Results 1 to 10 of about 27,715 (115)

Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering [PDF]

Nature Communications, 2023
While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge.
Candace C. Liu, Noah F. Greenwald, Alex Kong, Erin F. McCaffrey, Ke Xuan Leow, Dunja Mrdjen, Bryan J. Cannon, Josef Lorenz Rumberger, Sricharan Reddy Varra, Michael Angelo   +9 more
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A review on deep learning applications in highly multiplexed tissue imaging data analysis [PDF]

Frontiers in Bioinformatics, 2023
Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics ...
Mohammed Zidane, Ahmad Makky, Matthias Bruhns, Matthias Bruhns, Alexander Rochwarger, Sepideh Babaei, Manfred Claassen, Manfred Claassen, Christian M. Schürch   +8 more
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Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics [PDF]

Journal of Translational Medicine
Background Tissue microenvironment heterogeneity, including cellular composition, functional states, and intercellular interactions, shapes complex biological processes and disease mechanisms.
Linyu Yang, Zhenyu Yang
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Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging. [PDF]

PLoS Computational Biology
Highly multiplexed single-cell imaging technologies have revolutionized our ability to capture spatial protein expression at the single-cell level, thereby enabling a deeper understanding of tissue organization and function.
Matthias Bruhns, Jan T Schleicher, Maximilian Wirth, Marcello Zago, Sepideh Babaei, Manfred Claassen   +5 more
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Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. [PDF]

Eur J Immunol, 2021
Kennedy-Darling J, Bhate SS, Hickey JW, Black S, Barlow GL, Vazquez G, Venkataraaman VG, Samusik N, Goltsev Y, Schürch CM, Nolan GP.   +10 more
europepmc   +2 more sources

A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data

Frontiers in Immunology, 2022
Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging ...
Guolan Lu, Guolan Lu, Guolan Lu, Marc A. Baertsch, Marc A. Baertsch, Marc A. Baertsch, John W. Hickey, John W. Hickey, Yury Goltsev, Yury Goltsev, Andrew J. Rech, Andrew J. Rech, Lucas Mani, Erna Forgó, Christina Kong, Sizun Jiang, Garry P. Nolan, Garry P. Nolan, Eben L. Rosenthal, Eben L. Rosenthal   +19 more
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Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease

Communications Biology, 2021
Allam, Hu, et al. present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data.
Mayar Allam, Thomas Hu, Shuangyi Cai, Krishnan Laxminarayanan, Robert B. Hughley, Ahmet F. Coskun   +5 more
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IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry

Nature Communications, 2023
Multiplexed imaging technologies can reveal the complex cellular and molecular profiles of tissue. Here, the authors develop and implement a denoising pipeline to significantly enhance imaging mass cytometry quality and improve single-cell analyses.
Peng Lu, Karolyn A. Oetjen, Diane E. Bender, Marianna B. Ruzinova, Daniel A. C. Fisher, Kevin G. Shim, Russell K. Pachynski, W. Nathaniel Brennen, Stephen T. Oh, Daniel C. Link, Daniel L. J. Thorek   +10 more
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A framework for multiplex imaging optimization and reproducible analysis

Communications Biology, 2022
An approach for tissue image analysis applicable to highly multiplexed immunofluorescence imaging of the spatial distribution of multiple protein biomarkers is proposed, here applied to the analysis of multiplex IF using the multiplex imaging platform ...
Jennifer Eng, Elmar Bucher, Zhi Hu, Ting Zheng, Summer L. Gibbs, Koei Chin, Joe W. Gray   +6 more
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CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

BMC Bioinformatics, 2022
Background Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter ...
Michael Y. Lee, Jacob S. Bedia, Salil S. Bhate, Graham L. Barlow, Darci Phillips, Wendy J. Fantl, Garry P. Nolan, Christian M. Schürch   +7 more
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