Multiplex imaging in immuno-oncology

被引：5

作者：

Zhao, Chen ^{[1
,2
]}

Germain, Ronald N. ^{[2
]}

机构：

[1] NCI, NIH, Thorac & GI Malignancies Branch, CCR, Bethesda, MD USA

[2] NIAID, Lymphocyte Biol Sect, Lab Immune Syst Biol, Bethesda, MD 20892 USA

来源：

JOURNAL FOR IMMUNOTHERAPY OF CANCER | 2023年 / 11卷 / 10期

关键词：

tumor microenvironment; immunotherapy; DENDRITIC CELLS; BREAST-CANCER; IMMUNOHISTOCHEMISTRY; TISSUES; LOCALIZATION; RESOLUTION; MICROSCOPY; CYTOMETRY;

D O I：

10.1136/jitc-2023-006923

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Multiplex imaging has emerged as an invaluable tool for immune-oncologists and translational researchers, enabling them to examine intricate interactions among immune cells, stroma, matrix, and malignant cells within the tumor microenvironment (TME). It holds significant promise in the quest to discover improved biomarkers for treatment stratification and identify novel therapeutic targets. Nonetheless, several challenges exist in the realms of study design, experiment optimization, and data analysis. In this review, our aim is to present an overview of the utilization of multiplex imaging in immuno-oncology studies and inform novice researchers about the fundamental principles at each stage of the imaging and analysis process.

引用

页数：9

共 85 条

[1] Multiplexed ion beam imaging of human breast tumors [J].

Angelo, Michael ;

Bendall, Sean C. ;

Finck, Rachel ;

Hale, Matthew B. ;

Hitzman, Chuck ;

Borowsky, Alexander D. ;

Levenson, Richard M. ;

Lowe, John B. ;

Liu, Scot D. ;

Zhao, Shuchun ;

Natkunam, Yasodha ;

Nolan, Garry P. .

NATURE MEDICINE, 2014, 20 (04) :436-+

[2] Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images [J].

Bai, Yunhao ;

Zhu, Bokai ;

Rovira-Clave, Xavier ;

Chen, Han ;

Markovic, Maxim ;

Chan, Chi Ngai ;

Su, Tung-Hung ;

McIlwain, David R. ;

Estes, Jacob D. ;

Keren, Leeat ;

Nolan, Garry P. ;

Jiang, Sizun .

FRONTIERS IN IMMUNOLOGY, 2021, 12

[3] Developing image analysis methods for digital pathology [J].

Bankhead, Peter .

JOURNAL OF PATHOLOGY, 2022, 257 (04) :391-402

[4] MAUI (MBI Analysis User Interface)-An image processing pipeline for Multiplexed Mass Based Imaging [J].

Baranski, Alex ;

Milo, Idan ;

Greenbaum, Shirley ;

Oliveria, John-Paul ;

Mrdjen, Dunja ;

Angelo, Michael ;

Keren, Leeat .

PLOS COMPUTATIONAL BIOLOGY, 2021, 17 (04)

[5] Image Segmentation, Registration and Characterization in R with SimpleITK [J].

Beare, Richard ;

Lowekamp, Bradley ;

Yaniv, Ziv .

JOURNAL OF STATISTICAL SOFTWARE, 2018, 86 (08) :1-35

[6] Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction [J].

Belthangady, Chinmay ;

Royer, Loic A. .

NATURE METHODS, 2019, 16 (12) :1215-1225

[7] T-cell activation by dendritic cells in the lymph node: lessons from the movies [J].

Bousso, Philippe .

NATURE REVIEWS IMMUNOLOGY, 2008, 8 (09) :675-684

[8] Diving into the mechanism of action of tumor immunotherapies with intravital imaging* [J].

Bousso, Philippe .

IMMUNOLOGICAL REVIEWS, 2022, 306 (01) :218-223

[9] The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy [J].

Bruni, Daniela ;

Angell, Helen K. ;

Galon, Jerome .

NATURE REVIEWS CANCER, 2020, 20 (11) :662-680

[10] Imaging Mass Cytometry [J].

Chang, Qing ;

Ornatsky, Olga I. ;

Siddiqui, Iram ;

Loboda, Alexander ;

Baranov, Vladimir I. ;

Hedley, David W. .

CYTOMETRY PART A, 2017, 91A (02) :160-169

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