101
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Seferbekova Z, Lomakin A, Yates LR, Gerstung M. Spatial biology of cancer evolution. Nat Rev Genet 2022; 24:295-313. [PMID: 36494509 DOI: 10.1038/s41576-022-00553-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
Abstract
The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.
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102
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Yuan Z, Li Y, Shi M, Yang F, Gao J, Yao J, Zhang MQ. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat Commun 2022; 13:7330. [PMID: 36443314 PMCID: PMC9705407 DOI: 10.1038/s41467-022-34867-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Tencent AI Lab, Shenzhen, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yisi Li
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | | | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
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103
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Rovira-Clavé X, Drainas AP, Jiang S, Bai Y, Baron M, Zhu B, Dallas AE, Lee MC, Chu TP, Holzem A, Ayyagari R, Bhattacharya D, McCaffrey EF, Greenwald NF, Markovic M, Coles GL, Angelo M, Bassik MC, Sage J, Nolan GP. Spatial epitope barcoding reveals clonal tumor patch behaviors. Cancer Cell 2022; 40:1423-1439.e11. [PMID: 36240778 PMCID: PMC9673683 DOI: 10.1016/j.ccell.2022.09.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/22/2022] [Accepted: 09/21/2022] [Indexed: 01/09/2023]
Abstract
Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.
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Affiliation(s)
- Xavier Rovira-Clavé
- Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA
| | - Alexandros P Drainas
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Maya Baron
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305, USA
| | - Alec E Dallas
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Myung Chang Lee
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Theresa P Chu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Alessandra Holzem
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Ramya Ayyagari
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Debadrita Bhattacharya
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Erin F McCaffrey
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Maxim Markovic
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Garry L Coles
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Michael C Bassik
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Julien Sage
- Department of Pediatrics, Stanford University, Stanford, CA 94305, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA 94305, USA.
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104
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Hsieh WC, Budiarto BR, Wang YF, Lin CY, Gwo MC, So DK, Tzeng YS, Chen SY. Spatial multi-omics analyses of the tumor immune microenvironment. J Biomed Sci 2022; 29:96. [DOI: 10.1186/s12929-022-00879-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 11/02/2022] [Indexed: 11/16/2022] Open
Abstract
AbstractIn the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell–cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial ‘omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.
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105
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Vijayaragavan K, Cannon BJ, Tebaykin D, Bossé M, Baranski A, Oliveria JP, Bukhari SA, Mrdjen D, Corces MR, McCaffrey EF, Greenwald NF, Sigal Y, Marquez D, Khair Z, Bruce T, Goldston M, Bharadwaj A, Montine KS, Angelo RM, Montine TJ, Bendall SC. Single-cell spatial proteomic imaging for human neuropathology. Acta Neuropathol Commun 2022; 10:158. [PMID: 36333818 PMCID: PMC9636771 DOI: 10.1186/s40478-022-01465-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.
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Affiliation(s)
| | - Bryan J Cannon
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dmitry Tebaykin
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Marc Bossé
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Alex Baranski
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - J P Oliveria
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Syed A Bukhari
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Dunja Mrdjen
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Erin F McCaffrey
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Noah F Greenwald
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Diana Marquez
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Zumana Khair
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Trevor Bruce
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Mako Goldston
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Anusha Bharadwaj
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Kathleen S Montine
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - R Michael Angelo
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Thomas J Montine
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA.
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106
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Chen W, Wang G, Zhang G. Insights into the transition of ductal carcinoma in situ to invasive ductal carcinoma: morphology, molecular portraits, and the tumor microenvironment. Cancer Biol Med 2022; 19:1487-1495. [PMID: 36350000 PMCID: PMC9630521 DOI: 10.20892/j.issn.2095-3941.2022.0440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/05/2022] [Indexed: 07/02/2024] Open
Affiliation(s)
- Weiling Chen
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang’An Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
- Key Laboratory for Endocrine-Related Cancer Precision Medicine of Xiamen, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
- Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
| | - Guimei Wang
- Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
- Department of Pathology, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
| | - Guojun Zhang
- Department of Breast-Thyroid-Surgery and Cancer Center, Xiang’An Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
- Key Laboratory for Endocrine-Related Cancer Precision Medicine of Xiamen, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
- Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiang’An Hospital of Xiamen University, Xiamen 361101, China
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107
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Walton RT, Singh A, Blainey PC. Pooled genetic screens with image‐based profiling. Mol Syst Biol 2022; 18:e10768. [PMID: 36366905 PMCID: PMC9650298 DOI: 10.15252/msb.202110768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 09/12/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships.
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Affiliation(s)
- Russell T Walton
- Broad Institute of MIT and Harvard Cambridge MA USA
- Department of Biological Engineering MIT Cambridge MA USA
| | - Avtar Singh
- Broad Institute of MIT and Harvard Cambridge MA USA
| | - Paul C Blainey
- Broad Institute of MIT and Harvard Cambridge MA USA
- Department of Biological Engineering MIT Cambridge MA USA
- Koch Institute for Integrative Cancer Research MIT Cambridge MA USA
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108
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Zhao J, Liu Y, Wang M, Ma J, Yang P, Wang S, Wu Q, Gao J, Chen M, Qu G, Wang J, Jiang G. Insights into highly multiplexed tissue images: A primer for Mass Cytometry Imaging data analysis. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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109
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Fusco L, Gazzi A, Shuck CE, Orecchioni M, Alberti D, D'Almeida SM, Rinchai D, Ahmed E, Elhanani O, Rauner M, Zavan B, Grivel JC, Keren L, Pasqual G, Bedognetti D, Ley K, Gogotsi Y, Delogu LG. Immune Profiling and Multiplexed Label-Free Detection of 2D MXenes by Mass Cytometry and High-Dimensional Imaging. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2205154. [PMID: 36207284 PMCID: PMC10915970 DOI: 10.1002/adma.202205154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/22/2022] [Indexed: 06/16/2023]
Abstract
There is a critical unmet need to detect and image 2D materials within single cells and tissues while surveying a high degree of information from single cells. Here, a versatile multiplexed label-free single-cell detection strategy is proposed based on single-cell mass cytometry by time-of-flight (CyTOF) and ion-beam imaging by time-of-flight (MIBI-TOF). This strategy, "Label-free sINgle-cell tracKing of 2D matErials by mass cytometry and MIBI-TOF Design" (LINKED), enables nanomaterial detection and simultaneous measurement of multiple cell and tissue features. As a proof of concept, a set of 2D materials, transition metal carbides, nitrides, and carbonitrides (MXenes), is selected to ensure mass detection within the cytometry range while avoiding overlap with more than 70 currently available tags, each able to survey multiple biological parameters. First, their detection and quantification in 15 primary human immune cell subpopulations are demonstrated. Together with the detection, mass cytometry is used to capture several biological aspects of MXenes, such as their biocompatibility and cytokine production after their uptake. Through enzymatic labeling, MXenes' mediation of cell-cell interactions is simultaneously evaluated. In vivo biodistribution experiments using a mixture of MXenes in mice confirm the versatility of the detection strategy and reveal MXene accumulation in the liver, blood, spleen, lungs, and relative immune cell subtypes. Finally, MIBI-TOF is applied to detect MXenes in different organs revealing their spatial distribution. The label-free detection of 2D materials by mass cytometry at the single-cell level, on multiple cell subpopulations and in multiple organs simultaneously, will enable exciting new opportunities in biomedicine.
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Affiliation(s)
- Laura Fusco
- ImmuneNano Laboratory, Department of Biomedical Sciences, University of Padua, Padua, 35129, Italy
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, 19104, USA
- Human Immunology Division, Translational Medicine Department, Sidra Medicine, Doha, 26999, Qatar
| | - Arianna Gazzi
- ImmuneNano Laboratory, Department of Biomedical Sciences, University of Padua, Padua, 35129, Italy
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, 34127, Italy
| | - Christopher E Shuck
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, 19104, USA
| | | | - Dafne Alberti
- Laboratory of Synthetic Immunology, Oncology and Immunology Section, Department of Surgery Oncology and Gastroenterology, University of Padua, Padua, 35124, Italy
| | - Sènan Mickael D'Almeida
- Flow Cytometry Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland
| | - Darawan Rinchai
- Human Immunology Division, Translational Medicine Department, Sidra Medicine, Doha, 26999, Qatar
| | - Eiman Ahmed
- Human Immunology Division, Translational Medicine Department, Sidra Medicine, Doha, 26999, Qatar
| | - Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Martina Rauner
- Department of Medicine III, Center for Healthy Aging, Technical University Dresden, 01307, Dresden, Germany
| | - Barbara Zavan
- Department of Medical Sciences, University of Ferrara, Ferrara, 44121, Italy
- Maria Cecilia Hospital, GVM Care & Research, Ravenna, 48033, Italy
| | | | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, 7610001, Israel
| | - Giulia Pasqual
- Laboratory of Synthetic Immunology, Oncology and Immunology Section, Department of Surgery Oncology and Gastroenterology, University of Padua, Padua, 35124, Italy
| | - Davide Bedognetti
- Human Immunology Division, Translational Medicine Department, Sidra Medicine, Doha, 26999, Qatar
- Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, 16132, Italy
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, 34110, Qatar
| | - Klaus Ley
- La Jolla Institute for Immunology, San Diego, CA, 92037, USA
- Immunology Center of Georgia (IMMCG), Augusta University, Augusta, GA, 30912, USA
| | - Yury Gogotsi
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, 19104, USA
| | - Lucia Gemma Delogu
- ImmuneNano Laboratory, Department of Biomedical Sciences, University of Padua, Padua, 35129, Italy
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110
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Xu C, Jin X, Wei S, Wang P, Luo M, Xu Z, Yang W, Cai Y, Xiao L, Lin X, Liu H, Cheng R, Pang F, Chen R, Su X, Hu Y, Wang G, Jiang Q. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Res 2022; 50:e131. [PMID: 36250636 PMCID: PMC9825193 DOI: 10.1093/nar/gkac901] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 09/20/2022] [Accepted: 10/04/2022] [Indexed: 01/29/2023] Open
Abstract
Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.
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Affiliation(s)
- Chang Xu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Songren Wei
- Department of Neuropharmacology and Novel Drug Discovery, School of Pharmaceutical Sciences, Southern Medical University, Guangzhou 510515, China
- Center for Brain Science and Brain-Inspired Intelligence, Guangdong-Hong Kong-Macao Greater Bay Area, Guangdong 523335, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Zhaochun Xu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Wenyi Yang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Yideng Cai
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Lixing Xiao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Xiaoyu Lin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Hongxin Liu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Rui Cheng
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Fenglan Pang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Rui Chen
- Department of Forensic Medicine, Guangdong Medical University, Dongguan 523808, China
| | - Xi Su
- ChinaFoshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan 528000, China
| | - Ying Hu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150000, China
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin 150076, China
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111
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Zheng Y, Chen Y, Ding X, Wong KH, Cheung E. Aquila: a spatial omics database and analysis platform. Nucleic Acids Res 2022; 51:D827-D834. [PMID: 36243967 PMCID: PMC9825501 DOI: 10.1093/nar/gkac874] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 09/06/2022] [Accepted: 10/07/2022] [Indexed: 01/30/2023] Open
Abstract
Spatial omics is a rapidly evolving approach for exploring tissue microenvironment and cellular networks by integrating spatial knowledge with transcript or protein expression information. However, there is a lack of databases for users to access and analyze spatial omics data. To address this limitation, we developed Aquila, a comprehensive platform for managing and analyzing spatial omics data. Aquila contains 107 datasets from 30 diseases, including 6500+ regions of interest, and 15.7 million cells. The database covers studies from spatial transcriptome and proteome analyses, 2D and 3D experiments, and different technologies. Aquila provides visualization of spatial omics data in multiple formats such as spatial cell distribution, spatial expression and co-localization of markers. Aquila also lets users perform many basic and advanced spatial analyses on any dataset. In addition, users can submit their own spatial omics data for visualization and analysis in a safe and secure environment. Finally, Aquila can be installed as an individual app on a desktop and offers the RESTful API service for power users to access the database. Overall, Aquila provides a detailed insight into transcript and protein expression in tissues from a spatial perspective. Aquila is available at https://aquila.cheunglab.org.
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Affiliation(s)
- Yimin Zheng
- Cancer Centre, University of Macau, Taipa 999078, Macau SAR,Centre for Precision Medicine Research and Training, University of Macau, Taipa 999078, Macau SAR,MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa 999078, Macau SAR,Faculty of Health Sciences, University of Macau, Taipa 999078, Macau SAR
| | - Yitian Chen
- Cancer Centre, University of Macau, Taipa 999078, Macau SAR,Centre for Precision Medicine Research and Training, University of Macau, Taipa 999078, Macau SAR,MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa 999078, Macau SAR,Faculty of Health Sciences, University of Macau, Taipa 999078, Macau SAR
| | - Xianting Ding
- Institute for Personalized Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Koon Ho Wong
- Cancer Centre, University of Macau, Taipa 999078, Macau SAR,Centre for Precision Medicine Research and Training, University of Macau, Taipa 999078, Macau SAR,MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa 999078, Macau SAR,Faculty of Health Sciences, University of Macau, Taipa 999078, Macau SAR
| | - Edwin Cheung
- To whom correspondence should be addressed. Tel: +853 8822 4992; Fax: +853 8822 2933;
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Cao Y, Lin Y, Patrick E, Yang P, Yang JYH. scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction. Bioinformatics 2022; 38:4745-4753. [PMID: 36040148 PMCID: PMC9563679 DOI: 10.1093/bioinformatics/btac590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/21/2022] [Accepted: 08/28/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into samples from individuals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each sample (e.g. individuals). RESULTS Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the sample level. We demonstrate that summarising a broad collection of features at the sample level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of individuals. AVAILABILITY scFeatures is publicly available as an R package at https://github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Methods section. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yue Cao
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
| | - Yingxin Lin
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, Australia
| | - Ellis Patrick
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Westmead, NSW, Australia
| | - Pengyi Yang
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,Computational Systems Biology Group, Children's Medical Research Institute, Westmead, NSW, Australia
| | - Jean Yee Hwa Yang
- Charles Perkins Centre, The University of Sydney, Sydney, Australia.,School of Mathematics and Statistics, The University of Sydney, Sydney, Australia.,Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
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113
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Su P, McGee JP, Durbin KR, Hollas MAR, Yang M, Neumann EK, Allen JL, Drown BS, Butun FA, Greer JB, Early BP, Fellers RT, Spraggins JM, Laskin J, Camarillo JM, Kafader JO, Kelleher NL. Highly multiplexed, label-free proteoform imaging of tissues by individual ion mass spectrometry. SCIENCE ADVANCES 2022; 8:eabp9929. [PMID: 35947651 PMCID: PMC9365283 DOI: 10.1126/sciadv.abp9929] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/24/2022] [Indexed: 05/25/2023]
Abstract
Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by fourfold compared to reported methods and reveals tissue localization of proteoforms at <80-μm spatial resolution. PiMS advances proteoform imaging by combining ambient nanospray desorption electrospray ionization with ion detection using individual ion mass spectrometry. We demonstrate highly multiplexed proteoform imaging of human kidney, annotating 169 of 400 proteoforms of <70 kDa using top-down MS and a database lookup of ~1000 kidney candidate proteoforms, including dozens of key enzymes in primary metabolism. PiMS images reveal distinct spatial localizations of proteoforms to both anatomical structures and cellular neighborhoods in the vasculature, medulla, and cortex regions of the human kidney. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of tissues.
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Affiliation(s)
- Pei Su
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - John P. McGee
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Kenneth R. Durbin
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Michael A. R. Hollas
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Manxi Yang
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Elizabeth K. Neumann
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Jamie L. Allen
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
| | - Bryon S. Drown
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | | | - Joseph B. Greer
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Bryan P. Early
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Ryan T. Fellers
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
| | - Jeffrey M. Spraggins
- Department of Biochemistry and Mass Spectrometry Research Center, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry and Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, IN, USA
| | - Jeannie M. Camarillo
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Jared O. Kafader
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
| | - Neil L. Kelleher
- Departments of Molecular Biosciences, Chemistry, and Chemical and Biological Engineering and the Feinberg School of Medicine, Northwestern University, Evanston, IL, USA
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, USA
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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Kuczkiewicz-Siemion O, Sokół K, Puton B, Borkowska A, Szumera-Ciećkiewicz A. The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy. Cancers (Basel) 2022; 14:cancers14153833. [PMID: 35954496 PMCID: PMC9367614 DOI: 10.3390/cancers14153833] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens. Abstract Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.
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Affiliation(s)
- Olga Kuczkiewicz-Siemion
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
| | - Kamil Sokół
- Diagnostic Hematology Department, Institute of Hematology and Transfusion Medicine, 02-776 Warsaw, Poland
| | - Beata Puton
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Aneta Borkowska
- Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
| | - Anna Szumera-Ciećkiewicz
- Department of Pathology, Maria Sklodowska-Curie National Research Institute of Oncology, 02-781 Warsaw, Poland
- Correspondence: (O.K.-S.); (A.S.-C.)
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115
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Moffitt JR, Lundberg E, Heyn H. The emerging landscape of spatial profiling technologies. Nat Rev Genet 2022; 23:741-759. [PMID: 35859028 DOI: 10.1038/s41576-022-00515-3] [Citation(s) in RCA: 120] [Impact Index Per Article: 60.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 01/04/2023]
Abstract
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.
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Affiliation(s)
- Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Emma Lundberg
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Pathology, Stanford University, Stanford, CA, USA.,Chan Zuckerberg Biohub, San Francisco, San Francisco, CA, USA
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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Prabhakaran S, Gatenbee C, Robertson-Tessi M, West J, Beg AA, Gray J, Antonia S, Gatenby RA, Anderson AR. Mistic: An open-source multiplexed image t-SNE viewer. PATTERNS (NEW YORK, N.Y.) 2022; 3:100523. [PMID: 35845830 PMCID: PMC9278502 DOI: 10.1016/j.patter.2022.100523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/10/2022] [Accepted: 05/09/2022] [Indexed: 01/02/2023]
Abstract
Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.
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Affiliation(s)
- Sandhya Prabhakaran
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Chandler Gatenbee
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Mark Robertson-Tessi
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jeffrey West
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Amer A. Beg
- Departments of Immunology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Jhanelle Gray
- Departments of Immunology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Scott Antonia
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Robert A. Gatenby
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
| | - Alexander R.A. Anderson
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA
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117
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Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF). J Transl Med 2022; 102:762-770. [PMID: 35351966 DOI: 10.1038/s41374-022-00778-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/23/2022] [Accepted: 02/25/2022] [Indexed: 11/09/2022] Open
Abstract
Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) is a form of mass spectrometry imaging that uses metal labeled antibodies and secondary ion mass spectrometry to image dozens of proteins simultaneously in the same tissue section. Working with the National Cancer Institute's (NCI) Cancer Immune Monitoring and Analysis Centers (CIMAC), we undertook a validation study, assessing concordance across a dozen serial sections of a tissue microarray of 21 samples that were independently processed and imaged by MIBI-TOF or single-plex immunohistochemistry (IHC) over 12 days. Pixel-level features were highly concordant across all 16 targets assessed in both staining intensity (R2 = 0.94 ± 0.04) and frequency (R2 = 0.95 ± 0.04). Comparison to digitized, single-plex IHC on adjacent serial sections revealed similar concordance (R2 = 0.85 ± 0.08) as well. Lastly, automated segmentation and clustering of eight cell populations found that cell frequencies between serial sections yielded an average correlation of R2 = 0.94 ± 0.05. Taken together, we demonstrate that MIBI-TOF, with well-vetted reagents and automated analysis, can generate consistent and quantitative annotations of clinically relevant cell states in archival human tissue, and more broadly, present a scalable framework for benchmarking multiplexed IHC approaches.
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118
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Almekinders MM, Bismeijer T, Kumar T, Yang F, Thijssen B, van der Linden R, van Rooijen C, Vonk S, Sun B, Parra Cuentas ER, Wistuba II, Krishnamurthy S, Visser LL, Seignette IM, Hofland I, Sanders J, Broeks A, Love JK, Menegaz B, Wessels L, Thompson AM, de Visser KE, Hooijberg E, Lips E, Futreal A, Wesseling J. Comprehensive multiplexed immune profiling of the ductal carcinoma in situ immune microenvironment regarding subsequent ipsilateral invasive breast cancer risk. Br J Cancer 2022; 127:1201-1213. [PMID: 35768550 PMCID: PMC9519539 DOI: 10.1038/s41416-022-01888-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/17/2022] [Accepted: 06/07/2022] [Indexed: 12/25/2022] Open
Abstract
Background Ductal carcinoma in situ (DCIS) is treated to prevent subsequent ipsilateral invasive breast cancer (iIBC). However, many DCIS lesions will never become invasive. To prevent overtreatment, we need to distinguish harmless from potentially hazardous DCIS. We investigated whether the immune microenvironment (IME) in DCIS correlates with transition to iIBC. Methods Patients were derived from a Dutch population-based cohort of 10,090 women with pure DCIS with a median follow-up time of 12 years. Density, composition and proximity to the closest DCIS cell of CD20+ B-cells, CD3+CD8+ T-cells, CD3+CD8− T-cells, CD3+FOXP3+ regulatory T-cells, CD68+ cells, and CD8+Ki67+ T-cells was assessed with multiplex immunofluorescence (mIF) with digital whole-slide analysis and compared between primary DCIS lesions of 77 women with subsequent iIBC (cases) and 64 without (controls). Results Higher stromal density of analysed immune cell subsets was significantly associated with higher grade, ER negativity, HER-2 positivity, Ki67 ≥ 14%, periductal fibrosis and comedonecrosis (P < 0.05). Density, composition and proximity to the closest DCIS cell of all analysed immune cell subsets did not differ between cases and controls. Conclusion IME features analysed by mIF in 141 patients from a well-annotated cohort of pure DCIS with long-term follow-up are no predictors of subsequent iIBC, but do correlate with other factors (grade, ER, HER2 status, Ki-67) known to be associated with invasive recurrences. ![]()
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Affiliation(s)
- Mathilde M Almekinders
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.,Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tycho Bismeijer
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Tapsi Kumar
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA.,Department of Genetics, MD Anderson Cancer Center, Houston, TX, USA.,MD Anderson Cancer Center UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Fei Yang
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - Bram Thijssen
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rianne van der Linden
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Charlotte van Rooijen
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Shiva Vonk
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.,Core Facility Molecular Pathology and Biobanking, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Baohua Sun
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin R Parra Cuentas
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, MD Anderson Cancer Center, Houston, TX, USA
| | | | - Lindy L Visser
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Iris M Seignette
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Ingrid Hofland
- Core Facility Molecular Pathology and Biobanking, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Joyce Sanders
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Annegien Broeks
- Core Facility Molecular Pathology and Biobanking, Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Jason K Love
- Breast Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Brian Menegaz
- Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Lodewyk Wessels
- Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Oncode Institute, Utrecht, The Netherlands
| | | | - Karin E de Visser
- Oncode Institute, Utrecht, The Netherlands.,Division of Tumour Biology & Immunology, Netherlands Cancer Institute, Amsterdam, The Netherlands.,Department of Immunology, Leiden University Medical Center, Leiden, The Netherlands
| | - Erik Hooijberg
- Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Esther Lips
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Andrew Futreal
- Department of Genomic Medicine, MD Anderson Cancer Center, Houston, TX, USA
| | - Jelle Wesseling
- Division of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands. .,Department of Pathology, Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. .,Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
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119
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Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment. Cancers (Basel) 2022; 14:cancers14133170. [PMID: 35804939 PMCID: PMC9264815 DOI: 10.3390/cancers14133170] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes. Abstract The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.
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120
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Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med 2022; 14:68. [PMID: 35761361 PMCID: PMC9238181 DOI: 10.1186/s13073-022-01075-1] [Citation(s) in RCA: 188] [Impact Index Per Article: 94.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/19/2022] [Indexed: 01/04/2023] Open
Abstract
Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.
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Affiliation(s)
- Cameron G Williams
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Hyun Jae Lee
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Takahiro Asatsuma
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Roser Vento-Tormo
- Cellular Genetics Group, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Ashraful Haque
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia.
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Tyramide-conjugated DNA barcodes enable signal amplification for multiparametric CODEX imaging. Commun Biol 2022; 5:627. [PMID: 35754060 PMCID: PMC9234042 DOI: 10.1038/s42003-022-03558-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/06/2022] [Indexed: 01/09/2023] Open
Abstract
Multiparametric imaging allows researchers to measure the expression of many biomarkers simultaneously, allowing detailed characterization of cell microenvironments. One such technique, CODEX, allows fluorescence imaging of >30 proteins in a single tissue section. In the commercial CODEX system, primary antibodies are conjugated to DNA barcodes. This modification can result in antibody dysfunction, and development of a custom antibody panel can be very costly and time consuming as trial and error of modified antibodies proceeds. To address these challenges, we developed novel tyramide-conjugated DNA barcodes that can be used with primary antibodies via peroxidase-conjugated secondary antibodies. This approach results in signal amplification and imaging without the need to conjugate primary antibodies. When combined with commercially available barcode-conjugated primary antibodies, we can very quickly develop working antibody panels. We also present methods to perform antibody staining using a commercially available automated tissue stainer and in situ hybridization imaging on a CODEX platform. Future work will include application of the combined tyramide-based and regular CODEX approach to image specific tumors with their immune cell infiltrates, including biomarkers that are currently difficult to image by regular CODEX. Tyramide-conjugated DNA barcodes can be used to perform CODEX imaging without modifying primary antibodies and could complement existing CODEX workflows for multiplexed imaging.
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Chin LK, Li H, Choi JH, Iwamoto Y, Oh J, Min J, Beak SK, Yoo D, Castro CM, Lee D, Im H. Hydrogel Stamping for Rapid, Multiplexed, Point-of-Care Immunostaining of Cells and Tissues. ACS APPLIED MATERIALS & INTERFACES 2022; 14:27613-27622. [PMID: 35671240 DOI: 10.1021/acsami.2c05071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the era of precision oncology, multicolor fluorescence imaging has become a core technology for multiplexed molecular analysis of cellular and tissue specimens. However, conventional solution-based staining is labor-intensive and time-consuming and requires considerable expertise to yield optimal results, which creates difficulties for employing this technology in resource-limited settings. Here, we report a new immunostaining method based on hydrogel stamping, which is simple, fast, easy to use, and reproducible. We showed that a hydrophilic hydrogel stamp could effectively transfer fluorescent antibodies to targets and withdraw an excess solution when the reaction is completed, obviating the need for extra washing. This unique property allows for quality immunostaining in 5 min for cells using one-eighth of antibody consumption compared to the conventional solution-based method. Furthermore, we implemented fluorescence quenching and immunocycling with hydrogel staining for multiplexed analysis of 9 protein markers at a single cell level. Finally, we applied the immunocycling method to human breast cancer tissue samples and showed quality immunostaining over a large area (∼2 cm2) in 30 min for molecular subtyping of breast cancer. The hydrogel immunostaining could open new opportunities for rapid, automated, and multiplexed profiling in compact point-of-care systems for molecular cancer diagnosis.
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Affiliation(s)
- Lip Ket Chin
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Electrical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong SAR
| | - Huiyan Li
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- School of Engineering, University of Guelph, 50 Stone Road East, Guelph N1G2W1, Canada
| | - Jae-Hyeok Choi
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Yoshiko Iwamoto
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Juhyun Oh
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Jouha Min
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
| | - Suk Kyung Beak
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Dahyeon Yoo
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Cesar M Castro
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Dongyoung Lee
- Noul Co. Limited, Gyeonggi-do, Yongin 16942, Republic of Korea
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5206, Boston, Massachusetts 02114, United States
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
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123
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Jiang S, Chan CN, Rovira-Clavé X, Chen H, Bai Y, Zhu B, McCaffrey E, Greenwald NF, Liu C, Barlow GL, Weirather JL, Oliveria JP, Nakayama T, Lee IT, Matter MS, Carlisle AE, Philips D, Vazquez G, Mukherjee N, Busman-Sahay K, Nekorchuk M, Terry M, Younger S, Bosse M, Demeter J, Rodig SJ, Tzankov A, Goltsev Y, McIlwain DR, Angelo M, Estes JD, Nolan GP. Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments. Immunity 2022; 55:1118-1134.e8. [PMID: 35447093 PMCID: PMC9220319 DOI: 10.1016/j.immuni.2022.03.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/13/2021] [Accepted: 03/25/2022] [Indexed: 12/12/2022]
Abstract
Understanding the mechanisms of HIV tissue persistence necessitates the ability to visualize tissue microenvironments where infected cells reside; however, technological barriers limit our ability to dissect the cellular components of these HIV reservoirs. Here, we developed protein and nucleic acid in situ imaging (PANINI) to simultaneously quantify DNA, RNA, and protein levels within these tissue compartments. By coupling PANINI with multiplexed ion beam imaging (MIBI), we measured over 30 parameters simultaneously across archival lymphoid tissues from healthy or simian immunodeficiency virus (SIV)-infected nonhuman primates. PANINI enabled the spatial dissection of cellular phenotypes, functional markers, and viral events resulting from infection. SIV infection induced IL-10 expression in lymphoid B cells, which correlated with local macrophage M2 polarization. This highlights a potential viral mechanism for conditioning an immunosuppressive tissue environment for virion production. The spatial multimodal framework here can be extended to decipher tissue responses in other infectious diseases and tumor biology.
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Affiliation(s)
- 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.
| | - Chi Ngai Chan
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | | | - Han Chen
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Erin McCaffrey
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Candace Liu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Graham L Barlow
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Jason L Weirather
- Center of Immuno-Oncology, Dana-Faber Cancer Institute, Boston, MA, USA
| | - John Paul Oliveria
- Department of Pathology, Stanford University, Stanford, CA, USA; Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Tsuguhisa Nakayama
- Department of Pathology, Stanford University, Stanford, CA, USA; Department of Otorhinolaryngology, Jikei University School of Medicine, Tokyo, Japan
| | - Ivan T Lee
- Department of Pathology, Stanford University, Stanford, CA, USA; Division of Allergy, Immunology, and Rheumatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Matthias S Matter
- Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Anne E Carlisle
- Center of Immuno-Oncology, Dana-Faber Cancer Institute, Boston, MA, USA
| | - Darci Philips
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Gustavo Vazquez
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Kathleen Busman-Sahay
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | - Michael Nekorchuk
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | - Margaret Terry
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | - Skyler Younger
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Janos Demeter
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham & Women's Hospital, Boston, MA, USA
| | - Alexandar Tzankov
- Pathology, Institute of Medical Genetics and Pathology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Yury Goltsev
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Jacob D Estes
- Vaccine and Gene Therapy Institute, Oregon Health & Science University, Beaverton, OR, USA; Division of Pathobiology & Immunology, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR, USA.
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA.
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124
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Brown CE, Bucktrout S, Butterfield LH, Futer O, Galanis E, Hormigo A, Lim M, Okada H, Prins R, Marr SS, Tanner K. The future of cancer immunotherapy for brain tumors: a collaborative workshop. J Transl Med 2022; 20:236. [PMID: 35606815 PMCID: PMC9125824 DOI: 10.1186/s12967-022-03438-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/12/2022] [Indexed: 11/26/2022] Open
Abstract
Harnessing the effector mechanisms of the immune system to combat brain tumors with antigen specificity and memory has been in research and clinical testing for many years. Government grant mechanisms and non-profit organizations have supported many innovative projects and trials while biotech companies have invested in the development of needed tools, assays and novel clinical approaches. The National Brain Tumor Society and the Parker Institute for Cancer Immunotherapy partnered to host a workshop to share recent data, ideas and identify both hurdles and new opportunities for harnessing immunotherapy against pediatric and adult brain tumors. Adoptively transferred cell therapies have recently shown promising early clinical results. Local cell delivery to the brain, new antigen targets and innovative engineering approaches are poised for testing in a new generation of clinical trials. Although several such advances have been made, several obstacles remain for the successful application of immunotherapies for brain tumors, including the need for more representative animal models that can better foreshadow human trial outcomes. Tumor and tumor microenvironment biopsies with multiomic analysis are critical to understand mechanisms of response and patient stratification, yet brain tumors are especially challenging for such biopsy collection. These workshop proceedings and commentary shed light on the status of immunotherapy in pediatric and adult brain tumor patients, including current research as well as opportunities for improving future efforts to bring immunotherapy to the forefront in the management of brain tumors.
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Affiliation(s)
| | - Samantha Bucktrout
- Parker Institute for Cancer Immunotherapy, 1 Letterman Dr. D3500, San Francisco, CA, 94129, USA
| | - Lisa H Butterfield
- Parker Institute for Cancer Immunotherapy, 1 Letterman Dr. D3500, San Francisco, CA, 94129, USA.
| | - Olga Futer
- National Brain Tumor Society, Newton, MA, 02458, USA
| | | | - Adilia Hormigo
- Icahn School of Medicine at Mount Sinai, The Tisch Cancer Institute, New York, NY, 10029, USA
| | - Michael Lim
- Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Hideho Okada
- Department of Neurosurgery, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Robert Prins
- Department of Neurosurgery and Molecular & Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90095, USA
| | - Sara Siebel Marr
- Parker Institute for Cancer Immunotherapy, 1 Letterman Dr. D3500, San Francisco, CA, 94129, USA.,Centivax, Inc, South San Francisco, CA, 94080, USA
| | - Kirk Tanner
- National Brain Tumor Society, Newton, MA, 02458, USA.
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125
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Zheng B, Fang L. Spatially resolved transcriptomics provide a new method for cancer research. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2022; 41:179. [PMID: 35590346 PMCID: PMC9118771 DOI: 10.1186/s13046-022-02385-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/06/2022] [Indexed: 12/22/2022]
Abstract
A major feature of cancer is the heterogeneity, both intratumoral and intertumoral. Traditional single-cell techniques have given us a comprehensive understanding of the biological characteristics of individual tumor cells, but the lack of spatial context of the transcriptome has limited the study of cell-to-cell interaction patterns and hindered further exploration of tumor heterogeneity. In recent years, the advent of spatially resolved transcriptomics (SRT) technology has made possible the multidimensional analysis of the tumor microenvironment in the context of intact tissues. Different SRT methods are applicable to different working ranges due to different working principles. In this paper, we review the advantages and disadvantages of various current SRT methods and the overall idea of applying these techniques to oncology studies, hoping to help researchers find breakthroughs. Finally, we discussed the future direction of SRT technology, and deeper investigation into the complex mechanisms of tumor development from different perspectives through multi-omics fusion, paving the way for precisely targeted tumor therapy.
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Affiliation(s)
- Bowen Zheng
- Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China
| | - Lin Fang
- Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
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126
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Badve SS, Gökmen-Polar Y. Protein Profiling of Breast Cancer for Treatment Decision-Making. Am Soc Clin Oncol Educ Book 2022; 42:1-9. [PMID: 35580295 DOI: 10.1200/edbk_351207] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.
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Affiliation(s)
- Sunil S Badve
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
| | - Yesim Gökmen-Polar
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA
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127
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Verkhoturov DS, Eller MJ, Han YD, Crulhas B, Verkhoturov SV, Revzin A, Schweikert EA. New Methodology for Accurate Determination of Molecular Co-localization at the Nanoscale. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2022; 38:5626-5632. [PMID: 35465673 DOI: 10.1021/acs.langmuir.2c00217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
A new methodology using nanoparticle projectile secondary ion mass spectrometry was developed to identify statistically significant co-localization of tagged proteins versus random aggregations at the nanoscale. The custom instrument was run in the unique event-by-event bombardment detection mode with 1040 keV Au28008+ individual projectiles each probing an area with a diameter of ∼20 nm. In a model experiment, antibodies tagged with fluorine, iodine, and bromine were attached on a silicon wafer in a 1:1:1 ratio. To determine whether the three different antibodies were homogeneously distributed at the nanoscale or if there were fluctuations due to the slightly different physical properties of the tags, a "co-localization factor" was introduced. It is shown for the first time that the differences in the hydrophobicity of the tags induced fluctuations, causing differential attachment of the tags at the nanoscale. When tags with the same physical and chemical properties were used, the analysis of co-localization factors shows that the attachment became random.
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Affiliation(s)
- D S Verkhoturov
- Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States
| | - M J Eller
- Department of Chemistry and Biochemistry, California State University, Northridge, California 91330, United States
| | - Y D Han
- Mayo Clinic, 200 1st Street SW St-11-14, Rochester, Minnesota 55905, United States
| | - B Crulhas
- Mayo Clinic, 200 1st Street SW St-11-14, Rochester, Minnesota 55905, United States
| | - S V Verkhoturov
- Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States
| | - A Revzin
- Mayo Clinic, 200 1st Street SW St-11-14, Rochester, Minnesota 55905, United States
| | - E A Schweikert
- Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States
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128
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Verberk SG, de Goede KE, Gorki FS, van Dierendonck XA, Argüello RJ, Van den Bossche J. An integrated toolbox to profile macrophage immunometabolism. CELL REPORTS METHODS 2022; 2:100192. [PMID: 35497494 PMCID: PMC9046227 DOI: 10.1016/j.crmeth.2022.100192] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 12/23/2021] [Accepted: 03/07/2022] [Indexed: 10/25/2022]
Abstract
Macrophages are dynamic immune cells that can adopt several activation states. Fundamental to these functional activation states is the regulation of cellular metabolic processes. Especially in mice, metabolic alterations underlying pro-inflammatory or homeostatic phenotypes have been assessed using various techniques. However, researchers new to the field may encounter ambiguity in choosing which combination of techniques is best suited to profile immunometabolism. To address this need, we have developed a toolbox to assess cellular metabolism in a semi-high-throughput 96-well-plate-based format. Application of the toolbox to activated mouse and human macrophages enables fast metabolic pre-screening and robust measurement of extracellular fluxes, mitochondrial mass and membrane potential, and glucose and lipid uptake. Moreover, we propose an application of SCENITH technology for ex vivo metabolic profiling. We validate established activation-induced metabolic rewiring in mouse macrophages and report new insights into human macrophage metabolism. By thoroughly discussing each technique, we hope to guide readers with practical workflows for investigating immunometabolism.
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Affiliation(s)
- Sanne G.S. Verberk
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Kyra E. de Goede
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Friederike S. Gorki
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Institute of Innate Immunity, University Hospital Bonn, University of Bonn, 53127 Bonn, Germany
| | - Xanthe A.M.H. van Dierendonck
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Rafael J. Argüello
- Aix Marseille Univ, CNRS, INSERM, CIML, Centre d’Immunologie de Marseille-Luminy, Marseille, France
| | - Jan Van den Bossche
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam Institute for Infection and Immunity, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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129
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Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution. Cancers (Basel) 2022; 14:cancers14081999. [PMID: 35454904 PMCID: PMC9026731 DOI: 10.3390/cancers14081999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 02/01/2023] Open
Abstract
The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.
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130
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Drown BS, Jooß K, Melani RD, Lloyd-Jones C, Camarillo JM, Kelleher NL. Mapping the Proteoform Landscape of Five Human Tissues. J Proteome Res 2022; 21:1299-1310. [PMID: 35413190 PMCID: PMC9087339 DOI: 10.1021/acs.jproteome.2c00034] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
A functional understanding of the human body requires structure-function studies of proteins at scale. The chemical structure of proteins is controlled at the transcriptional, translational, and post-translational levels, creating a variety of products with modulated functions within the cell. The term "proteoform" encapsulates this complexity at the level of chemical composition. Comprehensive mapping of the proteoform landscape in human tissues necessitates analytical techniques with increased sensitivity and depth of coverage. Here, we took a top-down proteomics approach, combining data generated using capillary zone electrophoresis (CZE) and nanoflow reversed-phase liquid chromatography (RPLC) hyphenated to mass spectrometry to identify and characterize proteoforms from the human lungs, heart, spleen, small intestine, and kidneys. CZE and RPLC provided complementary post-translational modification and proteoform selectivity, thereby enhancing the overall proteome coverage when used in combination. Of the 11,466 proteoforms identified in this study, 7373 (64%) were not reported previously. Large differences in the protein and proteoform level were readily quantified, with initial inferences about proteoform biology operative in the analyzed organs. Differential proteoform regulation of defensins, glutathione transferases, and sarcomeric proteins across tissues generate hypotheses about how they function and are regulated in human health and disease.
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Affiliation(s)
- Bryon S Drown
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Kevin Jooß
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Rafael D Melani
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Cameron Lloyd-Jones
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Jeannie M Camarillo
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
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131
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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat Biotechnol 2022; 40:555-565. [PMID: 34795433 PMCID: PMC9010346 DOI: 10.1038/s41587-021-01094-0] [Citation(s) in RCA: 232] [Impact Index Per Article: 116.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 09/14/2021] [Indexed: 02/07/2023]
Abstract
A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.
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132
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Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets. Int J Mol Sci 2022; 23:ijms23073811. [PMID: 35409169 PMCID: PMC8998543 DOI: 10.3390/ijms23073811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/26/2022] [Accepted: 03/29/2022] [Indexed: 12/10/2022] Open
Abstract
Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.
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133
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Cereceda K, Jorquera R, Villarroel-Espíndola F. Advances in mass cytometry and its applicability to digital pathology in clinical-translational cancer research. ADVANCES IN LABORATORY MEDICINE 2022; 3:5-29. [PMID: 37359436 PMCID: PMC10197474 DOI: 10.1515/almed-2021-0075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/16/2021] [Indexed: 06/28/2023]
Abstract
The development and subsequent adaptation of mass cytometry for the histological analysis of tissue sections has allowed the simultaneous spatial characterization of multiple components. This is useful to find the correlation between the genotypic and phenotypic profile of tumor cells and their environment in clinical-translational studies. In this revision, we provide an overview of the most relevant hallmarks in the development, implementation and application of multiplexed imaging in the study of cancer and other conditions. A special focus is placed on studies based on imaging mass cytometry (IMC) and multiplexed ion beam imaging (MIBI). The purpose of this review is to help our readers become familiar with the verification techniques employed on this tool and outline the multiple applications reported in the literature. This review will also provide guidance on the use of IMC or MIBI in any field of biomedical research.
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Affiliation(s)
- Karina Cereceda
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Roddy Jorquera
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
| | - Franz Villarroel-Espíndola
- Laboratorio de Medicina Traslacional, Instituto Oncológico Fundación Arturo López Pérez, Santiago, Chile
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134
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Abstract
Metals are essential components in life processes and participate in many important biological processes. Dysregulation of metal homeostasis is correlated with many diseases. Metals are also frequently incorporated into diagnosis and therapeutics. Understanding of metal homeostasis under (patho)physiological conditions and the molecular mechanisms of action of metallodrugs in biological systems has positive impacts on human health. As an emerging interdisciplinary area of research, metalloproteomics involves investigating metal-protein interactions in biological systems at a proteome-wide scale, has received growing attention, and has been implemented into metal-related research. In this review, we summarize the recent advances in metalloproteomics methodologies and applications. We also highlight emerging single-cell metalloproteomics, including time-resolved inductively coupled plasma mass spectrometry, mass cytometry, and secondary ion mass spectrometry. Finally, we discuss future perspectives in metalloproteomics, aiming to attract more original research to develop more advanced methodologies, which could be utilized rapidly by biochemists or biologists to expand our knowledge of how metal functions in biology and medicine. Expected final online publication date for the Annual Review of Biochemistry, Volume 91 is June 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Ying Zhou
- Department of Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, University of Hong Kong, Hong Kong SAR, China; ,
| | - Hongyan Li
- Department of Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, University of Hong Kong, Hong Kong SAR, China; ,
| | - Hongzhe Sun
- Department of Chemistry and CAS-HKU Joint Laboratory of Metallomics on Health and Environment, University of Hong Kong, Hong Kong SAR, China; ,
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135
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Baskar R, Kimmey SC, Bendall SC. Revealing new biology from multiplexed, metal-isotope-tagged, single-cell readouts. Trends Cell Biol 2022; 32:501-512. [PMID: 35181197 DOI: 10.1016/j.tcb.2022.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 11/26/2022]
Abstract
Mass cytometry (MC) is a recent technology that pairs plasma-based ionization of cells in suspension with time-of-flight (TOF) mass spectrometry to sensitively quantify the single-cell abundance of metal-isotope-tagged affinity reagents to key proteins, RNA, and peptides. Given the ability to multiplex readouts (~50 per cell) and capture millions of cells per experiment, MC offers a robust way to assay rare, transitional cell states that are pertinent to human development and disease. Here, we review MC approaches that let us probe the dynamics of cellular regulation across multiple conditions and sample types in a single experiment. Additionally, we discuss current limitations and future extensions of MC as well as computational tools commonly used to extract biological insight from single-cell proteomic datasets.
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Affiliation(s)
- Reema Baskar
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sam C Kimmey
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA.
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136
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Abstract
Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-β, regulatory T cells and IDO1+ PD-L1+ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.
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137
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Abstract
AbstractSpatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
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138
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Risom T, Glass DR, Averbukh I, Liu CC, Baranski A, Kagel A, McCaffrey EF, Greenwald NF, Rivero-Gutiérrez B, Strand SH, Varma S, Kong A, Keren L, Srivastava S, Zhu C, Khair Z, Veis DJ, Deschryver K, Vennam S, Maley C, Hwang ES, Marks JR, Bendall SC, Colditz GA, West RB, Angelo M. Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell 2022; 185:299-310.e18. [PMID: 35063072 PMCID: PMC8792442 DOI: 10.1016/j.cell.2021.12.023] [Citation(s) in RCA: 140] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 08/05/2021] [Accepted: 12/16/2021] [Indexed: 01/16/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.
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Affiliation(s)
- Tyler Risom
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Department of Research Pathology, Genentech, South San Francisco, CA, USA
| | - David R Glass
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Inna Averbukh
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Candace C Liu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alex Baranski
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Adam Kagel
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Erin F McCaffrey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Siri H Strand
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sushama Varma
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alex Kong
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Leeat Keren
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sucheta Srivastava
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chunfang Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Zumana Khair
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Deborah J Veis
- Departments of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Katherine Deschryver
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Sujay Vennam
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Carlo Maley
- Biodesign institute, Arizona State University, Tempe, AZ, USA
| | | | | | - Sean C Bendall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Graham A Colditz
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Robert B West
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Michael Angelo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Departments of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA.
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139
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Kenry, Nicolson F, Clark L, Panikkanvalappil SR, Andreiuk B, Andreou C. Advances in Surface Enhanced Raman Spectroscopy for in Vivo Imaging in Oncology. Nanotheranostics 2022; 6:31-49. [PMID: 34976579 PMCID: PMC8671959 DOI: 10.7150/ntno.62970] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 06/21/2021] [Indexed: 12/13/2022] Open
Abstract
In the last two decades, the application of surface enhanced Raman scattering (SERS) nanoparticles for preclinical cancer imaging has attracted increasing attention. Raman imaging with SERS nanoparticles offers unparalleled sensitivity, providing a platform for molecular targeting, and granting multiplexed and multimodal imaging capabilities. Recent progress has been facilitated not only by the optimization of the SERS contrast agents themselves, but also by the developments in Raman imaging approaches and instrumentation. In this article, we review the principles of Raman scattering and SERS, present advances in Raman instrumentation specific to cancer imaging, and discuss the biological means of ensuring selective in vivo uptake of SERS contrast agents for targeted, multiplexed, and multimodal imaging applications. We offer our perspective on areas that must be addressed in order to facilitate the clinical translation of SERS contrast agents for in vivo imaging in oncology.
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Affiliation(s)
- Kenry
- Department of Imaging, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Fay Nicolson
- Department of Imaging, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.,Department of Cancer Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Louise Clark
- Department of Imaging, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | | | - Bohdan Andreiuk
- Department of Imaging, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA.,Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02215, USA
| | - Chrysafis Andreou
- Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus
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140
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From imaging a single cell to implementing precision medicine: an exciting new era. Emerg Top Life Sci 2021; 5:837-847. [PMID: 34889448 PMCID: PMC8786301 DOI: 10.1042/etls20210219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/24/2021] [Accepted: 11/24/2021] [Indexed: 11/17/2022]
Abstract
In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.
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141
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Phongpreecha T, Gajera CR, Liu CC, Vijayaragavan K, Chang AL, Becker M, Fallahzadeh R, Fernandez R, Postupna N, Sherfield E, Tebaykin D, Latimer C, Shively CA, Register TC, Craft S, Montine KS, Fox EJ, Poston KL, Keene CD, Angelo M, Bendall SC, Aghaeepour N, Montine TJ. Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE. SCIENCE ADVANCES 2021; 7:eabk0473. [PMID: 34910503 PMCID: PMC8673771 DOI: 10.1126/sciadv.abk0473] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Synaptic molecular characterization is limited for Alzheimer’s disease (AD). Our newly invented mass cytometry–based method, synaptometry by time of flight (SynTOF), was used to measure 38 antibody probes in approximately 17 million single-synapse events from human brains without pathologic change or with pure AD or Lewy body disease (LBD), nonhuman primates (NHPs), and PS/APP mice. Synaptic molecular integrity in humans and NHP was similar. Although not detected in human synapses, Aβ was in PS/APP mice single-synapse events. Clustering and pattern identification of human synapses showed expected disease-specific differences, like increased hippocampal pathologic tau in AD and reduced caudate dopamine transporter in LBD, and revealed previously unidentified findings including increased hippocampal CD47 and lowered DJ1 in AD and higher ApoE in AD with dementia. Our results were independently supported by multiplex ion beam imaging of intact tissue. This highlights the higher depth and breadth of insight on neurodegenerative diseases obtainable through SynTOF.
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Affiliation(s)
- Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | | | - Candace C. Liu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Alan L. Chang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Nadia Postupna
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Emily Sherfield
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Dmitry Tebaykin
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Caitlin Latimer
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Carol A. Shively
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Thomas C. Register
- Department of Pathology/Comparative Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Suzanne Craft
- Department of Internal Medicine–Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | | | - Edward J. Fox
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Kathleen L. Poston
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - C. Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Sean C. Bendall
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | - Thomas J. Montine
- Department of Pathology, Stanford University, Stanford, CA, USA
- Corresponding author.
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142
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Oligonucleotide conjugated antibody strategies for cyclic immunostaining. Sci Rep 2021; 11:23844. [PMID: 34903759 PMCID: PMC8668956 DOI: 10.1038/s41598-021-03135-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/26/2021] [Indexed: 11/09/2022] Open
Abstract
A number of highly multiplexed immunostaining and imaging methods have advanced spatial proteomics of cancer for improved treatment strategies. While a variety of methods have been developed, the most widely used methods are limited by harmful signal removal techniques, difficulties with reagent production and antigen sensitivity. Multiplexed immunostaining employing oligonucleotide (oligos)-barcoded antibodies is an alternative approach that is growing in popularity. However, challenges remain in consistent conjugation of oligos to antibodies with maintained antigenicity as well as non-destructive, robust and cost-effective signal removal methods. Herein, a variety of oligo conjugation and signal removal methods were evaluated in the development of a robust oligo conjugated antibody cyclic immunofluorescence (Ab-oligo cyCIF) methodology. Both non- and site-specific conjugation strategies were assessed to label antibodies, where site-specific conjugation resulted in higher retained binding affinity and antigen-specific staining. A variety of fluorescence signal removal methods were also evaluated, where incorporation of a photocleavable link (PCL) resulted in full fluorescence signal removal with minimal tissue disruption. In summary, this work resulted in an optimized Ab-oligo cyCIF platform capable of generating high dimensional images to characterize the spatial proteomics of the hallmarks of cancer.
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143
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Borniger JC. Cancer as a tool for preclinical psychoneuroimmunology. Brain Behav Immun Health 2021; 18:100351. [PMID: 34988496 PMCID: PMC8710415 DOI: 10.1016/j.bbih.2021.100351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 09/03/2021] [Accepted: 09/17/2021] [Indexed: 12/20/2022] Open
Abstract
Cancer represents a novel homeostatic challenge to the host system. How the brain senses and responds to changes in peripheral physiology elicited by tumor growth is a largely untapped area of research. This is especially relevant given the widespread prevalence of systemic problems that people with various types of cancer experience. These include disruptions in sleep/wake cycles, cognitive function, depression, and changes in appetite/food intake, among others. Critically, many of these problems are evident prior to diagnosis, indicating that their etiology is potentially distinct from the effects of cancer treatment or the stress of a cancer diagnosis. Psychoneuroimmunology (PNI) is well equipped to tackle these types of problems, as it uses approaches from multiple disciplines to understand how specific stimuli (endogenous and environmental) are transduced into neural, endocrine, and immune signals that ultimately regulate health and behavior. In this article, I first provide a brief historical perspective of cancer and PNI, introduce the idea of cancer as a systemic homeostatic challenge, and provide examples from preclinical literature supporting this hypothesis. Given the rise of advanced tools in neuroscience (e.g., calcium imaging), we can now monitor and manipulate genetically defined neural circuits over the extended time scales necessary to disentangle distal communication between peripheral tumors and the brain.
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144
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Elhanani O, Keren L, Angelo M. High-Dimensional Tissue Profiling by Multiplexed Ion Beam Imaging. Methods Mol Biol 2021; 2386:147-156. [PMID: 34766270 DOI: 10.1007/978-1-0716-1771-7_10] [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: 05/13/2023]
Abstract
Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) enables high-dimensional imaging in situ of clinical specimens at single-cell resolution. In MIBI-TOF, tissue sections are stained with dozens of metal-labeled antibodies, whose abundance and location are read by secondary ionization mass spectrometry. The result is a multi-dimensional image, depicting sub-cellular expression and localization for dozens of distinct proteins in situ. Here, we describe the staining and imaging procedures of a MIBI-TOF experiment.
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Affiliation(s)
- Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA.
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145
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Whiteaker JR, Lundeen RA, Zhao L, Schoenherr RM, Burian A, Huang D, Voytovich U, Wang T, Kennedy JJ, Ivey RG, Lin C, Murillo OD, Lorentzen TD, Thiagarajan M, Colantonio S, Caceres TW, Roberts RR, Knotts JG, Reading JJ, Kaczmarczyk JA, Richardson CW, Garcia-Buntley SS, Bocik W, Hewitt SM, Murray KE, Do N, Brophy M, Wilz SW, Yu H, Ajjarapu S, Boja E, Hiltke T, Rodriguez H, Paulovich AG. Targeted Mass Spectrometry Enables Multiplexed Quantification of Immunomodulatory Proteins in Clinical Biospecimens. Front Immunol 2021; 12:765898. [PMID: 34858420 PMCID: PMC8632241 DOI: 10.3389/fimmu.2021.765898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 10/22/2021] [Indexed: 12/11/2022] Open
Abstract
Immunotherapies are revolutionizing cancer care, producing durable responses and potentially cures in a subset of patients. However, response rates are low for most tumors, grade 3/4 toxicities are not uncommon, and our current understanding of tumor immunobiology is incomplete. While hundreds of immunomodulatory proteins in the tumor microenvironment shape the anti-tumor response, few of them can be reliably quantified. To address this need, we developed a multiplex panel of targeted proteomic assays targeting 52 peptides representing 46 proteins using peptide immunoaffinity enrichment coupled to multiple reaction monitoring-mass spectrometry. We validated the assays in tissue and plasma matrices, where performance figures of merit showed over 3 orders of dynamic range and median inter-day CVs of 5.2% (tissue) and 21% (plasma). A feasibility study in clinical biospecimens showed detection of 48/52 peptides in frozen tissue and 38/52 peptides in plasma. The assays are publicly available as a resource for the research community.
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Affiliation(s)
- Jeffrey R. Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Rachel A. Lundeen
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Lei Zhao
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Regine M. Schoenherr
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Aura Burian
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Dongqing Huang
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Ulianna Voytovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Tao Wang
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Jacob J. Kennedy
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Richard G. Ivey
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Chenwei Lin
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Oscar D. Murillo
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | - Travis D. Lorentzen
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | - Simona Colantonio
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Tessa W. Caceres
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Rhonda R. Roberts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joseph G. Knotts
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Joshua J. Reading
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Jan A. Kaczmarczyk
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Christopher W. Richardson
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Sandra S. Garcia-Buntley
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - William Bocik
- Cancer Research Technology Program, Antibody Characterization Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Stephen M. Hewitt
- Experimental Pathology Laboratory, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Karen E. Murray
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
| | - Nhan Do
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Mary Brophy
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
| | - Stephen W. Wilz
- Department of Medicine, Boston University School of Medicine, Boston, MA, United States
- Pathology and Laboratory Medicine Service, Program, Veteran’s Administration (VA) Boston Healthcare System, Jamaica Plain, MA, United States
| | - Hongbo Yu
- Pathology and Laboratory Medicine Service, Program, Veteran’s Administration (VA) Boston Healthcare System, Jamaica Plain, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
| | - Samuel Ajjarapu
- Veteran’s Administration (VA) Cooperative Studies Program, Veteran’s Administration (VA) Boston Healthcare System (151MAV), Jamaica Plain, MA, United States
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Emily Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD, United States
| | - Amanda G. Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
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146
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Liu CC, McCaffrey EF, Greenwald NF, Soon E, Risom T, Vijayaragavan K, Oliveria JP, Mrdjen D, Bosse M, Tebaykin D, Bendall SC, Angelo M. Multiplexed Ion Beam Imaging: Insights into Pathobiology. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE 2021; 17:403-423. [PMID: 34752710 DOI: 10.1146/annurev-pathmechdis-030321-091459] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Candace C Liu
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Erin F McCaffrey
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Noah F Greenwald
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Erin Soon
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Tyler Risom
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , , .,Current affiliation: Genentech, South San Francisco, California 94080; USA
| | - Kausalia Vijayaragavan
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - John-Paul Oliveria
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , , .,Current affiliation: Genentech, South San Francisco, California 94080; USA
| | - Dunja Mrdjen
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Dmitry Tebaykin
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, California 94304 USA; , , , , , , , , , , ,
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147
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van der Leun AM, Hoekstra ME, Reinalda L, Scheele CLGJ, Toebes M, van de Graaff MJ, Chen LYY, Li H, Bercovich A, Lubling Y, David E, Thommen DS, Tanay A, van Rheenen J, Amit I, van Kasteren SI, Schumacher TN. Single-cell analysis of regions of interest (SCARI) using a photosensitive tag. Nat Chem Biol 2021; 17:1139-1147. [PMID: 34504322 PMCID: PMC7611907 DOI: 10.1038/s41589-021-00839-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 06/24/2021] [Indexed: 11/25/2022]
Abstract
The functional activity and differentiation potential of cells are determined by their interactions with surrounding cells. Approaches that allow unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a tradeoff between spatial resolution and cell profiling depth. Here, we develop a photocage-based technology that allows isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including primary human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran) cage coupled to a set of nanobodies allows high-resolution photo-uncaging of different cell types in areas of interest. Single-cell RNA-sequencing of spatially defined CD8+ T cells is used to exemplify the feasibility of identifying location-dependent cell states. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.
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Affiliation(s)
- Anne M van der Leun
- Division of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Mirjam E Hoekstra
- Division of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Luuk Reinalda
- Department of Bio-Organic Synthesis, Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands
| | - Colinda L G J Scheele
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
- VIB-KULeuven Center for Cancer Biology, Leuven, Belgium
| | - Mireille Toebes
- Division of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Michel J van de Graaff
- Department of Bio-Organic Synthesis, Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands
- SeraNovo, Leiden, Netherlands
| | - Linda Y Y Chen
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Hanjie Li
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
- Shenzhen Institute of Synthetic Biology, Shenzhen, China
| | - Akhiad Bercovich
- Department of Computer Science and Applied Mathematics and Department of Biological Regulation, Weizmann Institute, Rehovot, Israel
| | - Yaniv Lubling
- Department of Computer Science and Applied Mathematics and Department of Biological Regulation, Weizmann Institute, Rehovot, Israel
- Cancer Research UK Cambridge Institute, Cambridge, UK
| | - Eyal David
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Daniela S Thommen
- Division of Molecular Oncology & Immunology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Amos Tanay
- Department of Computer Science and Applied Mathematics and Department of Biological Regulation, Weizmann Institute, Rehovot, Israel
| | - Jacco van Rheenen
- Division of Molecular Pathology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Ido Amit
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Sander I van Kasteren
- Department of Bio-Organic Synthesis, Leiden Institute of Chemistry, Leiden University, Leiden, Netherlands.
| | - Ton N Schumacher
- Division of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands.
- Department of Hematology, Leiden University Medical Center, Leiden, Netherlands.
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148
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Chen Y, Liu Y, Gao X. The Application of Single-Cell Technologies in Cardiovascular Research. Front Cell Dev Biol 2021; 9:751371. [PMID: 34708045 PMCID: PMC8542723 DOI: 10.3389/fcell.2021.751371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/21/2021] [Indexed: 12/21/2022] Open
Abstract
Cardiovascular diseases (CVDs) are the leading cause of deaths in the world. The intricacies of the cellular composition and tissue microenvironment in heart and vasculature complicate the dissection of molecular mechanisms of CVDs. Over the past decade, the rapid development of single-cell omics technologies generated vast quantities of information at various biological levels, which have shed light on the cellular and molecular dynamics in cardiovascular development, homeostasis and diseases. Here, we summarize the latest single-cell omics techniques, and show how they have facilitated our understanding of cardiovascular biology. We also briefly discuss the clinical value and future outlook of single-cell applications in the field.
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Affiliation(s)
- Yinan Chen
- Fuwai Hospital, Chinese Academy of Medical Sciences, Shenzhen, China.,State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yang Liu
- Department of Vascular Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiang Gao
- Department of Vascular Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China
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149
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Luca BA, Steen CB, Matusiak M, Azizi A, Varma S, Zhu C, Przybyl J, Espín-Pérez A, Diehn M, Alizadeh AA, van de Rijn M, Gentles AJ, Newman AM. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 2021; 184:5482-5496.e28. [PMID: 34597583 DOI: 10.1016/j.cell.2021.09.014] [Citation(s) in RCA: 115] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 06/21/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.
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Affiliation(s)
- Bogdan A Luca
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Chloé B Steen
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | | | - Armon Azizi
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Sushama Varma
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Chunfang Zhu
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Joanna Przybyl
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Almudena Espín-Pérez
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Maximilian Diehn
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Ash A Alizadeh
- Division of Oncology, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Division of Hematology, Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Matt van de Rijn
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Andrew J Gentles
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA.
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150
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DePriest BP, Vieira N, Bidgoli A, Paczesny S. An overview of multiplexed analyses of CAR T-cell therapies: insights and potential. Expert Rev Proteomics 2021; 18:767-780. [PMID: 34628995 PMCID: PMC8626704 DOI: 10.1080/14789450.2021.1992276] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
INTRODUCTION Cancer immunotherapy is a rapidly growing field with exponential advancement in engineered immune cell-based therapies. For instance, an engineered chimeric antigen receptor (CAR) can be introduced in T-cells or other immune cells and adoptively transferred to target and kill cancer cells in hematologic malignancies or solid tumors. The first CAR-T-cell (CAR-T) therapy has been developed against CD19, a B-cell marker expressed on lymphoma and lymphoblastic leukemia. To allow for personalized treatment, proteomics approaches could provide insights into biomarkers for CAR-T therapy efficacy and toxicity. AREAS COVERED We researched the most recent technology methods of biomarker evaluation used in the laboratory and clinical setting. Publications of CAR-T biomarkers were then systematically reviewed to provide a narrative of the most validated biomarkers of CAR-T efficacy and toxicity. Examples of biomarkers include CAR-T functionality and phenotype as well as interleukin-6 and other cytokines. EXPERT COMMENTARY Biomarkers of CAR-T efficacy and toxicity have been identified, but still need to be validated and standardized across institutions. Moreover, few are used in the clinical setting due to limitations in real-time technology. Expansion of biomarker research could provide better understanding of patient response and risk of life-threatening side effects with potential for improved precision medicine.
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Affiliation(s)
- Brittany Paige DePriest
- Department of Microbiology and Immunology and Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Noah Vieira
- Department of Microbiology and Immunology and Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Alan Bidgoli
- Department of Microbiology and Immunology and Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Sophie Paczesny
- Department of Microbiology and Immunology and Pediatrics, Medical University of South Carolina, Charleston, SC, USA
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