Literature DB >> 27135535

Multiplexed Epitope-Based Tissue Imaging for Discovery and Healthcare Applications.

Bernd Bodenmiller1.   

Abstract

The study of organs and tissues on a molecular level is necessary as we seek an understanding of health and disease. Over the last few years, powerful highly multiplexed epitope-based imaging approaches that rely on the serial imaging of tissues with fluorescently labeled antibodies and the simultaneous analysis using metal-labeled antibodies have emerged. These techniques enable analysis of dozens of epitopes in thousands of cells in a single experiment providing a systems level view of normal and disease processes at the single-cell level with spatial resolution in tissues. In this Review, I discuss, first, the highly multiplexed epitope-based imaging approaches and the generated data. Second, I describe challenges that must be overcome to implement these imaging methods from bench to bedside, including issues with tissue processing and analyses of the large amounts of data generated. Third, I discuss how these methods can be integrated with readouts of genome, transcriptome, metabolome, and live cell information, and fourth, the novel applications possible in tissue biology, drug development, and biomarker discovery. I anticipate that highly multiplexed epitope-based imaging approaches will broadly complement existing imaging methods and will become a cornerstone of tissue biology and biomedical research and of precision medical applications.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Tissue systems biology; clinical pathology; computational biology; imaging mass cytometry; multiplexed imaging; multiplexed ion beam imaging; precision medicine; quantitative pathology; serial immunofluorescence microscopy; single-cell analysis

Mesh:

Substances:

Year:  2016        PMID: 27135535     DOI: 10.1016/j.cels.2016.03.008

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  71 in total

1.  The role of proteomics in assessing beta-cell dysfunction and death in type 1 diabetes.

Authors:  Ernesto S Nakayasu; Wei-Jun Qian; Carmella Evans-Molina; Raghavendra G Mirmira; Decio L Eizirik; Thomas O Metz
Journal:  Expert Rev Proteomics       Date:  2019-06-24       Impact factor: 3.940

2.  Deciphering tumor heterogeneity from FFPE tissues: Its promise and challenges.

Authors:  Alan J Simmons; Ken S Lau
Journal:  Mol Cell Oncol       Date:  2016-11-18

Review 3.  Emerging Role of the Pathologist in Precision Medicine for HCC.

Authors:  Thomas Longerich; Peter Schirmacher
Journal:  Dig Dis Sci       Date:  2019-04       Impact factor: 3.199

4.  Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging.

Authors:  Lauren E Himmel; Troy A Hackett; Jessica L Moore; Wilson R Adams; Giju Thomas; Tatiana Novitskaya; Richard M Caprioli; Andries Zijlstra; Anita Mahadevan-Jansen; Kelli L Boyd
Journal:  ILAR J       Date:  2018-12-01

Review 5.  Spatial proteomics: a powerful discovery tool for cell biology.

Authors:  Emma Lundberg; Georg H H Borner
Journal:  Nat Rev Mol Cell Biol       Date:  2019-05       Impact factor: 94.444

6.  A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry.

Authors:  Nicolas Damond; Stefanie Engler; Vito R T Zanotelli; Denis Schapiro; Clive H Wasserfall; Irina Kusmartseva; Harry S Nick; Fabrizio Thorel; Pedro L Herrera; Mark A Atkinson; Bernd Bodenmiller
Journal:  Cell Metab       Date:  2019-01-31       Impact factor: 27.287

Review 7.  Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics.

Authors:  Sophia K Longo; Margaret G Guo; Andrew L Ji; Paul A Khavari
Journal:  Nat Rev Genet       Date:  2021-06-18       Impact factor: 53.242

Review 8.  The tissue proteome in the multi-omic landscape of kidney disease.

Authors:  Markus M Rinschen; Julio Saez-Rodriguez
Journal:  Nat Rev Nephrol       Date:  2020-10-07       Impact factor: 28.314

Review 9.  High-dimension single-cell analysis applied to cancer.

Authors:  Lili Wang; Kenneth J Livak; Catherine J Wu
Journal:  Mol Aspects Med       Date:  2017-08-30

10.  Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

Authors:  Robert Krueger; Johanna Beyer; Won-Dong Jang; Nam Wook Kim; Artem Sokolov; Peter K Sorger; Hanspeter Pfister
Journal:  IEEE Trans Vis Comput Graph       Date:  2019-09-10       Impact factor: 4.579

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