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HUANG D, LIU X, XU G. [Research progress of deep learning applications in mass spectrometry imaging data analysis]. Se Pu 2024; 42:669-680. [PMID: 38966975 PMCID: PMC11224939 DOI: 10.3724/sp.j.1123.2023.10035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Indexed: 07/06/2024] Open
Abstract
Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.
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Mutithu DW, Aremu OO, Mokaila D, Bana T, Familusi M, Taylor L, Martin LJ, Heathfield LJ, Kirwan JA, Wiesner L, Adeola HA, Lumngwena EN, Manganyi R, Skatulla S, Naidoo R, Ntusi NAB. A study protocol to characterise pathophysiological and molecular markers of rheumatic heart disease and degenerative aortic stenosis using multiparametric cardiovascular imaging and multiomics techniques. PLoS One 2024; 19:e0303496. [PMID: 38739622 PMCID: PMC11090351 DOI: 10.1371/journal.pone.0303496] [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: 01/08/2024] [Accepted: 04/26/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION Rheumatic heart disease (RHD), degenerative aortic stenosis (AS), and congenital valve diseases are prevalent in sub-Saharan Africa. Many knowledge gaps remain in understanding disease mechanisms, stratifying phenotypes, and prognostication. Therefore, we aimed to characterise patients through clinical profiling, imaging, histology, and molecular biomarkers to improve our understanding of the pathophysiology, diagnosis, and prognosis of RHD and AS. METHODS In this cross-sectional, case-controlled study, we plan to recruit RHD and AS patients and compare them to matched controls. Living participants will undergo clinical assessment, echocardiography, CMR and blood sampling for circulatory biomarker analyses. Tissue samples will be obtained from patients undergoing valve replacement, while healthy tissues will be obtained from cadavers. Immunohistology, proteomics, metabolomics, and transcriptome analyses will be used to analyse circulatory- and tissue-specific biomarkers. Univariate and multivariate statistical analyses will be used for hypothesis testing and identification of important biomarkers. In summary, this study aims to delineate the pathophysiology of RHD and degenerative AS using multiparametric CMR imaging. In addition to discover novel biomarkers and explore the pathomechanisms associated with RHD and AS through high-throughput profiling of the tissue and blood proteome and metabolome and provide a proof of concept of the suitability of using cadaveric tissues as controls for cardiovascular disease studies.
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Affiliation(s)
- Daniel W. Mutithu
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Olukayode O. Aremu
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Dipolelo Mokaila
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Tasnim Bana
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
| | - Mary Familusi
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Department of Civil Engineering, University of Cape Town, Cape Town, South Africa
| | - Laura Taylor
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Lorna J. Martin
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Laura J. Heathfield
- Division of Forensic Medicine and Toxicology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Jennifer A. Kirwan
- Metabolomics Platform, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Max-Delbrück-Center (MDC) for Molecular Medicine, Helmholtz Association, Berlin, Germany
| | - Lubbe Wiesner
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Henry A. Adeola
- Division of Dermatology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Evelyn N. Lumngwena
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
- School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Rodgers Manganyi
- Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Sebastian Skatulla
- Department of Civil Engineering, University of Cape Town, Cape Town, South Africa
| | - Richard Naidoo
- Division of Anatomical Pathology, Department of Pathology, University of Cape Town and National Health Laboratory Service, Cape Town, South Africa
| | - Ntobeko A. B. Ntusi
- Department of Medicine, Cape Heart Institute, University of Cape Town, Cape Town, South Africa
- Division of Cardiology, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
- Extramural Unit on Intersection of Noncommunicable Diseases and Infectious Diseases, South African Medical Research Council, Cape Town, South Africa
- Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa
- Wellcome Centre for Infectious Disease Research, University of Cape Town, Cape Town, South Africa
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Li CMY, Briggs MT, Lee YR, Tin T, Young C, Pierides J, Kaur G, Drew P, Maddern GJ, Hoffmann P, Klingler-Hoffmann M, Fenix K. Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases. Clin Exp Med 2024; 24:53. [PMID: 38492056 PMCID: PMC10944452 DOI: 10.1007/s10238-024-01311-5] [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: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/18/2024]
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC liver metastases (CRLM) are often resistant to conventional treatments, with high rates of recurrence. Therefore, it is crucial to identify biomarkers for CRLM patients that predict cancer progression. This study utilised matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to spatially map the CRLM tumour proteome. CRLM tissue microarrays (TMAs) of 84 patients were analysed using tryptic peptide MALDI-MSI to spatially monitor peptide abundances across CRLM tissues. Abundance of peptides was compared between tumour vs stroma, male vs female and across three groups of patients based on overall survival (0-3 years, 4-6 years, and 7+ years). Peptides were then characterised and matched using LC-MS/MS. A total of 471 potential peptides were identified by MALDI-MSI. Our results show that two unidentified m/z values (1589.876 and 1092.727) had significantly higher intensities in tumours compared to stroma. Ten m/z values were identified to have correlation with biological sex. Survival analysis identified three peptides (Histone H4, Haemoglobin subunit alpha, and Inosine-5'-monophosphate dehydrogenase 2) and two unidentified m/z values (1305.840 and 1661.060) that were significantly higher in patients with shorter survival (0-3 years relative to 4-6 years and 7+ years). This is the first study using MALDI-MSI, combined with LC-MS/MS, on a large cohort of CRLM patients to identify the spatial proteome in this malignancy. Further, we identify several protein candidates that may be suitable for drug targeting or for future prognostic biomarker development.
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Affiliation(s)
- Celine Man Ying Li
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Matthew T Briggs
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Yea-Rin Lee
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - Teresa Tin
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Clifford Young
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | - John Pierides
- SA Pathology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia
| | - Gurjeet Kaur
- Institute for Research in Molecular Medicine, University Sains Malaysia, 11800, Pulau Pinang, Malaysia
| | - Paul Drew
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Guy J Maddern
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia
| | - Peter Hoffmann
- Clinical and Health Sciences, University of South Australia, Adelaide, SA, 5000, Australia
| | | | - Kevin Fenix
- Discipline of Surgery, Adelaide Medical School, The University of Adelaide, Adelaide, SA, 5005, Australia.
- The Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, SA, 5011, Australia.
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Truong JXM, Rao SR, Ryan FJ, Lynn DJ, Snel MF, Butler LM, Trim PJ. Spatial MS multiomics on clinical prostate cancer tissues. Anal Bioanal Chem 2024; 416:1745-1757. [PMID: 38324070 DOI: 10.1007/s00216-024-05178-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
Mass spectrometry (MS) and MS imaging (MSI) are used extensively for both the spatial and bulk characterization of samples in lipidomics and proteomics workflows. These datasets are typically generated independently due to different requirements for sample preparation. However, modern omics technologies now provide higher sample throughput and deeper molecular coverage, which, in combination with more sophisticated bioinformatic and statistical pipelines, make generating multiomics data from a single sample a reality. In this workflow, we use spatial lipidomics data generated by matrix-assisted laser desorption/ionization MSI (MALDI-MSI) on prostate cancer (PCa) radical prostatectomy cores to guide the definition of tumor and benign tissue regions for laser capture microdissection (LCM) and bottom-up proteomics all on the same sample and using the same mass spectrometer. Accurate region of interest (ROI) mapping was facilitated by the SCiLS region mapper software and dissected regions were analyzed using a dia-PASEF workflow. A total of 5525 unique protein groups were identified from all dissected regions. Lysophosphatidylcholine acyltransferase 1 (LPCAT1), a lipid remodelling enzyme, was significantly enriched in the dissected regions of cancerous epithelium (CE) compared to benign epithelium (BE). The increased abundance of this protein was reflected in the lipidomics data with an increased ion intensity ratio for pairs of phosphatidylcholines (PC) and lysophosphatidylcholines (LPC) in CE compared to BE.
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Affiliation(s)
- Jacob X M Truong
- The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
- Freemasons Centre for Male Health and Wellbeing, University of Adelaide, North Terrace, Adelaide, South Australia, 5000, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), North Terrace, Adelaide, South Australia, 5000, Australia
| | - Sushma R Rao
- The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
| | - Feargal J Ryan
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
- Flinders Health and Medical Research Institute, Flinders University, Bedford Park, South Australia, 5042, Australia
| | - David J Lynn
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
- Flinders Health and Medical Research Institute, Flinders University, Bedford Park, South Australia, 5042, Australia
| | - Marten F Snel
- The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
| | - Lisa M Butler
- The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia
- Freemasons Centre for Male Health and Wellbeing, University of Adelaide, North Terrace, Adelaide, South Australia, 5000, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), North Terrace, Adelaide, South Australia, 5000, Australia
| | - Paul J Trim
- The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, South Australia, 5000, Australia.
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods 2024; 21:521-530. [PMID: 38366241 PMCID: PMC10927565 DOI: 10.1038/s41592-024-02171-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel C Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Timothy J Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Jonathan V Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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Holbrook JH, Kemper GE, Hummon AB. Quantitative mass spectrometry imaging: therapeutics & biomolecules. Chem Commun (Camb) 2024; 60:2137-2151. [PMID: 38284765 PMCID: PMC10878071 DOI: 10.1039/d3cc05988j] [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: 12/08/2023] [Accepted: 01/22/2024] [Indexed: 01/30/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly utilized in the analysis of biological molecules. MSI grants the ability to spatially map thousands of molecules within one experimental run in a label-free manner. While MSI is considered by most to be a qualitative method, recent advancements in instrumentation, sample preparation, and development of standards has made quantitative MSI (qMSI) more common. In this feature article, we present a tailored review of recent advancements in qMSI of therapeutics and biomolecules such as lipids and peptides/proteins. We also provide detailed experimental considerations for conducting qMSI studies on biological samples, aiming to advance the methodology.
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Affiliation(s)
- Joseph H Holbrook
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, USA.
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Gabrielle E Kemper
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Amanda B Hummon
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio 43210, USA.
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [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: 09/05/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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Wang M, Chen L, Li J, You Y, Qian Z, Liu J, Jiang Y, Zhou T, Gu Y, Zhang Y. An omics review and perspective of researches on intrahepatic cholestasis of pregnancy. Front Endocrinol (Lausanne) 2024; 14:1267195. [PMID: 38260124 PMCID: PMC10801044 DOI: 10.3389/fendo.2023.1267195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Intrahepatic cholestasis of pregnancy (ICP) is one of the common pregnancy complications that may threaten the health of both pregnant women and their fetuses. Hence, it is of vital importance to identify key moleculars and the associated functional pathways of ICP, which will help us to better understand the pathological mechanisms as well as to develop precise clinical biomarkers. The emerging and developing of multiple omics approaches enable comprehensive studies of the genome, transcriptome, proteome and metabolome of clinical samples. The present review collected and summarized the omics based studies of ICP, aiming to provide an overview of the current progress, limitations and future directions. Briefly, these studies covered a broad range of research contents by the comparing of different experimental groups including ICP patients, ICP subtypes, ICP fetuses, ICP models and other complications. Correspondingly, the studied samples contain various types of clinical samples, in vitro cultured tissues, cell lines and the samples from animal models. According to the main research objectives, we further categorized these studies into two groups: pathogenesis and diagnosis analyses. The pathogenesis studies identified tens of functional pathways that may represent the key regulatory events for the occurrence, progression, treatment and fetal effects of ICP. On the other hand, the diagnosis studies tested more than 40 potential models for the early-prediction, diagnosis, grading, prognosis or differential diagnosis of ICP. Apart from these achievements, we also evaluated the limitations of current studies, and emphasized that many aspects of clinical characteristics, sample processing, and analytical method can greatly affect the reliability and repeatability of omics results. Finally, we also pointed out several new directions for the omics based analyses of ICP and other perinatal associated conditions in the future.
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Affiliation(s)
- Min Wang
- Center for Reproductive Medicine, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Lingyan Chen
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Jingyang Li
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Yilan You
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Zhiwen Qian
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Jiayu Liu
- Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Ying Jiang
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Tao Zhou
- Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Ying Gu
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
- Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Yan Zhang
- Department of Gynaecology and Obstetrics, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
- Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, Wuxi, China
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Ma M, Yu Q, Delafield DG, Cui Y, Li Z, Li M, Wu W, Shi X, Westmark PR, Gutierrez A, Ma G, Gao A, Xu M, Xu W, Westmark CJ, Li L. On-Tissue Spatial Proteomics Integrating MALDI-MS Imaging with Shotgun Proteomics Reveals Soy Consumption-Induced Protein Changes in a Fragile X Syndrome Mouse Model. ACS Chem Neurosci 2024; 15:119-133. [PMID: 38109073 PMCID: PMC11127747 DOI: 10.1021/acschemneuro.3c00497] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023] Open
Abstract
Fragile X syndrome (FXS), the leading cause of inherited intellectual disability and autism, is caused by the transcriptional silencing of the FMR1 gene, which encodes the fragile X messenger ribonucleoprotein (FMRP). FMRP interacts with numerous brain mRNAs that are involved in synaptic plasticity and implicated in autism spectrum disorders. Our published studies indicate that single-source, soy-based diets are associated with increased seizures and autism. Thus, there is an acute need for an unbiased protein marker identification in FXS in response to soy consumption. Herein, we present a spatial proteomics approach integrating mass spectrometry imaging with label-free proteomics in the FXS mouse model to map the spatial distribution and quantify levels of proteins in the hippocampus and hypothalamus brain regions. In total, 1250 unique peptides were spatially resolved, demonstrating the diverse array of peptidomes present in the tissue slices and the broad coverage of the strategy. A group of proteins that are known to be involved in glycolysis, synaptic transmission, and coexpression network analysis suggest a significant association between soy proteins and metabolic and synaptic processes in the Fmr1KO brain. Ultimately, this spatial proteomics work represents a crucial step toward identifying potential candidate protein markers and novel therapeutic targets for FXS.
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Affiliation(s)
- Min Ma
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Qinying Yu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Daniel G. Delafield
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Yusi Cui
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Zihui Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Miyang Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Wenxin Wu
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Xudong Shi
- Division of Otolaryngology, Department of Surgery, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Pamela R. Westmark
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Alejandra Gutierrez
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
- Molecular Environmental Toxicology Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Gui Ma
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Ang Gao
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Meng Xu
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Wei Xu
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Cara J. Westmark
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
- Molecular Environmental Toxicology Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53705, United States
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA
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10
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Fu J, Gu J, Bao Z, Zhou Y, Hu H, Yang C, Wu R, Liu H, Qin L, Xu H, Li J, Guo H, Wang L, Zhou Y, Wang X, Li G. 2,5-Dihydroxyterephthalic Acid: A Matrix for Improved Detection and Imaging of Amino Acids. Anal Chem 2023; 95:18709-18718. [PMID: 38018128 DOI: 10.1021/acs.analchem.3c01731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Amino acids (AAs), which are low-molecular-weight (low-MW) metabolites, serve as essential building blocks not only for protein synthesis but also for maintaining the nitrogen balance in living systems. In situ detection and imaging of AAs are crucial for understanding more complex biological processes. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a label-free mass spectrometric imaging technique that enables the simultaneous detection and imaging of the spatial distribution and relative abundance of different endogenous/exogenous compounds in biological samples. The excellent efficiency of MALDI-MSI is attributed to the choice of the MALDI matrix. However, to the best of our knowledge, no matrix has been specifically developed for AAs. Herein, we report a MALDI matrix, 2,5-dihydroxyterephthalic acid (DHT), which can improve the detection and imaging of AAs in biological samples by MALDI-MS. Our results indicated that DHT exhibited strong ultraviolet-visible (UV-vis) absorption, uniform matrix deposition, and high vacuum stability. Moreover, the matrix-related ion signals produced from DHT were reduced by 50 and 71.8% at m/z < 500 compared to the commonly used matrices of 2,5-dihydroxybenzoic acid (DHB) and α-cyano-4-hydroxycinnamic acid (CHCA), respectively, in their respective organic solvents. In terms of quantitative performance, arginine, glutamic acid, glutamine, and proline can be detected with limits of detection of 6, 4, 6, and 4 ng/mL, respectively, using the DHT as the matrix. Using DHT as the matrix, all 20 protein AAs were successfully detected in human serum by MALDI-MS, whereas only 7 and 10 AAs were detected when DHB and CHCA matrices were used, respectively. Furthermore, 20 protein AAs and taurine were successfully detected and imaged in a section of edible Crassostrea gigas (oyster) tissue for the first time. Our study demonstrates that using DHT as a matrix can improve the detection and imaging of AAs in biological samples by MALDI-MS.
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Affiliation(s)
- Jinxiang Fu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Jianchi Gu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Zhibin Bao
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Yunpeng Zhou
- General Surgery Department, Shanxi Bethune Hospital, Taiyuan 030032, China
| | - Hao Hu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Chenyu Yang
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Ran Wu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Haiqiang Liu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Liang Qin
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Hualei Xu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Jinrong Li
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Hua Guo
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Lei Wang
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Yijun Zhou
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Xiaodong Wang
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China
- Centre for Imaging & Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| | - Gaopeng Li
- General Surgery Department, Shanxi Bethune Hospital, Taiyuan 030032, China
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11
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis. Nat Methods 2023; 20:1883-1886. [PMID: 37996752 DOI: 10.1038/s41592-023-02070-z] [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: 03/06/2023] [Accepted: 10/06/2023] [Indexed: 11/25/2023]
Abstract
Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v.3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass recalibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multitissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Institute of Surgical Pathology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
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12
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Cooke JP, Lai L. Transflammation in tissue regeneration and response to injury: How cell-autonomous inflammatory signaling mediates cell plasticity. Adv Drug Deliv Rev 2023; 203:115118. [PMID: 37884127 PMCID: PMC10842620 DOI: 10.1016/j.addr.2023.115118] [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: 10/18/2022] [Revised: 08/01/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
Inflammation is a first responder against injury and infection and is also critical for the regeneration and repair of tissue after injury. The role of professional immune cells in tissue healing is well characterized. Professional immune cells respond to pathogens with humoral and cytotoxic responses; remove cellular debris through efferocytosis; secrete angiogenic cytokines and growth factors to repair the microvasculature and parenchyma. However, non-immune cells are also capable of responding to damage or pathogens. Non-immune somatic cells express pattern recognition receptors (PRRs) to detect pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). The PRRs activation leads to the release of inflammatory cytokines required for tissue defense and repair. Notably, the activation of PRRs also triggers epigenetic changes that promote DNA accessibility and cellular plasticity. Thus, non-immune cells directly respond to the local inflammatory cues and can undergo phenotypic modifications or even cell lineage transitions to facilitate tissue regeneration. This review will focus on the novel role of cell-autonomous inflammatory signaling in mediating cell plasticity, a process which is termed transflammation. We will discuss the regulation of this process by changes in the functions and expression levels of epigenetic modifiers, as well as metabolic and ROS/RNS-mediated epigenetic modulation of DNA accessibility during cell fate transition. We will highlight the recent technological developments in detecting cell plasticity and potential therapeutic applications of transflammation in tissue regeneration.
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Affiliation(s)
- John P Cooke
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, United States
| | - Li Lai
- Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, TX, United States.
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13
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543144. [PMID: 37398021 PMCID: PMC10312594 DOI: 10.1101/2023.05.31.543144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Daniel C. Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Timothy J. Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
- Carl R. Woese Institute for Genomic Biology. University of Illinois Urbana-Champaign, Urbana, IL 61801, United States
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14
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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15
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Wittek O, Jahreis B, Römpp A. MALDI MS Imaging of Chickpea Seeds ( Cicer arietinum) and Crab's Eye Vine ( Abrus precatorius) after Tryptic Digestion Allows Spatially Resolved Identification of Plant Proteins. Anal Chem 2023; 95:14972-14980. [PMID: 37749896 PMCID: PMC10568532 DOI: 10.1021/acs.analchem.3c02428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) imaging following in situ enzymatic digestion is a versatile analytical method for the untargeted investigation of protein distributions, which has rarely been used for plants so far. The present study describes a workflow for in situ tryptic digestion of plant seed tissue for MALDI MS imaging. Substantial modifications to the sample preparation procedure for mammalian tissues were necessary to cater to the specific properties of plant materials. For the first time, distributions of tryptic peptides were successfully visualized in plant tissue using MS imaging with accurate mass detection. Sixteen proteins were visualized and identified in chickpea seeds showing different distribution patterns, e.g., in the cotyledons, radicle, or testa. All tryptic peptides were detected with a mass resolution higher than 60,000 as well as a mass accuracy better than 1.5 ppm root-mean-square error and were matched to results from complementary liquid chromatography-MS/MS (LC-MS/MS) data. The developed method was also applied to crab's eye vine seeds for targeted MS imaging of the toxic protein abrin, showing the presence of abrin-a in all compartments. Abrin (59 kDa), as well as the majority of proteins visualized in chickpeas, was larger than 50 kDa and would thus not be readily accessible by top-down MS imaging. Since antibodies for plant proteins are often not readily available, in situ digestion MS imaging provides unique information, as it makes the distribution and identification of larger proteins in plant tissues accessible in an untargeted manner. This opens up new possibilities in the field of plant science as well as to assess the nutritional quality and/or safety of crops.
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Affiliation(s)
| | - Bastian Jahreis
- Bioanalytical Sciences and
Food Analysis, University of Bayreuth, Universitaetsstrasse 30, D-95447 Bayreuth, Germany
| | - Andreas Römpp
- Bioanalytical Sciences and
Food Analysis, University of Bayreuth, Universitaetsstrasse 30, D-95447 Bayreuth, Germany
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16
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Baquer G, Sementé L, Ràfols P, Martín-Saiz L, Bookmeyer C, Fernández JA, Correig X, García-Altares M. rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation. J Cheminform 2023; 15:80. [PMID: 37715285 PMCID: PMC10504721 DOI: 10.1186/s13321-023-00756-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/29/2023] [Indexed: 09/17/2023] Open
Abstract
Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC-MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain.
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain.
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain.
| | - Lucía Martín-Saiz
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Christoph Bookmeyer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Institute of Hygiene, University of Münster, Münster, Germany
| | - José A Fernández
- Department of Physical Chemistry, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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17
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Degnan DJ, Zemaitis KJ, Lewis LA, McCue LA, Bramer LM, Fulcher JM, Veličković D, Paša-Tolić L, Zhou M. IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2061-2064. [PMID: 37523489 DOI: 10.1021/jasms.3c00180] [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: 08/02/2023]
Abstract
Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data do not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant MALDI-MS software tools developed to confirm compound identifications are focused on small molecules (e.g., metabolites, lipids) and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes: intact proteins, peptides, and glycans. This tool accepts already derived molecular formulas or, for proteomics applications, can derive molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. Visualization of all matched isotopic profiles is provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.
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Affiliation(s)
- David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kevin J Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Logan A Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lee Ann McCue
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - James M Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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18
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Yannarell SM, Beaudoin ES, Talley HS, Schoenborn AA, Orr G, Anderton CR, Chrisler WB, Shank EA. Extensive cellular multi-tasking within Bacillus subtilis biofilms. mSystems 2023; 8:e0089122. [PMID: 37527273 PMCID: PMC10469600 DOI: 10.1128/msystems.00891-22] [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: 09/13/2022] [Accepted: 03/08/2023] [Indexed: 08/03/2023] Open
Abstract
Bacillus subtilis is a soil-dwelling bacterium that can form biofilms, or communities of cells surrounded by a self-produced extracellular matrix. In biofilms, genetically identical cells often exhibit heterogeneous transcriptional phenotypes, so that subpopulations of cells carry out essential yet costly cellular processes that allow the entire population to thrive. Surprisingly, the extent of phenotypic heterogeneity and the relationships between subpopulations of cells within biofilms of even in well-studied bacterial systems like B. subtilis remains largely unknown. To determine relationships between these subpopulations of cells, we created 182 strains containing pairwise combinations of fluorescent transcriptional reporters for the expression state of 14 different genes associated with potential cellular subpopulations. We determined the spatial organization of the expression of these genes within biofilms using confocal microscopy, which revealed that many reporters localized to distinct areas of the biofilm, some of which were co-localized. We used flow cytometry to quantify reporter co-expression, which revealed that many cells "multi-task," simultaneously expressing two reporters. These data indicate that prior models describing B. subtilis cells as differentiating into specific cell types, each with a specific task or function, were oversimplified. Only a few subpopulations of cells, including surfactin and plipastatin producers, as well as sporulating and competent cells, appear to have distinct roles based on the set of genes examined here. These data will provide us with a framework with which to further study and make predictions about the roles of diverse cellular phenotypes in B. subtilis biofilms. IMPORTANCE Many microbes differentiate, expressing diverse phenotypes to ensure their survival in various environments. However, studies on phenotypic differentiation have typically examined only a few phenotypes at one time, thus limiting our knowledge about the extent of differentiation and phenotypic overlap in the population. We investigated the spatial organization and gene expression relationships for genes important in B. subtilis biofilms. In doing so, we mapped spatial gene expression patterns and expanded the number of cell populations described in the B. subtilis literature. It is likely that other bacteria also display complex differentiation patterns within their biofilms. Studying the extent of cellular differentiation in other microbes may be important when designing therapies for disease-causing bacteria, where studying only a single phenotype may be masking underlying phenotypic differentiation relevant to infection outcomes.
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Affiliation(s)
- Sarah M. Yannarell
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Eric S. Beaudoin
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Hunter S. Talley
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Alexi A. Schoenborn
- Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Galya Orr
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Christopher R. Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - William B. Chrisler
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Elizabeth A. Shank
- Department of Systems Biology, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
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19
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Wang Z, Gletten RB, Schey KL. Spatially Resolved Proteomics Reveals Lens Suture-Related Cell-Cell Junctional Protein Distributions. Invest Ophthalmol Vis Sci 2023; 64:28. [PMID: 37603353 PMCID: PMC10445239 DOI: 10.1167/iovs.64.11.28] [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: 05/17/2023] [Accepted: 07/31/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose Lens transparency relies on the precise organization of lens fiber cells. The formation of the highly ordered lens architecture results from not only cell-cell adhesion along the lateral interfaces, but also from proper organization of fiber cells tips at lens sutures. Little is known about the cell adhesion between fiber tips at the sutures. The purpose of this study is to map suture-specific protein distributions. Methods Tissue sections were obtained from fresh frozen bovine lenses and washes were performed to remove soluble proteins and to retain membrane and membrane associated proteins. Imaging mass spectrometry (IMS) combined with on-tissue trypsin digestion was used to visualize protein spatial distributions. Sutures and adjacent regions were captured by laser capture microdissection and samples were digested by trypsin. Proteins were analyzed by liquid chromatography tandem MS and quantified by label-free quantification. Protein spatial distributions were confirmed by immunofluorescence. Results IMS results showed enrichment of adherens junction proteins cadherin-2 and armadillo repeat gene deleted in velo-cardio-facial syndrome (ARVCF) in both anterior and posterior sutures of bovine lenses. Liquid chromatography tandem MS confirmed higher expression of cadherin-2 and ARVCF and other adherens junction proteins including catenin α2 (CTNNA2) and catenin β1 (CTNNB1) in sutures. In contrast, IMS indicated low expression of gap junction protein connexin 50 and connexin 46 in the suture regions. The localization of cadherin-2 and connexin 50 was confirmed by immunofluorescence. Conclusions The complementary expression of adherens junction proteins and gap junction proteins in lens suture regions implicates adherens junctions in fiber cell tip adhesion and in maintaining the integrity of the lens.
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Affiliation(s)
- Zhen Wang
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Romell B. Gletten
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
| | - Kevin L. Schey
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
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20
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Pierobon M, Petricoin EF. Functional proteomic analysis, a missing piece for understanding clonal evolution and cooperation in the tissue microecology. Expert Rev Mol Diagn 2023; 23:1057-1059. [PMID: 37902050 DOI: 10.1080/14737159.2023.2277371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/20/2023] [Indexed: 10/31/2023]
Affiliation(s)
- Mariaelena Pierobon
- School of Systems Biology, Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
| | - Emanuel F Petricoin
- School of Systems Biology, Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
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21
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v3 - a versatile open source software for mass spectrometry imaging analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529280. [PMID: 36865170 PMCID: PMC9980127 DOI: 10.1101/2023.02.20.529280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Cardinal v3 is an open source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass re-calibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multi-tissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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22
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Xu H, Hao Q, Liu H, Chen L, Wu R, Qin L, Guo H, Li J, Yang C, Hu H, Xue K, Feng J, Zhou Y, Liu B, Li G, Wang X. A concentration-descending washing strategy with methanol for the enhancement of protein imaging in biological tissues by MALDI-MS. Analyst 2023; 148:823-831. [PMID: 36637134 DOI: 10.1039/d2an01678h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful approach that has been widely used for in situ detection of various endogenous compounds in tissues. However, there are still challenges with in situ analysis of proteins using MALDI-MSI due to the ion suppression effects of small molecules in tissue sections. Therefore, tissue-washing steps are crucial for protein MALDI tissue imaging to remove these interfering molecules. Here, we successfully developed a new method named the concentration-descending washing strategy (CDWS) with methanol (MeOH), i.e., washing of biological tissue with 100%, 95%, and 70% MeOH solutions, for the enhancement of endogenous in situ protein detection and imaging in tissues using MALDI-MS. The method of MeOH-based CDWS (MeOH-CDWS) led to the successful in situ detection of 272 ± 3, 185 ± 4, and 134 ± 2 protein ion signals from rat liver, rat brain, and germinating Chinese-yew seed tissue sections, respectively. By comparison, 161 ± 2, 121 ± 1, and 114 ± 2 protein ions were detected by three commonly used methods, i.e., Carnoy's wash, ethanol (EtOH)-based CAWS (i.e., concentration-ascending washing strategy, 70% EtOH followed by 90% EtOH/9% AcOH), and isopropanol (iPrOH)-based CAWS (70% iPrOH followed by 95% iPrOH), respectively, in rat liver tissue sections, indicating that 68.9 ± 3.1%, 124.8 ± 3.3%, and 138.6 ± 4.4% more protein ion signals could be detected by the use of MeOH-CDWS than the three abovementioned washing strategies. Our results show that the use of MeOH-CDWS improves the performance of MALDI-MSI for in situ protein detection such as the number and intensity of proteins. The use of MeOH-CDWS improves the fixation of proteins and thus reduces the loss of proteins, which significantly reduces protein delocalization in tissue and enhances the performance of MALDI tissue imaging of protein. Thus, the use of MeOH-CDWS improves the quality of protein images in tissue sections through MALDI-MSI and has the potential to be used as standard practice for MALDI tissue imaging of proteins.
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Affiliation(s)
- Hualei Xu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Qichen Hao
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Haiqiang Liu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Lulu Chen
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Ran Wu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Liang Qin
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Hua Guo
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Jinrong Li
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Chenyu Yang
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Hao Hu
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Kun Xue
- College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Jinchao Feng
- College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Yijun Zhou
- College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
| | - Biao Liu
- Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of China, Nanjing 210042, China.
| | - Gaopeng Li
- General Surgery Department, Shanxi Bethune Hospital, Taiyuan 030032, China.
| | - Xiaodong Wang
- Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), State Ethnic Affairs Commission, Beijing 100081, China. .,College of Life and Environmental Sciences, Centre for Imaging & Systems Biology, Minzu University of China, Beijing 100081, China
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23
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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24
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Quinlan RA, Clark JI. Insights into the biochemical and biophysical mechanisms mediating the longevity of the transparent optics of the eye lens. J Biol Chem 2022; 298:102537. [PMID: 36174677 PMCID: PMC9638808 DOI: 10.1016/j.jbc.2022.102537] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 11/18/2022] Open
Abstract
In the human eye, a transparent cornea and lens combine to form the "refracton" to focus images on the retina. This requires the refracton to have a high refractive index "n," mediated largely by extracellular collagen fibrils in the corneal stroma and the highly concentrated crystallin proteins in the cytoplasm of the lens fiber cells. Transparency is a result of short-range order in the spatial arrangement of corneal collagen fibrils and lens crystallins, generated in part by post-translational modifications (PTMs). However, while corneal collagen is remodeled continuously and replaced, lens crystallins are very long-lived and are not replaced and so accumulate PTMs over a lifetime. Eventually, a tipping point is reached when protein aggregation results in increased light scatter, inevitably leading to the iconic protein condensation-based disease, age-related cataract (ARC). Cataracts account for 50% of vision impairment worldwide, affecting far more people than other well-known protein aggregation-based diseases. However, because accumulation of crystallin PTMs begins before birth and long before ARC presents, we postulate that the lens protein PTMs contribute to a "cataractogenic load" that not only increases with age but also has protective effects on optical function by stabilizing lens crystallins until a tipping point is reached. In this review, we highlight decades of experimental findings that support the potential for PTMs to be protective during normal development. We hypothesize that ARC is preventable by protecting the biochemical and biophysical properties of lens proteins needed to maintain transparency, refraction, and optical function.
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Affiliation(s)
- Roy A Quinlan
- Department of Biosciences, Durham University, South Road Science Site, Durham, United Kingdom; Department of Biological Structure, University of Washington, Seattle, Washington, USA.
| | - John I Clark
- Department of Biological Structure, University of Washington, Seattle, Washington, USA.
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25
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Zhao C, Dong J, Deng L, Tan Y, Jiang W, Cai Z. Molecular network strategy in multi-omics and mass spectrometry imaging. Curr Opin Chem Biol 2022; 70:102199. [PMID: 36027696 DOI: 10.1016/j.cbpa.2022.102199] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 06/01/2022] [Accepted: 07/10/2022] [Indexed: 11/30/2022]
Abstract
Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.
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Affiliation(s)
- Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Lingli Deng
- Department of Information Engineering, East China University of Technology, China
| | - Yawen Tan
- Department of Breast and Thyroid Surgery, Shenzhen Second People's Hospital, Shenzhen, China
| | - Wei Jiang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong SAR, China.
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26
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Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2022:e21794. [PMID: 35822576 DOI: 10.1002/mas.21794] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Toufik Mahamdi
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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27
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Meng L, Han J, Chen J, Wang X, Huang X, Liu H, Nie Z. Development of an Automatic Ultrasonic Matrix Sprayer for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging. Anal Chem 2022; 94:6457-6462. [PMID: 35438954 DOI: 10.1021/acs.analchem.2c00403] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has emerged as a powerful tool for studying the spatial distribution of various types of molecules. Matrix deposition is a critical step for obtaining high-quality imaging data. An automatic device named SoniCoat was fabricated based on an ultrasonic nozzle driven by a pizeoelectric ceramic for matrix coating in the present study. Compared with the minihumidifier developed previously by our group for matrix deposition, SoniCoat realized automation, and the ultrasonic nozzle of this device is easy to clean and less likely to get clogged, indicating a longer life. Compared with commercial and homemade instruments such as the TM-Sprayer and electrospray matrix coating device, our newly developed device achieved a good nebulization effect without the need for high-pressure gas flow and high voltage. Matrix deposition by SoniCoat generated more homogeneous matrix crystals with diameters of about 10 μm and reduced the occurrence of molecular diffusion to a greater extent, compared with the results by TM-Sprayer. Repeatable high-spatial-resolution MS imaging data were obtained with the help of SoniCoat. Furthermore, the newly developed device can also be successfully applied for in situ derivatization.
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Affiliation(s)
- Lingwei Meng
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Han
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Junyu Chen
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Wang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Huang
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huihui Liu
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Zongxiu Nie
- Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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28
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Affiliation(s)
- Roy Quinlan
- Biomedical Sciences, Department of Biosciences, The University of Durham, Upper Mountjoy Science Site, Durham, DH1 3LE, UK.
| | - Frank Giblin
- Biomedical Sciences Emeritus, Eye Research Institute, Oakland University, Rochester, MI, 48309, USA.
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29
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Kruse ARS, Spraggins JM. Uncovering Molecular Heterogeneity in the Kidney With Spatially Targeted Mass Spectrometry. Front Physiol 2022; 13:837773. [PMID: 35222094 PMCID: PMC8874197 DOI: 10.3389/fphys.2022.837773] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/04/2022] [Indexed: 02/06/2023] Open
Abstract
The kidney functions through the coordination of approximately one million multifunctional nephrons in 3-dimensional space. Molecular understanding of the kidney has relied on transcriptomic, proteomic, and metabolomic analyses of kidney homogenate, but these approaches do not resolve cellular identity and spatial context. Mass spectrometry analysis of isolated cells retains cellular identity but not information regarding its cellular neighborhood and extracellular matrix. Spatially targeted mass spectrometry is uniquely suited to molecularly characterize kidney tissue while retaining in situ cellular context. This review summarizes advances in methodology and technology for spatially targeted mass spectrometry analysis of kidney tissue. Profiling technologies such as laser capture microdissection (LCM) coupled to liquid chromatography tandem mass spectrometry provide deep molecular coverage of specific tissue regions, while imaging technologies such as matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) molecularly profile regularly spaced tissue regions with greater spatial resolution. These technologies individually have furthered our understanding of heterogeneity in nephron regions such as glomeruli and proximal tubules, and their combination is expected to profoundly expand our knowledge of the kidney in health and disease.
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Affiliation(s)
- Angela R. S. Kruse
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, United States
| | - Jeffrey M. Spraggins
- Department of Biochemistry, Vanderbilt University, Nashville, TN, United States
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- *Correspondence: Jeffrey M. Spraggins,
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30
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Muñoz-Castro C, Noori A, Magdamo CG, Li Z, Marks JD, Frosch MP, Das S, Hyman BT, Serrano-Pozo A. Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer's disease. J Neuroinflammation 2022; 19:30. [PMID: 35109872 PMCID: PMC8808995 DOI: 10.1186/s12974-022-02383-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/10/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer's disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of these glial cells but lack spatial information. We have developed a methodology of cyclic multiplex fluorescent immunohistochemistry on human postmortem brains and image analysis that enables a comprehensive morphological quantitative characterization of astrocytes and microglia in the context of their spatial relationships with plaques and tangles. METHODS Single FFPE sections from the temporal association cortex of control and AD subjects were subjected to 8 cycles of multiplex fluorescent immunohistochemistry, including 7 astroglial, 6 microglial, 1 neuronal, Aβ, and phospho-tau markers. Our analysis pipeline consisted of: (1) image alignment across cycles; (2) background subtraction; (3) manual annotation of 5172 ALDH1L1+ astrocytic and 6226 IBA1+ microglial profiles; (4) local thresholding and segmentation of profiles; (5) machine learning on marker intensity data; and (6) deep learning on image features. RESULTS Spectral clustering identified three phenotypes of astrocytes and microglia, which we termed "homeostatic," "intermediate," and "reactive." Reactive and, to a lesser extent, intermediate astrocytes and microglia were closely associated with AD pathology (≤ 50 µm). Compared to homeostatic, reactive astrocytes contained substantially higher GFAP and YKL-40, modestly elevated vimentin and TSPO as well as EAAT1, and reduced GS. Intermediate astrocytes had markedly increased EAAT2, moderately increased GS, and intermediate GFAP and YKL-40 levels. Relative to homeostatic, reactive microglia showed increased expression of all markers (CD68, ferritin, MHC2, TMEM119, TSPO), whereas intermediate microglia exhibited increased ferritin and TMEM119 as well as intermediate CD68 levels. Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. AD diagnoses at the single-cell level. CONCLUSIONS Cyclic multiplex fluorescent immunohistochemistry combined with machine learning models holds promise to advance our understanding of the complexity and heterogeneity of glial responses as well as inform transcriptomics studies. Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary "homeostatic vs. reactive" classification to a third state, which could represent "transitional" or "resilient" glia.
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Affiliation(s)
- Clara Muñoz-Castro
- Departamento de Bioquímica y Biología Molecular, Facultad de Farmacia, Universidad de Sevilla, 41012, Sevilla, Spain
- Instituto de Biomedicina de Sevilla (IBiS)-Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013, Sevilla, Spain
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Ayush Noori
- Harvard College, Boston, MA, 02138, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
| | - Colin G Magdamo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
| | - Zhaozhi Li
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
| | - Jordan D Marks
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
| | - Matthew P Frosch
- Department of Pathology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Bradley T Hyman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - Alberto Serrano-Pozo
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA.
- MassGeneral Institute for Neurodegenerative Disease, 114 16th Street, Charlestown, MA, 02129, USA.
- Massachusetts Alzheimer's Disease Research Center, Charlestown, MA, 02129, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
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Murphy KJ, Reed DA, Trpceski M, Herrmann D, Timpson P. Quantifying and visualising the nuances of cellular dynamics in vivo using intravital imaging. Curr Opin Cell Biol 2021; 72:41-53. [PMID: 34091131 DOI: 10.1016/j.ceb.2021.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/23/2021] [Accepted: 04/28/2021] [Indexed: 12/14/2022]
Abstract
Intravital imaging is a powerful technology used to quantify and track dynamic changes in live cells and tissues within an intact environment. The ability to watch cell biology in real-time 'as it happens' has provided novel insight into tissue homeostasis, as well as disease initiation, progression and response to treatment. In this minireview, we highlight recent advances in the field of intravital microscopy, touching upon advances in awake versus anaesthesia-based approaches, as well as the integration of biosensors into intravital imaging. We also discuss current challenges that, in our opinion, need to be overcome to further advance the field of intravital imaging at the single-cell, subcellular and molecular resolution to reveal nuances of cell behaviour that can be targeted in complex disease settings.
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Affiliation(s)
- Kendelle J Murphy
- Garvan Institute of Medical Research & The Kinghorn Cancer Centre, Cancer Theme, Sydney, NSW, 2010, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Daniel A Reed
- Garvan Institute of Medical Research & The Kinghorn Cancer Centre, Cancer Theme, Sydney, NSW, 2010, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, 2010, Australia
| | - Michael Trpceski
- Garvan Institute of Medical Research & The Kinghorn Cancer Centre, Cancer Theme, Sydney, NSW, 2010, Australia
| | - David Herrmann
- Garvan Institute of Medical Research & The Kinghorn Cancer Centre, Cancer Theme, Sydney, NSW, 2010, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, 2010, Australia.
| | - Paul Timpson
- Garvan Institute of Medical Research & The Kinghorn Cancer Centre, Cancer Theme, Sydney, NSW, 2010, Australia; St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, Sydney, NSW, 2010, Australia.
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