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

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 experiments routinely produce terabytes of data...

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Autores principales: Lu, Guolan, Baertsch, Marc A., Hickey, John W., Goltsev, Yury, Rech, Andrew J., Mani, Lucas, Forgó, Erna, Kong, Christina, Jiang, Sizun, Nolan, Garry P., Rosenthal, Eben L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539451/
https://www.ncbi.nlm.nih.gov/pubmed/36211386
http://dx.doi.org/10.3389/fimmu.2022.981825
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author Lu, Guolan
Baertsch, Marc A.
Hickey, John W.
Goltsev, Yury
Rech, Andrew J.
Mani, Lucas
Forgó, Erna
Kong, Christina
Jiang, Sizun
Nolan, Garry P.
Rosenthal, Eben L.
author_facet Lu, Guolan
Baertsch, Marc A.
Hickey, John W.
Goltsev, Yury
Rech, Andrew J.
Mani, Lucas
Forgó, Erna
Kong, Christina
Jiang, Sizun
Nolan, Garry P.
Rosenthal, Eben L.
author_sort Lu, Guolan
collection PubMed
description 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 experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.
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spelling pubmed-95394512022-10-08 A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data Lu, Guolan Baertsch, Marc A. Hickey, John W. Goltsev, Yury Rech, Andrew J. Mani, Lucas Forgó, Erna Kong, Christina Jiang, Sizun Nolan, Garry P. Rosenthal, Eben L. Front Immunol Immunology 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 experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data. Frontiers Media S.A. 2022-09-23 /pmc/articles/PMC9539451/ /pubmed/36211386 http://dx.doi.org/10.3389/fimmu.2022.981825 Text en Copyright © 2022 Lu, Baertsch, Hickey, Goltsev, Rech, Mani, Forgó, Kong, Jiang, Nolan and Rosenthal https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Lu, Guolan
Baertsch, Marc A.
Hickey, John W.
Goltsev, Yury
Rech, Andrew J.
Mani, Lucas
Forgó, Erna
Kong, Christina
Jiang, Sizun
Nolan, Garry P.
Rosenthal, Eben L.
A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title_full A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title_fullStr A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title_full_unstemmed A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title_short A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
title_sort real-time gpu-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539451/
https://www.ncbi.nlm.nih.gov/pubmed/36211386
http://dx.doi.org/10.3389/fimmu.2022.981825
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