1
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Liu Y, Yang C. Computational methods for alignment and integration of spatially resolved transcriptomics data. Comput Struct Biotechnol J 2024; 23:1094-1105. [PMID: 38495555 PMCID: PMC10940867 DOI: 10.1016/j.csbj.2024.03.002] [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: 01/06/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/19/2024] Open
Abstract
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.
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Affiliation(s)
- Yuyao Liu
- Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
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2
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Wai KC, Okholm TLH, Ha PK, Marquez DM, Tenvooren I, Jones KB, Spitzer MH. The tumor microenvironment of benign and malignant salivary gland tumors. Head Neck 2024; 46:1625-1636. [PMID: 38454566 DOI: 10.1002/hed.27716] [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: 12/01/2023] [Revised: 02/08/2024] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Treatment of salivary gland tumors (SGTs) remains challenging. Little is known about the immune landscape of SGTs. We aimed to characterize the tumor microenvironment in benign and malignant SGTs. METHODS Eleven benign and nine malignant tumors were collected from patients undergoing curative intent surgery. Specimens were analyzed using mass cytometry by time-of-flight. Immune cell populations were manually gated, and T cells were clustered using the FlowSOM algorithm. Population frequencies were compared between high-grade and low-grade malignancies, corrected for multiple hypothesis testing. RESULTS There were trends towards increased CD4+ and CD8+ T cells among malignant tumors. High-grade malignancies exhibited trends towards higher frequencies of CD8+ PD-1+ CD39+ CD103+ exhausted T cells, CD4+ FoxP3+ TCF-1+ CD127- Tregs, and CD69+ CD25- CD4+ T cells compared to low-grade malignancies. CONCLUSION SGTs exhibit significant immunologic diversity. High-grade malignancies tended to have greater infiltration of exhausted CD8+ T cells and Tregs, which may guide future studies for immunotherapy strategies.
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Affiliation(s)
- Katherine C Wai
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Trine Line H Okholm
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Patrick K Ha
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Diana M Marquez
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Iliana Tenvooren
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
| | - Kyle B Jones
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
- Department of Orofacial Sciences, University of California San Francisco, San Francisco, California, USA
- Pharma Technical Cell and Gene Therapy, Genentech, Inc., South San Francisco, California, USA
| | - Matthew H Spitzer
- Department of Otolaryngology - Head and Neck Surgery, University of California San Francisco, San Francisco, California, USA
- Department of Microbiology and Immunology, University of California San Francisco, San Francisco, California, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
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3
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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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4
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McCarthy ME, Lu X, Ogunleye O, Latham DR, Abravanel M, Pritko D, Huggins JR, Haskell CV, Patel ND, Pittman ZA, Sanabria H, Birtwistle MR. Increasing Signal Intensity of Fluorescent Oligo-Labeled Antibodies to Enable Combination Multiplexing. Bioconjug Chem 2024. [PMID: 38889324 DOI: 10.1021/acs.bioconjchem.4c00246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Full-spectrum flow cytometry has increased antibody-based multiplexing, yet further increases remain potentially impactful. We recently proposed how fluorescence multiplexing using spectral imaging and combinatorics (MuSIC) could do so using tandem dyes and an oligo-based antibody labeling method. In this work, we found that such labeled antibodies had significantly lower signal intensities than conventionally labeled antibodies in human cell experiments. To improve signal intensity, we tested moving the fluorophores from the original external (ext.) 5' or 3' end-labeled orientation to internal (int.) fluorophore modifications. Cell-free spectrophotometer measurements showed a ∼6-fold signal intensity increase of the new int. configuration compared to the previous ext. configuration. Time-resolved fluorescence and fluorescence correlation spectroscopy showed that the ∼3-fold brightness difference is due to static quenching most likely by the oligo or solution in the ext. configuration. Spectral flow cytometry experiments using peripheral blood mononuclear cells show int. MuSIC probe-labeled antibodies (i) retained increased signal intensity while having no significant difference in the estimated % of CD8+ lymphocytes and (ii) labeled with Atto488, Atto647, and Atto488/647 combinations can be demultiplexed in triple-stained samples. The antibody labeling approach is general and can be broadly applied to many biological and diagnostic applications where spectral detection is available.
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Affiliation(s)
- Madeline E McCarthy
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Xiaoming Lu
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Oluwaferanmi Ogunleye
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Danielle R Latham
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Megan Abravanel
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Daniel Pritko
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Jonah R Huggins
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Charlotte V Haskell
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Nishi D Patel
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Zachariah A Pittman
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Hugo Sanabria
- Department of Chemistry, Clemson University, Clemson, South Carolina 29634-0002, United States
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, South Carolina 29634-0002, United States
- Department of Bioengineering, Clemson University, Clemson, South Carolina 29634-0002, United States
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VanderDoes J, Marceaux C, Yokote K, Asselin-Labat ML, Rice G, Hywood JD. Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments. PLoS Comput Biol 2024; 20:e1011361. [PMID: 38875302 DOI: 10.1371/journal.pcbi.1011361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/31/2024] [Indexed: 06/16/2024] Open
Abstract
Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.
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Affiliation(s)
- Jeremy VanderDoes
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Claire Marceaux
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Kenta Yokote
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Marie-Liesse Asselin-Labat
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Gregory Rice
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Jack D Hywood
- Department of Anatomical Pathology, Royal Melbourne Hospital, Melbourne, Australia
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6
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [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: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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7
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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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8
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Zhang Y, Lee RY, Tan CW, Guo X, Yim WWY, Lim JC, Wee FY, Yang WU, Kharbanda M, Lee JYJ, Ngo NT, Leow WQ, Loo LH, Lim TK, Sobota RM, Lau MC, Davis MJ, Yeong J. Spatial omics techniques and data analysis for cancer immunotherapy applications. Curr Opin Biotechnol 2024; 87:103111. [PMID: 38520821 DOI: 10.1016/j.copbio.2024.103111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.
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Affiliation(s)
- Yue Zhang
- Duke-NUS Medical School, Singapore 169856, Singapore
| | - Ren Yuan Lee
- Yong Loo Lin School of Medicine, National University of Singapore, 169856 Singapore; Singapore Thong Chai Medical Institution, Singapore 169874, Singapore
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Xue Guo
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Willa W-Y Yim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Jeffrey Ct Lim
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Felicia Yt Wee
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - W U Yang
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Malvika Kharbanda
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jia-Ying J Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Nye Thane Ngo
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Wei Qiang Leow
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Lit-Hsin Loo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore
| | - Tony Kh Lim
- Department of Anatomical Pathology, Singapore General Hospital, Singapore 169856, Singapore
| | - Radoslaw M Sobota
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore
| | - Mai Chan Lau
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A⁎STAR), Singapore 138648, Singapore
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia; Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia; immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria 3010, Australia
| | - Joe Yeong
- Institute of Molecular Cell Biology (IMCB), Agency of Science, Technology and Research (A⁎STAR), Singapore 169856, Singapore; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A⁎STAR), Singapore 138671, Singapore.
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Tornaas S, Kleftogiannis D, Fromreide S, Smeland HYH, Aarstad HJ, Vintermyr OK, Akslen LA, Costea DE, Dongre HN. Development of a high dimensional imaging mass cytometry panel to investigate spatial organization of tissue microenvironment in formalin-fixed archival clinical tissues. Heliyon 2024; 10:e31191. [PMID: 38803925 PMCID: PMC11128903 DOI: 10.1016/j.heliyon.2024.e31191] [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: 10/12/2023] [Revised: 05/10/2024] [Accepted: 05/12/2024] [Indexed: 05/29/2024] Open
Abstract
To decipher the interactions between various components of the tumor microenvironment (TME) and tumor cells in a preserved spatial context, a multiparametric approach is essential. In this pursuit, imaging mass cytometry (IMC) emerges as a valuable tool, capable of concurrently analyzing up to 40 parameters at subcellular resolution. In this study, a set of antibodies was selected to spatially resolve multiple cell types and TME elements, including a comprehensive panel targeted at dissecting the heterogeneity of cancer-associated fibroblasts (CAF), a pivotal TME component. This antibody panel was standardized and optimized using formalin-fixed paraffin-embedded tissue (FFPE) samples from different organs/lesions known to express the markers of interest. The final composition of the antibody panel was determined based on the performance of conjugated antibodies in both immunohistochemistry (IHC) and IMC. Tissue images were segmented employing the Steinbock framework. Unsupervised clustering of single-cell data was carried out using a bioinformatics pipeline developed in R program. This paper provides a detailed description of the staining procedure and analysis workflow. Subsequently, the panel underwent validation on clinical FFPE samples from head and neck squamous cell carcinoma (HNSCC). The panel and bioinformatics pipeline established here proved to be robust in characterizing different TME components of HNSCC while maintaining a high degree of spatial detail. The platform we describe shows promise for understanding the clinical implications of TMA heterogeneity in large patient cohorts with FFPE tissues available in diagnostic biobanks worldwide.
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Affiliation(s)
- Stian Tornaas
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
| | - Dimitrios Kleftogiannis
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
- Computional Biology Unit, Department of Informatics, University of Bergen, Norway
| | - Siren Fromreide
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
| | - Hilde Ytre-Hauge Smeland
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Hans Jørgen Aarstad
- Department for Ear-Nose-and-Throat, Head and Neck Clinic, Haukeland University Hospital, Bergen, Norway
| | | | - Lars Andreas Akslen
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Daniela Elena Costea
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
- Department of Pathology, Haukeland University Hospital, Bergen, Norway
| | - Harsh Nitin Dongre
- Center for Cancer Biomarkers (CCBIO) and Department of Clinical Medicine, University of Bergen, Norway
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10
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McCarthy ME, Lu X, Ogunleye O, Latham DR, Abravanel M, Pritko D, Huggins JR, Haskell CV, Patel ND, Pittman ZA, Sanabria H, Birtwistle MR. Increasing Signal Intensity of Fluorescent Oligo-Labeled Antibodies to Enable Combination Multiplexing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.06.547965. [PMID: 37461453 PMCID: PMC10350089 DOI: 10.1101/2023.07.06.547965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Full-spectrum flow cytometry has increased antibody-based multiplexing, yet further increases remain potentially impactful. We recently proposed how fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC) could do so using tandem dyes and an oligo-based antibody labeling method. In this work, we found that such labeled antibodies had significantly lower signal intensity than conventionally-labeled antibodies in human cell experiments. To improve signal intensity, we tested moving the fluorophores from the original external (ext.) 5' or 3' end-labeled orientation to internal (int.) fluorophore modifications. Cell-free spectrophotometer measurements showed a ~6-fold signal intensity increase of the new int. configuration compared to the previous ext. configuration. Time-resolved fluorescence and fluorescence correlation spectroscopy showed that ~3-fold brightness difference is due to static quenching most likely by the oligo or solution in the ext. configuration. Spectral flow cytometry experiments using peripheral blood mononuclear cells show int. MuSIC probe-labeled antibodies (i) retained increased signal intensity while having no significant difference in the estimated % of CD8+ lymphocytes and (ii) labeled with Atto488, Atto647, and Atto488/647 combinations can be demultiplexed in triple-stained samples. The antibody labeling approach is general and can be broadly applied to many biological and diagnostic applications where spectral detection is available.
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Affiliation(s)
| | - Xiaoming Lu
- Department of Chemical and Biomolecular Engineering, Clemson University
| | | | | | - Megan Abravanel
- Department of Chemical and Biomolecular Engineering, Clemson University
| | - Daniel Pritko
- Department of Chemical and Biomolecular Engineering, Clemson University
| | - Jonah R. Huggins
- Department of Chemical and Biomolecular Engineering, Clemson University
| | | | - Nishi D. Patel
- Department of Chemical and Biomolecular Engineering, Clemson University
| | | | | | - Marc R. Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University
- Department of Bioengineering, Clemson University
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Nirmal AJ, Sorger PK. SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. JOURNAL OF OPEN SOURCE SOFTWARE 2024; 9:6604. [PMID: 38873023 PMCID: PMC11173324 DOI: 10.21105/joss.06604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Multiplexed imaging data are revolutionizing our understanding of the composition and organization of tissues and tumors ("Catching up with Multiplexed Tissue Imaging," 2022). A critical aspect of such "tissue profiling" is quantifying the spatial relationships among cells at different scales from the interaction of neighboring cells to recurrent communities of cells of multiple types. This often involves statistical analysis of 107 or more cells in which up to 100 biomolecules (commonly proteins) have been measured. While software tools currently cater to the analysis of spatial transcriptomics data (Liu et al., 2022), there remains a need for toolkits explicitly tailored to the complexities of multiplexed imaging data including the need to seamlessly integrate image visualization with data analysis and exploration. We introduce SCIMAP, a Python package specifically crafted to address these challenges. With SCIMAP, users can efficiently preprocess, analyze, and visualize large datasets, facilitating the exploration of spatial relationships and their statistical significance. SCIMAP's modular design enables the integration of new algorithms, enhancing its capabilities for spatial analysis.
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Affiliation(s)
- Ajit J Nirmal
- Department of Dermatology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States of America
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States of America
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States of America
| | - Peter K Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States of America
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States of America
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12
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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13
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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14
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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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15
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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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16
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Winter S, Götze KS, Hecker JS, Metzeler KH, Guezguez B, Woods K, Medyouf H, Schäffer A, Schmitz M, Wehner R, Glauche I, Roeder I, Rauner M, Hofbauer LC, Platzbecker U. Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche. Leukemia 2024; 38:936-946. [PMID: 38514772 PMCID: PMC11073997 DOI: 10.1038/s41375-024-02226-6] [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: 11/16/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.
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Affiliation(s)
- Susann Winter
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katharina S Götze
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- German MDS Study Group (D-MDS), Leipzig, Germany
| | - Judith S Hecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich (TUM), Munich, Germany
| | - Klaus H Metzeler
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany
| | - Borhane Guezguez
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Kevin Woods
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Hind Medyouf
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Frankfurt am Main, Germany
| | - Alexander Schäffer
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Marc Schmitz
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Rebekka Wehner
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Ingo Roeder
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Martina Rauner
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany
| | - Lorenz C Hofbauer
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany.
| | - Uwe Platzbecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- German MDS Study Group (D-MDS), Leipzig, Germany.
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany.
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17
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McMahon NP, Solanki A, Wang LG, Montaño AR, Jones JA, Samkoe KS, Tichauer KM, Gibbs SL. In situ single-cell therapeutic response imaging facilitated by the TRIPODD fluorescence imaging platform. Theranostics 2024; 14:2816-2834. [PMID: 38773974 PMCID: PMC11103495 DOI: 10.7150/thno.93256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/21/2024] [Indexed: 05/24/2024] Open
Abstract
Purpose: Small molecule drugs such as tyrosine kinase inhibitors (TKIs) targeting tumoral molecular dependencies have become standard of care for numerous cancer types. Notably, epidermal growth factor receptor (EGFR) TKIs (e.g., erlotinib, afatinib, osimertinib) are the current first-line treatment for non-small cell lung cancer (NSCLC) due to their improved therapeutic outcomes for EGFR mutated and overexpressing disease over traditional platinum-based chemotherapy. However, many NSCLC tumors develop resistance to EGFR TKI therapy causing disease progression. Currently, the relationship between in situ drug target availability (DTA), local protein expression and therapeutic response cannot be accurately assessed using existing analytical tools despite being crucial to understanding the mechanism of therapeutic efficacy. Procedure: We have previously reported development of our fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution) that is capable of simultaneous quantification of single-cell DTA and protein expression with preserved spatial context within a tumor. TRIPODD combines two complementary fluorescence imaging techniques: intracellular paired agent imaging (iPAI) to measure DTA and cyclic immunofluorescence (cyCIF), which utilizes oligonucleotide conjugated antibodies (Ab-oligos) for spatial proteomic expression profiling on tissue samples. Herein, TRIPODD was modified and optimized to provide a downstream analysis of therapeutic response through single-cell DTA and proteomic response imaging. Results: We successfully performed sequential imaging of iPAI and cyCIF resulting in high dimensional imaging and biomarker assessment to quantify single-cell DTA and local protein expression on erlotinib treated NSCLC models. Pharmacodynamic and pharmacokinetic studies of the erlotinib iPAI probes revealed that administration of 2.5 mg/kg each of the targeted and untargeted probe 4 h prior to tumor collection enabled calculation of DTA values with high Pearson correlation to EGFR, the erlotinib molecular target, expression in the tumors. Analysis of single-cell biomarker expression revealed that a single erlotinib dose was insufficient to enact a measurable decrease in the EGFR signaling cascade protein expression, where only the DTA metric detected the presence of bound erlotinib. Conclusion: We demonstrated the capability of TRIPODD to evaluate therapeutic response imaging to erlotinib treatment as it relates to signaling inhibition, DTA, proliferation, and apoptosis with preserved spatial context.
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Affiliation(s)
- Nathan P. McMahon
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Allison Solanki
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Lei G. Wang
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Antonio R. Montaño
- 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
| | - Kimberley S. Samkoe
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Kenneth M. Tichauer
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, 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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18
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Xu AM, Haro M, Walts AE, Hu Y, John J, Karlan BY, Merchant A, Orsulic S. Spatiotemporal architecture of immune cells and cancer-associated fibroblasts in high-grade serous ovarian carcinoma. SCIENCE ADVANCES 2024; 10:eadk8805. [PMID: 38630822 PMCID: PMC11023532 DOI: 10.1126/sciadv.adk8805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 03/15/2024] [Indexed: 04/19/2024]
Abstract
High-grade serous ovarian carcinoma (HGSOC), the deadliest form of ovarian cancer, is typically diagnosed after it has metastasized and often relapses after standard-of-care platinum-based chemotherapy, likely due to advanced tumor stage, heterogeneity, and immune evasion and tumor-promoting signaling from the tumor microenvironment. To understand how spatial heterogeneity contributes to HGSOC progression and early relapse, we profiled an HGSOC tissue microarray of patient-matched longitudinal samples from 42 patients. We found spatial patterns associated with early relapse, including changes in T cell localization, malformed tertiary lymphoid structure (TLS)-like aggregates, and increased podoplanin-positive cancer-associated fibroblasts (CAFs). Using spatial features to compartmentalize the tissue, we found that plasma cells distribute in two different compartments associated with TLS-like aggregates and CAFs, and these distinct microenvironments may account for the conflicting reports about the role of plasma cells in HGSOC prognosis.
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Affiliation(s)
- Alexander M. Xu
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Hematology and Cellular Therapy, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Marcela Haro
- Department of Obstetrics and Gynecology and Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ann E. Walts
- Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Ye Hu
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Joshi John
- Department of Veterans Affairs, Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
- Department of Medicine, Division of Geriatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Beth Y. Karlan
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Akil Merchant
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Hematology and Cellular Therapy, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Sandra Orsulic
- Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
- Department of Veterans Affairs, Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA 90095, USA
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19
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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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20
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Seal S, Neelon B, Angel PM, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta AS, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA. J Proteome Res 2024; 23:1131-1143. [PMID: 38417823 PMCID: PMC11002919 DOI: 10.1021/acs.jproteome.3c00462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 03/01/2024]
Abstract
Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.
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Affiliation(s)
- Souvik Seal
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Brian Neelon
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Peggi M. Angel
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth C. O’Quinn
- Translational
Science Laboratory, Hollings Cancer Center, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth Hill
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Thao Vu
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Debashis Ghosh
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Anand S. Mehta
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Kristin Wallace
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Alexander V. Alekseyenko
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
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21
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Sims Z, Mills GB, Chang YH. MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation. Commun Biol 2024; 7:409. [PMID: 38570598 PMCID: PMC10991424 DOI: 10.1038/s42003-024-06110-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024] Open
Abstract
Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
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Affiliation(s)
- Zachary Sims
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
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22
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Guo X, Bian X, Li Y, Zhu X, Zhou X. The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167098. [PMID: 38412927 DOI: 10.1016/j.bbadis.2024.167098] [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: 11/16/2023] [Revised: 01/14/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.
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Affiliation(s)
- Xiaojun Guo
- Department of Immunology, School of Medicine, Nantong University, Nantong, China; The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiaonan Bian
- Department of Immunology, School of Medicine, Nantong University, Nantong, China
| | - Yitong Li
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China
| | - Xiao Zhu
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang, China.
| | - Xiaorong Zhou
- Department of Immunology, School of Medicine, Nantong University, Nantong, China.
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23
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [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: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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24
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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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25
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Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
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Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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26
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Unterauer EM, Shetab Boushehri S, Jevdokimenko K, Masullo LA, Ganji M, Sograte-Idrissi S, Kowalewski R, Strauss S, Reinhardt SCM, Perovic A, Marr C, Opazo F, Fornasiero EF, Jungmann R. Spatial proteomics in neurons at single-protein resolution. Cell 2024; 187:1785-1800.e16. [PMID: 38552614 DOI: 10.1016/j.cell.2024.02.045] [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: 05/17/2023] [Revised: 11/28/2023] [Accepted: 02/29/2024] [Indexed: 04/02/2024]
Abstract
To understand biological processes, it is necessary to reveal the molecular heterogeneity of cells by gaining access to the location and interaction of all biomolecules. Significant advances were achieved by super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of proteomics. Here, we introduce secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method that is capable of achieving virtually unlimited multiplexing at better than 15 nm resolution. Using SUM-PAINT, we generated 30-plex single-molecule resolved datasets in neurons and adapted omics-inspired analysis for data exploration. This allowed us to reveal the complexity of synaptic heterogeneity, leading to the discovery of a distinct synapse type. We not only provide a resource for researchers, but also an integrated acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution.
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Affiliation(s)
- Eduard M Unterauer
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Data & Analytics, Roche Pharma Research and Early Development, Roche Innovation Center Munich, Munich, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Kristina Jevdokimenko
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany
| | | | - Mahipal Ganji
- Max Planck Institute of Biochemistry, Planegg, Germany; Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Shama Sograte-Idrissi
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany
| | - Rafal Kowalewski
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sebastian Strauss
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Susanne C M Reinhardt
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ana Perovic
- Max Planck Institute of Biochemistry, Planegg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany; Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Felipe Opazo
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany; NanoTag Biotechnologies GmbH, Göttingen, Germany
| | - Eugenio F Fornasiero
- Institute of Neuro- and Sensory Physiology, University Medical Center Göttingen, Göttingen, Germany; Department of Life Sciences, University of Trieste, Trieste, Italy.
| | - Ralf Jungmann
- Max Planck Institute of Biochemistry, Planegg, Germany; Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität, Munich, Germany.
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27
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Forjaz A, Vaz E, Romero VM, Joshi S, Braxton AM, Jiang AC, Fujikura K, Cornish T, Hong SM, Hruban RH, Wu PH, Wood LD, Kiemen AL, Wirtz D. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.04.569986. [PMID: 38106231 PMCID: PMC10723352 DOI: 10.1101/2023.12.04.569986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.
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Affiliation(s)
- André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Eduarda Vaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Valentina Matos Romero
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Ann C. Jiang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Kohei Fujikura
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Toby Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Laura D. Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
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28
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Baker GJ, Novikov E, Zhao Z, Vallius T, Davis JA, Lin JR, Muhlich JL, Mittendorf EA, Santagata S, Guerriero JL, Sorger PK. Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565120. [PMID: 37961235 PMCID: PMC10634977 DOI: 10.1101/2023.11.01.565120] [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
Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.
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Affiliation(s)
- Gregory J. Baker
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Edward Novikov
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Ziyuan Zhao
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA
| | - Tuulia Vallius
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Janae A. Davis
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Jeremy L. Muhlich
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Elizabeth A. Mittendorf
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jennifer L. Guerriero
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Peter K. Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
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29
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Scheuermann S, Kristmann B, Engelmann F, Nuernbergk A, Scheuermann D, Koloseus M, Abed T, Solass W, Seitz CM. Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging. Front Immunol 2024; 15:1383932. [PMID: 38566984 PMCID: PMC10985204 DOI: 10.3389/fimmu.2024.1383932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.
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Affiliation(s)
- Sophia Scheuermann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
| | - Beate Kristmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Fabienne Engelmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Alice Nuernbergk
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - David Scheuermann
- School of Business and Economics, Faculty of Economics and Social Sciences, University of Tuebingen, Tuebingen, Germany
| | - Marie Koloseus
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Tayeb Abed
- Institute of Pathology and Neuropathology, University Hospital Tuebingen and Comprehensive Cancer Center, Tuebingen, Germany
| | - Wiebke Solass
- Institute of Tissue Medicine and Pathology (ITMP), University of Bern, Bern, Switzerland
| | - Christian M. Seitz
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
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30
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Yeo YY, Cramer P, Deisher A, Bai Y, Zhu B, Yeo WJ, Shipp MA, Rodig SJ, Jiang S. A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging. Methods Cell Biol 2024; 186:213-231. [PMID: 38705600 DOI: 10.1016/bs.mcb.2024.02.018] [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
Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.
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Affiliation(s)
- Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States
| | - Precious Cramer
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Addison Deisher
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yunhao Bai
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Bokai Zhu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Wan-Jin Yeo
- Department of Physics, Institute of Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Margaret A Shipp
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States; Department of Pathology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
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31
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Yeo YY, Qiu H, Bai Y, Zhu B, Chang Y, Yeung J, Michel HA, Wright K, Shaban M, Sadigh S, Nkosi D, Shanmugam V, Rock P, Tung Yiu SP, Cramer P, Paczkowska J, Stephan P, Liao G, Huang AY, Wang H, Chen H, Frauenfeld L, Mitra B, Gewurz BE, Schürch CM, Zhao B, Nolan GP, Zhang B, Shalek AK, Angelo M, Mahmood F, Ma Q, Burack WR, Shipp MA, Rodig SJ, Jiang S. Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583586. [PMID: 38496566 PMCID: PMC10942289 DOI: 10.1101/2024.03.05.583586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.
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32
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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33
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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34
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Macdonald JK, Mehta AS, Drake RR, Angel PM. Molecular analysis of the extracellular microenvironment: from form to function. FEBS Lett 2024; 598:602-620. [PMID: 38509768 PMCID: PMC11049795 DOI: 10.1002/1873-3468.14852] [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: 11/29/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.
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Affiliation(s)
- Jade K Macdonald
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Anand S Mehta
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Richard R Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
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35
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Aung TN, Bates KM, Rimm DL. High-Plex Assessment of Biomarkers in Tumors. Mod Pathol 2024; 37:100425. [PMID: 38219953 DOI: 10.1016/j.modpat.2024.100425] [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: 08/30/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.
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Affiliation(s)
- Thazin N Aung
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Katherine M Bates
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, Connecticut.
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36
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Patton A, Dermawan JK. Current updates in sarcoma biomarker discovery: emphasis on next-generation sequencing-based methods. Pathology 2024; 56:274-282. [PMID: 38185613 DOI: 10.1016/j.pathol.2023.10.015] [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: 10/02/2023] [Revised: 10/25/2023] [Accepted: 10/29/2023] [Indexed: 01/09/2024]
Abstract
Soft tissue sarcomas comprise a heterogeneous group of neoplasms. Although soft tissue malignancies make up only 2% of adult cancers, classification based on histomorphology presents a diagnostic challenge. Characterisation of soft tissue sarcomas by molecular analysis is rapidly evolving to improve diagnostic accuracy and develop targeted therapies. This review highlights the advances in molecular techniques, including current next-generation sequencing-based assays (fusion detection by RNA sequencing, targeted/whole exome sequencing, microRNA profiling), as well as emerging methods (liquid biopsies, DNA methylation profiling, single-cell molecular profiling and next-generation immunohistochemistry) for future clinical applications.
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Affiliation(s)
- Ashley Patton
- Department of Pathology & Laboratory Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Josephine K Dermawan
- Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
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37
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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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38
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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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39
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Tu C, Kulasinghe A, Barbour A, Souza-Fonseca-Guimaraes F. Leveraging spatial omics for the development of precision sarcoma treatments. Trends Pharmacol Sci 2024; 45:134-144. [PMID: 38212196 DOI: 10.1016/j.tips.2023.12.006] [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: 11/08/2023] [Revised: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 01/13/2024]
Abstract
Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.
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Affiliation(s)
- Cui Tu
- Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, QLD 4102, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, QLD 4102, Australia
| | - Andrew Barbour
- Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, QLD 4102, Australia; Department of Surgery, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
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40
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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Patel AG, Ashenberg O, Collins NB, Segerstolpe Å, Jiang S, Slyper M, Huang X, Caraccio C, Jin H, Sheppard H, Xu K, Chang TC, Orr BA, Shirinifard A, Chapple RH, Shen A, Clay MR, Tatevossian RG, Reilly C, Patel J, Lupo M, Cline C, Dionne D, Porter CBM, Waldman J, Bai Y, Zhu B, Barrera I, Murray E, Vigneau S, Napolitano S, Wakiro I, Wu J, Grimaldi G, Dellostritto L, Helvie K, Rotem A, Lako A, Cullen N, Pfaff KL, Karlström Å, Jané-Valbuena J, Todres E, Thorner A, Geeleher P, Rodig SJ, Zhou X, Stewart E, Johnson BE, Wu G, Chen F, Yu J, Goltsev Y, Nolan GP, Rozenblatt-Rosen O, Regev A, Dyer MA. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574538. [PMID: 38260392 PMCID: PMC10802404 DOI: 10.1101/2024.01.07.574538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
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Affiliation(s)
- Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- These authors contributed equally
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally
| | - Natalie B Collins
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- These authors contributed equally
| | - Åsa Segerstolpe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Huang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chiara Caraccio
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Heather Sheppard
- Comparative Pathology Core, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ke Xu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ti-Cheng Chang
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Abbas Shirinifard
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Amber Shen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael R Clay
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ruth G Tatevossian
- Cancer Biomarkers Laboratory, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Colleen Reilly
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jaimin Patel
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Marybeth Lupo
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cynthia Cline
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sébastien Vigneau
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Napolitano
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Isaac Wakiro
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jingyi Wu
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Grace Grimaldi
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Dellostritto
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Asaf Rotem
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kathleen L Pfaff
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Åsa Karlström
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Judit Jané-Valbuena
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen Todres
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron Thorner
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Elizabeth Stewart
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yury Goltsev
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
- Lead contacts
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Lead contacts
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Magrill J, Moldoveanu D, Gu J, Lajoie M, Watson IR. Mapping the single cell spatial immune landscapes of the melanoma microenvironment. Clin Exp Metastasis 2024:10.1007/s10585-023-10252-4. [PMID: 38217840 DOI: 10.1007/s10585-023-10252-4] [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: 07/15/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.
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Affiliation(s)
- Jamie Magrill
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Dan Moldoveanu
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Jiayao Gu
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mathieu Lajoie
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Ian R Watson
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Department of Biochemistry, McGill University, Montréal, QC, Canada.
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada.
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43
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Meyer L, Eling N, Bodenmiller B. cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. BMC Bioinformatics 2024; 25:9. [PMID: 38172724 PMCID: PMC10765786 DOI: 10.1186/s12859-023-05546-z] [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: 08/07/2023] [Accepted: 10/27/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R. RESULTS Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples. CONCLUSIONS The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .
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Affiliation(s)
- 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/University of Zurich, Zurich, Switzerland
| | - Nils Eling
- 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.
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44
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Lee E, Lee D, Fan W, Lytle A, Fu Y, Scott DW, Steidl C, Aparicio S, Roth A. ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images. Bioinformatics 2024; 40:btad783. [PMID: 38152895 PMCID: PMC10783950 DOI: 10.1093/bioinformatics/btad783] [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: 07/07/2023] [Revised: 12/17/2023] [Accepted: 12/27/2023] [Indexed: 12/29/2023] Open
Abstract
MOTIVATION Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy. RESULTS In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. AVAILABILITY AND IMPLEMENTATION Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.
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Affiliation(s)
- Eric Lee
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia V5Z1L3, Canada
- Graduate Bioinformatics Training Program, University of British Columbia, Vancouver, British Columbia V5T4S6, Canada
| | - Dongkyu Lee
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
| | - Wayne Fan
- BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z4H4, Canada
| | - Andrew Lytle
- Centre for Lymphoid Cancer, BC Cancer and University of British Columbia, Vancouver, British Columbia V5Z1L3, Canada
| | - Yuxiang Fu
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
| | - IMAXT Consortium
- CRUK IMAXT Grand Challenge Consortium, Cambridge CB20RE, United Kingdom
| | - David W Scott
- Centre for Lymphoid Cancer, BC Cancer and University of British Columbia, Vancouver, British Columbia V5Z1L3, Canada
| | - Christian Steidl
- Centre for Lymphoid Cancer, BC Cancer and University of British Columbia, Vancouver, British Columbia V5Z1L3, Canada
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia V5Z1L3, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T1Z7, Canada
| | - Andrew Roth
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia V5Z1L3, Canada
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T1Z7, Canada
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Weirather JL, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 2024; 15:28. [PMID: 38167832 PMCID: PMC10761896 DOI: 10.1038/s41467-023-44188-w] [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: 05/15/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jason L Weirather
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Chen Y, Yang S, Yu K, Zhang J, Wu M, Zheng Y, Zhu Y, Dai J, Wang C, Zhu X, Dai Y, Sun Y, Wu T, Wang S. Spatial omics: An innovative frontier in aging research. Ageing Res Rev 2024; 93:102158. [PMID: 38056503 DOI: 10.1016/j.arr.2023.102158] [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: 08/28/2023] [Revised: 11/25/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Disentangling the impact of aging on health and disease has become critical as population aging progresses rapidly. Studying aging at the molecular level is complicated by the diverse aging profiles and dynamics. However, the examination of cellular states within aging tissues in situ is hampered by the lack of high-resolution spatial data. Emerging spatial omics technologies facilitate molecular and spatial analysis of tissues, providing direct access to precise information on various functional regions and serving as a favorable tool for unraveling the heterogeneity of aging. In this review, we summarize the recent advances in spatial omics application in multi-organ aging research, which has enhanced the understanding of aging mechanisms from multiple standpoints. We also discuss the main challenges in spatial omics research to date, the opportunities for further developing the technology, and the potential applications of spatial omics in aging and aging-related diseases.
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Affiliation(s)
- Ying Chen
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Shuhao Yang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Kaixu Yu
- Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinjin Zhang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Meng Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yongqiang Zheng
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Centre, Sun Yat-sen University, Guangzhou, China
| | - Yun Zhu
- Department of Internal Medicine, Southern Illinois University School of Medicine, 801 N. Rutledge, P.O. Box 19628, Springfield, IL 62702, USA
| | - Jun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Chunyan Wang
- College of Science & Engineering Jinan University, Guangzhou, China
| | - Xiaoran Zhu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yun Dai
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China
| | - Yunhong Sun
- Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tong Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
| | - Shixuan Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; National Clinical Research Center for Obstetrical and Gynecological Diseases, Wuhan, China; Ministry of Education, Key Laboratory of Cancer Invasion and Metastasis, Wuhan, China.
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47
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Rodríguez-Bejarano OH, Roa L, Vargas-Hernández G, Botero-Espinosa L, Parra-López C, Patarroyo MA. Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate. Biochim Biophys Acta Rev Cancer 2024; 1879:189064. [PMID: 38158026 DOI: 10.1016/j.bbcan.2023.189064] [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: 08/23/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.
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Affiliation(s)
- Oscar Hernán Rodríguez-Bejarano
- Health Sciences Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Calle 222#55-37, Bogotá 111166, Colombia; Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; PhD Programme in Biotechnology, Faculty of Sciences, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Leonardo Roa
- Veterinary Clinic, Faculty of Agricultural Sciences, Universidad de La Salle, Carrera 7 #179-03, Bogotá 110141, Colombia
| | - Giovanni Vargas-Hernández
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Lucía Botero-Espinosa
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Carlos Parra-López
- Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
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48
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Hunter B, Nicorescu I, Foster E, McDonald D, Hulme G, Fuller A, Thomson A, Goldsborough T, Hilkens CMU, Majo J, Milross L, Fisher A, Bankhead P, Wills J, Rees P, Filby A, Merces G. OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration. Cytometry A 2024; 105:36-53. [PMID: 37750225 PMCID: PMC10952805 DOI: 10.1002/cyto.a.24803] [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: 03/28/2023] [Revised: 09/13/2023] [Accepted: 09/18/2023] [Indexed: 09/27/2023]
Abstract
Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.
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Affiliation(s)
- Bethany Hunter
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Ioana Nicorescu
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Emma Foster
- Image Analysis Unit, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - David McDonald
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Gillian Hulme
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew Fuller
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Amanda Thomson
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | | | - Catharien M. U. Hilkens
- Translational and Clinical Research Institute, Immunity and Inflammation Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Joaquim Majo
- Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation TrustNewcastle upon TyneUK
| | - Luke Milross
- Transplantation and Regenerative Medicine, Newcastle University Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Andrew Fisher
- Transplantation and Regenerative Medicine, Newcastle University Translational and Clinical Research Institute, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - Peter Bankhead
- Centre for Genomic and Experimental Medicine, CRUK Scotland Centre, and Edinburgh PathologyUniversity of EdinburghEdinburghUK
| | - John Wills
- Department of Veterinary MedicineCambridge UniversityCambridgeUK
- Department of Biomedical EngineeringSwansea UniversitySwansea, WalesUK
| | - Paul Rees
- Department of Biomedical EngineeringSwansea UniversitySwansea, WalesUK
- Imaging PlatformBroad Institute of MIT and HarvardCambridgeMassachusettsUSA
| | - Andrew Filby
- Flow Cytometry Core Facility, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
| | - George Merces
- Biosciences Institute, Innovation, Methodology and Application (IMA) Research Theme, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
- Image Analysis Unit, Faculty of Medical SciencesNewcastle UniversityNewcastle upon TyneUK
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49
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Kumar G, Pandurengan RK, Parra ER, Kannan K, Haymaker C. Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications. Front Immunol 2023; 14:1288802. [PMID: 38179056 PMCID: PMC10765501 DOI: 10.3389/fimmu.2023.1288802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.
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Affiliation(s)
| | | | | | - Kasthuri Kannan
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
| | - Cara Haymaker
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, United States
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50
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Xiang P, Liyu A, Kwon Y, Hu D, Williams SM, Veličković D, Markillie LM, Chrisler WB, Paša-Tolić L, Zhu Y. Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation. ACS MEASUREMENT SCIENCE AU 2023; 3:459-468. [PMID: 38145026 PMCID: PMC10740121 DOI: 10.1021/acsmeasuresciau.3c00033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/17/2023] [Accepted: 09/22/2023] [Indexed: 12/26/2023]
Abstract
Multiplexed molecular profiling of tissue microenvironments, or spatial omics, can provide critical insights into cellular functions and disease pathology. The coupling of laser microdissection with mass spectrometry-based proteomics has enabled deep and unbiased mapping of >1000 proteins. However, the throughput of laser microdissection is often limited due to tedious two-step procedures, sequential laser cutting, and sample collection. The two-step procedure also hinders the further improvement of spatial resolution to <10 μm as needed for subcellular proteomics. Herein, we developed a high-throughput and high-resolution spatial proteomics platform by seamlessly coupling deep ultraviolet (DUV) laser ablation (LA) with nanoPOTS (Nanodroplet Processing in One pot for Trace Samples)-based sample preparation. We demonstrated the DUV-LA system can quickly isolate and collect tissue samples at a throughput of ∼30 spots/min and a spatial resolution down to 2 μm from a 10 μm thick human pancreas tissue section. To improve sample recovery, we developed a proximity aerosol collection approach by placing DMSO droplets close to LA spots. We demonstrated the DUV-LA-nanoPOTS platform can detect an average of 1312, 1533, and 1966 proteins from ablation spots with diameters of 7, 13, and 19 μm, respectively. In a proof-of-concept study, we isolated and profiled two distinct subcellular regions of the pancreas tissue revealed by hematoxylin and eosin (H&E) staining. Quantitative proteomics revealed proteins specifically enriched to subcellular compartments.
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Affiliation(s)
- Piliang Xiang
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Andrey Liyu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Yumi Kwon
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Dehong Hu
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Sarah M. Williams
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Dušan Veličković
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Lye Meng Markillie
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - William B. Chrisler
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Ying Zhu
- Department
of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, 1 DNA Way, South San Francisco, California 94080, United States
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