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Ak Ç, Sayar Z, Thibault G, Burlingame EA, Kuykendall MJ, Eng J, Chitsazan A, Chin K, Adey AC, Boniface C, Spellman PT, Thomas GV, Kopp RP, Demir E, Chang YH, Stavrinides V, Eksi SE. Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment. iScience 2024; 27:110668. [PMID: 39246442 PMCID: PMC11379676 DOI: 10.1016/j.isci.2024.110668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 07/19/2024] [Accepted: 08/01/2024] [Indexed: 09/10/2024] Open
Abstract
Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.
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Affiliation(s)
- Çiğdem Ak
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Zeynep Sayar
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Erik A Burlingame
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - M J Kuykendall
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Jennifer Eng
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | - Alex Chitsazan
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Koei Chin
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Andrew C Adey
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Christopher Boniface
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - Paul T Spellman
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Knight Cancer Institute, OHSU, Portland, OR 97239, USA
| | - George V Thomas
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, School of Medicine, OHSU, Portland, OR 97239, USA
| | - Ryan P Kopp
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Urology, School of Medicine, Knight Cancer Institute, Portland, OR 97239, USA
| | - Emek Demir
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Division of Oncological Sciences, School of Medicine, OHSU, Portland, OR 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
| | | | - Sebnem Ece Eksi
- Cancer Early Detection Advanced Research (CEDAR), Knight Cancer Institute, OHSU, Portland, OR 97239, USA
- Department of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97209, USA
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2
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Dezem FS, Arjumand W, DuBose H, Morosini NS, Plummer J. Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives. Annu Rev Biomed Data Sci 2024; 7:131-153. [PMID: 38768396 DOI: 10.1146/annurev-biodatasci-102523-103640] [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: 05/22/2024]
Abstract
Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.
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Affiliation(s)
- Felipe Segato Dezem
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Wani Arjumand
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Hannah DuBose
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Natalia Silva Morosini
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
| | - Jasmine Plummer
- Department of Cellular and Molecular Biology and Comprehensive Cancer Center, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Center for Spatial Omics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA;
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3
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Mi H, Sivagnanam S, Ho WJ, Zhang S, Bergman D, Deshpande A, Baras AS, Jaffee EM, Coussens LM, Fertig EJ, Popel AS. Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology. Brief Bioinform 2024; 25:bbae421. [PMID: 39179248 PMCID: PMC11343572 DOI: 10.1093/bib/bbae421] [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: 05/29/2024] [Revised: 07/11/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024] Open
Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Shamilene Sivagnanam
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
| | - Won Jin Ho
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Daniel Bergman
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
| | - Atul Deshpande
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Alexander S Baras
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Pathology, Johns Hopkins University School of Medicine, MD 21205, United States
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Elizabeth M Jaffee
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
| | - Lisa M Coussens
- The Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97201, United States
- Department of Cell, Development and Cancer Biology, Oregon Health and Science University, Portland, OR 97201, United States
- Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, OR 97201, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
- Convergence Institute, Johns Hopkins University, Baltimore, MD 21205, United States
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD 21218, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, United States
- Department of Oncology, Johns Hopkins University School of Medicine, MD 21205, United States
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Dey AK, Das S, Jose SM, Sreedharan S, Kandoth N, Barman S, Patra A, Das A, Pramanik SK. Surface functionalized perovskite nanocrystals: a design strategy for organelle-specific fluorescence lifetime multiplexing. Chem Sci 2024; 15:10935-10944. [PMID: 39027267 PMCID: PMC11253202 DOI: 10.1039/d4sc01447b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/06/2024] [Indexed: 07/20/2024] Open
Abstract
Fluorescent molecules or materials with high photoluminescence quantum yields and stability towards photobleaching are ideally suited for multiplex imaging. Despite complying with such properties, perovskite nanocrystals (Pv-NCs) are rarely used for bioimaging owing to their toxicity and limited stability in aqueous media and towards human physiology. We aim to address these deficiencies by designing core-shell structures with Pv-NCs as the core and surface-engineered silica as the shell (SiO2@Pv-NCs) since silica is recognized as a biologically benign carrier material and is known to be excreted through urine. The post-grafting methodology is adopted for developing [SiO2@Pv-NCs]tpm and [SiO2@Pv-NCs]tsy (tpm: triphenylphosphonium ion, tsy: tosylsulfonamide) for specific imaging of mitochondria and endoplasmic reticulum (ER) of the live HeLa cell, respectively. A subtle difference in their average fluorescence decay times ([SiO2@Pv-NCs]tpm: tpm τ av = 3.1 ns and [SiO2@Pv-NCs]tsy: tsy τ av = 2.1 ns) is used for demonstrating a rare example of perovskite nanocrystals in fluorescence lifetime multiplex imaging.
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Affiliation(s)
- Anik Kumar Dey
- CSIR - Central Salt and Marine Chemicals Research Institute Gijubhai Badheka Marg Bhavnagar Gujarat 364002 India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Human Resource Development Centre Ghaziabad Uttar Pradesh 201 002 India
| | - Subhadeep Das
- Department of Chemistry, Indian Institute of Science Education and Research Bhopal India
| | - Sharon Mary Jose
- Department of Biological Sciences, Indian Institute of Science Education and Research, Kolkata Mohanpur West Bengal India
| | - Sreejesh Sreedharan
- Human Science Research Centre, University of Derby Kedleston Road DE22 1GB UK
| | - Noufal Kandoth
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research Kolkata West Bengal India
| | - Surajit Barman
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research Kolkata West Bengal India
| | - Abhijit Patra
- Department of Chemistry, Indian Institute of Science Education and Research Bhopal India
| | - Amitava Das
- Department of Chemical Sciences and Centre for Advanced Functional Materials, Indian Institute of Science Education and Research Kolkata West Bengal India
| | - Sumit Kumar Pramanik
- CSIR - Central Salt and Marine Chemicals Research Institute Gijubhai Badheka Marg Bhavnagar Gujarat 364002 India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-Human Resource Development Centre Ghaziabad Uttar Pradesh 201 002 India
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5
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Tonnerre P, Baumert TF. Unraveling the liver antiviral immunity in functional cure of chronic hepatitis B using scRNAseq. J Hepatol 2024; 81:14-16. [PMID: 38513812 DOI: 10.1016/j.jhep.2024.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 03/11/2024] [Indexed: 03/23/2024]
Affiliation(s)
- Pierre Tonnerre
- Institut de Recherche Saint-Louis, Université Paris-Cité, Inserm U976, Team ATIP-Avenir, Paris, France.
| | - Thomas F Baumert
- University of Strasbourg, Inserm, Institute for Translational Medicine and Liver Disease, Inserm UMR_S1110, Strasbourg, France; Gastroenterology and Hepatology Service, Strasbourg University Hospitals, Strasbourg, France; Institut Hospitalo-Universitaire (IHU), University of Strasbourg, France; Institut Universitaire de France (IUF), Paris, France.
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6
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Ram S, Mojtahedzadeh S, Aguilar JK, Coskran T, Powell EL, O'Neil SP. Quantitative performance assessment of Ultivue multiplex panels in formalin-fixed, paraffin-embedded human and murine tumor specimens. Sci Rep 2024; 14:8496. [PMID: 38605049 PMCID: PMC11009312 DOI: 10.1038/s41598-024-58372-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
We present a rigorous validation strategy to evaluate the performance of Ultivue multiplex immunofluorescence panels. We have quantified the accuracy and precision of four different multiplex panels (three human and one mouse) in tumor specimens with varying levels of T cell density. Our results show that Ultivue panels are typically accurate wherein the relative difference in cell proportion between a multiplex image and a 1-plex image is less than 20% for a given biomarker. Ultivue panels exhibited relatively high intra-run precision (CV ≤ 25%) and relatively low inter-run precision (CV >> 25%) which can be remedied by using local intensity thresholding to gate biomarker positivity. We also evaluated the reproducibility of cell-cell distance estimates measured from multiplex images which show high intra- and inter-run precision. We introduce a new metric, multiplex labeling efficiency, which can be used to benchmark the overall fidelity of the multiplex data across multiple batch runs. Taken together our results provide a comprehensive characterization of Ultivue panels and offer practical guidelines for analyzing multiplex images.
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Affiliation(s)
- Sripad Ram
- Drug Safety Research and Development, Pfizer Inc., Groton, CT, USA.
| | | | | | - Timothy Coskran
- Drug Safety Research and Development, Pfizer Inc., Groton, CT, USA
| | - Eric L Powell
- Oncology Research and Development, Pfizer Inc., San Diego, CA, USA
| | - Shawn P O'Neil
- Drug Safety Research and Development, Pfizer Inc., Groton, CT, USA
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7
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Navikas V, Kowal J, Rodriguez D, Rivest F, Brajkovic S, Cassano M, Dupouy D. Semi-automated approaches for interrogating spatial heterogeneity of tissue samples. Sci Rep 2024; 14:5025. [PMID: 38424144 PMCID: PMC10904364 DOI: 10.1038/s41598-024-55387-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.
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Affiliation(s)
| | - Joanna Kowal
- Lunaphore Technologies SA, Tolochenaz, Switzerland
| | | | | | | | | | - Diego Dupouy
- Lunaphore Technologies SA, Tolochenaz, Switzerland.
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8
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Shetab Boushehri S, Kornivetc A, Winter DJE, Kazeminia S, Essig K, Schmich F, Marr C. PXPermute reveals staining importance in multichannel imaging flow cytometry. CELL REPORTS METHODS 2024; 4:100715. [PMID: 38412831 PMCID: PMC10921034 DOI: 10.1016/j.crmeth.2024.100715] [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: 05/28/2023] [Revised: 11/08/2023] [Accepted: 01/29/2024] [Indexed: 02/29/2024]
Abstract
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibodies for IFC analysis is time consuming, expensive, and potentially harmful to cell viability. To streamline experimental workflows and reduce costs, it is crucial to identify the most relevant channels for downstream analysis. In this study, we introduce PXPermute, a user-friendly and powerful method for assessing the significance of IFC channels, particularly for cell profiling. Our approach evaluates channel importance by permuting pixel values within each channel and analyzing the resulting impact on machine learning or deep learning models. Through rigorous evaluation of three multichannel IFC image datasets, we demonstrate PXPermute's potential in accurately identifying the most informative channels, aligning with established biological knowledge. PXPermute can assist biologists with systematic channel analysis, experimental design optimization, and biomarker identification.
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Affiliation(s)
- Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Munich, Germany; Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Aleksandra Kornivetc
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; University of Hamburg, Department of Informatics, 22527 Hamburg, Germany
| | - Domink J E Winter
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Institute of Computational Biology, Helmholtz Zentrum MünchenMunich - Helmholtz Munich - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, School of Life Sciences, 85354 Weihenstephan, Germany
| | - Salome Kazeminia
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Munich, Germany
| | - Katharina Essig
- Large Molecule Research (LMR), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Fabian Schmich
- Data & Analytics (D&A), Roche Pharma Research and Early Development (pRED), Roche Innovation Center Munich, 82377 Penzberg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
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Vierdag WMAM, Saka SK. A perspective on FAIR quality control in multiplexed imaging data processing. FRONTIERS IN BIOINFORMATICS 2024; 4:1336257. [PMID: 38405548 PMCID: PMC10885342 DOI: 10.3389/fbinf.2024.1336257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
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Affiliation(s)
| | - Sinem K. Saka
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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10
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Bialy N, Alber F, Andrews B, Angelo M, Beliveau B, Bintu L, Boettiger A, Boehm U, Brown CM, Maina MB, Chambers JJ, Cimini BA, Eliceiri K, Errington R, Faklaris O, Gaudreault N, Germain RN, Goscinski W, Grunwald D, Halter M, Hanein D, Hickey JW, Lacoste J, Laude A, Lundberg E, Ma J, Malacrida L, Moore J, Nelson G, Neumann EK, Nitschke R, Onami S, Pimentel JA, Plant AL, Radtke AJ, Sabata B, Schapiro D, Schöneberg J, Spraggins JM, Sudar D, Adrien Maria Vierdag WM, Volkmann N, Wählby C, Wang SS, Yaniv Z, Strambio-De-Castillia C. Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ARXIV 2024:arXiv:2401.13022v4. [PMID: 38351940 PMCID: PMC10862930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
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Affiliation(s)
- Nikki Bialy
- Morgridge Institute for Research, Madison, USA
| | | | | | | | | | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA
| | - Kevin Eliceiri
- Morgridge Institute for Research, Madison, USA
- University of Wisconsin-Madison, Madison, USA
| | | | | | | | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | - Michael Halter
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Alex Laude
- Newcastle University, Newcastle upon Tyne, UK
| | - Emma Lundberg
- Stanford University, Palo Alto, USA
- SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, USA
| | - Leonel Malacrida
- Institut Pasteur de Montevideo, & Universidad de la República, Montevideo, Uruguay
| | - Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany
| | - Glyn Nelson
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Anne L Plant
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | | | | | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, USA
| | | | | | | | | | - Ziv Yaniv
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
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11
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Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
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Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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12
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Doha ZO, Wang X, Calistri NL, Eng J, Daniel CJ, Ternes L, Kim EN, Pelz C, Munks M, Betts C, Kwon S, Bucher E, Li X, Waugh T, Tatarova Z, Blumberg D, Ko A, Kirchberger N, Pietenpol JA, Sanders ME, Langer EM, Dai MS, Mills G, Chin K, Chang YH, Coussens LM, Gray JW, Heiser LM, Sears RC. MYC Deregulation and PTEN Loss Model Tumor and Stromal Heterogeneity of Aggressive Triple-Negative Breast Cancer. Nat Commun 2023; 14:5665. [PMID: 37704631 PMCID: PMC10499828 DOI: 10.1038/s41467-023-40841-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 08/14/2023] [Indexed: 09/15/2023] Open
Abstract
Triple-negative breast cancer (TNBC) patients have a poor prognosis and few treatment options. Mouse models of TNBC are important for development of new therapies, however, few mouse models represent the complexity of TNBC. Here, we develop a female TNBC murine model by mimicking two common TNBC mutations with high co-occurrence: amplification of the oncogene MYC and deletion of the tumor suppressor PTEN. This Myc;Ptenfl model develops heterogeneous triple-negative mammary tumors that display histological and molecular features commonly found in human TNBC. Our research involves deep molecular and spatial analyses on Myc;Ptenfl tumors including bulk and single-cell RNA-sequencing, and multiplex tissue-imaging. Through comparison with human TNBC, we demonstrate that this genetic mouse model develops mammary tumors with differential survival and therapeutic responses that closely resemble the inter- and intra-tumoral and microenvironmental heterogeneity of human TNBC, providing a pre-clinical tool for assessing the spectrum of patient TNBC biology and drug response.
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Affiliation(s)
- Zinab O Doha
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Department of medical laboratory technology, Taibah University, Al-Madinah al-Munawwarah, Saudi Arabia
| | - Xiaoyan Wang
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Nicholas L Calistri
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Jennifer Eng
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Colin J Daniel
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Luke Ternes
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Eun Na Kim
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Carl Pelz
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Michael Munks
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
- Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA
| | - Courtney Betts
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Sunjong Kwon
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Elmar Bucher
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Xi Li
- Division of Oncologic Sciences, Oregon Health and Science University, Portland, OR, USA
| | - Trent Waugh
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Zuzana Tatarova
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Dylan Blumberg
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
| | - Aaron Ko
- Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, OR, USA
| | - Nell Kirchberger
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR, USA
| | - Jennifer A Pietenpol
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melinda E Sanders
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ellen M Langer
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Mu-Shui Dai
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Gordon Mills
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
- Division of Oncologic Sciences, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Koei Chin
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Lisa M Coussens
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Joe W Gray
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- OHSU Center for Spatial Systems Biomedicine, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Rosalie C Sears
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA.
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
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13
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Speranza E. Understanding virus-host interactions in tissues. Nat Microbiol 2023; 8:1397-1407. [PMID: 37488255 DOI: 10.1038/s41564-023-01434-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 06/20/2023] [Indexed: 07/26/2023]
Abstract
Although virus-host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus-host interactions in the infection microenvironment.
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Affiliation(s)
- Emily Speranza
- Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL, USA.
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14
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Prabhakaran S, Yapp C, Baker GJ, Beyer J, Chang YH, Creason AL, Krueger R, Muhlich J, Patterson NH, Sidak K, Sudar D, Taylor AJ, Ternes L, Troidl J, Xie Y, Sokolov A, Tyson DR. Addressing persistent challenges in digital image analysis of cancerous tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.548450. [PMID: 37547011 PMCID: PMC10401923 DOI: 10.1101/2023.07.21.548450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.
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15
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Bolognesi MM, Antoranz A, Bosisio FM, Cattoretti G. Quantitative multiplex immunohistochemistry with colorimetric staining (QUIVER) may still benefit from MILAN. Acta Neuropathol Commun 2023; 11:91. [PMID: 37287032 DOI: 10.1186/s40478-023-01585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 06/09/2023] Open
Affiliation(s)
- Maddalena M Bolognesi
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Via Cadore 48, Monza, MI, Italy
| | - Asier Antoranz
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- The Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Francesca Maria Bosisio
- Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Giorgio Cattoretti
- Pathology, Department of Medicine and Surgery, Università di Milano-Bicocca, Via Cadore 48, Monza, MI, Italy.
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16
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McMahon NP, Jones JA, Anderson AN, Dietz MS, Wong MH, Gibbs SL. Flexible Cyclic Immunofluorescence (cyCIF) Using Oligonucleotide Barcoded Antibodies. Cancers (Basel) 2023; 15:827. [PMID: 36765785 PMCID: PMC9913741 DOI: 10.3390/cancers15030827] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 01/25/2023] [Indexed: 02/03/2023] Open
Abstract
Advances in our understanding of the complex, multifaceted interactions between tumor epithelia, immune infiltrate, and tumor microenvironmental cells have been driven by highly multiplexed imaging technologies. These techniques are capable of labeling many more biomarkers than conventional immunostaining methods. However, multiplexed imaging techniques suffer from low detection sensitivity, cell loss-particularly in fragile samples-, and challenges with antibody labeling. Herein, we developed and optimized an oligonucleotide antibody barcoding strategy for cyclic immunofluorescence (cyCIF) that can be amplified to increase the detection efficiency of low-abundance antigens. Stained fluorescence signals can be readily removed using ultraviolet light treatment, preserving tissue and fragile cell sample integrity. We also extended the oligonucleotide barcoding strategy to secondary antibodies to enable the inclusion of difficult-to-label primary antibodies in a cyCIF panel. Using both the amplification oligonucleotides to label DNA barcoded antibodies and in situ hybridization of multiple fluorescently labeled oligonucleotides resulted in signal amplification and increased signal-to-background ratios. This procedure was optimized through the examination of staining parameters including staining oligonucleotide concentration, staining temperature, and oligonucleotide sequence design, resulting in a robust amplification technique. As a proof-of-concept, we demonstrate the flexibility of our cyCIF strategy by simultaneously imaging with the original oligonucleotide conjugated antibody (Ab-oligo) cyCIF strategy, the novel Ab-oligo cyCIF amplification strategy, as well as direct and indirect immunofluorescence to generate highly multiplexed images.
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Affiliation(s)
- Nathan P. McMahon
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Jocelyn A. Jones
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Ashley N. Anderson
- Department of Cell, Development & Cancer Biology Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Matthew S. Dietz
- Department of Cell, Development & Cancer Biology Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Melissa H. Wong
- Department of Cell, Development & Cancer Biology Department, Oregon Health & Science University, Portland, OR 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Summer L. Gibbs
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
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17
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Chiriboga L, Callis GM, Wang Y, Chlipala E. Guide for collecting and reporting metadata on protocol variables and parameters from slide-based histotechnology assays to enhance reproducibility. J Histotechnol 2022; 45:132-147. [DOI: 10.1080/01478885.2022.2134022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Luis Chiriboga
- Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA
- NYULH Center for Biospecimen Research and Development, New York, NY, USA
| | | | - Yongfu Wang
- Stowers Institute for Medical Research, Kansas, MO, USA
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