1
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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2
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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3
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Seal S, Neelon B, Angel PM, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta AS, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA. J Proteome Res 2024; 23:1131-1143. [PMID: 38417823 PMCID: PMC11002919 DOI: 10.1021/acs.jproteome.3c00462] [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: 07/30/2023] [Revised: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 03/01/2024]
Abstract
Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.
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Affiliation(s)
- Souvik Seal
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Brian Neelon
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Peggi M. Angel
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth C. O’Quinn
- Translational
Science Laboratory, Hollings Cancer Center, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth Hill
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Thao Vu
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Debashis Ghosh
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Anand S. Mehta
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Kristin Wallace
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Alexander V. Alekseyenko
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
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4
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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5
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Bull JA, Mulholland EJ, Leedham SJ, Byrne HM. Extended correlation functions for spatial analysis of multiplex imaging data. BIOLOGICAL IMAGING 2024; 4:e2. [PMID: 38516631 PMCID: PMC10951806 DOI: 10.1017/s2633903x24000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 01/11/2024] [Accepted: 01/28/2024] [Indexed: 03/23/2024]
Abstract
Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.
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Affiliation(s)
- Joshua A. Bull
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
| | - Eoghan J. Mulholland
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
| | - Simon J. Leedham
- Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
- Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, OxfordOX3 9DU, UK
| | - Helen M. Byrne
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, OxfordOX2 6GG, UK
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7DQ, UK
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6
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Guldberg SM, Okholm TLH, McCarthy EE, Spitzer MH. Computational Methods for Single-Cell Proteomics. Annu Rev Biomed Data Sci 2023; 6:47-71. [PMID: 37040735 PMCID: PMC10621466 DOI: 10.1146/annurev-biodatasci-020422-050255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Advances in single-cell proteomics technologies have resulted in high-dimensional datasets comprising millions of cells that are capable of answering key questions about biology and disease. The advent of these technologies has prompted the development of computational tools to process and visualize the complex data. In this review, we outline the steps of single-cell and spatial proteomics analysis pipelines. In addition to describing available methods, we highlight benchmarking studies that have identified advantages and pitfalls of the currently available computational toolkits. As these technologies continue to advance, robust analysis tools should be developed in tandem to take full advantage of the potential biological insights provided by these data.
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Affiliation(s)
- Sophia M Guldberg
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
| | - Trine Line Hauge Okholm
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
| | - Elizabeth E McCarthy
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Biomedical Sciences Graduate Program, University of California, San Francisco, California, USA
- Institute for Human Genetics; Division of Rheumatology, Department of Medicine; Medical Scientist Training Program; and Biological and Medical Informatics Graduate Program, University of California, San Francisco, California, USA
| | - Matthew H Spitzer
- Department of Otolaryngology-Head and Neck Surgery and Department of Microbiology and Immunology, University of California, San Francisco, California, USA;
- Gladstone-UCSF Institute for Genomic Immunology, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
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7
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Liu CC, Greenwald NF, Kong A, McCaffrey EF, Leow KX, Mrdjen D, Cannon BJ, Rumberger JL, Varra SR, Angelo M. Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nat Commun 2023; 14:4618. [PMID: 37528072 PMCID: PMC10393943 DOI: 10.1038/s41467-023-40068-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.
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Affiliation(s)
- Candace C Liu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Alex Kong
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Ke Xuan Leow
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Dunja Mrdjen
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bryan J Cannon
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Josef Lorenz Rumberger
- Max-Delbrueck-Center for Molecular Medicine, Berlin, Germany
- Charité University Medicine, Berlin, Germany
| | | | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA.
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8
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Seal S, Neelon B, Angel P, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta A, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.548034. [PMID: 37461579 PMCID: PMC10350074 DOI: 10.1101/2023.07.06.548034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/31/2023]
Abstract
Motivation Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. Results We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. Availability The associated R package can be found here, https://github.com/sealx017/SpaceANOVA.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Brian Neelon
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Peggi Angel
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina
| | - Elizabeth C. O’Quinn
- Translational Science Laboratory, Hollings Cancer Center, Medical University of South Carolina, Charleston, South Carolina
| | - Elizabeth Hill
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Thao Vu
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, Colorado
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado CU Anschutz Medical Campus, Aurora, Colorado
| | - Anand Mehta
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina
| | - Kristin Wallace
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Alexander V. Alekseyenko
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina
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9
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Li BZ, Sumera A, Booker SA, McCullagh EA. Current Best Practices for Analysis of Dendritic Spine Morphology and Number in Neurodevelopmental Disorder Research. ACS Chem Neurosci 2023; 14:1561-1572. [PMID: 37070364 PMCID: PMC10161226 DOI: 10.1021/acschemneuro.3c00062] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/07/2023] [Indexed: 04/19/2023] Open
Abstract
Quantitative methods for assessing neural anatomy have rapidly evolved in neuroscience and provide important insights into brain health and function. However, as new techniques develop, it is not always clear when and how each may be used to answer specific scientific questions posed. Dendritic spines, which are often indicative of synapse formation and neural plasticity, have been implicated across many brain regions in neurodevelopmental disorders as a marker for neural changes reflecting neural dysfunction or alterations. In this Perspective we highlight several techniques for staining, imaging, and quantifying dendritic spines as well as provide a framework for avoiding potential issues related to pseudoreplication. This framework illustrates how others may apply the most rigorous approaches. We consider the cost-benefit analysis of the varied techniques, recognizing that the most sophisticated equipment may not always be necessary for answering some research questions. Together, we hope this piece will help researchers determine the best strategy toward using the ever-growing number of techniques available to determine neural changes underlying dendritic spine morphology in health and neurodevelopmental disorders.
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Affiliation(s)
- Ben-Zheng Li
- Department
of Physiology and Biophysics, University
of Colorado Anschutz Medical Campus, Aurora, Colorado 80045, United States
| | - Anna Sumera
- Simons
Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9XD, U.K.
| | - Sam A Booker
- Simons
Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh EH8 9XD, U.K.
| | - Elizabeth A. McCullagh
- Department
of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma 74078, United States
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10
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Yang M, Unsihuay D, Hu H, Nguele Meke F, Qu Z, Zhang ZY, Laskin J. Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution. Anal Chem 2023; 95:5214-5222. [PMID: 36917636 DOI: 10.1021/acs.analchem.2c04795] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful tool for label-free mapping of the spatial distribution of proteins in biological tissues. We have previously demonstrated imaging of individual proteoforms in biological tissues using nanospray desorption electrospray ionization (nano-DESI), an ambient liquid extraction-based MSI technique. Nano-DESI MSI generates multiply charged protein ions, which is advantageous for their identification using top-down proteomics analysis. In this study, we demonstrate proteoform mapping in biological tissues with a spatial resolution down to 7 μm using nano-DESI MSI. A substantial decrease in protein signals observed in high-spatial-resolution MSI makes these experiments challenging. We have enhanced the sensitivity of nano-DESI MSI experiments by optimizing the design of the capillary-based probe and the thickness of the tissue section. In addition, we demonstrate that oversampling may be used to further improve spatial resolution at little or no expense to sensitivity. These developments represent a new step in MSI-based spatial proteomics, which complements targeted imaging modalities widely used for studying biological systems.
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Affiliation(s)
- Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Daisy Unsihuay
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Hang Hu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Frederick Nguele Meke
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zihan Qu
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Zhong-Yin Zhang
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
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Advances in Mass Spectrometry-Based Single Cell Analysis. BIOLOGY 2023; 12:biology12030395. [PMID: 36979087 PMCID: PMC10045136 DOI: 10.3390/biology12030395] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.
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12
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Glasson Y, Chépeaux LA, Dumé AS, Jay P, Pirot N, Bonnefoy N, Michaud HA. A 31-plex panel for high-dimensional single-cell analysis of murine preclinical models of solid tumors by imaging mass cytometry. Front Immunol 2023; 13:1011617. [PMID: 36741363 PMCID: PMC9893499 DOI: 10.3389/fimmu.2022.1011617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Currently, the study of resistance mechanisms and disease progression in cancer relies on the capacity to analyze tumors as a complex ecosystem of healthy and malignant cells. Therefore, one of the current challenges is to decipher the intra-tumor heterogeneity and especially the spatial distribution and interactions of the different cellular actors within the tumor. Preclinical mouse models are widely used to extend our understanding of the tumor microenvironment (TME). Such models are becoming more sophisticated and allow investigating questions that cannot be addressed in clinical studies. Indeed, besides studying the tumor cell interactions within their environment, mouse models allow evaluating the efficacy of new drugs and delivery approaches, treatment posology, and toxicity. Spatially resolved analyses of the intra-tumor heterogeneity require global approaches to identify and localize a large number of different cell types. For this purpose, imaging mass cytometry (IMC) is a major asset in the field of human immuno-oncology. However, the paucity of validated IMC panels to study TME in pre-clinical mouse models remains a critical obstacle to translational or basic research in oncology. Here, we validated a panel of 31 markers for studying at the single-cell level the TME and the immune landscape for discovering/characterizing cells with complex phenotypes and the interactions shaping the tumor ecosystem in mouse models.
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Affiliation(s)
- Yaël Glasson
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Plateforme de Cytométrie et d’Imagerie de Masse, Montpellier, France,Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Montpellier, France
| | - Laure-Agnès Chépeaux
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Plateforme de Cytométrie et d’Imagerie de Masse, Montpellier, France
| | - Anne-Sophie Dumé
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Plateforme de Cytométrie et d’Imagerie de Masse, Montpellier, France
| | - Philippe Jay
- Institut de Génomique Fonctionnelle (IGF), University of Montpellier, Centre national de la recherche scientifique (CNRS), Inserm, Montpellier, France
| | - Nelly Pirot
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Montpellier, France,BioCampus Montpellier, Univ Montpellier, Centre national de la recherche scientifique (CNRS), Inserm, Réseau d’Histologie Expérimentale de Montpellier, Montpellier, France
| | - Nathalie Bonnefoy
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Plateforme de Cytométrie et d’Imagerie de Masse, Montpellier, France,Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Montpellier, France
| | - Henri-Alexandre Michaud
- Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Plateforme de Cytométrie et d’Imagerie de Masse, Montpellier, France,Institut de Recherche en Cancérologie de Montpellier (IRCM), Univ Montpellier, Inserm, Institut régional du Cancer de Montpellier (ICM), Montpellier, France,*Correspondence: Henri-Alexandre Michaud,
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13
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Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers. Semin Immunopathol 2023; 45:61-69. [PMID: 36625902 DOI: 10.1007/s00281-022-00981-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/11/2022] [Indexed: 01/11/2023]
Abstract
Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.
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Zhao J, Liu Y, Wang M, Ma J, Yang P, Wang S, Wu Q, Gao J, Chen M, Qu G, Wang J, Jiang G. Insights into highly multiplexed tissue images: A primer for Mass Cytometry Imaging data analysis. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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15
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Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF). J Transl Med 2022; 102:762-770. [PMID: 35351966 DOI: 10.1038/s41374-022-00778-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 11/09/2022] Open
Abstract
Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) is a form of mass spectrometry imaging that uses metal labeled antibodies and secondary ion mass spectrometry to image dozens of proteins simultaneously in the same tissue section. Working with the National Cancer Institute's (NCI) Cancer Immune Monitoring and Analysis Centers (CIMAC), we undertook a validation study, assessing concordance across a dozen serial sections of a tissue microarray of 21 samples that were independently processed and imaged by MIBI-TOF or single-plex immunohistochemistry (IHC) over 12 days. Pixel-level features were highly concordant across all 16 targets assessed in both staining intensity (R2 = 0.94 ± 0.04) and frequency (R2 = 0.95 ± 0.04). Comparison to digitized, single-plex IHC on adjacent serial sections revealed similar concordance (R2 = 0.85 ± 0.08) as well. Lastly, automated segmentation and clustering of eight cell populations found that cell frequencies between serial sections yielded an average correlation of R2 = 0.94 ± 0.05. Taken together, we demonstrate that MIBI-TOF, with well-vetted reagents and automated analysis, can generate consistent and quantitative annotations of clinically relevant cell states in archival human tissue, and more broadly, present a scalable framework for benchmarking multiplexed IHC approaches.
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