1
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Khadem H, Mangini M, Farazpour S, De Luca AC. Correlative Raman Imaging: Development and Cancer Applications. BIOSENSORS 2024; 14:324. [PMID: 39056600 PMCID: PMC11274409 DOI: 10.3390/bios14070324] [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: 05/24/2024] [Revised: 06/26/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Despite extensive research efforts, cancer continues to stand as one of the leading causes of death on a global scale. To gain profound insights into the intricate mechanisms underlying cancer onset and progression, it is imperative to possess methodologies that allow the study of cancer cells at the single-cell level, focusing on critical parameters such as cell morphology, metabolism, and molecular characteristics. These insights are essential for effectively discerning between healthy and cancerous cells and comprehending tumoral progression. Recent advancements in microscopy techniques have significantly advanced the study of cancer cells, with Raman microspectroscopy (RM) emerging as a particularly powerful tool. Indeed, RM can provide both biochemical and spatial details at the single-cell level without the need for labels or causing disruptions to cell integrity. Moreover, RM can be correlated with other microscopy techniques, creating a synergy that offers a spectrum of complementary insights into cancer cell morphology and biology. This review aims to explore the correlation between RM and other microscopy techniques such as confocal fluoresce microscopy (CFM), atomic force microscopy (AFM), digital holography microscopy (DHM), and mass spectrometry imaging (MSI). Each of these techniques has their own strengths, providing different perspectives and parameters about cancer cell features. The correlation between information from these various analysis methods is a valuable tool for physicians and researchers, aiding in the comprehension of cancer cell morphology and biology, unraveling mechanisms underlying cancer progression, and facilitating the development of early diagnosis and/or monitoring cancer progression.
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Affiliation(s)
- Hossein Khadem
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Maria Mangini
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Somayeh Farazpour
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
| | - Anna Chiara De Luca
- Institute for Experimental Endocrinology and Oncology 'G. Salvatore', IEOS-Second Unit, National Research Council, 80131 Naples, Italy
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2
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Yang E, Kim JH, Tressler CM, Shen XE, Brown DR, Johnson CC, Hahm TH, Barman I, Glunde K. RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section. Biosens Bioelectron 2023; 239:115597. [PMID: 37597501 PMCID: PMC10544780 DOI: 10.1016/j.bios.2023.115597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/02/2023] [Accepted: 08/11/2023] [Indexed: 08/21/2023]
Abstract
Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.
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Affiliation(s)
- Ethan Yang
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Jeong Hee Kim
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Caitlin M Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Xinyi Elaine Shen
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Dalton R Brown
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Cole C Johnson
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Tae-Hun Hahm
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Ishan Barman
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA; Departments of Oncology and Biological Chemistry, The Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
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3
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Holtkamp HU, Aguergaray C, Prangnell K, Pook C, Amirapu S, Grey A, Simpson C, Nieuwoudt M, Jarrett P. Raman spectroscopy and mass spectrometry identifies a unique group of epidermal lipids in active discoid lupus erythematosus. Sci Rep 2023; 13:16452. [PMID: 37777584 PMCID: PMC10542761 DOI: 10.1038/s41598-023-43331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/22/2023] [Indexed: 10/02/2023] Open
Abstract
Discoid lupus erythematosus (DLE) is the most common form of cutaneous lupus1. It can cause permanent scarring. The pathophysiology of is not fully understood. Plasmacytoid dendritic cells are found in close association with apoptotic keratinocytes inferring close cellular signalling. Matrix Associated Laser Desorption Ionisation (MALDI) combined with Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) is an exquisitely sensitive combination to examine disease processes at the cellular and molecular level. Active areas of discoid lupus erythematosus were compared with normal perilesional skin using MALDI combined with FT-ICR-MS. A unique set of biomarkers, including epidermal lipids is identified in active discoid lupus. These were assigned as sphingomyelins, phospholipids and ceramides. Additionally, increased levels of proteins from the keratin, and small proline rich family, and aromatic amino acids (tryptophan, phenylalanine, and tyrosine) in the epidermis are observed. These techniques, applied to punch biopsies of the skin, have shown a distinctive lipid profile of active discoid lupus. This profile may indicate specific lipid signalling pathways. Lipid rich microdomains (known as lipid rafts) are involved in cell signalling and lipid abnormalities have been described with systemic lupus erythematosus which correlate with disease activity.
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Affiliation(s)
- Hannah U Holtkamp
- The Photon Factory, The University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
| | - Claude Aguergaray
- The Photon Factory, The University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- Department of Physics, The University of Auckland, Auckland, New Zealand
| | - Kalita Prangnell
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Christopher Pook
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Satya Amirapu
- Department of Anatomy and Medical Imaging, The University of Auckland, Auckland, New Zealand
| | - Angus Grey
- Department of Physiology, The University of Auckland, Auckland, New Zealand
| | - Cather Simpson
- The Photon Factory, The University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- Department of Physics, The University of Auckland, Auckland, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
| | - Michel Nieuwoudt
- The Photon Factory, The University of Auckland, Auckland, New Zealand
- School of Chemical Sciences, The University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
| | - Paul Jarrett
- Department of Dermatology, Middlemore Hospital, Auckland, New Zealand.
- Department of Medicine, The University of Auckland, Auckland, New Zealand.
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4
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Woodhouse N, Majer J, Marshall P, Hood S, Notingher I. Quantification of Drugs in Brain and Liver Mimetic Tissue Models Using Raman Spectroscopy. APPLIED SPECTROSCOPY 2023; 77:246-260. [PMID: 36320126 PMCID: PMC10034474 DOI: 10.1177/00037028221139494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Quantitative analysis of drug delivery with in biological systems is an integral challenge in drug development. Analytical techniques are important for assessing both drug target delivery, target action, and drug toxicology. Using mimetic tissue models, we have investigated the efficacy of Raman spectroscopy in quantitative detection of alkyne group and deuterated drugs in rat brain and rat liver tissue models. Lasers with 671 nm and 785 nm wavelengths were assessed for their feasibility in this application due to opposing relative benefits and disadvantages. Thin tissue sections have been tested as a practical means of reducing autofluorescent background by minimizing out-of-focus tissue and therefore maximizing photobleaching rates. Alkyne-tagged drugs were quantitatively measured at 18 ± 5 μg/g drug/tissue mass ratio in rat brain and at 34 ± 6 μg/g in rat liver. Quantification calibration curves were generated for a range of concentrations from 0-500 μg/g. These results show the potential of Raman spectroscopy as a diffraction-limited spatially resolved imaging technique for assessing drug delivery in tissue applications.
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Affiliation(s)
- Nathan Woodhouse
- School of Physics and Astronomy,
University
of Nottingham, Nottingham, UK
| | | | | | | | - Ioan Notingher
- School of Physics and Astronomy,
University
of Nottingham, Nottingham, UK
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5
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Azam KSF, Ryabchykov O, Bocklitz T. A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules 2022; 27:7448. [PMID: 36364272 PMCID: PMC9655963 DOI: 10.3390/molecules27217448] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/19/2022] [Accepted: 10/21/2022] [Indexed: 08/05/2024] Open
Abstract
Data fusion aims to provide a more accurate description of a sample than any one source of data alone. At the same time, data fusion minimizes the uncertainty of the results by combining data from multiple sources. Both aim to improve the characterization of samples and might improve clinical diagnosis and prognosis. In this paper, we present an overview of the advances achieved over the last decades in data fusion approaches in the context of the medical and biomedical fields. We collected approaches for interpreting multiple sources of data in different combinations: image to image, image to biomarker, spectra to image, spectra to spectra, spectra to biomarker, and others. We found that the most prevalent combination is the image-to-image fusion and that most data fusion approaches were applied together with deep learning or machine learning methods.
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Affiliation(s)
- Kazi Sultana Farhana Azam
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology, Member of Leibniz-Research Alliance “Health Technologies”, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
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6
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Mittal S, Kim J, Bhargava R. Statistical Considerations and Tools to Improve Histopathologic Protocols with Spectroscopic Imaging. APPLIED SPECTROSCOPY 2022; 76:428-438. [PMID: 35296146 PMCID: PMC9202564 DOI: 10.1177/00037028211066327] [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] [Indexed: 06/14/2023]
Abstract
Advances in infrared (IR) spectroscopic imaging instrumentation and data science now present unique opportunities for large validation studies of the concept of histopathology using spectral data. In this study, we examine the discrimination potential of IR metrics for different histologic classes to estimate the sample size needed for designing validation studies to achieve a given statistical power and statistical significance. Next, we present an automated annotation transfer tool that can allow large-scale training/validation, overcoming the limitations of sparse ground truth data with current manual approaches by providing a tool to transfer pathologist annotations from stained images to IR images across diagnostic categories. Finally, the results of a combination of supervised and unsupervised analysis provide a scheme to identify diagnostic groups/patterns and isolating pure chemical pixels for each class to better train complex histopathological models. Together, these methods provide essential tools to take advantage of the emerging capabilities to record and utilize large spectroscopic imaging datasets.
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Affiliation(s)
- Shachi Mittal
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Chemical Engineering, University of Washington, Seattle, WA, USA
| | - Jonathan Kim
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Rohit Bhargava
- Department of Bioengineering and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Departments of Mechanical Science and Engineering, Electrical and Computer Engineering, Chemical and Biomolecular Engineering, and Chemistry, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Cancer Center at Illinois, University of Illinois at Urbana–Champaign, Urbana, IL, USA
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7
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Jiang H, Zhang Y, Liu Z, Wang X, He J, Jin H. Advanced applications of mass spectrometry imaging technology in quality control and safety assessments of traditional Chinese medicines. JOURNAL OF ETHNOPHARMACOLOGY 2022; 284:114760. [PMID: 34678417 PMCID: PMC9715987 DOI: 10.1016/j.jep.2021.114760] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/05/2021] [Accepted: 10/18/2021] [Indexed: 05/13/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Traditional Chinese medicines (TCMs) have made great contributions to the prevention and treatment of human diseases in China, and especially in cases of COVID-19. However, due to quality problems, the lack of standards, and the diversity of dosage forms, adverse reactions to TCMs often occur. Moreover, the composition of TCMs makes them extremely challenging to extract and isolate, complicating studies of toxicity mechanisms. AIM OF THE REVIEW The aim of this paper is therefore to summarize the advanced applications of mass spectrometry imaging (MSI) technology in the quality control, safety evaluations, and determination of toxicity mechanisms of TCMs. MATERIALS AND METHODS Relevant studies from the literature have been collected from scientific databases, such as "PubMed", "Scifinder", "Elsevier", "Google Scholar" using the keywords "MSI", "traditional Chinese medicines", "quality control", "metabolomics", and "mechanism". RESULTS MSI is a new analytical imaging technology that can detect and image the metabolic changes of multiple components of TCMs in plants and animals in a high throughput manner. Compared to other chemical analysis methods, such as liquid chromatography-mass spectrometry (LC-MS), this method does not require the complex extraction and separation of TCMs, and is fast, has high sensitivity, is label-free, and can be performed in high-throughput. Combined with chemometrics methods, MSI can be quickly and easily used for quality screening of TCMs. In addition, this technology can be used to further focus on potential biomarkers and explore the therapeutic/toxic mechanisms of TCMs. CONCLUSIONS As a new type of analysis method, MSI has unique advantages to metabolic analysis, quality control, and mechanisms of action explorations of TCMs, and contributes to the establishment of quality standards to explore the safety and toxicology of TCMs.
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Affiliation(s)
- Haiyan Jiang
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Yaxin Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Zhigang Liu
- School of Biological Science and Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing 100050, China.
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; Beijing Union-Genius Pharmaceutical Technology Development Co., Ltd., Beijing 100176, China; NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing 100050, China.
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8
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Iakab SA, Baquer G, Lafuente M, Pina MP, Ramírez JL, Ràfols P, Correig-Blanchar X, García-Altares M. SALDI-MS and SERS Multimodal Imaging: One Nanostructured Substrate to Rule Them Both. Anal Chem 2022; 94:2785-2793. [PMID: 35102738 PMCID: PMC8851428 DOI: 10.1021/acs.analchem.1c04118] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Imaging techniques
based on mass spectrometry or spectroscopy methods
inform in situ about the chemical composition of
biological tissues or organisms, but they are sometimes limited by
their specificity, sensitivity, or spatial resolution. Multimodal
imaging addresses these limitations by combining several imaging modalities;
however, measuring the same sample with the same preparation using
multiple imaging techniques is still uncommon due to the incompatibility
between substrates, sample preparation protocols, and data formats.
We present a multimodal imaging approach that employs a gold-coated
nanostructured silicon substrate to couple surface-assisted laser
desorption/ionization mass spectrometry (SALDI-MS) and surface-enhanced
Raman spectroscopy (SERS). Our approach integrates both imaging modalities
by using the same substrate, sample preparation, and data analysis
software on the same sample, allowing the coregistration of both images.
We transferred molecules from clean fingertips and fingertips covered
with plasticine modeling clay onto our nanostructure and analyzed
their chemical composition and distribution by SALDI-MS and SERS.
Multimodal analysis located the traces of plasticine on fingermarks
and provided chemical information on the composition of the clay.
Our multimodal approach effectively combines the advantages of mass
spectrometry and vibrational spectroscopy with the signal enhancing
abilities of our nanostructured substrate.
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Affiliation(s)
- Stefania-Alexandra Iakab
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid 28029, Spain
| | - Gerard Baquer
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain
| | - Marta Lafuente
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza 50009, Spain.,Departamento de Ingeniería Química y Tecnologías del Medio Ambiente, Universidad de Zaragoza, Campus Río Ebro-Edificio I+D+i, C/Mariano Esquillor s/n, Zaragoza 50018, Spain
| | - Maria Pilar Pina
- Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza 50009, Spain.,Departamento de Ingeniería Química y Tecnologías del Medio Ambiente, Universidad de Zaragoza, Campus Río Ebro-Edificio I+D+i, C/Mariano Esquillor s/n, Zaragoza 50018, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, CIBER-BBN, Madrid 28029, Spain
| | - José Luis Ramírez
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid 28029, Spain
| | - Xavier Correig-Blanchar
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid 28029, Spain.,Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus 43204, Spain
| | - María García-Altares
- Department of Electronic Engineering, Rovira i Virgili University, Tarragona 43007, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid 28029, Spain
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Iakab S, Ràfols P, Correig-Blanchar X, García-Altares M. Perspective on Multimodal Imaging Techniques Coupling Mass Spectrometry and Vibrational Spectroscopy: Picturing the Best of Both Worlds. Anal Chem 2021; 93:6301-6310. [PMID: 33856207 PMCID: PMC8491157 DOI: 10.1021/acs.analchem.0c04986] [Citation(s) in RCA: 15] [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: 11/27/2020] [Accepted: 04/07/2021] [Indexed: 01/19/2023]
Abstract
Studies on complex biological phenomena often combine two or more imaging techniques to collect high-quality comprehensive data directly in situ, preserving the biological context. Mass spectrometry imaging (MSI) and vibrational spectroscopy imaging (VSI) complement each other in terms of spatial resolution and molecular information. In the past decade, several combinations of such multimodal strategies arose in research fields as diverse as microbiology, cancer, and forensics, overcoming many challenges toward the unification of these techniques. Here we focus on presenting the advantages and challenges of multimodal imaging from the point of view of studying biological samples as well as giving a perspective on the upcoming trends regarding this topic. The latest efforts in the field are discussed, highlighting the purpose of the technique for clinical applications.
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Affiliation(s)
- Stefania
Alexandra Iakab
- Rovira
i Virgili University, Department of Electronic
Engineering, IISPV, 43007 Tarragona, Spain
- Spanish
Biomedical Research Centre in Diabetes and Associated Metabolic Disorders
(CIBERDEM), 28029 Madrid, Spain
| | - Pere Ràfols
- Rovira
i Virgili University, Department of Electronic
Engineering, IISPV, 43007 Tarragona, Spain
- Spanish
Biomedical Research Centre in Diabetes and Associated Metabolic Disorders
(CIBERDEM), 28029 Madrid, Spain
| | - Xavier Correig-Blanchar
- Rovira
i Virgili University, Department of Electronic
Engineering, IISPV, 43007 Tarragona, Spain
- Spanish
Biomedical Research Centre in Diabetes and Associated Metabolic Disorders
(CIBERDEM), 28029 Madrid, Spain
| | - María García-Altares
- Rovira
i Virgili University, Department of Electronic
Engineering, IISPV, 43007 Tarragona, Spain
- Spanish
Biomedical Research Centre in Diabetes and Associated Metabolic Disorders
(CIBERDEM), 28029 Madrid, Spain
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10
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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11
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Introduction to Infrared and Raman-Based Biomedical Molecular Imaging and Comparison with Other Modalities. Molecules 2020; 25:molecules25235547. [PMID: 33256052 PMCID: PMC7731440 DOI: 10.3390/molecules25235547] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 01/18/2023] Open
Abstract
Molecular imaging has rapidly developed to answer the need of image contrast in medical diagnostic imaging to go beyond morphological information to include functional differences in imaged tissues at the cellular and molecular levels. Vibrational (infrared (IR) and Raman) imaging has rapidly emerged among the molecular imaging modalities available, due to its label-free combination of high spatial resolution with chemical specificity. This article presents the physical basis of vibrational spectroscopy and imaging, followed by illustration of their preclinical in vitro applications in body fluids and cells, ex vivo tissues and in vivo small animals and ending with a brief discussion of their clinical translation. After comparing the advantages and disadvantages of IR/Raman imaging with the other main modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography/single-photon emission-computed tomography (PET/SPECT), ultrasound (US) and photoacoustic imaging (PAI), the design of multimodal probes combining vibrational imaging with other modalities is discussed, illustrated by some preclinical proof-of-concept examples.
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12
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Iakab SA, Sementé L, García-Altares M, Correig X, Ràfols P. Raman2imzML converts Raman imaging data into the standard mass spectrometry imaging format. BMC Bioinformatics 2020; 21:448. [PMID: 33036551 PMCID: PMC7547406 DOI: 10.1186/s12859-020-03789-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/29/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Multimodal imaging that combines mass spectrometry imaging (MSI) with Raman imaging is a rapidly developing multidisciplinary analytical method used by a growing number of research groups. Computational tools that can visualize and aid the analysis of datasets by both techniques are in demand. RESULTS Raman2imzML was developed as an open-source converter that transforms Raman imaging data into imzML, a standardized common data format created and adopted by the mass spectrometry community. We successfully converted Raman datasets to imzML and visualized Raman images using open-source software designed for MSI applications. CONCLUSION Raman2imzML enables both MSI and Raman images to be visualized using the same file format and the same software for a straightforward exploratory imaging analysis.
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Affiliation(s)
- Stefania Alexandra Iakab
- Department of Electronic Engineering, Rovira i Virgili University, 43007, Tarragona, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, Rovira i Virgili University, 43007, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, Rovira i Virgili University, 43007, Tarragona, Spain. .,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain.
| | - Xavier Correig
- Department of Electronic Engineering, Rovira i Virgili University, 43007, Tarragona, Spain.,Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain.,Institut d'Investigació Sanitària Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, Rovira i Virgili University, 43007, Tarragona, Spain
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13
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Abstract
Abstract
A potential role of optical technologies in medicine including micro-Raman spectroscopy is diagnosis of bacteria, cells and tissues which is covered in this chapter. The main advantage of Raman-based methods to complement and augment diagnostic tools is that unsurpassed molecular specificity is achieved without labels and in a nondestructive way. Principles and applications of micro-Raman spectroscopy in the context of medicine will be described. First, Raman spectra of biomolecules representing proteins, nucleic acids, lipids and carbohydrates are introduced. Second, microbial applications are summarized with the focus on typing on species and strain level, detection of infections, antibiotic resistance and biofilms. Third, cytological applications are presented to classify single cells and study cell metabolism and drug–cell interaction. Fourth, applications to tissue characterization start with discussion of lateral resolution for Raman imaging followed by Raman-based detection of pathologies and combination with other modalities. Finally, an outlook is given to translate micro-Raman spectroscopy as a clinical tool to solve unmet needs in point-of-care applications and personalized treatment of diseases.
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14
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Lasch P, Noda I. Two-Dimensional Correlation Spectroscopy (2D-COS) for Analysis of Spatially Resolved Vibrational Spectra. APPLIED SPECTROSCOPY 2019; 73:359-379. [PMID: 30488717 DOI: 10.1177/0003702818819880] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The last two decades have seen tremendous progress in the application of two-dimensional correlation spectroscopy (2D-COS) as a versatile analysis method for data series obtained using a large variety of different spectroscopic modalities, including infrared (IR) and Raman spectroscopy. The analysis technique is applicable to a series of spectra recorded under the influence of an external sample perturbation. Two-dimensional COS analysis is not only helpful to decipher correlations, which may exist between distinct spectral features, but can also be utilized to obtain the sequence of individual spectral changes. The focus of this review article is on the application of 2D-COS for analyzing spatially resolved data with special emphasis on hyperspectral imaging (HSI) study. In this review, we briefly introduce the fundamentals of the generalized 2D-COS analysis approach, discuss specific points of 2D-COS application to spatially resolved spectra and demonstrate essential aspects of data pre-processing for 2D-COS analysis of spatially resolved spectra. Based on illustrative examples, we show that 2D-COS is useful for spectral band assignment in HSI applications and demonstrate its utility for detecting subtle correlations between spectra features, or between features from different imaging modalities in the case of heterospectral (multimodal) HSI. Furthermore, a short overview on existing 2D-COS software tools is provided. It is hoped that this article represents not only a useful guideline for 2D-COS analyses of spatially resolved hyperspectral data but supports also further dissemination of the 2D-COS analysis method as a whole.
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Affiliation(s)
- Peter Lasch
- 1 Robert Koch-Institute, ZBS6-Proteomics and Spectroscopy, Berlin, Germany
| | - Isao Noda
- 2 Department of Materials Science and Engineering, University of Delaware, Newark, DE, USA
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15
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Abstract
Abstract
This chapter is a short introduction into the data analysis pipeline, which is typically utilized to analyze Raman spectra. We empathized in the chapter that this data analysis pipeline must be tailored to the specific application of interest. Nevertheless, the tailored data analysis pipeline consists always of the same general procedures applied sequentially. The utilized procedures correct for artefacts, standardize the measured spectral data and translate the spectroscopic signals into higher level information. These computational procedures can be arranged into separate groups namely data pre-treatment, pre-processing and modeling. Thereby the pre-treatment aims to correct for non-sample-dependent artefacts, like cosmic spikes and contributions of the measurement device. The block of procedures, which needs to be applied next, is called pre-processing. This group consists of smoothing, baseline correction, normalization and dimension reduction. Thereafter, the analysis model is constructed and the performance of the models is evaluated. Every data analysis pipeline should be composed of procedures of these three groups and we describe every group in this chapter. After the description of data pre-treatment, pre-processing and modeling, we summarized trends in the analysis of Raman spectra namely model transfer approaches and data fusion. At the end of the chapter we tried to condense the whole chapter into guidelines for the analysis of Raman spectra.
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16
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Svirkova A, Turyanskaya A, Perneczky L, Streli C, Marchetti-Deschmann M. Multimodal imaging of undecalcified tissue sections by MALDI MS and μXRF. Analyst 2019; 143:2587-2595. [PMID: 29737333 DOI: 10.1039/c8an00313k] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Matrix-assisted laser desorption/ionisation mass spectrometric imaging (MALDI MSI) is a technique that provides localized information on intact molecules in a sample. Micro X-ray fluorescence (μXRF) imaging allows the examination of the spatial distribution of elements in a sample without any morphological changes. These methods have already been applied separately to different tissues, organs, plants and bacterial films, but, to the best of our knowledge, they have yet to be coupled in a multimodal analysis. In this proof-of-principle study, we established and tested sample preparation strategies, allowing the multimodal analysis of lipids (sphingomyelin and phosphatidylcholines) and elements relevant to bone structures as calcium, phosphorous and sulphur in the very same sample section of a chicken phalanx without tissue decalcification. The results of the investigation of such parameters as adhesive tapes supporting tissue sections, and the sequence of the imaging experiments are presented. We show specific lipid distributions in skin, cartilage, muscle, nail, and the intact morphology of bone by calcium and phosphorus imaging. A combination of molecular and elemental imaging was achieved, thus, providing now for the first time the possibility of gathering MALDI MSI and μXRF information from the very same sample without any washing steps omitting therefore the analytical artifacts that inevitably occur in approaches using consecutive tissue sections. The proposed combination can benefit in research studies regarding bone diseases, osteoporosis, osteoarthritis, cartilage failure, bone/tendon distinguishing, where elemental and lipid interaction play an essential role.
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Affiliation(s)
- Anastasiya Svirkova
- Institute of Chemical Technology and Analytics (CTA), TU Wien, Vienna, Austria.
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17
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de Juan A, Tauler R. Data Fusion by Multivariate Curve Resolution. DATA HANDLING IN SCIENCE AND TECHNOLOGY 2019. [DOI: 10.1016/b978-0-444-63984-4.00008-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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18
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Rodner E, Bocklitz T, von Eggeling F, Ernst G, Chernavskaia O, Popp J, Denzler J, Guntinas-Lichius O. Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study. Head Neck 2018; 41:116-121. [PMID: 30548511 DOI: 10.1002/hed.25489] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 03/11/2018] [Accepted: 07/05/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. METHODS Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. RESULTS A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. CONCLUSION Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.
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Affiliation(s)
- Erik Rodner
- Department of Computer Science, Friedrich Schiller University, Jena, Germany.,Corporate Research and Technology, Carl Zeiss AG, Jena, Germany
| | - Thomas Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.,Leibniz Institute of Photonic Technology, Jena, Germany
| | - Ferdinand von Eggeling
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.,Leibniz Institute of Photonic Technology, Jena, Germany.,Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | - Günther Ernst
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany
| | | | - Jürgen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany.,Leibniz Institute of Photonic Technology, Jena, Germany
| | - Joachim Denzler
- Department of Computer Science, Friedrich Schiller University, Jena, Germany
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19
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Ryabchykov O, Popp J, Bocklitz T. Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples. Front Chem 2018; 6:257. [PMID: 30062092 PMCID: PMC6055053 DOI: 10.3389/fchem.2018.00257] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 06/08/2018] [Indexed: 01/03/2023] Open
Abstract
Despite of a large number of imaging techniques for the characterization of biological samples, no universal one has been reported yet. In this work, a data fusion approach was investigated for combining Raman spectroscopic data with matrix-assisted laser desorption/ionization (MALDI) mass spectrometric data. It betters the image analysis of biological samples because Raman and MALDI information can be complementary to each other. While MALDI spectrometry yields detailed information regarding the lipid content, Raman spectroscopy provides valuable information about the overall chemical composition of the sample. The combination of Raman spectroscopic and MALDI spectrometric imaging data helps distinguishing different regions within the sample with a higher precision than would be possible by using either technique. We demonstrate that a data weighting step within the data fusion is necessary to reveal additional spectral features. The selected weighting approach was evaluated by examining the proportions of variance within the data explained by the first principal components of a principal component analysis (PCA) and visualizing the PCA results for each data type and combined data. In summary, the presented data fusion approach provides a concrete guideline on how to combine Raman spectroscopic and MALDI spectrometric imaging data for biological analysis.
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Affiliation(s)
- Oleg Ryabchykov
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
| | - Juergen Popp
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
| | - Thomas Bocklitz
- Spectroscopy and Imaging Research Department, Leibniz Institute of Photonic Technology, Member of Leibniz Health Technology, Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Jena, Germany
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20
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Rabe JH, A Sammour D, Schulz S, Munteanu B, Ott M, Ochs K, Hohenberger P, Marx A, Platten M, Opitz CA, Ory DS, Hopf C. Fourier Transform Infrared Microscopy Enables Guidance of Automated Mass Spectrometry Imaging to Predefined Tissue Morphologies. Sci Rep 2018; 8:313. [PMID: 29321555 PMCID: PMC5762902 DOI: 10.1038/s41598-017-18477-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 12/12/2017] [Indexed: 12/27/2022] Open
Abstract
Multimodal imaging combines complementary platforms for spatially resolved tissue analysis that are poised for application in life science and personalized medicine. Unlike established clinical in vivo multimodality imaging, automated workflows for in-depth multimodal molecular ex vivo tissue analysis that combine the speed and ease of spectroscopic imaging with molecular details provided by mass spectrometry imaging (MSI) are lagging behind. Here, we present an integrated approach that utilizes non-destructive Fourier transform infrared (FTIR) microscopy and matrix assisted laser desorption/ionization (MALDI) MSI for analysing single-slide tissue specimen. We show that FTIR microscopy can automatically guide high-resolution MSI data acquisition and interpretation without requiring prior histopathological tissue annotation, thus circumventing potential human-annotation-bias while achieving >90% reductions of data load and acquisition time. We apply FTIR imaging as an upstream modality to improve accuracy of tissue-morphology detection and to retrieve diagnostic molecular signatures in an automated, unbiased and spatially aware manner. We show the general applicability of multimodal FTIR-guided MALDI-MSI by demonstrating precise tumor localization in mouse brain bearing glioma xenografts and in human primary gastrointestinal stromal tumors. Finally, the presented multimodal tissue analysis method allows for morphology-sensitive lipid signature retrieval from brains of mice suffering from lipidosis caused by Niemann-Pick type C disease.
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Affiliation(s)
- Jan-Hinrich Rabe
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Denis A Sammour
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Sandra Schulz
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Bogdan Munteanu
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany
| | - Martina Ott
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Katharina Ochs
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center of Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Hohenberger
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Alexander Marx
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Michael Platten
- German Cancer Consortium (DKTK) CCU Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Center Mannheim of Heidelberg University, Mannheim, Germany
| | - Christiane A Opitz
- Brain Cancer Metabolism Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neurology and National Center of Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
| | - Daniel S Ory
- Diabetic Cardiovascular Disease Center and Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, 63110, USA
| | - Carsten Hopf
- Center for Applied Research in Applied Biomedical Mass Spectrometry (ABIMAS), Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany.
- Institute of Medical Technology, Heidelberg University and Mannheim University of Applied Sciences, Paul-Wittsack Str. 10, 68163, Mannheim, Germany.
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21
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Prentice BM, Caprioli RM, Vuiblet V. Label-free molecular imaging of the kidney. Kidney Int 2017; 92:580-598. [PMID: 28750926 PMCID: PMC6193761 DOI: 10.1016/j.kint.2017.03.052] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 03/27/2017] [Accepted: 03/28/2017] [Indexed: 12/25/2022]
Abstract
In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.
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Affiliation(s)
- Boone M Prentice
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA; Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee, USA; Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA; Departments of Pharmacology and Medicine, Vanderbilt University, Nashville, Tennessee, USA; Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA.
| | - Vincent Vuiblet
- Biophotonic Laboratory, UMR CNRS 7369 URCA, Reims, France; Nephropathology, Department of Biopathology Laboratory, CHU de Reims, Reims, France; Nephrology and Renal Transplantation department, CHU de Reims, Reims, France.
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22
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Lasch P, Noda I. Two-Dimensional Correlation Spectroscopy for Multimodal Analysis of FT-IR, Raman, and MALDI-TOF MS Hyperspectral Images with Hamster Brain Tissue. Anal Chem 2017; 89:5008-5016. [PMID: 28365985 DOI: 10.1021/acs.analchem.7b00332] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hyperspectral imaging (HSI) techniques are useful for obtaining very detailed structural and compositional information from biomedical, pharmaceutical, or clinical samples, among others. The informative value of these methods can be further increased through the application of different HSI techniques and joint analysis of the data. However, interpretation and understanding of multimodal HSI have been impeded by difficulties in registration of the different HSI data sets and by the lack of integrative analysis concepts. Here, we introduce two-dimensional correlation spectroscopy (2DCOS) as a novel technique for jointly analyzing HSI data which allows one to obtain deeper insights into the chemistry of complex samples by decrypting auto- and heterospectral correlations that may exist between features of the different HSI data. The general workflow of 2DCOS analysis is demonstrated by HSI examples acquired from cryo-sections of hamster brain tissue using Fourier-transform infrared (FT-IR) microspectroscopy, confocal Raman microspectroscopy (CRM), and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Multimodal hyperspectral image analysis by 2DCOS opens up new opportunities for spectral band assignments and thus the interpretation of structure-spectra and composition-spectra relationships. We foresee wide application potential for describing complex samples in various fields ranging from biomedicine to industrial applications.
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Affiliation(s)
- Peter Lasch
- ZBS6 "Proteomics and Spectroscopy", Robert Koch-Institute , Nordufer 20, D-13353 Berlin, Germany
| | - Isao Noda
- Department of Materials Science and Engineering, University of Delaware , Newark, Delaware 19716, United States
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23
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Krafft C, Schmitt M, Schie IW, Cialla-May D, Matthäus C, Bocklitz T, Popp J. Markerfreie molekulare Bildgebung biologischer Zellen und Gewebe durch lineare und nichtlineare Raman-spektroskopische Ansätze. Angew Chem Int Ed Engl 2017. [DOI: 10.1002/ange.201607604] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Christoph Krafft
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
| | - Michael Schmitt
- Institut für Physikalische Chemie und Abbe Center of Photonics; Friedrich-Schiller-Universität Jena; Helmholtzweg 4 07743 Jena Deutschland
| | - Iwan W. Schie
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
| | - Dana Cialla-May
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
- Institut für Physikalische Chemie und Abbe Center of Photonics; Friedrich-Schiller-Universität Jena; Helmholtzweg 4 07743 Jena Deutschland
| | - Christian Matthäus
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
- Institut für Physikalische Chemie und Abbe Center of Photonics; Friedrich-Schiller-Universität Jena; Helmholtzweg 4 07743 Jena Deutschland
| | - Thomas Bocklitz
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
- Institut für Physikalische Chemie und Abbe Center of Photonics; Friedrich-Schiller-Universität Jena; Helmholtzweg 4 07743 Jena Deutschland
| | - Jürgen Popp
- Leibniz-Institut für Photonische Technologien; Albert-Einstein-Straße 9 07745 Jena Deutschland
- Institut für Physikalische Chemie und Abbe Center of Photonics; Friedrich-Schiller-Universität Jena; Helmholtzweg 4 07743 Jena Deutschland
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24
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Krafft C, Schmitt M, Schie IW, Cialla-May D, Matthäus C, Bocklitz T, Popp J. Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches. Angew Chem Int Ed Engl 2017; 56:4392-4430. [PMID: 27862751 DOI: 10.1002/anie.201607604] [Citation(s) in RCA: 134] [Impact Index Per Article: 19.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Revised: 11/04/2016] [Indexed: 12/20/2022]
Abstract
Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.
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Affiliation(s)
- Christoph Krafft
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany
| | - Michael Schmitt
- Institut für Physikalische Chemie und Abbe Center für Photonics, Friedrich Schiller Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Iwan W Schie
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany
| | - Dana Cialla-May
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany.,Institut für Physikalische Chemie und Abbe Center für Photonics, Friedrich Schiller Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Christian Matthäus
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany.,Institut für Physikalische Chemie und Abbe Center für Photonics, Friedrich Schiller Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany.,Institut für Physikalische Chemie und Abbe Center für Photonics, Friedrich Schiller Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
| | - Jürgen Popp
- Leibniz-Institut für Photonische Technologien, Albert-Einstein-Strasse 9, 07745, Jena, Germany.,Institut für Physikalische Chemie und Abbe Center für Photonics, Friedrich Schiller Universität Jena, Helmholtzweg 4, 07743, Jena, Germany
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25
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Quanico J, Franck J, Wisztorski M, Salzet M, Fournier I. Integrated mass spectrometry imaging and omics workflows on the same tissue section using grid-aided, parafilm-assisted microdissection. Biochim Biophys Acta Gen Subj 2017; 1861:1702-1714. [PMID: 28300637 DOI: 10.1016/j.bbagen.2017.03.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Revised: 03/08/2017] [Accepted: 03/10/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND In spite of the number of applications describing the use of MALDI MSI, one of its major drawbacks is the limited capability of identifying multiple compound classes directly on the same tissue section. METHODS We demonstrate the use of grid-aided, parafilm-assisted microdissection to perform MALDI MS imaging and shotgun proteomics and metabolomics in a combined workflow and using only a single tissue section. The grid is generated by microspotting acid dye 25 using a piezoelectric microspotter, and this grid was used as a guide to locate regions of interest and as an aid during manual microdissection. Subjecting the dissected pieces to the modified Folch method allows to separate the metabolites from proteins. The proteins can then be subjected to digestion under controlled conditions to improve protein identification yields. RESULTS The proof of concept experiment on rat brain generated 162 and 140 metabolite assignments from three ROIs (cerebellum, hippocampus and midbrain/hypothalamus) in positive and negative modes, respectively, and 890, 1303 and 1059 unique proteins. Integrated metabolite and protein overrepresentation analysis identified pathways associated with the biological functions of each ROI, most of which were not identified when looking at the protein and metabolite lists individually. CONCLUSIONS This combined MALDI MS imaging and multi-omics approach further extends the amount of information that can be generated from single tissue sections. GENERAL SIGNIFICANCE To the best of our knowledge, this is the first report combining both imaging and multi-omics analyses in the same workflow and on the same tissue section.
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Affiliation(s)
- Jusal Quanico
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Julien Franck
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Maxence Wisztorski
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Michel Salzet
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France
| | - Isabelle Fournier
- Université de Lille 1, INSERM, U1192-Laboratoire Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (PRISM), F-59000 Lille, France.
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Matrix Metalloproteinases in the Interstitial Space. Protein Sci 2016. [DOI: 10.1201/9781315374307-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Multigrid MALDI mass spectrometry imaging (mMALDI MSI). Anal Bioanal Chem 2016; 408:3769-81. [PMID: 27039200 DOI: 10.1007/s00216-016-9465-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 03/02/2016] [Accepted: 03/03/2016] [Indexed: 10/22/2022]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an important technique for the spatially resolved molecular analysis of tissue sections. The selection of matrices influences the resulting mass spectra to a high degree. For extensive and simultaneous analysis, the application of different matrices to one tissue sample is desirable. To date, only a single matrix could be applied to a tissue section per experiment. However, repetitive removal of the matrix makes this approach time-consuming and damaging to tissue samples. To overcome these drawbacks, we developed a multigrid MALDI MSI technique (mMALDI MSI) that relies on automated inkjet printing to place differing matrices onto predefined dot grids. We used a cooled printhead to prevent cavitation of low viscosity solvents in the printhead nozzle. Improved spatial resolution of the dot grids was achieved by using a triple-pulse procedure that reduced droplet volume. The matrices can either be applied directly to the thaw-mounted tissue sample or by precoating the slide followed by mounting of the tissue sample. During the MALDI imaging process, we were able to precisely target different matrix point grids with the laser to simultaneously produce distinct mass spectra. Unlike the standard method, the prespotting approach optimizes the spectra quality, avoids analyte delocalization, and enables subsequent hematoxylin and eosin (H&E) staining. Graphical Abstract Scheme of the pre-spotted multigrid MALDI MSI workflow.
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de Juan A, Tauler R. Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data. DATA HANDLING IN SCIENCE AND TECHNOLOGY 2016. [DOI: 10.1016/b978-0-444-63638-6.00002-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bocklitz TW, Guo S, Ryabchykov O, Vogler N, Popp J. Raman Based Molecular Imaging and Analytics: A Magic Bullet for Biomedical Applications!? Anal Chem 2015; 88:133-51. [DOI: 10.1021/acs.analchem.5b04665] [Citation(s) in RCA: 72] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Thomas W. Bocklitz
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
| | - Shuxia Guo
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Oleg Ryabchykov
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Nadine Vogler
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
| | - Jürgen Popp
- Institute
of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Institute of Photonic Technology (IPHT), Albert-Einstein-Strasse 9, 07745 Jena, Germany
- InfectoGnostics
Forschungscampus Jena e.V., Zentrum für Angewandte Forschung, Philosophenweg 7, 07743 Jena, Germany
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