1
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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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2
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Guazzini M, Reisach AG, Weichwald S, Seiler C. spillR: spillover compensation in mass cytometry data. Bioinformatics 2024; 40:btae337. [PMID: 38848472 PMCID: PMC11189660 DOI: 10.1093/bioinformatics/btae337] [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: 10/16/2023] [Revised: 04/29/2024] [Accepted: 06/05/2024] [Indexed: 06/09/2024] Open
Abstract
MOTIVATION Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. Chevrier et al. introduce an experimental and computational procedure to estimate and compensate for spillover implemented in their R package CATALYST. They assume spillover can be described by a spillover matrix that encodes the ratio between the signal in the unstained spillover receiving and stained spillover emitting channel. They estimate the spillover matrix from experiments with beads. We propose to skip the matrix estimation step and work directly with the full bead distributions. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. Spillover correction is often a pre-processing step followed by downstream analyses, and choosing a flexible model reduces the chance of introducing biases that can propagate downstream. RESULTS We implement our method in an R package spillR using expectation-maximization to fit the mixture model. We test our method on simulated, semi-simulated, and real data from CATALYST. We find that our method compensates low counts accurately, does not introduce negative counts, avoids overcompensating high counts, and preserves correlations between markers that may be biologically meaningful. AVAILABILITY AND IMPLEMENTATION Our new R package spillR is on bioconductor at bioconductor.org/packages/spillR. All experiments and plots can be reproduced by compiling the R markdown file spillR_paper.Rmd at github.com/ChristofSeiler/spillR_paper.
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Affiliation(s)
- Marco Guazzini
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | | | - Sebastian Weichwald
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christof Seiler
- Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
- Mathematics Centre Maastricht, Maastricht University, Maastricht, The Netherlands
- Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zurich, University of Zurich, Schlieren, Switzerland
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3
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Pham T, Chen Y, Labaer J, Guo J. Ultrasensitive and Multiplexed Protein Imaging with Clickable and Cleavable Fluorophores. Anal Chem 2024; 96:7281-7288. [PMID: 38663032 DOI: 10.1021/acs.analchem.4c01273] [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: 05/07/2024]
Abstract
Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction, and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity, or are technically demanding. To tackle these issues, here, we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through repeated cycles of target staining, fluorescence imaging, and fluorophore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ∼820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed that different subregions of the tissue are composed of cells from specific clusters.
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Affiliation(s)
- Thai Pham
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Yi Chen
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Joshua Labaer
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Jia Guo
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
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4
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Ali HR, West RB. Spatial Biology of Breast Cancer. Cold Spring Harb Perspect Med 2024; 14:a041335. [PMID: 38110242 PMCID: PMC11065165 DOI: 10.1101/cshperspect.a041335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.
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Affiliation(s)
- H Raza Ali
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom
| | - Robert B West
- Department of Pathology, Stanford University Medical Center, Stanford, California 94305, USA
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5
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Ermann J, Lefton M, Wei K, Gutierrez-Arcelus M. Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling. Curr Rheumatol Rep 2024; 26:144-154. [PMID: 38227172 DOI: 10.1007/s11926-023-01132-7] [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] [Accepted: 12/28/2023] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis. RECENT FINDINGS The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.
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Affiliation(s)
- Joerg Ermann
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Micah Lefton
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Maria Gutierrez-Arcelus
- Harvard Medical School, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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6
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De Biasi S, Gigan JP, Borella R, Santacroce E, Lo Tartaro D, Neroni A, Paschalidis N, Piwocka K, Argüello RJ, Gibellini L, Cossarizza A. Cell metabolism: Functional and phenotypic single cell approaches. Methods Cell Biol 2024; 186:151-187. [PMID: 38705598 DOI: 10.1016/bs.mcb.2024.02.024] [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] [Indexed: 05/07/2024]
Abstract
Several metabolic pathways are essential for the physiological regulation of immune cells, but their dysregulation can cause immune dysfunction. Hypermetabolic and hypometabolic states represent deviations in the magnitude and flexibility of effector cells in different contexts, for example in autoimmunity, infections or cancer. To study immunometabolism, most methods focus on bulk populations and rely on in vitro activation assays. Nowadays, thanks to the development of single-cell technologies, including multiparameter flow cytometry, mass cytometry, RNA cytometry, among others, the metabolic state of individual immune cells can be measured in a variety of samples obtained in basic, translational and clinical studies. Here, we provide an overview of different single-cell approaches that are employed to investigate both mitochondrial functions and cell dependence from mitochondria metabolism. Moreover, besides the description of the appropriate experimental settings, we discuss the strengths and weaknesses of different approaches with the aim to suggest how to study cell metabolism in the settings of interest.
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Affiliation(s)
- Sara De Biasi
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy.
| | - Julien Paul Gigan
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | - Rebecca Borella
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Elena Santacroce
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Anita Neroni
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Katarzyna Piwocka
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Rafael José Argüello
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | - Lara Gibellini
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
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7
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Jahangir CA, Page DB, Broeckx G, Gonzalez CA, Burke C, Murphy C, Reis-Filho JS, Ly A, Harms PW, Gupta RR, Vieth M, Hida AI, Kahila M, Kos Z, van Diest PJ, Verbandt S, Thagaard J, Khiroya R, Abduljabbar K, Acosta Haab G, Acs B, Adams S, Almeida JS, Alvarado-Cabrero I, Azmoudeh-Ardalan F, Badve S, Baharun NB, Bellolio ER, Bheemaraju V, Blenman KR, Botinelly Mendonça Fujimoto L, Burgues O, Chardas A, Cheang MCU, Ciompi F, Cooper LA, Coosemans A, Corredor G, Dantas Portela FL, Deman F, Demaria S, Dudgeon SN, Elghazawy M, Fernandez-Martín C, Fineberg S, Fox SB, Giltnane JM, Gnjatic S, Gonzalez-Ericsson PI, Grigoriadis A, Halama N, Hanna MG, Harbhajanka A, Hart SN, Hartman J, Hewitt S, Horlings HM, Husain Z, Irshad S, Janssen EA, Kataoka TR, Kawaguchi K, Khramtsov AI, Kiraz U, Kirtani P, Kodach LL, Korski K, Akturk G, Scott E, Kovács A, Laenkholm AV, Lang-Schwarz C, Larsimont D, Lennerz JK, Lerousseau M, Li X, Madabhushi A, Maley SK, Manur Narasimhamurthy V, Marks DK, McDonald ES, Mehrotra R, Michiels S, Kharidehal D, Minhas FUAA, Mittal S, Moore DA, Mushtaq S, Nighat H, Papathomas T, Penault-Llorca F, Perera RD, Pinard CJ, Pinto-Cardenas JC, Pruneri G, Pusztai L, Rajpoot NM, Rapoport BL, Rau TT, Ribeiro JM, Rimm D, Vincent-Salomon A, Saltz J, Sayed S, Hytopoulos E, Mahon S, Siziopikou KP, Sotiriou C, Stenzinger A, Sughayer MA, Sur D, Symmans F, Tanaka S, Taxter T, Tejpar S, Teuwen J, Thompson EA, Tramm T, Tran WT, van der Laak J, Verghese GE, Viale G, Wahab N, Walter T, Waumans Y, Wen HY, Yang W, Yuan Y, Bartlett J, Loibl S, Denkert C, Savas P, Loi S, Specht Stovgaard E, Salgado R, Gallagher WM, Rahman A. Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer. J Pathol 2024; 262:271-288. [PMID: 38230434 DOI: 10.1002/path.6238] [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: 06/15/2023] [Accepted: 11/17/2023] [Indexed: 01/18/2024]
Abstract
Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Chowdhury Arif Jahangir
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - David B Page
- Earle A Chiles Research Institute, Providence Cancer Institute, Portland, OR, USA
| | - Glenn Broeckx
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
- Centre for Oncological Research (CORE), MIPPRO, Faculty of Medicine, Antwerp University, Antwerp, Belgium
| | - Claudia A Gonzalez
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Caoimbhe Burke
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Clodagh Murphy
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Jorge S Reis-Filho
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Paul W Harms
- Departments of Pathology and Dermatology, University of Michigan, Ann Arbor, MI, USA
| | - Rajarsi R Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Akira I Hida
- Department of Pathology, Matsuyama Shimin Hospital, Matsuyama, Japan
| | - Mohamed Kahila
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Zuzana Kos
- Department of Pathology and Laboratory Medicine, University of British Columbia, BC Cancer, Vancouver, British Columbia, Canada
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
- Johns Hopkins Oncology Center, Baltimore, MD, USA
| | - Sara Verbandt
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jeppe Thagaard
- Technical University of Denmark, Kgs. Lyngby, Denmark
- Visiopharm A/S, Hørsholm, Denmark
| | - Reena Khiroya
- Department of Cellular Pathology, University College Hospital, London, UK
| | - Khalid Abduljabbar
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | | | - Balazs Acs
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Sylvia Adams
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
- Department of Medicine, NYU Grossman School of Medicine, Manhattan, NY, USA
| | - Jonas S Almeida
- Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), Rockville, MD, USA
| | | | | | - Sunil Badve
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Emory University Winship Cancer Institute, Atlanta, GA, USA
| | | | - Enrique R Bellolio
- Departamento de Anatomía Patológica, Facultad de Medicina, Universidad de La Frontera, Temuco, Chile
| | | | - Kim Rm Blenman
- Department of Internal Medicine Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | | | - Octavio Burgues
- Pathology Department, Hospital Cliníco Universitario de Valencia/Incliva, Valencia, Spain
| | - Alexandros Chardas
- Department of Pathobiology & Population Sciences, The Royal Veterinary College, London, UK
| | - Maggie Chon U Cheang
- Head of Integrative Genomics Analysis in Clinical Trials, ICR-CTSU, Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Lee Ad Cooper
- Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - An Coosemans
- Department of Oncology, Laboratory of Tumor Immunology and Immunotherapy, KU Leuven, Leuven, Belgium
| | - Germán Corredor
- Biomedical Engineering Department, Emory University, Atlanta, GA, USA
| | | | - Frederik Deman
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Sandra Demaria
- Department of Radiation Oncology, Weill Cornell Medical College, New York, NY, USA
- Department of Pathology, Weill Cornell Medicine, New York, NY, USA
| | - Sarah N Dudgeon
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Mahmoud Elghazawy
- University of Surrey, Guildford, UK
- Ain Shams University, Cairo, Egypt
| | - Claudio Fernandez-Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
| | - Susan Fineberg
- Montefiore Medical Center and the Albert Einstein College of Medicine, New York, NY, USA
| | - Stephen B Fox
- Pathology, Peter MacCallum Cancer Centre and Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | | | - Sacha Gnjatic
- Department of Oncological Sciences, Medicine Hem/Onc, and Pathology, Tisch Cancer Institute - Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Anita Grigoriadis
- Cancer Bioinformatics, Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- The Breast Cancer Now Research Unit, Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Niels Halama
- Department of Translational Immunotherapy, German Cancer Research Center, Heidelberg, Germany
| | | | | | - Steven N Hart
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Johan Hartman
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Stephen Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hugo M Horlings
- Division of Pathology, Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | | | - Sheeba Irshad
- King's College London & Guys & St Thomas NHS Trust, London, UK
| | - Emiel Am Janssen
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | | | - Kosuke Kawaguchi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Andrey I Khramtsov
- Department of Pathology and Laboratory Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, Stavanger, Norway
- Department of Chemistry, Bioscience and Environmental Technology, University of Stavanger, Stavanger, Norway
| | - Pawan Kirtani
- Histopathology, Aakash Healthcare Super Speciality Hospital, New Delhi, India
| | - Liudmila L Kodach
- Department of Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Konstanty Korski
- Data, Analytics and Imaging, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Guray Akturk
- Translational Molecular Biomarkers, Merck & Co., Inc., Kenilworth, NJ, USA
| | - Ely Scott
- Translational Medicine, Bristol Myers Squibb, Princeton, NJ, USA
| | - Anikó Kovács
- Department of Clinical Pathology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anne-Vibeke Laenkholm
- Department of Surgical Pathology, Zealand University Hospital, Roskilde, Denmark
- Department of Surgical Pathology, University of Copenhagen, Copenhagen, Denmark
| | - Corinna Lang-Schwarz
- Institute of Pathology, Klinikum Bayreuth GmbH, Friedrich-Alexander-University Erlangen-Nuremberg, Bayreuth, Germany
| | - Denis Larsimont
- Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Jochen K Lennerz
- Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Marvin Lerousseau
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM U900, Paris, France
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics, Pathology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sai K Maley
- NRG Oncology/NSABP Foundation, Pittsburgh, PA, USA
| | | | - Douglas K Marks
- Perlmutter Cancer Center, NYU Langone Health, New York, NY, USA
| | - Elizabeth S McDonald
- Breast Cancer Translational Research Group, University of Pennsylvania, Philadelphia, PA, USA
| | - Ravi Mehrotra
- Indian Cancer Genomic Atlas, Pune, India
- Centre for Health, Innovation and Policy Foundation, Noida, India
| | - Stefan Michiels
- Office of Biostatistics and Epidemiology, Gustave Roussy, Oncostat U1018, Inserm, University Paris-Saclay, Ligue Contre le Cancer labeled Team, Villejuif, France
| | - Durga Kharidehal
- Department of Pathology, Narayana Medical College and Hospital, Nellore, India
| | - Fayyaz Ul Amir Afsar Minhas
- Tissue Image Analytics Centre, Warwick Cancer Research Centre, PathLAKE Consortium, Department of Computer Science, University of Warwick, Coventry, UK
| | - Shachi Mittal
- Department of Chemical Engineering, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - David A Moore
- CRUK Lung Cancer Centre of Excellence, UCL and Cellular Pathology Department, UCLH, London, UK
| | - Shamim Mushtaq
- Department of Biochemistry, Ziauddin University, Karachi, Pakistan
| | - Hussain Nighat
- Pathology and Laboratory Medicine, All India Institute of Medical Sciences, Raipur, India
| | - Thomas Papathomas
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Department of Clinical Pathology, Drammen Sykehus, Vestre Viken HF, Drammen, Norway
| | - Frederique Penault-Llorca
- Service de Pathologie et Biopathologie, Centre Jean PERRIN, INSERM U1240 Imagerie Moléculaire et Stratégies Théranostiques (IMoST), Université Clermont Auvergne, Clermont-Ferrand, France
| | - Rashindrie D Perera
- School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, Victoria, Australia
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Christopher J Pinard
- Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- Department of Oncology, Lakeshore Animal Health Partners, Mississauga, Ontario, Canada
- Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI), University of Guelph, Guelph, Ontario, Canada
| | | | - Giancarlo Pruneri
- Department of Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- Faculty of Medicine and Surgery, University of Milan, Milan, Italy
| | - Lajos Pusztai
- Yale Cancer Center, Yale University, New Haven, CT, USA
- Department of Medical Oncology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | | | - Bernardo Leon Rapoport
- The Medical Oncology Centre of Rosebank, Johannesburg, South Africa
- Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Tilman T Rau
- Institute of Pathology, University Hospital Düsseldorf and Heinrich-Heine-University, Düsseldorf, Germany
| | | | - David Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theranostic Medicine, Institut Curie, University Paris-Sciences et Lettres, Paris, France
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook Medicine, New York, NY, USA
| | - Shahin Sayed
- Department of Pathology, Aga Khan University, Nairobi, Kenya
| | - Evangelos Hytopoulos
- Department of Pathology, Aga Khan University, Nairobi, Kenya
- iRhythm Technologies Inc., San Francisco, CA, USA
| | - Sarah Mahon
- Mater Misericordiae University Hospital, Dublin, Ireland
| | - Kalliopi P Siziopikou
- Department of Pathology, Section of Breast Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Christos Sotiriou
- Breast Cancer Translational Research Laboratory J.-C. Heuson, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles (ULB), Brussels, Belgium
- Medical Oncology Department, Institut Jules Bordet, Hôpital Universitaire de Bruxelles (H.U.B), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Albrecht Stenzinger
- Institute of Pathology, University Hospital Heidelberg, Centers for Personalized Medicine (ZPM), Heidelberg, Germany
| | | | - Daniel Sur
- Department of Medical Oncology, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania
| | - Fraser Symmans
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sabine Tejpar
- Digestive Oncology, Department of Oncology, KU Leuven, Leuven, Belgium
| | - Jonas Teuwen
- AI for Oncology Lab, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Trine Tramm
- Department of Pathology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - William T Tran
- Department of Radiation Oncology, University of Toronto and Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Jeroen van der Laak
- Head of Integrative Genomics Analysis in Clinical Trials, ICR-CTSU, Division of Clinical Studies, The Institute of Cancer Research, London, UK
| | - Gregory E Verghese
- Cancer Bioinformatics, Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK
- The Breast Cancer Now Research Unit, Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Giuseppe Viale
- Department of Pathology, European Institute of Oncology & University of Milan, Milan, Italy
| | - Noorul Wahab
- Tissue Image Analytics Centre, Department of Computer Science, University of Warwick, Coventry, UK
| | - Thomas Walter
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France
- Institut Curie, PSL University, Paris, France
- INSERM U900, Paris, France
| | | | - Hannah Y Wen
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Wentao Yang
- Fudan Medical University Shanghai Cancer Center, Shanghai, PR China
| | - Yinyin Yuan
- Department of Translational Molecular Pathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Sibylle Loibl
- Department of Medicine and Research, German Breast Group, Neu-Isenburg, Germany
| | - Carsten Denkert
- Institut für Pathologie, Philipps-Universität Marburg und Universitätsklinikum Marburg, Marburg, Germany
| | - Peter Savas
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Sir Peter MacCallum Department of Medical Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Sherene Loi
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | | | - Roberto Salgado
- Department of Pathology PA2, GZA-ZNA Hospitals, Antwerp, Belgium
- Division of Cancer Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - William M Gallagher
- UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Dublin, Ireland
| | - Arman Rahman
- UCD School of Medicine, UCD Conway Institute, University College Dublin, Dublin, Ireland
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8
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Feng S, Calinawan A, Pugliese P, Wang P, Ceccarelli M, Petralia F, Gosline SJC. Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution. CELL REPORTS METHODS 2024; 4:100708. [PMID: 38412834 PMCID: PMC10921018 DOI: 10.1016/j.crmeth.2024.100708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 10/23/2023] [Accepted: 01/18/2024] [Indexed: 02/29/2024]
Abstract
Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.
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Affiliation(s)
- Song Feng
- Pacific Northwest National Laboratory, Seattle, WA, USA
| | - Anna Calinawan
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
| | | | - Pei Wang
- Icahn School of Medicine at Mount Sinai School, New York, NY, USA
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9
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Pascual-Reguant A, Kroh S, Hauser AE. Tissue niches and immunopathology through the lens of spatial tissue profiling techniques. Eur J Immunol 2024; 54:e2350484. [PMID: 37985207 DOI: 10.1002/eji.202350484] [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: 05/31/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/22/2023]
Abstract
Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.
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Affiliation(s)
- Anna Pascual-Reguant
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
- Spatial Genomics, Centre Nacional d'Anàlisi Genòmica, Barcelona, 08028, Spain
| | - Sandy Kroh
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
| | - Anja E Hauser
- Department of Rheumatology and Clinical Immunology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Immune Dynamics, Deutsches Rheuma-Forschungszentrum (DRFZ), Leibniz Institute, Berlin, Germany
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10
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Hu T, Allam M, Cai S, Henderson W, Yueh B, Garipcan A, Ievlev AV, Afkarian M, Beyaz S, Coskun AF. Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology. Nat Commun 2023; 14:8260. [PMID: 38086839 PMCID: PMC10716522 DOI: 10.1038/s41467-023-43917-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
Metabolic reprogramming in cancer and immune cells occurs to support their increasing energy needs in biological tissues. Here we propose Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling of single immune and cancer cells in male human tissues by incorporating untargeted spatial metabolomics and targeted multiplexed protein imaging in a single pipeline. We utilized the scSpaMet to profile cell types and spatial metabolomic maps of 19507, 31156, and 8215 single cells in human lung cancer, tonsil, and endometrium tissues, respectively. The scSpaMet analysis revealed cell type-dependent metabolite profiles and local metabolite competition of neighboring single cells in human tissues. Deep learning-based joint embedding revealed unique metabolite states within cell types. Trajectory inference showed metabolic patterns along cell differentiation paths. Here we show scSpaMet's ability to quantify and visualize the cell-type specific and spatially resolved metabolic-protein mapping as an emerging tool for systems-level understanding of tissue biology.
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Affiliation(s)
- Thomas Hu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Mayar Allam
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Shuangyi Cai
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Walter Henderson
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Brian Yueh
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Anton V Ievlev
- Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Oak Ridge, TN, USA
| | - Maryam Afkarian
- Division of Nephrology, Department of Internal Medicine, University of California, Davis, CA, USA
| | - Semir Beyaz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ahmet F Coskun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
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11
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Tsimberidou AM, Kahle M, Vo HH, Baysal MA, Johnson A, Meric-Bernstam F. Molecular tumour boards - current and future considerations for precision oncology. Nat Rev Clin Oncol 2023; 20:843-863. [PMID: 37845306 DOI: 10.1038/s41571-023-00824-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 10/18/2023]
Abstract
Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.
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Affiliation(s)
- Apostolia M Tsimberidou
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Michael Kahle
- Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Henry Hiep Vo
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mehmet A Baysal
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amber Johnson
- Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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12
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Lötstedt B, Stražar M, Xavier R, Regev A, Vickovic S. Spatial host-microbiome sequencing reveals niches in the mouse gut. Nat Biotechnol 2023:10.1038/s41587-023-01988-1. [PMID: 37985876 DOI: 10.1038/s41587-023-01988-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/12/2023] [Indexed: 11/22/2023]
Abstract
Mucosal and barrier tissues, such as the gut, lung or skin, are composed of a complex network of cells and microbes forming a tight niche that prevents pathogen colonization and supports host-microbiome symbiosis. Characterizing these networks at high molecular and cellular resolution is crucial for understanding homeostasis and disease. Here we present spatial host-microbiome sequencing (SHM-seq), an all-sequencing-based approach that captures tissue histology, polyadenylated RNAs and bacterial 16S sequences directly from a tissue by modifying spatially barcoded glass surfaces to enable simultaneous capture of host transcripts and hypervariable regions of the 16S bacterial ribosomal RNA. We applied our approach to the mouse gut as a model system, used a deep learning approach for data mapping and detected spatial niches defined by cellular composition and microbial geography. We show that subpopulations of gut cells express specific gene programs in different microenvironments characteristic of regional commensal bacteria and impact host-bacteria interactions. SHM-seq should enhance the study of native host-microbe interactions in health and disease.
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Affiliation(s)
- Britta Lötstedt
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- New York Genome Center, New York, NY, USA
| | | | - Ramnik Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Molecular Biology, Center for Computational and Integrative Biology, Massachusetts, General Hospital, Harvard Medical School, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Sanja Vickovic
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- New York Genome Center, New York, NY, USA.
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA.
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden.
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13
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Pham T, Chen Y, Labaer J, Guo J. Ultrasensitive and multiplexed protein imaging with clickable and cleavable fluorophores. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563323. [PMID: 37961266 PMCID: PMC10634699 DOI: 10.1101/2023.10.20.563323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity or technically demanding. To tackle these issues, here we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through reiterative cycles of target staining, fluorescence imaging, and fluoropohore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in a human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ~820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed different subregions of the tissue are composed of cells from specific clusters.
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Affiliation(s)
- Thai Pham
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Yi Chen
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Joshua Labaer
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Jia Guo
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
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14
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Jin Z, Zhou Q, Cheng JN, Jia Q, Zhu B. Heterogeneity of the tumor immune microenvironment and clinical interventions. Front Med 2023; 17:617-648. [PMID: 37728825 DOI: 10.1007/s11684-023-1015-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/24/2023] [Indexed: 09/21/2023]
Abstract
The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.
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Affiliation(s)
- Zheng Jin
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
- Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
- Research Institute, GloriousMed Clinical Laboratory (Shanghai) Co. Ltd., Shanghai, 201318, China
- Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, China
| | - Qin Zhou
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
- Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
- School of Pharmacy and Bioengineering, Chongqing University of Technology, Chongqing, 400054, China
| | - Jia-Nan Cheng
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.
- Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China.
| | - Qingzhu Jia
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.
- Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China.
| | - Bo Zhu
- Department of Oncology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China.
- Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China.
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15
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Gosline SJC, Veličković M, Pino JC, Day LZ, Attah IK, Swensen AC, Danna V, Posso C, Rodland KD, Chen J, Matthews CE, Campbell-Thompson M, Laskin J, Burnum-Johnson K, Zhu Y, Piehowski PD. Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine-Exocrine Signaling Sphere of Influence. Mol Cell Proteomics 2023; 22:100592. [PMID: 37328065 PMCID: PMC10460696 DOI: 10.1016/j.mcpro.2023.100592] [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] [Received: 11/04/2022] [Revised: 04/24/2023] [Accepted: 06/05/2023] [Indexed: 06/18/2023] Open
Abstract
The need for a clinically accessible method with the ability to match protein activity within heterogeneous tissues is currently unmet by existing technologies. Our proteomics sample preparation platform, named microPOTS (Microdroplet Processing in One pot for Trace Samples), can be used to measure relative protein abundance in micron-scale samples alongside the spatial location of each measurement, thereby tying biologically interesting proteins and pathways to distinct regions. However, given the smaller pixel/voxel number and amount of tissue measured, standard mass spectrometric analysis pipelines have proven inadequate. Here we describe how existing computational approaches can be adapted to focus on the specific biological questions asked in spatial proteomics experiments. We apply this approach to present an unbiased characterization of the human islet microenvironment comprising the entire complex array of cell types involved while maintaining spatial information and the degree of the islet's sphere of influence. We identify specific functional activity unique to the pancreatic islet cells and demonstrate how far their signature can be detected in the adjacent tissue. Our results show that we can distinguish pancreatic islet cells from the neighboring exocrine tissue environment, recapitulate known biological functions of islet cells, and identify a spatial gradient in the expression of RNA processing proteins within the islet microenvironment.
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Affiliation(s)
- Sara J C Gosline
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | | | - James C Pino
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Le Z Day
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Isaac K Attah
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Adam C Swensen
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Vincent Danna
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Camilo Posso
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Karin D Rodland
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Clayton E Matthews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana, USA
| | | | - Ying Zhu
- Pacific Northwest National Laboratories, Richland, Washington, USA
| | - Paul D Piehowski
- Pacific Northwest National Laboratories, Richland, Washington, USA.
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16
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Zidane M, Makky A, Bruhns M, Rochwarger A, Babaei S, Claassen M, Schürch CM. A review on deep learning applications in highly multiplexed tissue imaging data analysis. FRONTIERS IN BIOINFORMATICS 2023; 3:1159381. [PMID: 37564726 PMCID: PMC10410935 DOI: 10.3389/fbinf.2023.1159381] [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: 02/05/2023] [Accepted: 07/12/2023] [Indexed: 08/12/2023] Open
Abstract
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.
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Affiliation(s)
- Mohammed Zidane
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Ahmad Makky
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Matthias Bruhns
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sepideh Babaei
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Manfred Claassen
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Christian M. Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
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17
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Nabhan M, Egan D, Kreileder M, Zhernovkov V, Timosenko E, Slidel T, Dovedi S, Glennon K, Brennan D, Kolch W. Deciphering the tumour immune microenvironment cell by cell. IMMUNO-ONCOLOGY TECHNOLOGY 2023; 18:100383. [PMID: 37234284 PMCID: PMC10206805 DOI: 10.1016/j.iotech.2023.100383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Immune checkpoint inhibitors (ICIs) have rejuvenated therapeutic approaches in oncology. Although responses tend to be durable, response rates vary in many cancer types. Thus, the identification and validation of predictive biomarkers is a key clinical priority, the answer to which is likely to lie in the tumour microenvironment (TME). A wealth of data demonstrates the huge impact of the TME on ICI response and resistance. However, these data also reveal the complexity of the TME composition including the spatiotemporal interactions between different cell types and their dynamic changes in response to ICIs. Here, we briefly review some of the modalities that sculpt the TME, in particular the metabolic milieu, hypoxia and the role of cancer-associated fibroblasts. We then discuss recent approaches to dissect the TME with a focus on single-cell RNA sequencing, spatial transcriptomics and spatial proteomics. We also discuss some of the clinically relevant findings these multi-modal analyses have yielded.
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Affiliation(s)
- M. Nabhan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - D. Egan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - M. Kreileder
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - V. Zhernovkov
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
| | - E. Timosenko
- ICC, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, , UK
| | - T. Slidel
- Oncology Data Science, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - S. Dovedi
- ICC, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, , UK
| | - K. Glennon
- UCD Gynaecological Oncology Group, UCD School of Medicine Mater Misericordiae University Hospital, Dublin, Ireland
| | - D. Brennan
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
- UCD Gynaecological Oncology Group, UCD School of Medicine Mater Misericordiae University Hospital, Dublin, Ireland
| | - W. Kolch
- Systems Biology Ireland, School of Medicine, University College Dublin, Belfield, Ireland
- Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Ireland
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18
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Gaglia G, Burger ML, Ritch CC, Rammos D, Dai Y, Crossland GE, Tavana SZ, Warchol S, Jaeger AM, Naranjo S, Coy S, Nirmal AJ, Krueger R, Lin JR, Pfister H, Sorger PK, Jacks T, Santagata S. Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma. Cancer Cell 2023; 41:871-886.e10. [PMID: 37059105 PMCID: PMC10193529 DOI: 10.1016/j.ccell.2023.03.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 01/31/2023] [Accepted: 03/22/2023] [Indexed: 04/16/2023]
Abstract
Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.
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Affiliation(s)
- Giorgio Gaglia
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Megan L Burger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR 97212, USA; School of Medicine, Division of Hematology and Oncology, Oregon Health & Science University, Portland, OR 97212, USA
| | - Cecily C Ritch
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Danae Rammos
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yang Dai
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Grace E Crossland
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sara Z Tavana
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Simon Warchol
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Alex M Jaeger
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Santiago Naranjo
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Robert Krueger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
| | - Tyler Jacks
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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19
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Husain SS, Ong EJ, Minskiy D, Bober-Irizar M, Irizar A, Bober M. Single-cell subcellular protein localisation using novel ensembles of diverse deep architectures. Commun Biol 2023; 6:489. [PMID: 37147530 PMCID: PMC10163260 DOI: 10.1038/s42003-023-04840-z] [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: 10/21/2022] [Accepted: 04/12/2023] [Indexed: 05/07/2023] Open
Abstract
Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our AI-trains-AI approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL is best performing in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived.
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Affiliation(s)
| | - Eng-Jon Ong
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
| | - Dmitry Minskiy
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
| | - Mikel Bober-Irizar
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
- ForecomAI, London, W1W 5PF, UK
| | | | - Miroslaw Bober
- CVSSP, University of Surrey, Guildford, GU27XH, Surrey, UK
- ForecomAI, London, W1W 5PF, UK
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20
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Fischer JR, Jackson HW, de Souza N, Varga Z, Schraml P, Moch H, Bodenmiller B. Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers. Cell Rep Med 2023; 4:100977. [PMID: 36921599 PMCID: PMC10040454 DOI: 10.1016/j.xcrm.2023.100977] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/29/2022] [Accepted: 02/21/2023] [Indexed: 03/15/2023]
Abstract
Although breast cancer mortality is largely caused by metastasis, clinical decisions are based on analysis of the primary tumor and on lymph node involvement but not on the phenotype of disseminated cells. Here, we use multiplex imaging mass cytometry to compare single-cell phenotypes of primary breast tumors and matched lymph node metastases in 205 patients. We observe extensive phenotypic variability between primary and metastatic sites and that disseminated cell phenotypes frequently deviate from the clinical disease subtype. We identify single-cell phenotypes and spatial organizations of disseminated tumor cells that are associated with patient survival and a weaker survival association for high-risk phenotypes in the primary tumor. We show that p53 and GATA3 in lymph node metastases provide prognostic information beyond clinical classifiers and can be measured with standard methods. Molecular characterization of disseminated tumor cells is an untapped source of clinically applicable prognostic information for breast cancer.
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Affiliation(s)
- Jana Raja Fischer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland; Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | | | - Natalie de Souza
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland; Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Zsuzsanna Varga
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Peter Schraml
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University and University Hospital Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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21
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Einhaus J, Rochwarger A, Mattern S, Gaudillière B, Schürch CM. High-multiplex tissue imaging in routine pathology-are we there yet? Virchows Arch 2023; 482:801-812. [PMID: 36757500 PMCID: PMC10156760 DOI: 10.1007/s00428-023-03509-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/22/2023] [Accepted: 01/31/2023] [Indexed: 02/10/2023]
Abstract
High-multiplex tissue imaging (HMTI) approaches comprise several novel immunohistological methods that enable in-depth, spatial single-cell analysis. Over recent years, studies in tumor biology, infectious diseases, and autoimmune conditions have demonstrated the information gain accessible when mapping complex tissues with HMTI. Tumor biology has been a focus of innovative multiparametric approaches, as the tumor microenvironment (TME) contains great informative value for accurate diagnosis and targeted therapeutic approaches: unraveling the cellular composition and structural organization of the TME using sophisticated computational tools for spatial analysis has produced histopathologic biomarkers for outcomes in breast cancer, predictors of positive immunotherapy response in melanoma, and histological subgroups of colorectal carcinoma. Integration of HMTI technologies into existing clinical workflows such as molecular tumor boards will contribute to improve patient outcomes through personalized treatments tailored to the specific heterogeneous pathological fingerprint of cancer, autoimmunity, or infection. Here, we review the advantages and limitations of existing HMTI technologies and outline how spatial single-cell data can improve our understanding of pathological disease mechanisms and determinants of treatment success. We provide an overview of the analytic processing and interpretation and discuss how HMTI can improve future routine clinical diagnostic and therapeutic processes.
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Affiliation(s)
- Jakob Einhaus
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Alexander Rochwarger
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Sven Mattern
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Brice Gaudillière
- Department of Anaesthesiology, Perioperative & Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M Schürch
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
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22
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Mayer AT, Holman DR, Sood A, Tandon U, Bhate SS, Bodapati S, Barlow GL, Chang J, Black S, Crenshaw EC, Koron AN, Streett SE, Gambhir SS, Sandborn WJ, Boland BS, Hastie T, Tibshirani R, Chang JT, Nolan GP, Schürch CM, Rogalla S. A tissue atlas of ulcerative colitis revealing evidence of sex-dependent differences in disease-driving inflammatory cell types and resistance to TNF inhibitor therapy. SCIENCE ADVANCES 2023; 9:eadd1166. [PMID: 36662860 PMCID: PMC9858501 DOI: 10.1126/sciadv.add1166] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/16/2022] [Indexed: 06/01/2023]
Abstract
Although literature suggests that resistance to TNF inhibitor (TNFi) therapy in patients with ulcerative colitis (UC) is partially linked to immune cell populations in the inflamed region, there is still substantial uncertainty underlying the relevant spatial context. Here, we used the highly multiplexed immunofluorescence imaging technology CODEX to create a publicly browsable tissue atlas of inflammation in 42 tissue regions from 29 patients with UC and 5 healthy individuals. We analyzed 52 biomarkers on 1,710,973 spatially resolved single cells to determine cell types, cell-cell contacts, and cellular neighborhoods. We observed that cellular functional states are associated with cellular neighborhoods. We further observed that a subset of inflammatory cell types and cellular neighborhoods are present in patients with UC with TNFi treatment, potentially indicating resistant niches. Last, we explored applying convolutional neural networks (CNNs) to our dataset with respect to patient clinical variables. We note concerns and offer guidelines for reporting CNN-based predictions in similar datasets.
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Affiliation(s)
- Aaron T. Mayer
- Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Enable Medicine LLC, Menlo Park, CA, USA
| | - Derek R. Holman
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Anav Sood
- Department of Biomedical Data Science and of Statistics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Salil S. Bhate
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Graham L. Barlow
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeff Chang
- Enable Medicine LLC, Menlo Park, CA, USA
| | - Sarah Black
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Erica C. Crenshaw
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexander N. Koron
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sarah E. Streett
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sanjiv S. Gambhir
- Department of Bioengineering, Stanford University School of Medicine, Stanford, CA, USA
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - William J. Sandborn
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Brigid S. Boland
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Science and of Statistics, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert Tibshirani
- Department of Biomedical Data Science and of Statistics, Stanford University School of Medicine, Stanford, CA, USA
| | - John T. Chang
- Division of Gastroenterology, Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Garry P. Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Christian M. Schürch
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany
| | - Stephan Rogalla
- Molecular Imaging Program at Stanford, Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Division of Gastroenterology and Hepatology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
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23
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Rangamuwa K, Aloe C, Christie M, Asselin-Labat ML, Batey D, Irving L, John T, Bozinovski S, Leong TL, Steinfort D. Methods for assessment of the tumour microenvironment and immune interactions in non-small cell lung cancer. A narrative review. Front Oncol 2023; 13:1129195. [PMID: 37143952 PMCID: PMC10151669 DOI: 10.3389/fonc.2023.1129195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 03/28/2023] [Indexed: 05/06/2023] Open
Abstract
Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer death worldwide. Immunotherapy with immune checkpoint inhibitors (ICI) has significantly improved outcomes in some patients, however 80-85% of patients receiving immunotherapy develop primary resistance, manifesting as a lack of response to therapy. Of those that do have an initial response, disease progression may occur due to acquired resistance. The make-up of the tumour microenvironment (TME) and the interaction between tumour infiltrating immune cells and cancer cells can have a large impact on the response to immunotherapy. Robust assessment of the TME with accurate and reproducible methods is vital to understanding mechanisms of immunotherapy resistance. In this paper we will review the evidence of several methodologies to assess the TME, including multiplex immunohistochemistry, imaging mass cytometry, flow cytometry, mass cytometry and RNA sequencing.
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Affiliation(s)
- Kanishka Rangamuwa
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine Royal Melbourne Hospital (RMH), University of Melbourne, Parkville, VIC, Australia
- *Correspondence: Kanishka Rangamuwa,
| | - Christian Aloe
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Michael Christie
- Department of Pathology, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | | | - Daniel Batey
- Personalised Oncology Division, Walter Eliza Hall Institute, Melbourne, VIC, Australia
| | - Lou Irving
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Thomas John
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Steven Bozinovski
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, Australia
| | - Tracy L. Leong
- Personalised Oncology Division, Walter Eliza Hall Institute, Melbourne, VIC, Australia
- Department of Respiratory Medicine, Austin Hospital, Heidelberg, VIC, Australia
| | - Daniel Steinfort
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine Royal Melbourne Hospital (RMH), University of Melbourne, Parkville, VIC, Australia
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24
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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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25
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Brbić M, Cao K, Hickey JW, Tan Y, Snyder MP, Nolan GP, Leskovec J. Annotation of spatially resolved single-cell data with STELLAR. Nat Methods 2022; 19:1411-1418. [PMID: 36280720 DOI: 10.1038/s41592-022-01651-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 09/14/2022] [Indexed: 11/09/2022]
Abstract
Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.
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Affiliation(s)
- Maria Brbić
- Department of Computer Science, Stanford University, Stanford, CA, USA
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kaidi Cao
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - John W Hickey
- Baxter Laboratories Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Yuqi Tan
- Baxter Laboratories Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Baxter Laboratories Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.
- Department of Pathology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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26
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Coy S, Wang S, Stopka SA, Lin JR, Yapp C, Ritch CC, Salhi L, Baker GJ, Rashid R, Baquer G, Regan M, Khadka P, Cole KA, Hwang J, Wen PY, Bandopadhayay P, Santi M, De Raedt T, Ligon KL, Agar NYR, Sorger PK, Touat M, Santagata S. Single cell spatial analysis reveals the topology of immunomodulatory purinergic signaling in glioblastoma. Nat Commun 2022; 13:4814. [PMID: 35973991 PMCID: PMC9381513 DOI: 10.1038/s41467-022-32430-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 07/29/2022] [Indexed: 12/11/2022] Open
Abstract
How the glioma immune microenvironment fosters tumorigenesis remains incompletely defined. Here, we use single-cell RNA-sequencing and multiplexed tissue-imaging to characterize the composition, spatial organization, and clinical significance of extracellular purinergic signaling in glioma. We show that microglia are the predominant source of CD39, while tumor cells principally express CD73. In glioblastoma, CD73 is associated with EGFR amplification, astrocyte-like differentiation, and increased adenosine, and is linked to hypoxia. Glioblastomas enriched for CD73 exhibit inflammatory microenvironments, suggesting that purinergic signaling regulates immune adaptation. Spatially-resolved single-cell analyses demonstrate a strong spatial correlation between tumor-CD73 and microglial-CD39, with proximity associated with poor outcomes. Similar spatial organization is present in pediatric high-grade gliomas including H3K27M-mutant diffuse midline glioma. These data reveal that purinergic signaling in gliomas is shaped by genotype, lineage, and functional state, and that core enzymes expressed by tumor and myeloid cells are organized to promote adenosine-rich microenvironments potentially amenable to therapeutic targeting.
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Affiliation(s)
- Shannon Coy
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Boston, MA, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Cecily C Ritch
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
| | - Lisa Salhi
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle Epinière, and AP-HP Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Gregory J Baker
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Rumana Rashid
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Pitt-CMU Medical Scientist Training Program, University of Pittsburgh-Carnegie Mellon, Pittsburgh, PA, USA
| | - Gerard Baquer
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Prasidda Khadka
- Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristina A Cole
- Children's Hospital of Philadelphia, University of Pennsylvania, Pennsylvania, PA, USA
| | - Jaeho Hwang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Patrick Y Wen
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Pratiti Bandopadhayay
- Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mariarita Santi
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Thomas De Raedt
- Children's Hospital of Philadelphia, University of Pennsylvania, Pennsylvania, PA, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pediatric Oncology, Dana-Farber Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pathology, Boston Children's Hospital, Boston, MA, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Mehdi Touat
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle Epinière, and AP-HP Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France.
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Sandro Santagata
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
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27
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Kuczkiewicz-Siemion O, Sokół K, Puton B, Borkowska A, Szumera-Ciećkiewicz A. The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy. Cancers (Basel) 2022; 14:cancers14153833. [PMID: 35954496 PMCID: PMC9367614 DOI: 10.3390/cancers14153833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens. Abstract Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.
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Affiliation(s)
- Olga Kuczkiewicz-Siemion
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
| | - Kamil Sokół
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
| | - Beata Puton
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Aneta Borkowska
- Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Anna Szumera-Ciećkiewicz
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
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28
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Moffitt JR, Lundberg E, Heyn H. The emerging landscape of spatial profiling technologies. Nat Rev Genet 2022; 23:741-759. [PMID: 35859028 DOI: 10.1038/s41576-022-00515-3] [Citation(s) in RCA: 120] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 01/04/2023]
Abstract
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.
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Affiliation(s)
- Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Pathology, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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29
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Elishaev M, Hodonsky CJ, Ghosh SKB, Finn AV, von Scheidt M, Wang Y. Opportunities and Challenges in Understanding Atherosclerosis by Human Biospecimen Studies. Front Cardiovasc Med 2022; 9:948492. [PMID: 35872917 PMCID: PMC9300954 DOI: 10.3389/fcvm.2022.948492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Over the last few years, new high-throughput biotechnologies and bioinformatic methods are revolutionizing our way of deep profiling tissue specimens at the molecular levels. These recent innovations provide opportunities to advance our understanding of atherosclerosis using human lesions aborted during autopsies and cardiac surgeries. Studies on human lesions have been focusing on understanding the relationship between molecules in the lesions with tissue morphology, genetic risk of atherosclerosis, and future adverse cardiovascular events. This review will highlight ways to utilize human atherosclerotic lesions in translational research by work from large cardiovascular biobanks to tissue registries. We will also discuss the opportunities and challenges of working with human atherosclerotic lesions in the era of next-generation sequencing.
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Affiliation(s)
- Maria Elishaev
- Department of Pathology and Laboratory Medicine, Center for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
| | - Chani J. Hodonsky
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | | | - Aloke V. Finn
- Cardiovascular Pathology Institute, Gaithersburg, MD, United States
| | - Moritz von Scheidt
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Munich, Germany
- Deutsches Zentrum für Herz-Kreislauf-Forschung, Partner Site Munich Heart Alliance, Munich, Germany
| | - Ying Wang
- Department of Pathology and Laboratory Medicine, Center for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada
- *Correspondence: Ying Wang ; orcid.org/0000-0002-1444-5778
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30
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Martin NG, Malacrino S, Wojciechowska M, Campo L, Jones H, Wedge DC, Holmes C, Sirinukunwattana K, Sailem H, Verrill C, Rittscher J. A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3063-3067. [PMID: 36085678 DOI: 10.1109/embc48229.2022.9871251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.
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31
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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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32
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Chin LK, Li H, Choi JH, Iwamoto Y, Oh J, Min J, Beak SK, Yoo D, Castro CM, Lee D, Im H. Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues. ACS APPLIED MATERIALS & INTERFACES 2022; 14:27613-27622. [PMID: 35671240 DOI: 10.1021/acsami.2c05071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the era of precision oncology, multicolor fluorescence imaging has become a core technology for multiplexed molecular analysis of cellular and tissue specimens. However, conventional solution-based staining is labor-intensive and time-consuming and requires considerable expertise to yield optimal results, which creates difficulties for employing this technology in resource-limited settings. Here, we report a new immunostaining method based on hydrogel stamping, which is simple, fast, easy to use, and reproducible. We showed that a hydrophilic hydrogel stamp could effectively transfer fluorescent antibodies to targets and withdraw an excess solution when the reaction is completed, obviating the need for extra washing. This unique property allows for quality immunostaining in 5 min for cells using one-eighth of antibody consumption compared to the conventional solution-based method. Furthermore, we implemented fluorescence quenching and immunocycling with hydrogel staining for multiplexed analysis of 9 protein markers at a single cell level. Finally, we applied the immunocycling method to human breast cancer tissue samples and showed quality immunostaining over a large area (∼2 cm2) in 30 min for molecular subtyping of breast cancer. The hydrogel immunostaining could open new opportunities for rapid, automated, and multiplexed profiling in compact point-of-care systems for molecular cancer diagnosis.
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Affiliation(s)
- Lip Ket Chin
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Electrical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Huiyan Li
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph N1G2W1, Canada
| | - Jae-Hyeok Choi
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Yoshiko Iwamoto
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Juhyun Oh
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Jouha Min
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Suk Kyung Beak
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Dahyeon Yoo
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Dongyoung Lee
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
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33
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Mund A, Brunner AD, Mann M. Unbiased spatial proteomics with single-cell resolution in tissues. Mol Cell 2022; 82:2335-2349. [PMID: 35714588 DOI: 10.1016/j.molcel.2022.05.022] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 05/05/2022] [Accepted: 05/18/2022] [Indexed: 12/19/2022]
Abstract
Mass spectrometry (MS)-based proteomics has become a powerful technology to quantify the entire complement of proteins in cells or tissues. Here, we review challenges and recent advances in the LC-MS-based analysis of minute protein amounts, down to the level of single cells. Application of this technology revealed that single-cell transcriptomes are dominated by stochastic noise due to the very low number of transcripts per cell, whereas the single-cell proteome appears to be complete. The spatial organization of cells in tissues can be studied by emerging technologies, including multiplexed imaging and spatial transcriptomics, which can now be combined with ultra-sensitive proteomics. Combined with high-content imaging, artificial intelligence and single-cell laser microdissection, MS-based proteomics provides an unbiased molecular readout close to the functional level. Potential applications range from basic biological questions to precision medicine.
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Affiliation(s)
- Andreas Mund
- Proteomics Program, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - Andreas-David Brunner
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany; Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Str. 65, D-88397, Biberach Riss, Germany
| | - Matthias Mann
- Proteomics Program, The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Faculty of Health and Medical Sciences, Blegdamsvej 3B, 2200 Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nat Biomed Eng 2022; 6:515-526. [PMID: 34750536 PMCID: PMC9079188 DOI: 10.1038/s41551-021-00789-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 06/02/2021] [Indexed: 01/20/2023]
Abstract
Multiplexed tissue imaging facilitates the diagnosis and understanding of complex disease traits. However, the analysis of such digital images heavily relies on the experience of anatomical pathologists for the review, annotation and description of tissue features. In addition, the wider use of data from tissue atlases in basic and translational research and in classrooms would benefit from software that facilitates the easy visualization and sharing of the images and the results of their analyses. In this Perspective, we describe the ecosystem of software available for the analysis of tissue images and discuss the need for interactive online guides that help histopathologists make complex images comprehensible to non-specialists. We illustrate this idea via a software interface (Minerva), accessible via web browsers, that integrates multi-omic and tissue-atlas features. We argue that such interactive narrative guides can effectively disseminate digital histology data and aid their interpretation.
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35
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Millian DE, Saldarriaga OA, Wanninger T, Burks JK, Rafati YN, Gosnell J, Stevenson HL. Cutting-Edge Platforms for Analysis of Immune Cells in the Hepatic Microenvironment-Focus on Tumor-Associated Macrophages in Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:1861. [PMID: 35454766 PMCID: PMC9026790 DOI: 10.3390/cancers14081861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/26/2022] [Accepted: 03/30/2022] [Indexed: 12/11/2022] Open
Abstract
The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each.
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Affiliation(s)
- Daniel E. Millian
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA; (D.E.M.); (O.A.S.); (J.G.)
| | - Omar A. Saldarriaga
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA; (D.E.M.); (O.A.S.); (J.G.)
| | - Timothy Wanninger
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX 77555, USA;
| | - Jared K. Burks
- Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Yousef N. Rafati
- School of Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA;
| | - Joseph Gosnell
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA; (D.E.M.); (O.A.S.); (J.G.)
| | - Heather L. Stevenson
- Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA; (D.E.M.); (O.A.S.); (J.G.)
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36
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Qian N, Min W. Super-multiplexed vibrational probes: Being colorful makes a difference. Curr Opin Chem Biol 2022; 67:102115. [PMID: 35077919 PMCID: PMC8940683 DOI: 10.1016/j.cbpa.2021.102115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 11/03/2022]
Abstract
Biological systems with intrinsic complexity require multiplexing techniques to comprehensively describe the phenotype, interaction, and heterogeneity. Recent years have witnessed the development of super-multiplexed vibrational microscopy, overcoming the 'color barrier' of fluorescence-based optical techniques. Here, we will review the recent progress in the design and applications of super-multiplexed vibrational probes. We hope to illustrate how rainbow-like vibrational colors can be generated from systematic studies on structure-spectroscopy relationships and how being colorful makes a difference to various biomedical applications.
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37
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Ung N, Goldbeck C, Man C, Hoeflich J, Sun R, Barbetta A, Matasci N, Katz J, Lee JSH, Chopra S, Asgharzadeh S, Warren M, Sher L, Kohli R, Akbari O, Genyk Y, Emamaullee J. Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection. Front Immunol 2022; 13:831103. [PMID: 35432320 PMCID: PMC9009043 DOI: 10.3389/fimmu.2022.831103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/08/2022] [Indexed: 12/14/2022] Open
Abstract
Rejection continues to be an important cause of graft loss in solid organ transplantation, but deep exploration of intragraft alloimmunity has been limited by the scarcity of clinical biopsy specimens. Emerging single cell immunoprofiling technologies have shown promise in discerning mechanisms of autoimmunity and cancer immunobiology. Within these applications, Imaging Mass Cytometry (IMC) has been shown to enable highly multiplexed, single cell analysis of immune phenotypes within fixed tissue specimens. In this study, an IMC panel of 10 validated markers was developed to explore the feasibility of IMC in characterizing the immune landscape of chronic rejection (CR) in clinical tissue samples obtained from liver transplant recipients. IMC staining was highly specific and comparable to traditional immunohistochemistry. A single cell segmentation analysis pipeline was developed that enabled detailed visualization and quantification of 109,245 discrete cells, including 30,646 immune cells. Dimensionality reduction identified 11 unique immune subpopulations in CR specimens. Most immune subpopulations were increased and spatially related in CR, including two populations of CD45+/CD3+/CD8+ cytotoxic T-cells and a discrete CD68+ macrophage population, which were not observed in liver with no rejection (NR). Modeling via principal component analysis and logistic regression revealed that single cell data can be utilized to construct statistical models with high consistency (Wilcoxon Rank Sum test, p=0.000036). This study highlights the power of IMC to investigate the alloimmune microenvironment at a single cell resolution during clinical rejection episodes. Further validation of IMC has the potential to detect new biomarkers, identify therapeutic targets, and generate patient-specific predictive models of clinical outcomes in solid organ transplantation.
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Affiliation(s)
- Nolan Ung
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, United States
| | - Cameron Goldbeck
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Cassandra Man
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Julianne Hoeflich
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Ren Sun
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, United States
| | - Arianna Barbetta
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Naim Matasci
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jonathan Katz
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, United States
| | - Jerry S. H. Lee
- Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Chemical Engineering and Material Sciences, University of Southern California, Los Angeles, CA, United States
| | - Shefali Chopra
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Pathology, University of Southern California, Los Angeles, CA, United States
| | - Shahab Asgharzadeh
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Pediatrics, Children’s Hospital-Los Angeles, Los Angeles, CA, United States
| | - Mika Warren
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Pathology, Children’s Hospital-Los Angeles, Los Angeles, CA, United States
| | - Linda Sher
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Rohit Kohli
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Pediatrics, Children’s Hospital-Los Angeles, Los Angeles, CA, United States
| | - Omid Akbari
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Molecular Microbiology and Immunology, University of Southern California, Los Angeles, CA, United States
| | - Yuri Genyk
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Juliet Emamaullee
- Division of Hepatobiliary and Abdominal Organ Transplant Surgery, Department of Surgery, University of Southern California, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Molecular Microbiology and Immunology, University of Southern California, Los Angeles, CA, United States
- *Correspondence: Juliet Emamaullee,
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38
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Correia C, Weiskittel TM, Ung CY, Villasboas Bisneto JC, Billadeau DD, Kaufmann SH, Li H. Uncovering Pharmacological Opportunities for Cancer Stem Cells-A Systems Biology View. Front Cell Dev Biol 2022; 10:752326. [PMID: 35359437 PMCID: PMC8962639 DOI: 10.3389/fcell.2022.752326] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 02/10/2022] [Indexed: 12/14/2022] Open
Abstract
Cancer stem cells (CSCs) represent a small fraction of the total cancer cell population, yet they are thought to drive disease propagation, therapy resistance and relapse. Like healthy stem cells, CSCs possess the ability to self-renew and differentiate. These stemness phenotypes of CSCs rely on multiple molecular cues, including signaling pathways (for example, WNT, Notch and Hedgehog), cell surface molecules that interact with cellular niche components, and microenvironmental interactions with immune cells. Despite the importance of understanding CSC biology, our knowledge of how neighboring immune and tumor cell populations collectively shape CSC stemness is incomplete. Here, we provide a systems biology perspective on the crucial roles of cellular population identification and dissection of cell regulatory states. By reviewing state-of-the-art single-cell technologies, we show how innovative systems-based analysis enables a deeper understanding of the stemness of the tumor niche and the influence of intratumoral cancer cell and immune cell compositions. We also summarize strategies for refining CSC systems biology, and the potential role of this approach in the development of improved anticancer treatments. Because CSCs are amenable to cellular transitions, we envision how systems pharmacology can become a major engine for discovery of novel targets and drug candidates that can modulate state transitions for tumor cell reprogramming. Our aim is to provide deeper insights into cancer stemness from a systems perspective. We believe this approach has great potential to guide the development of more effective personalized cancer therapies that can prevent CSC-mediated relapse.
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Affiliation(s)
- Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Taylor M Weiskittel
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States
| | | | - Daniel D Billadeau
- Department of Immunology, Mayo Clinic, Rochester, MN, United States,Division of Oncology Research, Mayo Clinic, Rochester, MN, United States
| | - Scott H Kaufmann
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States,Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, United States,Division of Oncology Research, Mayo Clinic, Rochester, MN, United States
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States,*Correspondence: Hu Li,
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39
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Cereceda K, Jorquera R, Villarroel-Espíndola F. Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research. ADVANCES IN LABORATORY MEDICINE 2022; 3:5-29. [PMID: 37359436 PMCID: PMC10197474 DOI: 10.1515/almed-2021-0075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/16/2021] [Indexed: 06/28/2023]
Abstract
The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.
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Affiliation(s)
- Karina Cereceda
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Roddy Jorquera
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Franz Villarroel-Espíndola
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
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40
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Hickey JW, Neumann EK, Radtke AJ, Camarillo JM, Beuschel RT, Albanese A, McDonough E, Hatler J, Wiblin AE, Fisher J, Croteau J, Small EC, Sood A, Caprioli RM, Angelo RM, Nolan GP, Chung K, Hewitt SM, Germain RN, Spraggins JM, Lundberg E, Snyder MP, Kelleher NL, Saka SK. Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nat Methods 2022; 19:284-295. [PMID: 34811556 PMCID: PMC9264278 DOI: 10.1038/s41592-021-01316-y] [Citation(s) in RCA: 136] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 09/15/2021] [Indexed: 02/07/2023]
Abstract
Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.
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Affiliation(s)
- John W Hickey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Elizabeth K Neumann
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Andrea J Radtke
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA.
| | - Jeannie M Camarillo
- Departments of Chemistry, Molecular Biosciences and the National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, IL, USA
| | - Rebecca T Beuschel
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Alexandre Albanese
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Boston Children's Hospital, Division of Hematology/Oncology, Boston, MA, USA
| | | | - Julia Hatler
- Antibody Development Department, Bio-Techne, Minneapolis, MN, USA
| | - Anne E Wiblin
- Department of Research and Development, Abcam, Cambridge, UK
| | - Jeremy Fisher
- Department of Research and Development, Cell Signaling Technology, Danvers, MA, USA
| | - Josh Croteau
- Department of Applications Science, BioLegend, San Diego, CA, USA
| | | | | | - Richard M Caprioli
- Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - R Michael Angelo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea
- Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald N Germain
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Neil L Kelleher
- Departments of Chemistry, Molecular Biosciences and the National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, IL, USA
| | - Sinem K Saka
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA.
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
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41
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Gaglia G, Kabraji S, Rammos D, Dai Y, Verma A, Wang S, Mills CE, Chung M, Bergholz JS, Coy S, Lin JR, Jeselsohn R, Metzger O, Winer EP, Dillon DA, Zhao JJ, Sorger PK, Santagata S. Temporal and spatial topography of cell proliferation in cancer. Nat Cell Biol 2022; 24:316-326. [PMID: 35292783 PMCID: PMC8959396 DOI: 10.1038/s41556-022-00860-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 01/31/2022] [Indexed: 02/06/2023]
Abstract
Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.
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Affiliation(s)
- Giorgio Gaglia
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sheheryar Kabraji
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Danae Rammos
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yang Dai
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana Verma
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shu Wang
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA
| | - Caitlin E Mills
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Mirra Chung
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Johann S Bergholz
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Shannon Coy
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Rinath Jeselsohn
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Otto Metzger
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Eric P Winer
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean J Zhao
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
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42
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Schapiro D, Yapp C, Sokolov A, Reynolds SM, Chen YA, Sudar D, Xie Y, Muhlich J, Arias-Camison R, Arena S, Taylor AJ, Nikolov M, Tyler M, Lin JR, Burlingame EA, Chang YH, Farhi SL, Thorsson V, Venkatamohan N, Drewes JL, Pe'er D, Gutman DA, Herrmann MD, Gehlenborg N, Bankhead P, Roland JT, Herndon JM, Snyder MP, Angelo M, Nolan G, Swedlow JR, Schultz N, Merrick DT, Mazzili SA, Cerami E, Rodig SJ, Santagata S, Sorger PK. MITI minimum information guidelines for highly multiplexed tissue images. Nat Methods 2022; 19:262-267. [PMID: 35277708 PMCID: PMC9009186 DOI: 10.1038/s41592-022-01415-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
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Affiliation(s)
- Denis Schapiro
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Image and Data Analysis Core, Harvard Medical School, Boston, MA, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Yu-An Chen
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR, USA
| | - Yubin Xie
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jeremy Muhlich
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Raquel Arias-Camison
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Sarah Arena
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | | | | | - Madison Tyler
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA
| | - Erik A Burlingame
- Oregon Health and Science University, Portland, OR, USA
- Indica Labs, Albuquerque, NM, USA
| | - Young H Chang
- Oregon Health and Science University, Portland, OR, USA
| | - Samouil L Farhi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Julia L Drewes
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dana Pe'er
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Peter Bankhead
- Edinburgh Pathology, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Joseph T Roland
- Vanderbilt University School of Medicine, Nashville, TN, USA
| | - John M Herndon
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Michael Angelo
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Garry Nolan
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Jason R Swedlow
- Division of Computational Biology and Centre for Gene Regulation and Expression, University of Dundee, Dundee, UK
| | - Nikolaus Schultz
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA.
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
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43
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Lo YC, Keyes TJ, Jager A, Sarno J, Domizi P, Majeti R, Sakamoto KM, Lacayo N, Mullighan CG, Waters J, Sahaf B, Bendall SC, Davis KL. CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors. Nat Commun 2022; 13:934. [PMID: 35177627 PMCID: PMC8854441 DOI: 10.1038/s41467-022-28484-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/27/2022] [Indexed: 11/09/2022] Open
Abstract
The increasing use of mass cytometry for analyzing clinical samples offers the possibility to perform comparative analyses across public datasets. However, challenges in batch normalization and data integration limit the comparison of datasets not intended to be analyzed together. Here, we present a data integration strategy, CytofIn, using generalized anchors to integrate mass cytometry datasets from the public domain. We show that low-variance controls, such as healthy samples and stable channels, are inherently homogeneous, robust against stimulation, and can serve as generalized anchors for batch correction. Single-cell quantification comparing mass cytometry data from 989 leukemia files pre- and post normalization with CytofIn demonstrates effective batch correction while recapitulating the gold-standard bead normalization. CytofIn integration of public cancer datasets enabled the comparison of immune features across histologies and treatments. We demonstrate the ability to integrate public datasets without necessitating identical control samples or bead standards for fast and robust analysis using CytofIn. Challenges in batch normalization and data integration limit the comparison of existing mass cytometry datasets. Here, the authors report CytofIn that can integrate mass cytometry datasets from the public domain and reveal cellular features associated with immune oncology by analyzing five public cancer datasets.
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Affiliation(s)
- Yu-Chen Lo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Timothy J Keyes
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.,Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Astraea Jager
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jolanda Sarno
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Pablo Domizi
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ravindra Majeti
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Kathleen M Sakamoto
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Norman Lacayo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Charles G Mullighan
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jeffrey Waters
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA
| | - Bita Sahaf
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA
| | - Sean C Bendall
- Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.,Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kara L Davis
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA. .,Center for Cancer Cellular Therapy, Cancer Correlative Sciences Unit, Stanford University School of Medicine, Stanford, CA, USA.
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44
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Vickovic S, Lötstedt B, Klughammer J, Mages S, Segerstolpe Å, Rozenblatt-Rosen O, Regev A. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat Commun 2022; 13:795. [PMID: 35145087 PMCID: PMC8831571 DOI: 10.1038/s41467-022-28445-y] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
The spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial transcriptomics has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of spatial transcriptomics at scale, by presenting Spatial Multi-Omics (SM-Omics) as a fully automated, high-throughput all-sequencing based platform for combined and spatially resolved transcriptomics and antibody-based protein measurements. SM-Omics uses DNA-barcoded antibodies, immunofluorescence or a combination thereof, to scale and combine spatial transcriptomics and spatial antibody-based multiplex protein detection. SM-Omics allows processing of up to 64 in situ spatial reactions or up to 96 sequencing-ready libraries, of high complexity, in a ~2 days process. We demonstrate SM-Omics in the mouse brain, spleen and colorectal cancer model, showing its broad utility as a high-throughput platform for spatial multi-omics.
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Affiliation(s)
- S Vickovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,New York Genome Center, New York, NY, USA. .,Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
| | - B Lötstedt
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J Klughammer
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - S Mages
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Å Segerstolpe
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - O Rozenblatt-Rosen
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - A Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA. .,Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Genentech, 1 DNA Way, South San Francisco, CA, USA.
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45
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Boyraz B, Saatz J, Pompös IM, Gad M, Dernedde J, Maier AKB, Moscovitz O, Seeberger PH, Traub H, Tauber R. Imaging Keratan Sulfate in Ocular Tissue Sections by Immunofluorescence Microscopy and LA-ICP-MS. ACS APPLIED BIO MATERIALS 2022; 5:853-861. [PMID: 35076201 DOI: 10.1021/acsabm.1c01240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Carbohydrate-specific antibodies can serve as valuable tools to monitor alterations in the extracellular matrix resulting from pathologies. Here, the keratan sulfate-specific monoclonal antibody MZ15 was characterized in more detail by immunofluorescence microscopy as well as laser ablation ICP-MS using tissue cryosections and paraffin-embedded samples. Pretreatment with keratanase II prevented staining of samples and therefore demonstrated efficient enzymatic keratan sulfate degradation. Random fluorescent labeling and site-directed introduction of a metal cage into MZ15 were successful and allowed for a highly sensitive detection of the keratan sulfate landscape in the corneal stroma from rats and human tissue.
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Affiliation(s)
- Burak Boyraz
- Institut für Laboratoriumsmedizin, Klinische Chemie und Pathobiochemie, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Augustenburger Platz 1, Berlin 13353, Germany.,Freie Universität Berlin, Fachbereich Biologie, Chemie, Pharmazie, Arnimallee 22, Berlin 14195, Germany
| | - Jessica Saatz
- Bundesanstalt für Materialforschung und -prüfung (BAM), Richard-Willstätter-Strasse, 11, Berlin 12489, Germany
| | - Inga-Marie Pompös
- Klinik für Augenheilkunde, Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Augustenburger Platz 1, Berlin 13353, Germany
| | - Michel Gad
- Bundesanstalt für Materialforschung und -prüfung (BAM), Richard-Willstätter-Strasse, 11, Berlin 12489, Germany.,Department Chemie und Biologie, Universität Siegen, Adolf-Reichwein-Strasse 2, Siegen 57076, Germany
| | - Jens Dernedde
- Institut für Laboratoriumsmedizin, Klinische Chemie und Pathobiochemie, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Augustenburger Platz 1, Berlin 13353, Germany
| | - Anna-Karina B Maier
- Klinik für Augenheilkunde, Campus Virchow-Klinikum, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Augustenburger Platz 1, Berlin 13353, Germany
| | - Oren Moscovitz
- Biomolecular Systems Department, Max-Planck-Institute for Colloids and Interfaces, Am Mühlenberg 1, Potsdam 14476, Germany
| | - Peter H Seeberger
- Biomolecular Systems Department, Max-Planck-Institute for Colloids and Interfaces, Am Mühlenberg 1, Potsdam 14476, Germany
| | - Heike Traub
- Bundesanstalt für Materialforschung und -prüfung (BAM), Richard-Willstätter-Strasse, 11, Berlin 12489, Germany
| | - Rudolf Tauber
- Institut für Laboratoriumsmedizin, Klinische Chemie und Pathobiochemie, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Augustenburger Platz 1, Berlin 13353, Germany
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46
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Radtke AJ, Chu CJ, Yaniv Z, Yao L, Marr J, Beuschel RT, Ichise H, Gola A, Kabat J, Lowekamp B, Speranza E, Croteau J, Thakur N, Jonigk D, Davis JL, Hernandez JM, Germain RN. IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nat Protoc 2022; 17:378-401. [PMID: 35022622 DOI: 10.1038/s41596-021-00644-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/05/2021] [Indexed: 01/02/2023]
Abstract
High-content imaging is needed to catalog the variety of cellular phenotypes and multicellular ecosystems present in metazoan tissues. We recently developed iterative bleaching extends multiplexity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with >250 commercially available antibodies and 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and nonproprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labeling, imaging and chemical bleaching that can be completed at relatively low cost in 2-5 d by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open-source software and freeware. In summary, IBEX is a noncommercial method that can be readily implemented by academic laboratories and scaled to achieve high-content mapping of diverse tissues in support of a Human Reference Atlas or other such applications.
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Affiliation(s)
- Andrea J Radtke
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
| | - Colin J Chu
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ziv Yaniv
- Bioinformatics and Computational Bioscience Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Li Yao
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - James Marr
- Leica Microsystems Inc., Wetzlar, Germany
| | - Rebecca T Beuschel
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Hiroshi Ichise
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Anita Gola
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Howard Hughes Medical Institute, Robin Neustein Laboratory of Mammalian Cell Biology and Development, The Rockefeller University, New York, NY, USA
| | - Juraj Kabat
- Biological Imaging Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Bradley Lowekamp
- Bioinformatics and Computational Bioscience Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Emily Speranza
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.,Innate Immunity and Pathogenesis Section, Laboratory of Virology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Hamilton, MT, USA
| | - Joshua Croteau
- Department of Business Development, BioLegend, Inc, San Diego, CA, USA
| | - Nishant Thakur
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Danny Jonigk
- Institute of Pathology, Hannover Medical School, Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Jeremy L Davis
- Surgical Oncology Program, Metastasis Biology Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan M Hernandez
- Surgical Oncology Program, Metastasis Biology Section, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ronald N Germain
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA. .,Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
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47
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Lopez BGC, Kohale IN, Du Z, Korsunsky I, Abdelmoula WM, Dai Y, Stopka SA, Gaglia G, Randall EC, Regan MS, Basu SS, Clark AR, Marin BM, Mladek AC, Burgenske DM, Agar JN, Supko JG, Grossman SA, Nabors LB, Raychaudhuri S, Ligon KL, Wen PY, Alexander B, Lee EQ, Santagata S, Sarkaria J, White FM, Agar NYR. Multimodal platform for assessing drug distribution and response in clinical trials. Neuro Oncol 2022; 24:64-77. [PMID: 34383057 PMCID: PMC8730776 DOI: 10.1093/neuonc/noab197] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.
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Affiliation(s)
- Begoña G C Lopez
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ishwar N Kohale
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Ziming Du
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ilya Korsunsky
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yang Dai
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Giorgio Gaglia
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sankha S Basu
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amanda R Clark
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bianca-Maria Marin
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ann C Mladek
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA
| | - Jeffrey G Supko
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Stuart A Grossman
- Brain Cancer Program, Johns Hopkins Hospital, Baltimore, Maryland, USA
| | - Louis B Nabors
- University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Brian Alexander
- Department of Radiation Oncology, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Eudocia Q Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Sandro Santagata
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jann Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Forest M White
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
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48
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Multi-Omics Profiling of the Tumor Microenvironment. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:283-326. [DOI: 10.1007/978-3-030-91836-1_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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49
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Chen HY, Palendira U, Feng CG. Navigating the cellular landscape in tissue: Recent advances in defining the pathogenesis of human disease. Comput Struct Biotechnol J 2022; 20:5256-5263. [PMID: 36212528 PMCID: PMC9519395 DOI: 10.1016/j.csbj.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/04/2022] [Accepted: 09/04/2022] [Indexed: 11/19/2022] Open
Affiliation(s)
- Helen Y. Chen
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Umaimainthan Palendira
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
| | - Carl G. Feng
- Immunology and Host Defence Group, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Centenary Institute, The University of Sydney, NSW, Australia
- Corresponding author at: Level 5 (East) The Charles Perkins Centre (D17), The University of Sydney, NSW, 2006, Australia
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50
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Kakade VR, Weiss M, Cantley LG. Using Imaging Mass Cytometry to Define Cell Identities and Interactions in Human Tissues. Front Physiol 2021; 12:817181. [PMID: 35002783 PMCID: PMC8727440 DOI: 10.3389/fphys.2021.817181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 11/25/2021] [Indexed: 12/30/2022] Open
Abstract
In the evolving landscape of highly multiplexed imaging techniques that can be applied to study complex cellular microenvironments, this review characterizes the use of imaging mass cytometry (IMC) to study the human kidney. We provide technical details for antibody validation, cell segmentation, and data analysis specifically tailored to human kidney samples, and elaborate on phenotyping of kidney cell types and novel insights that IMC can provide regarding pathophysiological processes in the injured or diseased kidney. This review will provide the reader with the necessary background to understand both the power and the limitations of IMC and thus support better perception of how IMC analysis can improve our understanding of human disease pathogenesis and can be integrated with other technologies such as single cell sequencing and proteomics to provide spatial context to cellular data.
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Affiliation(s)
| | | | - Lloyd G. Cantley
- Section of Nephrology, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, United States
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