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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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2
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Stillger MN, Li MJ, Hönscheid P, von Neubeck C, Föll MC. Advancing rare cancer research by MALDI mass spectrometry imaging: Applications, challenges, and future perspectives in sarcoma. Proteomics 2024; 24:e2300001. [PMID: 38402423 DOI: 10.1002/pmic.202300001] [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: 06/08/2023] [Revised: 02/10/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
MALDI mass spectrometry imaging (MALDI imaging) uniquely advances cancer research, by measuring spatial distribution of endogenous and exogenous molecules directly from tissue sections. These molecular maps provide valuable insights into basic and translational cancer research, including tumor biology, tumor microenvironment, biomarker identification, drug treatment, and patient stratification. Despite its advantages, MALDI imaging is underutilized in studying rare cancers. Sarcomas, a group of malignant mesenchymal tumors, pose unique challenges in medical research due to their complex heterogeneity and low incidence, resulting in understudied subtypes with suboptimal management and outcomes. In this review, we explore the applicability of MALDI imaging in sarcoma research, showcasing its value in understanding this highly heterogeneous and challenging rare cancer. We summarize all MALDI imaging studies in sarcoma to date, highlight their impact on key research fields, including molecular signatures, cancer heterogeneity, and drug studies. We address specific challenges encountered when employing MALDI imaging for sarcomas, and propose solutions, such as using formalin-fixed paraffin-embedded tissues, and multiplexed experiments, and considerations for multi-site studies and digital data sharing practices. Through this review, we aim to spark collaboration between MALDI imaging researchers and clinical colleagues, to deploy the unique capabilities of MALDI imaging in the context of sarcoma.
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Affiliation(s)
- Maren Nicole Stillger
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Mujia Jenny Li
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- Institute for Pharmaceutical Sciences, University of Freiburg, Freiburg, Germany
| | - Pia Hönscheid
- Institute of Pathology, Faculty of Medicine, University Hospital Carl Gustav Carus at the Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases, Partner Site Dresden, German Cancer Research Center Heidelberg, Dresden, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cläre von Neubeck
- Department of Particle Therapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Faculty of Medicine, University Medical Center, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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3
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Bitto V, Hönscheid P, Besso MJ, Sperling C, Kurth I, Baumann M, Brors B. Enhancing mass spectrometry imaging accessibility using convolutional autoencoders for deriving hypoxia-associated peptides from tumors. NPJ Syst Biol Appl 2024; 10:57. [PMID: 38802379 PMCID: PMC11130291 DOI: 10.1038/s41540-024-00385-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: 01/12/2024] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Mass spectrometry imaging (MSI) allows to study cancer's intratumoral heterogeneity through spatially-resolved peptides, metabolites and lipids. Yet, in biomedical research MSI is rarely used for biomarker discovery. Besides its high dimensionality and multicollinearity, mass spectrometry (MS) technologies typically output mass-to-charge ratio values but not the biochemical compounds of interest. Our framework makes particularly low-abundant signals in MSI more accessible. We utilized convolutional autoencoders to aggregate features associated with tumor hypoxia, a parameter with significant spatial heterogeneity, in cancer xenograft models. We highlight that MSI captures these low-abundant signals and that autoencoders can preserve them in their latent space. The relevance of individual hyperparameters is demonstrated through ablation experiments, and the contribution from original features to latent features is unraveled. Complementing MSI with tandem MS from the same tumor model, multiple hypoxia-associated peptide candidates were derived. Compared to random forests alone, our autoencoder approach yielded more biologically relevant insights for biomarker discovery.
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Affiliation(s)
- Verena Bitto
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- HIDSS4Health - Helmholtz Information and Data Science School for Health, Karlsruhe/Heidelberg, Heidelberg, Germany.
- Faculty for Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Pia Hönscheid
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - María José Besso
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christian Sperling
- National Center for Tumor Diseases (NCT), Partner Site Dresden, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Hospital Carl Gustav Carus (UKD), Technische Universität Dresden, Institute of Pathology, Dresden, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ina Kurth
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Michael Baumann
- Division of Radiooncology/Radiobiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Consortium (DKTK), Core Center Heidelberg, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg, Germany
- Medical Faculty Heidelberg and Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
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4
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Ngai YT, Briggs MT, Mittal P, Young C, Parkinson-Lawrence E, Klingler-Hoffmann M, Orgeig S, Hoffmann P. Mass spectrometry imaging protocol for spatial mapping of lipids, N-glycans and peptides in murine lung tissue. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2024; 38:e9721. [PMID: 38525810 DOI: 10.1002/rcm.9721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 03/26/2024]
Abstract
RATIONALE The application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to murine lungs is challenging due to the spongy nature of the tissue. Lungs consist of interconnected air sacs (alveoli) lined by a single layer of flattened epithelial cells, which requires inflation to maintain its natural structure. Therefore, a protocol that is compatible with both lung instillation and high spatial resolution is essential to enable multi-omic studies on murine lung disease models using MALDI-MSI. METHODS AND RESULTS To maintain the structural integrity of the tissue, murine lungs were inflated with 8% (w/v) gelatin for lipid MSI of fresh frozen tissues or 4% (v/v) paraformaldehyde neutral buffer for N-glycan and peptide MSI of FFPE tissues. Tissues were sectioned and prepared for enzymatic digestion and/or matrix deposition. Glycerol-free PNGase F was applied for N-glycan MSI, while Trypsin Gold was applied for peptide MSI using the iMatrixSpray and ImagePrep Station, respectively. For lipid, N-glycan and peptide MSI, α-cyano-4-hydroxycinnamic acid matrix was deposited using the iMatrixSpray. MS data were acquired with 20 μm spatial resolution using a timsTOF fleX MS instrument followed by MS fragmentation of lipids, N-glycans and peptides. For lipid MSI, trapped ion mobility spectrometry was used to separate isomeric/isobaric lipid species. SCiLS™ Lab was used to visualize all MSI data. For analyte identification, MetaboScape®, GlycoMod and Mascot were used to annotate MS fragmentation spectra of lipids, N-glycans and tryptic peptides, respectively. CONCLUSIONS Our protocol provides instructions on sample preparation for high spatial resolution MALDI-MSI, MS/MS data acquisition and lipid, N-glycan and peptide annotation and identification from murine lungs. This protocol will allow non-biased analyses of diseased lungs from preclinical murine models and provide further insight into disease models.
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Affiliation(s)
- Yuen T Ngai
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Matthew T Briggs
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Parul Mittal
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Clifford Young
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | | | | | - Sandra Orgeig
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Peter Hoffmann
- Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
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5
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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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6
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Ellin NR, Guo Y, Miranda-Quintana RA, Prentice BM. Extended similarity methods for efficient data mining in imaging mass spectrometry. DIGITAL DISCOVERY 2024; 3:805-817. [PMID: 38638647 PMCID: PMC11022984 DOI: 10.1039/d3dd00165b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often contain negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of imaging mass spectrometry spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1 : 1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Yingchan Guo
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
- Quantum Theory Project, University of Florida Gainesville FL 32611-7200 USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida Gainesville FL 32611-7200 USA
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7
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Klein D, Rivera ES, Caprioli RM, Spraggins JM. Imaging Mass Spectrometry of Isotopically Resolved Intact Proteins on a Trapped Ion-Mobility Quadrupole Time-of-Flight Mass Spectrometer. Anal Chem 2024; 96:5065-5070. [PMID: 38517028 PMCID: PMC10993197 DOI: 10.1021/acs.analchem.3c05252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/15/2024] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
In this work, we demonstrate rapid, high spatial, and high spectral resolution imaging of intact proteins by matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on a hybrid quadrupole-reflectron time-of-flight (qTOF) mass spectrometer equipped with trapped ion mobility spectrometry (TIMS). Historically, untargeted MALDI IMS of proteins has been performed on TOF mass spectrometers. While advances in TOF instrumentation have enabled rapid, high spatial resolution IMS of intact proteins, TOF mass spectrometers generate relatively low-resolution mass spectra with limited mass accuracy. Conversely, the implementation of MALDI sources on high-resolving power Fourier transform (FT) mass spectrometers has allowed IMS experiments to be conducted with high spectral resolution with the caveat of increasingly long data acquisition times. As illustrated here, qTOF mass spectrometers enable protein imaging with the combined advantages of TOF and FT mass spectrometers. Protein isotope distributions were resolved for both a protein standard mixture and proteins detected from a whole-body mouse pup tissue section. Rapid (∼10 pixels/s) 10 μm lateral spatial resolution IMS was performed on a rat brain tissue section while maintaining isotopic spectral resolution. Lastly, proof-of-concept MALDI-TIMS data was acquired from a protein mixture to demonstrate the ability to differentiate charge states by ion mobility. These experiments highlight the advantages of qTOF and timsTOF platforms for resolving and interpreting complex protein spectra generated from tissue by IMS.
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Affiliation(s)
- Dustin
R. Klein
- Mass
Spectrometry Research Center, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Biochemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Emilio S. Rivera
- Mass
Spectrometry Research Center, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Biochemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Richard M. Caprioli
- Mass
Spectrometry Research Center, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Biochemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Medicine, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Pharmacology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jeffrey M. Spraggins
- Mass
Spectrometry Research Center, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Biochemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department
of Cell and Developmental Biology, Vanderbilt
University, Nashville, Tennessee 37235, United States
- Department
of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37235, United States
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8
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Ma X, Fernández FM. Advances in mass spectrometry imaging for spatial cancer metabolomics. MASS SPECTROMETRY REVIEWS 2024; 43:235-268. [PMID: 36065601 PMCID: PMC9986357 DOI: 10.1002/mas.21804] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 05/09/2023]
Abstract
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
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Affiliation(s)
- Xin Ma
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry and Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, USA
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9
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Xiang P, Liyu A, Kwon Y, Hu D, Williams SM, Veličković D, Markillie LM, Chrisler WB, Paša-Tolić L, Zhu Y. Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation. ACS MEASUREMENT SCIENCE AU 2023; 3:459-468. [PMID: 38145026 PMCID: PMC10740121 DOI: 10.1021/acsmeasuresciau.3c00033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 12/26/2023]
Abstract
Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.
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Affiliation(s)
- Piliang Xiang
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Andrey Liyu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Yumi Kwon
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Dehong Hu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Sarah M. Williams
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Dušan Veličković
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Lye Meng Markillie
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - William B. Chrisler
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Ying Zhu
- Department
of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
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10
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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11
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Trimpin S. A tutorial: Laserspray ionization and related laser-based ionization methods for use in mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:2234-2267. [PMID: 37462443 DOI: 10.1002/mas.21762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 09/30/2021] [Accepted: 11/15/2021] [Indexed: 08/09/2023]
Abstract
This Tutorial is to provide a summary of parameters useful for successful outcomes of laserspray ionization (LSI) and related methods that employ a laser to ablate a matrix:analyte sample to produce highly charged ions. In these methods the purpose of the laser is to transfer matrix-analyte clusters into the gas phase. Ions are hypothesized to be produced by a thermal process where emitted matrix:analyte gas-phase particles/clusters are charged and loss of matrix from the charged particles leads to release of the analyte ions into the gas phase. The thermal energy responsible for the charge-separation process is relatively low and not necessarily supplied by the laser; a heated inlet tube linking atmospheric pressure with the first vacuum stage of a mass spectrometer is sufficient. The inlet becomes the "ion source", and inter alia, pressure, temperature, and the matrix, which can be a solid, liquid, or combinations, become critical parameters. Injecting matrix:analyte into a heated inlet tube using laser ablation, a shockwave, or simply tapping, all produce the similar mass spectra. Applications are provided that showcase new opportunities in the field of mass spectrometry.
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Affiliation(s)
- Sarah Trimpin
- Department of Chemistry, Wayne State University, Detroit, Michigan, USA
- MSTM, LLC, Newark, Delaware, USA
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12
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Moore JL, Patterson NH, Norris JL, Caprioli RM. Prospective on Imaging Mass Spectrometry in Clinical Diagnostics. Mol Cell Proteomics 2023; 22:100576. [PMID: 37209813 PMCID: PMC10545939 DOI: 10.1016/j.mcpro.2023.100576] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/10/2023] [Accepted: 05/12/2023] [Indexed: 05/22/2023] Open
Abstract
Imaging mass spectrometry (IMS) is a molecular technology utilized for spatially driven research, providing molecular maps from tissue sections. This article reviews matrix-assisted laser desorption ionization (MALDI) IMS and its progress as a primary tool in the clinical laboratory. MALDI mass spectrometry has been used to classify bacteria and perform other bulk analyses for plate-based assays for many years. However, the clinical application of spatial data within a tissue biopsy for diagnoses and prognoses is still an emerging opportunity in molecular diagnostics. This work considers spatially driven mass spectrometry approaches for clinical diagnostics and addresses aspects of new imaging-based assays that include analyte selection, quality control/assurance metrics, data reproducibility, data classification, and data scoring. It is necessary to implement these tasks for the rigorous translation of IMS to the clinical laboratory; however, this requires detailed standardized protocols for introducing IMS into the clinical laboratory to deliver reliable and reproducible results that inform and guide patient care.
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Affiliation(s)
| | - Nathan Heath Patterson
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Jeremy L Norris
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Richard M Caprioli
- Frontier Diagnostics, Nashville, Tennessee, USA; Vanderbilt University Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Departments of Biochemistry, Pharmacology, Chemistry, and Medicine, Vanderbilt University, Nashville, Tennessee, USA.
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13
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. Anal Chem 2023; 95:12913-12922. [PMID: 37579019 PMCID: PMC10561690 DOI: 10.1021/acs.analchem.3c02389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often assessed and visualized using various supervised and unsupervised statistical approaches. However, these approaches tend to fall short in identifying and concisely visualizing subtle, phenotype-relevant molecular changes. To address these shortcomings, we developed aggregated molecular phenotype (AMP) scores. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores, therefore, allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes. Due to the ensembled approach, AMP scores are able to overcome limitations associated with individual models, leading to high diagnostic accuracy and interpretability. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization MSI. Initial comparisons of cancerous human tissues to their normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Mary E King
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States
| | - Livia S Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, Texas 77030, United States
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, North Carolina 27709, United States
| | - Erin S Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, United States
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14
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Ellin NR, Miranda-Quintana RA, Prentice BM. Extended Similarity Methods for Efficient Data Mining in Imaging Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550838. [PMID: 37546817 PMCID: PMC10402165 DOI: 10.1101/2023.07.27.550838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x , y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O ( N ) , of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue.
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Affiliation(s)
- Nicholas R Ellin
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
- Quantum Theory Project, University of Florida, Gainesville, FL, 32611-7200; USA
| | - Boone M Prentice
- Department of Chemistry, University of Florida, Gainesville, FL, 32611-7200; USA
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15
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Chappel JR, King ME, Fleming J, Eberlin LS, Reif DM, Baker ES. Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.01.543306. [PMID: 37333214 PMCID: PMC10274704 DOI: 10.1101/2023.06.01.543306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.
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Affiliation(s)
- Jessie R. Chappel
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Mary E. King
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Jonathon Fleming
- Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Livia S. Eberlin
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - David M. Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Erin S. Baker
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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16
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Sharman K, Patterson NH, Weiss A, Neumann EK, Guiberson ER, Ryan DJ, Gutierrez DB, Spraggins JM, Van de Plas R, Skaar EP, Caprioli RM. Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue. J Proteome Res 2023; 22:1394-1405. [PMID: 35849531 PMCID: PMC9845430 DOI: 10.1021/acs.jproteome.2c00206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.
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Affiliation(s)
- Kavya Sharman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Program in Chemical & Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Andy Weiss
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Elizabeth K Neumann
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Emma R Guiberson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Daniel J Ryan
- Pfizer Inc., Chesterfield, Missouri 63017, United States
| | - Danielle B Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Eric P Skaar
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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17
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Joignant AN, Xi Y, Muddiman DC. Impact of wavelength and spot size on laser depth of focus: Considerations for mass spectrometry imaging of non-flat samples. JOURNAL OF MASS SPECTROMETRY : JMS 2023; 58:e4914. [PMID: 36916474 DOI: 10.1002/jms.4914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/09/2023] [Accepted: 02/19/2023] [Indexed: 06/18/2023]
Abstract
Biospecimens with nearly flat surfaces on a flat stage are typically required for laser-based mass spectrometry imaging (MSI) techniques. However, sampling stages are rarely perfectly level, and accounting for this and the need to accommodate non-flat samples requires a deeper understanding of the laser beam depth of focus. In ablation-based MSI methods, a laser is focused on top of the sample surface, ensuring that the sample is at the focal point or remains within depth of focus. In general, the depth of focus of a given laser is related to the beam quality (M2 ) and the wavelength (λ). However, because laser is applied on a biological sample, other variables can also impact the depth of focus, which could affect the robustness of the MSI techniques for diverse sample types. When the height of a sample ranges outside of the depth of focus, ablated area and the corresponding ion abundances may vary as well, increasing the variability of results. In this tutorial, we examine the parameters and equations that describe the depth of focus of a Gaussian laser beam. Additionally, we describe several approaches that account for surface roughness exceeding the depth of focus of the laser.
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Affiliation(s)
- Alena N Joignant
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
| | - Ying Xi
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
| | - David C Muddiman
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, North Carolina, USA
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18
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Kanter F, Lellmann J, Thiele H, Kalloger S, Schaeffer DF, Wellmann A, Klein O. Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks. Cancers (Basel) 2023; 15:cancers15030686. [PMID: 36765644 PMCID: PMC9913229 DOI: 10.3390/cancers15030686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 01/25/2023] Open
Abstract
Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
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Affiliation(s)
- Frederic Kanter
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
| | - Jan Lellmann
- Institute of Mathematics and Image Computing, Universität zu Lübeck, 23562 Luebeck, Germany
- Correspondence: (J.L.); (O.K.)
| | - Herbert Thiele
- Fraunhofer Institute for Digital Medicine MEVIS, 23562 Luebeck, Germany
| | - Steve Kalloger
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - David F. Schaeffer
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- Pancreas Centre BC, Vancouver, BC V5Z 1G1, Canada
- Division of Anatomic Pathology, Vancouver General Hospital, Vancouver, BC V5Z 1M9, Canada
| | - Axel Wellmann
- Institute of Pathology, Wittinger Strasse 14, 29223 Celle, Germany
| | - Oliver Klein
- BIH Center for Regenerative Therapies, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
- Correspondence: (J.L.); (O.K.)
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19
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Bai H, Manni JG, Muddiman DC. Transforming a Mid-infrared Laser Profile from Gaussian to a Top-Hat with a Diffractive Optical Element for Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:10-16. [PMID: 36542595 PMCID: PMC9975536 DOI: 10.1021/jasms.2c00203] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Many mass spectrometry imaging (MSI) applications such as infrared matrix-assisted electrospray ionization (IR-MALDESI) employ an infrared (IR) laser with a Gaussian profile where laser irradiance is highest in the center and decreases exponentially. To enable full ablation of a square region of interest, oversampling is often needed, which results in nonuniform ablation and leads to decreased image quality. A diffractive optical element (DOE) was integrated into the optical path to generate homogeneous intensity distributions while maintaining laser energy above the ablation threshold, to enable complete sample removal from laser pulses without oversampling. 2D and 3D imaging with the DOE inserted show clear and sharp ablation patterns with satisfactory biological signals gained. Further improvements will optimize the beam profile and generate a square top-hat laser beam for MSI application at higher spatial resolution.
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Affiliation(s)
- Hongxia Bai
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA
| | | | - David C. Muddiman
- FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC 27695, USA
- Molecular Education, Technology, and Research Innovation Center (METRIC), North Carolina State University, Raleigh, NC 27695 USA
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20
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Lukowski JK, Olson H, Velickovic M, Wang J, Kyle JE, Kim YM, Williams SM, Zhu Y, Huyck HL, McGraw MD, Poole C, Rogers L, Misra R, Alexandrov T, Ansong C, Pryhuber GS, Clair G, Adkins JN, Carson JP, Anderton CR. An optimized approach and inflation media for obtaining complimentary mass spectrometry-based omics data from human lung tissue. Front Mol Biosci 2022; 9:1022775. [PMID: 36465564 PMCID: PMC9709465 DOI: 10.3389/fmolb.2022.1022775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 11/02/2022] [Indexed: 04/23/2024] Open
Abstract
Human disease states are biomolecularly multifaceted and can span across phenotypic states, therefore it is important to understand diseases on all levels, across cell types, and within and across microanatomical tissue compartments. To obtain an accurate and representative view of the molecular landscape within human lungs, this fragile tissue must be inflated and embedded to maintain spatial fidelity of the location of molecules and minimize molecular degradation for molecular imaging experiments. Here, we evaluated agarose inflation and carboxymethyl cellulose embedding media and determined effective tissue preparation protocols for performing bulk and spatial mass spectrometry-based omics measurements. Mass spectrometry imaging methods were optimized to boost the number of annotatable molecules in agarose inflated lung samples. This optimized protocol permitted the observation of unique lipid distributions within several airway regions in the lung tissue block. Laser capture microdissection of these airway regions followed by high-resolution proteomic analysis allowed us to begin linking the lipidome with the proteome in a spatially resolved manner, where we observed proteins with high abundance specifically localized to the airway regions. We also compared our mass spectrometry results to lung tissue samples preserved using two other inflation/embedding media, but we identified several pitfalls with the sample preparation steps using this preservation method. Overall, we demonstrated the versatility of the inflation method, and we can start to reveal how the metabolome, lipidome, and proteome are connected spatially in human lungs and across disease states through a variety of different experiments.
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Affiliation(s)
| | - Heather Olson
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Marija Velickovic
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Juan Wang
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Jennifer E. Kyle
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Young-Mo Kim
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Sarah M. Williams
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Ying Zhu
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Heidi L. Huyck
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Matthew D. McGraw
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Cory Poole
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Lisa Rogers
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Ravi Misra
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Charles Ansong
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Gloria S. Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States
| | - Geremy Clair
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - Joshua N. Adkins
- Pacific Northwest National Laboratory (PNNL), Richland, WA, United States
| | - James P. Carson
- Texas Advanced Computing Center (TACC), University of Texas at Austin, Austin, TX, United States
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21
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Körber A, Keelor JD, Claes BSR, Heeren RMA, Anthony IGM. Fast Mass Microscopy: Mass Spectrometry Imaging of a Gigapixel Image in 34 Minutes. Anal Chem 2022; 94:14652-14658. [PMID: 36223179 PMCID: PMC9607864 DOI: 10.1021/acs.analchem.2c02870] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022]
Abstract
Mass spectrometry imaging (MSI) maps the spatial distributions of chemicals on surfaces. MSI requires improvements in throughput and spatial resolution, and often one is compromised for the other. In microprobe-mode MSI, improvements in spatial resolution increase the imaging time quadratically, thus limiting the use of high spatial resolution MSI for large areas or sample cohorts and time-sensitive measurements. Here, we bypass this quadratic relationship by combining a Timepix3 detector with a continuously sampling secondary ion mass spectrometry mass microscope. By reconstructing the data into large-field mass images, this new method, fast mass microscopy, enables orders of magnitude higher throughput than conventional MSI albeit yet at lower mass resolution. We acquired submicron, gigapixel images of fingerprints and rat tissue at acquisition speeds of 600,000 and 15,500 pixels s-1, respectively. For the first image, a comparable microprobe-mode measurement would take more than 2 months, whereas our approach took 33.3 min.
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Affiliation(s)
- Aljoscha Körber
- The
Maastricht MultiModal Molecular Imaging Institute (M4i), Division
of Imaging Mass Spectrometry, Maastricht
University, Universiteitssingel 50, Maastricht 6229 ER, The Netherlands
| | - Joel D. Keelor
- Amsterdam
Scientific Instruments B.V. (ASI), Science Park 106, Amsterdam 1098 XG, The Netherlands
| | - Britt S. R. Claes
- The
Maastricht MultiModal Molecular Imaging Institute (M4i), Division
of Imaging Mass Spectrometry, Maastricht
University, Universiteitssingel 50, Maastricht 6229 ER, The Netherlands
| | - Ron M. A. Heeren
- The
Maastricht MultiModal Molecular Imaging Institute (M4i), Division
of Imaging Mass Spectrometry, Maastricht
University, Universiteitssingel 50, Maastricht 6229 ER, The Netherlands
| | - Ian G. M. Anthony
- The
Maastricht MultiModal Molecular Imaging Institute (M4i), Division
of Imaging Mass Spectrometry, Maastricht
University, Universiteitssingel 50, Maastricht 6229 ER, The Netherlands
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22
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Hu H, Helminiak D, Yang M, Unsihuay D, Hilger RT, Ye DH, Laskin J. High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling. ACS MEASUREMENT SCIENCE AU 2022; 2:466-474. [PMID: 36281292 PMCID: PMC9585637 DOI: 10.1021/acsmeasuresciau.2c00031] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 05/25/2023]
Abstract
Mass spectrometry imaging (MSI) enables label-free mapping of hundreds of molecules in biological samples with high sensitivity and unprecedented specificity. Conventional MSI experiments are relatively slow, limiting their utility for applications requiring rapid data acquisition, such as intraoperative tissue analysis or 3D imaging. Recent advances in MSI technology focus on improving the spatial resolution and molecular coverage, further increasing the acquisition time. Herein, a deep learning approach for dynamic sampling (DLADS) was employed to reduce the number of required measurements, thereby improving the throughput of MSI experiments in comparison with conventional methods. DLADS trains a deep learning model to dynamically predict molecularly informative tissue locations for active mass spectra sampling and reconstructs high-fidelity molecular images using only the sparsely sampled information. Experimental hardware and software integration of DLADS with nanospray desorption electrospray ionization (nano-DESI) MSI is reported for the first time, which demonstrates a 2.3-fold improvement in throughput for a linewise acquisition mode. Meanwhile, simulations indicate that a 5-10-fold throughput improvement may be achieved using the pointwise acquisition mode.
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Affiliation(s)
- Hang Hu
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - David Helminiak
- Electrical
and Computer Engineering, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Manxi Yang
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Daisy Unsihuay
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Ryan T. Hilger
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Dong Hye Ye
- Electrical
and Computer Engineering, Marquette University, Milwaukee, Wisconsin 53233, United States
| | - Julia Laskin
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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23
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Johnson AL, Laterra J, Lopez-Bertoni H. Exploring glioblastoma stem cell heterogeneity: Immune microenvironment modulation and therapeutic opportunities. Front Oncol 2022; 12:995498. [PMID: 36212415 PMCID: PMC9532940 DOI: 10.3389/fonc.2022.995498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/02/2022] [Indexed: 11/29/2022] Open
Abstract
Despite its growing use in cancer treatment, immunotherapy has been virtually ineffective in clinical trials for gliomas. The inherently cold tumor immune microenvironment (TIME) in gliomas, characterized by a high ratio of pro-tumor to anti-tumor immune cell infiltrates, acts as a seemingly insurmountable barrier to immunotherapy. Glioma stem cells (GSCs) within these tumors are key contributors to this cold TIME, often functioning indirectly through activation and recruitment of pro-tumor immune cell types. Furthermore, drivers of GSC plasticity and heterogeneity (e.g., reprogramming transcription factors, epigenetic modifications) are associated with induction of immunosuppressive cell states. Recent studies have identified GSC-intrinsic mechanisms, including functional mimicry of immune suppressive cell types, as key determinants of anti-tumor immune escape. In this review, we cover recent advancements in our understanding of GSC-intrinsic mechanisms that modulate GSC-TIME interactions and discuss cutting-edge techniques and bioinformatics platforms available to study immune modulation at high cellular resolution with exploration of both malignant (i.e., GSC) and non-malignant (i.e., immune) cell fractions. Finally, we provide insight into the therapeutic opportunities for targeting immunomodulatory GSC-intrinsic mechanisms to potentiate immunotherapy response in gliomas.
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Affiliation(s)
- Amanda L. Johnson
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - John Laterra
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: John Laterra, ; Hernando Lopez-Bertoni,
| | - Hernando Lopez-Bertoni
- Hugo W. Moser Research Institute at Kennedy Krieger, Baltimore, MD, United States
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: John Laterra, ; Hernando Lopez-Bertoni,
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24
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Zemaitis KJ, Veličković D, Kew W, Fort KL, Reinhardt-Szyba M, Pamreddy A, Ding Y, Kaushik D, Sharma K, Makarov AA, Zhou M, Paša-Tolić L. Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection. Anal Chem 2022; 94:12604-12613. [PMID: 36067026 PMCID: PMC10064997 DOI: 10.1021/acs.analchem.2c01034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.
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Affiliation(s)
- Kevin J Zemaitis
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - William Kew
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kyle L Fort
- Thermo Fisher Scientific (Bremen) GmbH, 28199 Bremen, Germany
| | | | - Annapurna Pamreddy
- Center for Renal Precision Medicine, Department of Medicine, University of Texas Health, San Antonio, Texas 78284, United States
| | - Yanli Ding
- Department of Pathology and Laboratory Medicine, University of Texas Health, San Antonio, Texas 78284, United States
| | - Dharam Kaushik
- Department of Urology, University of Texas Health, San Antonio, Texas 78284, United States
| | - Kumar Sharma
- Center for Renal Precision Medicine, Department of Medicine, University of Texas Health, San Antonio, Texas 78284, United States.,Audie L. Murphy Memorial VA Hospital, South Texas Veterans Health Care System, San Antonio, Texas 78284, United States
| | - Alexander A Makarov
- Thermo Fisher Scientific (Bremen) GmbH, 28199 Bremen, Germany.,Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht 3584, The Netherlands
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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25
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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: 13] [Impact Index Per Article: 6.5] [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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26
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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: 3] [Impact Index Per Article: 1.5] [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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27
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Andreou C, Weissleder R, Kircher MF. Multiplexed imaging in oncology. Nat Biomed Eng 2022; 6:527-540. [PMID: 35624151 DOI: 10.1038/s41551-022-00891-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 09/06/2021] [Indexed: 01/24/2023]
Abstract
In oncology, technologies for clinical molecular imaging are used to diagnose patients, establish the efficacy of treatments and monitor the recurrence of disease. Multiplexed methods increase the number of disease-specific biomarkers that can be detected simultaneously, such as the overexpression of oncogenic proteins, aberrant metabolite uptake and anomalous blood perfusion. The quantitative localization of each biomarker could considerably increase the specificity and the accuracy of technologies for clinical molecular imaging to facilitate granular diagnoses, patient stratification and earlier assessments of the responses to administered therapeutics. In this Review, we discuss established techniques for multiplexed imaging and the most promising emerging multiplexing technologies applied to the imaging of isolated tissues and cells and to non-invasive whole-body imaging. We also highlight advances in radiology that have been made possible by multiplexed imaging.
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Affiliation(s)
- Chrysafis Andreou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Center for Molecular Imaging and Nanotechnology (CMINT), Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
| | - Moritz F Kircher
- Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA.,Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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28
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Bartels B, Svatoš A. Influence of Ion Source Geometry on the Repeatability of Topographically Guided LAESI-MSI. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:265-272. [PMID: 35020389 PMCID: PMC8815068 DOI: 10.1021/jasms.1c00262] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/07/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Spatially resolving the relative distribution of analyte molecules in biological matter holds great promise in the life sciences. Mass spectrometry imaging (MSI) is a technique that can provide such spatial resolution but remains underused in fields such as chemical ecology, as traditional MSI sample preparation is often chemically or morphologically invasive. Laser ablation electrospray ionization (LAESI)-MSI is a variation of MSI particularly well-suited for situations where chemical sample preparation is too invasive but provides new challenges related to the repeatability of measurement outcomes. We assess the repeatability of LAESI-MSI by sampling a droplet of [ring-13C6]l-phenylalanine with known concentration and expressing the resulting variability as a coefficient of variation, cv. In doing so, we entirely eliminate variability caused by surface morphology or underlying true sample gradients. We determine the limit of detection (LOD) for13C6-Phe by sampling from droplets with successively decreasing but known concentration. We assess the influence of source geometry on the LOD and repeatability by performing these experiments using four distinct variations of sources: one commercial and three custom-built ones. Finally, we extend our study to leaf and stem samples Arabidopsis thaliana and Gossypium hirsutum. We overcome the challenges of LAESI associated with three-dimensional surface morphology by relying on work previously published. Our measurements on both controlled standard and realistic samples give strong evidence that LAESI-MSI's repeatability in current implementations is insufficient for MSI in chemical ecology.
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29
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Houdelet C, Arafah K, Bocquet M, Bulet P. Molecular histoproteomy by MALDI mass spectrometry imaging to uncover markers of the impact of Nosema on Apis mellifera. Proteomics 2022; 22:e2100224. [PMID: 34997678 DOI: 10.1002/pmic.202100224] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 12/29/2021] [Accepted: 01/03/2022] [Indexed: 12/12/2022]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful technology used to investigate the spatio-temporal distribution of a huge number of molecules throughout a body/tissue section. In this paper, we report the use of MALDI IMS to follow the molecular impact of an experimental infection of Apis mellifera with the microsporidia Nosema ceranae. We performed representative molecular mass fingerprints of selected tissues obtained by dissection. This was followed by MALDI IMS workflows optimization including specimen embedding and positioning as well as washing and matrix application. We recorded the local distribution of peptides/proteins within different tissues from experimentally infected versus non infected honeybees. As expected, a distinction in these molecular profiles between the two conditions was recorded from different anatomical sections of the gut tissue. More importantly, we observed differences in the molecular profiles in the brain, thoracic ganglia, hypopharyngeal glands, and hemolymph. We introduced MALDI IMS as an effective approach to monitor the impact of N. ceranae infection on A. mellifera. This opens perspectives for the discovery of molecular changes in peptides/proteins markers that could contribute to a better understanding of the impact of stressors and toxicity on different tissues of a bee in a single experiment.
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Affiliation(s)
- Camille Houdelet
- CR Université Grenoble Alpes, Institute for Advanced Biosciences, Inserm U1209, CNRS UMR 5309, Grenoble, France.,Saint Julien-en Genevois, Plateforme BioPark d'Archamps, France
| | - Karim Arafah
- Saint Julien-en Genevois, Plateforme BioPark d'Archamps, France
| | | | - Philippe Bulet
- CR Université Grenoble Alpes, Institute for Advanced Biosciences, Inserm U1209, CNRS UMR 5309, Grenoble, France.,Saint Julien-en Genevois, Plateforme BioPark d'Archamps, France
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30
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Engel KM, Prabutzki P, Leopold J, Nimptsch A, Lemmnitzer K, Vos DRN, Hopf C, Schiller J. A new update of MALDI-TOF mass spectrometry in lipid research. Prog Lipid Res 2022; 86:101145. [PMID: 34995672 DOI: 10.1016/j.plipres.2021.101145] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 12/06/2021] [Accepted: 12/29/2021] [Indexed: 01/06/2023]
Abstract
Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.
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Affiliation(s)
- Kathrin M Engel
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Patricia Prabutzki
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Jenny Leopold
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Ariane Nimptsch
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - Katharina Lemmnitzer
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany
| | - D R Naomi Vos
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Strasse 10, D-68163 Mannheim, Germany
| | - Carsten Hopf
- Center for Biomedical Mass Spectrometry and Optical Spectroscopy (CeMOS), Mannheim University of Applied Sciences, Paul-Wittsack-Strasse 10, D-68163 Mannheim, Germany
| | - Jürgen Schiller
- Leipzig University, Faculty of Medicine, Institute for Medical Physics and Biophysics, Härtelstraße 16-18, D-04107, Germany.
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31
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McCaughey C, Trebino M, Yildiz FH, Sanchez LM. Utilizing imaging mass spectrometry to analyze microbial biofilm chemical responses to exogenous compounds. Methods Enzymol 2022; 665:281-304. [PMID: 35379438 PMCID: PMC9022628 DOI: 10.1016/bs.mie.2021.11.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an appealing label-free method for imaging biological samples which focuses on the spatial distribution of chemical signals. This approach has been used to study the chemical ecology of microbes and can be applied to study the chemical responses of microbes to treatment with exogenous compounds. Specific conjugated cholic acids such as taurocholic acid (TCA), have been shown to inhibit biofilm formation in the enteric pathogen Vibrio cholerae and MALDI-IMS can be used to directly observe the chemical responses of V. cholerae biofilm colonies to treatment with TCA. A major challenge of MALDI-IMS is optimizing the sample preparation and drying for a particular growth condition and microbial strain. Here we demonstrate how V. cholerae is cultured and prepared for MALDI-IMS analysis and highlight critical steps to ensure proper sample adherence to a MALDI target plate and maintain spatial distributions when applying this technique to any microbial strain. We additionally show how to use both manual interrogation and statistical analyses of MALDI-IMS data to establish the adequacy of the sample preparation protocol. This protocol can serve as a guideline for the development of sample preparation techniques and the acquisition of high quality MALDI-IMS data.
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Affiliation(s)
- Catherine McCaughey
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA 95064
| | - Michael Trebino
- Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA 95064
| | - Fitnat H. Yildiz
- Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA 95064
| | - Laura M Sanchez
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, 1156 High St, Santa Cruz, CA 95064,Corresponding author, , phone: 831-459-4676
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32
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Kip AM, Valverde JM, Altelaar M, Heeren RMA, Hundscheid IHR, Dejong CHC, Olde Damink SWM, Balluff B, Lenaerts K. Combined Quantitative (Phospho)proteomics and Mass Spectrometry Imaging Reveal Temporal and Spatial Protein Changes in Human Intestinal Ischemia-Reperfusion. J Proteome Res 2021; 21:49-66. [PMID: 34874173 PMCID: PMC8750167 DOI: 10.1021/acs.jproteome.1c00447] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
![]()
Intestinal ischemia–reperfusion
(IR) injury is a severe
clinical condition, and unraveling its pathophysiology is crucial
to improve therapeutic strategies and reduce the high morbidity and
mortality rates. Here, we studied the dynamic proteome and phosphoproteome
in the human intestine during ischemia and reperfusion, using liquid
chromatography-tandem mass spectrometry (LC-MS/MS) analysis to gain
quantitative information of thousands of proteins and phosphorylation
sites, as well as mass spectrometry imaging (MSI) to obtain spatial
information. We identified a significant decrease in abundance of
proteins related to intestinal absorption, microvillus, and cell junction,
whereas proteins involved in innate immunity, in particular the complement
cascade, and extracellular matrix organization increased in abundance
after IR. Differentially phosphorylated proteins were involved in
RNA splicing events and cytoskeletal and cell junction organization.
In addition, our analysis points to mitogen-activated protein kinase
(MAPK) and cyclin-dependent kinase (CDK) families to be active kinases
during IR. Finally, matrix-assisted laser desorption ionization time-of-flight
(MALDI-TOF) MSI presented peptide alterations in abundance and distribution,
which resulted, in combination with Fourier-transform ion cyclotron
resonance (FTICR) MSI and LC-MS/MS, in the annotation of proteins
related to RNA splicing, the complement cascade, and extracellular
matrix organization. This study expanded our understanding of the
molecular changes that occur during IR in the human intestine and
highlights the value of the complementary use of different MS-based
methodologies.
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Affiliation(s)
- Anna M Kip
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Juan Manuel Valverde
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Maarten Altelaar
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands
| | - Ron M A Heeren
- Maastricht Multimodal Molecular Imaging Institute (M4i), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Inca H R Hundscheid
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
| | - Cornelis H C Dejong
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.,Department of General, Visceral- and Transplantation Surgery, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Steven W M Olde Damink
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.,Department of General, Visceral- and Transplantation Surgery, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074 Aachen, Germany
| | - Benjamin Balluff
- Maastricht Multimodal Molecular Imaging Institute (M4i), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Kaatje Lenaerts
- Department of Surgery, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
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33
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Ahmed R, Augustine R, Valera E, Ganguli A, Mesaeli N, Ahmad IS, Bashir R, Hasan A. Spatial mapping of cancer tissues by OMICS technologies. Biochim Biophys Acta Rev Cancer 2021; 1877:188663. [PMID: 34861353 DOI: 10.1016/j.bbcan.2021.188663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/15/2021] [Accepted: 11/26/2021] [Indexed: 12/14/2022]
Abstract
Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.
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Affiliation(s)
- Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar; Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA
| | - Robin Augustine
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar
| | - Enrique Valera
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA
| | - Anurup Ganguli
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA
| | - Nasrin Mesaeli
- Department of Biochemistry, Weill Cornell Medicine in Qatar, Qatar Foundation, Doha, Qatar
| | - Irfan S Ahmad
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA
| | - Rashid Bashir
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana Champaign, IL, USA; Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar; Biomedical Research Center (BRC), Qatar University, Doha 2713, Qatar.
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Lippold S, Thavarajah R, Reusch D, Wuhrer M, Nicolardi S. Glycoform analysis of intact erythropoietin by MALDI FT-ICR mass spectrometry. Anal Chim Acta 2021; 1185:339084. [PMID: 34711323 DOI: 10.1016/j.aca.2021.339084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/13/2021] [Accepted: 09/19/2021] [Indexed: 10/20/2022]
Abstract
Recombinant human erythropoietin (EPO) is a complex therapeutic glycoprotein with three N- and one O-glycosylation sites. Glycosylation of EPO influences its safety and efficacy and is defined as a critical quality attribute. Thus, analytical methods for profiling EPO glycosylation are highly demanded. Owing to the complexity of the intact protein, information about EPO glycosylation is commonly derived from released glycan and glycopeptide analysis using mass spectrometry (MS). Alternatively, comprehensive insights into the glycoform heterogeneity of intact EPO are obtained using ESI MS-based methods with or without upfront separation of EPO glycoforms. MALDI MS, typically performed with TOF mass analyzers, has been also used for the analysis of intact EPO but, due to the poor glycoform resolution, has only provided limited glycoform information. Here, we present a MALDI FT-ICR MS method for the glycosylation profiling of intact EPO with improved glycoform resolution and without loss of sialic acid residues commonly observed in MALDI analysis. Three EPO variants were characterized in-depth and up to 199 glycoform compositions were assigned from the evaluation of doubly-charged ions, without any deconvolution of the mass spectra. Key glycosylation features such as sialylation, acetylation, and N-acetyllactosamine repeats were determined and found to agree with previously reported data obtained from orthogonal analyses. The developed method allowed for a fast and straightforward data acquisition and evaluation and can be potentially used for the high-throughput comparison of EPO samples throughout its manufacturing process.
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Affiliation(s)
- Steffen Lippold
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Raashina Thavarajah
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Dietmar Reusch
- Pharma Technical Development Penzberg, Roche Diagnostics GmbH, Penzberg, Germany
| | - Manfred Wuhrer
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Simone Nicolardi
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands.
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Zhang J, Sans M, Garza KY, Eberlin LS. MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE. MASS SPECTROMETRY REVIEWS 2021; 40:692-720. [PMID: 33094861 DOI: 10.1002/mas.21664] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 09/09/2020] [Accepted: 09/09/2020] [Indexed: 06/11/2023]
Abstract
Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.
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Affiliation(s)
- Jialing Zhang
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Marta Sans
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Kyana Y Garza
- Department of Chemistry, University of Texas at Austin, Austin, TX
| | - Livia S Eberlin
- Department of Chemistry, University of Texas at Austin, Austin, TX
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Buerger M, Klein O, Kapahnke S, Mueller V, Frese JP, Omran S, Greiner A, Sommerfeld M, Kaschina E, Jannasch A, Dittfeld C, Mahlmann A, Hinterseher I. Use of MALDI Mass Spectrometry Imaging to Identify Proteomic Signatures in Aortic Aneurysms after Endovascular Repair. Biomedicines 2021; 9:biomedicines9091088. [PMID: 34572274 PMCID: PMC8465851 DOI: 10.3390/biomedicines9091088] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 08/15/2021] [Accepted: 08/24/2021] [Indexed: 11/16/2022] Open
Abstract
Endovascular repair (EVAR) has become the standard procedure in treating thoracic (TAA) or abdominal aortic aneurysms (AAA). Not entirely free of complications, a persisting perfusion of the aneurysm after EVAR, called Endoleak (EL), leads to reintervention and risk of secondary rupture. How the aortic wall responds to the implantation of a stentgraft and EL is mostly uncertain. We present a pilot study to identify peptide signatures and gain new insights in pathophysiological alterations of the aortic wall after EVAR using matrix-assisted laser desorption or ionization mass spectrometry imaging (MALDI-MSI). In course of or accompanying an open aortic repair, tissue sections from 15 patients (TAA = 5, AAA = 5, EVAR = 5) were collected. Regions of interest (tunica media and tunica adventitia) were defined and univariate (receiver operating characteristic analysis) statistical analysis for subgroup comparison was used. This proof-of-concept study demonstrates that MALDI-MSI is feasible to identify discriminatory peptide signatures separating TAA, AAA and EVAR. Decreased intensity distributions for actin, tropomyosin, and troponin after EVAR suggest impaired contractility in vascular smooth muscle cells. Furthermore, inability to provide energy caused by impaired respiratory chain function and continuous degradation of extracellular matrix components (collagen) might support aortic wall destabilization. In case of EL after EVAR, this mechanism may result in a weakened aortic wall with lacking ability to react on reinstating pulsatile blood flow.
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Affiliation(s)
- Matthias Buerger
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Oliver Klein
- BIH Center for Regenerative Therapies BCRT, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany;
| | - Sebastian Kapahnke
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Verena Mueller
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Jan Paul Frese
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Safwan Omran
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Andreas Greiner
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
| | - Manuela Sommerfeld
- Center for Cardiovascular Research (CCR), Institute of Pharmacology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hessische Str. 3-4, 10115 Berlin, Germany; (M.S.); (E.K.)
| | - Elena Kaschina
- Center for Cardiovascular Research (CCR), Institute of Pharmacology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hessische Str. 3-4, 10115 Berlin, Germany; (M.S.); (E.K.)
| | - Anett Jannasch
- Department of Cardiac Surgery, Herzzentrum Dresden, Medical Faculty Carl Gustav Carus Dresden, Technische Universität Dresden, 01307 Dresden, Germany; (A.J.); (C.D.)
| | - Claudia Dittfeld
- Department of Cardiac Surgery, Herzzentrum Dresden, Medical Faculty Carl Gustav Carus Dresden, Technische Universität Dresden, 01307 Dresden, Germany; (A.J.); (C.D.)
| | - Adrian Mahlmann
- University Center for Vascular Medicine, Department of Medicine—Section Angiology, University Hospital Carl Gustav Carus, Technische Universität, 01307 Dresden, Germany;
| | - Irene Hinterseher
- Berlin Institute of Health, Vascular Surgery Clinic, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; (M.B.); (S.K.); (V.M.); (J.P.F.); (S.O.); (A.G.)
- Medizinische Hochschule Brandenburg Theordor Fontane, 16816 Neuruppin, Germany
- Correspondence: ; Tel.: +49-30-450-522725
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Skaar EP. Imaging Infection Across Scales of Size: From Whole Animals to Single Molecules. Annu Rev Microbiol 2021; 75:407-426. [PMID: 34343016 DOI: 10.1146/annurev-micro-041521-121457] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Infectious diseases are a leading cause of global morbidity and mortality, and the threat of infectious diseases to human health is steadily increasing as new diseases emerge, existing diseases reemerge, and antimicrobial resistance expands. The application of imaging technology to the study of infection biology has the potential to uncover new factors that are critical to the outcome of host-pathogen interactions and to lead to innovations in diagnosis and treatment of infectious diseases. This article reviews current and future opportunities for the application of imaging to the study of infectious diseases, with a particular focus on the power of imaging objects across a broad range of sizes to expand the utility of these approaches. Expected final online publication date for the Annual Review of Microbiology, Volume 75 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Eric P Skaar
- Vanderbilt Institute for Infection, Immunology, and Inflammation, Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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Frisby TS, Gong Z, Langmead CJ. Asynchronous parallel Bayesian optimization for AI-driven cloud laboratories. Bioinformatics 2021; 37:i451-i459. [PMID: 34252975 PMCID: PMC8275326 DOI: 10.1093/bioinformatics/btab291] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/26/2021] [Indexed: 11/20/2022] Open
Abstract
Motivation The recent emergence of cloud laboratories—collections of automated wet-lab instruments that are accessed remotely, presents new opportunities to apply Artificial Intelligence and Machine Learning in scientific research. Among these is the challenge of automating the process of optimizing experimental protocols to maximize data quality. Results We introduce a new deterministic algorithm, called PaRallel OptimizaTiOn for ClOud Laboratories (PROTOCOL), that improves experimental protocols via asynchronous, parallel Bayesian optimization. The algorithm achieves exponential convergence with respect to simple regret. We demonstrate PROTOCOL in both simulated and real-world cloud labs. In the simulated lab, it outperforms alternative approaches to Bayesian optimization in terms of its ability to find optimal configurations, and the number of experiments required to find the optimum. In the real-world lab, the algorithm makes progress toward the optimal setting. Data availability and implementation PROTOCOL is available as both a stand-alone Python library, and as part of a R Shiny application at https://github.com/clangmead/PROTOCOL. Data are available at the same repository. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Trevor S Frisby
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Zhiyun Gong
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Christopher James Langmead
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Geier B, Oetjen J, Ruthensteiner B, Polikarpov M, Gruber-Vodicka HR, Liebeke M. Connecting structure and function from organisms to molecules in small-animal symbioses through chemo-histo-tomography. Proc Natl Acad Sci U S A 2021; 118:e2023773118. [PMID: 34183413 PMCID: PMC8300811 DOI: 10.1073/pnas.2023773118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their bodies is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. We addressed this challenge and developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines chemical imaging of metabolites based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (micro-CT) on the same animal. Both high-resolution MSI and micro-CT allowed us to correlate the distribution of metabolites to the same animal's three-dimensional (3D) histology down to submicrometer resolutions. Our protocol is compatible with tissue-specific DNA sequencing and fluorescence in situ hybridization for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and micro-CT, we present a methodological groundwork for connecting metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem functioning.
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Affiliation(s)
- Benedikt Geier
- Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany;
| | - Janina Oetjen
- MALDI Imaging Lab, University of Bremen, 28334 Bremen, Germany
| | | | - Maxim Polikarpov
- European Molecular Biology Laboratory, Hamburg Unit c/o Deutsches Elektronen-Synchrotron, 22607 Hamburg, Germany
| | | | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany;
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Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging. Cancers (Basel) 2021; 13:cancers13133184. [PMID: 34202325 PMCID: PMC8269054 DOI: 10.3390/cancers13133184] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The childhood tumor, neuroblastoma, has a broad clinical presentation. Risk assessment at diagnosis is particularly difficult in molecularly heterogeneous high-risk cases. Here we investigate the potential of imaging mass spectrometry to directly detect intratumor heterogeneity on the protein level in tissue sections. We show that this approach can produce discriminatory peptide signatures separating high- from low- and intermediate-risk tumors, identify 8 proteins aassociated with these signatures and validate two marker proteins using tissue immunostaining that have promise for further basic and translational research in neuroblastoma. We provide proof-of-concept that mass spectrometry-based technology could assist early risk assessment in neuroblastoma and provide insights into peptide signature-based detection of intratumor heterogeneity. Abstract Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.
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Tang W, Gordon A, Wang F, Chen Y, Li B. Hydralazine as a Versatile and Universal Matrix for High-Molecular Coverage and Dual-Polarity Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging. Anal Chem 2021; 93:9083-9093. [PMID: 34152727 DOI: 10.1021/acs.analchem.1c00498] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Few matrices have the potential to be universally applicable for imaging vast endogenous compounds ranging from micro to macromolecules. In this article, we present hydralazine (HZN) as a versatile and universal matrix for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) of a wide range of endogenous compounds between 50.0 and 20,000.0 Da. HZN was prepared from its hydrochloride by alkalizing HZN·HCl with ammonia to enhance the optical absorptivity at the preferred MALDI UV laser wavelength. To further improve its performance for MALDI MS, HZN was doped with NH4OH or TFA, resulting in matrix superior performance for imaging biologically relevant compounds in the negative and positive-ion modes, respectively. The analyte-matrix interaction was also enhanced by the optimized matrix solvent and the deposition amount. Compared with conventional matrices such as 2,5-dihydroxybenzoic acid, α-cyano-4-hydroxycinnamic acid, and 9-aminoacridine (9-AA), the HZN matrix provided higher sensitivity, broader molecular coverage, and improved signal intensities. Its broad acquisition range makes it versatile for imaging small molecular metabolites and lipids, as well as proteins. In addition, HZN was applied successfully for the visualization of tissue-specific distributions and changes of small molecules, lipids, and proteins in the kidney and liver sections of obese ob/ob and diabetic db/db mice. The use of the HZN matrix shows great potential application in the field of pathological research.
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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
| | - Andrew Gordon
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Fang Wang
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Yanwen Chen
- State Key Laboratory of Natural Medicines and School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, 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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Coughlan C, Bruce KD, Burgy O, Boyd TD, Michel CR, Garcia-Perez JE, Adame V, Anton P, Bettcher BM, Chial HJ, Königshoff M, Hsieh EWY, Graner M, Potter H. Exosome Isolation by Ultracentrifugation and Precipitation and Techniques for Downstream Analyses. ACTA ACUST UNITED AC 2021; 88:e110. [PMID: 32633898 DOI: 10.1002/cpcb.110] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Exosomes are 50- to 150-nm-diameter extracellular vesicles secreted by all mammalian cells except mature red blood cells and contribute to diverse physiological and pathological functions within the body. Many methods have been used to isolate and analyze exosomes, resulting in inconsistencies across experiments and raising questions about how to compare results obtained using different approaches. Questions have also been raised regarding the purity of the various preparations with regard to the sizes and types of vesicles and to the presence of lipoproteins. Thus, investigators often find it challenging to identify the optimal exosome isolation protocol for their experimental needs. Our laboratories have compared ultracentrifugation and commercial precipitation- and column-based exosome isolation kits for exosome preparation. Here, we present protocols for exosome isolation using two of the most commonly used methods, ultracentrifugation and precipitation, followed by downstream analyses. We use NanoSight nanoparticle tracking analysis and flow cytometry (Cytek® ) to determine exosome concentrations and sizes. Imaging flow cytometry can be utilized to both size exosomes and immunophenotype surface markers on exosomes (ImageStream® ). High-performance liquid chromatography followed by nano-flow liquid chromatography-mass spectrometry (LCMS) of the exosome fractions can be used to determine the presence of lipoproteins, with LCMS able to provide a proteomic profile of the exosome preparations. We found that the precipitation method was six times faster and resulted in a ∼2.5-fold higher concentration of exosomes per milliliter compared to ultracentrifugation. Both methods yielded extracellular vesicles in the size range of exosomes, and both preparations included apoproteins. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Pre-analytic fluid collection and processing Basic Protocol 2: Exosome isolation by ultracentrifugation Alternate Protocol 1: Exosome isolation by precipitation Basic Protocol 3: Analysis of exosomes by NanoSight nanoparticle tracking analysis Alternate Protocol 2: Analysis of exosomes by flow cytometry and imaging flow cytometry Basic Protocol 4: Downstream analysis of exosomes using high-performance liquid chromatography Basic Protocol 5: Downstream analysis of the exosome proteome using nano-flow liquid chromatography-mass spectrometry.
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Affiliation(s)
- Christina Coughlan
- University of Colorado Alzheimer's and Cognition Center, Department of Neurology, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Kimberley D Bruce
- Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Olivier Burgy
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus. INSERM U1231, Faculty of Medicine and Pharmacy, University of Bourgogne-Franche Comté, Dijon, France
| | - Timothy D Boyd
- University of Colorado Alzheimer's and Cognition Center, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Cole R Michel
- School of Pharmacy, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Josselyn E Garcia-Perez
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Vanesa Adame
- University of Colorado Alzheimer's and Cognition Center, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Paige Anton
- Department of Pharmaceutical Sciences, University of Colorado Alzheimer's and Cognition Center, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Brianne M Bettcher
- University of Colorado Alzheimer's and Cognition Center, Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Heidi J Chial
- University of Colorado Alzheimer's and Cognition Center, Department of Neurology, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Melanie Königshoff
- Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Elena W Y Hsieh
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado.,Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, Colorado
| | - Michael Graner
- Department of Pediatrics, Section of Allergy and Immunology, University of Colorado School of Medicine, Children's Hospital Colorado, Aurora, Colorado
| | - Huntington Potter
- University of Colorado Alzheimer's and Cognition Center, Department of Neurology, Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, Colorado
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Angel PM, Rujchanarong D, Pippin S, Spruill L, Drake R. Mass Spectrometry Imaging of Fibroblasts: Promise and Challenge. Expert Rev Proteomics 2021; 18:423-436. [PMID: 34129411 PMCID: PMC8717608 DOI: 10.1080/14789450.2021.1941893] [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: 03/18/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Fibroblasts maintain tissue and organ homeostasis through output of extracellular matrix that affects nearby cell signaling within the stroma. Altered fibroblast signaling contributes to many disease states and extracellular matrix secreted by fibroblasts has been used to stratify patient by outcome, recurrence, and therapeutic resistance. Recent advances in imaging mass spectrometry allow access to single cell fibroblasts and their ECM niche within clinically relevant tissue samples. AREAS COVERED We review biological and technical challenges as well as new solutions to proteomic access of fibroblast expression within the complex tissue microenvironment. Review topics cover conventional proteomic methods for single fibroblast analysis and current approaches to accessing single fibroblast proteomes by imaging mass spectrometry approaches. Strategies to target and evaluate the single cell stroma proteome on the basis of cell signaling are presented. EXPERT OPINION The promise of defining proteomic signatures from fibroblasts and their extracellular matrix niches is the discovery of new disease markers and the ability to refine therapeutic treatments. Several imaging mass spectrometry approaches exist to define the fibroblast in the setting of pathological changes from clinically acquired samples. Continued technology advances are needed to access and understand the stromal proteome and apply testing to the clinic.
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Affiliation(s)
- Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Denys Rujchanarong
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Sarah Pippin
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
| | - Laura Spruill
- Department of Pathology and Laboratory Medicine, Medical University of South Carolina, Charleston, SC
| | - Richard Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston SC USA
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Guo G, Papanicolaou M, Demarais NJ, Wang Z, Schey KL, Timpson P, Cox TR, Grey AC. Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP. Nat Commun 2021; 12:3241. [PMID: 34050164 PMCID: PMC8163805 DOI: 10.1038/s41467-021-23461-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 04/29/2021] [Indexed: 12/12/2022] Open
Abstract
Spatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts.
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Affiliation(s)
- G Guo
- Mass Spectrometry Hub, University of Auckland, Auckland, New Zealand
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - M Papanicolaou
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, UNSW Sydney, Sydney, NSW, Australia
- School of Life Sciences, University of Technology Sydney, Sydney, NSW, Australia
| | - N J Demarais
- Mass Spectrometry Hub, University of Auckland, Auckland, New Zealand
- University of Auckland, School of Biological Sciences, Auckland, New Zealand
| | - Z Wang
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
| | - K L Schey
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
| | - P Timpson
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, UNSW Sydney, Sydney, NSW, Australia
- St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - T R Cox
- The Garvan Institute of Medical Research and The Kinghorn Cancer Centre, UNSW Sydney, Sydney, NSW, Australia.
- St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia.
| | - A C Grey
- Mass Spectrometry Hub, University of Auckland, Auckland, New Zealand.
- School of Biological Sciences, University of Auckland, Auckland, New Zealand.
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45
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Dai G, Feinberg AW, Wan LQ. Recent Advances in Cellular and Molecular Bioengineering for Building and Translation of Biological Systems. Cell Mol Bioeng 2021; 14:293-308. [PMID: 34055096 PMCID: PMC8147909 DOI: 10.1007/s12195-021-00676-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 05/07/2021] [Indexed: 11/24/2022] Open
Abstract
In January of 2020, the Biomedical Engineering Society (BMES)- Cellular and Molecular Bioengineering (CMBE) conference was held in Puerto Rico and themed “Vision 2020: Emerging Technologies to Elucidate the Rule of Life.” The annual BME-CMBE conference gathered worldwide leaders and discussed successes and challenges in engineering biological systems and their translation. The goal of this report is to present the research frontiers in this field and provide perspectives on successful engineering and translation towards the clinic. We hope that this report serves as a constructive guide in shaping the future of research and translation of engineered biological systems.
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Affiliation(s)
- Guohao Dai
- Department of Bioengineering, Northeastern University, 805 Columbus Ave, ISEC 224, Boston, MA 02115 USA
| | - Adam W Feinberg
- Departments of Biomedical Engineering & Materials Science & Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 USA
| | - Leo Q Wan
- Departments of Biomedical Engineering & Biological Sciences, Rensselaer Polytechnic Institute, Biotech 2147, 110 8th Street, Troy, NY 12180 USA
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46
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Zago G, Saavedra PHV, Keshari KR, Perry JSA. Immunometabolism of Tissue-Resident Macrophages - An Appraisal of the Current Knowledge and Cutting-Edge Methods and Technologies. Front Immunol 2021; 12:665782. [PMID: 34025667 PMCID: PMC8138590 DOI: 10.3389/fimmu.2021.665782] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 04/07/2021] [Indexed: 12/23/2022] Open
Abstract
Tissue-resident macrophages exist in unique environments, or niches, that inform their identity and function. There is an emerging body of literature suggesting that the qualities of this environment, such as the types of cells and debris they eat, the intercellular interactions they form, and the length of time spent in residence, collectively what we call habitare, directly inform their metabolic state. In turn, a tissue-resident macrophage’s metabolic state can inform their function, including whether they resolve inflammation and protect the host from excessive perturbations of homeostasis. In this review, we summarize recent work that seeks to understand the metabolic requirements for tissue-resident macrophage identity and maintenance, for how they respond to inflammatory challenges, and for how they perform homeostatic functions or resolve inflammatory insults. We end with a discussion of the emerging technologies that are enabling, or will enable, in situ study of tissue-resident macrophage metabolism.
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Affiliation(s)
- Giulia Zago
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Pedro H V Saavedra
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Kayvan R Keshari
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Justin S A Perry
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Department of Immunology and Microbial Pathogenesis, Weill Cornell Medical College, New York, NY, United States
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47
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Taylor M, Lukowski JK, Anderton CR. Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:872-894. [PMID: 33656885 PMCID: PMC8033567 DOI: 10.1021/jasms.0c00439] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 05/02/2023]
Abstract
Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.
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Affiliation(s)
- Michael
J. Taylor
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jessica K. Lukowski
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Christopher R. Anderton
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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48
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Guiberson ER, Weiss A, Ryan DJ, Monteith AJ, Sharman K, Gutierrez DB, Perry WJ, Caprioli RM, Skaar EP, Spraggins JM. Spatially Targeted Proteomics of the Host-Pathogen Interface during Staphylococcal Abscess Formation. ACS Infect Dis 2021; 7:101-113. [PMID: 33270421 DOI: 10.1021/acsinfecdis.0c00647] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Staphylococcus aureus is a common cause of invasive and life-threatening infections that are often multidrug resistant. To develop novel treatment approaches, a detailed understanding of the complex host-pathogen interactions during infection is essential. This is particularly true for the molecular processes that govern the formation of tissue abscesses, as these heterogeneous structures are important contributors to staphylococcal pathogenicity. To fully characterize the developmental process leading to mature abscesses, temporal and spatial analytical approaches are required. Spatially targeted proteomic technologies such as micro-liquid extraction surface analysis offer insight into complex biological systems including detection of bacterial proteins and their abundance in the host environment. By analyzing the proteomic constituents of different abscess regions across the course of infection, we defined the immune response and bacterial contribution to abscess development through spatial and temporal proteomic assessment. The information gathered was mapped to biochemical pathways to characterize the metabolic processes and immune strategies employed by the host. These data provide insights into the physiological state of bacteria within abscesses and elucidate pathogenic processes at the host-pathogen interface.
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Affiliation(s)
- Emma R. Guiberson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Andy Weiss
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, United States
| | - Daniel J. Ryan
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Andrew J. Monteith
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, United States
| | - Kavya Sharman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Danielle B. Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - William J. Perry
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Richard M. Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37203, United States
| | - Eric P. Skaar
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee 37203, United States
| | - Jeffrey M. Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37203, United States
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49
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Rivera ES, Djambazova KV, Neumann EK, Caprioli RM, Spraggins JM. Integrating ion mobility and imaging mass spectrometry for comprehensive analysis of biological tissues: A brief review and perspective. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4614. [PMID: 32955134 PMCID: PMC8211109 DOI: 10.1002/jms.4614] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 06/27/2020] [Accepted: 07/02/2020] [Indexed: 05/02/2023]
Abstract
Imaging mass spectrometry (IMS) technologies are capable of mapping a wide array of biomolecules in diverse cellular and tissue environments. IMS has emerged as an essential tool for providing spatially targeted molecular information due to its high sensitivity, wide molecular coverage, and chemical specificity. One of the major challenges for mapping the complex cellular milieu is the presence of many isomers and isobars in these samples. This challenge is traditionally addressed using orthogonal liquid chromatography (LC)-based analysis, though, common approaches such as chromatography and electrophoresis are not able to be performed at timescales that are compatible with most imaging applications. Ion mobility offers rapid, gas-phase separations that are readily integrated with IMS workflows in order to provide additional data dimensionality that can improve signal-to-noise, dynamic range, and specificity. Here, we highlight recent examples of ion mobility coupled to IMS and highlight their importance to the field.
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Key Words
- IMS
- desorption electrospray ionization, DESI
- drift tube ion mobility spectrometry, DTIMS
- high-field asymmetric waveform ion mobility, FAIMS
- imaging mass spectrometry
- infrared matrix-assisted laser desorption electrospray ionization, IR-MALDESI
- ion mobility
- laser ablation electrospray ionization, LAESI
- lipids
- liquid extraction surface analysis, LESA
- liquid microjunction, (LMJ)
- matrix-assisted laser desorption electrospray ionization, MALDI
- metabolites
- proteins
- tissue analysis
- trapped ion mobility spectrometry, TIMS
- travelling wave ion mobility spectrometry, TWIMS
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Affiliation(s)
- Emilio S. Rivera
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA
- Mass Spectrometry Research Center, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
| | - Katerina V. Djambazova
- Mass Spectrometry Research Center, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN 37235, USA
| | - Elizabeth K. Neumann
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA
- Mass Spectrometry Research Center, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
| | - Richard M. Caprioli
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA
- Mass Spectrometry Research Center, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN 37235, USA
- Department of Pharmacology, Vanderbilt University, 2220 Pierce Avenue, Nashville, TN 37232, USA
- Department of Medicine, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
| | - Jeffrey M. Spraggins
- Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA
- Mass Spectrometry Research Center, Vanderbilt University, 465 21 Ave S #9160, Nashville, TN 37235, USA
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN 37235, USA
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50
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McMillen JC, Fincher JA, Klein DR, Spraggins JM, Caprioli RM. Effect of MALDI matrices on lipid analyses of biological tissues using MALDI-2 postionization mass spectrometry. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4663. [PMID: 33241625 DOI: 10.26434/chemrxiv.12494705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/02/2020] [Accepted: 08/14/2020] [Indexed: 05/18/2023]
Abstract
Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) allows for highly multiplexed, untargeted detection of many hundreds of analytes from tissue. Recently, laser postionization (MALDI-2) has been developed for increased ion yield and sensitivity for lipid IMS. However, the dependence of MALDI-2 performance on the various lipid classes is largely unknown. To understand the effect of the applied matrix on MALDI-2 analysis of lipids, samples including an equimolar lipid standard mixture, various tissue homogenates, and intact rat kidney tissue sections were analyzed using the following matrices: α-cyano-4-hydroxycinnamic acid, 2',5'-dihydroxyacetophenone, 2',5'-dihydroxybenzoic acid (DHB), and norharmane (NOR). Lipid signal enhancement of protonated species using MALDI-2 technology varied based on the matrix used. Although signal improvements were observed for all matrices, the most dramatic effects using MALDI-2 were observed using NOR and DHB. For lipid standards analyzed by MALDI-2, NOR provided the broadest coverage, enabling the detection of all 13 protonated standards, including nonpolar lipids, whereas DHB gave less coverage but gave the highest signal increase for those lipids recorded. With respect to tissue homogenates and rat kidney tissue, mass spectra were compared and showed that the number and intensity of neutral lipids tentatively identified with MALDI-2 using NOR increased significantly (e.g., fivefold intensity increase for triacylglycerol). In the cases of DHB with MALDI-2, the number of protonated lipids identified from tissue homogenates doubled with 152 on average compared with 76 with MALDI alone. High spatial resolution imaging (~20 μm) of rat kidney tissue showed similar results using DHB with 125 lipids tentatively identified from MALDI-2 spectra versus just 72 using standard MALDI. From the four matrices tested, NOR provided the greatest increase in sensitivity for neutral lipids (triacylglycerol, diacylglycerol, monoacylglycerol, and cholesterol ester), and DHB provided the highest overall number of lipids detected using MALDI-2 technology.
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Affiliation(s)
- Josiah C McMillen
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, 37232, USA
| | - Jarod A Fincher
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Departments of Biochemistry, Pharmacology and Medicine, Vanderbilt University and Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
| | - Dustin R Klein
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Departments of Biochemistry, Pharmacology and Medicine, Vanderbilt University and Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Departments of Biochemistry, Pharmacology and Medicine, Vanderbilt University and Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
| | - Richard M Caprioli
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, 37232, USA
- Departments of Biochemistry, Pharmacology and Medicine, Vanderbilt University and Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
- Department of Pharmacology, Vanderbilt University
- Department of Medicine, Vanderbilt University, Nashville, Tennessee, 37232, USA
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