution in Tandem Mass Spectrometry Ion/Ion Reaction Imaging Experiments through Image Fusion. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1797-1805. [PMID: 38954826 DOI: 10.1021/jasms.4c00144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
We have recently developed a charge inversion ion/ion reaction to selectively derivatize phosphatidylserine lipids via gas-phase Schiff base formation. This tandem mass spectrometry (MS/MS) workflow enables the separation and detection of isobaric lipids in imaging mass spectrometry, but the images acquired using this workflow are limited to relatively poor spatial resolutions due to the current time and limit of detection requirements for these ion/ion reaction imaging mass spectrometry experiments. This trade-off between chemical specificity and spatial resolution can be overcome by using computational image fusion, which combines complementary information from multiple images. Herein, we demonstrate a proof-of-concept workflow that fuses a low spatial resolution (i.e., 125 μm) ion/ion reaction product ion image with higher spatial resolution (i.e., 25 μm) ion images from a full scan experiment performed using the same tissue section, which results in a predicted ion/ion reaction product ion image with a 5-fold improvement in spatial resolution. Linear regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN) predictive models were tested for this workflow. Linear regression and 2D CNN models proved optimal for predicted ion/ion images of PS 40:6 and SHexCer d38:1, respectively.
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Affiliation(s)
- Zhongling Liang
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Yingchan Guo
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Xizheng Diao
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
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4
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Liang Z, Guo Y, Sharma A, McCurdy CR, Prentice BM. Multimodal Image Fusion Workflow Incorporating MALDI Imaging Mass Spectrometry and Microscopy for the Study of Small Pharmaceutical Compounds. Anal Chem 2024; 96:11869-11880. [PMID: 38982936 DOI: 10.1021/acs.analchem.4c01553] [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: 07/11/2024]
Abstract
Multimodal imaging analyses of dosed tissue samples can provide more comprehensive insights into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multimodal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity coregistration with other higher-resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high-spatial resolution microscopy image. As a proof of concept, our multimodal workflow was applied to brain tissue extracted from a Sprague-Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models, including linear regression, partial least-squares regression, random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then coregistered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess colocalization of corynantheidine with brain cells. Our study also provides insights into the different evaluation parameters to consider when utilizing image fusion for biological applications.
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Affiliation(s)
- Zhongling Liang
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Yingchan Guo
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
| | - Abhisheak Sharma
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Christopher R McCurdy
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
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5
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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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6
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Colley ME, Esselman AB, Scott CF, Spraggins JM. High-Specificity Imaging Mass Spectrometry. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2024; 17:1-24. [PMID: 38594938 DOI: 10.1146/annurev-anchem-083023-024546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Imaging mass spectrometry (IMS) enables highly multiplexed, untargeted tissue mapping for a broad range of molecular classes, facilitating in situ biological discovery. Yet, challenges persist in molecular specificity, which is the ability to discern one molecule from another, and spatial specificity, which is the ability to link untargeted imaging data to specific tissue features. Instrumental developments have dramatically improved IMS spatial resolution, allowing molecular observations to be more readily associated with distinct tissue features across spatial scales, ranging from larger anatomical regions to single cells. High-performance mass analyzers and systems integrating ion mobility technologies are also becoming more prevalent, further improving molecular coverage and the ability to discern chemical identity. This review provides an overview of recent advancements in high-specificity IMS that are providing critical biological context to untargeted molecular imaging, enabling integrated analyses, and addressing advanced biomedical research applications.
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Affiliation(s)
- Madeline E Colley
- 1Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA;
- 2Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Allison B Esselman
- 2Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
- 3Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
| | - Claire F Scott
- 2Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
- 4Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Jeffrey M Spraggins
- 1Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA;
- 2Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
- 3Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA
- 4Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
- 5Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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7
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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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8
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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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9
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Chen LC, Lee C, Hsu CC. Towards developing a matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) compatible tissue expansion protocol. Anal Chim Acta 2024; 1297:342345. [PMID: 38438227 DOI: 10.1016/j.aca.2024.342345] [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: 09/19/2023] [Revised: 01/05/2024] [Accepted: 02/04/2024] [Indexed: 03/06/2024]
Abstract
Mass spectrometry imaging (MSI) visualizes spatial distribution of molecules in a biological tissue. However, compared with traditional microscopy-based imaging, conventional MSI is limited to its spatial resolution, resulting in difficulties in identifying detailed tissue morphological characters, such as lesion boundary or nanoscale structures. On the other hand, expansion microscopy, a tissue expansion method widely used in optical imaging to improve morphological details, has great potential to solve insufficient spatial resolution in mass spectrometry imaging (MSI). However, expansion microscopy was not originally designed for MSI, resulting in problems while combining expansion microscopy and MSI such as expanded sample fragility, vacuum stability and molecule loss during sample preparation. In this research we developed a MALDI MSI compatible expansion protocol by adjusting sample preparation methods during tissue expansion, successfully combining expansion microscopy with MSI. After tissue expansion the expanded sample can be readily applied to MALDI MSI sample preparation and further data acquisition. The MALDI MSI compatible expansion protocol has great potential to be widely applied in MALDI MSI sample preparation to facilitate improvement of MSI spatial resolution.
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Affiliation(s)
- Li-Cyun Chen
- Department of Chemistry, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei City, 106319, Taiwan.
| | - Chuping Lee
- Department of Chemistry, National Chung Hsing University, No.145, Xingda Rd., South Dist., Taichung City, 40227, Taiwan.
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei City, 106319, Taiwan.
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10
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Liang Z, Guo Y, Sharma A, McCurdy CR, Prentice BM. A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.584673. [PMID: 38559145 PMCID: PMC10980041 DOI: 10.1101/2024.03.12.584673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 μm spatial resolutions, a significant improvement compared to the original images acquired at 25 μm spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the μ-opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.
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Affiliation(s)
- Zhongling Liang
- Department of Chemistry, University of Florida, Gainesville, FL 32611
| | - Yingchan Guo
- Department of Chemistry, University of Florida, Gainesville, FL 32611
| | - Abhisheak Sharma
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610
| | - Christopher R. McCurdy
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610
- Department of Medicinal Chemistry, College of Pharmacy, University of Florida, Gainesville, FL 32610
| | - Boone M. Prentice
- Department of Chemistry, University of Florida, Gainesville, FL 32611
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11
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods 2024; 21:521-530. [PMID: 38366241 PMCID: PMC10927565 DOI: 10.1038/s41592-024-02171-3] [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: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel C Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Timothy J Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Jonathan V Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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12
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Croslow SW, Trinklein TJ, Sweedler JV. Advances in multimodal mass spectrometry for single-cell analysis and imaging enhancement. FEBS Lett 2024; 598:591-601. [PMID: 38243373 PMCID: PMC10963143 DOI: 10.1002/1873-3468.14798] [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: 11/12/2023] [Revised: 12/11/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024]
Abstract
Multimodal mass spectrometry (MMS) incorporates an imaging modality with probe-based mass spectrometry (MS) to enable precise, targeted data acquisition and provide additional biological and chemical data not available by MS alone. Two categories of MMS are covered; in the first, an imaging modality guides the MS probe to target individual cells and to reduce acquisition time by automatically defining regions of interest. In the second category, imaging and MS data are coupled in the data analysis pipeline to increase the effective spatial resolution using a higher resolution imaging method, correct for tissue deformation, and incorporate fine morphological features in an MS imaging dataset. Recent methodological and computational developments are covered along with their application to single-cell and imaging analyses.
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Affiliation(s)
- Seth W. Croslow
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jonathan V. Sweedler
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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13
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Kumar BS. Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 16:8-32. [PMID: 38088775 DOI: 10.1039/d3ay01267k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.
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Affiliation(s)
- Bharath Sampath Kumar
- Independent researcher, 21, B2, 27th Street, Lakshmi Flats, Nanganallur, Chennai 600061, India.
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14
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Good CJ, Butrico CE, Colley ME, Gibson-Corley KN, Cassat JE, Spraggins JM, Caprioli RM. In situ lipidomics of Staphylococcus aureus osteomyelitis using imaging mass spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569690. [PMID: 38077019 PMCID: PMC10705574 DOI: 10.1101/2023.12.01.569690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Osteomyelitis occurs when Staphylococcus aureus invades the bone microenvironment, resulting in a bone marrow abscess with a spatially defined architecture of cells and biomolecules. Imaging mass spectrometry and microscopy are invaluable tools that can be employed to interrogate the lipidome of S. aureus-infected murine femurs to reveal metabolic and signaling consequences of infection. Here, nearly 250 lipids were spatially mapped to healthy and infection-associated morphological features throughout the femur, establishing composition profiles for tissue types. Ether lipids and arachidonoyl lipids were significantly altered between cells and tissue structures in abscesses, suggesting their roles in abscess formation and inflammatory signaling. Sterols, triglycerides, bis(monoacylglycero)phosphates, and gangliosides possessed ring-like distributions throughout the abscess, indicating dysregulated lipid metabolism in a subpopulation of leukocytes that cannot be discerned with traditional microscopy. These data provide chemical insight into the signaling function and metabolism of cells in the fibrotic border of abscesses, likely characteristic of lipid-laden macrophages.
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Affiliation(s)
- Christopher J. Good
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Casey E. Butrico
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Madeline E. Colley
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
| | - Katherine N. Gibson-Corley
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - James E. Cassat
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jeffrey M. Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37235, USA
| | - Richard M. Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Biochemistry, Vanderbilt University, Nashville, TN 37235, USA
- Department of Medicine, Vanderbilt University, Nashville, TN 37235, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37235, USA
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15
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Tang W, Li Z, Zou Y, Liao J, Li B. A multimodal pipeline for image correction and registration of mass spectrometry imaging with microscopy. Anal Chim Acta 2023; 1283:341969. [PMID: 37977791 DOI: 10.1016/j.aca.2023.341969] [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: 05/28/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 11/19/2023]
Abstract
The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) and histology plays a pivotal role in advancing our understanding of complex heterogeneous tissues, which provides a comprehensive description of biological tissue with both wide molecule coverage and high lateral resolution. Herein, we proposed a novel strategy for the correction and registration of MALDI MSI data with hematoxylin & eosin (H&E) staining images. To overcome the challenges of discrepancies in spatial resolution towards the unification of the two imaging modalities, a deep learning-based interpolation algorithm for MALDI MSI data was constructed, which enables spatial coherence and the following orientation matching between images. Coupled with the affine transformation (AT) and the subsequent moving least squares algorithm, the two types of images from one rat brain tissue section were aligned automatically with high accuracy. Moreover, we demonstrated the practicality of the developed pipeline by projecting it to a rat cerebral ischemia-reperfusion injury model, which would help decipher the link between molecular metabolism and pathological interpretation towards microregion. This new approach offers the chance for other types of bioimaging to boost the field of multimodal image fusion.
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Affiliation(s)
- Weiwei Tang
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Zhen Li
- School of Science, China Pharmaceutical University, Nanjing, 211198, China
| | - Yuchen Zou
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, 211198, China.
| | - Bin Li
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
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16
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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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17
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Yang E, Shen XE, West‐Foyle H, Hahm T, Siegler MA, Brown DR, Johnson CC, Kim JH, Roker LA, Tressler CM, Barman I, Kuo SC, Glunde K. FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2304343. [PMID: 37908150 PMCID: PMC10724403 DOI: 10.1002/advs.202304343] [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: 06/28/2023] [Revised: 09/15/2023] [Indexed: 11/02/2023]
Abstract
Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.
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Affiliation(s)
- Ethan Yang
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Xinyi Elaine Shen
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Hoku West‐Foyle
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Tae‐Hun Hahm
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | | | - Dalton R. Brown
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Cole C. Johnson
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Jeong Hee Kim
- Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
| | - LaToya Ann Roker
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
| | - Caitlin M. Tressler
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
| | - Ishan Barman
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreMD21218USA
- Sidney Kimmel Comprehensive Cancer CancerJohns Hopkins University School of MedicineBaltimoreMD21231USA
| | - Scot C. Kuo
- Microscope FacilityJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Cell BiologyJohns Hopkins University School of MedicineBaltimoreMD21205USA
- Department of Biomedical EngineeringJohns Hopkins University School of MedicineBaltimoreMD21218USA
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Applied Imaging Mass Spectrometry CoreJohns Hopkins University School of MedicineBaltimoreMD21287USA
- Sidney Kimmel Comprehensive Cancer CancerJohns Hopkins University School of MedicineBaltimoreMD21231USA
- Department of Biological ChemistryJohns Hopkins University School of MedicineBaltimoreMD21205USA
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18
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543144. [PMID: 37398021 PMCID: PMC10312594 DOI: 10.1101/2023.05.31.543144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Daniel C. Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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19
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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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20
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Chen P, Han Y, Wang L, Zheng Y, Zhu Z, Zhao Y, Zhang M, Chen X, Wang X, Sun C. Spatially Resolved Metabolomics Combined with the 3D Tumor-Immune Cell Coculture Spheroid Highlights Metabolic Alterations during Antitumor Immune Response. Anal Chem 2023; 95:15153-15161. [PMID: 37800909 DOI: 10.1021/acs.analchem.2c05734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
The metabolic cross-talk between tumor and immune cells plays key roles in immune cell function and immune checkpoint blockade therapy. However, the characterization of tumor immunometabolism and its spatiotemporal alterations during immune response in a complex tumor microenvironment is challenging. Here, a 3D tumor-immune cell coculture spheroid model was developed to mimic tumor-immune interactions, combined with mass spectrometry imaging-based spatially resolved metabolomics to visualize tumor immunometabolic alterations during immune response. The inhibition of T cells was simulated by coculturing breast tumor spheroids with Jurkat T cells, and the reactivation of T cells can be monitored through diminishing cancer PD-L1 expressions by berberine. This system enables simultaneously screening and imaging discriminatory metabolites that are altered during T cell-mediated antitumor immune response and characterizing the distributions of berberine and its metabolites in tumor spheroids. We discovered that the transport and catabolism of glutamine were significantly reprogrammed during the antitumor immune response at both metabolite and enzyme levels, corresponding to its indispensable roles in energy metabolism and building new biomass. The combination of spatially resolved metabolomics with the 3D tumor-immune cell coculture spheroid visually reveals metabolic interactions between tumor and immune cells and possibly helps decipher the role of immunometabolic alterations in tumor immunotherapy.
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Affiliation(s)
- Panpan Chen
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Yuhao Han
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Lei Wang
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Yurong Zheng
- School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Zihan Zhu
- School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
| | - Yuan Zhao
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Mingqi Zhang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Xiangfeng Chen
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Xiao Wang
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
| | - Chenglong Sun
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
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21
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Wang L, Han Y, Zhang Y, Geng H, Zhu Z, Chen P, Cui X, Wang X, Sun C. In-depth profiling of carbohydrate isomers in biological tissues by chemical derivatization-assisted mass spectrometry imaging. Anal Chim Acta 2023; 1278:341741. [PMID: 37709472 DOI: 10.1016/j.aca.2023.341741] [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: 05/18/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023]
Abstract
Carbohydrates play crucial regulatory roles in various physiological and pathological processes. However, the low ionization efficiency and the presence of linkage pattern, monosaccharide composition and anomeric configuration isomers make their in-depth analysis very challenging, especially for heterogeneous biological tissues. In this study, we propose a high-sensitive and isomer-specific imaging approach to visualize the spatial distributions of monosaccharide and disaccharide isomers by integrating chemical derivatization and matrix-assisted laser desorption/ionization tandem mass spectrometry imaging (MALDI-MS2I). 2-Pyridinecarbohydrazide (PYD) is developed as a novel derivatization reagent which can not only improves the MS sensitivity of carbohydrates, but also enables the identification and visualization of ketose and aldose monosaccharide isomers, as well as linkage pattern, monosaccharide composition and anomeric configuration disaccharide isomers by mass spectrometry imaging of isomer-specific MS/MS fragment ions. Moreover, we build quantitative MALDI-MS2 and MALDI-MS2I methods for disaccharide isomers based on the diagnostic fragment ions, and good linear relationships could be achieved both in solution and on glass slides. We expect that this study should provide new ideas for in-depth profiling of the spatial signatures of carbohydrates in biological tissues and lay the foundation for a deeper understanding of carbohydrates' structure.
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Affiliation(s)
- Lei Wang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Yuhao Han
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Yaqi Zhang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Haoyuan Geng
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Zihan Zhu
- School of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Panpan Chen
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiaoqing Cui
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiao Wang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Chenglong Sun
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China; Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.
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22
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Rodriguez GG, Yu Z, Shaykevich S, O’Donnell LF, Aguilera L, Cloos MA, Madelin G. Super-resolution of sodium images from simultaneous 1 H MRF/ 23 Na MRI acquisition. NMR IN BIOMEDICINE 2023; 36:e4959. [PMID: 37186038 PMCID: PMC10527031 DOI: 10.1002/nbm.4959] [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: 08/12/2022] [Revised: 04/17/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023]
Abstract
In this work, we introduce a super-resolution method that generates a high-resolution (HR) sodium (23 Na) image from simultaneously acquired low-resolution (LR) 23 Na density-weighted MRI and HR proton density, T1 , and T2 maps from proton (1 H) MR fingerprinting in the brain at 7 T. The core of our method is a partial least squares regression between the HR (1 H) images and the LR (23 Na) image. An iterative loop and deconvolution with the point spread function of each acquired image were included in the algorithm to generate a final HR 23 Na image without losing features from the LR 23 Na image. The method was applied to simultaneously acquired HR proton and LR sodium data with in-plane resolution ratios between sodium and proton data of 3.8 and 1.9 and the same slice thickness. Four volunteers were scanned to evaluate the method's performance. For the data with a resolution ratio of 3.8, the mean absolute difference between the generated and ground truth HR 23 Na images was in the range of 1.5%-7.2% of the ground truth with a multiscale structural similarity index (M-SSIM) of 0.93 ± 0.03. For the data with a resolution ratio of 1.9, the mean absolute difference was in the range of 4.8%-6.3% with an M-SSIM of 0.95 ± 0.01.
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Affiliation(s)
- Gonzalo G. Rodriguez
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Zidan Yu
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
- Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health
- Departement of Medicine, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
| | - Sarah Shaykevich
- Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health
| | - Lauren F. O’Donnell
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Liz Aguilera
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
| | - Martijn A. Cloos
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, QLD, Australia
| | - Guillaume Madelin
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine
- Vilcek Institute of Graduate Biomedical Sciences, NYU Langone Health
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23
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Desyaterik Y, Mwangi JN, McRae M, Jones AM, Kashuba ADM, Rosen EP. Application of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry for morphine imaging in brain tissue. Anal Bioanal Chem 2023; 415:5809-5817. [PMID: 37490153 PMCID: PMC10474208 DOI: 10.1007/s00216-023-04861-x] [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: 04/21/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/26/2023]
Abstract
Here, we present a method developed for the analysis of spatial distributions of morphine in mouse brain tissue using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) coupled to a Q Exactive Plus mass spectrometer. The method is also capable of evaluating spatial distributions of the antiretroviral drug abacavir. To maximize sensitivity to morphine, we analyze various Orbitrap mass spectrometry acquisition modes utilizing signal abundance and frequency of detection as evaluation criteria. We demonstrate detection of morphine in mouse brain and establish that the selected ion monitoring mode provides 2.5 times higher sensitivity than the full-scan mode. We find that distributions of morphine and abacavir are highly correlated with the Pearson correlation coefficient R = 0.87. Calibration showed that instrument response is linear up to 40 pg/mm2 (3.8 μg/g of tissue).
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Affiliation(s)
- Yury Desyaterik
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | | | - MaryPeace McRae
- School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Austin M Jones
- School of Pharmacy, Virginia Commonwealth University, Richmond, VA, USA
| | - Angela D M Kashuba
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elias P Rosen
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Lorents A, Colin ME, Bjerke IE, Nougaret S, Montelisciani L, Diaz M, Verschure P, Vezoli J. Human Brain Project Partnering Projects Meeting: Status Quo and Outlook. eNeuro 2023; 10:ENEURO.0091-23.2023. [PMID: 37669867 PMCID: PMC10481639 DOI: 10.1523/eneuro.0091-23.2023] [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: 03/19/2023] [Revised: 08/03/2023] [Accepted: 08/04/2023] [Indexed: 09/07/2023] Open
Abstract
As the European Flagship Human Brain Project (HBP) ends in September 2023, a meeting dedicated to the Partnering Projects (PPs), a collective of independent research groups that partnered with the HBP, was held on September 4-7, 2022. The purpose of this meeting was to allow these groups to present their results, reflect on their collaboration with the HBP and discuss future interactions with the European Research Infrastructure (RI) EBRAINS that has emerged from the HBP. In this report, we share the tour-de-force that the Partnering Projects that were present in the meeting have made in furthering knowledge concerning various aspects of Brain Research with the HBP. We describe briefly major achievements of the HBP Partnering Projects in terms of a systems-level understanding of the functional architecture of the brain and its possible emulation in artificial systems. We then recapitulate open discussions with EBRAINS representatives about the evolution of EBRAINS as a sustainable Research Infrastructure for the Partnering Projects after the HBP, and also for the wider scientific community.
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Affiliation(s)
| | | | - Ingvild Elise Bjerke
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo 0372, Norway
| | - Simon Nougaret
- Institut de Neurosciences de la Timone, Unité Mixte de Recherche 7289, Aix Marseille Université, Centre National de la Recherche Scientifique, Marseille 13005, France
| | - Luca Montelisciani
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam 1098XH, The Netherlands
| | - Marissa Diaz
- Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich GmbH, Jülich 52428, Germany
| | - Paul Verschure
- Donders Center for Neuroscience (DCN-FNWI), Radboud University, Nijmegen 6500HD, The Netherlands
| | - Julien Vezoli
- Ernst Strügmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main 60528, Germany
- Institut National de la Santé et de la Recherche Médicale Unité 1208, Stem Cell and Brain Research Institute, Université Claude Bernard Lyon 1, Bron 69500, France
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25
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Jain S, Pei L, Spraggins JM, Angelo M, Carson JP, Gehlenborg N, Ginty F, Gonçalves JP, Hagood JS, Hickey JW, Kelleher NL, Laurent LC, Lin S, Lin Y, Liu H, Naba A, Nakayasu ES, Qian WJ, Radtke A, Robson P, Stockwell BR, Van de Plas R, Vlachos IS, Zhou M, Börner K, Snyder MP. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat Cell Biol 2023; 25:1089-1100. [PMID: 37468756 PMCID: PMC10681365 DOI: 10.1038/s41556-023-01194-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.
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Affiliation(s)
- Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Liming Pei
- Center for Mitochondrial and Epigenomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology and the Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Michael Angelo
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - James P Carson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Joana P Gonçalves
- Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
| | - James S Hagood
- Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John W Hickey
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Neil L Kelleher
- Departments of Medicine, Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Louise C Laurent
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Shin Lin
- Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
| | - Huiping Liu
- Departments of Pharmacology, Medicine (Hematology and Oncology), Lurie Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexandra Naba
- Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, USA
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Andrea Radtke
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Brent R Stockwell
- Department of Biological Sciences and Department of Chemistry, Columbia University, New York, NY, USA
| | - Raf Van de Plas
- Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
| | - Ioannis S Vlachos
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA.
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26
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Zhou Y, Jiang X, Wang X, Huang J, Li T, Jin H, He J. Promise of spatially resolved omics for tumor research. J Pharm Anal 2023; 13:851-861. [PMID: 37719191 PMCID: PMC10499658 DOI: 10.1016/j.jpha.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.
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Affiliation(s)
- Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xinyi Jiang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Tong Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
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27
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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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28
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Wang L, Cheng JX. Nanoscale bond-selective imaging by computational fusion of atomic force microscopy and coherent anti-Stokes Raman scattering microscopy. Analyst 2023; 148:2975-2982. [PMID: 37305950 PMCID: PMC10349369 DOI: 10.1039/d3an00662j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Vibrational microscopy based on coherent Raman scattering is a powerful tool for high-speed chemical imaging, but its lateral resolution is bound to the optical diffraction limit. On the other hand, atomic force microscopy (AFM) provides nano-scale spatial resolution, yet with lower chemical specificity. In this study, we leverage a computational approach called pan-sharpening to merge AFM topography images and coherent anti-Stokes Raman scattering (CARS) images. The hybrid system combines the advantages of both modalities, providing informative chemical mapping with ∼20 nm spatial resolution. CARS and AFM images were sequentially acquired on a single multimodal platform, which facilitates image co-localization. Our image fusion approach allowed for discerning merged neighboring features previously invisible due to the diffraction limit and identifying subtle unobservable structures with the input from AFM images. Compared to tip-enhanced CARS measurement, sequential acquisition of CARS and AFM images enables higher laser power to be used and avoids any tip damage caused by the incident laser beams, resulting in a significantly improved CARS image quality. Together, our work suggests a new direction for achieving super-resolution coherent Raman scattering imaging of materials through a computational approach.
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Affiliation(s)
- Le Wang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
| | - Ji-Xin Cheng
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
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29
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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: 6] [Impact Index Per Article: 6.0] [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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30
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Li Z, Sun Y, An F, Chen H, Liao J. Self-supervised clustering analysis of colorectal cancer biomarkers based on multi-scale whole slides image and mass spectrometry imaging fused images. Talanta 2023; 263:124727. [PMID: 37247451 DOI: 10.1016/j.talanta.2023.124727] [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: 02/20/2023] [Revised: 05/13/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023]
Abstract
Mass spectrometry imaging (MSI) is widely used for unlabeled molecular co-localization in biological samples and is also commonly used for screening cancer biomarkers. The main issues affecting the screening of cancer biomarkers are: 1) low-resolution MSI and pathological slices cannot be accurately matched; 2) a large amount of MSI data cannot be directly analyzed without manual annotation. This paper proposes a self-supervised cluster analysis method for colorectal cancer biomarkers based on multi-scale whole slide images (WSI) and MSI fusion images without manual annotation, which can accurately determine the correlation between molecules and lesion areas. This paper uses the combination of WSI multi-scale high-resolution and MSI high-dimensional data to obtain high-resolution fusion images. This method can observe the spatial distribution of molecules in pathological slices and use this method as an evaluation index for self-supervised screening of cancer biomarkers. The experimental results show that the method proposed in this chapter can train the image fusion model with a small amount of MSI and WSI data, and the mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) evaluation metrics of the fused images can reach 0.9587 and 0.8745. And self-supervised clustering using MSI features and fused image features can obtain good classification results, and the precision, recall, and F1-score values of the self-supervised model reach 0.9074, 0.9065, and 0.9069, respectively. This method effectively combines the advantages of WSI and MSI, which will significantly expand the application scenarios of MSI and facilitate the screening of disease markers.
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Affiliation(s)
- Zhen Li
- School of Science, China Pharmaceutical University, Nanjing, 211198, China
| | - Yusong Sun
- School of Science, China Pharmaceutical University, Nanjing, 211198, China
| | - Feng An
- Zhejiang Lab, #1818 Wenyi West Road, Yuhang District, Hangzhou, 311100, Zhengjiang province, China
| | - Hongyang Chen
- Zhejiang Lab, #1818 Wenyi West Road, Yuhang District, Hangzhou, 311100, Zhengjiang province, China
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, 211198, China; Zhejiang Lab, #1818 Wenyi West Road, Yuhang District, Hangzhou, 311100, Zhengjiang province, China.
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31
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Xiang Y, Metodiev M, Wang M, Cao B, Bunch J, Takats Z. Enhancement of Ambient Mass Spectrometry Imaging Data by Image Restoration. Metabolites 2023; 13:metabo13050669. [PMID: 37233710 DOI: 10.3390/metabo13050669] [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: 03/24/2023] [Revised: 05/05/2023] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Mass spectrometry imaging (MSI) has been a key driver of groundbreaking discoveries in a number of fields since its inception more than 50 years ago. Recently, MSI development trends have shifted towards ambient MSI (AMSI) as the removal of sample-preparation steps and the possibility of analysing biological specimens in their natural state have drawn the attention of multiple groups across the world. Nevertheless, the lack of spatial resolution has been cited as one of the main limitations of AMSI. While significant research effort has presented hardware solutions for improving the resolution, software solutions are often overlooked, although they can usually be applied in a cost-effective manner after image acquisition. In this vein, we present two computational methods that we have developed to directly enhance the image resolution post-acquisition. Robust and quantitative resolution improvement is demonstrated for 12 cases of openly accessible datasets across laboratories around the globe. Using the same universally applicable Fourier imaging model, we discuss the possibility of true super-resolution by software for future studies.
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Affiliation(s)
- Yuchen Xiang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Martin Metodiev
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory (NPL), Teddington TW11 0LW, UK
| | - Meiqi Wang
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Boxuan Cao
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
| | - Josephine Bunch
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory (NPL), Teddington TW11 0LW, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
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32
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Sun C, Wang A, Zhou Y, Chen P, Wang X, Huang J, Gao J, Wang X, Shu L, Lu J, Dai W, Bu Z, Ji J, He J. Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer. Nat Commun 2023; 14:2692. [PMID: 37164975 PMCID: PMC10172194 DOI: 10.1038/s41467-023-38360-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open
Abstract
Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.
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Affiliation(s)
- Chenglong Sun
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Anqiang Wang
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Panpan Chen
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jiamin Gao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiao Wang
- Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
- Key Laboratory for Natural Active Pharmaceutical Constituents Research in Universities of Shandong Province, School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China
| | - Liebo Shu
- Shanghai Luming Biological Technology co.Ltd, Shanghai, 201102, China
| | - Jiawei Lu
- Shanghai Luming Biological Technology co.Ltd, Shanghai, 201102, China
| | - Wentao Dai
- NHC Key Lab of Reproduction Regulation (Shanghai Institute for Biomedical and Pharmaceutical Technologies) & Shanghai Engineering Research Center of Pharmaceutical Translation, Fudan University, Shanghai, 200080, China.
- Shanghai Key Laboratory of Gastric Neoplasms, Department of General Surgery, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Zhaode Bu
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China.
| | - Jiafu Ji
- Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China.
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
- NMPA Key Laboratory of safety research and evaluation of Innovative Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
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33
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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: 3] [Impact Index Per Article: 3.0] [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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34
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Geier B, Gil-Mansilla E, Liutkevičiūtė Z, Hellinger R, Vanden Broeck J, Oetjen J, Liebeke M, Gruber CW. Multiplexed neuropeptide mapping in ant brains integrating microtomography and three-dimensional mass spectrometry imaging. PNAS NEXUS 2023; 2:pgad144. [PMID: 37215633 PMCID: PMC10194420 DOI: 10.1093/pnasnexus/pgad144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 04/14/2023] [Indexed: 05/24/2023]
Abstract
Neuropeptides are important regulators of animal physiology and behavior. Hitherto the gold standard for the localization of neuropeptides have been immunohistochemical methods that require the synthesis of antibody panels, while another limiting factor has been the brain's opacity for subsequent in situ light or fluorescence microscopy. To address these limitations, we explored the integration of high-resolution mass spectrometry imaging (MSI) with microtomography for a multiplexed mapping of neuropeptides in two evolutionary distant ant species, Atta sexdens and Lasius niger. For analyzing the spatial distribution of chemically diverse peptide molecules across the brain in each species, the acquisition of serial mass spectrometry images was essential. As a result, we have comparatively mapped the three-dimensional (3D) distributions of eight conserved neuropeptides throughout the brain microanatomy. We demonstrate that integrating the 3D MSI data into high-resolution anatomy models can be critical for studying organs with high plasticity such as brains of social insects. Several peptides, like the tachykinin-related peptides (TK) 1 and 4, were widely distributed in many brain areas of both ant species, whereas others, for instance myosuppressin, were restricted to specific regions only. Also, we detected differences at the species level; many peptides were identified in the optic lobe of L. niger, but only one peptide (ITG-like) was found in this region in A. sexdens. Building upon MS imaging studies on neuropeptides in invertebrate model systems, our approach leverages correlative MSI and computed microtomography for investigating fundamental neurobiological processes by visualizing the unbiased 3D neurochemistry in its complex anatomic environment.
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Affiliation(s)
| | | | - Zita Liutkevičiūtė
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna 1090, Austria
| | - Roland Hellinger
- Center for Physiology and Pharmacology, Medical University of Vienna, Vienna 1090, Austria
| | - Jozef Vanden Broeck
- Molecular Developmental Physiology and Signal Transduction Group, Zoological Institute, KU Leuven, Leuven 3000, Belgium
| | - Janina Oetjen
- To whom correspondence should be addressed: (J.O.); (M.L.); (C.W.G.)
| | - Manuel Liebeke
- To whom correspondence should be addressed: (J.O.); (M.L.); (C.W.G.)
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35
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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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36
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Abu Sammour D, Cairns JL, Boskamp T, Marsching C, Kessler T, Ramallo Guevara C, Panitz V, Sadik A, Cordes J, Schmidt S, Mohammed SA, Rittel MF, Friedrich M, Platten M, Wolf I, von Deimling A, Opitz CA, Wick W, Hopf C. Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging. Nat Commun 2023; 14:1823. [PMID: 37005414 PMCID: PMC10067847 DOI: 10.1038/s41467-023-37394-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/13/2023] [Indexed: 04/04/2023] Open
Abstract
Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.
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Affiliation(s)
- Denis Abu Sammour
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - James L Cairns
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Tobias Boskamp
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
- Center for Industrial Mathematics, University of Bremen, Bremen, Germany
| | - Christian Marsching
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Tobias Kessler
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carina Ramallo Guevara
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Verena Panitz
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Ahmed Sadik
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Bioscience, Heidelberg University, Heidelberg, Germany
| | - Jonas Cordes
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Stefan Schmidt
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
| | - Shad A Mohammed
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Miriam F Rittel
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirco Friedrich
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKTK Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Platten
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKTK Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ivo Wolf
- Faculty of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany
| | - Andreas von Deimling
- Department of Neuropathology, University Hospital Heidelberg, Heidelberg, Germany
- DKTK Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christiane A Opitz
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
- DKTK Metabolic Crosstalk in Cancer, German Consortium of Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Carsten Hopf
- Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Mannheim, Germany.
- Mannheim Center for Translational Neuroscience (MCTN), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
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37
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Wong A. A roadmap to high-resolution standard microcoil MAS NMR spectroscopy for metabolomics. NMR IN BIOMEDICINE 2023; 36:e4683. [PMID: 34970795 DOI: 10.1002/nbm.4683] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/06/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
Current microcoil probe technology has emerged as a significant advancement in NMR applications to biofluids research. It has continued to excel as a hyphenated tool with other prominent microdevices, opening many new possibilities in multiple omics fields. However, this does not hold for biological samples such as intact tissue or organisms, due to the considerable challenges of incorporating the microcoil in a magic-angle spinning (MAS) probe without relinquishing the high-resolution spectral data. Not until 2012 did a microcoil MAS probe show promise in profiling the metabolome in a submilligram tissue biopsy with spectral resolution on par with conventional high-resolution MAS (HR-MAS) NMR. This result subsequently triggered a great interest in the possibility of NMR analysis with microgram tissues and striving toward the probe development of "high-resolution" capable microcoil MAS NMR spectroscopy. This review gives an overview of the issues and challenges in the probe development and summarizes the advancements toward metabolomics.
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Affiliation(s)
- Alan Wong
- NIMBE, CEA, CNRS, Université Paris-Saclay, CEA Saclay, Gif-sur-Yvette, France
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38
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Wehrli P, Ge J, Michno W, Koutarapu S, Dreos A, Jha D, Zetterberg H, Blennow K, Hanrieder J. Correlative Chemical Imaging and Spatial Chemometrics Delineate Alzheimer Plaque Heterogeneity at High Spatial Resolution. JACS AU 2023; 3:762-774. [PMID: 37006756 PMCID: PMC10052239 DOI: 10.1021/jacsau.2c00492] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/19/2023]
Abstract
We present a novel, correlative chemical imaging strategy based on multimodal matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI), hyperspectral microscopy, and spatial chemometrics. Our workflow overcomes challenges associated with correlative MSI data acquisition and alignment by implementing 1 + 1-evolutionary image registration for precise geometric alignment of multimodal imaging data and their integration in a common, truly multimodal imaging data matrix with maintained MSI resolution (10 μm). This enabled multivariate statistical modeling of multimodal imaging data using a novel multiblock orthogonal component analysis approach to identify covariations of biochemical signatures between and within imaging modalities at MSI pixel resolution. We demonstrate the method's potential through its application toward delineating chemical traits of Alzheimer's disease (AD) pathology. Here, trimodal MALDI MSI of transgenic AD mouse brain delineates beta-amyloid (Aβ) plaque-associated co-localization of lipids and Aβ peptides. Finally, we establish an improved image fusion approach for correlative MSI and functional fluorescence microscopy. This allowed for high spatial resolution (300 nm) prediction of correlative, multimodal MSI signatures toward distinct amyloid structures within single plaque features critically implicated in Aβ pathogenicity.
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Affiliation(s)
- Patrick
M. Wehrli
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Junyue Ge
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Wojciech Michno
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Srinivas Koutarapu
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Ambra Dreos
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Durga Jha
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
| | - Henrik Zetterberg
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
- U.
K. Dementia Research Institute at University College London, London WC1N 3BG, U.K.
- Hong
Kong Center for Neurodegenerative Diseases, Sha Tin, N.T. 1512-1518, Hong Kong, China
| | - Kaj Blennow
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
| | - Jörg Hanrieder
- Department
of Psychiatry and Neurochemistry, Institute
of Neuroscience and Physiology, Sahlgrenska Academy, University of
Gothenburg, Mölndal 431 80, Sweden
- Clinical
Neurochemistry Laboratory, Sahlgrenska University
Hospital Mölndal, Mölndal 431 80, Sweden
- Department
of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London WC1N 3BG, U.K.
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39
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Liu Y, Nie X, Wang J, Zhao Z, Wang Z, Ju F. Visualizing the distribution of flavonoids in litchi ( Litchi chinenis) seeds through matrix-assisted laser desorption/ionization mass spectrometry imaging. FRONTIERS IN PLANT SCIENCE 2023; 14:1144449. [PMID: 36909412 PMCID: PMC9998689 DOI: 10.3389/fpls.2023.1144449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Flavonoids are one of the most important bioactive components in litchi (Litchi chinensis Sonn.) seeds and have broad-spectrum antiviral and antitumor activities. Litchi seeds have been shown to inhibit the proliferation of cancer cells and induce apoptosis, particularly effective against breast and liver cancers. Elucidating the distribution of flavonoids is important for understanding their physiological and biochemical functions and facilitating their efficient extraction and utilization. However, the spatial distribution patterns and expression states of flavonoids in litchi seeds remain unclear. Herein, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used for in situ detection and imaging of the distribution of flavonoids in litchi seed tissue sections for the first time. Fifteen flavonoid ion signals, including liquiritigenin, apigenin, naringenin, luteolin, dihydrokaempferol, daidzein, quercetin, taxifolin, kaempferol, isorhamnetin, myricetin, catechin, quercetin 3-β-d-glucoside, baicalin, and rutin, were successfully detected and imaged in situ through MALDI-MSI in the positive ion mode using 2-mercaptobenzothiazole as a matrix. The results clearly showed the heterogeneous distribution of flavonoids, indicating the potential of litchi seeds for flavonoid compound extraction. MALDI-MS-based multi-imaging enhanced the visualization of spatial distribution and expression states of flavonoids. Thus, apart from improving our understanding of the spatial distribution of flavonoids in litchi seeds, our findings also facilitate the development of MALDI-MSI-based metabolomics as a novel effective molecular imaging tool for evaluating the spatial distribution of endogenous compounds.
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Affiliation(s)
- Yukun Liu
- Department of Breast Surgery, Breast Disease Center, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Xiaofei Nie
- Department of Oncology, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Jilong Wang
- Department of Acupuncture and Moxibustion, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zhenqi Zhao
- Department of Radiology, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Zhimei Wang
- Department of Gynecological Neoplasms, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Fang Ju
- Department of Oncology, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China
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40
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Haque MIU, Mukherjee D, Stopka SA, Agar NYR, Hinkle J, Ovchinnikova OS. Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:227-235. [PMID: 36625762 PMCID: PMC10479534 DOI: 10.1021/jasms.2c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.
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Affiliation(s)
- Md Inzamam Ul Haque
- The Bredesen Center, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Debangshu Mukherjee
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, United States
| | - Jacob Hinkle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
| | - Olga S Ovchinnikova
- The Bredesen Center, University of Tennessee, Knoxville, Tennessee 37996, United States
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41
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Lim MJ, Yagnik G, Henkel C, Frost SF, Bien T, Rothschild KJ. MALDI HiPLEX-IHC: multiomic and multimodal imaging of targeted intact proteins in tissues. Front Chem 2023; 11:1182404. [PMID: 37201132 PMCID: PMC10187789 DOI: 10.3389/fchem.2023.1182404] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 04/14/2023] [Indexed: 05/20/2023] Open
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is one of the most widely used methods for imaging the spatial distribution of unlabeled small molecules such as metabolites, lipids and drugs in tissues. Recent progress has enabled many improvements including the ability to achieve single cell spatial resolution, 3D-tissue image reconstruction, and the precise identification of different isomeric and isobaric molecules. However, MALDI-MSI of high molecular weight intact proteins in biospecimens has thus far been difficult to achieve. Conventional methods normally require in situ proteolysis and peptide mass fingerprinting, have low spatial resolution, and typically detect only the most highly abundant proteins in an untargeted manner. In addition, MSI-based multiomic and multimodal workflows are needed which can image both small molecules and intact proteins from the same tissue. Such a capability can provide a more comprehensive understanding of the vast complexity of biological systems at the organ, tissue, and cellular levels of both normal and pathological function. A recently introduced top-down spatial imaging approach known as MALDI HiPLEX-IHC (MALDI-IHC for short) provides a basis for achieving this high-information content imaging of tissues and even individual cells. Based on novel photocleavable mass-tags conjugated to antibody probes, high-plex, multimodal and multiomic MALDI-based workflows have been developed to image both small molecules and intact proteins on the same tissue sample. Dual-labeled antibody probes enable multimodal mass spectrometry and fluorescent imaging of targeted intact proteins. A similar approach using the same photocleavable mass-tags can be applied to lectin and other probes. We detail here several examples of MALDI-IHC workflows designed to enable high-plex, multiomic and multimodal imaging of tissues at a spatial resolution as low as 5 µm. This approach is compared to other existing high-plex methods such as imaging mass cytometry, MIBI-TOF, GeoMx and CODEX. Finally, future applications of MALDI-IHC are discussed.
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Affiliation(s)
- Mark J. Lim
- AmberGen, Inc., Billerica, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
| | | | | | | | - Tanja Bien
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | - Kenneth J. Rothschild
- AmberGen, Inc., Billerica, MA, United States
- Department of Physics and Photonics Center, Boston University, Boston, MA, United States
- *Correspondence: Mark J. Lim, ; Kenneth J. Rothschild,
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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: 5] [Impact Index Per Article: 2.5] [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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43
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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: 20] [Impact Index Per Article: 10.0] [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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Azam KSF, Ryabchykov O, Bocklitz T. A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules 2022; 27:7448. [PMID: 36364272 PMCID: PMC9655963 DOI: 10.3390/molecules27217448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/19/2022] [Accepted: 10/21/2022] [Indexed: 08/05/2024] Open
Abstract
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.
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Affiliation(s)
- Kazi Sultana Farhana Azam
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
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45
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Perry WJ, Grunenwald CM, Van de Plas R, Witten JC, Martin DR, Apte SS, Cassat JE, Pettersson GB, Caprioli RM, Skaar EP, Spraggins JM. Visualizing Staphylococcus aureus pathogenic membrane modification within the host infection environment by multimodal imaging mass spectrometry. Cell Chem Biol 2022; 29:1209-1217.e4. [PMID: 35654040 PMCID: PMC9308753 DOI: 10.1016/j.chembiol.2022.05.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/10/2021] [Accepted: 05/11/2022] [Indexed: 11/30/2022]
Abstract
Bacterial pathogens have evolved virulence factors to colonize, replicate, and disseminate within the vertebrate host. Although there is an expanding body of literature describing how bacterial pathogens regulate their virulence repertoire in response to environmental signals, it is challenging to directly visualize virulence response within the host tissue microenvironment. Multimodal imaging approaches enable visualization of host-pathogen molecular interactions. Here we demonstrate multimodal integration of high spatial resolution imaging mass spectrometry and microscopy to visualize Staphylococcus aureus envelope modifications within infected murine and human tissues. Data-driven image fusion of fluorescent bacterial reporters and matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance imaging mass spectrometry uncovered S. aureus lysyl-phosphatidylglycerol lipids, localizing to select bacterial communities within infected tissue. Absence of lysyl-phosphatidylglycerols is associated with decreased pathogenicity during vertebrate colonization as these lipids provide protection against the innate immune system. The presence of distinct staphylococcal lysyl-phosphatidylglycerol distributions within murine and human infections suggests a heterogeneous, spatially oriented microbial response to host defenses.
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Affiliation(s)
- William J Perry
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN 37232, USA
| | - Caroline M Grunenwald
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN 37232, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA; Delft Center for Systems and Control, Delft University of Technology - TU Delft, Delft, the Netherlands; Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA
| | - James C Witten
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Heart and Vascular Institute, Cleveland, OH 44195, USA
| | - Daniel R Martin
- Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
| | - Suneel S Apte
- Department of Biomedical Engineering, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
| | - James E Cassat
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN 37232, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA; Department of Pediatrics, Division of Pediatric Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Gösta B Pettersson
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Heart and Vascular Institute, Cleveland, OH 44195, USA
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA; Department of Pharmacology, Vanderbilt University, Nashville, TN 37212, USA; Department of Medicine, Vanderbilt University, Nashville, TN 37212, USA
| | - Eric P Skaar
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Vanderbilt University, Nashville, TN 37232, USA; Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN 37212, USA.
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN 37232, USA; Department of Chemistry, Vanderbilt University, Nashville, TN 37212, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN 37212, USA; Department of Cell & Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA.
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46
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Burnum-Johnson KE, Conrads TP, Drake RR, Herr AE, Iyengar R, Kelly RT, Lundberg E, MacCoss MJ, Naba A, Nolan GP, Pevzner PA, Rodland KD, Sechi S, Slavov N, Spraggins JM, Van Eyk JE, Vidal M, Vogel C, Walt DR, Kelleher NL. New Views of Old Proteins: Clarifying the Enigmatic Proteome. Mol Cell Proteomics 2022; 21:100254. [PMID: 35654359 PMCID: PMC9256833 DOI: 10.1016/j.mcpro.2022.100254] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/09/2022] [Accepted: 05/27/2022] [Indexed: 11/23/2022] Open
Abstract
All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.
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Affiliation(s)
- Kristin E Burnum-Johnson
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
| | - Thomas P Conrads
- Inova Women's Service Line, Inova Health System, Falls Church, Virginia, USA
| | - Richard R Drake
- Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Amy E Herr
- Department of Bioengineering, University of California, Berkeley, California, USA
| | - Ravi Iyengar
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA
| | - Emma Lundberg
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Alexandra Naba
- Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California at San Diego, San Diego, California, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Salvatore Sechi
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology, Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Institute in the Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Marc Vidal
- Department of Genetics, Harvard University, Cambridge, Massachusetts, USA
| | - Christine Vogel
- New York University Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - David R Walt
- Department of Pathology, Harvard Medical School, Brigham and Women's Hospital, Wyss Institute at Harvard University, Boston, Massachusetts, USA
| | - Neil L Kelleher
- Department of Chemistry, Northwestern University, Evanston, Illinois, USA.
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47
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Spruill ML, Maletic-Savatic M, Martin H, Li F, Liu X. Spatial analysis of drug absorption, distribution, metabolism, and toxicology using mass spectrometry imaging. Biochem Pharmacol 2022; 201:115080. [PMID: 35561842 PMCID: PMC9744413 DOI: 10.1016/j.bcp.2022.115080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 12/14/2022]
Abstract
Mass spectrometry imaging (MSI) is emerging as a powerful analytical tool for detection, quantification, and simultaneous spatial molecular imaging of endogenous and exogenous molecules via in situ mass spectrometry analysis of thin tissue sections without the requirement of chemical labeling. The MSI generates chemically specific and spatially resolved ion distribution information for administered drugs and metabolites, which allows numerous applications for studies involving various stages of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). MSI-based pharmacokinetic imaging analysis provides a histological context and cellular environment regarding dynamic drug distribution and metabolism processes, and facilitates the understanding of the spatial pharmacokinetics and pharmacodynamic properties of drugs. Herein, we discuss the MSI's current technological developments that offer qualitative, quantitative, and spatial location information of small molecule drugs, antibody, and oligonucleotides macromolecule drugs, and their metabolites in preclinical and clinical tissue specimens. We highlight the macro and micro drug-distribution in the whole-body, brain, lung, liver, kidney, stomach, intestine tissue sections, organoids, and the latest applications of MSI in pharmaceutical ADMET studies.
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Affiliation(s)
- Michelle L Spruill
- Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, TX 77204, USA
| | - Mirjana Maletic-Savatic
- Department of Pediatrics, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | | | - Feng Li
- Center for Drug Discovery and Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, USA; NMR and Drug Metabolism Core, Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Xinli Liu
- Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, TX 77204, USA.
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48
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Mass Spectrometry-Based Analysis of Lipid Involvement in Alzheimer’s Disease Pathology—A Review. Metabolites 2022; 12:metabo12060510. [PMID: 35736443 PMCID: PMC9228715 DOI: 10.3390/metabo12060510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/31/2022] [Accepted: 05/31/2022] [Indexed: 01/27/2023] Open
Abstract
Irregularities in lipid metabolism have been linked to numerous neurodegenerative diseases. The roles of abnormal brain, plasma, and cerebrospinal fluid (CSF) lipid levels in Alzheimer’s disease (AD) onset and progression specifically have been described to a great extent in the literature. Apparent hallmarks of AD include, but are not limited to, genetic predisposition involving the APOE Ɛ4 allele, oxidative stress, and inflammation. A common culprit tied to many of these hallmarks is disruption in brain lipid homeostasis. Therefore, it is important to understand the roles of lipids, under normal and abnormal conditions, in each process. Lipid influences in processes such as inflammation and blood–brain barrier (BBB) disturbance have been primarily studied via biochemical-based methods. There is a need, however, for studies focused on uncovering the relationship between lipid irregularities and AD by molecular-based quantitative analysis in transgenic animal models and human samples alike. In this review, mass spectrometry as it has been used as an analytical tool to address the convoluted relationships mentioned above is discussed. Additionally, molecular-based mass spectrometry strategies that should be used going forward to further relate structure and function relationships of lipid irregularities and hallmark AD pathology are outlined.
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49
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Tuck M, Grélard F, Blanc L, Desbenoit N. MALDI-MSI Towards Multimodal Imaging: Challenges and Perspectives. Front Chem 2022; 10:904688. [PMID: 35615316 PMCID: PMC9124797 DOI: 10.3389/fchem.2022.904688] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/14/2022] [Indexed: 01/22/2023] Open
Abstract
Multimodal imaging is a powerful strategy for combining information from multiple images. It involves several fields in the acquisition, processing and interpretation of images. As multimodal imaging is a vast subject area with various combinations of imaging techniques, it has been extensively reviewed. Here we focus on Matrix-assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) coupling other imaging modalities in multimodal approaches. While MALDI-MS images convey a substantial amount of chemical information, they are not readily informative about the morphological nature of the tissue. By providing a supplementary modality, MALDI-MS images can be more informative and better reflect the nature of the tissue. In this mini review, we emphasize the analytical and computational strategies to address multimodal MALDI-MSI.
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50
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Müller WH, Verdin A, De Pauw E, Malherbe C, Eppe G. Surface-assisted laser desorption/ionization mass spectrometry imaging: A review. MASS SPECTROMETRY REVIEWS 2022; 41:373-420. [PMID: 33174287 PMCID: PMC9292874 DOI: 10.1002/mas.21670] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/22/2020] [Accepted: 10/24/2020] [Indexed: 05/04/2023]
Abstract
In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the analytical strategies to perform SALDI-MSI are extensively discussed. Emphasis is placed on the sample preparation but also on the selection of the nanosubstrate (in terms of chemical composition and morphology) as well as its functionalization possibilities for the selective analysis of specific compounds in targeted approaches. Subsequently, some selected applications of SALDI-MSI in various fields (i.e., biomedical, biological, environmental, and forensic) are presented. The strengths and the remaining limitations of SALDI-MSI are finally summarized in the conclusion and some perspectives of this technique, which has a bright future, are proposed in this section.
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Affiliation(s)
- Wendy H. Müller
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Alexandre Verdin
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Edwin De Pauw
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Cedric Malherbe
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
| | - Gauthier Eppe
- Mass Spectrometry Laboratory, MolSys Research Unit, Chemistry DepartmentUniversity of LiègeLiègeBelgium
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